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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Mou, Chenglin Wu, Kaiqi Du, Pengcheng Yang, Kaibo School of Civil Engineering and Architecture Henan University of Science and Technology Department of Engineering Mechanics Luoyang471099 China School of Information Engineering Henan University of Science and Technology Department of Computer Science and Technology Luoyang471099 China School of Information Engineering Henan University of Science and Technology Department of Automation Luoyang471099 China School of International Education Henan University of Science and Technology Department of Civil Engineering Luoyang471099 China

ISBN: (纸本)9798350374315

Tower solar thermal power generation is a new type of low-carbon and environmentally friendly clean energy technology. In this paper, a single-objective optimization model is established with the maximum annual average thermal output per unit mirror area of the heliostat field as the objective function. By optimizing the heliostat width, heliostat height and heliostat coordinate, the annual average output thermal power can be maximized while meeting the rated power. The heliostat field is distributed in a circular shape. As the seasons change, the light intensity received by the heliostat field is uneven, so the optical efficiency of the heliostats is also different. We simplify the heliostat field into S area (area with higher optical efficiency) and W area (area with lower optical efficiency) based on the optical efficiency distribution of heliostats. At different locations, According to the heliostat width, heliostat height and heliostat coordinate generated by random functions, substituting these parameters into the efficiency model in this paper, and the random search algorithm is used to traverse the maximum annual output thermal power and the corresponding heliostat parameters. In the shadow occlusion loss model, we have grid to set up the heliostat and use the coordinate conversion to solve the shadow occlusion loss efficiency. © 2024 IEEE.

关键词： Heliostats (instruments)

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Design and Implementation of an Astute Infant Monitoring System Based on YOLO v8 Algorithm 15

Design and Implementation of an Astute Infant Monitoring Sys...

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

作者： Arunkumar, T. Maheswaran, S. Dineshkumar, P. Geetha, K. Sureshkumar, A. Praveenkumar, S. Kongu Engineering College Department of Computer Science and Engineering Tamilnadu Perundurai India Kongu Engineering College Department of Electronics and Communication Engineering Tamilnadu Perundurai India K.S.Rangasamy college of Technology Department of Information Technology Tamilnadu Tiruchengode India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Namakkal India

ISBN: (纸本)9798350370249

Parents today have a greater workload because they are working hard at their careers. As a result, there is less opportunity of giving babies better care. Children's health may suffer greatly as a result of this. Even if the infants are appropriately and consistently monitored, there is a risk of losing life. For this, we use cameras to monitor the external conditions. The system is designed to monitor and alert the baby emotions to the parents. The monitoring of baby's motion, emotions and surrounding is done by the camera that is fixed in an angle that it can cover the baby to the maximum extend. The camera that are fixed turn according to the baby's movement, so that it continuously monitors the baby. The system will alert the user if there in some negative emotion given by the baby. The system is trained in a way that it monitors the surroundings around the baby carefully. (eg: fire). This is done using YOLO v8. YOLO v8 is an anchor-free model. © 2024 IEEE.

关键词： Neonatal monitoring

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Bamboo Forest Growth Optimization Algorithm for Night Image Enhancement 15th

Bamboo Forest Growth Optimization Algorithm for Night Image...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Shi, Hao-Jie Pan, Jeng-Shyang Chu, Shu-Chuan Kong, Lingping Snášel, Václav College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

ISBN: (纸本)9789819700677

The Bamboo Forest Growth Optimization(BFGO) algorithm is an algorithm proposed by the growth law of bamboo forest, which has the advantages of fast convergence and not easily falling into local optimum. In this paper we have experimented the BFGO with several other population intelligence optimization algorithms on 23 benchmark test functions and proved its superiority. Also we apply the bamboo forest optimization algorithm to nighttime image enhancement in this paper, and the algorithm achieves better results on this application. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Cooperative Learning and Co-Regulation: Exploring Students' Teamwork Strategies in Higher Education 53

Cooperative Learning and Co-Regulation: Exploring Students' ...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Aggrawal, Sakhi Cristancho, Jorge A. Patel, Devang A. Magana, Alejandra J. Purdue University Department of Computer and Information Technology West LafayetteIN United States School of Engineering Education Purdue University West LafayetteIN United States School of Engineering Education Purdue University Department of Computer and Information Technology West LafayetteIN United States

