Procedia Computer Science最新文献_第10页

Sustainable development of solar power through the investigation of Partial Shading effect of solar module in terms of experimental set up and MATLAB simulation

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.030

Mohiuddin Khan , Mahbub Ferdous , Suman Chowdhury

{"title":"Sustainable development of solar power through the investigation of Partial Shading effect of solar module in terms of experimental set up and MATLAB simulation","authors":"Mohiuddin Khan , Mahbub Ferdous , Suman Chowdhury","doi":"10.1016/j.procs.2025.01.030","DOIUrl":"10.1016/j.procs.2025.01.030","url":null,"abstract":"<div><div>The analysis on partial shading for a PV module is presented in this paper as a critical criterion for sustainable development in practical life. Nowadays it is an important challenge for the sustainable development in the energy sector where solar panel can take a vital role in this regard. A partial shading experiment on a PV module will be shown in this investigation, as well as comparisons of IV properties and PV panel efficiency with and without shade. The many causes of partial shade will be examined and there will be corrective ways to avoid partial shading. This project is developed into two stages namely, hardware and software implementation. For hardware implementation two 20W polycrystalline solar panel and different types of variable loads were used. In addition to this, the impact of partial shading of single and two different solar PV configurations are investigated: 1) Series 2) Parallel. The SIMULINK environment of MATLAB is utilized for simulation. Under partial shading conditions, the effect of two environmental elements, irradiance and temperature, will be seen in the simulated features. The bypass and blocking diodes are employed to increase the performance of PV arrays, and their influence can be observed in the simulation. From the practical observation it is seen that almost 86.2 % more power can be obtained from the solar module from 25 % shading to 0% shading on module.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 702-707"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integration of Coalescent Theory and Generative Adversarial Network (GAN) for Synthesizing High-Fidelity Textual Financial Data

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.019

Ashwini Dalvi , Shriya Pingulkar , Aryaman Tiwary , Diti Divekar , Irfan N A Siddavatam , Nilkamal More

{"title":"Integration of Coalescent Theory and Generative Adversarial Network (GAN) for Synthesizing High-Fidelity Textual Financial Data","authors":"Ashwini Dalvi , Shriya Pingulkar , Aryaman Tiwary , Diti Divekar , Irfan N A Siddavatam , Nilkamal More","doi":"10.1016/j.procs.2025.01.019","DOIUrl":"10.1016/j.procs.2025.01.019","url":null,"abstract":"<div><div>Financial data analysis faces significant challenges due to limitations in the quality, scope, and biases of existing datasets. This research work introduces a novel approach to creating synthetic financial datasets using coalescent theory, a principle from evolutionary biology, combined with deep learning methodologies to address constraints on scope, accessibility, and diversity in financial datasets. While methods such as the Synthetic Minority Over-sampling Technique (SMOTE) and Generative Adversarial Networks (GANs) have shown some success in generating synthetic data, particularly in textual domains, they still face significant challenges in producing realistic and balanced textual data. The proposed method in this research improves the stability and quality of synthetic data generation by integrating coalescent theory with GANs, resulting in a more stable architecture that mitigates mode collapse and captures complex temporal dependencies and non-linear relationships in financial datasets. The generated data accurately mirrors the intricacies of real-world financial markets, enhancing the quality, diversity, and authenticity of synthetic data for robust predictive modelling. This research works details the integration of evolutionary algorithms with deep learning to create datasets that authentically represent financial contexts and are nearly indistinguishable from genuine data. By introducing this interdisciplinary approach, this research aims to enrich the toolkit for financial analysis and set a new standard in synthetic data generation.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 593-602"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evolutionary Computation in Early Detection and Classification of Plant Diseases from Aerial View of Agricultural lands

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.001

K. Sujatha , R.S. Ponmagal , Prameeladevi Chillakuru , U. Jayalatsumi , N. Janaki , N.P.G. Bhavani

{"title":"Evolutionary Computation in Early Detection and Classification of Plant Diseases from Aerial View of Agricultural lands","authors":"K. Sujatha , R.S. Ponmagal , Prameeladevi Chillakuru , U. Jayalatsumi , N. Janaki , N.P.G. Bhavani","doi":"10.1016/j.procs.2024.12.001","DOIUrl":"10.1016/j.procs.2024.12.001","url":null,"abstract":"<div><div>This research presents a new combined deep learning system for effective and reliable identification of plant diseases in complicated agricultural environments. One of the most difficult jobs in agriculture is identifying plant diseases early on. Early disease detection in plants is crucial for increasing agricultural yield. With the application of machine learning and deep learning techniques, this issue has been resolved. Large crop farms can now detect plant illnesses automatically, which is advantageous as it reduces the monitoring time. The suggested approach consists of multiple important stages. To begin with, image quality of the agricultural lands is improved through preprocessing techniques like noise reduction, gamma correction and white balancing. Data augmentation is incorporated to expand the dataset and improve the generalization capacity of the model. Efficient methods such as EfficientDet and Squeeze Net, as well as color and shape based features, are included in feature extraction. The most relevant features are selected by a Hybrid Optimization Algorithm (HOA), which integrates Mother Optimization Algorithm (MOA), Teaching learning-based optimization (TLBO) and Improved Wild Horse Optimization to detect the various plant diseases like Bacterial Blight, Tungro, Blast and Brown spot. At last, a deep learning detector, which may include Recurrent Convolutional Neural Networks (R-CNNs) and Recurrent Neural Network (RNN), identifies the location and type of objects. The use of hyper parameter tuning techniques is also implemented to avoid over fitting and improve the overall generalization. This comprehensive approach depicts encouraging results in overcoming challenges in plant disease detection.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Hybrid Genetic-Ant Colony Optimization for Dynamic Self-Healing and Network Performance Optimization in 5G/6G Networks

