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Improvisation of Node Mobility Using Cluster Routing-based Group Adaptive in MANET

computer Systems science & engineering 2023年第3期44卷 2619-2636页

作者： J.Shanthini P.Punitha S.Karthik Department of Computer Science&Engineering Dr.N.G.P.Institute of EngineeringCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

In today's Internet routing infrastructure,designers have addressed scal-ing concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary *** tactical Mobile Ad-hoc Network(MANET),hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out,self-mending and *** in the routing process is one of the key aspects to increase MANET performance by coordinat-ing the pathways using multiple criteria and *** present a Group Adaptive Hybrid Routing Algorithm(GAHRA)for gathering portability,which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile *** on this aspect,the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach,with the objectives of enhancing the out-put of MANET routing computation in each *** analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.

关键词： Diplomatic mobile Ad-hoc network grouping mobility interior stable hybrid routing scheme adaptive switch structure clustering communication

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A novel composite machine learning model for the prediction of compressive strength of blended concrete

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Journal of Building Pathology and Rehabilitation 2025年第1期10卷 1-18页

作者： Prasad, E.V. Rama Krishna, S. Singha, S. Department of Civil Engineering School of Technology GITAM (Deemed to be University) Telangana Hyderabad India Department of Computer Science and Engineering School of Technology GITAM (Deemed to be University) Telangana Hyderabad India

Blended cement, comprising clinker and supplementary cementitious materials (SCMs), such as fly ash, slag, and silica fume, forms blended concrete when combined with aggregates. This study introduced a novel ensemble approach, the LightGBM and ANN Fusion (LAF) model, to check the prediction of compressive strength in blended concrete using eight key input features, including cement content, slag, fly ash, water, and curing age, derived from 1030 samples. The LAF model synergistically combines predictions from LightGBM and ANN with the fusion weight (alpha) dynamically optimized using Support Vector Machine (SVM) analysis. This integration leverages the strengths of both models while mitigating their individual limitations. The LAF model exhibited superior performance, with an R² of 0.9514 during training and 0.8863 during testing, significantly surpassing standalone models such as LightGBM (R² = 0.9385) and ANN (R² = 0.9136). It achieved the lowest error rates, including an MSE of 15.7384 and RMSE of 3.9672 during training, underscoring its robust generalization capabilities. The novel utilization of SVM-tuned alpha for dynamic model adaptation reduces overfitting and enhances predictive accuracy compared to traditional ensemble and single models. Comparative analysis with existing ensemble techniques, including stacking and gradient-boosted ANN, further validated the superior accuracy and adaptability of the LAF model. This approach represents a significant advancement in predictive modeling, offering a scientifically sound and versatile framework for concrete strength prediction. Future research will refine the SVM tuning, expand the model’s application to other material properties, and explore real-time deployment, positioning the LAF model as a pioneering tool for smart construction materials. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： ANN Compressive strength LAF LGBM Performance indicators Sensitivity analysis SVM

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Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

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computers, Materials & Continua 2024年第9期80卷 4791-4812页

作者： Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi Sophers James Vincent Omollo Nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan Department of Accounting and Finance Faculty of BusinessMiddle East UniversityAmman11831Jordan Faculty of Engineering Al-Balqa Applied UniversitySalt19117Jordan Department of Mathematics Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo210-40601Kenya Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering Saveetha Engineering College(Autonomous)Chennai602105India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAR approaches are not suitable in a real-time ***,an updated HAR model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR *** EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.

关键词： Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units

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LED-Based Adaptive Algorithm: A Novel Integration of Logit and Exponential Decay for Self-Organizing Sequential Search Problem 5

LED-Based Adaptive Algorithm: A Novel Integration of Logit a...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Baisakh Swain, Bishnu Prasad Patel, Nileshkumar Kalia, Sarthak Gmr Institute of Technology Department of Computer Science and Engineering Andhra Pradesh Rajam India Sambalpur University Institute of Information Technology Department of Computer Science and Engineering Odisha Sambalpur India Nirma University School Of Technology Computer Science and Engineering Department Gujarat Ahmedabad India

ISBN: (纸本)9798331522100

This research presents an innovative adaptive list reorganization strategy aimed at minimizing the total access cost for dynamic sequences. The proposed algorithm leverages a dual-parameter approach combining Logit and Exponential Decay (LED) model for the self-organizing linear list problem, also known as the list update problem. The proposed method presented in this article, is one of the few existing approaches that offers an alternative to deterministic strategies, specifically designed to handle random input sequences for this *** strategy significantly improves performance compared to deterministic algorithms like Move-to-Front (MTF) and Move-to-Middle (MTM), which fail to account for varying patterns of locality and frequency in request sequences. The comparative analysis is performed with the well-known deterministic list update algorithms such as MTF and MFM by considering the Calgary Corpus standard dataset. The experimental results show that the adaptive reorganization strategy outperformed both algorithms. © 2025 IEEE.

