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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sujai, S. Tiwaskar, S.A. Thangarasu, N. Jain, Romil Preetha, M. Goyal, Shubhi School of Engineering and Technology Mechaical Engineering Karnataka Bangalore India Computer Engg Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research for Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Quantum University Research Center Quantum University India

ISBN: (纸本)9798350370249

Mobile advert hoc networks (MANETs) are self-organizing, infrastructure-less networks together with cellular nodes that talk with each other without delay over wireless links. In spite of the numerous benefits they offer, MANETs need more bandwidth and strength assets and numerous inherent obstacles such as route preservation price and transmission reliability. To address those issues, this paper gift a routing method based on multipath routing and community coding for MANETs. Multipath routing is based on packet-degree duplication to stabilize the traffic load among multiple paths to be had in the network. In contrast, network coding combines more than one packet at community interface nodes, which will increase throughput and reduce manipulation overhead when packets traverse intermediate nodes. The proposed routing method combines each of these techniques, which ends up in efficient and dependable information transmission, even as concurrently decreasing the stress on the network sources. The proposed approach changed into simulating the usage of an OMNeT++ simulator and evaluated in terms of shipping ratio and common stop-to-give-up postponement. The simulation outcomes confirmed that the proposed technique outperforms multipath routing without network coding and single-direction routing in phrases of delivery ratio and average end-to-give-up delay. © 2024 IEEE.

关键词： Infrastructure Manipulation Multipath Numerous Preservation Strength Technique

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Hybrid Deep Learning Approaches for Data Security in Cloud Environments 2

Hybrid Deep Learning Approaches for Data Security in Cloud E...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Rekha, S. Sharma, Manish Kannan, Sathish Upadhye, Vijay Jagdish Patil, Harshal Rajkumar, S. PSG College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore641 014 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India Parul Institute of Applied Sciences Parul University Research and Development Cell Department of Microbiology Gujarat Waghodia India Computer Science and Engineering Maharashtra Pune412115 India Institute of Technology Hawassa University Faculty of Manufacturing Department of Mechanical Engineering Ethiopia

ISBN: (纸本)9798350327533

More recent studies have concentrated on methods to improve the security and privacy of cloud operations for the benefit of user data. For example, in order to safeguard sensitive data kept by a cloud-deployed procedure, it is important to guarantee secure transmission of sensitive data across multiple stakeholders. researchers have investigated deeply into the subject of cloud security concerns, which include data breaches, denial of service, data loss, service rejection, and threats from malicious users. These issues typically stem from problems like multi-tenancy, lack of control over data, and trust issues. Cloud providers are primarily responsible for ensuring the security of the cloud platform. This study proposes a CNN-BiLSTM-AM hybrid model that combines Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and Attention Mechanism (AM). In order to determine how well our proposed hybrid model worked, this study tested it extensively against the traditional CNN and Long Short-Term Memory (LSTM) techniques. An established standard for evaluating intrusion detection systems, the CICIDS 2017 dataset was used in the study. The experimental results demonstrated that the proposed hybrid model outperformed the competition. The proposed hybrid model delivers the highest performance measure of accuracy (99.2%), detection rate (98.807%), and precision (99.59%). © 2024 IEEE.

关键词： Cardiovascular Disease Deep Learning Electrocardiogram Heartbeat Normalization

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La-substituted W-type barium-nickel ferrites for tunable and high-performance electromagnetic wave absorption

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International Journal of Minerals,Metallurgy and Materials 2025年第3期32卷 645-656页

作者： Long Wang Jiurong Liu Shenghui Xie Yanli Deng Zhou Wang Shanyue Hou Shengying Yue Gang Wang Na Wu Zhihui Zeng Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials(Ministry of Education) School of Materials Science and EngineeringShandong UniversityJinan 250061China Electronic Materials Research Laboratory Key Laboratory of the Ministry of Education and International Center for Dielectric ResearchSchool of Electronic Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China Army Engineering University of PLA College of Field Engineering Nanjing 210007China Laboratory for Multiscale Mechanics and Medical Science State Key Laboratory for Strength and Vibration of Mechanical StructuresSchool of AerospaceXi’an Jiaotong UniversityXi’an 710049China Department of Electrical and Computer Engineering Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-ChampaignUrbana 61801USA School of Chemistry and Chemical Engineering Shandong UniversityJinan 250100China

