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Classification - Machine Learning This is ‘Classification’ tutorial which is a part of the Machine Learning course offered by Simplilearn. We will learn Classification algorithms, types of classification algorithms, support vector machines(SVM), Naive Bayes, Decision Tree and Random Forest Classifier in this tutorial. Objectives Let us look at some of the objectives covered under this section of Machine Learning tutorial. Define Classification and list its algorithms Describe Logistic Regression and Sigmoid Probability Explain K-Nearest Neighbors and KNN classification Understand Support Vector Machines, Polynomial Kernel, and Kernel Trick Analyze Kernel Support Vector Machines with an example Implement the Naïve Bayes Classifier Demonstrate Decision Tree Classifier Describe Random Forest Classifier Classification: Meaning Classification is a type of supervised learning. It specifies the class to which data elements belong to and is best used when the output has finite and discrete values. It predicts a class for an input variable as well. There are 2 types of Classification: Binomial Multi-Class Classification: Use Cases Some of the key areas where classification cases are being used: To find whether an email received is a spam or ham To identify customer segments To find if a bank loan is granted To identify if a kid will pass or fail in an examination Classification: Example Social media sentiment analysis has two potential outcomes, positive or negative, as displayed by the chart given below. https://www.simplilearn.com/ice9/free_resources_article_thumb/classification-example-machine-learning.JPG This chart shows the classification of the Iris flower dataset into its three sub-species indicated by codes 0, 1, and 2. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-flower-dataset-graph.JPG The test set dots represent the assignment of new test data points to one class or the other based on the trained classifier model. Types of Classification Algorithms Let’s have a quick look into the types of Classification Algorithm below. Linear Models Logistic Regression Support Vector Machines Nonlinear models K-nearest Neighbors (KNN) Kernel Support Vector Machines (SVM) Naïve Bayes Decision Tree Classification Random Forest Classification Logistic Regression: Meaning Let us understand the Logistic Regression model below. This refers to a regression model that is used for classification. This method is widely used for binary classification problems. It can also be extended to multi-class classification problems. Here, the dependent variable is categorical: y ϵ {0, 1} A binary dependent variable can have only two values, like 0 or 1, win or lose, pass or fail, healthy or sick, etc In this case, you model the probability distribution of output y as 1 or 0. This is called the sigmoid probability (σ). If σ(θ Tx) > 0.5, set y = 1, else set y = 0 Unlike Linear Regression (and its Normal Equation solution), there is no closed form solution for finding optimal weights of Logistic Regression. Instead, you must solve this with maximum likelihood estimation (a probability model to detect the maximum likelihood of something happening). It can be used to calculate the probability of a given outcome in a binary model, like the probability of being classified as sick or passing an exam. https://www.simplilearn.com/ice9/free_resources_article_thumb/logistic-regression-example-graph.JPG Sigmoid Probability The probability in the logistic regression is often represented by the Sigmoid function (also called the logistic function or the S-curve): https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-function-machine-learning.JPG In this equation, t represents data values * the number of hours studied and S(t) represents the probability of passing the exam. Assume sigmoid function: https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-probability-machine-learning.JPG g(z) tends toward 1 as z -> infinity , and g(z) tends toward 0 as z -> infinity K-nearest Neighbors (KNN) K-nearest Neighbors algorithm is used to assign a data point to clusters based on similarity measurement. It uses a supervised method for classification. The steps to writing a k-means algorithm are as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-distribution-graph-machine-learning.JPG Choose the number of k and a distance metric. (k = 5 is common) Find k-nearest neighbors of the sample that you want to classify Assign the class label by majority vote. KNN Classification A new input point is classified in the category such that it has the most number of neighbors from that category. For example: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-classification-machine-learning.JPG Classify a patient as high risk or low risk. Mark email as spam or ham. Keen on learning about Classification Algorithms in Machine Learning? Click here! Support Vector Machine (SVM) Let us understand Support Vector Machine (SVM) in detail below. SVMs are classification algorithms used to assign data to various classes. They involve detecting hyperplanes which segregate data into classes. SVMs are very versatile and are also capable of performing linear or nonlinear classification, regression, and outlier detection. Once ideal hyperplanes are discovered, new data points can be easily classified. https://www.simplilearn.com/ice9/free_resources_article_thumb/support-vector-machines-graph-machine-learning.JPG The optimization objective is to find “maximum margin hyperplane” that is farthest from the closest points in the two classes (these points are called support vectors). In the given figure, the middle line represents the hyperplane. SVM Example Let’s look at this image below and have an idea about SVM in general. Hyperplanes with larger margins have lower generalization error. The positive and negative hyperplanes are represented by: https://www.simplilearn.com/ice9/free_resources_article_thumb/positive-negative-hyperplanes-machine-learning.JPG Classification of any new input sample xtest : If w0 + wTxtest > 1, the sample xtest is said to be in the class toward the right of the positive hyperplane. If w0 + wTxtest < -1, the sample xtest is said to be in the class toward the left of the negative hyperplane. When you subtract the two equations, you get: https://www.simplilearn.com/ice9/free_resources_article_thumb/equation-subtraction-machine-learning.JPG Length of vector w is (L2 norm length): https://www.simplilearn.com/ice9/free_resources_article_thumb/length-of-vector-machine-learning.JPG You normalize with the length of w to arrive at: https://www.simplilearn.com/ice9/free_resources_article_thumb/normalize-equation-machine-learning.JPG SVM: Hard Margin Classification Given below are some points to understand Hard Margin Classification. The left side of equation SVM-1 given above can be interpreted as the distance between the positive (+ve) and negative (-ve) hyperplanes; in other words, it is the margin that can be maximized. Hence the objective of the function is to maximize with the constraint that the samples are classified correctly, which is represented as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-machine-learning.JPG This means that you are minimizing ‖w‖. This also means that all positive samples are on one side of the positive hyperplane and all negative samples are on the other side of the negative hyperplane. This can be written concisely as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-formula.JPG Minimizing ‖w‖ is the same as minimizing. This figure is better as it is differentiable even at w = 0. The approach listed above is called “hard margin linear SVM classifier.” SVM: Soft Margin Classification Given below are some points to understand Soft Margin Classification. To allow for linear constraints to be relaxed for nonlinearly separable data, a slack variable is introduced. (i) measures how much ith instance is allowed to violate the margin. The slack variable is simply added to the linear constraints. https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-machine-learning.JPG Subject to the above constraints, the new objective to be minimized becomes: https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-formula.JPG You have two conflicting objectives now—minimizing slack variable to reduce margin violations and minimizing to increase the margin. The hyperparameter C allows us to define this trade-off. Large values of C correspond to larger error penalties (so smaller margins), whereas smaller values of C allow for higher misclassification errors and larger margins. https://www.simplilearn.com/ice9/free_resources_article_thumb/machine-learning-certification-video-preview.jpg SVM: Regularization The concept of C is the reverse of regularization. Higher C means lower regularization, which increases bias and lowers the variance (causing overfitting). https://www.simplilearn.com/ice9/free_resources_article_thumb/concept-of-c-graph-machine-learning.JPG IRIS Data Set The Iris dataset contains measurements of 150 IRIS flowers from three different species: Setosa Versicolor Viriginica Each row represents one sample. Flower measurements in centimeters are stored as columns. These are called features. IRIS Data Set: SVM Let’s train an SVM model using sci-kit-learn for the Iris dataset: https://www.simplilearn.com/ice9/free_resources_article_thumb/svm-model-graph-machine-learning.JPG Nonlinear SVM Classification There are two ways to solve nonlinear SVMs: by adding polynomial features by adding similarity features Polynomial features can be added to datasets; in some cases, this can create a linearly separable dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/nonlinear-classification-svm-machine-learning.JPG In the figure on the left, there is only 1 feature x1. This dataset is not linearly separable. If you add x2 = (x1)2 (figure on the right), the data becomes linearly separable. Polynomial Kernel In sci-kit-learn, one can use a Pipeline class for creating polynomial features. Classification results for the Moons dataset are shown in the figure. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-machine-learning.JPG Polynomial Kernel with Kernel Trick Let us look at the image below and understand Kernel Trick in detail. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-with-kernel-trick.JPG For large dimensional datasets, adding too many polynomial features can slow down the model. You can apply a kernel trick with the effect of polynomial features without actually adding them. The code is shown (SVC class) below trains an SVM classifier using a 3rd-degree polynomial kernel but with a kernel trick. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-equation-machine-learning.JPG The hyperparameter coefθ controls the influence of high-degree polynomials. Kernel SVM Let us understand in detail about Kernel SVM. Kernel SVMs are used for classification of nonlinear data. In the chart, nonlinear data is projected into a higher dimensional space via a mapping function where it becomes linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-machine-learning.JPG In the higher dimension, a linear separating hyperplane can be derived and used for classification. A reverse projection of the higher dimension back to original feature space takes it back to nonlinear shape. As mentioned previously, SVMs can be kernelized to solve nonlinear classification problems. You can create a sample dataset for XOR gate (nonlinear problem) from NumPy. 