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Hybrid Fuzzy Data Aggregation and Optimization-Based Routing for Energy Efficiency in Heterogeneous Wireless Sensor Networks

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Informatica (Slovenia) 2024年第20期48卷 95-106页

作者： Alhijaj, Asaad A. Marzook, Ali K. Hussein, Dheyaa Mezaal Alkenani, Jawad Department of Computer Information Systems College of Computer Science and Information Technology University of Basrah Basrah Iraq University of Basrah College of Engineering Department of Electrical Engineering Basrah Iraq University of Technology Office of the President of the University Baghdad Iraq Department of Computer Science Shatt Al-Arab University College Basra Iraq

Wireless sensor networks (WSNs) are networks with many sensor nodes that are utilized for various purposes, including the military and medical. In hazardous circumstances, precise data aggregation and routing are essential, and the energy consumption of the sensors needs to be closely controlled. Nonetheless, there is a significant chance of redundant data because of external factors and nearby sensors. A multitude of information can be found in large datasets, some of it unnecessary and others useful. This redundancy negatively impacts performance in terms of redundant transmission and computing costs. However, data aggregation might help a network get rid of unnecessary data. In this work, we present a hybrid protocol called fuzzy data aggregation with fuzzy spider monkey optimization routing protocol (FDA-FSMORP) that represents an intelligent approach to collecting sensor data in HWSNs considering energy consumption. The results indicated that the suggested method beat in minimizing data latency our approach reduced energy consumption by 73% using energy more effectively when compared to our simulated outcomes. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Sensor nodes

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Message Priority Classification Framework for Autonomous Vehicles in Supply Chain Management 2

Message Priority Classification Framework for Autonomous Veh...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Virani, Mili Kansara, Drashti Pathak, Lakshit Gupta, Rajesh Tanwar, Sudeep Sawhney, Sahil Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Department of Computer Science and Engineering Model Institute of Engineering & Technology J&K India

ISBN: (纸本)9798331530389

In modern supply chain management, efficient communication and timely decision-making are essential to ensuring smooth operations and avoiding descriptions. This is ensured by efficient communication and timely decision-making. Prioritizing essential messages with these systems becomes essential as autonomous technologies become more integrated. This work presents a message priority classification framework formulated to refine communication processes within autonomous vehicles (AVs). This plays a growing role in supply chain logistics. Our framework uses machine learning (ML) models such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LR) which are implemented to classify message priorities in real-time, assuring efficient and uninterrupted communication. The experimental outcomes show that the RF model outperformed other models and achieved the highest accuracy of 0.9714. Further, the proposed framework enhances the modularity of AV communication systems and supports efficiency in decision making in complex and variable supply chains. This study emphasizes the significance of ML in optimizing communication leading to more dependable and efficient supply chain operations for AVs. © 2025 IEEE.

关键词： Supply chain management

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Spatiotemporal sequence data mining: Traffic flow prediction algorithm based on Recurrent Neural Networks

Advances in Transportation Studies

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Advances in Transportation Studies 2024年 64卷 25-40页

作者： Liu, Y.P. Liu, B.N. Department of Computer Engineering Taiyuan Institute of Technology Shanxi Taiyuan030008 China Department of Traffic Equipment and Control Engineering School of Traffic Electrical and Control Engineering North China University of Technology Beijing100043 China

Traffic congestion is one of the most common problems in the transportation system. In urban planning and construction, traffic congestion increases the difficulty of control and scheduling, hindering the pace of urban modernization development. The development of intelligent transportation systems has to some extent solved these problems, among which traffic flow prediction is particularly crucial. A period processing layer containing concatenation, dimension amplification, and diffusion convolution was studied and designed. On the grounds of this, a graph neural network prediction model combining spatiotemporal period characteristics was designed. And it further introduced a traffic flow prediction method on the grounds of dynamic topology maps. This model combined adaptive topology generation and parameter learning mechanisms, synchronously processing the complex correlation of traffic data and the heterogeneity between nodes through gated recurrent neural networks. The outcomes indicated that the research model is more excellent than other graph learning methods in three evaluation indicators: Mean square error, mean absolute percentage error, and root mean square error. Their values were 15.63%, 9.87%, and 25.08%, respectively. The evaluation on all four datasets showed excellent performance of the research model, confirming the effectiveness of its adaptive topology and parameter learning strategy. This study had important practical significance for improving the traffic management system, alleviating traffic pressure, and enhancing the travel experience of urban residents. © 2024, Aracne Editrice. All rights reserved.

