A multi-user, multi-group todo/ticketing system for Django projects. Includes CSV import and integrated mail tracking.

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using Neural Networks (SSD) on Tensorflow. This repo documents steps and scripts used to train a hand detector using Tensorflow (Object Detection API). As with any DNN based task, the most expensive (and riskiest) part of the process has to do with finding or creating the right (annotated) dataset. I was interested mainly in detecting hands on a table (egocentric view point). I experimented first with the [Oxford Hands Dataset](http://www.robots.ox.ac.uk/~vgg/data/hands/) (the results were not good). I then tried the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) which was a much better fit to my requirements. The goal of this repo/post is to demonstrate how neural networks can be applied to the (hard) problem of tracking hands (egocentric and other views). Better still, provide code that can be adapted to other uses cases. If you use this tutorial or models in your research or project, please cite [this](#citing-this-tutorial). Here is the detector in action. <img src="images/hand1.gif" width="33.3%"><img src="images/hand2.gif" width="33.3%"><img src="images/hand3.gif" width="33.3%"> Realtime detection on video stream from a webcam . <img src="images/chess1.gif" width="33.3%"><img src="images/chess2.gif" width="33.3%"><img src="images/chess3.gif" width="33.3%"> Detection on a Youtube video. Both examples above were run on a macbook pro **CPU** (i7, 2.5GHz, 16GB). Some fps numbers are: | FPS | Image Size | Device| Comments| | ------------- | ------------- | ------------- | ------------- | | 21 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run without visualizing results| | 16 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | | 11 | 640 * 480 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | > Note: The code in this repo is written and tested with Tensorflow `1.4.0-rc0`. Using a different version may result in [some errors](https://github.com/tensorflow/models/issues/1581). You may need to [generate your own frozen model](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/) graph using the [model checkpoints](model-checkpoint) in the repo to fit your TF version. **Content of this document** - Motivation - Why Track/Detect hands with Neural Networks - Data preparation and network training in Tensorflow (Dataset, Import, Training) - Training the hand detection Model - Using the Detector to Detect/Track hands - Thoughts on Optimizations. > P.S if you are using or have used the models provided here, feel free to reach out on twitter ([@vykthur](https://twitter.com/vykthur)) and share your work! ## Motivation - Why Track/Detect hands with Neural Networks? There are several existing approaches to tracking hands in the computer vision domain. Incidentally, many of these approaches are rule based (e.g extracting background based on texture and boundary features, distinguishing between hands and background using color histograms and HOG classifiers,) making them not very robust. For example, these algorithms might get confused if the background is unusual or in situations where sharp changes in lighting conditions cause sharp changes in skin color or the tracked object becomes occluded.(see [here for a review](https://www.cse.unr.edu/~bebis/handposerev.pdf) paper on hand pose estimation from the HCI perspective) With sufficiently large datasets, neural networks provide opportunity to train models that perform well and address challenges of existing object tracking/detection algorithms - varied/poor lighting, noisy environments, diverse viewpoints and even occlusion. The main drawbacks to usage for real-time tracking/detection is that they can be complex, are relatively slow compared to tracking-only algorithms and it can be quite expensive to assemble a good dataset. But things are changing with advances in fast neural networks. Furthermore, this entire area of work has been made more approachable by deep learning frameworks (such as the tensorflow object detection api) that simplify the process of training a model for custom object detection. More importantly, the advent of fast neural network models like ssd, faster r-cnn, rfcn (see [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models) ) etc make neural networks an attractive candidate for real-time detection (and tracking) applications. Hopefully, this repo demonstrates this. > If you are not interested in the process of training the detector, you can skip straight to applying the [pretrained model I provide in detecting hands](#detecting-hands). Training a model is a multi-stage process (assembling dataset, cleaning, splitting into training/test partitions and generating an inference graph). While I lightly touch on the details of these parts, there are a few other tutorials cover training a custom object detector using the tensorflow object detection api in more detail[ see [here](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) and [here](https://towardsdatascience.com/how-to-train-your-own-object-detector-with-tensorflows-object-detector-api-bec72ecfe1d9) ]. I recommend you walk through those if interested in training a custom object detector from scratch. ## Data preparation and network training in Tensorflow (Dataset, Import, Training) **The Egohands Dataset** The hand detector model is built using data from the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) dataset. This dataset works well for several reasons. It contains high quality, pixel level annotations (>15000 ground truth labels) where hands are located across 4800 images. All images are captured from an egocentric view (Google glass) across 48 different environments (indoor, outdoor) and activities (playing cards, chess, jenga, solving puzzles etc). <img src="images/egohandstrain.jpg" width="100%"> If you will be using the Egohands dataset, you can cite them as follows: > Bambach, Sven, et al. "Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions." Proceedings of the IEEE International Conference on Computer Vision. 