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7th IEEE International Conference on Electrical, Electronics, and system Engineering, ICEESE 2024

作者： Luangrungruang, Tidarat Phatai, Gawalee Department of Computer Faculty of Science and Technology Sakon Nakhon Rajabhat University Sakon Nahon Thailand

ISBN: (纸本)9798350367447

This study presents a predictive modeling approach for estimating monthly wind production in Thailand using advanced machine learning methodologies. Logistic Regression (LR), Extreme Learning Machines (ELM), and Support Vector Regression (SVR) were employed and evaluated through 5-fold and 10-fold cross-validation techniques. The dataset spans from 2018 to 2023, obtained from the Thai Meteorological department and department of Alternative Energy Development and Efficiency, encompassing monthly wind production data. Performance metrics including Mean Squared Error (MSE) and Mean Absolute Error (MAE) were used to assess model accuracy. Results indicate that SVR consistently outperforms LR and ELM in terms of predictive accuracy, demonstrating lower MSE and MAE values during both training and testing phases. Specifically, during the testing phase of the 10-fold cross-validation, the model achieved an MSE of 67.3646 and an MAE of 6.5194. This study contributes to the advancement of predictive modeling in renewable energy forecasting and provides valuable insights for policymakers and stakeholders in Thailand's energy sector. © 2024 IEEE.

关键词： Adversarial machine learning

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Transforming Public Service: AI Chatbot Integration in SI-PEKA Mobile App Development 2

Transforming Public Service: AI Chatbot Integration in SI-PE...

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2nd International Symposium on information technology and Digital Innovation, ISITDI 2024

作者： Oksama, Muhammad Haikel Marsal, Arif Hamzah, Muhammad Luthfi Syaifullah Rozanda, Nesdi Evrilyan Universitas Islam Negeri Sultan Syarif Kasim Faculty of Science and Technology Department of Information System Pekanbaru Indonesia

ISBN: (纸本)9798350389241

The rapid advancement of Artificial Intelligence (AI) and its integration into chatbot applications have opened new opportunities for enhancing government administration and public services. However, developing and maintaining chatbots poses significant challenges due to the necessity of proficiency in machine learning and natural language processing, distinguishing it from conventional software development. To address these challenges, this paper introduces SI-PEKA, an innovative public service mobile application featuring the intelligent chatbot, Tanya Jaksa, integrated with advanced AI technology. By leveraging the GPT-4 assistant, facilitated by the OpenAI Platform API, SI-PEKA overcomes these development hurdles, ensuring precise and accurate responses without requiring extensive expertise in AI from developers. Currently, public service delivery at the Public Prosecutor's Office in Pekanbaru is conducted manually. SI-PEKA revolutionizes this process by offering AI-driven legal consultations, thereby enhancing efficiency and convenience for the public. This paper details the development, implementation, and testing of SI-PEKA, highlighting its transformative potential in public service delivery. The development process includes system analysis and design, application implementation, chatbot integration, testing, and iterative improvement. The application underwent rigorous Blackbox testing, achieving a 100% acceptance rate. Results indicate that SI-PEKA functions effectively, providing accurate responses to user queries and demonstrating its efficacy in addressing user needs. This study underscores SI-PEKA's role in driving innovation and adapting to the rapid advancements in AI technology to meet evolving user requirements. © 2024 IEEE.

关键词： Acceptance tests

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Analyzing User Satisfaction with the DANA E-Wallet Using the E-Service Quality, CSI, and Kano Methods

Analyzing User Satisfaction with the DANA E-Wallet Using the...

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2024 International Conference on Circuit, systems and Communication, ICCSC 2024

作者： Kamila, Muthia Hamzah, Muhammad Luthfi Ahsyar, Tengku Khairil Syaifullah, Syaifullah Universitas Islam Negeri Sultan Syarif Kasim Faculty of Science and Technology Department of Information System Pekanbaru Indonesia

ISBN: (纸本)9798350365306

Advancements in technology and information have enabled various types of services to be conducted digitally. E-wallet is an application system used for cashless payments. Some applications that fall under E-Wallet include Dana, GoPay, LinkAja, OVO, and ShopeePay. According to research findings, these e-wallet applications compete with each other to provide quality services to their users. This study uses three satisfaction calculation methodologies to assess the DANA e-wallet's quality and gauge customer satisfaction: E-Service Quality, CSI, and Kano. Based on the calculation results in this research, DANA is already categorized as satisfactory. However, there are several attributes that should always be prioritized because they impact user satisfaction based on user needs, particularly in the efficiency dimension, where 'the DANA application should be easy to use and user-friendly, and transactions should be done quickly.' © 2024 IEEE.

