AI/ML, Computer Vision, DLL Show Case
AI Empowering the Future.
Audio
Speech sentiment analysis using AI/ML involves processing audio recordings to detect and interpret emotional tones, predicting sentiments such as neutral, angry, calm, happy, sad, fearful, disgust, and surprised. This involves using Natural Language Processing (NLP) for spoken content analysis and machine learning algorithms to classify these specific emotions, providing valuable insights for applications like customer service, market research, and social media monitoring.
Text
Text-to-speech (TTS) and speech-to-text (STT) technologies using AI/ML convert written text into natural-sounding speech and spoken language into written text, respectively. TTS leverages neural networks to generate human-like voices, enhancing accessibility and user interaction, while STT employs advanced speech recognition models to transcribe audio accurately, facilitating tasks like voice commands and automated transcription services.
NLP / GI
NLP Question and Answer (Q&A) systems using AI/ML involve creating models that can understand and respond to human language queries. These systems leverage natural language processing techniques to interpret the context and intent of questions, providing accurate and relevant answers from vast datasets. Applications include virtual assistants, customer support, and information retrieval systems.
Computer Vision
Computer Vision for face recognition and lip movement analysis using AI/ML involves detecting and identifying faces in images or videos and analyzing lip movements to understand speech or expressions. Advanced machine learning models are trained to recognize facial features and track lip motion, enabling applications in security, authentication, and silent speech interfaces. Demo
ML/DLL
Deep language learning, a machine learning subfield, is key for vehicle identification, classification, and traffic rule violation detection. It uses multi-layer neural networks to analyze large datasets, recognizing complex patterns in traffic images and videos. These models accurately identify vehicle types and detect violations like speeding and red light infractions, improving traffic management and law enforcement efficiency. By mimicking the human brain's structure, deep learning advances intelligent transportation systems, contributing to safer roads and effective traffic monitoring. Demo
Blockchain
Our innovative betting application leverages React and Blockchain technology to provide a secure and transparent platform for Ethereum-based betting. Users can create and join one-to-one or one-to-many bets seamlessly. Upon match completion, the admin resolves the bets, and results are displayed in the "Declared Bets" section. Winners have their earnings transferred to a "Pending to Withdrawal" wallet, from which they can easily withdraw to their main wallet. Losers forfeit their staked amounts. The platform ensures real-time updates and secure transactions through integration with Ethereum wallets and smart contracts, delivering a modern and trustworthy betting experience.
DocChat
DocChat is a telemedicine platform that lets in sufferers to have video consultations with healthcare specialists from the comfort of their personal houses. It gives a handy and steady manner for sufferers to get right of entry to medical advice, prognosis, and treatment plans with no need to go to a doctor's workplace in man or woman. By leveraging video era, DocChat enhances accessibility to healthcare services, making it less complicated for people to get hold of well timed hospital treatment, in particular in faraway or underserved regions.
Synchronize Utility
A synchronize utility is a tool designed to ensure that files, data, and settings are consistently updated across multiple devices and platforms. This utility automatically detects changes and replicates them in real-time, allowing users to access the most recent versions of their data from anywhere. By streamlining the process of data synchronization, it enhances productivity and minimizes the risk of data loss.
IPFS (InterPlanetary File System) - IPFS
Application built with React and IPFS, allows users to securely store their personal information such as name and email in a decentralized manner. Upon submission, users receive a file containing their ID, name, email, CID, and a timestamp. This information is securely stored using IPFS, providing a unique CID for each submission. Users can easily retrieve their stored information via the CID, ensuring data integrity, privacy, and accessibility without reliance on centralized servers.
Voice Spectrum Analysis
Develop a system to analyze the frequency spectrum of recorded or live voice data. This involves using signal processing techniques to visualize and interpret the audio's unique characteristics. Useful for applications in voice recognition, emotion detection, and audio diagnostics.
Safety Kit Detection
Create a solution to detect the presence of safety kits in various environments, such as construction sites or industrial areas. This involves using image recognition technology to ensure compliance with safety regulations and promote workplace safety.
Vehicle Detection, Classification, and Counter
Develop a model that can detect, classify, and count vehicles in real-time from video feeds. This system can identify different types of vehicles and provide a count, supporting traffic management and urban planning initiatives.
Speed Tracker and Violation Detector
Build a tool to monitor vehicle speeds and detect speed limit violations. This system uses video analysis to track the speed of moving vehicles and identify instances where they exceed speed limits, enhancing road safety through automated enforcement.
U-turn Detector and Violation Detector
Implement a system to detect illegal U-turns and identify violations at intersections. This involves analyzing video feeds to monitor vehicle movements and ensure compliance with traffic rules, reducing the risk of accidents at critical points.
People Counter
Develop an AI-based solution to accurately count the number of people in a specified area. This system can be used for crowd management, event planning, and security monitoring, providing real-time data on the number of individuals present in a given space.
Accident Detection
Implement a deep learning model to automatically detect traffic accidents in video footage. This system can analyze real-time or recorded videos to identify potential accidents, helping to enhance road safety and prompt emergency response.
Chain Snatching Detection
This project addresses a targeted use case by detecting chain snatching incidents with a model trained on a specialized, custom dataset. Developed to capture real-world scenarios, the model achieves reliable detection within defined environments, ensuring high accuracy and robustness with focused data. It offers an efficient solution for monitoring specific safety concerns in controlled settings.