Open Source Windows Artificial Intelligence Software - Page 2
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Artificial Intelligence Software for Windows
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AI learning
AiLearning, data analysis plus machine learning practice
We actively respond to the Research Open Source Initiative (DOCX) . Open source today is not just open source, but datasets, models, tutorials, and experimental records. We are also exploring other categories of open source solutions and protocols. I hope you will understand this initiative, combine this initiative with your own interests, and do what you can. Everyone's tiny contributions, together, are the entire open source ecosystem. We are iBooker, a large open-source community, we-media, and online earning community, with a QQ group of more than 10,000 people and at least 10,000 subscribers. The number of Github Stars exceeds 60k, and it ranks in the top 100 of all Github organizations. The daily up of all its websites exceeds 4k, and the peak of Alexa ranking is 20k. Our core members are certified as CSDN blog experts and short-book programmers as excellent authors. We have established ApacheCN, a non-profit document, and tutorial translation project. -
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AI-Blocks
A powerful and intuitive WYSIWYG to create Machine Learning models
A powerful and intuitive WYSIWYG interface that allows anyone to create Machine Learning models! The concept of AI-Blocs is to have a simple scene with draggable objects that have scripts attached to them. The model can be run directly on the editor or be exported to a standalone script that runs on Tensorflow. Variables are parsed from python scripts and can be edited from the AI-Blocs properties panel. To run your model simply press the "Play" button and let the magic happen! The project requires Python and Tensorflow to run projects. You can still create and edit projects without these dependencies. To run AI-Blocs, download the project archive and launch AI-Blocs.exe. -
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Argo for Jason
A Jason architecture for programming embedded robotic agents
In this architecture, Javino enables processing the data coming from sensors as perceptions in ARGO's agent reasoning cycle. Then, one can restrict the list of perceptions delivered by Javino based on filters designed by the agent's programmer. The main contribution of ARGO is to enable the use of perception filters for programming robotic agents. Moreover, ARGO allows an agent to decide when to start or to stop perceiving from its sensors, to fix the interval between each perception and to control these filtering behavior in runtime. LATEST VERSION available at: https://github.com/chon-group/argo -
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Armageddon AI
To create a mathematical equation to run a super military grade A.I.
The author here seeks to take information that he has learned from other programming projects to create a mathematical equation that can be used to run a super artificial intelligent A.I. capable of multitasking and performing task as well as a human or even better than a human. This project will use trained regression lines to predict the weights of the A.I.'s neurons. Then using the equation's of the regression lines to make an equation for a A.I. that can self train and update its own A.I. equation to out perform humans at completing task. The purpose of this A.I. is to be able to put it into any environment and have it perform better than a human can. -
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Awesome Community Detection Research
A curated list of community detection research papers
A collection of community detection papers. A curated list of community detection research papers with implementations. Similar collections about graph classification, classification/regression tree, fraud detection, and gradient boosting papers with implementations. -
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Awesome Decision Tree Papers
A collection of research papers on decision, classification, etc.
A collection of research papers on decision, classification and regression trees with implementations. -
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Awesome Fraud Detection Research Papers
A curated list of data mining papers about fraud detection
A curated list of data mining papers about fraud detection from several conferences. -
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Awesome Graph Classification
Graph embedding, classification and representation learning papers
A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations. Relevant graph classification benchmark datasets are available. Similar collections about community detection, classification/regression tree, fraud detection, Monte Carlo tree search, and gradient boosting papers with implementations. -
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Awesome-FL
Comprehensive and timely academic information on federated learning
A “awesome” curated list of federated learning (FL) academic resources: research papers, tools, frameworks, datasets, tutorials, and workshops. A hub for FL knowledge maintained by the academic community. -
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BOS-Ontologie
Die BOS-Ontologie ist eine formale Ontologie der BOS-Domäne.