关键词： Electronic money

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IALS: Innovative Approach for Lung Segmentation Applying Artificial Intelligence and Deep Learning

IALS: Innovative Approach for Lung Segmentation Applying Art...

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2025 International Conference on computer, Electrical and Communication Engineering, ICCECE 2025

作者： Hossain, Mohammad Alamgir Shahi, Fazal Imam Husain, Sadia Suliman, Abdelrazig Kamal, Mohammad Shahid Ahmad, Yasir Talukdar, Mohammad Mazedul Huq Martin, R. John Jazan University College of Engineering & Computer Science Department of computer Science Jazan Saudi Arabia Jazan University ELearning Center Jazan Saudi Arabia University of Bisha Faculty of Computers and Information Technology Department of Information Technology Saudi Arabia Jazan University College of Arts and Humanities Department of Foreign Languages Jazan Saudi Arabia

ISBN: (纸本)9798331513894

The automated segmentation of lungs within chest CT scans remains crucial for many medical applications that require disease diagnosis and treatment design. The presented deep incremental learning (DIL) system addresses fundamental limitations that arise during lung segmentation processes. The exceptional segmentation outcomes result from the neural network structure which includes masked RCNN+UNet for tissue segmentation and GAN+VARMA for classification. Medical literature identifies lung tissue structures as difficult to study because they present irregular forms and complex structures. The task of obtaining precise segmentation proves very challenging. The application of masked RCNN+UNet enables shape control and improves segmentation precision of the lung system. The DIL function helps maintain improved features within segmented images. The applied framework enables improved performance through DSC and MAE and JI metrics evaluation. Experimental evidence shows that the new framework achieves superior performance compared to current top-ranking methods for lung segmentation. The proposed framework exhibits excellent precision and detection efficiency with a DSC value of 0.96 and JI value of 0.93 and a low MAE © 2025 IEEE.

关键词： Medical applications

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A Hybrid WSVM-Levy Approach for Energy-Efficient Manufacturing Using Big Data and IoT

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computers, Materials & Continua 2024年第12期81卷 4895-4914页

作者： Surbhi Bhatia Khan Mohammad Alojail Mahesh Thyluru Ramakrishna Hemant Sharma School of Science Engineering and EnvironmentUniversity of SalfordManchesterM68HDUK University Centre for Research and Development Chandigarh UniversityMohali140307India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11495Saudi Arabia Department of Computer Science and Engineering JAIN(Deemed-to-be University)Bangalore560030India Department of Computer Science and Engineering IES College of TechnologyBhopal462044India

In Intelligent Manufacturing,Big Data and industrial information enable enterprises to closely monitor and respond to precise changes in both internal processes and external environmental factors,ensuring more informed decision-making and adaptive system *** also promotes decision making and provides scientific analysis to enhance the efficiency of the operation,cost reduction,maximizing the process of production and so *** methods are employed to enhance productivity,yet achieving sustainable manufacturing remains a complex challenge that requires careful *** study aims to develop a methodology for effective manufacturing sustainability by proposing a novel Hybrid Weighted Support Vector-based Lévy flight(HWS-LF)*** objective of the HWS-LF method is to improve the environmental,economic,and social aspects of manufacturing *** this approach,Support Vector Machines(SVM)are used to classify data points by identifying the optimal hyperplane to separate different classes,thereby supporting predictive maintenance and quality control in *** Forest is applied to boost efficiency,resource allocation,and production optimization.A Weighted Average Ensemble technique is employed to combine predictions from multiple models,assigning different weights to ensure an accurate system for evaluating manufacturing ***,Lévy flight Optimization is incorporated to enhance the performance of the HWS-LF method *** method’s effectiveness is assessed using various evaluation metrics,including accuracy,precision,recall,F1-score,and *** show that the proposed HWS-LF method outperforms other state-of-the-art techniques,demonstrating superior productivity and system performance.

