Himanshu Kumar’s Post

🚀 Excited to announce a major technical achievement for our AI interviews platform. We've integrated advanced computer vision based proctoring using OpenCV, an open source computer vision library. 🎉 With robust algorithms and extensive documentation, I was able to integrate this cutting-edge proctoring tech within a few days of dev work. Can't imagine, 6 months back, we had to partner with a third party service provider to do this. Using OpenCV, we've implemented: - Real-time Facial Recognition: Via Haar Cascades and Deep Neural Networks (DNNs) for candidate verification. - Behavioral Analysis: Utilizing optical flow and pose estimation to detect suspicious activities. - Environmental Scanning: Using background subtraction and image segmentation for environment verification. Key Data Points - Adoption: OpenCV is used by 47,000+ companies, proving its reliability. - Accuracy: Achieving 95% accuracy in facial recognition with state-of-the-art DNNs. #GenerativeAI #ComputerVision #OpenSource #AIInterviews #Proctoring #OpenCV #MachineLearning

Excited to share a face recognition system utilizing DeepFace, face_recognition, and OpenCV. DeepFace Library, renowned for its robust CNN algorithms, enabling advanced facial analysis and feature extraction. Leveraging face_recognition library, which utilizes HOG (Histogram of Oriented Gradients) and CNN (Convolutional Neural Networks) to accurately detect facial landmarks and positions. Complemented by OpenCV, providing essential tools for image processing, such as image loading, manipulation, and feature extraction. Despite the heavy computational load imposed by these libraries (yet the best libraries for this task), my laptop struggled, taking approximately 40 seconds to process each frame, which unfortunately prevented me from uploading the video solution of my work. My team:- Het Patel, Ronak Gopani github link:- https://lnkd.in/dHqJKFhU #facerecognition #machinelearning #computervision #opencv #deepface #facialrecognition

Day 3 of 15-day Image Processing Journey! Topics: RGB to Binary Image: Converting color images to binary images for thresholding, edge detection, and feature extraction. Bitwise Operations: Exploring AND, OR, XOR, and NOT operations for image manipulation and feature extraction. Drawing Text and Shapes: Adding text, lines, rectangles, circles, and polygons to images for image processing and computer vision applications. I'm looking forward to diving deeper into more advanced topics like filtering, transformations, and object detection in the next 12 days! Stay tuned for more updates on my image processing journey! #ImageProcessing #ComputerVision #MachineLearning #DeepLearning #DataScience #AI #ArtificialIntelligence #ImageManipulation #FeatureExtraction #ObjectDetection #BitwiseOperations #RGBtoBinary #ImageThresholding #EdgeDetection #ImageAnalysis #DataAnalysis #DataVisualization #LearningJourney #CareerDevelopment #ProfessionalGrowth #TechCareer #Innovation

Welcome to Day 63 of #100daysamplifierdesign Machine Learning for Automated Op-Amp Design Traditional op-amp design is time-consuming, requiring extensive expertise. Neural networks can automate this process, rapidly exploring the design space. Leveraging machine learning enables efficient exploration of the op-amp design space, streamlining the development process. Key steps: 1. Generate comprehensive dataset of op-amp designs via circuit simulation 2. Train neural network to learn relationship between specs and circuit parameters 3. Deploy trained model to quickly predict optimal parameters for target performance 4. Validate against traditional methods and continuously refine the model #OpAmp #MachineLearning #NeuralNetworks #AutomatedDesign #IntegratedCircuits

https://lnkd.in/gZbu4Waj Published article: Deep Semantic Segmentation New Model of Natural and Medical Images Author: Pei-Yu Chen Abstract: Semantic segmentation is the most significant deep learning technology. At present, automatic assisted driving (Autopilot) is widely used in real-time driving, but if there is a deviation in object detection in real vehicles, it can easily lead to misjudgment. Turning and even crashing can be quite dangerous. This paper seeks to propose a model for this problem to increase the accuracy of discrimination and improve security. It proposes a Convolutional Neural Network (CNN)+ Holistically-Nested Edge Detection (HED) combined with Spatial Pyramid Pooling (SPP). Traditionally, CNN is used to detect the shape of objects, and the edge may be ignored. Therefore, adding HED increases the robustness of the edge, and finally adds SPP to obtain modules of different sizes, and strengthen the detection of undetected objects. The research results are trained in the CityScapes street view data set. The accuracy of Class mIoU for small objects reaches 77.51%, and Category mIoU for large objects reaches 89.95%. #Convolutionalneuralnetwork #Edgedetection #Cityscapes #Semanticsegmentation #CloudComputing #ComputerArchitecture #DataArchitecture #DataMining #DataModeling #DataScience #DataSecurity #DimensionalAnalysis #EducationalTechnology #ElectricalSytems #ImageProcessing #InformationSecurity #Instrumentation #InternetSecurity #ITStrategy #MachineDesign #MachineLearning #MaterialsScience #MobilityManagement #NetworkProtocols #NetworkSecurity #OperatingSystems #PowerElectronics #ProjectTechnology #QualityControl #ReliabilityTech #Robotics #SemiconductorTechnology #Simulation #SoftwareTesting #SystemDynamics #SystemsDesign #SystemsTechnology #TechnoMechnics #TechnologyandSociety #TechProduction #VehicleTechnology #WirelessSensorNetworks

