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共 71201 条记录，以下是341-350 订阅

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Cyberbullying Detection using Deep Learning Algorithms 3

Cyberbullying Detection using Deep Learning Algorithms

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Nirmala Devi, K. Jayanthi, P. Rajasekar, Vani Rathinasamy, Rahul Dhandapani, Pradeep Rasil Risam, F. Dept. of Computer Science Kongu Engineering College Erode India

ISBN: (纸本)9798331527549

Social media's widespread use has made it easier for people to communicate openly, but it has also fuelled the growth of cyberbullying, which has seriously harmed victims' emotional and mental health. The intricacy of human language and the variety of abusive behaviours make it difficult to detect cyberbullying. To solve this problem, numerous studies have used deep learning (DL) and machine learning (ML) algorithms over time. However, deep learning (DL) algorithms have transformed text analysis by automatically extracting features and comprehending contextual dependencies in language, while machine learning (ML) techniques are limited in their ability to handle complex and contextual linguistic patterns. Building upon this framework, this study investigates the use of transformer-based models—more especially, BERT—for the detection of cyberbullying. Out of the proposed models, DistlBERT gives the best accuracy, precision, recall, and F1-score of 98.87%,98.87%, 98.87 and 98.86% respectively. © 2025 IEEE.

关键词： Adversarial machine learning

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Robust Human Pose Estimation and Action Recognition over Multi-level Perceptron 3

Robust Human Pose Estimation and Action Recognition over Mul...

引用

3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Hanzla, Muhammad Wahid, Wasim Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331504861

This research proposes a novel method that combines human pose estimation (HPE) and sustainable event classification (SEC), focusing on skeleton and context-aware feature extraction for precise activity recognition through machine learning techniques. In contrast to traditional state-of-the-art approaches, our method improves posture and event identification in daily activities by integrating individual detection, skeletal structure creation, and advanced feature extraction methods. The process begins with video-to-image conversion, followed by sliding window application for gradient-magnitude images and background removal during preprocessing. Human detection is achieved using Otsu's thresholding, and key points are identified with the Shi-Tomasi algorithm. The pose estimation step generates human skeletonization, and feature extraction is performed using color histograms, Harris corner detection, and Speeded-Up Robust Features (SURF), optimized through Hill Climbing. Classification using a multi-level perceptron on the UCF-101 and Sports Videos in the Wild (SVW) datasets achieves accuracies of 86.9% and 86.7%, respectively. Our method’s unique combination of advanced techniques represents a significant enhancement over existing solutions, with promising applications in areas such as augmented reality, service robotics, e-health, fitness, and security surveillance. ©2024 IEEE.

关键词： Musculoskeletal system

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Drone-Based Human Action Recognition for Surveillance: A Multi-Feature Approach

Drone-Based Human Action Recognition for Surveillance: A Mul...

引用

2024 International Conference on Engineering and Computing, ICECT 2024

作者： Abbas, Yawar Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350349719

Understanding human actions is crucial for enabling machines to interpret human behavior, with applications spanning video-based surveillance applications, sports monitoring, and entertainment systems. However, the diversity in human motion and appearance poses significant challenges for action recognition, especially when using drones for video capture, which exacerbates complexities such as dynamic backgrounds, motion blur, varying capture angles, and exposure issues. In this study, we propose a system tailored to address these challenges, specifically in drone-recorded RGB videos. Our approach involves splitting the video into frames and applying some preprocessing steps to these RGB frames. In preprocessing, we focus on reducing the computational cost, resizing the image quality, and enhancing foreground objects by removing the background. Subsequently, we also detect humans from images by applying YOLOv5. By using this, we can detect humans from the original frames, from which we extract the human skeleton and identify key points representing various body parts. These key points, encompassing critical joints such as the head, wrists, elbows, thighs, knees, and ankles, are used to derive normalized positions, angular relationships, distance measures, and 3D point clouds. For feature optimization, we use the Linear Discriminant Analysis (LDA), followed by classification via a multi-class SVM. Experimental validation using the Drone Action dataset demonstrates notable action recognition accuracies of 83.2%, showcasing the efficacy of our proposed system. © 2024 IEEE.

关键词： Drones

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Cybersecurity for Industrial IoT: A Review on Vulnerabilities and Attacks 8

Cybersecurity for Industrial IoT: A Review on Vulnerabilitie...

