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4th International Conference on Inventive Research in computing Applications, ICIRCA 2022

作者： Bhardwaj, Aditya Mutaher, Hamza International Inistitute of Technology Dept. Computer Science and Engineering Bhubaneswar India School of Computing and Innovative Technologies British University Vietnam Hanoi Viet Nam

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Scalability is one of the Software Defined Network features that makes it more efficient than the traditional network. Due to this feature, a huge number of hosts connect to the network and require network rules and policies from the network controller. The huge number of connected hosts makes the network vulnerable to unauthorized access, which may cause network damage. In this paper, a secure hosts login technique is proposed to assure the authenticity of the hosts that need to access the network and use its resources. The proposed technique uses the key agreement protocol to allow the hosts and controller to establish secure communication. Then the procedure moves to the registration phase and login phase, which are responsible for creating and verifying the hosts' credentials while claiming network access. The secrecy and authenticity of the proposed technique was simulated, verified, and proved using AVISPA tool. The result of the simulation also proved the immunity of the proposed technique against MITM and replay attacks. © 2022 IEEE.

关键词： Controllers

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STEBS: Spatio-Temporal Entropy-Based Scoring Handover Model for LEO Satellite

STEBS: Spatio-Temporal Entropy-Based Scoring Handover Model ...

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International Wireless Communications and Mobile computing Conference, IWCMC

作者： Mohammad A. Massad Abdallah Y. Alma’Aitah Hossam S. Hassanein School of Computing Queen’s University Kingston ON Canada Department of Network Engineering and Security University of Science and Technology Irbid Jordan

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

In 6G and beyond network infrastructure, Low Earth Orbit (LEO)-based Non-terrestrial Networks (NTN) are poised to play a significant role in delivering global connectivity. To serve users effectively, multiple satellites must collaborate. Each satellite has only a brief window of time to communicate with the user. Therefore, an optimal handover strategy is needed to ensure seamless user transitions between satellites while minimizing unnecessary and frequent handovers, which leads to user satisfaction. However, existing handover strategies, primarily based on system geometry, may lack efficiency in dynamically changing user demand, particularly in deep urban canyon environments. This paper introduces a Spatio-temporal Entropy-based Scoring (STEBS) handover strategy. STEBS is a multi-objective dynamic handover strategy designed to minimize the number of handovers while consistently meeting user demand. Simulation results demonstrate that STEBS reduces the number of handovers and increases throughput, achieving an exceptionally low blocking rate of one-eighth compared to benchmark schemes, resulting in a $99 \%$ user satisfaction rate.

关键词： Wireless communication Geometry Satellites Heuristic algorithms Simulation Computational modeling Low earth orbit satellites

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Cultivating Expertise: Unravelling Type 2 Diabetes Associations through Incremental Knowledge-Based System Development: Ripple Down Rules or Machine Learning 34

Cultivating Expertise: Unravelling Type 2 Diabetes Associati...

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34th Australasian Conference on Information Systems, ACIS 2023

作者： Omar, Adel Beydoun, Ghassan Win, Khin Shukla, Nagesh Jelinek, Herbert F. Elias, Hector School of Systems Management and Leadership University of Technology Sydney Sydney Australia School of Computing and Information Technology Wollongong University Wollongong Australia Department of Business Strategy & Innovation Griffith University Griffith Australia Department of Biomedical Engineering Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Wollongong University College Wollongong University Wollongong Australia

Type 2 diabetes is a chronic disease that is caused by a combination of genetic and environmental factors, with social determinants playing a significant role. These social determinants include but not limited to factors such as education level, occupation, family history, ethnicity and place of residence etc. The prevalence of Type 2 diabetes (T2D) necessitates inventive management strategies. This study explores the potential of utilizing Ripple Down Rules (RDR) in comparison with Machine Learning (ML) approaches for the incremental development of a Knowledge-Based System (KBS). The aim is to create a robust Decision Support System (DSS) for effective T2D management, focusing on the dynamic influence of evolving social determinants. The research outcomes substantiate the viability of this approach. The KBS, fortified by RDRs, demonstrated notable performance metrics. Specifically, the system achieved an impressive accuracy rate of 90.2%, accompanied by a specificity of 96.9%. The sensitivity, crucial for identifying potential T2D cases, reached 73.6%, indicating the system's proficiency in recognizing diverse instances. Copyright © 2023 Omar, Beydoun, Win, Shukla, Jelinek and Elias.