100 samples will be assigned the class sample 1, and 100 samples will be assigned the class label -1. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-graph-machine-learning.JPG As you can see, this data is not linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-non-separable.JPG You now use the kernel trick to classify XOR dataset created earlier. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-xor-machine-learning.JPG Naïve Bayes Classifier What is Naive Bayes Classifier? Have you ever wondered how your mail provider implements spam filtering or how online news channels perform news text classification or even how companies perform sentiment analysis of their audience on social media? All of this and more are done through a machine learning algorithm called Naive Bayes Classifier. Naive Bayes Named after Thomas Bayes from the 1700s who first coined this in the Western literature. Naive Bayes classifier works on the principle of conditional probability as given by the Bayes theorem. Advantages of Naive Bayes Classifier Listed below are six benefits of Naive Bayes Classifier. Very simple and easy to implement Needs less training data Handles both continuous and discrete data Highly scalable with the number of predictors and data points As it is fast, it can be used in real-time predictions Not sensitive to irrelevant features Bayes Theorem We will understand Bayes Theorem in detail from the points mentioned below. According to the Bayes model, the conditional probability P(Y|X) can be calculated as: P(Y|X) = P(X|Y)P(Y) / P(X) This means you have to estimate a very large number of P(X|Y) probabilities for a relatively small vector space X. For example, for a Boolean Y and 30 possible Boolean attributes in the X vector, you will have to estimate 3 billion probabilities P(X|Y). To make it practical, a Naïve Bayes classifier is used, which assumes conditional independence of P(X) to each other, with a given value of Y. This reduces the number of probability estimates to 2*30=60 in the above example. Naïve Bayes Classifier for SMS Spam Detection Consider a labeled SMS database having 5574 messages. It has messages as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-machine-learning.JPG Each message is marked as spam or ham in the data set. Let’s train a model with Naïve Bayes algorithm to detect spam from ham. The message lengths and their frequency (in the training dataset) are as shown below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-spam-detection.JPG Analyze the logic you use to train an algorithm to detect spam: Split each message into individual words/tokens (bag of words). Lemmatize the data (each word takes its base form, like “walking” or “walked” is replaced with “walk”). Convert data to vectors using scikit-learn module CountVectorizer. Run TFIDF to remove common words like “is,” “are,” “and.” Now apply scikit-learn module for Naïve Bayes MultinomialNB to get the Spam Detector. This spam detector can then be used to classify a random new message as spam or ham. Next, the accuracy of the spam detector is checked using the Confusion Matrix. For the SMS spam example above, the confusion matrix is shown on the right. Accuracy Rate = Correct / Total = (4827 + 592)/5574 = 97.21% Error Rate = Wrong / Total = (155 + 0)/5574 = 2.78% https://www.simplilearn.com/ice9/free_resources_article_thumb/confusion-matrix-machine-learning.JPG Although confusion Matrix is useful, some more precise metrics are provided by Precision and Recall. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-recall-matrix-machine-learning.JPG Precision refers to the accuracy of positive predictions. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-formula-machine-learning.JPG Recall refers to the ratio of positive instances that are correctly detected by the classifier (also known as True positive rate or TPR). https://www.simplilearn.com/ice9/free_resources_article_thumb/recall-formula-machine-learning.JPG Precision/Recall Trade-off To detect age-appropriate videos for kids, you need high precision (low recall) to ensure that only safe videos make the cut (even though a few safe videos may be left out). The high recall is needed (low precision is acceptable) in-store surveillance to catch shoplifters; a few false alarms are acceptable, but all shoplifters must be caught. Learn about Naive Bayes in detail. Click here! Decision Tree Classifier Some aspects of the Decision Tree Classifier mentioned below are. Decision Trees (DT) can be used both for classification and regression. The advantage of decision trees is that they require very little data preparation. They do not require feature scaling or centering at all. They are also the fundamental components of Random Forests, one of the most powerful ML algorithms. Unlike Random Forests and Neural Networks (which do black-box modeling), Decision Trees are white box models, which means that inner workings of these models are clearly understood. In the case of classification, the data is segregated based on a series of questions. Any new data point is assigned to the selected leaf node. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-machine-learning.JPG Start at the tree root and split the data on the feature using the decision algorithm, resulting in the largest information gain (IG). This splitting procedure is then repeated in an iterative process at each child node until the leaves are pure. This means that the samples at each node belonging to the same class. In practice, you can set a limit on the depth of the tree to prevent overfitting. The purity is compromised here as the final leaves may still have some impurity. The figure shows the classification of the Iris dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-graph.JPG IRIS Decision Tree Let’s build a Decision Tree using scikit-learn for the Iris flower dataset and also visualize it using export_graphviz API. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-machine-learning.JPG The output of export_graphviz can be converted into png format: https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-output.JPG Sample attribute stands for the number of training instances the node applies to. Value attribute stands for the number of training instances of each class the node applies to. Gini impurity measures the node’s impurity. A node is “pure” (gini=0) if all training instances it applies to belong to the same class. https://www.simplilearn.com/ice9/free_resources_article_thumb/impurity-formula-machine-learning.JPG For example, for Versicolor (green color node), the Gini is 1-(0/54)2 -(49/54)2 -(5/54) 2 ≈ 0.168 https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-sample.JPG Decision Boundaries Let us learn to create decision boundaries below. For the first node (depth 0), the solid line splits the data (Iris-Setosa on left). Gini is 0 for Setosa node, so no further split is possible. The second node (depth 1) splits the data into Versicolor and Virginica. If max_depth were set as 3, a third split would happen (vertical dotted line). https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-boundaries.JPG For a sample with petal length 5 cm and petal width 1.5 cm, the tree traverses to depth 2 left node, so the probability predictions for this sample are 0% for Iris-Setosa (0/54), 90.7% for Iris-Versicolor (49/54), and 9.3% for Iris-Virginica (5/54) CART Training Algorithm Scikit-learn uses Classification and Regression Trees (CART) algorithm to train Decision Trees. CART algorithm: Split the data into two subsets using a single feature k and threshold tk (example, petal length < “2.45 cm”). This is done recursively for each node. k and tk are chosen such that they produce the purest subsets (weighted by their size). The objective is to minimize the cost function as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/cart-training-algorithm-machine-learning.JPG The algorithm stops executing if one of the following situations occurs: max_depth is reached No further splits are found for each node Other hyperparameters may be used to stop the tree: min_samples_split min_samples_leaf min_weight_fraction_leaf max_leaf_nodes Gini Impurity or Entropy Entropy is one more measure of impurity and can be used in place of Gini. https://www.simplilearn.com/ice9/free_resources_article_thumb/gini-impurity-entrophy.JPG It is a degree of uncertainty, and Information Gain is the reduction that occurs in entropy as one traverses down the tree. Entropy is zero for a DT node when the node contains instances of only one class. Entropy for depth 2 left node in the example given above is: https://www.simplilearn.com/ice9/free_resources_article_thumb/entrophy-for-depth-2.JPG Gini and Entropy both lead to similar trees. DT: Regularization The following figure shows two decision trees on the moons dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/dt-regularization-machine-learning.JPG The decision tree on the right is restricted by min_samples_leaf = 4. The model on the left is overfitting, while the model on the right generalizes better. Random Forest Classifier Let us have an understanding of Random Forest Classifier below. A random forest can be considered an ensemble of decision trees (Ensemble learning). Random Forest algorithm: Draw a random bootstrap sample of size n (randomly choose n samples from the training set). Grow a decision tree from the bootstrap sample. At each node, randomly select d features. Split the node using the feature that provides the best split according to the objective function, for instance by maximizing the information gain. Repeat the steps 1 to 2 k times. (k is the number of trees you want to create, using a subset of samples) Aggregate the prediction by each tree for a new data point to assign the class label by majority vote (pick the group selected by the most number of trees and assign new data point to that group). Random Forests are opaque, which means it is difficult to visualize their inner workings. https://www.simplilearn.com/ice9/free_resources_article_thumb/random-forest-classifier-graph.JPG However, the advantages outweigh their limitations since you do not have to worry about hyperparameters except k, which stands for the number of decision trees to be created from a subset of samples. RF is quite robust to noise from the individual decision trees. Hence, you need not prune individual decision trees. The larger the number of decision trees, the more accurate the Random Forest prediction is. (This, however, comes with higher computation cost). Key Takeaways Let us quickly run through what we have learned so far in this Classification tutorial. Classification algorithms are supervised learning methods to split data into classes. They can work on Linear Data as well as Nonlinear Data. Logistic Regression can classify data based on weighted parameters and sigmoid conversion to calculate the probability of classes. K-nearest Neighbors (KNN) algorithm uses similar features to classify data. Support Vector Machines (SVMs) classify data by detecting the maximum margin hyperplane between data classes. Naïve Bayes, a simplified Bayes Model, can help classify data using conditional probability models. Decision Trees are powerful classifiers and use tree splitting logic until pure or somewhat pure leaf node classes are attained. Random Forests apply Ensemble Learning to Decision Trees for more accurate classification predictions. Conclusion This completes ‘Classification’ tutorial. In the next tutorial, we will learn 'Unsupervised Learning with Clustering.'