引用

8th International Conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2025

作者： Kumari, Reetu Kumar, Sunil Amandeep Dutta, Kamlesh Kumari, Aparna Guru Jambheshwar University of Science and Technology Dept. of Computer Science & Engineering Haryana Hisar India Guru Jambheshwar University of Science and Technology Dept. of AI & Data Science Haryana Hisar India National Institute of Technology Hamirpur Dept. of Computer Science & Engineering Himachal Pardesh India Institute of Technology Nirma University Dept. of Computer Science & Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798331520762

Internet has evolved from a network of connecting people to a network of connecting things, leading to a more complex and sophisticated network of Industrial Things, known as Industrial IoT (IIoT) today. This evolution has revolutionized the traditional Industrial system into real-time smarter systems that enhanced overall efficiency and productivity. Despite its benefits, the interconnected nature of IIoT, expanded the attack surface, exposing the systems to new crucial and complex cybersecurity threats. Several real time attacks on various industries as mentioned in this paper and the increasing cybersecurity cost over the years, clearly states the need of robust security solutions. To deeply understand the cybersecurity aspect, we provided a comprehensive study of attacks and vulnerabilities in this paper. In this paper, we did a indept. study of various IIoT related severe attacks and aspects related to these attacks, like vulnerabilities, impacts, potential solutions etc. We also provided future research directions to help the research community, to work upon and develop more robust and efficient cybersecurity solutions. © 2025 IEEE.

关键词： attacks cybersecurity etc IIoT Industrial IoT IoT vulnerabilities

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Federated Few-shot Medical Image Classification

Federated Few-shot Medical Image Classification

引用

2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Makhanov, Nursultan Tu, Nguyen Anh Nazarbayev University Dept. of Computer Science Astana Kazakhstan

ISBN: (纸本)9798350353983

Medical image analysis (MIA) holds significant promise for advancing healthcare diagnostics, but privacy concerns often impede data sharing between institutions. Traditional methods require extensive labeled datasets, but annotating medical images is time-consuming and costly, leading to insufficient data for robust deep learning (DL) model training. Moreover, conventional federated learning (FL) approaches struggle with the heterogeneity of data across institutions and adapting to unseen labels, compromising model performance. This work addresses these challenges by integrating few-shot learning (FSL) with FL, leveraging the strengths of both methodologies. The proposed approach enables effective learning from limited and diverse data distributions while preserving patient privacy. By in-corporating meta-learning algorithms, our models quickly adapt to new, unseen data, enhancing robustness and generalizability in non-IID settings. This innovative framework promises significant improvements in MIA tasks, paving the way for more effective and privacy-preserving healthcare solutions. © 2024 IEEE.

关键词： Federated learning

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Multimodal Objects Categorization by Fusing GMM and Multi-layer Perceptron 5

Multimodal Objects Categorization by Fusing GMM and Multi-la...

引用

5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Naseer, Aysha Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350394764

Multi-object recognition resolves the need to instantaneously identify numerous things, which is essential for developing an in-dept. understanding of the world of vision. Variations in object features, occlusions, scale, perspective, and intricate backgrounds make this task challenging. The practical strategy for categorizing objects into multiple categories using well-known benchmark datasets is presented in this work. The method extracts multi-object segmentation from difficult images using an advanced Gaussian mixture model (GMM) technique. A multi-layer perceptron (MLP) architecture that incorporates local descriptors and detectors optimizes the classification process. The method's ability to handle concerns like object occlusion and spatial complexity is validated by extensive experiments using a well-known dataset, Corel 10k. The advised strategy overtakes cutting-edge techniques in terms of accuracy, with scores of 87.40% accuracy for the Corel 10k dataset. © 2024 IEEE.

关键词： Object detection

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Parallelized 0/1 Knapsack Algorithm Optimization in CPU-GPU-Based Heterogeneous System with Algorithm-based Fault Tolerance 18

Parallelized 0/1 Knapsack Algorithm Optimization in CPU-GPU-...