关键词： Decision support systems

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AKGIN: An API Knowledge Graph and Intent Network based Mashup-Oriented API Recommendation Method

AKGIN: An API Knowledge Graph and Intent Network based Mashu...

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International Conference on Computer Supported Cooperative Work in Design

作者： Changyong Yu Rong Hu Bangjun Wang Computing and Novel Software Technology HNUST School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

With the continuous development of the Web API ecosystem, mashup-oriented API recommendation gets a lot of attention. Collaborative filtering, deep learning and their combination based methods are recently proposed for API recommendation. However, the recommendation results often perform poorly when data is sparse. Therefore, we propose an API knowledge graph (AKG) and intent network based mashup-oriented API recommendation method, whose main target is to recruit the semantic representation ability to improve the precision of API recommendation. Firstly, Latent Dirichlet Allocation (LDA) is used to extract API topics, which are important entities in the AKG. Then, the mashup intention is modeled based on the mashup-API interactions. The high-order relationship paths are recursively aggregated into the representation vector of Mashup and API. Finally, the inner product of their representations present the probability of the API recommendation for the mashup. The experimental results on real-world datasets show that our proposed method outperforms several baseline methods in mashup-oriented API recommendations.

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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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Computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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Blockchain-Enabled Energy Trading in Microgrids for Sustainable computing Infrastructure

Blockchain-Enabled Energy Trading in Microgrids for Sustaina...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： M. L. Sworna Kokila Sunitha R P. Kalyanakumar J. Relin Francis Raj R. Santhana Krishnan S. Gopikumar Department of Computing Technologies Faculty of Engineering and Technology School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Rajarajeswari College of Engineering Bangalore India Department of Information Technology Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology India Department of Civil Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This research investigates Blockchain-enabled energy trading within microgrids to enhance sustainable computing infrastructure. A novel algorithm, GridCoin, is proposed and evaluated against existing methodologies including Optimal Power Flow (OPF), Microgrid Energy Management System (MEMS), and Hierarchical Control Algorithms (HCA). The study employs simulation metrics such as Transaction Throughput, Energy Trading Efficiency, and Transaction Latency to compare the performance of these algorithms. GridCoin demonstrates superior capabilities in optimizing energy trading operations, achieving higher transaction throughput, improved efficiency in energy utilization, and reduced transaction latency. These results highlight the potential of Blockchain technology to revolutionize energy markets by providing secure, transparent, and decentralized transaction mechanisms. Future research directions include enhancing optimization techniques, integrating renewable energy sources, addressing scalability and security challenges, conducting real-world deployments, and analyzing economic and policy implications. This study contributes to advancing sustainable computing infrastructure through innovative Blockchain-based solutions.

关键词： Micromechanical devices Renewable energy sources System performance Microgrids Green computing Throughput Blockchains Energy management systems Optimization Load flow

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Malpractice Detection in Examination Hall using Deep Learning

Malpractice Detection in Examination Hall using Deep Learnin...

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International Conference on Inventive computing and Informatics (ICICI)

作者： Aruna S K Madhumitha A Selvanayaki Kolandapalayam Shanmugam Senthil Kumar Thangavel Maiga Chang Department of Computer Science and Engineering School of Engineering and Technology Bangalore CHRIST University India Department of Mathematics and Computer Science Ashland University Ohia USA Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India School of Computing Information and Systems Athabasca University Canada

ISBN: (数字)9798350373295

ISBN: (纸本)9798350373301

Various institutions administer tests at designated examination locations, chosen third-party and approved centers, and have established standards for installing CCTV cameras and conducting frisking under the supervision of designated personnel. Some institutions are using online proctoring, which enables students to take exams from any location. In all of the aforementioned scenarios, human monitoring is conducted, and maintaining a high level of vigilance may be challenging due to administrative oversight or intentional allowance of malpractice for personal gain. The malpractice detection may be attributed to acts like as plagiarism, unauthorized sharing of papers, and non-verbal communication. The study is conducted by capturing the dataset in the classroom of Christ University. The proposed approach is based on the YOLO framework. The movies are processed in real time to identify hand rotation, paper extraction, and classify the motion. The accuracy for the Head_right class is significantly higher than that of the Head_left class. The system is implemented using the programming language Python and has the potential for future expansion to provide real-time monitoring.

