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A New Handover Management Model for Two-Tier 5G Mobile Networks

computers, Materials & Continua 2022年第6期71卷 5491-5509页

作者： Mohammad Arifin Rahman Khan Mohammed Golam Kaosar Mohammad Shorfuzzaman Kire Jakimoski Department of Computer Science and Engineering Bangladesh UniversityDhaka1207Bangladesh Discipline of IT Media and CommunicationsMurdoch UniversityPerthWA6150Australia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Faculty of Informatics AUE-FON UniversitySkopjeRepublic of North Macedonia

There has been an exponential rise in mobile data traffic in recent times due to the increasing popularity of portable devices like tablets,smartphones,and *** rapid rise in the use of these portable devices has put extreme stress on the network service providers while forcing telecommunication engineers to look for innovative solutions to meet the increased *** solution to the problem is the emergence of fifth-generation(5G)wireless communication,which can address the challenges by offering very broad wireless area capacity and potential cut-power *** application of small cells is the fundamental mechanism for the *** use of small cells can enhance the facility for higher capacity and ***,it must be noted that small cells deployment will lead to frequent handovers of mobile *** the importance of small cells in 5G,this paper aims to examine a new resource management scheme that can work to minimize the rate of handovers formobile phones through careful resources allocation in a two-tier ***,the resource management problem has been formulated as an optimization issue thatwe aim to overcome through an optimal *** find a solution to the existing problem of frequent handovers,a heuristic approach has been *** solution is then evaluated and validated through simulation and testing,during which the performance was noted to improve by 12%in the context of handover ***,this model has been observed to be more efficient as compared to the existing model.

关键词： Two-tier mobile networks small cell densification management of mobility handover cost average throughput power consumption

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Electrochemical Carbon Dioxide Reduction to Ethylene:from Mechanistic Understanding to Catalyst Surface engineering

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Nano-Micro Letters 2023年第10期15卷 382-415页

作者： Junpeng Qu Xianjun Cao Li Gao Jiayi Li Lu Li Yuhan Xie Yufei Zhao Jinqiang Zhang Minghong Wu Hao Liu Joint International Laboratory on Environmental and Energy Frontier Materials School of Environmental and Chemical EngineeringShanghai UniversityShanghai 200444People’s Republic of China Centre for Clean Energy Technology Faculty of ScienceUniversity of Technology SydneyBroadwaySydneyNSW 2007Australia Department of Electrical and Computer Engineering University of Toronto35 St George StreetTorontoON M5S 1A4Canada

Electrochemical carbon dioxide reduction reaction(CO_(2)RR)provides a promising way to convert CO_(2)to *** multicarbon(C_(2+))products,especially ethylene,are of great interest due to their versatile industrial ***,selectively reducing CO_(2)to ethylene is still challenging as the additional energy required for the C–C coupling step results in large overpotential and many competing ***,mechanistic understanding of the key steps and preferred reaction pathways/conditions,as well as rational design of novel catalysts for ethylene production have been regarded as promising approaches to achieving the highly efficient and selective CO_(2)*** this review,we first illustrate the key steps for CO_(2)RR to ethylene(e.g.,CO_(2)adsorption/activation,formation of~*CO intermediate,C–C coupling step),offering mechanistic understanding of CO_(2)RR conversion to *** the alternative reaction pathways and conditions for the formation of ethylene and competitive products(C_1 and other C_(2+)products)are investigated,guiding the further design and development of preferred conditions for ethylene *** strategies of Cu-based catalysts for CO_(2)RR-ethylene are further summarized,and the correlations of reaction mechanism/pathways,engineering strategies and selectivity are ***,major challenges and perspectives in the research area of CO_(2)RR are proposed for future development and practical applications.

关键词： Key steps in CO_(2)RR-ethylene Preferable reaction pathways Mechanism understanding Surface engineering strategies of Cu-based catalysts

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Flexible Localization Method with Motion Estimation for Underwater Wireless Sensor Networks 26

Flexible Localization Method with Motion Estimation for Unde...

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26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Ismail, A.S. Hawbani, Ammar Wang, Xingfu Aziz, Samah Abdel Zhao, Liang Saeed, Nasir School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China Zagazig University Faculty of Science Zagazig44519 Egypt School of Computer Science Shenyang Aerospace University Shenyang110136 China United Arab Emirates University Department of Electrical and Communication Engineering Al Ain15551 United Arab Emirates

ISBN: (纸本)9791188428120

Due to the challenging conditions of underwater environments, such as node mobility and large-scale networks, achieving localization in large-scale mobile underwater sensor networks (UWSN) is a difficult task. This paper introduces a scheme known as the Flexible Localization Method with Mobility Estimation (FLMME) for UWSNs by utilizing the expected mobility patterns of underwater objects. FLMME performs localization hierarchically by splitting the process into anchor and ordinary node localization. Each node estimates its next mobility pattern based on previous location information, enabling estimates about its next location. Anchor nodes, holding known locations, manage the localization process to balance accuracy and error trade-offs. Extensive simulations demonstrate that FLMME reduces localization errors and hence increases localization accuracy. © 2024 Global IT Research Institute - GIRI.