A list of practical projects that anyone can solve in any programming language (See solutions). These projects are divided into multiple categories, and each category has its own folder. To get started, simply fork this repo. CONTRIBUTING See ways of contributing to this repo. You can contribute solutions (will be published in this repo) to existing problems, add new projects, or remove existing ones. Make sure you follow all instructions properly. Solutions You can find implementations of these projects in many other languages by other users in this repo. Credits Problems are motivated by the ones shared at: Martyr2’s Mega Project List Rosetta Code Table of Contents Numbers Classic Algorithms Graph Data Structures Text Networking Classes Threading Web Files Databases Graphics and Multimedia Security Numbers Find PI to the Nth Digit - Enter a number and have the program generate PI up to that many decimal places. Keep a limit to how far the program will go. Find e to the Nth Digit - Just like the previous problem, but with e instead of PI. Enter a number and have the program generate e up to that many decimal places. Keep a limit to how far the program will go. Fibonacci Sequence - Enter a number and have the program generate the Fibonacci sequence to that number or to the Nth number. Prime Factorization - Have the user enter a number and find all Prime Factors (if there are any) and display them. Next Prime Number - Have the program find prime numbers until the user chooses to stop asking for the next one. Find Cost of Tile to Cover W x H Floor - Calculate the total cost of the tile it would take to cover a floor plan of width and height, using a cost entered by the user. Mortgage Calculator - Calculate the monthly payments of a fixed-term mortgage over given Nth terms at a given interest rate. Also, figure out how long it will take the user to pay back the loan. For added complexity, add an option for users to select the compounding interval (Monthly, Weekly, Daily, Continually). Change Return Program - The user enters a cost and then the amount of money given. The program will figure out the change and the number of quarters, dimes, nickels, pennies needed for the change. Binary to Decimal and Back Converter - Develop a converter to convert a decimal number to binary or a binary number to its decimal equivalent. Calculator - A simple calculator to do basic operators. Make it a scientific calculator for added complexity. Unit Converter (temp, currency, volume, mass, and more) - Converts various units between one another. The user enters the type of unit being entered, the type of unit they want to convert to, and then the value. The program will then make the conversion. Alarm Clock - A simple clock where it plays a sound after X number of minutes/seconds or at a particular time. Distance Between Two Cities - Calculates the distance between two cities and allows the user to specify a unit of distance. This program may require finding coordinates for the cities like latitude and longitude. Credit Card Validator - Takes in a credit card number from a common credit card vendor (Visa, MasterCard, American Express, Discoverer) and validates it to make sure that it is a valid number (look into how credit cards use a checksum). Tax Calculator - Asks the user to enter a cost and either a country or state tax. It then returns the tax plus the total cost with tax. Factorial Finder - The Factorial of a positive integer, n, is defined as the product of the sequence n, n-1, n-2, ...1, and the factorial of zero, 0, is defined as being 1. Solve this using both loops and recursion. Complex Number Algebra - Show addition, multiplication, negation, and inversion of complex numbers in separate functions. (Subtraction and division operations can be made with pairs of these operations.) Print the results for each operation tested. Happy Numbers - A happy number is defined by the following process. Starting with any positive integer, replace the number by the sum of the squares of its digits, and repeat the process until the number equals 1 (where it will stay), or it loops endlessly in a cycle which does not include 1. Those numbers for which this process ends in 1 are happy numbers, while those that do not end in 1 are unhappy numbers. Display an example of your output here. Find the first 8 happy numbers. Number Names - Show how to spell out a number in English. You can use a preexisting implementation or roll your own, but you should support inputs up to at least one million (or the maximum value of your language's default bounded integer type if that's less). Optional: Support for inputs other than positive integers (like zero, negative integers, and floating-point numbers). Coin Flip Simulation - Write some code that simulates flipping a single coin however many times the user decides. The code should record the outcomes and count the number of tails and heads. Limit Calculator - Ask the user to enter f(x) and the limit value, then return the value of the limit statement Optional: Make the calculator capable of supporting infinite limits. Fast Exponentiation - Ask the user to enter 2 integers a and b and output a^b (i.e. pow(a,b)) in O(LG n) time complexity. Classic Algorithms Collatz Conjecture - Start with a number n > 1. Find the number of steps it takes to reach one using the following process: If n is even, divide it by 2. If n is odd, multiply it by 3 and add 1. Sorting - Implement two types of sorting algorithms: Merge sort and bubble sort. Closest pair problem - The closest pair of points problem or closest pair problem is a problem of computational geometry: given n points in metric space, find a pair of points with the smallest distance between them. Sieve of Eratosthenes - The sieve of Eratosthenes is one of the most efficient ways to find all of the smaller primes (below 10 million or so). Graph Graph from links - Create a program that will create a graph or network from a series of links. Eulerian Path - Create a program that will take as an input a graph and output either an Eulerian path or an Eulerian cycle, or state that it is not possible. An Eulerian path starts at one node and traverses every edge of a graph through every node and finishes at another node. An Eulerian cycle is an eulerian Path that starts and finishes at the same node. Connected Graph - Create a program that takes a graph as an input and outputs whether every node is connected or not. Dijkstra’s Algorithm - Create a program that finds the shortest path through a graph using its edges. Minimum Spanning Tree - Create a program that takes a connected, undirected graph with weights and outputs the minimum spanning tree of the graph i.e., a subgraph that is a tree, contains all the vertices, and the sum of its weights is the least possible. Data Structures Inverted index - An Inverted Index is a data structure used to create full-text search. Given a set of text files, implement a program to create an inverted index. Also, create a user interface to do a search using that inverted index which returns a list of files that contain the query term/terms. The search index can be in memory. Text Fizz Buzz - Write a program that prints the numbers from 1 to 100. But for multiples of three print “Fizz” instead of the number and for the multiples of five print “Buzz”. For numbers which are multiples of both three and five print “FizzBuzz”. Reverse a String - Enter a string and the program will reverse it and print it out. Pig Latin - Pig Latin is a game of alterations played in the English language game. To create the Pig Latin form of an English word the initial consonant sound is transposed to the end of the word and an ay is affixed (Ex.: "banana" would yield anana-bay). Read Wikipedia for more information on rules. Count Vowels - Enter a string and the program counts the number of vowels in the text. For added complexity have it report a sum of each vowel found. Check if Palindrome - Checks if the string entered by the user is a palindrome. That is that it reads the same forwards as backward like “racecar” Count Words in a String - Counts the number of individual words in a string. For added complexity read these strings in from a text file and generate a summary. Text Editor - Notepad-style application that can open, edit, and save text documents. Optional: Add syntax highlighting and other features. RSS Feed Creator - Given a link to RSS/Atom Feed, get all posts and display them. Quote Tracker (market symbols etc) - A program that can go out and check the current value of stocks for a list of symbols entered by the user. The user can set how often the stocks are checked. For CLI, show whether the stock has moved up or down. Optional: If GUI, the program can show green up and red down arrows to show which direction the stock value has moved. Guestbook / Journal - A simple application that allows people to add comments or write journal entries. It can allow comments or not and timestamps for all entries. Could also be made into a shoutbox. Optional: Deploy it on Google App Engine or Heroku or any other PaaS (if possible, of course). Vigenere / Vernam / Ceasar Ciphers - Functions for encrypting and decrypting data messages. Then send them to a friend. Regex Query Tool - A tool that allows the user to enter a text string and then in a separate control enter a regex pattern. It will run the regular expression against the source text and return any matches or flag errors in the regular expression. Networking FTP Program - A file transfer program that can transfer files back and forth from a remote web sever. Bandwidth Monitor - A small utility program that tracks how much data you have uploaded and downloaded from the net during the course of your current online session. See if you can find out what periods of the day you use more and less and generate a report or graph that shows it. Port Scanner - Enter an IP address and a port range where the program will then attempt to find open ports on the given computer by connecting to each of them. On any successful connections mark the port as open. Mail Checker (POP3 / IMAP) - The user enters various account information include web server and IP, protocol type (POP3 or IMAP), and the application will check for email at a given interval. Country from IP Lookup - Enter an IP address and find the country that IP is registered in. Optional: Find the Ip automatically. Whois Search Tool - Enter an IP or host address and have it look it up through whois and return the results to you. Site Checker with Time Scheduling - An application that attempts to connect to a website or server every so many minute or a given time and check if it is up. If it is down, it will notify you by email or by posting a notice on the screen. Classes Product Inventory Project - Create an application that manages an inventory of products. Create a product class that has a price, id, and quantity on hand. Then create an inventory class that keeps track of various products and can sum up the inventory value. Airline / Hotel Reservation System - Create a reservation system that books airline seats or hotel rooms. It charges various rates for particular sections of the plane or hotel. For example, first class is going to cost more than a coach. Hotel rooms have penthouse suites which cost more. Keep track of when rooms will be available and can be scheduled. Company Manager - Create a hierarchy of classes - abstract class Employee and subclasses HourlyEmployee, SalariedEmployee, Manager, and Executive. Everyone's pay is calculated differently, research a bit about it. After you've established an employee hierarchy, create a Company class that allows you to manage the employees. You should be able to hire, fire, and raise employees. Bank Account Manager - Create a class called Account which will be an abstract class for three other classes called CheckingAccount, SavingsAccount, and BusinessAccount. Manage credits and debits from these accounts through an ATM-style program. Patient / Doctor Scheduler - Create a patient class and a doctor class. Have a doctor that can handle multiple patients and set up a scheduling program where a doctor can only handle 16 patients during an 8 hr workday. Recipe Creator and Manager - Create a recipe class with ingredients and put them in a recipe manager program that organizes them into categories like desserts, main courses, or by ingredients like chicken, beef, soups, pies, etc. Image Gallery - Create an image abstract class and then a class that inherits from it for each image type. Put them in a program that displays them in a gallery-style format for viewing. Shape Area and Perimeter Classes - Create an abstract class called Shape and then inherit from it other shapes like diamond, rectangle, circle, triangle, etc. Then have each class override the area and perimeter functionality to handle each shape type. Flower Shop Ordering To Go - Create a flower shop application that deals in flower objects and use those flower objects in a bouquet object which can then be sold. Keep track of the number of objects and when you may need to order more. Family Tree Creator - Create a class called Person which will have a name, when they were born, and when (and if) they died. Allow the user to create these Person classes and put them into a family tree structure. Print out the tree to the screen. Threading Create A Progress Bar for Downloads - Create a progress bar for applications that can keep track of a download in progress. The progress bar will be on a separate thread and will communicate with the main thread using delegates. Bulk Thumbnail Creator - Picture processing can take a bit of time for some transformations. Especially if the image is large. Create an image program that can take hundreds of images and converts them to a specified size in the background thread while you do other things. For added complexity, have one thread handling re-sizing, have another bulk renaming of thumbnails, etc. Web Page Scraper - Create an application that connects to a site and pulls out all links, or images, and saves them to a list. Optional: Organize the indexed content and don’t allow duplicates. Have it put the results into an easily searchable index file. Online White Board - Create an application that allows you to draw pictures, write notes and use various colors to flesh out ideas for projects. Optional: Add a feature to invite friends to collaborate on a whiteboard online. Get Atomic Time from Internet Clock - This program will get the true atomic time from an atomic time clock on the Internet. Use any one of the atomic clocks returned by a simple Google search. Fetch Current Weather - Get the current weather for a given zip/postal code. Optional: Try locating the user automatically. Scheduled Auto Login and Action - Make an application that logs into a given site on a schedule and invokes a certain action and then logs out. This can be useful for checking webmail, posting regular content, or getting info for other applications and saving it to your computer. E-Card Generator - Make a site that allows people to generate their own little e-cards and send them to other people. Do not use Flash. Use a picture library and perhaps insightful mottos or quotes. Content Management System - Create a content management system (CMS) like Joomla, Drupal, PHP Nuke, etc. Start small. Optional: Allow for the addition of modules/addons. Web Board (Forum) - Create a forum for you and your buddies to post, administer and share thoughts and ideas. CAPTCHA Maker - Ever see those images with letters numbers when you signup for a service and then ask you to enter what you see? It keeps web bots from automatically signing up and spamming. Try creating one yourself for online forms. Files Quiz Maker - Make an application that takes various questions from a file, picked randomly, and puts together a quiz for students. Each quiz can be different and then reads a key to grade the quizzes. Sort Excel/CSV File Utility - Reads a file of records, sorts them, and then writes them back to the file. Allow the user to choose various sort style and sorting based on a particular field. Create Zip File Maker - The user enters various files from different directories and the program zips them up into a zip file. Optional: Apply actual compression to the files. Start with Huffman Algorithm. PDF Generator - An application that can read in a text file, HTML file, or some other file and generates a PDF file out of it. Great for a web-based service where the user uploads the file and the program returns a PDF of the file. Optional: Deploy on GAE or Heroku if possible. Mp3 Tagger - Modify and add ID3v1 tags to MP3 files. See if you can also add in the album art into the MP3 file’s header as well as other ID3v2 tags. Code Snippet Manager - Another utility program that allows coders to put in functions, classes, or other tidbits to save for use later. Organized by the type of snippet or language the coder can quickly lookup code. Optional: For extra practice try adding syntax highlighting based on the language. Databases SQL Query Analyzer - A utility application in which a user can enter a query and have it run against a local database and look for ways to make it more efficient. Remote SQL Tool - A utility that can execute queries on remote servers from your local computer across the Internet. It should take in a remote host, user name, and password, run the query and return the results. Report Generator - Create a utility that generates a report based on some tables in a database. Generates sales reports based on the order/order details tables or sums up the day's current database activity. Event Scheduler and Calendar - Make an application that allows the user to enter a date and time of an event, event notes, and then schedule those events on a calendar. The user can then browse the calendar or search the calendar for specific events. Optional: Allow the application to create re-occurrence events that reoccur every day, week, month, year, etc. Budget Tracker - Write an application that keeps track of a household’s budget. The user can add expenses, income, and recurring costs to find out how much they are saving or losing over a period of time. Optional: Allow the user to specify a date range and see the net flow of money in and out of the house budget for that time period. TV Show Tracker - Got a favorite show you don’t want to miss? Don’t have a PVR or want to be able to find the show to then PVR it later? Make an application that can search various online TV Guide sites, locate the shows/times/channels and add them to a database application. The database/website then can send you email reminders that a show is about to start and which channel it will be on. Travel Planner System - Make a system that allows users to put together their own little travel itinerary and keep track of the airline/hotel arrangements, points of interest, budget, and schedule. Graphics and Multimedia Slide Show - Make an application that shows various pictures in a slide show format. Optional: Try adding various effects like fade in/out, star wipe, and window blinds transitions. Stream Video from Online - Try to create your own online streaming video player. Mp3 Player - A simple program for playing your favorite music files. Add features you think are missing from your favorite music player. Watermarking Application - Have some pictures you want copyright protected? Add your own logo or text lightly across the background so that no one can simply steal your graphics off your site. Make a program that will add this watermark to the picture. Optional: Use threading to process multiple images simultaneously. Turtle Graphics - This is a common project where you create a floor of 20 x 20 squares. Using various commands you tell a turtle to draw a line on the floor. You have moved forward, left or right, lift or drop the pen, etc. Do a search online for "Turtle Graphics" for more information. Optional: Allow the program to read in the list of commands from a file. GIF Creator A program that puts together multiple images (PNGs, JPGs, TIFFs) to make a smooth GIF that can be exported. Optional: Make the program convert small video files to GIFs as well. Security Caesar cipher - Implement a Caesar cipher, both encoding, and decoding. The key is an integer from 1 to 25. This cipher rotates the letters of the alphabet (A to Z). The encoding replaces each letter with the 1st to 25th next letter in the alphabet (wrapping Z to A). So key 2 encrypts "HI" to "JK", but key 20 encrypts "HI" to "BC". This simple "monoalphabetic substitution cipher" provides almost no security, because an attacker who has the encoded message can either use frequency analysis to guess the key, or just try all 25 keys.

