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Effectual Energy Optimization Stratagems for Wireless Sensor Network Collections Through Fuzzy-Based Inadequate Clustering

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SN computer science 2024年第8期5卷 1-10页

作者： S, Kavitha Rao, Deepak Dasaratha Jain, Anupriya Sharma, Seema Pandit, Shraddha V. Pandey, Rajeev Christ University Karanataka Banglore India Indian Institute of Technology Patna India School of Computer Applications Manav Rachna International Institute of Research and Studies(MRIIRS) Haryana Faridabad India Department of Artificial Intelligence and Data Science PES Modern College of Engineering Pune Shivajinagar 411005 India Department of Computer Science & amp Engineering University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal Madhya Pradesh India

Wireless Sensor Networks (WSNs) are crucial in the burgeoning Internet of Things (IoT) landscape, serving as a backbone technology that enables myriad applications across various industries. Originating as a simple methodology, WSNs have evolved significantly, propelled by rapid advancements in sensor technology and hardware capabilities. These networks play a pivotal role in collecting and transmitting data, which is essential for the infrastructure of most IoT systems. WSNs operate by deploying sensor nodes across diverse locations to gather environmental data. This scalability and adaptability of WSNs were demonstrated in studies where network coverage was expanded to include 100 and 200 nodes. Notably, the implementation of the innovative FLECH (Fuzzy Logic Energy-efficient Clustering Hierarchy) protocol significantly enhanced energy efficiency, reducing consumption by 12.69% in networks with 100 nodes and by 36.85% in those with 200 nodes, compared to the traditional LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol. This work innovatively combines fuzzy logic and Particle Swarm Optimization (PSO) for efficient Cluster Head selection in Wireless Sensor Networks. The evaluation of these protocols involved numerous simulations and communication tests to ascertain the First Node Die (FND) point—indicative of when a network begins to lose efficacy due to energy depletion. Results indicated that the LEACH protocol reached the FND point faster than FLECH, suggesting that FLECH may offer better longevity and durability for IoT applications, aligning with the needs for sustainable and efficient operation in expanding technological ecosystems. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： FLECH IoT LEACH WSN

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AI - Based Aircraft Recognition System 3

AI - Based Aircraft Recognition System

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3rd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2024

作者： Shiva Chandra, N.K. Rajath Anirudhan Manipal Institute of Technology Manipal Academy of Higher Education Department of Data Science and Computer Applications Karnataka Manipal576104 India

ISBN: (纸本)9798350391244

The project 'AI-Based Aircraft Recognition System' aims to develop an advanced system for automatically recognizing and identifying aircraft using AI techniques. The increasing role of artificial intelligence (AI) in various sectors, including aviation, has driven the need for more efficient and accurate recognition systems. Aircraft recognition has attracted attention due to its potential uses in areas like military operations, air traffic control, and aviation surveillance. This project proposes the use of advanced AI algorithms to address gaps in current aircraft recognition systems by enhancing accuracy, performance, and real-time application. © 2024 IEEE.

关键词： Deep learning

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Alzheimer’s Detection via Cepstral Domain Feature Fusion and Mann-Whitney U-Test Statistical Filtering

Alzheimer’s Detection via Cepstral Domain Feature Fusion an...

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Innovative Research in Applied science, Engineering and Technology (IRASET),Conference on

作者： Anass El Hallani Adil Chakhtouna Abdellah Adib Team Data Science and Artificial Intelligence Laboratory of Mathematics Computer Science and Applications (LMCSA) Faculty of Sciences and Technologies Mohammedia Hassan II University Casablanca Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

In this paper, we propose a robust approach for early detection of Alzheimer’s Disease (AD) using advanced cepstral speech features, fused to capture diverse acoustic signatures. The Mann-Whitney U-test is employed to filter out non-informative features, improving real-world applicability and reducing overfitting. We systematically compare Random Forest, Decision Tree, and Support Vector Machine classifiers, achieving an accuracy of 85.6% using only the most significant features, drawn from MFCCs, GTCCs, LPCCs, and others. The method addresses data sparsity challenges in clinical research by favoring domain-relevant indicators while maintaining computational feasibility. The results confirm that subtle vocal cues, captured using affordable devices, are promising biomarkers for AD screening, especially in resourcelimited or telehealth environments. By highlighting both practical implementation and scalability, our study contributes to the development of efficient, non-invasive diagnostic protocols. This, in turn, supports earlier intervention and improved patient outcomes. The framework thus paves the way for future multimodal expansions.

关键词： Support vector machines Protocols Cepstral analysis Telemedicine Scalability Feature extraction Decision trees Alzheimer's disease Random forests Overfitting

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Deciphering Student Coding Behavior: Interpretable Keystroke Features and Ensemble Strategies for Grade Prediction

Deciphering Student Coding Behavior: Interpretable Keystroke...

