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An Enhanced Fuzzy Routing Protocol for Energy Optimization in the Underwater Wireless Sensor Networks

Computers, Materials & Continua 2025年第5期83卷 1791-1820页

作者： Mehran Tarif Mohammadhossein Homaei Amir Mosavi Department of Computer Science University of VeronaVerona37134Italy Department of Computer Systems Engineering and Telematics University of ExtremaduraCaceres10003ExtremaduraSpain Institute of the Information Society Ludovika University of Public ServiceBudapest1083Hungary John Von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Department of Information and Computing Systems Abylkas Saginov Karaganda Technical UniversityKaraganda100000Kazakhstan

Underwater Wireless Sensor Networks(UWSNs)are gaining popularity because of their potential uses in oceanography,seismic activity monitoring,environmental preservation,and underwater ***,these networks are faced with challenges such as self-interference,long propagation delays,limited bandwidth,and changing network *** challenges are coped with by designing advanced routing *** this work,we present Under Water Fuzzy-Routing Protocol for Low power and Lossy networks(UWF-RPL),an enhanced fuzzy-based protocol that improves decision-making during path selection and traffic distribution over different network *** method extends RPL with the aid of fuzzy logic to optimize depth,energy,Received Signal Strength Indicator(RSSI)to Expected Transmission Count(ETX)ratio,and *** protocol outperforms other techniques in that it offersmore energy efficiency,better packet delivery,lowdelay,and no queue *** also exhibits better scalability and reliability in dynamic underwater networks,which is of very high importance in maintaining the network operations efficiency and the lifetime of UWSNs *** to other recent methods,it offers improved network convergence time(10%–23%),energy efficiency(15%),packet delivery(17%),and delay(24%).

关键词： Underwater sensor networks(UWSNs) routing energy fuzzy logic multipath load balancing

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Cognitive Load Optimization in Digital (ESL) Learning: A Hybrid BERT and FNN Approach for Adaptive Content Personalization

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International Journal of Advanced Computer science and Applications 2025年第4期16卷 564-576页

作者： Ramesh, Komminni Thomas, Christine Ann Osei-Asiamah, Joel Pagidipati, Bhuvaneswari Muniyandy, Elangovan Reddy, B. V. Suresh El-Ebiary, Yousef A. Baker BoS Anurag Engineering College Suryapet District Telangana Kodad India Department of English and Cultural Studies Christ University Bengaluru India Gauteng Province Pretoria South Africa West Godavari Dt Andhra Pradesh Bhimavaram534204 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Faculty of Informatics and Computing UniSZA University Malaysia

Traditional English as a Secondary Language (ESL) learning platform rely on static content delivery, often failing to adapt to individual learners’ cognitive capacities, leading to inefficient comprehension and increased cognitive load. A novel hybrid Feedforward Neural Network and Bidirectional Encoder Representation Transformer (FNN-BERT) framework stands as our solution because it performs dynamic content personalization through predictions of real-time cognitive load. The proposed approach incorporates Feedforward Neural Networks (FNN) alongside Bidirectional Encoder Representations from Transformers (BERT) to process behavioral analytics for optimized content complexity adjustment and adaptive and scalable learning delivery. Real-time adaptability, scalability and high computational needs of current models reduce their effectiveness in personalized learning environments. Through the application of Test of English for International Communication (TOEIC), International English Language Testing System (IELTS) and Test of English as a Foreign Language (TOEFL) datasets, our methodology uses Feedforward Neural Network (FNN) to forecast cognitive load based on student engagement behaviors and application errors then Bidirectional Encoders Representations from Transformer (BERT) processes content difficulty adjustments automatically. The proposed model delivers a 95.3% accuracy rate, 96.22% precision level, 96.1% recall capability and 97.2% F1-score which surpasses conventional Artificial Intelligence-based English as a Secondary Language (ESL) learning systems. The system makes use of Python for its implementation to improve understanding as well as student focus and mental processing speed. Personalized content presentation methods lead to lower cognitive strain which simultaneously advances student achievement numbers. The research adds value to smart educational frameworks through its introduction of a scalable framework that allows adaptable learning systems for E

关键词： Feedforward neural networks

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A comparative study between android phone and TV apps

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Automated Software Engineering 2025年第2期32卷 1-30页

作者： Liu, Yonghui Chen, Xiao Liu, Yue Kong, Pingfan Bissyandé, Tegawendé F. Klein, Jacques Sun, Xiaoyu Li, Li Chen, Chunyang Grundy, John Department of Software Systems and Cybersecurity Monash University Melbourne Australia School of Information and Physical Sciences University of Newcastle Newcastle Australia SnT University of Luxembourg Esch-sur-Alzette Luxembourg The School of Computing Australian National University Canberra Australia School of Software Beihang University Beijing China Department of Computer Science Technical University of Munich Munich Germany Faculty of Data Science City University of Macau China

