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Joint Power Control and Multi-Path Routing for Internet of Underwater Things in Varying Environments

IEEE Internet of Things Journal 2025年第11期12卷 15197-15210页

作者： Xu, Cangzhu Song, Shanshan Liu, Jun Pan, Miao Xu, Gaochao Cui, Jun-Hong Jilin University Department of Computer Science and Technology Changchun130012 China Beihang University School of Electronic and Information Engineering Beijing100191 China Smart Ocean Technology Co. Ltd Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Chengdu China Jilin University College of Computer Science and Technology Changchun130012 China

Internet of Underwater Thing (IoUT) stands as promising technology facilitating diverse underwater applications. Nevertheless, IoUT across vast marine regions is challenged by highly diverse and fluctuating channel environments, which results in unreliable point-to-point (PTP) transmissions. Moreover, its multi-hop nature exacerbates severe unreliable end-to-end (ETE) transmissions. Existing methods utilize routing protocols to address the above challenges by independently power control for PTP reliability or multi-path transmission for ETE reliability. However, these methods ignore the interdependencies between power control and multi-path transmission, which fail to guarantee high energy-efficient reliability in resource-constrained and harsh underwater environments. To this end, we propose a joint power Control And Multi-Path routing (CAMP) protocol for IoUTs in varying environments. Specifically, we develop PTP and ETE reliability models by analyzing the interrelation between power control and multi-path routing, incorporating historical, current, and predictive information. A hybrid routing strategy is designed based on the reliability models to accommodate changing environmental conditions, residual energy, and link quality. This strategy initiates multi-path routing at the source and single-path forwarding at relay nodes, combined with power control. Extensive simulations demonstrate that CAMP achieves superior reliability (packet delivery rate) and energy efficiency, while simultaneously improving network performance in terms of latency and throughput. © 2014 IEEE.

关键词： Routing Reliability Power control Routing protocols Energy efficiency Relays Internet of Things Sensors Reliability engineering computer network reliability

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Enhanced Emotion Recognition Using a Hybrid Autoencoder-LSTM Model Optimized with a Hybrid ACO-WOA Algorithm for Hyperparameter Tuning

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International Journal of advanced computer science and Applications 2025年第4期16卷 861-876页

作者： Waiker, Vinod Ramesh, Janjhyam Venkata Naga Bala, Ms Kiran Krishnaiah, V.V. Jaya Rama Jackulin, T. Muniyandy, Elangovan Shahin, Osama R. Datta Meghe Institute of Management Studies Maharashtra Nagpur India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of CSE Panimalar Engineering College Tamil Nadu Chennai India 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 College of Computer and Information Sciences Jouf University Saudi Arabia Physics and Mathematics Department Faculty of Engineering Helwan University Helwan Egypt

Emotion recognition is vital in the human computer interaction because it improves interaction. Therefore, this paper proposes an improved method for emotion recognition regarding the Hybrid Autoencoder-Long Short-Term Memory (LSTM) model and the newly developed hybrid approach of the Ant Colony Optimization (ACO) and Whale Optimization Algorithm (WOA) for hyperparameters tuning. In this case, Autoencoder can reduce input data dimensionality for input data and find the features relevant for the model’s work. In addition, LSTM is able to work with temporal structures of sequential inputs like speech and videos. The contribution of this research lies in the novel combination method of ACO-WOA which aims at tweaking hyperparameters of Autoencoder-LSTM model. Global aspect of ACO and WOA thereby improve the search efficiency and the accuracy of the proposed emotion recognition system and its generalization capacity. In context with the benchmark dataset for the experimentations of emotion recognition, it has established the efficiency of the proposed model in terms of the conventional methods. Recall rates in recognitive intended various emotions and different modalities were also higher in the hybrid Autoencoder-LSTM model. The optimization algorithms like the ACO-WOA also supported in reducing the computational cost which arose due to hyperparameters tuning. The implementation of this paper is done through Python Software. This implementation shows a high accuracy of 94.12% and 95.94% for audio datasets and image datasets respectively when compared with other deep learning models of Conv LSTM and VGG16. Therefore, the research shows that the presented hybrid approach can be a useful solution for successfully employing emotion recognition for enhancing the creation of the empathetic AI systems and for improving user interactions within various fields including healthcare, entertainment, and customer support. © (2025), (science and Information Organization). All rights

关键词： Ant colony optimization

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Binary-Level Formal Verification Based Automatic Security Ensurement for PLC in Industrial IoT

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2211-2226页

作者： Zhang, Xuankai Li, Jianhua Wu, Jun Chen, Guoxing Meng, Yan Zhu, Haojin Zhang, Xiaosong Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518055 China

Currently, the security of the control logic of Programmable Logic Controllers (PLCs) is facing a serious threat, significantly impacting industrial production. Consequently, ensuring the security of PLC control logic becomes imperative. Formal verification emerges as a promising methodology for verifing PLC security through behavioral modeling and security testing. However, existing formal verification approaches primarily focus on modeling the PLC source code, overlooking the identification of compile-time errors and real-time runtime logic checks. Therefore, it is essential to apply formal verification to PLC control logic at the binary level. In this study, we introduce VoICS, a system designed to facilitate binary-level formal verification. Using reverse engineering, VoICS automatically parses PLC programs written by various programming languages at the binary level and constructs control flow graphs (CFGs). Furthermore, we use an algorithm combining two model optimization methods (i.e., trim invalid states and unnecessary states compression) to convert the reversed PLC assembly program into nuXmv format model. Lastly, VoICS establishes the corresponding constraints and performs formal verification on the model using nuXmv. The evaluation results demonstrate the capability of VoICS in identifying instances of unreliable control logic within PLC control programs, thus reinforcing the dependability of the industrial automation system. © 2024 IEEE.