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IEEE International Conference on Big data

作者： Muhammad Fawad Akbar Khan John Edwards Paul Bodily Hamid Karimi Department of Computer Science Utah State University Data Science and Applications Lab Utah State University Department of Computer Science Idaho State University

Keystroke data in programming reveals intricate patterns that reflect the behavior of programmers. These patterns hold promise for predicting grades and other applications, providing insights into the skills of both proficient and less proficient programmers. Analyzing these patterns can yield tailored feedback for students who need support, enabling effective interventions. Our study utilizes a keystroke dataset from the CS1 (Introduction to computer science) course at Utah State University. We developed novel features by combining elements like key presses, timestamps, source locations, and programming terminology, drawing on prior research, our insights, and an analysis of programming behavior. An ensemble-based feature selection method identifies key features, which are then used in hyperparameter optimization and grade prediction with six classification and three regression algorithms. We categorized grades into three levels: Low, Average, and High. Despite challenges such as class imbalance, plagiarism, limited data per assignment, and the ceiling effect, we attained a notable weighted F1 score of 78%. We also introduce an ensemble classification strategy, merging Isolation Forest outlier detection with a refined Random Forest classifier, achieving 80% accuracy on our test set. Additionally, we provide a detailed interpretation of our features, supported by results and a case study of our dataset. This research aims to enhance computer science education at the undergraduate level, focusing on improving its overall quality. Code and data are available https://***/DSAatUSU/***.

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： data integrity

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Enhancing action discrimination via category-specific frame clustering for weakly-supervised temporal action localization

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Frontiers of Information Technology & Electronic Engineering 2024年第6期25卷 809-823页

作者： Huifen XIA Yongzhao ZHAN Honglin LIU Xiaopeng REN School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agricultural Applications Zhenjiang 212013China Changzhou Vocational Institute of Mechatronic Technology Changzhou 213164China

Temporal action localization (TAL) is a task of detecting the start and end timestamps of action instances and classifying them in an untrimmed video. As the number of action categories per video increases, existing weakly-supervised TAL (W-TAL) methods with only video-level labels cannot provide sufficient supervision. Single-frame supervision has attracted the interest of researchers. Existing paradigms model single-frame annotations from the perspective of video snippet sequences, neglect action discrimination of annotated frames, and do not pay sufficient attention to their correlations in the same category. Considering a category, the annotated frames exhibit distinctive appearance characteristics or clear action ***, a novel method to enhance action discrimination via category-specific frame clustering for W-TAL is proposed. Specifically,the K-means clustering algorithm is employed to aggregate the annotated discriminative frames of the same category, which are regarded as exemplars to exhibit the characteristics of the action category. Then, the class activation scores are obtained by calculating the similarities between a frame and exemplars of various categories. Category-specific representation modeling can provide complimentary guidance to snippet sequence modeling in the mainline. As a result, a convex combination fusion mechanism is presented for annotated frames and snippet sequences to enhance the consistency properties of action discrimination,which can generate a robust class activation sequence for precise action classification and localization. Due to the supplementary guidance of action discriminative enhancement for video snippet sequences, our method outperforms existing single-frame annotation based methods. Experiments conducted on three datasets (THUMOS14, GTEA, and BEOID) show that our method achieves high localization performance compared with state-of-the-art methods.

关键词： Weakly supervised Temporal action localization Single-frame annotation Category-specific Action discrimination

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Arabic Text Detection From Natural Images Using Transformers

Arabic Text Detection From Natural Images Using Transformers

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International Symposium on Signal, Image, Video and Communications (ISIVC)

作者： Ayad Abdessamad Ayad Habib Adib Abdellah Team Data Science & Artificial Intelligence Mathematics Computer Science & Applications Laboratory Faculty of Sciences and Technologies Mohammedia Hassan II University of Casablanca Morocco

ISBN: (数字)9798350385267

ISBN: (纸本)9798350385274

Text detection in the wild is an important stage for recognizing and interpreting text present in images or videos captured in uncontrolled environments in the wild. It helps in various applications such as scene understanding, robot navigation, augmented reality, and automatic captioning. However, it remains a difficult task, due to the vast differences in lighting, backgrounds, textures, and fonts. The research, in this topic, has predominantly concentrated on languages utilizing Latin characters, neglecting other languages with distinct features, such as the Arabic language. This work specifically addresses the topic of Arabic scene text detection. We propose a model for Arabic text detection from natural images based on transformers. We experiment two variations of this model: the first focuses on Arabic text, whereas the second is bilingual and handles both Latin and Arabic languages. We evaluate the performance of both models on existing datasets namely, the Everyday Arabic-English Scene Text dataset EvarEst [1] and the Arabic-Latin Scene Text Localization in Highway Traffic Panels dataset ASAYAR [2].