关键词： Mean square error

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DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

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computers, Materials & Continua 2023年第3期74卷 6429-6444页

作者： A.Arokiaraj Jovith S.Rama Sree Gudikandhula Narasimha Rao K.Vijaya Kumar Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Networking and Communications College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science Engineering Aditya Engineering CollegeSurampalemAndhra Pradesh533437India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of *** accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of *** quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression *** article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage *** proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data ***,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded *** addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably *** order to generate optimal codebook for LBG,the WSA is applied to *** performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several *** comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

关键词： DNA computing data storage image compression vector quantization ws algorithm space saving

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MCFusion: Frequency Domain Characteristics Enhancement and Feature Compensation Fusion Network for RGB-T Object Detection

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IEEE Sensors Journal 2025年第11期25卷 20880-20893页

作者： Gao, Yinbo Liao, Zhuhua Liu, Yizhi Yi, Aiping Zhang, Guoqiang Hunan University of Science and Technology China Hunan University of Science and Technology Department of Computer Science and Engineering Xiangtan China Hainan Normal University Department of Information Science and Technology Haikou China

The multi-modal object detection technology based on visible-thermal vision sensors has drawn significant attention as it is capable of achieving reliable object detection in complex scenes with challenging lighting conditions such as low light or backlight. However, there has been a lack of focus on the frequency domain feature information of the visible and thermal modalities themselves, as well as the complementarity of cross-modal features. Furthermore, current visible and thermal feature fusion methods only utilize feature information from the current layer, neglecting context information. Therefore, this paper proposes a novel network framework for enhancing frequency domain Characteristics and fusing cross-layer and cross-modal features to compensate for these limitations. This framework introduces two key modules: the Frequency Domain Characteristics Enhancement (FCE) module and the Cross-Layer and Cross-Modal Feature Compensation Fusion (CCF) module. The FCE module consists of two sub-modules. Reduce high-frequency information loss(FCE-RHFL) module operates on the visible modality to reduce high-frequency information loss, utilizing methods such as high-frequency masking and frequency recombination. Meanwhile, Enhance high-frequency information representation(FCE-EHFR) module enhances high-frequency information representation for the thermal modality through convolutions with different kernels and frequency domain enhancement techniques. In the CCF module, cross-modal and cross-layer feature compensation methods are employed to compensate for differences in modalities, followed by capture complementary information across modalities using a query-guided cross-attention mechanism. Finally, we conduct experimental comparisons on the KAIST and FLIR datasets, and the experimental results demonstrate that our method has excellent performance and robust detection results. © 2001-2012 IEEE.

关键词： Frequency domain analysis

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Adaptive Neighborhood Adjustment Strategy Based On MOHHO and NSGA-III Algorithms for Feature Selection

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IAENG International Journal of Applied Mathematics 2024年第5期54卷 917-935页

作者： Papasani, Anusha Durgam, Revathi Devarakonda, Nagaraju Computer Science and Engineering Department Koneru Lakshmaiah Education Foundation AndhraPradesh Vaddeswaram522502 India Computer Science and Engineering Department AVN Institute of Engineering and Technology Telangana Hyderabad501510 India Science and Engineering Department VIT-AP University AP Amaravati522237 India