2015. The Egohands dataset (zip file with labelled data) contains 48 folders of locations where video data was collected (100 images per folder). ``` -- LOCATION_X -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder -- LOCATION_Y -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder ``` **Converting data to Tensorflow Format** Some initial work needs to be done to the Egohands dataset to transform it into the format (`tfrecord`) which Tensorflow needs to train a model. This repo contains `egohands_dataset_clean.py` a script that will help you generate these csv files. - Downloads the egohands datasets - Renames all files to include their directory names to ensure each filename is unique - Splits the dataset into train (80%), test (10%) and eval (10%) folders. - Reads in `polygons.mat` for each folder, generates bounding boxes and visualizes them to ensure correctness (see image above). - Once the script is done running, you should have an images folder containing three folders - train, test and eval. Each of these folders should also contain a csv label document each - `train_labels.csv`, `test_labels.csv` that can be used to generate `tfrecords` Note: While the egohands dataset provides four separate labels for hands (own left, own right, other left, and other right), for my purpose, I am only interested in the general `hand` class and label all training data as `hand`. You can modify the data prep script to generate `tfrecords` that support 4 labels. Next: convert your dataset + csv files to tfrecords. A helpful guide on this can be found [here](https://pythonprogramming.net/creating-tfrecord-files-tensorflow-object-detection-api-tutorial/).For each folder, you should be able to generate `train.record`, `test.record` required in the training process. ## Training the hand detection Model Now that the dataset has been assembled (and your tfrecords), the next task is to train a model based on this. With neural networks, it is possible to use a process called [transfer learning](https://www.tensorflow.org/tutorials/image_retraining) to shorten the amount of time needed to train the entire model. This means we can take an existing model (that has been trained well on a related domain (here image classification) and retrain its final layer(s) to detect hands for us. Sweet!. Given that neural networks sometimes have thousands or millions of parameters that can take weeks or months to train, transfer learning helps shorten training time to possibly hours. Tensorflow does offer a few models (in the tensorflow [model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models)) and I chose to use the `ssd_mobilenet_v1_coco` model as my start point given it is currently (one of) the fastest models (read the SSD research [paper here](https://arxiv.org/pdf/1512.02325.pdf)). The training process can be done locally on your CPU machine which may take a while or better on a (cloud) GPU machine (which is what I did). For reference, training on my macbook pro (tensorflow compiled from source to take advantage of the mac's cpu architecture) the maximum speed I got was 5 seconds per step as opposed to the ~0.5 seconds per step I got with a GPU. For reference it would take about 12 days to run 200k steps on my mac (i7, 2.5GHz, 16GB) compared to ~5hrs on a GPU. > **Training on your own images**: Please use the [guide provided by Harrison from pythonprogramming](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) on how to generate tfrecords given your label csv files and your images. The guide also covers how to start the training process if training locally. [see [here] (https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/)]. If training in the cloud using a service like GCP, see the [guide here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_cloud.md). As the training process progresses, the expectation is that total loss (errors) gets reduced to its possible minimum (about a value of 1 or thereabout). By observing the tensorboard graphs for total loss(see image below), it should be possible to get an idea of when the training process is complete (total loss does not decrease with further iterations/steps). I ran my training job for 200k steps (took about 5 hours) and stopped at a total Loss (errors) value of 2.575.