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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Rajak, Nikhil Phulre, Ajay Kumar Chandra Patel, Subhash Patel, Narottam Das Khekare, Ganesh Vit Bhopal University School of Computing Science Engineering and Artificial Intelligence Sehore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350375442

This study addresses the pressing issue of hate speech on online platforms. Traditional text-based analysis is no longer adequate for identifying hate speech due to the growth of internet platform data. The project aims to create scalable automated methods for web platforms to detect hate speech in real-time. To find the best algorithms for recognizing hate speech, the study compares many machine learning models using a large dataset in Hindi from Twitter. Where the study matters is in the creation of advanced content filtering systems that offer a more secure and friendly online environment. Support Vector Machine (SVM) has the best performance, with outstanding precision (0.9468) and F1-score (0.944), showcasing the top-performing ones and talking about the difficulties and ways ahead in this field. The study recommends improving Hindi hate speech detection systems. With problems such as imbalanced data sets and the dynamic nature of online language, language research using hybrid models, constant model adjustments, and language trend monitoring are all necessary. © 2024 IEEE.

关键词： Adversarial machine learning

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POLYIE: A Dataset of Information Extraction from Polymer Material Scientific Literature

POLYIE: A Dataset of Information Extraction from Polymer Mat...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Cheung, Jerry Junyang Zhuang, Yuchen Li, Yinghao Shetty, Pranav Zhao, Wantian Grampurohit, Sanjeev Ramprasad, Rampi Zhang, Chao College of Computing United States School of Materials Science and Engineering Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9798891761148

Scientific information extraction (SciIE), which aims to automatically extract information from scientific literature, is becoming more important than ever. However, there are no existing SciIE datasets for polymer materials, which is an important class of materials used ubiquitously in our daily lives. To bridge this gap, we introduce POLYIE, a new SciIE dataset for polymer materials. POLYIE is curated from 146 full-length polymer scholarly articles, which are annotated with different named entities (i.e., materials, properties, values, conditions) as well as their N-ary relations by domain experts. POLYIE presents several unique challenges due to diverse lexical formats of entities, ambiguity between entities, and variable-length relations. We evaluate state-of-the-art named entity extraction and relation extraction models on POLYIE, analyze their strengths and weaknesses, and highlight some difficult cases for these models. To the best of our knowledge, POLYIE is the first SciIE benchmark for polymer materials, and we hope it will lead to more research efforts from the community on this challenging task. Our code and data are available on: https://***/jerry3027/PolyIE. © 2024 Association for Computational Linguistics.

关键词： Information retrieval

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DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring 5

DL-Based Somnolence Detection for Improved Driver Safety and...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Neeraja, Peddinti. Kumar, R.G. Kumar, M. Sunil Liyakat, Kazi Kutubuddin Sayyad Vani, M. Sowmya School of Computing Department of Computer Application AP Tirupathi India Siddharth Institute of Engineering & Technology Computer Science and Engineering AP Puttur India School of Computing Department of Computer Science and Engineering AP Tirupathi India Brahmdevdada Mane Institute of Technology Department of Computer Science and Engineering Maharashtra Solapur India

ISBN: (纸本)9798350383522

This abstract explores the utilization of deep learning for detecting driver somnolence, aiming to enhance driver safety and alertness monitoring. It investigates the integration of computer vision, physiological signals, and machine learning algorithms. Key considerations include real-time detection, accuracy, scalability, and driver intervention mechanisms. By leveraging deep learning techniques, effective driver somnolence detection systems can contribute to preventing accidents and promoting safer roads. © 2024 IEEE.

关键词： Convolution neural network deep learning Drowsiness detection transfer learning

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Smart Road Stud: Wireless Sensor Networks for Vehicle Sensing 100

Smart Road Stud: Wireless Sensor Networks for Vehicle Sensin...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Sun, Yanli Feng, Yimeng Wang, Hua Quan, Wei Li, Hao Sun, Jiayu Cheng, Jixuan Harbin Institute of Technology School of Transportation Science and Engineering Harbin China Macquarie University School of Computing Sydney Australia Broadvision Engineering Consultants Kunming China

ISBN: (纸本)9798331517786

This study introduces the Smart Road Stud (SRS), an innovative Anisotropic Magneto-Resistive (AMR) detector strategically placed on lane markings for advanced traffic monitoring. Unlike traditional detectors, the SRS, when deployed in pairs or more, facilitates simultaneous vehicle detection. Analysis reveals that single AMR detectors often face challenges, such as interference signals and misidentification of double-vehicle signals. To address these issues, a method leveraging multiple AMR detectors is proposed. Experimental results demonstrate over 95% accuracy in identifying interference signals and distinguishing double-vehicle signals. The approach achieves a remarkable 98.7% accuracy in lane-level traffic volume estimation. The SRS outperforms conventional detectors, showcasing its potential to enhance accuracy and versatility in traffic monitoring. © 2024 IEEE.

