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Video enabled crowd monitoring system and analysis 3

Video enabled crowd monitoring system and analysis

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mehra, Gunjan Joshi, Aditya Singh, Aakriti Shuaib, Mohd Diwakar, Manoj Singh, Prabhishek Graphic Era Deemed to be University Department of Computer Applications Dehradun India Graphic Era Deemed to be University Department of Cse Dehradun India School of Computer Science Engineering & Technology Bennett University Greater Noida India

ISBN: (纸本)9798331515911

One of the most victorious strategies for slowing the spread of the COVID-19 virus has been proposed: social isolation. A computer vision-based AI-assisted approach for aiding with social distance norms compliance is utilized. The solution consists of modules for person detection, tracking, and distance violations detection. You are given the option to select a tool-based mode for camera calibration. In this paper, the various measures for determining the likelihood of social distance violations and how to tell them apart from temporary violations from persistent ones are demonstrated. On countless real-world issues, AI/Deep Learning has in the past shown promising results. In this paper, how to observe crowds in public areas and workplaces using Python, image processing, and computer vision is discussed in depth. © 2025 IEEE.

关键词： Deep learning

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SMS Spam Detection using Advance Naive-Bayes Approach

SMS Spam Detection using Advance Naive-Bayes Approach

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Arora, Yojna Gupta, Neha Rathore, Yogesh Singh Bansal, Nidhi Mehta, Aina Chadha, Shikha Kumar, Ashwani Sharda University Department of Computer Science and Engineering Greater Noida India Symbiosis University of Applied Sciences School of Computer Science & Information Technology Madhya Pradesh Indore India Sharda University School of Engineering and Technology Department of Computer Science and Applications Uttar Pradesh Greater Noida India School of Engineering and Technology Department of Computer Science & Engineering Haryana Faridabad India Bennett University School of Computer Science Engineering and Technology India

ISBN: (纸本)9798331508685

Recently, with increased use of mobile phones, it has transformed into a multibillion-dollar Short Message Service or SMS. However, the drop in the cost of messaging services has led to an increased number of unsolicited commercial messages, or spam, being delivered to mobile phones. Several reasons exist for which traditional algorithms used in email filtering may not efficiently classify the spam in SMS such as the absence of strong SMS spam databases, with the short length and features of the message, the informal language used, etc. This paper uses a real SMS spam database After preprocessing the data and extraction of relevant features, Naïve Bayes machine learning techniques is applied to get the results. Then it is analyzed using confusion matrix, accuracy, precision and recall values. A learning curve with clearly defined relationship between the training and testing data set and overall error at the end. © 2025 IEEE.

关键词： Adversarial machine learning

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Ornithology Species Edge Detection Using Soft Computing Techniques 3rd

Ornithology Species Edge Detection Using Soft Computing Tech...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Himabindu, K. Deepak, Yadagiri Rama Department of CSE Parul Institute of Engineering and Technology Parul University Gujarat Vadodara India Department of CSA Parul Institute of Computer Applications Parul University Gujarat Vadodara India

ISBN: (纸本)9789819783540

A common hobby that provides relaxation from daily life is bird watching. In nature, birds are present. They support the environment and ecosystem. Given how visually similar many bird species are, it is a difficult challenge. As a practical aid for birdwatchers to recognize the species in their native habitat, for humans, it might be difficult to identify different bird species. From a human perspective, it is only natural for birds to appear in a variety of situations in a variety of sizes, shapes, colors, and perspectives. Some bird species are now only sometimes discovered, and even then, it can be challenging to classify them. Additionally, photos offer more distinct variants for bird species identification than audio categorization. The ability of humans to identify the birds from the images is also easier to understand. To identify the species from one another, shape feature is important. Edge detection is a key component of shape extraction. Edge is a significant visual feature that uses a limited number of pixels to communicate visual information. Because of climate factors, it can be uncertain to assess a bird's morphology. Therefore, in this study, we used the Bird Image Edge Detection using Fuzzy Algorithm (BIEDF) to implement this model to extract data from bird images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anthropogenic

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Meta-optimization for improved efficiency: an integrated methodology

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International Journal of Information technology (Singapore) 2025年第4期17卷 2375-2386页

作者： Singha, Anjani Kumar Yadav, Gaurav Hasanpuri, Vijay Patel, Rajiv Ranjan Department of Computer Applications SRM Institute of Science and Technology (‘Deemed-to-Be-University’) Delhi-NCR Campus Ghaziabad 201204 India Department of Computer Science Aligarh Muslim University Aligarh India UIET Kurukshetra University Kurukshetra India Haridwar University Roorkee India