(In retrospect, I could have stopped the training at about 50k steps and gotten a similar total loss value). With tensorflow, you can also run an evaluation concurrently that assesses your model to see how well it performs on the test data. A commonly used metric for performance is mean average precision (mAP) which is single number used to summarize the area under the precision-recall curve. mAP is a measure of how well the model generates a bounding box that has at least a 50% overlap with the ground truth bounding box in our test dataset. For the hand detector trained here, the mAP value was **0.9686@0.5IOU**. mAP values range from 0-1, the higher the better. <img src="images/accuracy.jpg" width="100%"> Once training is completed, the trained inference graph (`frozen_inference_graph.pb`) is then exported (see the earlier referenced guides for how to do this) and saved in the `hand_inference_graph` folder. Now its time to do some interesting detection. ## Using the Detector to Detect/Track hands If you have not done this yet, please following the guide on installing [Tensorflow and the Tensorflow object detection api](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). This will walk you through setting up the tensorflow framework, cloning the tensorflow github repo and a guide on - Load the `frozen_inference_graph.pb` trained on the hands dataset as well as the corresponding label map. In this repo, this is done in the `utils/detector_utils.py` script by the `load_inference_graph` method. ```python detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) print("> ====== Hand Inference graph loaded.") ``` - Detect hands. In this repo, this is done in the `utils/detector_utils.py` script by the `detect_objects` method. ```python (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) ``` - Visualize detected bounding detection_boxes. In this repo, this is done in the `utils/detector_utils.py` script by the `draw_box_on_image` method. This repo contains two scripts that tie all these steps together. - detect_multi_threaded.py : A threaded implementation for reading camera video input detection and detecting. Takes a set of command line flags to set parameters such as `--display` (visualize detections), image parameters `--width` and `--height`, videe `--source` (0 for camera) etc. - detect_single_threaded.py : Same as above, but single threaded. This script works for video files by setting the video source parameter videe `--source` (path to a video file). ```cmd # load and run detection on video at path "videos/chess.mov" python detect_single_threaded.py --source videos/chess.mov ``` > Update: If you do have errors loading the frozen inference graph in this repo, feel free to generate a new graph that fits your TF version from the model-checkpoint in this repo. Use the [export_inference_graph.py](https://github.com/tensorflow/models/blob/master/research/object_detection/export_inference_graph.py) script provided in the tensorflow object detection api repo. More guidance on this [here](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/). ## Thoughts on Optimization. A few things that led to noticeable performance increases. - Threading: Turns out that reading images from a webcam is a heavy I/O event and if run on the main application thread can slow down the program. I implemented some good ideas from [Adrian Rosebuck](https://www.pyimagesearch.com/2017/02/06/faster-video-file-fps-with-cv2-videocapture-and-opencv/) on parrallelizing image capture across multiple worker threads. This mostly led to an FPS increase of about 5 points. - For those new to Opencv, images from the `cv2.read()` method return images in [BGR format](https://www.learnopencv.com/why-does-opencv-use-bgr-color-format/). Ensure you convert to RGB before detection (accuracy will be much reduced if you dont). ```python cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) ``` - Keeping your input image small will increase fps without any significant accuracy drop.(I used about 320 x 240 compared to the 1280 x 720 which my webcam provides). - Model Quantization. Moving from the current 32 bit to 8 bit can achieve up to 4x reduction in memory required to load and store models. One way to further speed up this model is to explore the use of [8-bit fixed point quantization](https://heartbeat.fritz.ai/8-bit-quantization-and-tensorflow-lite-speeding-up-mobile-inference-with-low-precision-a882dfcafbbd). Performance can also be increased by a clever combination of tracking algorithms with the already decent detection and this is something I am still experimenting with. Have ideas for optimizing better, please share! <img src="images/general.jpg" width="100%"> Note: The detector does reflect some limitations associated with the training set. This includes non-egocentric viewpoints, very noisy backgrounds (e.g in a sea of hands) and sometimes skin tone. There is opportunity to improve these with additional data. ## Integrating Multiple DNNs. One way to make things more interesting is to integrate our new knowledge of where "hands" are with other detectors trained to recognize other objects. Unfortunately, while our hand detector can in fact detect hands, it cannot detect other objects (a factor or how it is trained). To create a detector that classifies multiple different objects would mean a long involved process of assembling datasets for each class and a lengthy training process. > Given the above, a potential strategy is to explore structures that allow us **efficiently** interleave output form multiple pretrained models for various object classes and have them detect multiple objects on a single image. An example of this is with my primary use case where I am interested in understanding the position of objects on a table with respect to hands on same table. I am currently doing some work on a threaded application that loads multiple detectors and outputs bounding boxes on a single image. More on this soon.