Die BOS-Ontologie ist eine formale Domänen- Ontologie der deutschen BOS (Behörden und Organisationen mit Sicherheitsaufgaben). Sie stellt eine terminologische Grundlage einer beschreibungslogischen Wissensbasis dar, anhand derer logische Schlussfolgerungsmechanismen eine intelligente Auswertung von Lageinformationen ermöglichen sollen. -
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BibleNLP
Natural Language Processing using The Holy Bible
This project attempts to develop natural language processing routines as applied to a Bible text domain. Many common technologies (e.g., tokenization, Brill POS tagger) are used in conjunction with theoretical paradigms (e.g., hierarchical word definition trees, phrasal concordance). -
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COMPETENCY APPLICATION ONTOLOGY
This ontology aims to represent competencies within a CS School
This ontology aims to represent competencies within a computer science school in order to improve its competency location system by facilitating comparison between different profiles, comparison between a profile and a job description for example. This ontology provides unique definitions to all relevant concepts related to the application cited previously. The ontology is in French. -
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Chat_Bot
A chat bot that talks to users on Omegle Site
Here is a chat bot that will talk to users on Omegle Site, it uses AIML, python and requires connection to internet to work. You'll be amused to see the bot chat with other people on Omegle. -
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CoTracker
CoTracker is a model for tracking any point (pixel) on a video
CoTracker is a learning-based point tracking system that jointly follows many user-specified points across a video, rather than tracking each point independently. By reasoning about all tracks together, it can maintain temporal consistency, handle mutual occlusions, and reduce identity swaps when trajectories cross. The model takes sparse point queries on one frame and predicts their sub-pixel locations and a visibility score for every subsequent frame, producing long, coherent trajectories. Its transformer-style architecture aggregates information both along time and across points, allowing it to recover tracks even after brief disappearances. The repository ships with inference scripts, pretrained weights, and simple interfaces to seed points, run tracking, and export trajectories for downstream tasks. Typical uses include correspondence building, motion analysis, dynamic SLAM priors, video editing masks, and evaluation of geometric consistency in real scenes. -
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Cpt Kirk
Obtain why and why-not justifications for answer set programs.
Since the method proposed in https://www.researchgate.net/publication/262599199_Unifying_Provenance_and_Debugging_for_Answer-Set_Programs?ev=prf_pub is based on meta-programming, it is possible to use existing state-of-the-art software systems that support well-founded and answer-set semantics, which allowed us to start developing this new tool by extending the one that exists related to a debugging approach: Spock, hence the name Cpt. Kirk. Furthermore and more importantly, one direction to explore is to use the technique of reification as described in "metaASP" to obtain the implicants via a saturation technique, and obtaining the prime implicants of provenance formulae by optimization and thus proper minimal justifications. -
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DE-HEoC
DE-based Weight Optimisation for Heterogeneous Ensemble
We propose the use of Differential Evolution algorithm for the weight adjustment of base classifiers used in weighted voting heterogeneous ensemble of classifier. Average Matthews Correlation Coefficient (MCC) score, calculated over 10-fold cross-validation, has been used as the measure of quality of an ensemble. DE/rand/1/bin algorithm has been utilised to maximize the average MCC score calculated using 10-fold cross-validation on training dataset. The voting weights of base classifiers are optimized for the heterogeneous ensemble of classifiers aiming to attain better generalization performances on testing datasets. -
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DOS Games
Simple DOS based games
This is a project to host DOS based games that were developed using tools from DOS era and demonstrates game development. Games in this project are developed as class project and most of them will be demonstration only. -
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Deep Learning with PyTorch
Latest techniques in deep learning and representation learning
This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include DS-GA 1001 Intro to Data Science or a graduate-level machine learning course. To be able to follow the exercises, you are going to need a laptop with Miniconda (a minimal version of Anaconda) and several Python packages installed. The following instruction would work as is for Mac or Ubuntu Linux users, Windows users would need to install and work in the Git BASH terminal. JupyterLab has a built-in selectable dark theme, so you only need to install something if you want to use the classic notebook interface. -
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DiffGen
Differentially-private algorithm based on Generalization
