This is a hardware and software system for real time monitoring and detection of forest fires. With its help remote recognition of wood fires is possible as well as high-accuracy positioning of flame base. Hardware part of the system consists of a set of intellectual sensors which are installed inside the forese. The action range of sensors is 250 - 500m depending on the RF device and type of sensor. As for the sensors, video cameras, infrared imagers and other intellectual equipment are used. They detect fire areas by a number of measures and under different conditions. If the sensor detects a fire, the information is transmitted to control unit via various communication channels: optical, radio, wire, GSM, etc. In such a way forest data are transmitted to the software part of our project where they are processed and analyzed. The system will automatically find and identify the fire area. Thereafter the information is passed to special departments via built-in alerting service, Internet and even mobile networks. Characteristics of the System • Fire detection accuracy - up to 250 m • Direction detection accuracy -- 0.5° • Possibility to integration data from other information sources (weather and satellite information). • Possibility of efficient scaling and broadening of the system for coverage range extension. • Number of users - without limit. • Possibility to get information on mobile phones. • Automatic detection of potentially dangerous objects (smoke and flame). Advantages of System 1. Automatization of monitoring 2. Centrally managed monitoring of large areas 3. Opportunity detect fires at an early stage and its spread 4. High accuracy of fire detection 5. Decrease of human factor role when detecting fires 6. Low cost of installation and exploitation of the system in comparison with other forms of monitoring 7. Flexibility of the system depending on relief and customer wants

Prediction of malicious network connection events with Random Forest and Elastic-Net Regression in R and Python

MindForest is a visual knowledge workspace with a two-layer navigation system: the Tree View lets you dive deep into a topic, while the Graph View reveals cross-topic connections. Equipped with a Markdown node editor and local persistence, it helps you build an immersive, full-scale knowledge forest.

Detect the forest fire as fast as possible and early alerting to forest unit ,thus making evacuation paths easier.

Make your personal connection with remote forests

Games from past game dev hackathons: Home Wars (2019), Connection: Lost (2018), Galaxy Waves (2017), The Forest Temple (2016)

Code from "Temporal connections between long-term Landsat time-series and tree-rings in an urban–rural temperate forest" (IJAEOG, 2021)

To detect diabetes Description: This project is done with the help of a random forest algorithm which is trained with live data and used flask for connection

Facebook Friend Recommendation using Graph Mining Predicts missing links in Facebook's social network using machine learning and graph mining techniques. Engineered 54 features from 1.86M users and 9.43M connections, achieving 92% F1-Score with Random Forest. Deployed as interactive Streamlit app.

- Studied the connection between the use of moral words in political tweets and the number of retweets - Used Twitter’s API and python library Tweepy to scrape and feature tag roughly 50k tweets from politicians - Identified Moral Words using Jesse Graham and Jonathan Haidt’s Moral Foundations Dictionary - Compiled, cleaned, and vectorized data into a Pandas data frame using Genism’s Doc2Vec API - Created a predictive model for retweet class with 68% accuracy using Sklearn’s Random Forest Classifier API

This project mainly aims at analysing the different fields in the given dataset and visualising it with Tableau. And here we are calculating Training accuracy, testing accuracy, and cross-validation accuracy to check the fit of the model by using 5 models Logistic Regression,Support Vector Machines,Random Forests,K-Nearest Neighbours,Gaussian Naive Bayes.Our aim here is to make management, comparison and visualization of the dataset of the airlines, Silk Air, more effective, simpler and meaningful. We will be to see connections more effectively as they occur between operating conditions and performance.

In this study, we try to compare the performance of prim’s and Kruskal algorithm by constructing a minimum spanning tree containing all the nodes for which the total distance is minimum. Kruskal algorithm finds a minimum spanning forest of an undirected edge-weighted graph. It is a greedy algorithm in graph theory as in each step it adds the next lowest-weight edge that will not form a cycle to the minimum spanning forest. While, Prim’s algorithm is a greedy algorithm that finds a minimum spanning tree for a weighted undirected graph. It operates by building this tree one vertex at a time, from an arbitrary starting vertex, at each step adding the cheapest possible connection from the tree to another vertex. We use java to show the working of above two algorithm and MATLAB to show the visualization of above two algorithm using particle swarm optimization technique. Our project reveals that Prim’s algorithm runs faster in dense graphs and Kruskal’s algorithm runs faster in sparse graphs.

Article classification and summarization have a genuinely backhanded connection as article classification fall into classification issues rather than summarization, where it is treated as an issue of semantics. A significant piece of the summarization procedure is the recognition of the point or subjects that are examined in an irregular document. In light of that, we attempt to find whether article classification can aid administered article summarization. This framework aims to summarize and classify a given new/text using NLP and ML techniques. The classification algorithm uses “Random Forest Classification” technique to classify the given text based on a trained model, which uses 2300 classified texts retrieved from BBC news and divided into 5 categories namely, business, entertainment, politics, sports and tech. Using this methodology we were able to achieve an accuracy of 96% .On the other hand, the summarizer algorithm uses the TF-IDF (Term frequency and Inverse document frequency) technique, which measures the frequency and uniqueness of each word and scores the sentences accordingly. The top 10 sentences based on their scores are shown as the summary for the given article. This summarization technique uses an extractive approach rather than an abstractive approach for article summarization. The whole system follows a single-document, extractive and domain-specific approach to meet the required results.

A revolutionary forest social ecosystem for animal connections

Decision Trees and Its Connection with Random Forest

Kayla and Jack's repository for Ghost Forest Bass Connections

The server for real-time connections for musica-forest-reactVR

ThemeForest Downloader.That download large theme file from theme forest for slow connection users

Predict a connection is normal or malicious attack based on machine learning techniques: Random Forest Classifier

Analyzes half-closed TCP connections using generalized linear models, regression trees, and random forest classification models.

C++ program to identify bridge edges and clusters in networks using DFS, listing critical connections and building cluster-bridge forests

🍄 Reveal hidden connections in codebases like mycelial networks in a forest. Phase 1: Semantic similarity detection, pattern analysis, DRY violation discovery.

This project is done with the help of a random forest algorithm which is trained with live data and used flask for connection

A beautiful portrait of a character with glowing runic tattoos in Arkham, standing in a forest, theme of connection to ancient nature, hyper-detailed

A beautiful portrait of a character with glowing runic tattoos in Arkham, standing in a forest, theme of connection to ancient nature, hyper-detailed

Industry 4.0 predictive maintenance using Random Forest ML on IoT sensor data. Built as part of MS CS application to Hof University — iisys CPS research connection.

This POC is a lightweight anomaly detection pipeline designed for network environments using Zeek logs. It preprocesses network connections, trains a Random Forest classifier, and detects suspicious patterns automatically.

A short fictional story about nature, exploring the connection between humans and the environment through the journey of a young boy who encounters the mysterious spirit of the forest.

An end-to-end Data Science project analyzing 5G network telemetry. Featuring custom feature engineering (Stress Score), interactive Streamlit dashboarding, and predictive modeling with Random Forest to forecast connection stability.

IntrusionNet Sentinel is a real-time Network Intrusion Detection System (NIDS) that uses a trained Machine Learning model (Random Forest) to analyze network traffic flows and identify threats. It continuously monitors connections, extracts statistical features (duration, byte counts), and instantly flags connections as Normal or Attack/Anomalous.