ISBN: (纸本)9798350336429

This research paper investigates the effectiveness of cooperative learning and co-regulation strategies in promoting teamwork and enhancing students' performance in higher education. The study was conducted in an in-person intermediate-level information system design course with 152 students divided into 31 teams. The students utilized the Scrum framework to manage a semester-long project with three milestones. Retrospective data were collected at the end of each milestone, and the first milestone data were analyzed in this study. Through a thematic analysis of retrospective data collected after the first milestone, the study examines students' planning, monitoring, and reflection strategies. The findings reveal that students demonstrated adaptive planning, equitable contribution, and task allocation based on individual strengths and preferences. In terms of monitoring, students adopted a proactive approach, displayed relational competence, and utilized both synchronous and asynchronous communication channels. Regarding reflection, students valued effective planning and execution but struggled with time management. They developed concrete improvement strategies for the next milestone, emphasizing realistic deadlines, improved communication, and a better understanding of team members' strengths. This research contributes to the understanding of cooperative learning and co-regulation in promoting effective teamwork in higher education. The findings have implications for pedagogical practices and suggest the importance of integrating cooperative learning and co-regulation strategies in team-based learning environments. Future research can further explore the application of these strategies in different educational contexts and investigate their long-term effects on students' performance and engagement. © 2023 IEEE.

关键词： co-regulated learning cooperative learning higher education Scrum teamwork

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Predicting Lung Cancer Disease Using Optimized Weighting-Based Enhanced Neural Network Classification 3

Predicting Lung Cancer Disease Using Optimized Weighting-Bas...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Chitra, T. Hemanth, S.V. Karthikeyan, S. Reddy, Vallem Ranadheer Banupriya, K.M. Amirthayogam, G. Christian College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Oddanchatram624619 India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana Hyderabad501401 India PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Vaagdevi Engineering College Department of Computer Science and Engineering Telangana Bollikunta506005 India Arunai Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvannamalai606603 India Department of Information Technology Tamilnadu Chennai603103 India

ISBN: (纸本)9798350372120

Lung cancer is a highly lethal disease, claiming the lives of approximately 1 million individuals annually. Cancer is characterized by abnormal and rapid cell growth, making it difficult to control, though early diagnosis through Computed Tomography (CT) scan images can significantly improve chances of survival. However, detecting lung cancer early is challenging and can increase the risk of complications such as infection, inflammation, and tumor growth in the lungs. Moreover, the current methods for analyzing lung cancer prognosis have low accuracy. To solve this problem, we proposed an Optimized Weighting-Based Enhanced Neural Network (OWENN) method to classify patients based on attributes accurately. Furthermore, they utilize the Adaptive Median Filter (AMF) technique during image pre-processing to calculate the mean value of each pixel and remove noise. Moreover, Improved Particle Swarm Optimization (IPSO) can be implemented to extract tumors from lung images efficiently. Finally, the OWENN method improves the accuracy of cancer or non-cancer detection by classifying patients based on their selected attributes. Afterward, the experimental results indicate that the suggested OWENN approach can attain lung cancer prediction by evaluating sensitivity, precision, accuracy, and time delay. © 2024 IEEE.

关键词： Median filters

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Evaluating Generative Adversarial Networks Performance in Image Synthesis with Graphical Analysis of Loss Functions and Quality Metrics 2

Evaluating Generative Adversarial Networks Performance in Im...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Biju, J. Jeevitha, R. Richard, Titus Chowdhury, Rini Kumar, Prashant Radhika, K. Department of Information Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of English Vels Institute of Science Technology & Advance Studies Chennai India Department of Information Technology Project Circle BSNL Lalkuthi Kolkata India Department of Mathematics Kongu Engineering College Erode India

ISBN: (纸本)9798331504960

Generative Adversarial Networks (GANs) have revolutionized image synthesis by using two neural networks, a generator and a discriminator, to create realistic images from random noise. In this adversarial process, the generator attempts to fool the discriminator, which distinguishes between real and fake images. Advanced variants like conditional GANs and CycleGANs enable tasks like specified image generation and style transfer. Our paper presents improvements in image quality and training stability for GANs by introducing new training methods and loss function modifications to address issues like mode collapse. Evaluated on CelebA and CIFAR-10, our model outperforms previous GANs with Inception Scores of 9.69 and 10.79 and Frechet Inception Distances of 7.91 and -9.69, respectively. These results demonstrate better convergence, more stable training, and higher-quality image generation, establishing a new benchmark for GAN research and applications. © 2024 IEEE.

关键词： Generative adversarial networks

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A Comparative Study of Machine Learning Models for House Price Prediction and Analysis in Smart Cities 1

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2nd International Conference on Electronic Governance with Emerging Technologies, EGETC 2023

作者： Kansal, Mrignainy Singh, Pancham Shukla, Shambhavi Srivastava, Sakshi Department of Information Technology Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India Department of Computer Science and Information Technology Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India