关键词： Adaptive algorithms

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An efficient integrity based multi-user blockchain framework for heterogeneous supply chain management applications

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International Journal of computers and Applications 2023年第4期45卷 337-351页

作者： Karumanchi, Mani Deep Sheeba, J.I. Devaneyan, S. Pradeep Department of Computer Science and Engineering Bapatla Engineering College Bapatla India Department of Computer Science and Engineering Puducherry Technological University Puducherry India Department of Mechanical Engineering Sri Venkateshwaraa College of Engineering and Technology Puducherry India

Most of the traditional cloud-based applications are insecure and difficult to compute the data integrity with variable hash size on heterogeneous supply chain datasets. Also, cloud storage systems are independent of integrity computational and data security due to structured data and computational memory. As the size of the cloud data files is increasing in the public and private cloud servers, it is difficult to provide strong data security due to file format and high dimensionality. The computational time and storage space of the conventional attribute-based encryption and decryption models are high during the data integrity verification in the traditional blockchain frameworks. This paper implements a hybrid variable size data integrity algorithm on the heterogeneous cloud supply chain data files for the strong data encryption and decryption process. This work implements an optimized blockchain framework using the advanced heterogeneous integrity computation approach and integrity policy-based attribute encryption and decryption approach for better cloud data security. Experimental results proved that the proposed integrity-based encryption model has better efficiency than the traditional integrity-based encryption frameworks on cloud heterogeneous data types. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Blockchain

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AVirtual Cloud Storage Architecture for Enhanced Data Security

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computer Systems science & engineering 2023年第2期44卷 1735-1747页

作者： M.Antony Joans Kumar C.Christopher Columbus E.Ben George T.Ajith Bosco Raj Department of Information Technology St.Xavier’s Catholic College of EngineeringNagercoil629003Tamil NaduIndia Department of Computer Science Engineering PSN College of Engineering and TechnologyTirunelveli627152India Department of Computer Science Engineering University of Technology and Applied SciencesMuscat112Oman Department of Electronics and Communications Engineering PSN College of Engineering and TechnologyTirunelveli627152India

The sensitive data stored in the public cloud by privileged users,such as corporate companies and government agencies are highly vulnerable in the hands of cloud providers and *** proposed Virtual Cloud Storage Archi-tecture is primarily concerned with data integrity and conﬁdentiality,as well as *** provide conﬁdentiality and availability,theﬁle to be stored in cloud storage should be encrypted using an auto-generated key and then encoded into distinct *** the encoded chunks ensured theﬁle integrity,and a newly proposed Circular Shift Chunk Allocation technique was used to determine the order of chunk ***ﬁle could be retrieved by performing the opera-tions in *** the regenerating code,the model could regenerate the missing and corrupted chunks from the *** proposed architecture adds an extra layer of security while maintaining a reasonable response time and sto-rage *** results analysis show that the proposed model has been tested with storage space and response time for storage and *** VCSA model consumes 1.5x(150%)storage *** was found that total storage required for the VCSA model is very low when compared with 2x Replication and completely satisﬁes the CIA *** response time VCSA model was tested with different sizedﬁles starting from 2 to 16 *** response time for storing and retrieving a 2 MBﬁle is 4.96 and 3.77 s respectively,and for a 16 MBﬁle,the response times are 11.06 s for storage and 5.6 s for retrieval.

关键词： Erasure code regenerating code cryptography virtual cloud storage architecture circular shift chunk allocation algorithm

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Recent progress in metal-organic frameworks(Part Ⅱ—material application)

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science China Chemistry 2025年第5期68卷 1642-1702页

作者： Jiandong Pang Wentao Jiang Xue-Wen Zhang Hao-Long Zhou Yuxiu Sun Wei Gong Bin Wang Fuyin Ma Linwei He Long Chen Qiang Chen He-Qi Zheng Yuanjing Cui Wen-Jie Shi Chao Zhang Mei Wang Yue-Biao Zhang Jie-Peng Zhang Chongli Zhong Yong Cui Shuao Wang Jian-Rong Li Guodong Qian Tong-Bu Lu Huan Pang Xian-He Bu School of Materials Science and Engineering Nankai University School of Physical Science and Technology Shanghai Tech University MOE Key Laboratory of Bioinorganic and Synthetic Chemistry GBRCE for Functional Molecular EngineeringSchool of ChemistryIGCMESun Yat-sen University Department of Chemistry Shantou University School of Chemical Engineering and Technology Tiangong University School of Chemistry and Chemical Engineering Shanghai Jiao Tong University State Key Laboratory of Radiation Medicine and Protection School for Radiological and Interdisciplinary SciencesSoochow University College of Materials Science & Engineering Beijing University of Technology State Key Laboratory of Silicon and Advanced Semiconductor Materials Cyrus Tang Center for Sensor Materials and ApplicationsSchool of Materials Science & EngineeringZhejiang University Institute for New Energy Materials and Low Carbon Technologies School of Materials Science and EngineeringTianjin University of Technology School of Chemistry and Chemical Engineering Yangzhou University

As a class of functional crystalline porous materials, metal-organic frameworks(MOFs) gained rapid development in the past three decades and a large number of MOFs with ordered structures, high surface areas, and functionalized channels have been investigated. MOFs and MOF-derived/composite materials show great potential in many application fields. In this review, we discussed the main applications of MOFs and MOF-derived/composite materials in small molecule storage, separation, luminescence, sensing, multitype catalysis, and energy storage. In addition, challenges and problems in the future research of MOFs-related fields are also discussed.