The efficiency of multi-objective evolutionary algorithms (MOEAs) in tackling issues with multiple objectives is examined. However, it is noted that current MOEA-based feature selection techniques often converge towards the center of the Pareto front due to inadequate selection forces. The study proposes the utilization of a novel approach known as MOEA/D, which partitions complex multi-objective problems into smaller, more feasible single-objective sub-problems. Each sub-problem may then be addressed using an equal amount of computational resources. The predetermined size of the neighborhood used by MOEA/D may lead to a delay in the algorithm's merging and reduce the effectiveness of the failure. The paper proposes the Adaptive Neighbourhood Adjustment Strategy (ANAS) as a novel approach to improve the efficiency of multi-objective optimisation algorithms in order to tackle this issue. The ANAS algorithm allows for adaptive adjustment of the subproblem neighborhood size, hence enhancing the trade-off between merging and variety. In the following section of the study, a novel feature selection technique called MOGHHNS3/D-ANA is introduced. This technique utilizes ANAS to expand the potential solutions for a particular subproblem. The approach evaluates the chosen features using the Regulated Extreme Learning Machine (RELM) classifier on sixteen benchmark datasets. The experimental results demonstrate that MOGHHNS3/D-ANA outperforms four commonly employed multi-objective techniques in terms of accuracy, precision, recall, F1 score, coverage, hamming loss, ranking loss, and training time, error. The APBI approach in decomposition-based multi-objective optimization focuses on handling constraints by adjusting penalty parameters to guide the search towards feasible solutions. On the other hand, the ANA approach focuses on dynamically adjusting the neighborhood size or search direction based on the proximity of solutions in the detached space to adapt the search process.

关键词： Multiobjective optimization

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Deep Neural Network and GAN-Based Reversible Data Hiding in Encrypted Images: A Privacy-Preserving Approach

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SN computer Science 2024年第1期5卷 45页

作者： Nalavade, Jagannath E. Patil, Akshta Buchade, Amar Jadhav, Nagesh Department of Computer Science and Engineering MIT Art Design and Technology University School of Computing Pune India Department of Computer Engineering Rajarambapu Institute of Technology Sakharale Sangli India

In light of recent incidents involving the leakage of private photographs of Hollywood celebrities from iCloud, the need for robust methods to safeguard image content has gained paramount importance. This paper addresses this concern by introducing a novel framework for reversible image editing (RIT) supported by reversible data hiding with encrypted images (RDH-EI) techniques. Unlike traditional approaches vulnerable to hacking, this framework ensures both efficient and secure data embedding while maintaining the original image’s privacy. The framework leverages two established methods: secret writing and knowledge activity. While secret writing is susceptible to hacking due to the complex nature of cipher languages, RDH-EI-supported RIT adopts a more secure approach. It replaces the linguistic content of the original image with the semantics of a different image, rendering the encrypted image visually indistinguishable from a plaintext image. This novel substitution prevents cloud servers from detecting encrypted data, enabling the adoption of reversible data hiding (RDH) methods designed for plaintext images. The proposed framework offers several distinct advantages. Firstly, it ensures the confidentiality of sensitive information by concealing the linguistic content of the original image. Secondly, it supports reversible image editing, enabling the restoration of the original image from the encrypted version without any loss of data. Lastly, the integration of RDH techniques designed for plaintext images empowers the cloud server to embed supplementary data while preserving image quality. Incorporating convolutional neural network (CNN) and generative adversarial network (GAN) models, the framework ensures accurate data extraction and high-quality image restoration. The applications of this concealed knowledge are vast, spanning law enforcement, medical data privacy, and military communication. By addressing limitations of previous methods, it opens new avenues

关键词： Deep hiding Deep neural network GAN model Reversible data hiding (RDH)

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Deep Learning-based Environmental Sound Classification Using Feature Fusion and Data Enhancement

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computers, Materials & Continua 2023年第1期74卷 1069-1091页

作者： Rashid Jahangir Muhammad Asif Nauman Roobaea Alroobaea Jasem Almotiri Muhammad Mohsin Malik Sabah M.Alzahrani Department of Computer Science COMSATS University IslamabadVehari CampusPakistan Department of Computer Science University of Engineering and Technology LahorePakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21974Saudi Arabia