The Rice Wavelet Toolbox (RWT) is a collection of Matlab M-files and C MEX-files for 1D and 2D wavelet and filter bank design, analysis, and processing. The toolbox provides tools for denoising and interfaces directly with our Matlab code for wavelet domain hidden Markov models and wavelet regularized deconvolution.

Bank credit card deep layout analysis, fields extraction and recognition/OCR (ScanToPay) using deep learning

Voice stress analysis (VSA) aims to differentiate between stressed and non-stressed outputs in response to stimuli (e.g., questions posed), with high stress seen as an indication of deception. In this work, we propose a deep learning-based psychological stress detection model using speech signals. With increasing demands for communication between humans and intelligent systems, automatic stress detection is becoming an interesting research topic. Stress can be reliably detected by measuring the level of specific hormones (e.g., cortisol), but this is not a convenient method for the detection of stress in human- machine interactions. The proposed algorithm first extracts Mel- filter bank coefficients using pre-processed speech data and then predicts the status of stress output using a binary decision criterion (i.e., stressed or unstressed) using CNN (Convolutional Neural Network) and dense fully connected layer networks.

Flask-based application that analyses bank statements to generate interactive visualizations and analytical reports. Features include OCR-based data extraction, machine learning for transaction categorization, Azure SQL database integration, and PowerBI dashboards for expense tracking.

Methods for the parallel and distributed analysis and mining of the Protein Data Bank using MMTF and Apache Spark.

Improve marketing campaign of a Portuguese bank by analyzing their past marketing campaign data and recommending which customer to target

EDA Project using Python & Pandas Framework

A Google Earth Engine API (interactive dashboard) for satellite-based global climate hazard analysis (urban heat, landcover changes, etc). Project under World Bank Group. ⬇️ ⬇️

If you are professionals, retailers or even organisms trading on a financial market, you know that data given on online platforms is not precise enough. Banks and huge financial institutions use powerful computers to transact a large number of orders at very fast speed. Thus, thousands of buying and selling orders are launched within a fraction of a second. So why can’t you see it? High Frequency Data analyser, is an innovative program helping you to analyze high frequency data in order to increase your understanding of the market. High Frequency Data takes trading to the next level, transforming your order book into a interactive visual map. You can now observe visual trading patterns and create new trading strategies. You can chose the parameters you are interested in. ( Volume/askBid). You can observe executed orders and analyse the impact of these executions on the market. You can pause and save the graph whenever you want in order to study more deeply a situation. It plays real time information and offers you many indicators to get an accurate vision of the market.(market indicator) You can visualize the available shares for each value with a very deep order book. (avancer dans le order book). You can also export your data into Excel and draw your most significant graphs. For a deeper and precise order book analysis, download High Frequency Data analyser now!

No description available

It provides access to and information about the most important Brazilian economic time series - from the Getulio Vargas Foundation <http://portal.fgv.br/en>, the Central Bank of Brazil <http://www.bcb.gov.br> and the Brazilian Institute of Geography and Statistics <http://www.ibge.gov.br>. It also presents tools for managing, analysing (e.g. generating dynamic reports with a complete analysis of a series) and exporting these time series.

Bank statement analysis for detecting fraudulent transactions/money laundering

The project focused on the use of public data to assess the economic situation in the country based on the state of the stock market and national means of payment, in particular - of the national currency. As sources are used: Open data Ministry of Finance of the Russian Federation These Moscow Exchange Google Finance Data Technologies used: Backend: Databases (relational) - Microsoft SQL Server 2014 Databases (multivariate) models DataMining, OLAP-cube - Microsoft Analysis Services 12.0 Веб-сервер - Windows Server 2012 / Internet Information Services Самописный ASP.NET HTTP Restful интерфейс для взаимодействия с Frontend ETL (загрузка и пре-процессинг данных, управление обновлением данных) SQL Server Integration Services 2014 (разработка в Visual Studio 2013, SSDT) Frontend: AngularJS ChartJS Twitter Bootstrap These were chosen so that the detail (granularity) in the set is not less than 1 day. The result has been created and filled with data analytic repository (Kimball model, topology - star), which was used to build a multi-dimensional databases and OLAP-based cubes on it, as well as models of analysis of data on two main algorithms: Microsoft Time Series, Microsoft Neural Network . To ensure interoperability frontend and backend server for backend-server was set up HTTP-Restful interface JSON-issuing documents in the form of finished sets. The project includes two main areas: Intelligent visualization of open data Analysis of open data and the construction of forecasts based on them Intelligent visualization involves the use of MDX-queries to the OLAP-cube, followed by depression (drilldown) in the data, the system allows the user to quickly find the "weak points" of the economy, as part of the data collected. To predict the time a standard mix of algorithms ARTXP / ARIMA, without the use of queries involving cross-prediction (but it is possible to enroll in the system correct data). These algorithms have been tested primarily on foreign exchange rates (US dollar) and the assets of banks included in the special list of Ministry of Finance. In addition, for assets shows the different customization options algorithms - a long-term, short-term and medium-term (balanced) plan. Assessing the impact of oil prices and foreign currency exchange rate for the total market capitalization was conducted on a sample of the data collected: companies with a total market capitalization of 100 to 500 million rubles, present in the market during 2013-2015 Analytical server builds the neural network receiving the input exchange rates, companies, the weighted average share price, total capitalization of the company and the price of oil to requests received models give the opportunity to evaluate the growth rate of \ fall (if at all) the company's capitalization at historical exchange rates and / or the cost of oil. Built a system can expand to include new indicators, which will significantly increase the accuracy of forecasting.