关键词： metal-organic frameworks storage and separation sensing and detection multitype catalysis energy storage

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Efficiency beyond Limits: LSTM and k-NN Synergy for Forecasting Energy Needs in Data Centers

Efficiency beyond Limits: LSTM and k-NN Synergy for Forecast...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Mogal, Amit K. Sonaje, Vaibhav P. Kumar, C.H.N. Santhosh Devi, V. Chithra Gottumukkala, Prasanthi Satheesh Kumar, D. Sandip University School of Computer Science and Engineering Department of Computer Science & Application Maharashtra Nashik India Sandip University School of Computer Science and Engineering Department of Computer Science & Applications Maharashtra Nashik India Anurag Engineering College Department of CSE Telangana Kodada India PSNA College of Engineering and Technology Tamilnadu Dindigul India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Hyderabad India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350361537

The objective of this study is to examine the effectiveness of a hybrid methodology that combines Long Short-Term Memory (LSTM) and k-Nearest Neighbors (k-NN) models in the context of energy prediction within data centres. The objective of this study is to improve the accuracy of predictions and the efficiency of operations by utilising the combined capabilities of these two machine learning approaches. The proposed LSTM-kNN model has superior performance compared to individual LSTM and k-NN models, as evidenced by a thorough assessment on sample data. It achieves a Mean Absolute Error (MAE) of 9.45, Root Mean Square Error (RMSE) of 12.97, and a coefficient of determination (R2) value of 0.89. The importance of model synergy in enhancing the accuracy of energy forecasts is shown by comparative analysis. The results of this study provide a significant contribution to the advancement of energy management methods in data centres. These findings provide vital insights for stakeholders, enabling them to optimise resource allocation, capacity planning, and sustainability initiatives. Proactive energy optimisation measures and cost-effective operational decisions are examples of practical applications. Potential avenues for future study encompass the exploration of supplementary machine learning techniques and the examination of the influence exerted by external factors on patterns of energy usage. In summary, this work highlights the significance of incorporating machine learning methods to tackle intricate issues in energy prediction and control in data centre settings. © 2024 IEEE.

关键词： k-nearest neighbors

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Enhanced Cyclone Intensity Estimation Through CNN Analysis of INSAT-3D IR Imagery 12th

Enhanced Cyclone Intensity Estimation Through CNN Analysis o...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Kumar, Divvela Vishnu Sai Arora, Deepak Tiwari, Shivam Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Lucknow Campus Lucknow India Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9789819788354

Cyclone forecasting using satellite pictures involves anticipating the cyclone’s intensity in advance of its arrival. The results of this study can inform people’s preparations for the cyclone. In order to save lives and reduce damage, cyclone strength forecast is crucial. As a result of the many dangers they pose, these cyclones are among the most expensive natural disasters that have ever occurred. Five separate CNN (Convolutional Neural Network) models are presented by the authors to assess the cyclone’s severity using INSAT-3D infrared pictures. While many methods exist for making such an assessment, the authors’ top model yields an RMSE of 10.02 kts for intensity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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ICRM: An intelligent citation recommendation mechanism based on BERT and weighted BoW models

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10135-10150页

作者： Chang, Chih-Yung Yang, Yu-Ting Zhang, Qiaoyun Lin, Yi-Ti Roy, Diptendu Sinha Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan Department of English Tamkang University New Taipei Taiwan Department of Computer Science and Engineering National Institute of Technology Shillong India

With the field of technology has witnessed rapid advancements, attracting an ever-growing community of researchers dedicated to developing theories and techniques. This paper proposes an innovative ICRM (Intelligent Citation Recommendation Mechanism), designed to automate the process of suggesting the appropriate number of citations for individual brackets within a document. The proposed ICRM comprises three phases: Coarse-grained Weighted Bag of Word (WCBW), Fine-grained SciBERT (FSB) and Citation Adjustment phases. Firstly, the WCBW phase employs TF-IDF to extract keywords from both target and candidate documents, forming vectors that capture word significance along with metadata like authorship, keywords, and titles. It aims to identify relevant papers from a database, serving as initial candidates for each bracket. Secondly, the FSB phase employs the SciBERT model to assess the similarity between candidate documents and the local context around brackets, enhancing the precision of recommendations. It refines this selection by analyzing candidate-document relationships within the proximity of the brackets. Lastly, the Citation Adjustment phase tackles overlapping citations and ensures that recommended citation numbers align with user-defined criteria, resolving issues of imbalance. The simulation results demonstrate that the proposed ICRM outperforms existing models significantly in terms of precision, recall and F1-score. © 2024 - IOS Press. All rights reserved.

关键词： Artificial intelligence

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