Environmental sound classification(ESC)involves the process of distinguishing an audio stream associated with numerous environmental *** common aspects such as the framework difference,overlapping of different sound events,and the presence of various sound sources during recording make the ESC task much more complicated and *** research is to propose a deep learning model to improve the recognition rate of environmental sounds and reduce the model training time under limited computation *** this research,the performance of transformer and convolutional neural networks(CNN)are *** audio features,chromagram,Mel-spectrogram,tonnetz,Mel-Frequency Cepstral Coefficients(MFCCs),delta MFCCs,delta-delta MFCCs and spectral contrast,are extracted fromtheUrbanSound8K,ESC-50,and ESC-10,***,this research also employed three data enhancement methods,namely,white noise,pitch tuning,and time stretch to reduce the risk of overfitting issue due to the limited audio *** evaluation of various experiments demonstrates that the best performance was achieved by the proposed transformer model using seven audio features on enhanced *** UrbanSound8K,ESC-50,and ESC-10,the highest attained accuracies are 0.98,0.94,and 0.97 *** experimental results reveal that the proposed technique can achieve the best performance for ESC problems.

关键词： Environmental sound classification convolutional neural network deep learning transformer data augmentation

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Knowledge-Empowered,Collaborative,and Co-Evolving AI Models:The Post-LLM Roadmap

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工程（英文） 2025年第1期44卷 87-100页

作者： Fei Wu Tao Shen Thomas Bäck Jingyuan Chen Gang Huang Yaochu Jin Kun Kuang Mengze Li Cewu Lu Jiaxu Miao Yongwei Wang Ying Wei Fan Wu Junchi Yan Hongxia Yang Yi Yang Shengyu Zhang Zhou Zhao Yueting Zhuang Yunhe Pan College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Leiden Institute of Advanced Computer Science Leiden UniversityLeiden 2333 CCNetherlands College of Electrical Engineering Zhejiang UniversityHangzhou 310027China School of Engineering Westlake UniversityHangzhou 310024China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Cyber Science and Technology Sun Yat-Sen UniversityShenzhen 518107China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China

Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple *** these achieve-ments,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific *** address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model ***,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge ***,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge ***,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual *** illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various *** conclude by outlining future pathways for further advancement and applications.

关键词： Artificial intelligence Large language models Knowledge empowerment Model collaboration Model co-evolution

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IoT Based Agricultural Monitoring and Alert System 4

IoT Based Agricultural Monitoring and Alert System

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priya, V Vishnu Nisanth, G. Dhanushree, D. Sindhu, M. Varshini, T. Preteka, A.T. Kongu Engineering College Department of Computer Science and Engineering Perundurai India Kongu Engineering College Department of Computer Applications Perundurai India Kongu Engineering College Department of Computer Technology Perundurai India

ISBN: (纸本)9798331523923

The goal of this project is to implement an Internet of Things (IoT)-based Agricultural Monitoring & Alert System (AMAS) that will integrate multiple sensors to continuously monitor agricultural parameters, such as soil moisture levels, soil temperature, water levels, irrigation pump operation status, and water quality, to improve precision farming practices. Enabling farmers to make well-informed decisions, maximize resources, and enhance crop output in real time is the primary objective of the AMAS system. The challenges of using IoT for agricultural monitoring, include issues with adoption among farmers, dependable technology, efficient data administration, and connectivity. Small-scale farmers' financial hardship and rural areas' restricted access to technology. Many obstacles stand in the way of the widespread adoption of IoT-based agricultural monitoring systems, such as smallscale farmers' limited financial resources, rural communities' restricted access to technology, and problems with connectivity, dependable data handling, and system scalability. Machine learning-based prediction models and IoT-integrated smart irrigation are two recent approaches that have showed promise in tackling these issues, but they still face obstacles such high implementation costs, technical complexity, and infrastructure dependence. By improving yields, increasing output, and facilitating remote monitoring, the AMAS system seeks to overcome these limits while taking into account the unique requirements and limitations of farmers. This study plan to enhance productivity, optimize yield, and implement remote monitoring. © 2025 IEEE.

关键词： Soil moisture

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