关键词： Sustainable manufacturing support vector machine random forest levy flight optimization weighted average ensemble

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Optimizing Acoustic Echo Cancellation with Variable Step Size in Adaptive Filtering

Optimizing Acoustic Echo Cancellation with Variable Step Siz...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Chen, Yen-Yi Wang, Jian-Hong Chan, Po-Cheng Liang, Kai-Wen Wang, Ze-Yu Wang, Jia-Ching School of Computer Science and Technology Shandong University of Technology Shandong Zibo China National Central University Department of Computer Science and Information Engineering Taoyuan Taiwan Advanced Technology Laboratory Chunghwa Telecom Laboratories Taiwan

ISBN: (纸本)9798350353983

This paper presents an innovative approach to acoustic echo cancellation (AEC) by applying Variable Step Size (VSS) separately to each adaptive filtering technique: Normalized Least Mean Square (NLMS), and Proportionate Normalized Least Mean Square (PNLMS). This application is tailored to overcome the challenges of step size adjustment and signal sparsity, significantly enhancing system performance. We demonstrate that utilizing VSS with each algorithm independently allows for optimized control over convergence rates-larger step sizes quicken convergence while smaller sizes ensure smoother transitions. This method improves the robustness and efficiency of echo cancellation systems, removing the need for manual step size adjustments and proving highly effective in real-time applications. © 2024 IEEE.

关键词： Echo suppression

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Explainable AI for Epileptic Seizure Prediction: A SHAPE and LIME Approach 25

Explainable AI for Epileptic Seizure Prediction: A SHAPE and...

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25th International Arab Conference on information technology, ACIT 2024

作者： Talukder, Md. Simul Hasan Mondal, Sohag Kumar Aditi, Oyshi Tabassum Aljaidi, Mohammad Sulaiman, Rejwan Bin Al-Qerem, Ahmad Dhaka University of Engineering & Technology Gazipur Bangladesh Khulna University of Engineering and Technology Khulna Bangladesh Daffodil International University Department of Computer Science and Engineering Savar Bangladesh Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Northumbria University School of Computer Science & Technology Newcastle Tayne United Kingdom

ISBN: (纸本)9798331540012

Epilepsy is a neurological chaos typified by frequent, spontaneous seizures. Early recognition of epilepsy by utilising AI techniques to scan EEG signals can enhance the prevention and management of it. This study aims to improve the classification of epileptic seizures by using the preprocessed version of the Epileptic Seizure (ERS) dataset. The preprocessed dataset is subjected to multiple stages in order to improve its quality and achieve balanced class representation. Data cleaning, outlier management, merging, and oversampling are those. After that, an 80:20 split of the dataset is made into subsets for testing and training. Next, training, testing, and evaluation are performed on the models of Gradient Boosting Classifier (GBC), Decision Tree Classifier (DTC), Random Forest Classifier (RFC), CatBoost Classifier (CBC), and ExtraTree Classifier (ETC). The ExtraTree Classifier is remarkably accurate, with a 99.51% accuracy rate, which is the highest accuracy. Finally, explainable artificial intelligence (SHAP and LIME) is used to clarify the ExtraTree Classifier's decision-making procedure. By providing a visual representation of significant variables and their influence on the model's predictions, this interpretability tool improves comprehension of the classification results. © 2024 IEEE.