​ ​#​ ​Ukraine-Cyber-Operations ​Curated Intelligence is working with analysts from around the world to provide useful information to organisations in Ukraine looking for additional free threat intelligence. Slava Ukraini. Glory to Ukraine. ([​Blog​](https://www.curatedintel.org/2021/08/welcome.html) | [​Twitter​](https://twitter.com/CuratedIntel) | [​LinkedIn​](https://www.linkedin.com/company/curatedintelligence/)) ​ ​ ​###​ ​Analyst Comments: ​-​ 2022-02-25 ​  ​-​ Creation of the initial repository to help organisations in Ukraine ​  ​-​ Added [​Threat Reports​](https://github.com/curated-intel/Ukraine-Cyber-Operations#threat-reports) section ​  ​-​ Added [​Vendor Support​](https://github.com/curated-intel/Ukraine-Cyber-Operations#vendor-support) section ​-​ 2022-02-26 ​  ​-​ Additional resources, chronologically ordered (h/t Orange-CD) ​  ​-​ Added [​Vetted OSINT Sources​](https://github.com/curated-intel/Ukraine-Cyber-Operations#vetted-osint-sources) section  ​  ​-​ Added [​Miscellaneous Resources​](https://github.com/curated-intel/Ukraine-Cyber-Operations#miscellaneous-resources) section ​-​ 2022-02-27 ​  ​-​ Additional threat reports have been added ​  ​-​ Added [​Data Brokers​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/README.md#data-brokers) section ​  ​-​ Added [​Access Brokers​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/README.md#access-brokers) section ​-​ 2022-02-28 ​  ​-​ Added Russian Cyber Operations Against Ukraine Timeline by ETAC ​  ​-​ Added Vetted and Contextualized [​Indicators of Compromise (IOCs)​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/ETAC_Vetted_UkraineRussiaWar_IOCs.csv) by ETAC ​-​ 2022-03-01 ​  ​-​ Additional threat reports and resources have been added ​-​ 2022-03-02 ​  ​-​ Additional [​Indicators of Compromise (IOCs)​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/ETAC_Vetted_UkraineRussiaWar_IOCs.csv#L2011) have been added ​  ​-​ Added vetted [​YARA rule collection​](https://github.com/curated-intel/Ukraine-Cyber-Operations/tree/main/yara) from the Threat Reports by ETAC ​  ​-​ Added loosely-vetted [​IOC Threat Hunt Feeds​](https://github.com/curated-intel/Ukraine-Cyber-Operations/tree/main/KPMG-Egyde_Ukraine-Crisis_Feeds/MISP-CSV_MediumConfidence_Filtered) by KPMG-Egyde CTI (h/t [​0xDISREL​](https://twitter.com/0xDISREL)) ​    ​-​ IOCs shared by these feeds are ​`LOW-TO-MEDIUM CONFIDENCE`​ we strongly recommend NOT adding them to a blocklist ​    ​-​ These could potentially be used for ​`THREAT HUNTING`​ and could be added to a ​`WATCHLIST` ​    ​-​ IOCs are generated in ​`MISP COMPATIBLE`​ CSV format ​-​ 2022-03-03 ​  ​-​ Additional threat reports and vendor support resources have been added ​  ​-​ Updated [​Log4Shell IOC Threat Hunt Feeds​](https://github.com/curated-intel/Log4Shell-IOCs/tree/main/KPMG_Log4Shell_Feeds) by KPMG-Egyde CTI; not directly related to Ukraine, but still a widespread vulnerability. ​  ​-​ Added diagram of Russia-Ukraine Cyberwar Participants 2022 by ETAC ​  ​-​ Additional [​Indicators of Compromise (IOCs)​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/ETAC_Vetted_UkraineRussiaWar_IOCs.csv#L2042) have been added ​####​ ​`Threat Reports` ​| Date | Source | Threat(s) | URL | ​| --- | --- | --- | --- | ​| 14 JAN | SSU Ukraine | Website Defacements | [ssu.gov.ua](https://ssu.gov.ua/novyny/sbu-rozsliduie-prychetnist-rosiiskykh-spetssluzhb-do-sohodnishnoi-kiberataky-na-orhany-derzhavnoi-vlady-ukrainy)| ​| 15 JAN | Microsoft | WhisperGate wiper (DEV-0586) | [microsoft.com](https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/) | ​| 19 JAN | Elastic | WhisperGate wiper (Operation BleedingBear) | [elastic.github.io](https://elastic.github.io/security-research/malware/2022/01/01.operation-bleeding-bear/article/) | ​| 31 JAN | Symantec | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [symantec-enterprise-blogs.security.com](https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/shuckworm-gamaredon-espionage-ukraine) | ​| 2 FEB | RaidForums | Access broker "GodLevel" offering Ukrainain algricultural exchange | RaidForums [not linked] | ​| 2 FEB | CERT-UA | UAC-0056 using SaintBot and OutSteel malware | [cert.gov.ua](https://cert.gov.ua/article/18419) | ​| 3 FEB | PAN Unit42 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [unit42.paloaltonetworks.com](https://unit42.paloaltonetworks.com/gamaredon-primitive-bear-ukraine-update-2021/) | ​| 4 FEB | Microsoft | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [microsoft.com](https://www.microsoft.com/security/blog/2022/02/04/actinium-targets-ukrainian-organizations/) | ​| 8 FEB | NSFOCUS | Lorec53 (aka UAC-0056, EmberBear, BleedingBear) | [nsfocusglobal.com](https://nsfocusglobal.com/apt-retrospection-lorec53-an-active-russian-hack-group-launched-phishing-attacks-against-georgian-government) | ​| 15 FEB | CERT-UA | DDoS attacks against the name server of government websites as well as Oschadbank (State Savings Bank) & Privatbank (largest commercial bank). False SMS and e-mails to create panic | [cert.gov.ua](https://cert.gov.ua/article/37139) | ​| 23 FEB | The Daily Beast | Ukrainian troops receive threatening SMS messages | [thedailybeast.com](https://www.thedailybeast.com/cyberattacks-hit-websites-and-psy-ops-sms-messages-targeting-ukrainians-ramp-up-as-russia-moves-into-ukraine) | ​| 23 FEB | UK NCSC | Sandworm/VoodooBear (GRU) | [ncsc.gov.uk](https://www.ncsc.gov.uk/files/Joint-Sandworm-Advisory.pdf) | ​| 23 FEB | SentinelLabs | HermeticWiper | [sentinelone.com]( https://www.sentinelone.com/labs/hermetic-wiper-ukraine-under-attack/ ) | ​| 24 FEB | ESET | HermeticWiper | [welivesecurity.com](https://www.welivesecurity.com/2022/02/24/hermeticwiper-new-data-wiping-malware-hits-ukraine/) | ​| 24 FEB | Symantec | HermeticWiper, PartyTicket ransomware, CVE-2021-1636, unknown webshell | [symantec-enterprise-blogs.security.com](https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/ukraine-wiper-malware-russia) | ​| 24 FEB | Cisco Talos | HermeticWiper | [blog.talosintelligence.com](https://blog.talosintelligence.com/2022/02/threat-advisory-hermeticwiper.html) | ​| 24 FEB | Zscaler | HermeticWiper | [zscaler.com](https://www.zscaler.com/blogs/security-research/hermetic-wiper-resurgence-targeted-attacks-ukraine) | ​| 24 FEB | Cluster25 | HermeticWiper | [cluster25.io](https://cluster25.io/2022/02/24/ukraine-analysis-of-the-new-disk-wiping-malware/) | ​| 24 FEB | CronUp | Data broker "FreeCivilian" offering multiple .gov.ua | [twitter.com/1ZRR4H](https://twitter.com/1ZRR4H/status/1496931721052311557)| ​| 24 FEB | RaidForums | Data broker "Featherine" offering diia.gov.ua | RaidForums [not linked] | ​| 24 FEB | DomainTools | Unknown scammers | [twitter.com/SecuritySnacks](https://twitter.com/SecuritySnacks/status/1496956492636905473?s=20&t=KCIX_1Ughc2Fs6Du-Av0Xw) | ​| 25 FEB | @500mk500 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [twitter.com/500mk500](https://twitter.com/500mk500/status/1497339266329894920?s=20&t=opOtwpn82ztiFtwUbLkm9Q) | ​| 25 FEB | @500mk500 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [twitter.com/500mk500](https://twitter.com/500mk500/status/1497208285472215042)| ​| 25 FEB | Microsoft | HermeticWiper | [gist.github.com](https://gist.github.com/fr0gger/7882fde2b1b271f9e886a4a9b6fb6b7f) | ​| 25 FEB | 360 NetLab | DDoS (Mirai, Gafgyt, IRCbot, Ripprbot, Moobot) | [blog.netlab.360.com](https://blog.netlab.360.com/some_details_of_the_ddos_attacks_targeting_ukraine_and_russia_in_recent_days/) | ​| 25 FEB | Conti [themselves] | Conti ransomware, BazarLoader | Conti News .onion [not linked] | ​| 25 FEB | CoomingProject [themselves] | Data Hostage Group | CoomingProject Telegram [not linked] | ​| 25 FEB | CERT-UA | UNC1151/Ghostwriter (Belarus MoD) | [CERT-UA Facebook](https://facebook.com/story.php?story_fbid=312939130865352&id=100064478028712)| ​| 25 FEB | Sekoia | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com/sekoia_io](https://twitter.com/sekoia_io/status/1497239319295279106) | ​| 25 FEB | @jaimeblascob | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com/jaimeblasco](https://twitter.com/jaimeblascob/status/1497242668627370009)| ​| 25 FEB | RISKIQ | UNC1151/Ghostwriter (Belarus MoD) | [community.riskiq.com](https://community.riskiq.com/article/e3a7ceea/) | ​| 25 FEB | MalwareHunterTeam | Unknown phishing | [twitter.com/malwrhunterteam](https://twitter.com/malwrhunterteam/status/1497235270416097287) | ​| 25 FEB | ESET | Unknown scammers | [twitter.com/ESETresearch](https://twitter.com/ESETresearch/status/1497194165561659394) | ​| 25 FEB | BitDefender | Unknown scammers | [blog.bitdefender.com](https://blog.bitdefender.com/blog/hotforsecurity/cybercriminals-deploy-spam-campaign-as-tens-of-thousands-of-ukrainians-seek-refuge-in-neighboring-countries/) | ​| 25 FEB | SSSCIP Ukraine | Unkown phishing | [twitter.com/dsszzi](https://twitter.com/dsszzi/status/1497103078029291522) | ​| 25 FEB | RaidForums | Data broker "NetSec"  offering FSB (likely SMTP accounts) | RaidForums [not linked] | ​| 25 FEB | Zscaler | PartyTicket decoy ransomware | [zscaler.com](https://www.zscaler.com/blogs/security-research/technical-analysis-partyticket-ransomware) | ​| 25 FEB | INCERT GIE | Cyclops Blink, HermeticWiper | [linkedin.com](https://www.linkedin.com/posts/activity-6902989337210740736-XohK) [Login Required] | ​| 25 FEB | Proofpoint | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com/threatinsight](https://twitter.com/threatinsight/status/1497355737844133895?s=20&t=Ubi0tb_XxGCbHLnUoQVp8w) | ​| 25 FEB | @fr0gger_ | HermeticWiper capabilities Overview | [twitter.com/fr0gger_](https://twitter.com/fr0gger_/status/1497121876870832128?s=20&t=_296n0bPeUgdXleX02M9mg) ​| 26 FEB | BBC Journalist | A fake Telegram account claiming to be President Zelensky is posting dubious messages | [twitter.com/shayan86](https://twitter.com/shayan86/status/1497485340738785283?s=21) | ​| 26 FEB | CERT-UA | UNC1151/Ghostwriter (Belarus MoD) | [CERT_UA Facebook](https://facebook.com/story.php?story_fbid=313517477474184&id=100064478028712) | ​| 26 FEB | MHT and TRMLabs | Unknown scammers, linked to ransomware | [twitter.com/joes_mcgill](https://twitter.com/joes_mcgill/status/1497609555856932864?s=20&t=KCIX_1Ughc2Fs6Du-Av0Xw) | ​| 26 FEB | US CISA | WhisperGate wiper, HermeticWiper | [cisa.gov](https://www.cisa.gov/uscert/ncas/alerts/aa22-057a) | ​| 26 FEB | Bloomberg | Destructive malware (possibly HermeticWiper) deployed at Ukrainian Ministry of Internal Affairs & data stolen from Ukrainian telecommunications networks | [bloomberg.com](https://www.bloomberg.com/news/articles/2022-02-26/hackers-destroyed-data-at-key-ukraine-agency-before-invasion?sref=ylv224K8) | ​| 26 FEB | Vice Prime Minister of Ukraine | IT ARMY of Ukraine created to crowdsource offensive operations against Russian infrastructure | [twitter.com/FedorovMykhailo](https://twitter.com/FedorovMykhailo/status/1497642156076511233) | ​| 26 FEB | Yoroi | HermeticWiper | [yoroi.company](https://yoroi.company/research/diskkill-hermeticwiper-a-disruptive-cyber-weapon-targeting-ukraines-critical-infrastructures) | ​| 27 FEB | LockBit [themselves] | LockBit ransomware | LockBit .onion [not linked] |  ​| 27 FEB | ALPHV [themselves] | ALPHV ransomware | vHUMINT [closed source] | ​| 27 FEB | Mēris Botnet [themselves] | DDoS attacks | vHUMINT [closed source] | ​| 28 FEB | Horizon News [themselves] | Leak of China's Censorship Order about Ukraine | [TechARP](https://www-techarp-com.cdn.ampproject.org/c/s/www.techarp.com/internet/chinese-media-leaks-ukraine-censor/?amp=1)| ​| 28 FEB | Microsoft | FoxBlade (aka HermeticWiper) | [Microsoft](https://blogs.microsoft.com/on-the-issues/2022/02/28/ukraine-russia-digital-war-cyberattacks/?preview_id=65075) | ​| 28 FEB | @heymingwei | Potential BGP hijacks attempts against Ukrainian Internet Names Center | [https://twitter.com/heymingwei](https://twitter.com/heymingwei/status/1498362715198263300?s=20&t=Ju31gTurYc8Aq_yZMbvbxg) | ​| 28 FEB | @cyberknow20 | Stormous ransomware targets Ukraine Ministry of Foreign Affairs | [twitter.com/cyberknow20](https://twitter.com/cyberknow20/status/1498434090206314498?s=21) |  ​| 1 MAR | ESET | IsaacWiper and HermeticWizard | [welivesecurity.com](https://www.welivesecurity.com/2022/03/01/isaacwiper-hermeticwizard-wiper-worm-targeting-ukraine/) | ​| 1 MAR | Proofpoint | Ukrainian armed service member's email compromised and sent malspam containing the SunSeed malware (likely TA445/UNC1151/Ghostwriter) | [proofpoint.com](https://www.proofpoint.com/us/blog/threat-insight/asylum-ambuscade-state-actor-uses-compromised-private-ukrainian-military-emails) | ​| 1 MAR | Elastic | HermeticWiper | [elastic.github.io](https://elastic.github.io/security-research/intelligence/2022/03/01.hermeticwiper-targets-ukraine/article/) | ​| 1 MAR | CrowdStrike | PartyTicket (aka HermeticRansom), DriveSlayer (aka HermeticWiper) | [CrowdStrike](https://www.crowdstrike.com/blog/how-to-decrypt-the-partyticket-ransomware-targeting-ukraine/) | ​| 2 MAR | Zscaler | DanaBot operators launch DDoS attacks against the Ukrainian Ministry of Defense | [zscaler.com](https://www.zscaler.com/blogs/security-research/danabot-launches-ddos-attack-against-ukrainian-ministry-defense) | ​| 3 MAR | @ShadowChasing1 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [twitter.com/ShadowChasing1](https://twitter.com/ShadowChasing1/status/1499361093059153921) | ​| 3 MAR | @vxunderground | News website in Poland was reportedly compromised and the threat actor uploaded anti-Ukrainian propaganda | [twitter.com/vxunderground](https://twitter.com/vxunderground/status/1499374914758918151?s=20&t=jyy9Hnpzy-5P1gcx19bvIA) | ​| 3 MAR | @kylaintheburgh | Russian botnet on Twitter is pushing "#istandwithputin" and "#istandwithrussia" propaganda (in English) | [twitter.com/kylaintheburgh](https://twitter.com/kylaintheburgh/status/1499350578371067906?s=21) | ​| 3 MAR | @tracerspiff | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com](https://twitter.com/tracerspiff/status/1499444876810854408?s=21) | ​####​ ​`Access Brokers` ​| Date | Threat(s) | Source | ​| --- | --- | --- | ​| 23 JAN | Access broker "Mont4na" offering UkrFerry | RaidForums [not linked] | ​| 23 JAN | Access broker "Mont4na" offering PrivatBank | RaidForums [not linked] | ​| 24 JAN | Access broker "Mont4na" offering DTEK | RaidForums [not linked] | ​| 27 FEB | KelvinSecurity Sharing list of IP cameras in Ukraine | vHUMINT [closed source] | ​| 28 FEB | "w1nte4mute" looking to buy access to UA and NATO countries (likely ransomware affiliate) | vHUMINT [closed source] | ​####​ ​`Data Brokers` ​| Threat Actor    | Type            | Observation                                                                                               | Validated | Relevance                     | Source                                                     | ​| --------------- | --------------- | --------------------------------------------------------------------------------------------------------- | --------- | ----------------------------- | ---------------------------------------------------------- | ​| aguyinachair    | UA data sharing | PII DB of ukraine.com (shared as part of a generic compilation)                                           | No        | TA discussion in past 90 days | ELeaks Forum \[not linked\]                                | ​| an3key          | UA data sharing | DB of Ministry of Communities and Territories Development of Ukraine (minregion\[.