Python package for generating multi-unit schematic symbols for KiCad from a CSV file.

Create and **automatically** update a list of all videos on a YouTube channel (in txt/csv/md form) via YouTube bot with end-to-end web scraping - no API tokens required. Multi-threaded support for YouTube videos list updates.

The data represents financial transactions -- bank transfers, purchases, credit card transactions, checks, etc. Most of the transactions are legitimate. A few represent money laundering. The data is in CSV format. The data is generated using a multi-agent virtual world model. All of the agents in the virtual world have actions governed by statistical distributions. Thus the model and data are NOT based on obfuscating or anonymizing real individuals. Everything is synthetic. More specifically the underlying model uses a virtual world of banks, individuals, and companies -- with individuals and companies buying items, and doing bank transfers to make payments, get supplies, pay salaries, etc. The underlying model has good and bad actors, with bad actors doing things like smuggling, extortion, illegal gambling, etc. The bad actors sometimes attempt to launder ill-gotten funds resulting in money-laundering transactions. NOTE : Although this repository is under the Apache-2.0 license, the actual data is released under the CDLA-Sharing-1.0 license.

Schema‑Action‑Querying: An open‑source multi‑table Text‑to‑Action system for Excel/CSV files. Use small local 3B‑parameter language models to automatically identify tables, columns and joins — no database setup, no cloud service required.

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SQL editor, ETL. Cross-platform. Runs as a Go HTTP server with a browser-based interface. Supports solo and multi-user modes with role-based permissions. Easily copy data between databases, Excel, JSON, and CSV.

Package bigcscvreader offers a multi-threaded approach for reading a large CSV file in order to improve the time of reading and processing it, in golang.

MultiReplace is a Notepad++ plugin for advanced multi-string replacements. It supports reusable lists, CSV column targeting, highlighting, and external data lookups. All replacements can be enhanced with scriptable rules, conditional logic, and math.

By invoking local large language models, this tool processes spreadsheets similar to multi-dimensional tables. It can batch-generate content for Excel/CSV data using AI. The tool supports simultaneous use of OpenAI API and local Ollama models, applicable to various scenarios such as text summarization, data extraction, and content translation.

AutoHotkey_L (AHK) functions to load from CSV files, sort, display and save as CSV collections of records using the Object data type. Files can be read and saved in any delimited format (CSV, semi-colon, tab delimited, single-line or multi-line, etc.). Collections can also be displayed, edited and read in GUI ListView objects.

Multi-format serialization library (JSON, XML, YAML, CSV, MsgPack)

cargo-thanku | Rust Dependency Acknowledgment Generator 🦀 ❤️‍🔥 Enhanced cargo-thanks, give an Acknowledgment to your rust project for thanks. Automatically generate polished acknowledgments for your Rust project's dependencies ✨ Multi-format outputs (Markdown/JSON/YAML/CSV) • 🚀 Concurrent GitHub/crates.io API integration • 🌐 Multi-languages