Smart TVs have surged in popularity, leading developers to create TV versions of mobile apps. Understanding the relationship between TV and mobile apps is key to building consistent, secure, and optimized cross-platform experiences while addressing TV-specific SDK challenges. Despite extensive research on mobile apps, TV apps have been given little attention, leaving the relationship between phone and TV apps unexplored. Our study addresses this gap by compiling an extensive collection of 3445 Android phone/TV app pairs from the Google Play Store, launching the first comparative analysis of its kind. We examined these pairs across multiple dimensions, including non-code elements, code structure, security, and privacy aspects. Our findings reveal that while these app pairs could get identified with the same package names, they deploy different artifacts with varying functionality across platforms. TV apps generally exhibit less complexity in terms of hardware-dependent features and code volume but maintain significant shared resource files and components with their phone versions. Interestingly, some categories of TV apps show similar or even severe security and privacy concerns compared to their mobile counterparts. This research aims to assist developers and researchers in understanding phone-TV app relationships, highlight domain-specific concerns necessitating TV-specific tools, and provide insights for migrating apps from mobile to TV platforms. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Television applications

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Effect of Dy2O3 on the phase formation and electrical properties of Tl-1223 HTS

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Fizika Nizkikh Temperatur 2025年第1期51卷 59-63页

作者： Metskhvarishvili, I.R. Menelaou, Melita Lobzhanidze, T.E. Dgebuadze, G.N. Surmanidze, D.L. Bendeliani, B.G. Gabunia, V.M. Metskhvarishvili, M.R. Jishiashvili, D.A. Ilia Vekua Sukhumi Institute of Physics and Technology Laboratory of Cryogenic Technique and Technologies Georgia Tbilisi0186 Georgia Georgian Technical University Faculty of Informatics and Control Systems Department of Microprocessor and Measurement Systems Tbilisi0175 Georgia Department of Mechanical Engineering and Materials Science and Engineering Cyprus University of Technology Limassol3036 Cyprus Ivane Javakhishvili Tbilisi State University Faculty of Exact and Natural Sciences Department of Chemistry Tbilisi0179 Georgia Petre Melikishvili Institute of Physical and Organic Chemistry the Iv. Javakhishvili Tbilisi State University Tbilisi0186 Georgia "Talga" Institute of Georgian Technical University Tbilisi0186 Georgia V. Chavchanidze Institute of Cybernetics the Georgian Technical University Tbilisi0186 Georgia

TlBa2Ca2Cu3DyxO8+δ superconductor samples (x = 0–0.075) were prepared by a two-step solid-state reaction technique to investigate the impact of Dy2O3 (dysprosium sesquioxide) addition on the formation and the transport properties of the Tl-1223 phase. The samples were analyzed using X-ray powder diffraction, ac susceptibility, and high-harmonics temperature dependence to study the effects of Dy2O3 on phase formation, superconducting transition temperature Tc, and the intergrain critical current density Jc. XRD results revealed that the prepared samples are nearly single-phases Tl-1223 with a tetragonal structure. Tc is not suppressed as Dy content increased, while Jc increased up to x = 0.025, then decreased with a further increase in x. Our findings show that the presence of dysprosium sesquioxide promotes the high-Tc phase and enhances the transport critical current densities. © I. R. Metskhvarishvili, Melita Menelaou, T. E. Lobzhanidze, G. N. Dgebuadze, D. L. Surmanidze, B. G. Bendeliani, V. M. Gabunia, M. R. Metskhvarishvili, and D. A. Jishiashvili, 2025.

关键词： X ray powder diffraction

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Deep Learning-Powered Edge Analytics for IoT-based Sensor Networks 2

Deep Learning-Powered Edge Analytics for IoT-based Sensor Ne...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Karthikeyan, M. Saravana Subramani, Ponnusamy Selvam, M. Panneer Sworna Kokila, M.L. Vijayalakshmi, R. Sundaravadivel, P. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Physics Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India School of Computing SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331505745

The rapid expansion of loT-based sensor networks has necessitated the development of efficient edge analytics frameworks to process vast amounts of data in real time while minimizing computational overhead. Deep learning-powered edge computing has emerged as a transformative approach, enhancing data processing capabilities at the network edge while addressing constraints related to latency, bandwidth, and energy consumption. This study introduces a novel algorithm, Lightweight AI Model Compression for Energy-Efficient IoT Edge Devices (LiteEdgeAI), designed to optimize deep learning models for resource-constrained edge environments. The proposed method employs quantization, pruning, and knowledge distillation techniques to achieve significant reductions in model size and computational complexity while maintaining high inference accuracy. To validate the efficiency of LiteEdgeAI, a comparative simulation analysis was conducted against existing state-of-the-art algorithms using key performance metrics such prediction accuracy, energy efficiency and computational complexity. Experimental results demonstrated that LiteEdgeAI outperformed baseline models. The findings highlight the potential of LiteEdgeAI in enabling scalable, low-power, and real-time edge intelligence for IoT-based sensor networks. © 2025 IEEE.