关键词： Road transportation Location awareness Deformable models Text recognition Navigation Text detection Transformers

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Real-time Energy Dispatching Strategy for Microgrid Based on Improved SD3 Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1338-1357页

作者： Hang, Tian-Xiao He, Wen-Wu Pei, Bo-Yu Lin, Mei-Li Li, Pei-Qiang School of Computer Science and Mathematics Fujian University of Technology Xuefu South Road Fujian Province Fuzhou City350118 China School of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Xuefu South Road Fujian Province Fuzhou City350118 China School of Electronic Electrical Engineering and Physics Fujian University of Technology Xuefu South Road Fujian Province Fuzhou City350118 China

Deep reinforcement learning algorithms has attracted attention in the field of energy management in recent years, which can effectively interact with the environment and learn relevant policies to achieve specific goals. To eliminate Q-value estimation bias, in this paper we propose a real-time energy dispatching strategy for microgrid based on SD3 algorithm, and introduce prioritized experience replay and Huber loss, to develop an improved algorithm, called SD3 with loss-adjusted prioritized experience replay (SD3-LAP). A grid-connected microgrid that includes photovoltaic system, wind turbine, micro-turbine, energy storage system and electrical load is built to simulate the environment and validate the proposed methods. The problem of real-time energy dispatching for microgrid is recast as a Markov Decision Process, which aims to minimize the operating costs under the condition that the constraints of power balance and the upper and lower limits of SOC of the energy storage system are satisfied. Experimental results show that compared with baseline models, the energy dispatching strategies provide by SD3 algorithm and SD3-LAP algorithm are more effective and robust, and the mechanism of LAP is a general purposed plug-in that can effectively improve the performance of related reinforcement learning algorithm. The daily average operating cost of SD3-LAP algorithm and SD3 algorithm are respectively 37.9% and 22.89% lower than TD3 algorithm, while the daily average operating cost of of TD3 algorithm is 21.9% lower than DQN. In addition, compared with the original algorithms, namely SD3, TD3 and DQN, the LAP mechanism reduces respectively the daily average operating cost by 19.5%, 11.7%, and 20.1%. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Reinforcement learning

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Prediction of Traffic Accidents Using Deep Learning Ensemble Model

Prediction of Traffic Accidents Using Deep Learning Ensemble...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Sekhar, G Soma Mahadik, Shubhangi Ramana, Attili Venkata Chandra Shaker Reddy, Pundru Pughazendi, N. Asif, S. Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Bharati Vidyapeeth's Institute of Management & Information Technology Master of Computer Applications Navi Mumbai India Hyderabad India SR University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai India Marri Laxman Reddy Institute of Technology and Management CSE-Data Science Telangana Hyderabad India

ISBN: (纸本)9798350319200

Because of the widespread negative effects that traffic accidents can have on people's lives, the study of how to assess and anticipate them using cutting-edge technology is an important area of study. The research of traffic accidents has traditionally relied on data made public by traffic-entities and road-police forces. However, with the advent of social-media, researchers now have access to detailed, real-time data on road accidents occurring across a region, allowing for more comprehensive analyses that take into account previously unreported incidents. This paper uses an ensemble-Deep-Learning(EDL) strategy made up of Gated-Recurrent Units(GRUs) and Convolutional-Neural-Networks(CNNs) to offer a model to forecast traffic accidents utilizing data mined from social-media and the open web. The acquired results are compared to those of published baseline methods and other researchers. The outcomes are encouraging, showing that the proposed EDL approach outperforms the conventional models and other DL approaches published in the literature when applied to the problem setting. Authorities in charge of preventing accidents on the roads can use the data generated by the program. © 2023 IEEE.

关键词： DL ML risk prediction traffic accident traffic accidents

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Automated Detection of Language Disorders in Children Using NLP and Machine Learning

Automated Detection of Language Disorders in Children Using ...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Malathi, P. Legapriyadharshini, N. Nair, Shruthi S Sujatha, M.P. Sadaieswaran, R. Thirumalaikumari, T. Simats Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India Simats Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Application Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Agni College of Technology Department of Computer Science and Engineering Chennai India Tagore College of Arts and Science Department of Computer Applications Chennai India Simats Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Chennai India

ISBN: (纸本)9798350309140

The purpose of the study is to address the essential need for a more efficient and objective technique for detecting language disorders in children, overcoming the constraints of standard manual evaluation systems. Existing systems, which utilize subjective evaluations and manual scoring, frequently result in lengthy processes and possible errors. To solve these issues, the proposed system automates the identification process by combining Natural Language Processing (NLP) and Machine Learning (ML). The system achieves a breakthrough in accuracy by normalizing text and extracting a comprehensive collection of linguistic features using advanced NLP techniques. The implementation involves difficult data preprocessing, extensive feature extraction, and the application of a cutting-edge Support Vector Machine (SVM) classifier. The proposed system achieves a 15% increase in accuracy, a 12% increase in precision, a 10% increase in recall, and an 11% increase in F1 score over existing systems. The confusion matrices show improved prediction skills, while accuracy across varied linguistic samples demonstrates the system's adaptability. Finally, the proposed system not only speeds up the detection of language disorders but also provides the groundwork for deeper insights into linguistic development, with revolutionary potential in educational and therapeutic settings. © 2024 IEEE.

关键词： Adversarial machine learning

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