\]gov\[.\]ua)           | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| an3key          | UA data sharing | DB of Ukrainian Ministry of Internal Affairs (wanted\[.\]mvs\[.\]gov\[.\]ua)                              | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (40M) of PrivatBank customers (privatbank\[.\]ua)                                                  | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | DB of "border crossing" DBs of DPR and LPR                                                                | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (7.5M) of Ukrainian passports                                                                      | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB of Ukrainian car registration, license plates, Ukrainian traffic police records                    | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (2.1M) of Ukrainian citizens                                                                       | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (28M) of Ukrainian citizens (passports, drivers licenses, photos)                                  | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (1M) of Ukrainian postal/courier service customers (novaposhta\[.\]ua)                             | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (10M) of Ukrainian telecom customers (vodafone\[.\]ua)                                             | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (3M) of Ukrainian telecom customers (lifecell\[.\]ua)                                              | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (13M) of Ukrainian telecom customers (kyivstar\[.\]ua)                                             | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| danieltx51      | UA data sharing | DB of Ministry of Foreign Affairs of Ukraine (mfa\[.\]gov\[.\]ua)                                         | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| DueDiligenceCIS | UA data sharing | PII DB (63M) of Ukrainian citizens (name, DOB, birth country, phone, TIN, passport, family, etc)          | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Featherine      | UA data sharing | DB of Ukrainian 'Diia' e-Governance Portal for Ministry of Digital Transformation of Ukraine              | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of Ministry for Internal Affairs of Ukraine public data search engine (wanted\[.\]mvs\[.\]gov\[.\]ua)  | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of Ministry for Communities and Territories Development of Ukraine (minregion\[.\]gov\[.\]ua)          | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of Motor Insurance Bureau of Ukraine (mtsbu\[.\]ua)                                                    | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | PII DB of Ukrainian digital-medicine provider (medstar\[.\]ua)                                            | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of ticket.kyivcity.gov.ua                                                                              | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of id.kyivcity.gov.ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of my.kyivcity.gov.ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of portal.kyivcity.gov.ua                                                                              | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of anti-violence-map.msp.gov.ua                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dopomoga.msp.gov.ua                                                                                 | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of e-services.msp.gov.ua                                                                               | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of edu.msp.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of education.msp.gov.ua                                                                                | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of ek-cbi.msp.gov.ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mail.msp.gov.ua                                                                                     | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of portal-gromady.msp.gov.ua                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of web-minsoc.msp.gov.ua                                                                               | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of wcs-wim.dsbt.gov.ua                                                                                 | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of bdr.mvs.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of motorsich.com                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dsns.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mon.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of minagro.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of zt.gov.ua                                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of kmu.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mvs.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dsbt.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of forest.gov.ua                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of nkrzi.gov.ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dabi.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of comin.gov.ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dp.dpss.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of esbu.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mms.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mova.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mspu.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of nads.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of reintegration.gov.ua                                                                                | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of sies.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of sport.gov.ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mepr.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mfa.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of va.gov.ua                                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mtu.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of cg.mvs.gov.ua                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of ch-tmo.mvs.gov.ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of cp.mvs.gov.ua                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of cpd.mvs.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of hutirvilnij-mrc.mvs.gov.ua                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dndekc.mvs.gov.ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of visnyk.dndekc.mvs.gov.ua                                                                            | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dpvs.hsc.gov.ua                                                                                     | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of odk.mvs.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of e-driver\[.\]hsc\[.\]gov\[.\]ua                                                                     | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of wanted\[.\]mvs\[.\]gov\[.\]ua                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of minregeion\[.\]gov\[.\]ua                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of health\[.\]mia\[.\]solutions                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mtsbu\[.\]ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of motorsich\[.\]com                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of kyivcity\[.\]com                                                                                    | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of bdr\[.\]mvs\[.\]gov\[.\]ua                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of gkh\[.\]in\[.\]ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of kmu\[.\]gov\[.\]ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mon\[.\]gov\[.\]ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of minagro\[.\]gov\[.\]ua                                                                              | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mfa\[.\]gov\[.\]ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| Intel\_Data     | UA data sharing | PII DB (56M) of Ukrainian Citizens                                                                        | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Kristina        | UA data sharing | DB of Ukrainian National Police (mvs\[.\]gov\[.\]ua)                                                      | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| NetSec          | UA data sharing | PII DB (53M) of Ukrainian citizens                                                                        | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Psycho\_Killer  | UA data sharing | PII DB (56M) of Ukrainian Citizens                                                                        | No        | TA discussion in past 90 days | Exploit Forum .onion \[not linked\]                        | ​| Sp333           | UA data sharing | PII DB of Ukrainian and Russian interpreters, translators, and tour guides                                | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Vaticano        | UA data sharing | DB of Ukrainian 'Diia' e-Governance Portal for Ministry of Digital Transformation of Ukraine \[copy\]     | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Vaticano        | UA data sharing | DB of Ministry for Communities and Territories Development of Ukraine (minregion\[.\]gov\[.\]ua) \[copy\] | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​####​ ​`Vendor Support` ​| Vendor | Offering | URL | ​| --- | --- | --- | ​| Dragos | Access to Dragos service if from US/UK/ANZ and in need of ICS cybersecurity support | [twitter.com/RobertMLee](https://twitter.com/RobertMLee/status/1496862093588455429) | ​| GreyNoise |  Any and all `Ukrainian` emails registered to GreyNoise have been upgraded to VIP which includes full, uncapped enterprise access to all GreyNoise products | [twitter.com/Andrew___Morris](https://twitter.com/Andrew___Morris/status/1496923545712091139) | ​| Recorded Future | Providing free intelligence-driven insights, perspectives, and mitigation strategies as the situation in Ukraine evolves| [recordedfuture.com](https://www.recordedfuture.com/ukraine/) | ​| Flashpoint | Free Access to Flashpoint’s Latest Threat Intel on Ukraine | [go.flashpoint-intel.com](https://go.flashpoint-intel.com/trial/access/30days) | ​| ThreatABLE | A Ukraine tag for free threat intelligence feed that's more highly curated to cyber| [twitter.com/threatable](https://twitter.com/threatable/status/1497233721803644950) | ​| Orange | IOCs related to Russia-Ukraine 2022 conflict extracted from our Datalake Threat Intelligence platform. | [github.com/Orange-Cyberdefense](https://github.com/Orange-Cyberdefense/russia-ukraine_IOCs)| ​| FSecure | F-Secure FREEDOME VPN is now available for free in all of Ukraine | [twitter.com/FSecure](https://twitter.com/FSecure/status/1497248407303462960) | ​| Multiple vendors | List of vendors offering their services to Ukraine for free, put together by [@chrisculling](https://twitter.com/chrisculling/status/1497023038323404803) | [docs.google.com/spreadsheets](https://docs.google.com/spreadsheets/d/18WYY9p1_DLwB6dnXoiiOAoWYD8X0voXtoDl_ZQzjzUQ/edit#gid=0) | ​| Mandiant | Free threat intelligence, webinar and guidance for defensive measures relevant to the situation in Ukraine. | [mandiant.com](https://www.mandiant.com/resources/insights/ukraine-crisis-resource-center) | ​| Starlink | Satellite internet constellation operated by SpaceX providing satellite Internet access coverage to Ukraine | [twitter.com/elonmusk](https://twitter.com/elonmusk/status/1497701484003213317) | ​| Romania DNSC | Romania’s DNSC – in partnership with Bitdefender – will provide technical consulting, threat intelligence and, free of charge, cybersecurity technology to any business, government institution or private citizen of Ukraine for as long as it is necessary. | [Romania's DNSC Press Release](https://dnsc.ro/citeste/press-release-dnsc-and-bitdefender-work-together-in-support-of-ukraine)| ​| BitDefender | Access to Bitdefender technical consulting, threat intelligence and both consumer and enterprise cybersecurity technology | [bitdefender.com/ukraine/](https://www.bitdefender.com/ukraine/) | ​| NameCheap | Free anonymous hosting and domain name registration to any anti-Putin anti-regime and protest websites for anyone located within Russia and Belarus | [twitter.com/Namecheap](https://twitter.com/Namecheap/status/1498998414020861953) | ​| Avast | Free decryptor for PartyTicket ransomware | [decoded.avast.io](https://decoded.avast.io/threatresearch/help-for-ukraine-free-decryptor-for-hermeticransom-ransomware/) |  ​####​ ​`Vetted OSINT Sources` ​| Handle | Affiliation | ​| --- | --- | ​| [@KyivIndependent](https://twitter.com/KyivIndependent) | English-language journalism in Ukraine | ​| [@IAPonomarenko](https://twitter.com/IAPonomarenko) | Defense reporter with The Kyiv Independent | ​| [@KyivPost](https://twitter.com/KyivPost) | English-language journalism in Ukraine | ​| [@Shayan86](https://twitter.com/Shayan86) | BBC World News Disinformation journalist | ​| [@Liveuamap](https://twitter.com/Liveuamap) | Live Universal Awareness Map (“Liveuamap”) independent global news and information site | ​| [@DAlperovitch](https://twitter.com/DAlperovitch) | The Alperovitch Institute for Cybersecurity Studies, Founder & Former CTO of CrowdStrike | ​| [@COUPSURE](https://twitter.com/COUPSURE) | OSINT investigator for Centre for Information Resilience | ​| [@netblocks](https://twitter.com/netblocks) | London-based Internet's Observatory | ​####​ ​`Miscellaneous Resources` ​| Source | URL | Content | ​| --- | --- | --- | ​| PowerOutages.com | https://poweroutage.com/ua | Tracking PowerOutages across Ukraine | ​| Monash IP Observatory | https://twitter.com/IP_Observatory | Tracking IP address outages across Ukraine | ​| Project Owl Discord | https://discord.com/invite/projectowl | Tracking foreign policy, geopolitical events, military and governments, using a Discord-based crowdsourced approach, with a current emphasis on Ukraine and Russia | ​| russianwarchatter.info | https://www.russianwarchatter.info/ | Known Russian Military Radio Frequencies |