Listado de API's Públicas en Chile Listado de API's Públicas para distintos tipos de servicios digitales nacionales Enlace Servicios Públicos / Gobierno API Biblioteca del Congreso Leyes, Proyectos de Ley y Normas. API Mercado Público: Todo lo que necesitas es estar conectado con los servicios de información disponibles en api.mercadopublico.cl para crear notificaciones y estar siempre actualizado de los negocios con el Estado. API División Político Administrativa: Permite obtener Regiones, Provincias y Comunas. API Portal ChileAtiende: API del Portal de Servicios del Estado - ChileAtiende. Plataforma Ley de Lobby La API de la plataforma Ley de Lobby implementada para el Gobierno de Chile, es la interfaz para programadores que permite integrar los contenidos de este portal en tu sitio web. API Energía Abierta - Comisión Nacional de Energía: API permite el acceso directo a los datos publicados en el sitio de datos abiertos de Comision Nacional de Energía. API Comisión Nacional de Energía: Provee el público acceso a la información que se genera dentro de la CNE considerando sus distintos sistemas de información. Usado en Bencina en línea API Datos Peñalolen - Municipalidad de Peñalolen: API que permite el acceso directo a los datos publicados en el sitio de datos abiertos de Peñalolén. API Datos Providencia - Municipalidad de Providencia: API que permite el acceso directo a los datos publicados en el sitio de datos abiertos de la Municipalidad de Providencia. Compras Transparentes: Desarrollada en Falcon y Python, contiene todos los detalles de la API de Compras Transparentes que permite explorar las transacciones entre el Estado de Chile y las empresas, las cuales se efectúan a través de la plataforma de compras públicas. ChileCompra: La API permite el acceso directo a los datos publicados en el portal de datos abiertos de ChileCompra desde tu aplicación. Usa una interfaz RESTful y retorna los datos en formato JSON. Las vistas invocadas a través de la API proveen un acceso estándar online a datos contenidos en páginas HTML, XLS, CSV y otros archivos similares disponibles en Internet. Portal de Datos Públicos: La versión actual de la API es 1.0. La mayoría de los métodos retorna sus resultados en formato JSON, excepto el metodo invoke donde puede elegirse entre varios formatos de salida. Cada key obtenida para la API del Portal de Datos Públicos del Gobierno de Chile está limitada a 10.000 reqs/mes y 1 req/seg. Seguimiento de pedidos de Correos de Chile: Módulo npm para hacer el seguimiento de uno o más pedidos de Correos de Chile. Correos Chile Tracking API: AfterShip Restful JSON APIs and webhooks allow developers to add Correos Chile tracking function easily. Support APIs Client Libraries for PHP, Java, Node.js, Python, .NET, Ruby. Correos Chile API: EasyPost is a multi-carrier shipping solution. The EasyPost API is one integration point for 60+ carriers, including Correos Chile. Feriados en Chile 2017: API con info de feriados en Chile año 2017. Feriados Legales en Chile: API sin restricciones y costos, que contiene todos los feriados legales de Chile. Turnos de Farmacia: URL para obtener en formato JSON el listado de farmacias del país y sus turnos nocturnos legales, directamente desde FARMANET del Ministerio de Salud. Transporte BIP: Get balance of bip card (Chile) by scrapping http://www.tarjetabip.cl/ Economía Indicadores económicos diarios: Este es un servicio open source (web service) que entrega los principales indicadores económicos para Chile en formato JSON. Tanto los indicadores diarios como históricos para que desarrolladores puedan utilizarlos en sus aplicaciones o sitios web. Indicadores del Día: Los indicadores que entregamos en el servicio, aparecen en el sitio del Banco Central de Chile (http://www.bcentral.cl/), estos datos son actualizados cada una hora y servidos en diferentes formatos como xml, json, csv y javascript. API SBIF: La API de SBIF permite obtener información de manera directa desde la base de datos del sitio web utilizando los servicios web provistos en esta plataforma. Buda.com: La API REST de Buda.com, exchange de criptomonedas por moneda local en Chile, Colombia, Perú y Argentina. Permite el manejo de ordenes de compra/venta, abonos/retiros e información del mercado en tiempo real. BCI: API públicas del Banco de Crédito e Inversiones. Permite obtener información sobre cuentas, indicadores económicos, información del banco, entre otros. Medios de Pago Khipu: API REST para crear cobros y recibir pagos con Khipu. Flow: Flow es una plataforma de pagos online que te permite pagar y recibir pagos de cualquier persona usando tarjetas de credito o débito. Sistemas de Alerta Sismos Chile: Últimos sismos en Chile. Chile Alerta - Api: Boletines de Tsunami en Chile, Últimos sismos en Chile y Últimos sismos en países específicos y el Mundo. Mapas / Geocodificación API Planos.cl: API Planos.cl de hibu está conformada por clases desarrolladas en lenguaje Javascript. API de Mapas y Geocodificación de Mapcity: La API de MapCity es una extensión de Openlayers y ExtCore. Los tipos básicos de la API y los controles son derivados de los tipos y controles de OpenLayers, por lo tanto la mayoría de las funciones de OpenLayers aplican a las funciones de la API. Entretención y ocio Horóscopo Yolanda Sultana: Obtiene el horóscopo del día desde Login.cl. No hay forma de obtener horóscopos anteriores, porque es de mala suerte. Clima API Tiempo Meteored.cl: La Api de Meteored.cl es una aplicación con la que usted puede obtener la predicción meteorológica de las localidades que desee a diario. Cómo Aportar: Seguir el siguiente formato: - [Nombre / Título sitio web](URL documentación API): Descripción corta de qué se trata este servicio, en general se encuentra