W-type barium-nickel ferrite(BaNi_(2)Fe_(16)O_(27))is a highly promising material for electromagnetic wave(EMW)absorption be-cause of its magnetic loss capability for EMW,low cost,large-scale production potential,high-temperature resistance,and excellent chemical ***,the poor dielectric loss of magnetic ferrites hampers their utilization,hindering enhancement in their EMW-absorption *** efficient strategies that improve the EMW-absorption performance of ferrite is highly desired but re-mains ***,an efficient strategy substituting Ba^(2+)with rare earth La^(3+)in W-type ferrite was proposed for the preparation of novel La-substituted ferrites(Ba_(1-x)LaxNi_(2)Fe_(15.4)O_(27)).The influences of La^(3+)substitution on ferrites’EMW-absorption performance and the dissipative mechanism toward EMW were systematically explored and ***^(3+)efficiently induced lattice defects,enhanced defect-induced polarization,and slightly reduced the ferrites’bandgap,enhancing the dielectric properties of the ***^(3+)also enhanced the ferromagnetic resonance loss and strengthened magnetic *** effects considerably improved the EMW-absorption perform-ance of Ba_(1-x)LaxNi_(2)Fe_(15.4)O_(27)compared with pure W-type *** x=0.2,the best EMW-absorption performance was achieved with a minimum reflection loss of-55.6 dB and effective absorption bandwidth(EAB)of 3.44 GHz.

关键词： electromagnetic wave absorption W-type barium ferrite rare earth dielectric

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LEGF-DST:LLMs-Enhanced Graph-Fusion Dual-Stream Transformer for Fine-Grained Chinese Malicious SMS Detection

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computers, Materials & Continua 2025年第2期82卷 1901-1924页

作者： Xin Tong Jingya Wang Ying Yang Tian Peng Hanming Zhai Guangming Ling School of Information and Cybersecurity People’s Public Security University of ChinaBeijing100038China Cyber Investigation Technology Research and Development Center The Third Research Institute of the Ministry of Public SecurityShanghai201204China Department of Cybersecurity Defense Beijing Police CollegeBeijing102202China School of Computer Science Henan Institute of EngineeringZhengzhou451191China

With the widespread use of SMS(Short Message Service),the proliferation of malicious SMS has emerged as a pressing societal *** deep learning-based text classifiers offer promise,they often exhibit suboptimal performance in fine-grained detection tasks,primarily due to imbalanced datasets and insufficient model representation *** address this challenge,this paper proposes an LLMs-enhanced graph fusion dual-stream Transformer model for fine-grained Chinese malicious SMS *** the data processing stage,Large Language Models(LLMs)are employed for data augmentation,mitigating dataset *** the data input stage,both word-level and character-level features are utilized as model inputs,enhancing the richness of features and preventing information loss.A dual-stream Transformer serves as the backbone network in the learning representation stage,complemented by a graph-based feature fusion *** the output stage,both supervised classification cross-entropy loss and supervised contrastive learning loss are used as multi-task optimization objectives,further enhancing the model’s feature *** results demonstrate that the proposed method significantly outperforms baselines on a publicly available Chinese malicious SMS dataset.

关键词： Transformers malicious SMS multi-task learning large language models

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Energy Management of Networked Smart Railway Stations Considering Regenerative Braking, Energy Storage System, and Photovoltaic Units

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Energy engineering 2023年第1期120卷 69-86页

作者： Saeed Akbari Seyed Saeed Fazel Hamed Hashemi-Dezaki School of Railway Engineering Iran University of Science and TechnologyTehran13114-16846Iran Department of Electrical and Computer Engineering University of KashanKashan***Iran Research and Innovational Center for Electrical Engineering(RICE) Faculty of Electrical EngineeringUniversity ofWest Bohemia(UWB)Pilsen30100Czech Republic

The networking of microgrids has received significant attention in the form of a smart *** this paper,a set of smart railway stations,which is assumed as microgrids,is connected *** has been tried to manage the energy exchanged between the networked microgrids to reduce received energy from the utility ***,the operational costs of stations under various conditions decrease by applying the proposed *** smart railway stations are studied in the presence of photovoltaic(PV)units,energy storage systems(ESSs),and regenerative braking *** regenerative braking is one of the essential ***,the stochastic behaviors of the ESS’s initial state of energy and the uncertainty of PV power generation are taken into account through a scenario-based *** networked microgrid scheme of railway stations(based on coordinated operation and scheduling)and independent operation of railway stations are *** proposed method is applied to realistic case studies,including three stations of Line 3 of Tehran Urban and Suburban Railway Operation Company(TUSROC).The rolling stock is simulated in the MATLAB ***,the coordinated operation of networked microgrids and independent operation of railway stations are optimized in the GAMS environment utilizing mixed-integer linear programming(MILP).