关键词： Location

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Generating A New Shilling Attack for Recommendation Systems

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computers, Materials & Continua 2022年第5期71卷 2827-2846页

作者： Pradeep Kumar Singh Pijush Kanti Dutta Pramanik Madhumita Sardar Anand Nayyar Mehedi Masud Prasenjit Choudhury Department of Computer Science&Engineering National Institute of TechnologyDurgapurIndia Graduate School Duy Tan UniversityDa NangVietnam Faculty of Information Technology Duy Tan UniversityDa NangVietnam Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

A collaborative filtering-based recommendation system has been an integral part of e-commerce and *** keep the recommendation systems reliable,authentic,and superior,the security of these systems is very *** the existing shilling attack detection methods in collaborative filtering are able to detect the standard attacks,in this paper,we prove that they fail to detect a new or unknown *** develop a new attack model,named Obscure attack,with unknown features and observed that it has been successful in biasing the overall top-N list of the target users as *** Obscure attack is able to push target items to the top-N list as well as remove the actual rated items from the *** proposed attack is more effective at a smaller number of k in top-k similar user as compared to other existing *** effectivity of the proposed attack model is tested on the MovieLens dataset,where various classifiers like SVM,J48,random forest,and naïve Bayes are utilized.

关键词： Shilling attack recommendation system collaborative filtering top-N recommendation biasing shuffling hit ratio

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Spatial-channel attention-based stochastic neighboring embedding pooling and long-short-Term memory for lung nodules classification 12

Spatial-channel attention-based stochastic neighboring embed...

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12th International Conference on computer and Knowledge engineering, ICCKE 2022

作者： Saihood, Ahmed Karshenas, Hossein Nilchi, Ahmad Reza Naghsh University of Isfahan Faculty of Computer Engineering Artificial Intelligence Department Isfahan Iran University of Thi-Qar Nasiriyah Faculty of Computer Science and Mathematics Thi-Qar Iraq

ISBN: (数字)9781665476133

ISBN: (纸本)9781665476133

Handling lesion size and location variance in lung nodules are one of the main shortcomings of traditional convolutional neural networks (CNNs). The pooling layer within CNNs reduces the resolution of the feature maps causing small local details loss that needs processing by the following layers. In this article, we proposed a new pooling-based stochastic neighboring embedding method (SNE-pooling) that is able to handle the long-range dependencies property of the lung nodules. Further, an attention-based SNE-pooling model is proposed that could perform spatial and channel attention. The experimental results conducted on LIDC and LUNGx datasets show that the attention-based SNE-pooling model significantly improves the performance for the state of the art. © 2022 IEEE.

关键词： Embeddings

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A Way to Mark Attentance using Face Recognition using PL

A Way to Mark Attentance using Face Recognition using PL

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Vivek V Mahesh TR Department of Computer Science and Engineering Faculty of Engineering and Technology Jain (Deemed-to-be University) Bangalore

The computer system that is used to take attendance online is going to be upgraded as part of this project. This attendance tracking system is able to hold the technology known as facial recognition, which is a useful feature. This device helps students take attendance by utilizing both a biometric technology and artificial intelligence algorithms in one convenient package. When pupils enter the classroom, the face recognition software can identify them, making it useful for educational institutions of any kind. The existing system has a number of flaws that make it difficult to accurately record the attendance of both students and teachers. The approach makes it simpler to mark attendance and lowers the number of mistakes that might occur when doing *** students and teachers will gain a significant amount by utilizing these techniques. The ability to recognize people based on their faces is becoming increasingly significant in practically every industry. Face recognition techniques will be used to understand the faces. An attendance record is compared to the record that corresponds to it that was previously saved in the database. A student's photograph is used as a check point for this comparison.

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Advanced Traffic Framework for Managing Congestion 9

Advanced Traffic Framework for Managing Congestion

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Alharbi, Awad Saleh Sen, Adnan Ahmed Abi Yamin, Mohammad Alhudhaif, Abdullah Alaboudi, Abdulellah A. School of Mathematics Computer Science and Engineering Department of Electrical Engineering London United Kingdom Islamic University Faculty of Computer and Information Systems Al-Madinah Saudi Arabia College of Engineering and Computer Science Australian National University Canberra Australia King Abdulaziz University Faculty of Economics and Administration Jeddah Saudi Arabia Shaqra University Department of Computer Sciences Shaqra Saudi Arabia