ACsv is a easy, multi-platform and powerful csv parsing library, includes: js, ts, haxe, php, java, python, c#, go

applehealth2csv is a multi platform command line tool to convert Apple Watch health data to CSV or JSON files

Text Object Java Objects (TOJOs): an object representation of a multi-line structured text file like CSV, YAML, or JSON

A multi-threaded fast script to check broken links on any WordPress website. Checks all the posts, looks for broken internal and external links and save them in a CSV file.

Export shortcuts for specific production pipelines. Includes presets for Unity 3D (FBX), ThreeJS (compressed GLB), Element3D (OBJ), Xcode (USDZ), 3D printing (STL with multi-object output), 3d texture strips (Unity VF, PNG, EXR), and data visualisation (CSV item and vertex position data).

A powerful eBay scraper built with Scrapy that extracts product listings, prices, seller data, and auction information from eBay marketplaces worldwide. Features anti-bot protection, price intelligence, multi-format export (CSV/JSON), and global eBay site support.

These functions are included the "Random Forest" and the hybrid Random Forest and Multi-Objective Particle Swarm Optimization ("RF_MOPSO") to predict the targets as learning approach and find the optimal parameters of a multi-feature process, respectively. The example of this version is a drilling process prediction and optimization. Instruction to use the codes: The Random Forest and hybrid RF_MOPSO algorithms are run via "Random_Forest.m" and "example_final.m" files, respectively. There is a dataset to implement each method, "data1.csv" is for Random Forest and "data.csv" is belonged to RF_MOPSO. These algorithms indicate the results of the prediction and optimization in MATLAB software. The MOPSO algorithm was obtained via: Víctor Martínez-Cagigal (2022). Multi-Objective Particle Swarm Optimization (MOPSO) (https://www.mathworks.com/matlabcentral/fileexchange/62074-multi-objective-particle-swarm-optimization-mopso), MATLAB Central File Exchange. Retrieved July 27, 2022. Besides, the MOPSO implementation is based on the paper of Coello et al. (2004), "Handling multiple objectives with particle swarm optimization". Cite As: Mohammdad Reza Delavar (2022). Optimization of Drilling Parameters Using Combined Multi-Objective Method and Presenting a Practical Factor. Computers & Geosciences.

A Python library that automates multi-agent machine learning workflows by generating, executing, and returning accurate code-driven results from user queries and CSV datasets.

A low cost multi channel data logging system using Python and Arduino UNO board that will log and save data to a CSV (Comma Separated Values) file on the disk.The system can monitor temperature data from 4 independent sources at the same time and log the data to the disk.

-Developed a supply chain network baseline MIP model for a glass manufacuterer with multiple products, manufacuting facilites, and production costs (Regular/Overtime) to find optimal product flow as per sourcing policies and capacity constraints. -To improve the service levels, developed a multi-objective MIP scenario model which finds the minmum number of warehouses to be built such that 80% of the demand is covered with in 500 miles of the nearest source. -Scenario model suggested to build 5 warehouses with their exact location and product flow information and was able to achieve reduction in transporatation cost by 19.75% with 80% demand served within 500 miles compared to 11% of demand within 500 miles in baseline model. -Coded in Python and performed optimization using Gurobi: pandas, dictionaries, loops, gurobi packages, csv package.

A multi language role based library management system with Algolia full text search, RESTful API, image cropping & compressing, GitHub login, CSV import, newsletter, reCAPTCHA, and so on!

Multi Platform CSV Log Analyzer Tested on Win/Linux/Android

The Llama-2-GGML-CSV-Chatbot is a conversational tool leveraging the powerful Llama-2 7B language model. It facilitates multi-turn interactions based on uploaded CSV data, allowing users to engage in seamless conversations.

Fast and multi threaded stock data scraper written in Java using HTMLUnit and minimal-json. Scrapes Finviz and Stocktwits for data, and stores the information in a csv file.