关键词： Energy management system(EMS) smart railway stations coordinated operation photovoltaic generation regenerative braking uncertainty scenario-based model mixed-integer linear programming(MILP)

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Novel Positive Multi-Layer Graph Based Method for Collaborative Filtering Recommender Systems

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Journal of computer science & Technology 2022年第4期37卷 975-990页

作者： Bushra Alhijawi Ghazi AL-Naymat Department of Computer Science Princess Sumaya University for TechnologyAmman 11941Jordan Artificial Intelligence Research Center College of Engineering and Information TechnologyAjman UniversityAjman 20550U.A.E.

Recommender systems play an increasingly important role in a wide variety of applications to help users find favorite products. Collaborative filtering has remarkable success in terms of accuracy and becomes one of the most popular recommendation methods. However, these methods have shown unpretentious performance in terms of novelty, diversity, and coverage. We propose a novel graph-based collaborative filtering method, namely Positive Multi-Layer Graph-Based Recommender System (PMLG-RS). PMLG-RS involves a positive multi-layer graph and a path search algorithm to generate recommendations. The positive multi-layer graph consists of two connected layers: the user and item layers. PMLG-RS requires developing a new path search method that finds the shortest path with the highest cost from a source node to every other node. A set of experiments are conducted to compare the PMLG-RS with well-known recommendation methods based on three benchmark datasets, MovieLens-100K, MovieLens-Last, and Film Trust. The results demonstrate the superiority of PMLG-RS and its high capability in making relevant, novel, and diverse recommendations for users.

关键词： recommender system collaborative filtering graph theory path search novelty coverage diversity

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A Comprehensive Evaluation of State-of-the-Art Deep Learning Models for Road Surface Type Classification

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Intelligent Automation & Soft Computing 2023年第8期37卷 1275-1291页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayao56000Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok10800Thailand

In recent years,as intelligent transportation systems(ITS)such as autonomous driving and advanced driver-assistance systems have become more popular,there has been a rise in the need for different sources of traffic situation *** classification of the road surface type,also known as the RST,is among the most essential of these situational data and can be utilized across the entirety of the ITS ***,the benefits of deep learning(DL)approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual *** ability to extract important features is vital in making RST classification more *** work investigates the most recent advances in DL algorithms for sensor-based RST classification and explores appropriate feature extraction *** used different convolutional neural networks to understand the functional architecture better;we constructed an enhanced DL model called SE-ResNet,which uses residual connections and squeeze-and-excitation mod-ules to improve the classification *** experiments with a publicly available benchmark dataset,the passive vehicular sensors dataset,have shown that SE-ResNet outperforms other state-of-the-art *** proposed model achieved the highest accuracy of 98.41%and the highest F1-score of 98.19%when classifying surfaces into segments of dirt,cobblestone,or asphalt ***,the proposed model significantly outperforms DL networks(CNN,LSTM,and CNN-LSTM).The proposed RE-ResNet achieved the classification accuracies of asphalt roads at 98.98,cobblestone roads at 97.02,and dirt roads at 99.56%,respectively.

关键词： Road surface type classification deep learning inertial sensor deep pyramidal residual network squeeze-and-excitation module

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Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

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computer Systems science & engineering 2023年第8期46卷 1533-1547页

作者： Saud Yonbawi Sultan Alahmari T.Satyanarayana Murthy Padmakar Maddala E.Laxmi Lydia Seifedine Kadry Jungeun Kim Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Civil Engineering Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop *** agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental *** control has become one of the significant problems in the agricultural *** traditional weed control,the entire field is treated uniformly by spraying the soil,a single herbicide dose,weed,and crops in the same *** more precise farming,robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed *** may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable *** study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection(HHOGCN-WD)technique for Precision *** HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision *** image pre-processing,the HHOGCN-WD model utilizes a bilateral normal filter(BNF)for noise *** addition,coupled convolutional neural network(CCNet)model is utilized to derive a set of feature *** detect and classify weed,the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection *** experimental results of the HHOGCN-WD technique are investigated under the benchmark *** results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches,with increased accuracy of 99.13%.

