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6th IEEE International Conference and Workshop Obuda on Electrical and Power Engineering, CANDO-EPE 2023

作者： Katona, Jozsef Kovari, Attila Institute of Electronics and Communication Systems Obuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John von Neumann University Gamf Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Digital Technology Eszterházy Károly Catholic University Faculty of Computer Science Eger Hungary Institute of Engineering University of Dunaujvaros Department of Natural Sciences Dunaujvaros Budapest Hungary

ISBN: (纸本)9798350328752

Human-computer interaction (HCI) is an evolving field of research that focuses on understanding and improving the communication and interaction between humans and computers. Over the past decades, we have seen many significant advances in this field, which have contributed to the widespread adoption and integration of technology into our daily lives. HCI research and development aims to design information technology systems to meet the needs and preferences of users. Usability, efficiency, accessibility and user satisfaction are important considerations in HCI design. This paper presents the results of a pilot study on user acceptance of HCIs. The results show that users are positive about and willing to use HCIs. © 2023 IEEE.

关键词： Human computer interaction

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Empirical Evaluation of Stock Market Prediction System using Intelligent Learning Scheme with Data Processing Logic 5

Empirical Evaluation of Stock Market Prediction System using...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Prabakar, S. Kumar, Ankit Jayakarthik, R. Venkatesh, D. Pratheeba, R.S. Khan, Bhola Isbr Business School Finance and Accounting Bangalore India Faculty of Science Swami Vivekanand Subharti University Department of Computer Application Meerut India Saveetha College of Liberal Arts and Sciences Simats Dept of Computer Science India Commerce School of Commerce and Management Mohan Babu University Tirupati India Velammal Institute of Technology Department of Computer Science and Engineering Panchetti601204 India Mjp Rohilkhand University Department of Regional Economics Bareilly India

ISBN: (纸本)9798350385649

The Stock Market Prediction System using Intelligent Learning Scheme (SMS-ILS) presented in this study integrates advanced methodologies to enhance the accuracy and reliability of stock market predictions. The system encompasses a preprocessing phase, addressing missing data and outliers, followed by feature extraction utilizing Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) to capture intricate temporal dependencies. A Deep Belief Network (DBN) is employed for classification, categorizing stocks based on learned representations from historical and technical indicators. Grey Wolf Optimization (GWO) optimizes the system's parameters, ensuring adaptability to dynamic market conditions. Notably, the system achieves an impressive accuracy of 97%, emphasizing its efficacy in making correct predictions. This multi-model approach, integrating LSTM, RNN, DBN, and GWO, provides a comprehensive and adaptive framework for navigating the complexities of stock market prediction. The study emphasizes the importance of choosing metrics aligned with specific goals and conducting thorough analysis to ensure the robustness of the evaluation results in the challenging and dynamic context of financial markets. © 2024 IEEE.

关键词： Long short-term memory

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User-Specified Conditional Preferences Driven Personalized Skyline Web Services Selection

User-Specified Conditional Preferences Driven Personalized S...

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Senfu Ke Xiaodong Fu Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan Province China Yunnan Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan Province China

ISBN: (数字)9798350389166

ISBN: (纸本)9798350389173

The proliferation of web services with similar functionalities challenges selecting the most relevant service based on Quality of Service (QoS). Skyline queries help by reducing the candidate pool, but the resulting service skyline can still be extensive, making selection difficult. A personalized Skyline service selection method provides a smaller, more manageable set. Current approaches often overlook inter-dependencies between user preferences, complicating complex preference management. This paper introduces CP-Skyline, an efficient method leveraging Conditional Preference Networks (CP-Nets) to incorporate user-specific preferences and personalize the service selection process. We present a personalized conditional preference model based on CP-Nets to prune candidate services and compress the query space, defining a new dominance relation for CP-Skyline computation, and validate our approach through extensive experiments on synthetic and real-world datasets.

关键词： Web services Computational modeling Quality of service Software engineering

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A Bayesian Optimization Framework for Adaptive Node Deployment in Wireless Sensor Networks

A Bayesian Optimization Framework for Adaptive Node Deployme...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Rahul Priyadarshi Jayant Kumar Rout Anish Kumar Vishwakarma Nikhil Dhengre Rakesh Ranjan Amit Verma Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India School of Computer Science UPES Dehradun Uttarakhand India Department of ECE IIIT Nagpur Maharashtra India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Wireless Sensor Networks (WSNs) require efficient node deployment strategies to optimize network longevity, energy consumption, and coverage. This paper introduces a novel approach for strategic node deployment in WSNs using Bayesian Optimization (BO). A BO-based framework that iteratively modifies node positions to maximize network performance metrics, such as energy efficiency and coverage ratio, while minimizing execution time is proposed. Through extensive simulations across diverse deployment scenarios, the proposed method is evaluated and demonstrates superior performance compared to both traditional and machine learning (ML)-based techniques. The results indicate significant improvements in coverage and energy efficiency, alongside competitive execution times. These findings highlight the potential of BO to enhance WSN deployment strategies, offering a scalable and adaptive solution for real-world applications. Future research will focus on integrating BO with other optimization techniques and extending the approach to dynamic environments and varied network configurations.