关键词： Studs (fasteners)

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A Shallow Convolutional Neural Network Model for Breast Cancer Histopathology Image Classification 11th

A Shallow Convolutional Neural Network Model for Breast Canc...

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11th International Conference on Recent Trends in computing, ICRTC 2022

作者： Saxena, Shweta Shukla, Praveen Kumar Ukalkar, Yash School of Computing Science Engineering VIT Bhopal University Bhopal India School of Computing and Information Technology Manipal University Jaipur Jaipur India

ISBN: (纸本)9789811988240

Identification of malignancy using histopathology image processing is a crucial method for cancer diagnosis. A model to classify images based on deep convolutional neural networks (CNNs) attains a promising performance in the computer vision for a large dataset but creates a computational complexity for a comparatively small histopathology dataset. A shallow CNN model is proposed in this paper, which classifies the histopathology images of breast tissues. The images are classified as either malignant or benign by the model. It is tested on two publicly available, benchmark breast cancer histopathology datasets. According to the result, the proposed model achieves (95.19 ± 8.50) accuracy and requires a less no. of parameters (8, 052,062) for training as compared to the existing deep CNN models implemented on the same protocol. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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Customer Segmentation Analysis Using Clustering Algorithms 3rd

Customer Segmentation Analysis Using Clustering Algorithms

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3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Reddy, Biyyapu Sri Vardhan Rishikeshan, C.A. Dagumati, VishnuVardhan Prasad, Ashwani Singh, Bhavya School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819939312

Customer segmentation has been deployed as a prudent marketing strategy by companies to ensure that their investments are less risky and more judicious. Segmenting customers helps the companies to divide the customers into groups that reflect similarity and maximize the value of each customer to the business. The main goal of this research is to use a machine learning clustering approach called K-means clustering to accomplish consumer segmentation. Besides, the research work is also focused on performing exploratory data analysis on the given dataset. To group the customers into clusters, a K-means clustering algorithm is performed on the customer dataset. To achieve optimization and validation of the clusters, popular heuristic, interpretation, and approximation methods have been included in this paper. Further, for analyzing and visualizing the important facets of the customer dataset and the operation of the K-means algorithm, the paper presents some colorful and informative representations. The implementation of this research work has been done in the R programming language. The outcome of this work includes visualizing the segments of the mall customers in the form of clusters based on their spending scores and annual incomes. Furthermore, a better customer segmentation could be achieved by taking product reviews and customer feedback into consideration. Nevertheless, customer segmentation remains a prospective topic for many researchers and companies due to dynamic customer behavior. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： K-means clustering

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XAI-Enhanced MRI Analysis for Alzheimer's Disease Prediction 15

XAI-Enhanced MRI Analysis for Alzheimer's Disease Prediction

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Raj, Sakhineti Aakash Kumar Manoj, Veluru Victoria, Helen Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institue of Science and Technology SRM Nagar Tamilnadu Kattankulathur India

ISBN: (纸本)9798331300579

This research delves into the application of Explainable Artificial Intelligence (XAI) in the healthcare sector, specifically focusing on Alzheimer's disease prediction. Alzheimer's, a debilitating brain dis-ease, impacts daily activities through cognitive and be-havioral changes. Leveraging XAI in medical diagnostics enhances interpretability, providing transparent insights crucial for informed decision-making by healthcare pro-fessionals. The study explores innovative approaches within the complex landscape of machine learning and data utilization, aiming to unravel the intricacies of Alz-heimer's prediction models. Through the interdiscipli-nary lens of interpretable machine learning, this research seeks to enhance our understanding of the disease, paving the way for personalized and reliable diagnostic and treatment strategies. © Grenze Scientific Society, 2024.