ISBN: (纸本)9789380544441

The most important challenge for any traffic system is to address traffic congestion and reduce it as much as possible. This process is complicated even for smart systems, as it requires the integration between many solutions, technologies, and services because a single solution cannot effectively address this issue. In this paper, we present a framework to support cooperation of many solutions, technologies, tools, and services in the traffic domain. To achieve this, the different service providers must be enabled to contribute to providing solutions, support services, and cooperate to obtain better quality, performance, availability, and greater flexibility in the available options. Therefore, we have added a new module that is responsible for supporting interoperability between devices and different heterogeneous services. We also added a special privacy protection component which is also a challenge that limits the ability to collaborate and share data between different apps and services. Finally, we discuss the benefits of the advanced traffic framework, with a case study that proves its importance. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Interoperability

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Heart-Net: AMulti-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

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computers, Materials & Continua 2024年第9期80卷 3967-3990页

作者： DeemaMohammed Alsekait Ahmed Younes Shdefat AymanNabil Asif Nawaz Muhammad Rizwan Rashid Rana Zohair Ahmed Hanaa Fathi Diaa Salama Abd Elminaam Department of Computer Science and Information Technology Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia College of Engineering and Technology American University of the Middle EastEgaila15453Kuwait Faculty of Computers Science Misr International UniversityCairo11800Egypt University Institute of Information Technology—PMAS Arid Agriculture University Rawalpjndi46000Pakistan Department of Computer Science International Islamic UniversityIslamabad04436Pakistan Jadara Research Center Jadara UniversityIrbid21110Jordan Information System Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13511Egypt MEU Research Unit Middle East UniversityAmman11831Jordan

Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic *** diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality ***,these methods often require significant time and expertise,making them less accessible in resource-limited *** technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare *** study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant *** is performed using Optimized *** approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health *** proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality *** findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

关键词： Heart diseases magnetic resonance imaging electrocardiogram deep learning classification

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Hybrid optimization for integration of renewable energy systems into smart grids

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Measurement: Sensors 2024年 33卷

作者： Verma, Vikas Sorathiya, Vishal Sonowal, Gunikhan Reddy, M. Sudhakara Department of Computer Science & Engineering Vivekananda Global University Rajasthan Jaipur India Department of Electronics and Communication Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Gujarat Vadodara India Department of Computer Science and Engineering Assam down town University Assam Guwahati India Bangalore560027 India

The significant increase in the human population, energy consumption in daily living is increasing. One approach is to raise the amount of power produced to match the growth in human population, although this is typically practically unfeasible. By controlling demand, electricity consumption and associated expenses may be reduced. As a consequence, we employ Demand-side Management (DSM) to plan different gadgets on loads to reduce energy usage. Incorporating renewable energy sources and reducing energy costs are two significant roles that the smart grid plays. As a result, we suggest a hybrid swarm based Harris Hawks' optimization (S–HHO) algorithm to overcome the optimization difficulties. The suggested approach includes loads of home and business variety. The proposed technique was compared to binary particle swarm optimization (BPSO), multi-objective particle swarm optimization (MOPSO), wind driven optimization (WDO), and multi-objective genetic algorithms (MOGA). Results from simulations were produced using MATLAB software. The results show that the improved hybrid optimization S–HHO reduces the Peak-to-average ratio (PAR) and computational time better than other methods such as WDO, BPSO, MOPSO, and MOGA. © 2024 The Authors

关键词： Particle swarm optimization (PSO)

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Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

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computers, Materials & Continua 2022年第12期73卷 6563-6577页

作者： Manar Ahmed Hamza Hadeel Alsolai Jaber S.Alzahrani Mohammad Alamgeer Mohamed Mahmoud Sayed Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science and Bioinformatics Singhania UniversityPacheri BariDistrict JhnujhunuRajasthanIndia Department of Architectural Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt

Intelligent Transportation System(ITS)is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic *** the help of big data and communication technologies,ITS offers real-time investigation and highly-effective traffic *** Flow Prediction(TFP)is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past *** Network(NN)and Machine Learning(ML)models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of *** Learning(DL)is a kind of ML technique which yields effective performance on data classification and prediction *** this motivation,the current study introduces a novel Slime Mould Optimization(SMO)model with Bidirectional Gated Recurrent Unit(BiGRU)model for Traffic Prediction(SMOBGRU-TP)in smart ***,data preprocessing is performed to normalize the input data in the range of[0,1]using minmax normalization ***,BiGRUmodel is employed for effective forecasting of traffic in smart ***,the novelty of the work lies in using SMO algorithm to effectively adjust the hyperparameters of BiGRU *** proposed SMOBGRU-TP model was experimentally validated and the simulation results established the model’s superior performance in terms of prediction compared to existing techniques.

关键词： Smart cities traffic flow prediction slime mould optimization algorithm deep learning intelligent models

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