Welcome to Day 63 #100daysamplifierdesign Machine Learning for Automated Op-Amp Design Traditional op-amp design is time-consuming, requiring extensive expertise. Neural networks can automate this process, rapidly exploring the design space. Leveraging machine learning enables efficient exploration of the op-amp design space, streamlining the development process. Key steps: 1. Generate comprehensive dataset of op-amp designs via circuit simulation 2. Train neural network to learn relationship between specs and circuit parameters 3. Deploy trained model to quickly predict optimal parameters for target performance 4. Validate against traditional methods and continuously refine the model

🌟 Exciting News! 🌟 Thrilled to share that my research paper titled "Driver Drowsiness Detection Using CNN Algorithm in Image Processing" has been PUBLISHED in the AIP Conference Proceedings (Volume 3022, Issue 1, August 2024)! 🚗💥 With drowsiness being responsible for over 30% of road accidents worldwide 🌍, our research leverages Deep Learning and CNN algorithms to detect driver fatigue in real time! 📷🧠 By analyzing eye-blink patterns, the system alerts drivers when they’re on the verge of dozing off, potentially saving lives and preventing crashes! 🚨🚘 Check it out here: DOI: 10.1063/5.0222519 https://lnkd.in/gdcgUu8J #AI #DeepLearning #RoadSafety #Innovation #DrowsinessDetection #MachineLearning #Research #FutureTech

Object Detection and Tracking with YOLO and OpenCV YOLO is state-of-the-art deep learning model for real time object detection, OpenCV is an open-source computer vision library that enables seamless integration of image and video processing. #OpenCV #YOLO #AI #DeepLearnind #RealTimeDetection

Neural Network Learns to Drive on a Track: Simple 2D Simulation with Piglet and NumPy Excited to share latest project where we developed a neural network capable of driving a car on a simulated track! Using Piglet for rendering and NumPy for computations, We created a 2D simulation to teach the neural network the intricacies of driving. Key Highlights: Neural Network Training: Implemented a simple yet effective neural network model. Simulation Environment: Utilized Piglet for a realistic 2D driving environment. Data Processing: Leveraged NumPy for efficient data handling and processing. Learning Process: The network learns through trial and error, improving its driving skills over time. This project showcases the power of AI in solving complex problems through simulations and neural networks. Looking forward to further refining this model and exploring more advanced applications! #AI #MachineLearning #NeuralNetworks #Simulation #TechInnovation #Piglet #NumPy

Task-4 🎉 Project Completed: Hand Gesture Recognition Model for Enhanced Human-Computer Interaction I’m thrilled to share that I have successfully developed a hand gesture recognition model that achieves an impressive 99.7% accuracy in identifying and classifying various hand gestures from image and video data. This project is aimed at facilitating intuitive human-computer interaction and enabling gesture-based control systems. Key Accomplishments: 1. Data Acquisition and Preparation: Curated and preprocessed a diverse dataset to ensure the model’s robustness and generalization. 2.Advanced Model Architecture: Implemented state-of-the-art deep learning techniques, including convolutional neural networks (CNNs), to achieve high accuracy in gesture classification. 3.Real-Time Inference: Optimized the model for real-time processing, allowing for instantaneous gesture recognition in interactive applications. This project has deepened my understanding of computer vision and machine learning, and I’m excited about the potential applications in areas such as gaming, virtual reality, and assistive technologies. #ProdigyInfotech #GestureRecognition #ComputerVision #MachineLearning #DeepLearning