ISBN: (数字)9783031439407

ISBN: (纸本)9783031439391

Developing any precise or exact prediction of house prices is an unsettled task for many years. It is the social as well as economic need for the welfare & comfort of the citizens. During the Covid-19 outbreak policy reforms were introduced and various businesses scaled down their workforce so prospective buyers needed to wait for the decision about the purchase of Properties. Thus it became important to provide accurate and accessible solutions to the buyers to mould their decision. The objective and aim of our research work are to provide digital-based solutions to real estate prices because of the increasing growth in online platforms that provide virtual tours. We performed a detailed study to understand the pertinent attributes and the most efficient model built to perform forecasting of the expected price. The results of this analysis verified the use of models like Linear Regression, Random forest Regression, XG Boost, and Voting Regressor as some efficient models. The model that performed fairly well as compared to other models is Random Forest with an accuracy of (98.207) while others with an accuracy of (73.12) for Linear Regression, an accuracy of (95.41) for XG Boost, the accuracy of (94.44) for Voting Regression. Our Findings in this research have advocated the idea that prices of any real estate property are governed by 2 major factors: Its Locality and Construction Composition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

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computer Systems Science & engineering 2023年第1期44卷 205-219页

作者： J.Jean Justus U.Sakthi K.Priyadarshini B.Thiyaneswaran Masoud Alajmi Marwa Obayya Manar Ahmed Hamza Department of Computer Science&Engineering St.Joseph’s College of EngineeringChennai600119India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Electronics and Communication Engineering Sona College of TechnologySalem636005India Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The developments of multi-core systems(MCS)have considerably improved the existing technologies in thefield of computer *** MCS comprises several processors that are heterogeneous for resource capacities,working environments,topologies,and so *** existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task *** the same time,the task scheduling process is yet to be explored in the multi-core *** paper presents a new hybrid genetic algorithm(GA)with a krill herd(KH)based energy-efficient scheduling techni-que for multi-core systems(GAKH-SMCS).The goal of the GAKH-SMCS tech-nique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy *** GAKH-SMCS model involves a multi-objectivefitness function using four parameters such as makespan,processor utilization,speedup,and energy consumption to schedule tasks *** performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark *** experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan,pro-cessor utilization,speedup,and energy *** overall simulation results depicted that the presented GAKH-SMCS model achieves energy effi-ciency by optimal task scheduling process in MCS.

关键词： Task scheduling energy efficiency multi-core systems fitness function makespan

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Deep Learning Approaches for Stress Detection: A Survey

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IEEE Transactions on Affective Computing 2024年第02期16卷 499-517页

作者： Kyrou, Maria Kompatsiaris, IoannisSenior Petrantonakis, Panagiotis C. Aristotle University of Thessaloniki Artificial Intelligence and Information Analysis Laboratory Dept. of Informatics GR 54124 Greece Information Technologies Institute Centre for Research and Technology Hellas GR 57001 Greece Aristotle University of Thessaloniki Dept. of Electrical and Computer Engineering GR 54124 Greece

Stress has a severe impact on individuals irrespective of age, sex, work, or background. The reliable development of stress detection techniques enhances the social, educational, physical, economic, and professional quality of life, preventing chronic stress and proposing alleviation strategies. Research studies examine psychological, cognitive, behavioral, and physiological reactions to identify stress adequately. Deep Learning (DL) has received significant attention in recent years as it deals with high-dimensional, heterogeneous data and automatically learns representative features. This paper presents a survey on stress detection with recent DL approaches, leveraging data from all possible sources (physiological, speech, facial expressions, gestures, and social media content). The methodological outlines, the best results, and the main contributions of each study are discussed. We also describe publicly available datasets used by several of the presented works. Finally, we emphasize various open issues within the field of research and highlight key directions for future work © 2024 IEEE.

关键词： Social psychology

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Traffic Aware Routing with Round Robin Technique for Equating the Load in Software Defined WSN 2

Traffic Aware Routing with Round Robin Technique for Equatin...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Senkamalavalli, R. Nalini, M. Kalaimani, G. Iyyanar, P. East Point College of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India S.A Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Shadan Women's College of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Sona College of Technology Department of Information Technology Tamil Nadu Salem India

ISBN: (纸本)9798350346640

Multiple uses of Wireless Sensor Networks (WSNs) in the real world would need the probable dispersion of a large number of sensor nodes. The adaptability and flexibility is very difficult in the traditional WSNs which uses balancing load technology. Software-defined networking (SDN) is a good choice because it lets you see what resources are used and also set up boundaries. In WSN, the highest traffic load causes an overflow of the data queue. As a result, important data should be lost. Also, the sensor node's energy is rapidly drained, and the whole network's lifespan is damaged. Traffic-aware routing with round robin technique (TARR) for equating the load in a software-defined WSN is proposed to solve this problem. The network contains two significant functions, namely, the formation of the network and the action of the network. This approach recognizes the traffic by obtaining or forwarding packets via control and data messages. In this approach, the round-robin technique is used during congestion situations. Thus, the data is forwarded constantly. As a result, the recipient node receives all data packets efficiently. The results of the simulation demonstrate that the TARR technique boosts network throughput while simultaneously reducing both packet loss and latency in a software-defined WSN. © 2023 IEEE.

关键词： Software defined networking

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