关键词： Weed detection precision agriculture graph convolutional network harris hawks optimizer hyperparameter tuning

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Robust Particle Swarm Optimization Algorithm for Modeling the Effectof Oxides Thermal Properties on AMIG 304L Stainless Steel Welds

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computer Modeling in engineering & sciences 2024年第11期141卷 1809-1825页

作者： Rachid Djoudjou Abdeljlil Chihaoui Hedhibi Kamel Touileb Abousoufiane Ouis Sahbi Boubaker Hani Said Abdo Department of Mechanical Engineering College of Engineering in Al-KharjPrince Sattam bin Abdulaziz UniversityP.O.Box 655Al-Kharj16273Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Laboratory of Mechanics of Sousse(LMS) National Engineering School of SousseUniversity of SousseSousse4054Tunisia Center of Excellence for Research in Engineering Materials(CEREM) King Saud UniversityP.O.Box 800Al-Riyadh11421Saudi Arabia Department of Metallurgical and Materials Engineering Faculty of Petroleum and Mining EngineeringSuez UniversitySuez43512Egypt

There are several advantages to the MIG(Metal Inert Gas)process,which explains its increased use in variouswelding sectors,such as automotive,marine,and construction.A variant of the MIG process,where the sameequipment is employed except for the deposition of a thin layer of flux before the welding operation,is the AMIG(Activated Metal Inert Gas)*** study focuses on investigating the impact of physical properties ofindividual metallic oxide fluxes for 304L stainless steel welding joint morphology and to what extent it can helpdetermine a relationship among weld depth penetration,the aspect ratio,and the input physical properties ofthe *** types of oxides,TiO_(2),SiO_(2),Fe_(2)O_(3),Cr_(2)O_(3),and Mn_(2)O_(3),are tested on butt joint design withoutpreparation of the edges.A robust algorithm based on the particle swarm optimization(PSO)technique is appliedto optimally tune the models’parameters,such as the quadratic error between the actual outputs(depth and aspectratio),and the error estimated by the models’outputs is *** results showed that the proposed PSOmodel is first and foremost robust against uncertainties in measurement devices and modeling errors,and second,that it is capable of accurately representing and quantifying the weld depth penetration and the weld aspect ratioto the oxides’thermal properties.

关键词： Activated metal inert gas welding stainless steel activating flux oxides’thermal properties particle swarm optimization

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Optimizing Antenna Design for 6G Applications Using Machine Learning Techniques

Optimizing Antenna Design for 6G Applications Using Machine ...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Sharma, Praveen Kumar Yadav, Vivek Prakash Mukesh, Aashiwal Nirmal Jawale, Sourabh Kumar, Ankit Research Center for Electrical and Information Technology Seoul National University of Science and Technology Seoul01811 Korea Republic of FEAL: Flexible Electronics and Antenna Lab Department of Electronics and Communication Engineering Pilani India FEAL: Flexible Electronics and Antenna Lab Department of Electronics and Communication Engineering Mumbai India Medicaps University Department of Computer Science and Engineering Indore India Seoul National University of Science and Technology Department of Computer Science and Engineering Seoul01811 Korea Republic of

ISBN: (纸本)9798350379716

The emergence of 6G networks requires innovative antenna designs that operate at extremely high frequencies. The proposed paper explores the application of advanced machine learning techniques to optimize the design of antennas for 6G applications at 15 GHz. Traditional design methods are often time-consuming and depend on expert knowledge, whereas machine learning can automate and expedite this process. We developed a dataset incorporating critical antenna design parameters such as geometric dimensions and applied algorithms to predict key performance metrics like return loss, gain, and bandwidth. Our results demonstrate that neural networks, in particular, offer superior predictive accuracy, significantly reducing design iteration times. This integration of machine learning into antenna design not only improves performance but also facilitates the development of adaptive antenna systems, meeting the stringent demands of 6G networks. This research highlights the potential of machine learning to revolutionize antenna design, enabling rapid and efficient development of next-generation communication systems. © 2024 IEEE.

关键词： Smart antennas

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