引用

18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Abid, Marzan Binte Shaha, Shuvo Kabir, Upama Arefin, Md. Fahim University of Dhaka Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350331011

A heterogeneous high-performance computing (HPC) system is an aggregation of CPUs and GPUs through high-speed interconnection. As graphics processing units (GPUs) can conduct rapid mathematical and vector operations due to their hardware properties, GPU-based heterogeneous systems can optimize algorithms that show data parallelism. Since the dynamic programming (DP) solution of the 0/1 knapsack algorithm is inherently data-parallel, this paper optimized the algorithm to maximize the advantages of a GPU-based parallel environment. Furthermore, this research identified that the algorithm was well suited to four distinct GPU-launch approaches. Among them, the "single-cell calculation by single thread with a single kernel launch"approach outperforms others by reducing the complexity from O(n∗w) to O(n). Besides, these HPC systems are susceptible to the slightest of errors. Checkpoint recovery (CP/R) is the most commonly used fault tolerance and recovery system, which suffers from huge checkpoint and recovery overhead. To reduce this overhead in CPU-GPU-based heterogeneous systems, an algorithm-based fault tolerance (ABFT) technique has been proposed for a particular class of algorithm: 0/1 knapsack. The experimental results verify the feasibility of the proposed ABFT technique and demonstrate that the system can recover from failures with minimal overhead. © 2024 IEEE.

关键词： Graphics processing unit

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Traffic Surveillance System: Robust Multiclass Vehicle Detection and Classification 5

Traffic Surveillance System: Robust Multiclass Vehicle Detec...

引用

5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Chughtai, Bisma Riaz Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350394764

In modern times, the identification and categorization of intelligent vehicles have attained significant recognition within the realm of highway management, as it holds paramount importance for effective traffic control and administration. The traditional vehicle detection and classification methods often cause imprecise results because they are restricted to a limited number of perspectives and viewpoints. We came up with a novel deep-learning strategy designed for the segmentation and detection of vehicles. Our innovative methodology for vehicle detection and classification involves the utilization of semantic segmentation using DeepLabv3_ResNet101 for the detection of vehicles we have used YOLOv5, followed by the extraction of vehicle features employing the FAST Fourier transform algorithm, blob detection algorithm, and KAZE features and ORB feature. Subsequently, we employ the CNN (Convolutional Neural Network) model for vehicle classification, achieving an impressive classification accuracy of 96%. These experimental investigations are conducted on the widely recognized benchmark dataset known as the ' Vehicle-OpenImage dataset'. We have used five class datasets including (car, bus, truck, motorbike, and ambulance). The results of our study establish the current innovatory performance in the domain of vehicle detection and classification. © 2024 IEEE.

关键词： Vehicles

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Multi-Modal Sensors Fusion for Fall Detection and Action Recognition in Indoor Environment 3

Multi-Modal Sensors Fusion for Fall Detection and Action Rec...

引用

3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Abro, Iqra Aijaz Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331504861

In computer vision and inertial sensors, recent research focuses on monitoring and identifying falls, as well as daily actions, through autonomous human action detection. In this paper, the proposed model utilizes multi-modal sensors for fall detection. The data from inertial sensors was filtered using a bilateral filter, known for its monotonic and maximum flat magnitude response in the passband, giving smoothness. Key properties, such as the Gaussian Mixture Model (GMM) and parseval’s energy, were retrieved. Additionally, fall detection was accomplished utilizing RGB (red-green-blue) videos, where features such as rectangles, triangles, full-body ridges, and full-body curves were generated. The final results from the inertial sensor and vision data were fused utilizing multimodal fusion. We then optimized the fused data using the Naive Bayes approach and trained Multi-layer perceptron (MLP) classifier for classification. The UR Fall Detection (URFD) dataset was utilized to evaluate the proposed system, demonstrating its usefulness by reaching an accuracy of 88%. ©2024 IEEE.

关键词： computer vision

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A computer science Perspective on Digital Sustainability: A Thematic Literature Review 22

A Computer Science Perspective on Digital Sustainability: A ...

引用

22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Delnevo, Giovanni Olaiya, Kelvin Salomoni, Paola University of Bologna Dept. of Computer Science and Engineering Bologna Italy

ISBN: (纸本)9798331508050

This paper explores the evolving concept of digital sustainability, emphasizing its growing importance as digital technologies become essential to addressing global sustainability challenges. Through a thematic literature review focused on computer science, we analyze papers from Scopus-indexed journals to identify key themes in the discourse on digital sustainability. Additionally, we map the identified topics to the traditional three-pillar model of sustainable development, noting areas of alignment and divergence. The study offers insights into how digital sustainability is framed within the computer science literature and its implications for future research. © 2025 IEEE.

关键词： Green development

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