The use of optimization algorithms is essential to train neural networks effectively. The usage of a combination of two different optimizers is proposed in this method that, used together, can perform single optimizers in various problems. The objective of this study is to develop a comprehensive framework that combines meta-optimization techniques with traditional optimization methods to enhance performance metrics such as convergence speed, solution quality, and robustness against local minima. The researcher presents ATMO (Adaptive Meta Optimizers), recent deep neural network (DNN) models that are combined with a new optimizer that combines two different optimizers simultaneously. Meta-optimizer considers all of their contributions at once, rather than trying to enhance each one separately. Using a wide range of techniques, DNNs have been the subject of numerous investigations on text and image categorization. Results from these tests demonstrated that the suggested Adaptive Meta Optimizer outperforms individual optimizers. These experiments show that adequate training is achieved by considering a variety of factors such as Keras standard learning rate and convergence, and degradation on the CIFAR-100, CIFAR-10, and MNIST datasets, and the results are obtained using Python. © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： AdapTiveMeta Optimizers-ATMO Deep learning Image classification Optimization algorithm Optimizers Text classification

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A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach to Trainable Freebie Optimization 3

A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Vinitha, A. Jeevitha, S. Kumar, N.B. Mahesh Sathish Kumar, L. Saravanan, D. Karpagam Academy of Higher Education Department of Computer Applications Tamilnadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil Tamilnadu Virudhunagar India Hindusthan Institute of Technology Computer Science and Engineering Tamil Nadu Coimbatore India Madhya Pradesh Sehore India

ISBN: (纸本)9798331509675

Significant progress has been made in real-time object detection, with YOLOv7 emerging as a state-of-the-art model that achieves the highest levels of accuracy and speed. This study combines Bag-of-Freebies (BoF) strategies with YOLOv7 to improve performance without incurring extra computational expenses. Our tests show that adding BoF preserves real-time inference capabilities while increasing mAP@0.5 by 3.5%. The accuracy and FPS of YOLOv7 with BoF are superior to those of the current YOLO-based models (YOLOv4, YOLOv5, YOLOv6). According to the results, YOLOv7 with BoF sets a new standard for real-time object detection by achieving a mAP@0.5 of 55.3% and 83 FPS on the COCO dataset. © 2025 IEEE.

关键词： Contrastive Learning

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Performance Analysis of YOLO versions for Real-time Pothole Detection

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Procedia computer Science 2025年 257卷 77-84页

作者： Leonhel V. Fortin Orven E. Llantos Department of Computer Applications College of Computer Studies MSU-Iligan Institute of Technology Tibanga Iligan City 9200 Philippines Department of Computer Science

The integration of lightweight computer vision models emerges as a promising solution for automating road inspection processes. The evolution of YOLO architectures, particularly their lightweight variants, enables significant improvements in both detection accuracy and computational efficiency. Recent iterations through YOLOv9-tiny, YOLOv10-nano, and YOLOv11-nano enhance real-time performance capabilities while maintaining acceptable accuracy through optimized network architectures. The implementation of these lightweight YOLO variants presents a compelling approach to automated pothole detection. Previous studies utilizing limited datasets of approximately 1,388 images established initial feasibility but left significant room for improvement in model robustness and generalization. This research addresses these limitations by employing an expanded dataset of 16,054 images, encompassing diverse environmental conditions and road surface characteristics. The comparative analysis establishes new benchmarks for pothole detection in resource-constrained environments, demonstrating the viability of nano/tiny models for practical applications.

关键词： Lightweight computer Vision YOLO Pothole Detection Real-time Object Detection Road Surface Inspection

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A Music Recommender System to Identify the Emotional State of a Person Using Machine Learning Algorithms 4

A Music Recommender System to Identify the Emotional State o...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Muruganantham, A. Malarvizhi, C. Divya Vahini, S. Saranya, D. Uthiramoorthy, A. Karnataka Bengaluru India Psg Institute of Advanced Studies Department of Computer Science Coimbatore India Dr.Mahalingam College of Engineering and Technology Department of Computer Applications Tamil Nadu Pollachi India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Rathinam College of Arts and Science Department of Computer Science India

ISBN: (纸本)9798331523923

In today's digital world, customized recommendation algorithms can improve user experiences in many different areas. Within the sphere of intelligent human-technology interaction, this paper specifically examines a music recommender system driven by machine learning algorithms. It figures out what mood users are in and can provide an appropriate response in real time. The system being propounded in this essay uses advanced technology in facial expression recognition. With the help of a webcam, it aims to accurately detect user emotions, including joy, sadness, and anger. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Support Vector Machines (SVM) have been employed to effectively classify people's mood and recommend an appropriate song. Our extensive tests and examinations show that CNN, RNN, and SVM models are all very good at guessing users' moods - with high precision, recall, F1-score, and accuracy percentages. Furthermore, in-depth analyses like confusion matrixes illuminate how well each model serves as an emotion recognizer. This study's findings are representative of future prospects in affective computing around the globe with major implications for intelligent recommendation systems to which it's important that the models are sensitive. In conclusion, the suggested Music Recommender System is something new for taking a more personal view: We can be as deeply entertained as the web allows us to be - make tailored entertainment paths in the digital era. © 2025 IEEE.