关键词： Deep learning

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A Comprehensive Survey on Intelligent Reflecting Surfaces and Unmanned Aerial Vehicles Integration for Enhanced Communication in Massive Networks 11

A Comprehensive Survey on Intelligent Reflecting Surfaces an...

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11th International Conference on Mechatronics and Robotics Engineering, ICMRE 2025

作者： Attar, Hani Ababneh, Jafar Hafez, Mohamed Rachini, Ali Solyman, Ahmed Bahy, Ramy M. Zarqa University Faculty of Engineering Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan INTI-IU-University Nilai Faculty of Engineering Feqs Malaysia Department of Computer Science and Information Technology Jounieh Lebanon School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow United Kingdom Technical Research Center Cairo Egypt

ISBN: (纸本)9798331509293

Intelligent Reflecting Surfaces (IRSs) offer a revolutionary approach to wireless communication by dynamically controlling the properties of electromagnetic waves. By manipulating wavefronts without requiring radio frequency links, IRSs can enhance spectrum and energy efficiency, reduce costs, and improve overall system performance. Unmanned Aerial Vehicles (UAVs) have garnered significant attention due to their mobility and adaptability. However, real-time data transmission between UAVs can be hindered by obstacles and security risks. By combining IRSs and UAVs, we can create unparalleled opportunities in demanding environments. Through coordinated movements and intelligent wave reflection, both systems can optimize wireless signal propagation. This paper provides a comprehensive review of IRS-assisted UAV communications. We explore existing research, introduce novel technologies and applications, and highlight potential research directions for developing low-cost and energy-efficient wireless systems. Additionally, we address current challenges and outline promising areas for future study. © 2025 IEEE.

关键词： Drones

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Adaptive AI-Based Personalized Learning for Accelerated Vocabulary and Syntax Mastery in Young English Learners

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International Journal of Advanced Computer science and Applications 2025年第4期16卷 678-687页

作者： Aravind, Angalakuduru Durairaj, M. Chitkara, Preeti Baker El-Ebiary, Yousef A. Muniyandy, Elangovan Ushasree, Linginedi Ammar, Mohamed Ben Kodad Telangana 508206 India Dept.of English Panimalar Engineering College Poonamallee Chennai600123 India Head PR & International Relations Department of Applied Sciences KIET Group of Institutions Delhi-NCR Ghaziabad India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur Dist. 522302 India Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

Language acquisition is an integral part of early schooling, but young English language learners struggle to learn vocabulary and syntax since they are not provided with specialized instruction. Conventional teaching may vary according to different learning speeds and it leads to unbalanced levels of proficiency among students and possibly leading to disengagement among slow learners. The present computer-assisted learning aids provide practice interactively but without real-time adaptation and personalized feedback, limiting their capacity to address learners’ unique problems. To overcome these constraints, this study suggests an Artificial Intelligence based personalized learning system that supports vocabulary and syntax learning via adaptive learning models, NLP-based chatbots and gamified interactive lessons. The system dynamically adapts content according to students’ most recent performance in real time to enable a personalized learning experience, which results in efficient Learning. The research has experimental study design, and two groups are considered, an AI-supported learning group and a traditional learning group. Pre-test and post-test design measures the effects of the system on vocabulary recall and syntax correctness. Other learner engagement rates like survey results and qualitative feedback inform learner experience and learning efficacy. Initial results indicate that learners working with the Artificial Intelligence powered learning system gained 25percent in recalling vocabulary and 30percent in syntax accuracy over the control group. Further, learner engagement rates are elevated because of real-time feedback and gamification components. These results emphasize the promise of AI-based personalized learning to boost language acquisition and lay the basis for further effective innovations in adaptive education technologies. © (2025), (science and Information Organization). All rights reserved.