关键词： Formal verification

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RGB Pixel Combination Method for Underwater Camouflaged Image Categorization using Graph Neural Networks

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IEEE Access 2025年 13卷 84341-84356页

作者： Dhamodharan, K. Pandi, Thanapal Vellore Institute of Technology School of Advanced Sciences Department of Mathematics Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore India

Underwater imaging applications require sophisticated image processing techniques aided by computer Vision (CV). The intensity and light variations under the water demand camouflaged object, plant, and living organism detection in deep seas. This article introduces the Edge-Recognition Method using Graph Neural Networks (ERM-GNN) to address visual differentiations of such particles with precise categorization. This method is reliable in detecting camouflaged object edges and classifying their foregrounds and backgrounds. In the edge detection and object classification process, color features are utilized, relying on RGB variants observed near the edges of the foreground and background images. The edge depth intensity feature is trained repeatedly using the GNN, forming layers between the initial and final pixels of the regions. This approach imposes an xC_y pixel combination to analyze depth intensity until the foreground and backgrounds are categorized. The neural network trains the hidden layers by differentiating categorized and non-categorized RGB variants for in-depth detection. Consequently, this combination increases the chances of camouflaged underwater object categorization with better precision. This method outperforms state-of-the-art approaches such as G+L-CET, FEGM, and MFEM by a wide margin, improving recognition accuracy by 14.48%, precision by 12.81%, and object classification by 9.82%. These noteworthy improvements highlight the method's efficacy and potential use in marine biology and environmental surveillance, necessitating accurate underwater object detection. © 2013 IEEE.

关键词： Camouflaged Image CV Depth Intensity GNN Underwater Objects

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A New Data-Free Backdoor Removal Method via Adversarial Self-Knowledge Distillation

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IEEE Internet of Things Journal 2025年第9期12卷 12267-12277页

作者： Li, Xuexiang Gao, Yafei Liu, Minglin Zhou, Xu Chen, Xianfu Wu, Celimuge Li, Jie Zhengzhou University School of Cyber Science and Engineering Zhengzhou450002 China Michigan Technological University Department of Computer Science HoughtonMI49931 United States Shenzhen CyberAray Network Technology Company Ltd Shenzhen518038 China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201204 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

In the context of Internet of Things edge devices, pretrained models are often sourced directly from cloud computing platforms due to the unavailability of training data. This lack of access during the training phase makes these models susceptible to backdoor attacks. To address this challenge, we introduce a novel data-free backdoor removal method that operates effectively even when only the poisoned model is accessible. Our innovative approach employs two end-to-end generators with identical architectures to create both clean and poisoned samples. These samples are crucial for transferring knowledge from the teacher model—the fixed poisoned model—to the student model, which is initialized with the poisoned model. Our method utilizes a channel shuffling technique during the distillation process to disrupt and eliminate the backdoor knowledge embedded in the teacher model. This process involves iterative updates of the generators and meticulous distillation of the student model, leading to efficient backdoor removal. We conducted extensive experiments on five sophisticated backdoor attacks across two benchmark datasets. The results demonstrate that our method not only significantly bolsters the model’s resistance to backdoor attacks but also maintains high recognition accuracy for clean samples, thereby outperforming existing methods. © 2014 IEEE.

关键词： Cloud platforms

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Performance Analysis of YOLO versions for Real-time Pothole Detection 16

Performance Analysis of YOLO versions for Real-time Pothole ...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

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

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. © 2025 Elsevier B.V.. All rights reserved.

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

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A Comparative Study of Deep Learning and Modern Machine Learning Methods for Predicting Australia's Precipitation

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International Journal of advanced computer science and Applications 2025年第4期16卷 943-966页

作者： Farman, Hira Mastoi, Qurat-Ul-Ain Abbas, Qaiser Ahmad, Saad Alshahrani, Abdulaziz Jan, Salman Syed, Toqeer Ali Department of Computer Science Iqra University Karachi Institute of Economics and Technology Karachi74600 Pakistan School of Computing and Creative Technologies University of the West of England Bristol United Kingdom Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia Faculty of Computer Science IQRA University Karachi Pakistan Faculty of Computer Studies Arab Open University 512 United Kingdom Islamic University of Madinah CO 42351 Madinah Saudi Arabia