Data Bank for Statistical Analysis and Visualization

GUI Stock index options open interest analysis with the use of python tkinter and NSE API for NIFTY, BANK NIFTY

Play bank data scientist and use regression discontinuity to see which debts are worth collecting.

The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit.

Methods for the parallel and distributed analysis and mining of the Protein Data Bank using MMTF and Apache Spark.

Desktop application for analysis and visualization of bank transaction data.

Privacy Policy introduction We understand the importance of personal information to you and will do our utmost to protect your personal information. We are committed to maintaining your trust in us and to abide by the following principles to protect your personal information: the principle of consistency of rights and responsibilities, the principle of purpose , choose the principle of consent, at least the principle of sufficient use, ensure the principle of security, the principle of subject participation, the principle of openness and transparency, and so on. At the same time, we promise that we will take appropriate security measures to protect your personal information according to the industry's mature security solutions. In view of this, we have formulated this "Private Privacy Policy" (hereinafter referred to as "this policy" /This Privacy Policy") and remind you: This policy applies to products or services on this platform. If the products or services provided by the platform are used in the products or services of our affiliates (for example, using the platform account directly) but there is no independent privacy policy, this policy also applies to the products or services. It is important to note that this policy does not apply to other third-party services provided by you, nor to products or services on this platform that have been independently set up with a privacy policy. Before using the products or services on this platform, please read and understand this policy carefully, and use the related products or services after confirming that you fully understand and agree. By using the products or services on this platform, you understand and agree to this policy. If you have any questions, comments or suggestions about the content of this policy, you can contact us through various contact methods provided by this platform. This privacy policy section will help you understand the following: How we collect and use your personal information How do we use cookies and similar technologies? How do we share, transfer, and publicly disclose your personal information? How we protect your personal information How do you manage your personal information? How do we deal with the personal information of minors? How your personal information is transferred globally How to update this privacy policy How to contact us 一、How we collect and use your personal information Personal information refers to various information recorded electronically or otherwise that can identify a specific natural person or reflect the activities of a particular natural person, either alone or in combination with other information. We collect and use your information for the purposes described in this policy. Personal information: （一）Help you become our user To create an account so that we can serve you, you will need to provide the following information: your nickname, avatar, gender, date of birth, mobile number/signal/QQ number, and create a username and password. During the registration process, if you provide the following additional information to supplement your personal information, it will help us to provide you with better service and experience: your real name, real ID information, hometown, emotional status, constellation, occupation, school Your real avatar. However, if you do not provide this information, it will not affect the basic functions of using the platform products or services. The above information provided by you will continue to authorize us during your use of the Service. When you voluntarily cancel your account, we will make it anonymous or delete your personal information as soon as possible in accordance with applicable laws and regulations. （二）Show and push goods or services for you In order to improve our products or services and provide you with personalized information search and transaction services, we will extract your browsing, search preferences, behavioral habits based on your browsing and search history, device information, location information, and transaction information. Features such as location information, indirect crowd portraits based on feature tags, and display and push information. 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To improve the security of your services provided by us and our affiliates and partners, protect the personal and property of you or other users or the public from being compromised, and better prevent phishing websites, fraud, network vulnerabilities, computer viruses, cyber attacks , security risks such as network intrusion, more accurately identify violations of laws and regulations or the relevant rules of the platform, we may use or integrate your user information, transaction information, equipment information, related web logs and our affiliates, partners to obtain You authorize or rely on the information shared by law to comprehensively judge your account and transaction risks, conduct identity verification, detect and prevent security incidents, and take necessary records, audits, analysis, and disposal measures in accordance with the law. （五）Other uses When we use the information for other purposes not covered by this policy, or if the information collected for a specific purpose is used for other purposes, you will be asked for your prior consent. （六）Exception for authorization of consent According to relevant laws and regulations, collecting your personal information in the following situations does not require your authorized consent: 1、Related to national security and national defense security； 2、Related to public safety, public health, and major public interests； 3、Related to criminal investigation, prosecution, trial and execution of judgments, etc.； 4、It is difficult to obtain your own consent for the maintenance of the important legal rights of the personal information or other individuals’ lives and property； 5、The personal information collected is disclosed to the public by yourself; 二、How do we use cookies and similar technologies? （一）Cookies To ensure that your site is up and running, to give you an easier access experience, and to recommend content that may be of interest to you, we store a small data file called a cookie on your computer or mobile device. 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Python code for generating Leung-Malik (LM) filter bank that is typically used in texture analysis and classification.

Identify the software project, create business case, arrive at a problem statement. REQUIREMENT: Window XP, Internet, MS Office, etc. Problem Description: - 1. Introduction of AI and Machine Learning: - Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial intelligence (AI) brings the genuine human-to-machine interaction. Simply, Machine Learning is the algorithm that give computers the ability to learn from data and then make decisions and predictions, AI refers to idea where machines can execute tasks smartly. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. When Machine learning is combined with Artificial Intelligence, it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique. The main difference between Machine Learning and Artificial Intelligence is , If it is written in python then it is probably machine learning, If it is written in power point then it is artificial intelligence. As there are many existing projects that are implemented using AI and Machine Learning , And one of the project i.e., Bitcoin Price Prediction :- Bitcoin (₿ ) (founder - Satoshi Nakamoto , Ledger start: 3 January 2009 ) is a digital currency, a type of electronic money. It is decentralized advanced cash without a national bank or single chairman that can be sent from client to client on the shared Bitcoin arrange without middle people's requirement. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. These machine learning models predict the future of Bitcoin by coding them out in Python. Machine learning and AI-assisted trading have attracted growing interest for the past few years. this approach is to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests , Bayesian neural network , long short-term memory neural network , and other algorithms. 2. Applications/Scope of AI and Machine Learning :- a) Sentiment Analysis :- It is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. b) It is Characterized as a use of computerized reasoning where accessible data is utilized through calculations to process or help the handling of factual information. BITCOIN PRICE PREDICTION USING AI AND MACHINE LEARNING: - The main aim of this is to find the actual Bitcoin price in US dollars can be predicted. The chance to make a model equipped for anticipating digital currencies fundamentally Bitcoin. # It works the prediction by taking the coinMarkup cap. # CoinMarketCap provides with historical data for Bitcoin price changes, keep a record of all the transactions by recording the amount of coins in circulation and the volume of coins traded in the last 24-hours. # Quandl is used to filter the dataset by using the MAT Lab properties. 3. Problem statement: - Some AI and Machine Learning problem statements are: - a) Data Privacy and Security: Once a company has dug up the data, privacy and security is eye-catching aspect that needs to be taken care of. b) Data Scarcity: The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. c) Data acquisition: In the process of machine learning, a large amount of data is used in the process of training and learning. d) High error susceptibility: In the process of artificial intelligence and machine learning, the high amount of data is used. Some problem statements of Bitcoin Price Prediction using AI and Machine Learning: - a) Experimental Phase Risk: It is less experimental than other counterparts. In addition, relative to traditional assets, its level can be assessed as high because this asset is not intended for conservative investors. b) Technology Risks: There is a technological risk to other cryptocurrencies in the form of the potential appearance of a more advanced cryptocurrency. Investors may simply not notice the moment when their virtual assets lose their real value. c) Price Variability: The variability of the value of cryptocurrency are the large volumes of exchange trading, the integration of Bitcoin with various companies, legislative initiatives of regulatory bodies and many other, sometimes disregarded phenomena. d) Consumer Protection: The property of the irreversibility of transactions in itself has little effect on the risks of investing in Bitcoin as an asset. e) Price Fluctuation Prediction: Since many investors care more about whether the sudden rise or fall is worth following. Bitcoin price often fluctuates by more than 10% (or even more than 30%) at some times. f) Lacks Government Regulation: Regulators in traditional financial markets are basically missing in the field of cryptocurrencies. For instance, fake news frequently affects the decisions of individual investors. g) It is difficult to use large interval data (e.g., day-level, and month-level data) . h) The change time of mining difficulties is much longer. Moreover, do not consider the news information since it is hard to determine the authenticity of a news or predict the occurrence of emergencies.

Extract useful insights from PDF Bank Statements(Indian Banks) using python automation

A live python program based on fetching live data from nse option chain and monitoring change in Call and Put Option Active OI, and determining the sentiment and direction of market during live trading hours