关键词： Students

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Adaptive neural finite-time self-triggered control for nonstrict-feedback nonlinear systems with sensor faults

Robotic Intelligence and Automation

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Robotic Intelligence and Automation 2025年第3期45卷 373-385页

作者： Zhang, Wenxin Xu, Ning Zhao, Ning Al-Barakati, Abdullah A. College of Control Science and Engineering Bohai University Jinzhou China Department of Information Systems Faculty of Computing and Information Technology Communication Systems and Networks Research Group King Abdulaziz University Jeddah Saudi Arabia

Purpose: This paper aims to investigate the problem of adaptive neural finite-time self-triggered tracking control for interconnected large scale nonlinear systems in nonstrict-feedback forms with sensor faults. Design/methodology/approach: To begin with, by combining backstepping techniques and neural networks (NNs), an adaptive NN controller is designed to compensate for sensor faults. Then, command filters are introduced to deal with the complexity explosion problem in backstepping design processes. Moreover, to reduce unnecessary data transmissions, a self-triggered control strategy is presented. Findings: Based on self-triggered strategy, an adaptive neural finite-time control scheme for interconnected large-scale systems with sensor faults is proposed. Originality/value: This article considers sensor faults in interconnected large-scale nonlinear systems with nonstrict-feedback forms. Moreover, the introduction of command filters not only effectively avoids the complexity explosion problem arising from the repetitive differentiation of virtual control inputs, but also simplifies the controller design process. Besides, this paper proposes a self-triggered mechanism that calculates the next trigger point based on current system data, overcoming the need for continuous monitoring of measurement errors in event-triggered mechanisms. Furthermore, the controller guarantees the finite-time stability of interconnected large-scale systems, with the tracking error converging to a small neighborhood of the origin within a finite time frame. © 2025, Emerald Publishing Limited.

关键词： Large scale systems

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Review the Challenges Associated with Steganography Using Artificial Intelligence Techniques

Review the Challenges Associated with Steganography Using Ar...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Rasmi Al-Mousa Sultan Mohammed Albilasi Malak Hamad Al-mashagbeh Mahmoud Asassfeh Mahmoud Odeh Khaled AlQawasmi Ghassan Samara Dena Abu Laila Department of Cybersecurity Zarqa University Zarqa Jordan Department of Cybersecurity and Cloud Computing Applied Science Private University Amman Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Cybersecurity Faculty Zarqa Technical Intermediate College Zarqa Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The importance of data masking techniques has increased due to the rapid expansion of covert communications and the mass sharing of multimedia content. Steganography is a technique that allows for the safe and covert sharing of data. It's the science of incorporating data that is hard to see with the unaided eye. File types that support Steganography include audio, video, and image formats. Artificial Intelligence (AI) can enhance Steganography while also complicating the discovery of hidden data and augmenting steganalysis. By looking into these issues, this study intends to compare the various uses and techniques of AI and investigate the difficulties involved in incorporating AI technologies into Steganography. The study used the comparative research approach, comparing the literature with works that addressed Traditional Steganography in general. According to this Paper, countries need to educate themselves on classical Steganography as soon as possible to enhance information security and protection in the future.

关键词： Steganography Technological innovation Reviews Information security Streaming media Media Multimedia communication Artificial intelligence Virtualization Protection

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StrideSense: Enriching Lower Extremity and Kinetics in ACLR Patients via Sonic Insights

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IEEE Internet of Things Journal 2025年

作者： Ahmed, Awais Yang, Panlong Butt, Abdul Haleem Rizwan, Muhammad Angin, Pelin Khan, Taha Nanjing University of Information Science and Technology University of Science and Technology of China China University of Bologna Department of Biomedical and Neuromotor Sciences Italy Air University Islamabad Department of Creative Technologies Faculty of Computing and Artificial Intelligence Pakistan University of Science and Technology of China School of Computer Science and Technology China Middle East Technical University Department of Computer Engineering Turkey Detectivio AB Sweden

Tearing the anterior cruciate ligament requires repair and rehabilitation to restore lower limb functionality fully. This study outlines a method for monitoring rehabilitation after knee surgery by analyzing footstep sounds and applying deep learning techniques. The process involves examining gait sounds during the initial four weeks of rehabilitation. The suggested system, StrideSense, recognizes walking sounds, enabling seamless and ongoing monitoring of patients' gait rehabilitation. The research proposes a novel method for event detection by analyzing walking patterns using dynamic time-warping and sequential footstep duration. It leverages physiological data like gait sound and energy descriptors to develop a deep-learning model for gait improvement assessment, evaluated by a physiotherapist through lower extremity functional scores. The suggested model, AtdNet, which utilizes Densenet169 and attention mechanisms, achieves 96% accuracy in classifying walking on various post-surgical days. It also predicts lower extremity functional scores with a mean absolute error of 4.63%. A deeper analysis of bone-conducted footstep sounds enriched the acoustic sensing method. We assessed the suggested acoustic model alongside existing methods, showing that rehabilitation monitoring driven by acoustics outperforms traditional clinic-based approach assessments. Future efforts will focus on validating the model with a larger dataset and integrating it into smart homes. © 2014 IEEE.

关键词： Physiological models

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