关键词： Learning systems Wireless sensor networks Energy consumption Adaptive systems Machine learning Energy efficiency Real-time systems Bayes methods Reliability Optimization

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Identification of personalized driver genes for individuals using graph convolution network

Identification of personalized driver genes for individuals ...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Peng, Wei Yu, Piaofang Dai, Wei Fu, Xiaodong Liu, Li Pan, Yi Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665468190

The correct identification of the driver genes that lead to cancer development is essential for understanding the mechanisms of cancer and developing drugs to treat it. Currently, most computational methods for identifying cancer driver genes are based on a cohort of patients. However, due to the heterogeneity of cancers, patients diagnosed with the same cancers may have different genomic characteristics and present varied clinical symptoms. It requires devising effective methods to identify personalized cancer driver genes in an individual. This work developed a novel method to predict personalized cancer driver genes of a single sample based on graph convolution networks, namely pDriverGCN. pDriverGCN constructed a mutant gene-sample heterogeneous network according to the known driver genes of samples. Then it employed two separate graph convolution network models to learn feature representations for genes and samples by gathering the features of themselves and their neighbors. Finally, pDriverGCN used the feature representations to reconstruct the association matrix between genes and samples through a linear correlation coefficient decoder. We apply our model to identify personalized driver genes of samples on the TCGA datasets. The experimental results show that our model outperforms state-of-the-art methods being evaluated at both population and individual levels. © 2022 IEEE.

关键词： Genes

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A Bayesian Optimization Framework for Adaptive Node Deployment in Wireless Sensor Networks 14

A Bayesian Optimization Framework for Adaptive Node Deployme...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Priyadarshi, Rahul Rout, Jayant Kumar Vishwakarma, Anish Kumar Dhengre, Nikhil Ranjan, Rakesh Verma, Amit Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India School of Computer Science UPES Uttarakhand Dehradun India Department of ECE IIIT Maharashtra Nagpur India

ISBN: (纸本)9798331531935

关键词： Bayesian Optimization Coverage Ratio Energy Efficiency Node Deployment Wireless Sensor Networks

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Estimating the Impact of COVID-19 Pandemic on the Research Community in the Kingdom of Saudi Arabia

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computer Modeling in Engineering & Sciences 2021年第1期126卷 419-436页

作者： Abdulaziz Attaallah Masood Ahmad Adil Hussain Seh Alka Agrawal Rajeev Kumar Raees Ahmad Khan Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025India Department of Computer Application Shri Ramswaroop Memorial UniversityBarabanki225003India

Ever since its outbreak inWuhan,COVID-19 has cloaked the entireworld in a pall of despondency and *** present study describes the exploratory analysis of all COVID cases in Saudi ***,the study has executed the forecastingmodel for predicting the possible number of COVID-19 cases in Saudi Arabia till a defined *** this intent,the study analyzed different age groups of patients(child,adult,elderly)who were affected by *** analysis was done city-wise and also included the number of recoveries recorded in different ***,the study also discusses the impact of COVID-19 on the *** conducting the stated analysis,the authors have created a list of factors that are known to cause the spread of *** an effective countermeasure to contain the spread of Coronavirus in Saudi Arabia,this study also proposes to identify the most effective computer Science technique that can be used by healthcare *** this,the study employs the Fuzzy-Analytic Hierarchy Process integrated with the Technique for Order Performance by Similar to Ideal Solution(***).After prioritizing the various computer Science techniques,the ranking order that was obtained for the different techniques/tools to contain COVID-19 was:A4>A1>A2>A5>*** the Blockchain technique obtained the highest priority,the study recommends that it must be used extensively as an efficacious and accurate means to combat COVID-19.