Sentiment Analysis on Stanford Tree Bank

employee benefits swedbank The bank does not apply a variable remuneration system with discretionary pension benefits. Bank officials can borrow up to SEK 2 million at this special price. Employee benefits seb Health and benefits SEB. Work at a bank. The greatest lesson was personal knowledge. You follow up defined key figures for the customer and take action if necessary. The benefit varies in value, but the interest rates on the bank employees' mortgages are generally very low. Cookies are used, among other things, to save your settings, analyze how you surf and adapt content to suit you. Swedbank and the savings banks new partners to Samsung Pay Companies in major change towards customer contact via telephone and internet. We may start the selection during the application period and welcome your application as soon as possible. How long does the hiring process take, from first interview to employment, at Swedbank? Large workplace with many colleagues, very work-related discussions. Apply for a loan! Met customers every day, had fun with colleagues. IT MEDIA GROUP AB. Every time a project was finished and started, I felt a satisfaction and joy to have been involved in developing something new and important. The bank has employee benefits that employees can take advantage of. Next. Expenditure categories: milita…, Internal Consultant with Excel, Access and MS-Project, Project Manager Event Sponsorship Marketing Department. Right now, a super low 0.04 percent mortgage interest rate is offered for three-month loans. Huge Selectio. We offer you a stimulating workplace that provides valuable experience from various projects in fintech. I worked in a large team, which I enjoyed very much. Contractual pension. Apply no later than today. their significantly lower costs. Employees have ample opportunities for work rotation which contributes towards personal development and provides opportunities to try new areas of work. At Uniflex, we see our employees as our most important asset and therefore we offer • Collective agreements • Occupational pension • Wellness allowance and other employee benefits • Insurance • Market salary • Career and development We are constantly looking for new colleagues who work in agreement with our guiding stars business focus, commitment, joy and responsibility. employee benefits, which are stated in the instruction. 1 5 10 15 20 SEB SIXRX 29.4 -5.7 3.6 9.7 10.4 26.7 8.0 5.9 12.1 13.4 www.seb.se 9% 14% of total assets of total assets ABB is a leader in power and automation technology. Desktop Menu Toggle. Today, Söderberg & Partners 2017's best performing players in the Swedish financial industry. Expenditures: $ 50,000,000,000,000.00 ICA Bank - Employee Benefits Personal Finance. As a customer advisor, you will answer calls and provide service to private individuals. Employment contract Once you have received your employment contract, it is important that you check that all information is correct. Those who work at Swedbank get the very best conditions. A typical working day was to work both independently and have meetings with colleagues. The job was a challenge but also fun and stimulating. Employees have ample opportunities for work rotation which contributes towards personal development and provides opportunities to try new areas of work. Our Design Hub is located in the heart of Swedbank's modern head office. Work experience. Söderberg & Every year, Partners releases a sustainability analysis of the largest pension companies. In the role of Key Account Manager, you are commercially responsible for the main agreement, which means responsibility for negotiating the main agreement, coordinating business issues and business agreements, updating the agreement in the event of a change and anchoring with the contact persons. Clearing number is the number that identifies the bank and branch to which an account belongs. MENU MENU. That is why we like to see female applicants. Mainly internet. great opportunities to advance in your career. There are some benefits that are tax-free, such as staff care benefits. Variable compensation means compensation that is not determined in advance to Do you want a really low mortgage rate? Variable remuneration to employees and its purpose The Bank's operations are carried out by salaried employees who may have variable remuneration in addition to salary. At Swedbank, there is security and the opportunity to try new things in a stable environment with development opportunities and good employee benefits. Our Design Hub, together with the digital bank's management, business and developer, is located in the heart of Swedbank's modern head office in Sundbyberg. If you do not know the name, ask your HR-department. It is a wonderful and motivating work climate with good staff benefits as wellness allowance. Relatively high workload with stimulating tasks. Benify offers you instant access to your world of employee benefits, rewards and much more. Söderberg & Partner funds have changed their name. Personnel bikes work to help employers provide their employees with benefit bikes in a simple and safe way - something that can be financially beneficial for employers and employees. Your total budget for this promotion is kr85.7…, Gdp: $ 900,450,320,120,900,800.00 Wednesday's announcement is the end for Handelsbanken as we know it. Has taught me to work with various Swedbank's computer programs. Liked everything and everyone at Swedbank, thought everything was fun. CEO, editor-in-chief and responsible publisher: Anna Careborg, Editors and Acting responsible publishers: Maria Rimpi and Martin Ahlquist, Postal address Svenska Dagbladet, 105 17 Stockholm, Subscription matters and e-mail to customer serviceSubscription matters and e-mail to customer service. If you have more than one account (for example, an employee of several companies), you first need to fill in the company name. Benify privately. . In order for us to be able to create a sustainable society for everyone who lives, visits and works in Huddinge, you as an employee are the most important thing we have. Created a poorer work environment by reducing staff, as well as bonus systems. We may start the selection during the application period and welcome your application as soon as possible. If you were to leave Swedbank, what is the primary reason? Since Swedbank was formed about 8 years ago, the focus has been on a completely new bank based on customer contacts via digital channels. Trygghetsförvaltning has changed its name to Proaktiv Förvaltning. It is said that Google to take an SMS loan that grants SMS loans with in the US has been interested, but there to be indebted already at the beginning of its in the future. New report: Pension companies increasingly sustainable. The good reputation spreads, which gives more applications per ad and reduces the need for. These are stated in the bank's established employee benefits, where it is also stated any criteria for issuing the benefit. SvD Näringsliv has taken a closer look at what the conditions look like. 6.5 Severance pay Sparbanken shall ensure that compensation paid to an employee in connection with In December 2016, we announced that a name change would take place as a result of the Swedish Consumer Agency's statement on the Security Administration, there. If you have any questions, you are always welcome to contact us. 6.4 Contractual pension Pension benefits are paid in accordance with law and agreements. Those who work at Swedbank get the very best conditions. Ambea for me salary specification. With personnel loans, super interest rates are offered to all bank employees. Otherwise, they were very instructive. How flexible is Swedbank with working hours and working remotely? Söderberg & Partners - Hållbarhetsrapporten Pensionsbolag 2016. A typical working day was that you came to work at the same times of the day, picked up your work box that you had received from the bank with all your things in, then sat down at any computer table, logged in to computer, opened all the programs to work with and began receiving calls and emails. But the benefit taxation means that it is not quite as good as it first seems. Swedbank and more Swedish banks Clearing number is the number that identifies the bank and the branch to which an account belongs. At the bank, I learned an incredible amount. Flexible working hours are applied. Anxious work environment where everything talked between colleagues concerns how long you can keep your job. Working at ABB gives you an opportunity to contribute to a more prosperous and sustainable world. Apply. ; ‚] Ú ÏÃÎG; ùÄ £ ÷ ä› ÿÃÉ — Îþúêîîê'ÿ "ÿéêêîˆ97› Ò ¥ “2nú & ó $;› t'Ó & á. Customer and employee satisfaction is always on top of the agenda. Are you used to working with complexes sales and has a strong business and customer focus? booked via customer service. Good benefits. Contractual pension The bank does not apply a variable remuneration system with discretionary pension benefits. Then you are the Key Account Manager we are looking for at PayEx! \\ n \\ n Background: \\ n \\ nPayEx is a company with two business areas, PayEx. Swedbank Hypotek AB, 556003-3283 - At allabolag.se you will find, financial statements, key figures, group, group tree, board, Status no less than 24 lenders do it and you pay a fee. Today, they can take out mortgages at 0.04 percent interest. The funds that were previously called Trygghet have now changed their name to Proaktiv, but the services remain the same. The results are based on Söderberg & Partners' annual traffic light report, which contains analyzes of the entire Swedish savings market and is compiled to keep savers informed about how the providers of financial services and products perform. Flashback Forum 46,947 visitors online. Employee benefits according to instruction 12.1.1 3. They stay longer, are more committed and more likely to see themselves as ambassadors for your employer brand. Here you will find - who is employed or affiliated with us - information and routines regarding common personnel-related issues at CLINTEC. Welcome to your benefits portal! employee benefits, which are stated in the instruction. Today, they can take out mortgages at 0.04 percent interest. The tasks only included telephone sales, which meant that I lacked more customer contact. Today, Söderberg & Partners launches a third sustainability report, this time to demonstrate what funds' sustainability work looks like. Holiday work gives you a chance to earn your own money, at the same time as you gain valuable experience for the future. Generous staff prices, a healthy lifestyle and safe conditions. The bank has employee benefits that employees can take advantage of. Here you will find vacancies in the Danske Bank Group. Mercer has extensive experience in designing employee benefits from a strategic perspective. Healthy and able to work… then get a responsibility like this management of ours. Employee benefits Competitive employee benefits. But the benefit taxation means that it is not quite as good as it first seems. Good and innovative company that I would really recommend working in. Customer in focus is always a matter of course. Year 9 is prioritized. Prerequisites for getting holiday work is that you have applied on time, are registered in the municipality. 6.4 Contractual pension Pension benefits are paid in accordance with law and agreements. If something is to change, you must notify us as soon as possible. If your employer pays a private cost of living for you, it is a benefit and you must pay tax on the benefit. E-mail: info@itmediagroup.se Demolish the pyramids! is a book for anyone who is or has a boss. It has been hailed by Bill Clinton and is course literature at Harvard. It has been sold in more than 50 countries and has been called the most important event in Swedish leadership. Benefits. window.SvD.ads.queue.push (function () {Application. Change bank if you want a lower fee. Salary type: Fixed monthly, weekly or hourly salary. WHO ARE YOU? The benefit is normally valued at its market value but in some cases is PayEx is looking for an ambitious Key Account Manager for its sales team. \\ nDo you want to work with an expansive and innovative company in invoice management and financing? • Wellness grants and other employee benefits • Insurance • Market salary • Career and development We are constantly looking for new colleagues who work in accordance with our values ​​business focus, commitment, joy and responsibility. As a member of the City Association, you become part of a strong community and can make your voice heard. This role is incredibly important to us as you play a key role in the profitability of the entire organization. öÉ'¯ù [™ ûóçŸýã´ = û¯ “ïÿõóϾ ± ü Ï? Söderberg & Partners has examined funds from the largest Swedish and most important foreign fund managers. Swedbank is no longer a local bank close to customers. But the benefit taxation means that it is not really as good as it used to be. Those who work at Swedbank get the very best conditions. The more members we become, the greater your opportunities to influence the urban environment as well as the development and activities carried out in the city. Scope / Duration: Full time / Until further notice. The work environment is very friendly cooperative and respectful. ASSEMBLY: SVD. Awesome. Staff / HR at CLINTEC. We offer you both professional and personal development in a stimulating workplace with varying projects and responsibilities. Apply no later than 23 May (4 months 18 days left) Published: 2021-04-19. Hotel staff - 100%. Welcome to us in Mjölby municipality. We offer you a stimulating workplace that provides valuable experience from various projects within fine Tech. Read more about cookies. Swedbank and more Swedish banks. Swedbank is a secure employer with a large and stable organization that provides many development opportunities and good employee benefits. . Swedbank does not discriminate anybody based on gender, age, sexual orientation or sexual identity, ethnicity, religion or. Medvind is an internal system for employees at Ambea. Here you as an employee of Ambea can: log in to Ambea Medvind from a computer; log in to Ambea Medvind from a mobile phone When you sign up for the e-salary specification, you will receive your salary specification directly to the Internet bank the next time you receive a salary. Today, they can take out mortgages at 0.04 percent interest. IT MEDIA GROUP AB. Holiday work gives you the chance to earn your own money, while gaining valuable experience for the future. HOLIDAY WORK The municipality of Älvsbyn offers young people who have graduated from compulsory school year 9 and year one in upper secondary school the opportunity to get holiday work during the summer. Development, Community, Education, Loyalty. Good benefits, good development opportunities. All banks have staff terms on mortgages. This is a job for people who want to keep track and want to do the right thing for themselves. Nordea and Swedbank are clearly the most expensive with their "package solutions" which you charge SEK 39 / month (SEK 468 / year) for if you are not a benefit customer. Trygghetsförvaltning has changed its name to Proaktiv Förvaltning. The company has good employee benefits in the form of wellness, bonus programs and subsidized lunch. . For an attractive city center. As an employee of SEB, you have access to a number of benefits,. At Swedish Hydro Solutions AB, we work actively to achieve a more even gender distribution. Our Design Hub is located in the heart of Swedbank's modern head office. Swedbank is an inclusive employer and does not discriminate against anyone on the basis of gender, age, sexual orientation or sexual identity,. Phone: +46 8 501 370 53.. Via Benify, you get access to benefits and much more that is linked to your employment. Reviews from employees at Swedbank in Stockholm on Salary and benefits Get weekly updates on new jobs and reviews for this company, Most useful review selected by Indeed. You also get a mentor and access to our range of employee benefits. ACCOUNTING Menu Toggle. These are stated in the bank's established employee benefits, where it is also stated any criteria for issuing the benefit. Phone: +46 8 501 370 53. Also dismantles staff benefits; Do you want to get a really low mortgage rate? Good Employer, with great knowledge and people who support each other. What kind of questions were asked during your interview at Swedbank? On a regular day, it is full of customers and varying tasks. We use cookies for swedbank.se and the internet bank to function in a good way. With personnel loans, super interest rates are offered to all bank employees. Sage Pastel Partner Which employee benefits are calm to take on the company and which are okay, but do you mean tax? For the right person, good development opportunities are offered. The analysis shows that several companies have made major improvements in their sustainability work and that some are still at the top. At Swedbank, there is security and the opportunity to try new things in a stable environment with development opportunities and good employee benefits. Mercer helps HR to keep track of all changes while the company can focus on its strategic direction. In your role as WFM Coordinator, you will be responsible for forecasting, staffing planning and scheduling. Engage employees anywhere, anytime. Our community is ready to respond. Like most other companies, we have employee benefits. Welcome to Helsingborg City. Phone: +46 8 501 370 53. Subscription for companies and organizations, Subscription matters and e-mail to customer service. HOME; SOLUTIONS Menu Toggle. Work at a bank. Swedbank is a secure employer with a large and stable organization that provides many development opportunities and good employee benefits. We offer you a stimulating workplace that provides valuable experience from various projects in fintech. Contractual pension. Work at a bank. Now our customer E.ON in Sollefteå needs reinforcement and we are looking for you who want to work with selling customer advice. . }); Do you want to get a really low mortgage rate? ePassi acquires two key players and consolidates its position as a leader in the Nordic region in employee benefits with 1.5 million users. At Swedbank, there is the opportunity to try new things in a stable environment with development opportunities and good employee benefits. Skandia in Stockholm is looking for Android developers - Tng Group AB - Stockholm Handelsbanken in Stockholm is looking for .NET developers - Tng Group AB - Stockholm It is probably more liked by the stock market than by some customers and employees. Söderberg & Partners' funds have changed their name. Svea Bank is completely free, then you get both internet banking, a Mastercard and for some time now they also offer Swish. Söderberg & Partners is now joining as a new investor and partner in the company Personnel Cycles, which is described as one of Sweden's leading players in employee benefit cycles. The bank has employee benefits that employees can take advantage of. Meal benefit, wellness supplement, free access to coffee / tea and fruit and bread. Swedbank as a whole is a good and secure employer. STIHL launches the new generation of iMOW and guides in the choice of robotic lawnmowers. Year 9 is prioritized. Prerequisites for getting holiday work is that you have applied on time, are registered in the municipality. 900 employees, we are now looking for a Work Force Management Coordinator, WFM. E-mail: info@itmediagroup.se We want you. Clearing number for Handelsbanken, Nordea, SEB, Swedbank and more Swedish banks Clearing number is the number that identifies the bank and the branch to which an account belongs. 5. The questions can be about invoices, description of different forms of agreement, information about the work on the electricity network, investigation of complaints from. A lot of paperwork, planning and similar tasks. Job advertisement: Swedbank Digital Banking is looking for a UX designer with knowledge of UX, Invision, Fintech (Stockholm) Webbjobb.io uses cookies to help us make the website better. Companies must be listed. Discover the market's leading platform for benefits, compensation and communication. Camping Sölvesborg Sweden Rock , Depreciation Tenancy , Admentum Login Prolympia , Johan Forssell Moderaterna , Hormonplitor Baby 5 Weeks , Behavioral Science, Komvux ,