Floods are chaotic weather patterns that cause irreversible and devastating harm to people's lives, crops, and the socioeconomic system. It causes extensive property damage, animal mortality, and even human fatalities. To mitigate the risk of flooding, it is imperative to create an early warning system that can accurately forecast the amount of rain that will fall tomorrow. Rainfall forecasting is essential to the lives of people and is absolutely important everywhere in the world. The rainfall prediction model reduces risk and helps to prevent further human deaths. Statistics cannot reliably forecast rainfall since the atmosphere is dynamic. Due to the preceding factors, this study uses machine learning and deep learning techniques to estimate precipitation. The purpose of this study is to develop and evaluate a prediction model for forecasting rainfall of 5 cities of Australia (Darwin, Sydney, Perth airport, Melbourne, Brisbane). The Dataset was gathered from the national meteo-rological organization of Australia is the Australian Government Bureau of Meteorology, also known as the BOM. To monitor and forecast meteorological conditions, climatic trends, and natural calamities like cyclones, storms, floods, the Bureau of Meteorology is essential. The dataset includes 14, 5460 size, 23 features detailed city-specific monthly averages for Australia from 2008 to 2017(10 years). An effective rainfall forecasting was produced by integration of a number of Machine Learning and Deep Learning techniques, including Random Forest model (RF), Decision Tree (DT) and Gradient Boosting classifier (GBC), Artificial Neural Network (ANN), and Recurrent Neural Network (RNN). The models were trained to forecast rainfall, reducing the potential impact of floods. Results indicate that combining neural networks and Random Forests provides the most accurate predictions. © (2025), (science and Information Organization). All rights reserved.

关键词： Rain

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Nanoimprint lithography-assisted block copolymer self-assembly for hyperfine fabrication of magnetic patterns based on L10-FePt nanoparticles

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science China Chemistry 2025年第5期68卷 2027-2034页

作者： Jie Zhang Yi Chen Guijun Li Zelin Sun Zhengong Meng Wai-Yeung Wong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute Division of Integrative Systems and Design and Department of Electronic and Computer EngineeringThe Hong Kong University of Science and Technology School of Flexible Electronics (Future Technology) and Institute of Advanced Materials (IAM) Nanjing Tech University

L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely,and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit *** from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.

关键词： organometallic precursor magnetic nanoparticles block copolymer self-assembly nanoimprint lithography

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Optimizing solar radiation prediction: a novel SVM approach for renewable energy systems

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International Journal of Information Technology (Singapore) 2025年 1-13页

作者： Basha, S. Sharief Rao, A. Nagaraja Department of Mathematics School of Advanced Science Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computational Intelligence School of Computer Science & Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India

The global transition toward renewable energy sources, driven by fossil fuel depletion and environmental imperatives, has positioned solar power as a crucial sustainable alternative. This study presents an advanced methodological framework for solar radiation (SR) prediction utilizing Support Vector Machine (SVM) techniques. The research analyzes comprehensive solar radiation data collected from India’s Vellore district throughout 2023, implementing a novel Support Vector Regression (SVR) approach with both polynomial and radial basis function (RBF) kernels. While conventional methodologies predominantly focus on minimizing training errors, our approach emphasizes reducing the generalization error bound to enhance predictive performance. The proposed model incorporates diverse meteorological parameters for SR prediction, demonstrating superior generalization capabilities compared to traditional approaches. Empirical results indicate that the SVR_rbf model significantly outperformed both SVR_poly and artificial neural network (ANN) models in SR prediction accuracy. The model's efficacy is validated through rigorous performance metrics: RMSE (10.3032), MAE (4.2728), MAPE (0.0152), and R² (0.9795). This research advances the understanding of machine learning applications in renewable energy forecasting, particularly for regions with limited meteorological data availability. Research findings enhance solar energy utilization and contribute to the transition towards sustainable energy. Future research directions may explore the integration of additional environmental parameters and the model’s adaptability across diverse geographical contexts. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Artificial neural network Predictive models Solar radiation Support vector regression

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SVM-RideNN: hybrid support vector machine-rider neural network based sentiment analysis using product review

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Australian Journal of Electrical and Electronics Engineering 2025年

作者： A S, Sridhar Nagasundaram, S. Department of Computer Applications Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

Nowadays, shopping on online sites has become most mainstream for users. Moreover, the social media comments and reviews serve a huge role in online shopping. The satisfactions of users have also been improved with the utilization of sentiment analysis (SA). In addition, the sentiment analysis determines the digital text, in which the messages are positive, negative, or neutral. SA has difficulty in examining sentiments that are expressed in different languages, dialects, or colloquialisms. Moreover, it faces trouble with slang, abbreviations, and emojis. To bridge this gap, a hybrid Support Vector Machine Rider Optimization Neural Network (SVM-RideNN)-based sentimental analysis is proposed in this research. This method aims to examine the type and identification of text representation, and performance of sentiment analysis. The input product reviews are obtained from the acquired dataset, and the essential features are refined. From the refined features, the sentimental analysis process is done using the SVM-RideNN. In addition, the metrics, namely precision, recall, F1-score, and rouge, are employed to validate the performance of the model, and it has attained the finest output of 91.8%, 94.9%, 93.3%, and 86.9%. © 2025 Engineers Australia.

关键词： Support vector machines

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