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.041

Aanchal Agrawal , A.K. Pal

{"title":"Adaptive Hybrid Genetic-Ant Colony Optimization for Dynamic Self-Healing and Network Performance Optimization in 5G/6G Networks","authors":"Aanchal Agrawal , A.K. Pal","doi":"10.1016/j.procs.2024.12.041","DOIUrl":"10.1016/j.procs.2024.12.041","url":null,"abstract":"<div><div>The rapid growth of 5G/6G networks requires resilient solutions to optimize network performance while ensuring adaptability against failures. This paper introduces a novel Adaptive Hybrid Genetic-Ant Colony Optimization (GA-ACO) framework, designed for dynamic self-healing and multi-objective performance optimization in next-generation mobile networks. The developed method combines the global optimization competencies of a Genetic Algorithm (GA) with the local rerouting performance of Ant Colony Optimization (ACO), developing a dynamic switching mechanism. When no faults are detected, GA optimizes critical objectives such as latency minimization, bandwidth utilization, and energy efficiency. After identifying network faults, such as base station failures, ACO quickly reroutes impacted devices to preserve fault tolerance and minimize downtime. Main network metrics, including latency, bandwidth utilization, energy efficiency, and fault tolerance, are optimized at the same time utilizing a weighted-sum fitness function. The model adjusts dynamically to changing network situations, making it perfectly appropriate for real-time applications in 5G/6G networks, such as smart cities and mission-critical communications. Simulation results show the efficiency of the GA-ACO hybrid, demonstrating improved network efficiency and rapid recovery during failures. This innovative adaptive approach guarantees a more effective, efficient, and sustainable mobile communication network, competent of facing the complex needs of future 5G/6G technologies.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 404-413"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Sustainable Supply Chain Forecasting Using Machine Learning for Sales Prediction

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.006

Md. Parvezur Rahman Mahin , Munem Shahriar , Ritu Rani Das , Anuradha Roy , Ahmed Wasif Reza

{"title":"Enhancing Sustainable Supply Chain Forecasting Using Machine Learning for Sales Prediction","authors":"Md. Parvezur Rahman Mahin , Munem Shahriar , Ritu Rani Das , Anuradha Roy , Ahmed Wasif Reza","doi":"10.1016/j.procs.2025.01.006","DOIUrl":"10.1016/j.procs.2025.01.006","url":null,"abstract":"<div><div>Managing the supply chain is crucial to success in the competitive business sector. Demand forecasting using sales data is one of the major things in supply chain management because it is directly connected to profit margins, inventory levels, sales, and customer satisfaction. This research tried to provide an innovative approach to sales prediction using advanced machine learning methods to enhance supply chain operations and boost the predictive accuracy of supply chain models after analyzing historical sales data and considering different factors like seasonality, trends, and stock. Various machine learning algorithms were applied, including Linear Regression, Elastic Net Regression, KNN, Random Forest, and the ensemble Voting Regressor. The performance of Random Forest and KNN is very well but the Voting Regressor is better than other models for its strength of multiple algorithms. The Voting Regressor provides the lowest RMSE of 1.54 and the highest R<sup>2</sup> of 0.9999. This ensemble method improves sales forecasting accuracy by reducing errors and ensuring computational efficiency. It also provides more reliable tools to manage inventory, prevent overstocks, and minimize holding costs. This research presents the importance of machine learning integration in supply chain management. It shows the Voting Regressor as the most effective approach for demand forecast. Future research could explore the model’s application in broader markets, integrating other key factors and deep learning algorithms to refine predictive capabilities later.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 470-479"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Instructional Design as a Key Factor for Industry 5.0 Engineering Education

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.160

Ane Arregi , Jose Alberto Eguren , Javier Retegi , Dorleta Ibarra , Juan Ignacio Igartua