关键词： YOLO Accuracy Plagiarism Real-time systems Classification algorithms Object recognition Monitoring

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Military Text Triplet Extraction Model Combining ERNIE and Conditional Normalization 3

Military Text Triplet Extraction Model Combining ERNIE and C...

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3rd International Conference on Electronic Communication and Artificial Intelligence, IWECAI 2022

作者： Guo, Dongliang Wang, Licai Liu, Zhonglin Du, Tianfeng North China Institute of Computing Technology Smart Plat Rd Department Beijing China School of Mangement And Engineering Capital University of Economics And Business Beijing China

ISBN: (纸本)9781665479974

There are a large number of information resources that can be analyzed and mined in the existing military information system. Extracting the key knowledge from these resources is of great significance for knowledge response and reasoning. Aiming at the problems of error propagation and relationship overlap in samples in the process of military text triplet extraction, this paper proposes a triplet extraction model combining Ernie and conditional normalization, which introduces Ernie knowledge enhanced pre training extraction model, and uses 'semi pointer semi Annotation' structure and conditional layer norm to make the model have better expression ability. The experimental results show that on the manually labeled military news data set, the extraction effect of the model is significantly better than the traditional pipeline extraction model and the joint extraction model based on a single pre training model, and the F1 value reaches 81.23%. © 2022 IEEE.

关键词： Extraction

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Joint SSK Assisted by Mirror Array Grouping and LEDs in Indoor VLC System

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IEEE Transactions on Vehicular technology 2025年

作者： Sun, Yicong Wang, Fasong Li, Rui Shi, Shijie Li, Yitong Li, Xingwang Xiang, Wei Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Henan University of Technology School of Sciences Zhengzhou450001 China Henan Polytechnic University School of Physics and Electronics Information Engineering Jiaozuo454003 China La Trobe University School of Computing Engineering and Mathematical Sciences BundooraVIC3086 Australia

In the mirror array-assisted indoor visible light communication (VLC) system, activating all mirrors simultaneously can result in a complex system design and excessive consumption of resources. To address this issue and enhance the spectral efficiency, this paper proposes a joint space shift keying (SSK) technique, which is achieved by jointly utilizing the mirror array grouping and LED activation procedures in indoor VLC systems. By doing so, the SSK scheme utilizes the activation status of mirror array blocks to transmit information and can avoid excessive utilization of mirror resources while achieving efficient and reliable communication. The fairness of the considered system is improved by introducing a mirror array grouping technique that relies on the channel gains. Concurrently, a novel joint SSK modulation scheme is proposed, whereby the mirror array block is activated in conjunction with the LED. This scheme employs a information bit-based activation technique to enhance spectral efficiency. Additionally, the low complexity orthogonal matching pursuit (OMP) detection and the optimal maximum likelihood (ML) detection algorithms are employed. The effectiveness of the proposed schemes has been confirmed through extensive simulations. © 1967-2012 IEEE.

关键词： Mirrors

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CurricularVPR: Curricular Contrastive Loss for Visual Place Recognition

CurricularVPR: Curricular Contrastive Loss for Visual Place ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Dongshuo Zhang Nanhua Chen Meiqing Wu Siew-Kei Lam College of Computing and Data Science Nanyang Technological University Singapore School of Aerospace Engineering Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Visual Place Recognition (VPR) techniques commonly utilize Contrastive Losses (CL) to train models that generate compact and discriminative global descriptors for images. These models often result in poor performance due to one of the following reasons during training: 1) loss functions that focus primarily on easier samples, 2) reliance on time-consuming hard sample mining methods to identify informative supervisory samples, which hinders effective learning from large-scale datasets. To enhance both learning efficiency and effectiveness, we propose a Curricular Contrastive Loss (CCL) and use graded similarity labels as a measure of sample difficulty. Inspired by human learning that begin with easier concepts and progressively tackle more challenging ones, our CCL dynamically emphasizes easier samples during the initial training stages to achieve rapid convergence. The learning gradually focuses on harder samples in later training stages to bolster robustness of the models under challenging conditions. Our proposed method has been extensively evaluated on popular datasets, and the results demonstrate its superior performance compared to the CL and Generalized CL functions.

关键词： Training Accuracy Big Data Loss measurement Robustness Intelligent robots Visual place recognition Convergence

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