关键词： Neurodegenerative diseases

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Enhanced Web Security Using Cross-Feature Analysis of Visual Design, Live Logs and Code Structures 9

Enhanced Web Security Using Cross-Feature Analysis of Visual...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Kumar, Rahul Sharma, Tanvi Saranya, G. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communication Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350377972

Threats using website visual designs and content structures, along with standard coding vulnerabilities, represent an increasing danger to web security. To enhance web security, this paper introduces a new method called cross-feature analysis that integrates the investigation of visual design, live logs, code structures and other vulnerabilities. Based on this threefold correlation, this method identifies discrepancies that might have otherwise gone undetected by traditional approaches reliant on discrete elements. This research rightly provides a thorough methodology for evaluating XSS detection, SQL injection error and time-based testing, CSRF vulnerability checking, clickjacking protection, information disclosure detection, and risk assessment are all included in this. It includes several test vectors for each vulnerability, port scanning with service discovery, analysis of SSL/TLS and security headers, assessment of DNS setup, and integration of WHOIS data. Live monitoring is supported by real-time logging, and actionable insights to strengthen security posture across infrastructure and online applications are provided via a risk-based grading system with recommendations. © 2024 IEEE.

关键词： Risk assessment

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Synergetic Focal Loss for Imbalanced Classification in Federated XGBoost

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 647-660页

作者： Tian, Jiao Tsai, Pei-Wei Zhang, Kai Cai, Xinyi Xiao, Hongwang Yu, Ke Zhao, Wenyu Chen, Jinjun Department of Computing Technologies Swinburne University of Technology MelbourneVIC3122 Australia Center for Health Computing Research Beijing Academy of Artificial Intelligence Beijing100084 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China

Applying sparsity- and overfitting-aware eXtreme Gradient Boosting (XGBoost) for classification in federated learning allows many participants to train a series of trees collaboratively. Since various local multiclass distributions and global aggregation diversity, model performance plummets as convergence slowly and accuracy decreases. Worse still, neither the participants nor the server can detect this problem and make timely adjustments. In this article, we provide a new local-global class imbalance inconsistency quantification and utilize softmax as the activation and focal loss, a dynamically scaled cross-entropy loss, in federated XGBoost to mitigate local class imbalance. Moreover, we propose a simple but effective hyperparameter determination strategy based on local data distribution to adjust the sample weights among noncommunicating participants, synergetic focal loss, to solve the inconsistency of local and global class imbalance, a unique characteristic of federated learning. This strategy is perfectly integrated into the original classification algorithm. It requires no additional detectors or information transmission. Furthermore, a dynamical for loop is designed to capture an optimum hyperparameter combination. Finally, we conduct comprehensive tabular- and image-based experiments to show that synergetic focal loss used in federated XGBoost achieves faster convergency and significant accuracy improvement. Simulation results prove the effectiveness of the proposed principle of configuring sample weights. © 2023 IEEE.

关键词： Heuristic algorithms

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Blockchain Interoperability in Secure E-Governance: A Systematic Literature Review

Blockchain Interoperability in Secure E-Governance: A System...

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2024 International Conference on Information technology Systems and Innovation, ICITSI 2024

作者： Fathiyana, Rana Zaini Ramli, Kalamullah Yutia, Syifa Nurgaida Universitas Indonesia Faculty of Engineering Electrical Engineering Departement Depok Indonesia Telkom Uviversity Information Technology School of Computing Jakarta Indonesia

ISBN: (纸本)9798331511470

Ensuring data security, integrity, and privacy despite increased cyber threats and hacking attempts is one of the primary implementation difficulties of secure e-governance. Interoperability and seamless exchange of data are key components of e-governance, enabling the secure sharing of data, information, and resources among diverse government institutions and agencies, irrespective of the platforms or technologies utilized. Blockchain technology has surfaced as a viable answer, offering enhanced trust, transparency, and collaborative capabilities within government intra-system networks. Its inherent characteristics position blockchain as a transformative technology for the development of secure, reliable, and collaborative digital governance frameworks. This article provides a comprehensive literature review that examines the application of blockchain technology in enhancing interoperability and secure data exchange across various sectors, particularly in the realm of government governance. This review also identifies the types of interoperability that blockchain supports. This article concludes by outlining the methodology and security requirements essential for building a secure e-governance system. © 2024 IEEE.

关键词： Data integrity

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