关键词： Coronavirus social impact safety precautions fuzzy-AHP.TOPSIS block-chain technique COVID-19 monitoring

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Impact Assessment of COVID-19 Pandemic Through Machine Learning Models

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computers, Materials & Continua 2021年第9期68卷 2895-2912页

作者： Fawaz Jaber Alsolami Abdullah Saad Al-Malaise ALGhamdi Asif Irshad Khan Yoosef B.Abushark Abdulmohsen Almalawi Farrukh Saleem Alka Agrawal Rajeev Kumar Raees Ahmad Khan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025India Department of Computer Application Shri Ramswaroop Memorial UniversityBarabanki225003India

Ever since its outbreak in the Wuhan city of China,COVID-19 pandemic has engulfed more than 211 countries in the world,leaving a trail of unprecedented *** more debilitating than the infection itself,were the restrictions like lockdowns and quarantine measures taken to contain the spread of *** enforced alienation affected both the mental and social condition of people *** interactions and congregations are not only integral part of work life but also form the basis of human ***,COVID-19 brought all such communication to a grinding *** interactions have failed to enthuse the fervor that one enjoys in face-to-face *** pandemic has shoved the entire planet into an unstable *** main focus and aim of the proposed study is to assess the impact of the pandemic on different aspects of the society in Saudi *** achieve this objective,the study analyzes two perspectives:the early approach,and the late approach of COVID-19 and the consequent effects on different aspects of the *** used a Machine Learning based framework for the prediction of the impact of COVID-19 on the key aspects of *** of this research study indicate that financial resources were the worst *** countries are facing economic upheavals due to the pandemic and COVID-19 has had a considerable impact on the lives as well as the livelihoods of *** the damage is not irretrievable and the world’s societies can emerge out of this setback through concerted efforts in all facets of life.

关键词： Coronavirus social impact safety COVID-19 monitoring machine learning framework fuzzy AHP-TOPSIS

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Novel Deep Learning Network Architectures for Time Series Forecasting

Novel Deep Learning Network Architectures for Time Series Fo...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative applications in Global Scenario, ICPSITIAGS 2023

作者： Katariya, Jitendra Kumar Yadav, Dhananjay Gayathri, C. Kolekar, Vikas K. Daivagna, Umesh M. Pushparajesh, V. Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Atlas SkillTech University Department of UGDX Maharastra Mumbai India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India

ISBN: (纸本)9798350329773

This paper presents novel deep-learning network architectures for time series forecasting. First, a singular deep gaining knowledge of network architecture is proposed and tested for the usage of the Google tendencies dataset as a benchmark. The proposed structure combines 1-dimensional convolution neural networks, recurrent neural networks, and long-term cells to seize and study lengthy-term styles in the time series information. The structure is compared to current deep getting-to-know architectures and is shown to outperform them in phrases of accuracy. Moreover, the proposed structure is applied to a secondary time collection dataset, the COVID-19 confirmed infection dataset, to attain compelling effects. Eventually, an open-source implementation of the proposed architecture is made available for use in addition to research. © 2023 IEEE.

关键词： Network architecture

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Improved DHOA-Fuzzy Based Load Scheduling in IoT Cloud Environment

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computers, Materials & Continua 2022年第5期71卷 4101-4114页

作者： R.Joshua Samuel Raj V.Ilango Prince Thomas V.R.Uma Fahd N.Al-Wesabi Radwa Marzouk Anwer Mustafa Hilal Department of Information Science&Engineering CMR Institute of TechnologyBengaluru560037India Department of Computer Application CMR Institute of TechnologyBangalore560037India School of Computing Woldia Institute of TechnologyWoldia UniversityEthiopia Department of Commerce School of CommerceFinance and AccountancyChrist UniversityBangalore560029India Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Internet of things (IoT) has been significantly raised owing to thedevelopment of broadband access network, machine learning (ML), big dataanalytics (BDA), cloud computing (CC), and so on. The development of IoTtechnologies has resulted in a massive quantity of data due to the existenceof several people linking through distinct physical components, indicatingthe status of the CC environment. In the IoT, load scheduling is realistictechnique in distinct data center to guarantee the network suitability by fallingthe computer hardware and software catastrophe and with right utilize ofresource. The ideal load balancer improves many factors of Quality of Service(QoS) like resource performance, scalability, response time, error tolerance,and efficiency. The scholar is assumed as load scheduling a vital problem inIoT environment. There are many techniques accessible to load scheduling inIoT environments. With this motivation, this paper presents an improved deerhunting optimization algorithm with Type II fuzzy logic (IDHOA-T2F) modelfor load scheduling in IoT environment. The goal of the IDHOA-T2F is todiminish the energy utilization of integrated circuit of IoT node and enhancethe load scheduling in IoT environments. The IDHOA technique is derivedby integrating the concepts of Nelder Mead (NM) with the DHOA. Theproposed model also synthesized the T2L based on fuzzy logic (FL) systemsto counterbalance the load distribution. The proposed model finds usefulto improve the efficiency of IoT system. For validating the enhanced loadscheduling performance of the IDHOA-T2F technique, a series of simulationstake place to highlight the improved performance. The experimental outcomesdemonstrate the capable outcome of the IDHOA-T2F technique over therecent techniques.

关键词： Load scheduling internet of things cloud computing metaheuristics fuzzy logic

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