关键词： Neurology

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Pathological Analysis of Blood Cells Using Deep Learning Techniques

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Recent Advances in computer science and Communications 2022年第3期15卷 397-403页

作者： Ranga, Virender Gupta, Shivam Agrawal, Priyansh Meena, Jyoti Department of Computer Engineering National Institute of Technology Haryana Kurukshetra India Department of Computer Science and Engineering Indian Institute of Information Technology Haryana Sonepat India

Introduction: Pathologists are majorly concerned with detecting the diseases and helping the patients in their healthcare and well-being. The present method used by pathologists for this purpose is manually viewing the slides using a microscope and other instruments. However, this method has a number of limitations such as there is no standard way of diagnosis, there are certain chances of human errors and besides, it burdenizes the laboratory personnel to diagnose a large number of slides on a daily basis. Methods: The slide viewing method is widely used and converted into digital form to produce high resolution images. This enables the area of deep learning and machine learning to get an insight into this field of medical sciences. In the present study, a neural based network has been proposed for classification of blood cells images into various categories. When an input image is passed through the proposed architecture and all the hyper parameters and dropout ratio values are applied in accordance with the proposed algorithm, then the model classifies the blood images with an accuracy of 95.24%. Result: After training the models on 20 epochs. The plots of training accuracy, testing accuracy and corresponding training loss, and testing loss for the proposed model is plotted using matplotlib and trends. Discussion: The performance of the proposed model is better than the existing standard architectures and other works done by various researchers. Thus, the proposed model enables the development of pathological system which will reduce human errors and daily load on laboratory personnel. . This can also in turn help the pathologists in carrying out their work more efficiently and effectively. Conclusion: In the present study, a neural based network has been proposed for classification of blood cells images into various categories. These categories have significance in the medical sciences. When input image is passed through the proposed architecture and all the hyp

关键词： Diagnosis

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Modeling Energy-Related CO2Emissions with Backpropagation and Metaheuristics 6

Modeling Energy-Related CO2Emissions with Backpropagation an...

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6th International Conference on Artificial Intelligence in information and Communication, ICAIIC 2024

作者： Phatai, Gawalee Luangrungruang, Tidarat Sakon Nakhon Rajabhat University Faculty of Science and Technology Department of Computer Sakhon Nakhon Thailand

ISBN: (纸本)9798350344349

In this paper, we propose a model that combines Backpropagation Neural Network (BPNN) with a metaheuristic algorithm to predict carbon dioxide (CO2) emissions. The model utilizes input variables directly influencing carbon dioxide emissions. Our objective is to monitor CO2 emissions based on energy consumption resulting from production processes driven by the corresponding energy demand. Model training and testing are conducted to identify the most suitable model. The results demonstrate that machine learning with the Particle Swarm Optimization (PSO) model achieves an optimal avoidance rate. To assess model accuracy using Mean Square Error (MSE) and Mean Absolute Error (MAE) the smallest error indicates a more precise prediction. Following established guidelines, this document aids managers in making informed business decisions and devising energy management policies that effectively monitor carbon emissions. © 2024 IEEE.

关键词： Carbon dioxide

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Implications of Onshore Development on Global Software Engineering

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computers, Materials & Continua 2023年第2期74卷 3029-3044页

作者： Abdulrahman M.Qahtani Ahmed S.Ghiduk College of Computers and Information Technology Taif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceBeni-Suef UniversityBeni-Suef62521Egypt

Recently software industry has paid significant attention to customizing software products across distributed *** the requirements of multiple clients across distributed borders is a crucial challenge for the software customization *** decision-making and local development at the client site are considered methods for reducing difficulties in communicating the requirements of multiple clients across distributed *** paper introduces a new model called the onshore development model(ODM)for accomplishing the customization requests in the distributed development process of *** model presents a scenario for enhancing the onsite development of specific requirements to reduce delays andmisunderstandings between the clients and the team *** model depends on moving the development process to the client’s *** empirical studies were conducted to evaluate the proposed model to measure its productivity,time performance,and cost *** proposed model has been compared with two other models:the basic model(BM),which allocates the decision-making process and the development process for teams at the vendor’s location,and the local decision-making model(LDec),which assigns the decision-making process for team at the client’s *** results of the empirical studies showed significant outperforming of the proposed model over the basic model and local decision-making model in productivity,time performance,and cost *** productivity of the proposed model improved by 39%and 10%more than the basic model and the local decision-making model,*** addition,the time performance of the proposed model became faster by 49%and 20.8%than the basic model and the local decision-making model,***,it reduced the total cost of the development process by 31%in terms of the salaries of all persons involved in requirements collecting,decision-making,and development.

关键词： Distributed development local development software customization

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