Privacy-preserving data publishing addresses the problem of disclosing sensitive data when mining for useful information. Among existing privacy models, epsilon-differential privacy provides one of the strongest privacy guarantees and has no assumptions about an adversary's background knowledge. All the existing solutions that ensure epsilon-differential privacy handle the problem of disclosing relational and set-valued data in a privacy preserving manner separately. We developed an algorithm that considers both relational and set-valued data in differentially private disclosure of healthcare data. -
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Dodge gpt
Bypass Ai content for GPTZero and others making text Undetectable
*New Update* ╔════════════════════════════════════════════════════════════════╗ ║ DODGE V10 - STEALTH EDITION ║ The Only AI Text Humanizer That Defeats GPTZero ╚════════════════════════════════════════════════════════════════╝ █████████████████████████████████████████████████████████████████ █ █ █ 🛡️ CURRENT STATUS: GPTZERO RESISTANT - VERIFIED 2026 █ █ 📊 SUCCESS RATE: 60.7% AGAINST ALL DETECTORS █ █ 🔬 BASED ON: REAL HUMAN CORPUS ANALYSIS █ █ █ █████████████████████████████████████████████████████████████████ Dodge V10 isn't just another "synonym replacer" or "typo adder". It is a sophisticated neural text transformation engine that rewrites content to match the EXACT statistical fingerprint of human writing. -----100% Free----- -
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DodgsonScoring
Several Implementations of the Voting Rule Proposed by C.L.Dodgson
This Project features 5 Algorithms which implement the Dodgson Rule. A menu class allows testing, benchmarking and (to some extend) debugging them. The project is intended to be expandable, in case someone has a good idea and wants to implement it. A base class is given, and a new rule can be added relatively easy just by implementing the function which calculates the Dodgson score, "getSCD". Multithreading is also supported in case the rule performance can be improved by it. But that is, at the moment, a bit more complicated. -
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DreamBooth Dataset
Text-to-Image Diffusion Models for Subject-Driven Generation
DreamBooth is a research project and dataset repository representing the official assets for the DreamBooth technique, a method for fine-tuning text-to-image generative diffusion models so they can generate specific, personalized subjects from just a handful of example images. Originally developed by researchers at Google Research and Boston University, DreamBooth works by associating a unique identifier token with a small set of photos of a person, object, or style, enabling the model to produce diverse and accurate images of that subject in new contexts once fine-tuned. This method addresses a common limitation of general-purpose diffusion models, which often struggle to faithfully reproduce lesser-known or custom subjects without extensive retraining. -
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Extract Objects from Image
Connected Component Labeling Algorithm - Extracting Objects From image
fast Connected Component Labeling Algorithm - java application - Extracting Objects From image -
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FALCON - Text Search Java Project
JSON based text search Java Project
----------------- - What is it? - ----------------- The "Falcon Search" is a JAVA API and tool to search inside the documents. It was originally started to search the content in pdf files under the project "HAWK Search". Searching with this tool is query-based not word-based as in most of the document search tools OR document readers. It also takes care of jumbling of words within query and spelling mistakes. Commonly used techniques in this project are Natural Language Processing, Information Extraction and Question-Answering Architecture. ---------------------- - Latest Version - ---------------------- Details of latest version can be found on project website - http://geekdadaji.com --------------------------- - CONTACT DETAILS - --------------------------- CREATOR : SWAPNIL A JADHAV (saj1919) EMAIL ID : dadajibudhau@gmail.com WEBSITE : http://geekdadaji.com LICENSE : CC BY-NC 4.0 -
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FaceAccess Facial Recognition System
FaceAccess is an Access Control System based on Facial Recognition
With the growing need to exchange information and share resources, information security has become more important than ever in both the public and private sectors. Although many technologies have been developed to control access to files or resources, to enforce security policies, and to audit network usages, there does not exist a technology that can verify that the user who is using the system is the same person who logged in. FaceAccess provides a prototype implementation as a "login module" of an information system. The goal is to enhance the level of system security by periodically checking the user’s identity without disrupting the user’s activities. Installation instructions can be found in the package. If you need anymore guidance, please use the Wiki to post any kind of inquiry. NB: Please Donate to support the development of this project. PM me for other means. Any kind of support will be very much appreciated. Thanks a bunch.