{"title":"Instructional Design as a Key Factor for Industry 5.0 Engineering Education","authors":"Ane Arregi , Jose Alberto Eguren , Javier Retegi , Dorleta Ibarra , Juan Ignacio Igartua","doi":"10.1016/j.procs.2025.01.160","DOIUrl":"10.1016/j.procs.2025.01.160","url":null,"abstract":"<div><div>The rapid advancement of Industry 4.0 and the emerging concept of Industry 5.0 are revolutionising the manufacturing landscape, demanding a paradigm shift in engineering education. There is a critical need to develop learning concepts for engineering education that will play a pivotal role in bridging the gap between academia and industry, ensuring that engineering graduates are equipped to spearhead sustainable and intelligent operations management practices in the automotive sector. The European project EE4M-CoVE is focused on that need. In the context of the project, this paper analyses the development of an educational concept for Industry 5.0 education. It proposes an instructional design (ID) model that has been designed, developed, and validated by a panel of experts to ensure its alignment with industry standards and best educational practices. The project’s ultimate goal is to empower future engineers with the skills and knowledge necessary to thrive in the Industry 5.0 era. This will be achieved by developing and implementing learning concepts focused on operation management within the mobility value chain, considering both digital and green transitions. The instructional design model serves as a key tool to ensure the quality, standardisation, and scalability of this educational approach along all project members.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 985-994"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-Performance Computing for Distributed Route Computation in Traffic Flow Models

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.02.263

Paulo Silva , Pavlína Smolková , Sofia Michailidu , Jakub Beránek , Roman Macháček , Kateřina Slaninová , Jan Martinovič , Radim Cmar

{"title":"High-Performance Computing for Distributed Route Computation in Traffic Flow Models","authors":"Paulo Silva , Pavlína Smolková , Sofia Michailidu , Jakub Beránek , Roman Macháček , Kateřina Slaninová , Jan Martinovič , Radim Cmar","doi":"10.1016/j.procs.2025.02.263","DOIUrl":"10.1016/j.procs.2025.02.263","url":null,"abstract":"<div><div>In the dynamic landscape of smart cities and traffic management, it is necessary to further explore the synergistic potential of realtime traffic data and high-performance computing to optimise traffic flow through dynamic re-routing strategies. High-performance computing plays an essential role in achieving effective traffic flow optimisation. Our research builds upon existing studies highlighting the positive correlation between the integration of live traffic updates and route optimisation. The methodology involves simulations with our Ruth traffic simulator, where vehicles dynamically adjust routes based on up to date traffic information available to them at different levels. Scalability tests are conducted with varying numbers of CPUs and nodes to assess the simulator's capacity to scale. The results showcase the impact of live traffic data on both driving time and average speed, emphasising the adaptability of our approach for broader applications. In conclusion, our work not only advances the understanding of real-time traffic optimisation but also underscores the critical role of high-performance computing in achieving scalable solutions. The findings present practical implications for the implementation of dynamic re-routing strategies in transportation systems, paving the way for future research and real-world applications on smart cities.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"255 ","pages":"Pages 83-92"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast, and Accurate Radiative Transfer for Land Surface Models

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.02.255

Kazem Ardaneh , Fabienne Maignan , Sebastiaan Luyssaert , Philippe Peylin , Olivier Boucher

引用次数: 0

Digital Sovereignty and Digital Transformation Practice Recommendation for the Software Life Cycle Process

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.02.081

Martha Klare , Razvan Hrestic , Aida Stelter , Ulrike Lechner

引用次数: 0

Unbiased AI for a Sovereign Digital Future: A Bias Detection Framework

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.02.070

Razi Iqbal , Shereen Ismail

{"title":"Unbiased AI for a Sovereign Digital Future: A Bias Detection Framework","authors":"Razi Iqbal , Shereen Ismail","doi":"10.1016/j.procs.2025.02.070","DOIUrl":"10.1016/j.procs.2025.02.070","url":null,"abstract":"<div><div>The recent advancements in Artificial Intelligence (AI) have opened avenues on all fronts of life that were never even imagined before. The possibilities of incorporating AI tools to various domains of the computing and non-computing world to enhance efficiency, performance and reliability are skyrocketing in the current era of technology. While computing power is a crucial factor driving the development of these AI tools, the role of data is equally significant. One of the key reasons for AI tools to perform the way they perform is the presence of an enormous amount of data they can work with. However, the reliance on vast datasets also raises concerns about control over data and digital sovereignty, especially when such data impacts critical decision-making processes in recruitment, policy making, loan approvals, and beyond. If a feature in the data related to intersectionality, e.g., gender, race, cultural background, etc. dictates the outcome, the data is most probably biased. The bias of data can lead to injustice, inequality and unfairness and hence it is extremely important to tackle it. Ensuring that the data used is ethically managed, especially in line with national and regional data sovereignty regulations, is an integral aspect of mitigating these issues. The first step in the process is to identify the bias in data. This paper explores a methodology for detecting bias in data, based on a general AI-based framework that can be applied across various domains. The paper goes into the details of evaluating the identified bias for the gender feature and explains how this feature influences the outcome of a machine learning (ML) model.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"254 ","pages":"Pages 118-126"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0