关键词： Convolutional neural networks

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Personal Workout Trainer Using MediaPipe and OpenCV for Real-Time Guidance and Feedback

Personal Workout Trainer Using MediaPipe and OpenCV for Real...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： S.P. Priyanka Sophia Janit Department of Computer Applications Hindustan Institute Of Technology and Science

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Home workouts have gained significant popularity, but the lack of professional guidance often leads to improper exercise techniques, increasing the risk of injuries and reducing workout efficiency. To address this challenge, a virtual fitness tracker integrates cutting-edge technologies like OpenCV, MediaPipe, and Gemini LLMa to provide an intelligent and adaptive fitness solution. OpenCV processes video feeds to capture user movements accurately, while MediaPipe detects and tracks key body landmarks in real-time, ensuring precise pose estimation. By analyzing this data, Gemini LLM delivers personalized feedback, dynamically adjusting suggestions to correct user form and optimize performance. The system not only bridges the gap between professional trainers and home fitness but also enhances accessibility by offering an interactive and user-friendly experience. With capabilities like exercise repetition tracking, posture correction, and injury prevention, this solution transforms workouts into a safe, engaging, and results-driven experience. The approach demonstrates the potential of integrating computer vision and advanced language models to revolutionize personal fitness training.

关键词： Training computer vision Tracking Visual analytics Pose estimation Transforms Streaming media Real-time systems Injuries Standards

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Improving Handwritten Stenography Recognition Using Domain Specific Encoding and Synthetic Data Augmentation

Improving Handwritten Stenography Recognition Using Domain S...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： S. Nivetha C. Bala Kamatchi Department of Computer Applications Hindustan Institute of Technology and Science

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Handwritten form of stenography recognition is a challenging task due to high variations in shorthand symbols and a lack of annotated datasets. A strong baseline for stenographic text recognition, leveraging the newly created LION dataset of shorthand manuscripts. To alleviate symbol ambiguity as well as low-resource constraints in this domain, incorporating stenographic domain expertise is proposed through encoding schemes and synthetic data generation. In the experiments, a Gated-CNN-BGRU architecture is used, trained with Connectionist Temporal Classification (CTC) loss. Pre-train on synthetic data and fine-tuning on real data greatly improve CER and WER. The experiments showed the effectiveness of stenography-specific encoding schemes in improving WER (Word Error Rate) over baseline models by up to 10% when using Melin-based encodings. This work opens pathways for transcribing historical stenographic manuscripts more effectively.

关键词： Deep learning Handwriting recognition Text recognition Error analysis Symbols computer architecture Logic gates Writing Encoding Synthetic data

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GUARDEV: Guided Unified Adaptive Response for Defending Electric Vehicles 27th

GUARDEV: Guided Unified Adaptive Response for Defending Elec...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Mondal, Koustav Kumar Das, Debasis Inter-Disciplinary Research Division - IoT and Applications Indian Institute of Technology Jodhpur Rajasthan Jodhpur India Department of Computer Science Engineering Indian Institute of Technology Jodhpur Rajasthan Jodhpur India

ISBN: (纸本)9783031783821

In the rapidly evolving landscape of electric vehicles (EVs), integrating the Internet of Vehicles (IoV) has opened up new dimensions in automotive technology. While enhancing vehicle performance, safety, and security, this advancement also exposes EVs to heightened cyber threats. To address this critical concern, we introduce GUARDEV: Guided Unified Adaptive Response for Defending Electric Vehicles, a novel Machine Learning (ML)-enhanced conflict resolution-based ensemble Intrusion Detection System (IDS) architecture. GUARDEV leverages the power of adaptive ML techniques to provide a unified and guided response in safeguarding EVs against sophisticated cyber-attacks. Our framework evaluates the effectiveness of three prominent ML models - XGBoost, Random Forest, and Extra Trees - in detecting specific categories of cyber threats. By harnessing the prediction confidence scores from these models, GUARDEV offers a nuanced and intelligent approach to recognizing and thwarting diverse cyber-attacks. Our research, grounded in rigorous experimentation using public IoV security datasets, demonstrates GUARDEV’s exceptional accuracy of 99.47% in intrusion detection. This remarkable achievement not only underscores the potential of ML in enhancing vehicular cybersecurity but also establishes GUARDEV as a powerful tool for creating safer, more secure EVs. GUARDEV paves the way for robust protection of internet-connected electric vehicles in today’s digital age by providing a guided, unified, and adaptive response to cyber threats. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network intrusion

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