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2023 International Conference on Artificial Intelligence science and Applications in Industry and Society, CAISAIS 2023

作者： Abdelfattah, Aly Maher Ali, Nabila Ahmed Elaziz, Mohamed Abd Ammar, Hany H Galala University Faculty of Computer Science and Engineering Egypt

ISBN: (纸本)9798350314786

This paper presents a novel method for teaching software engineering using the AI tool, ChatGPT, to create an engaging and immersive learning platform. The technique emphasizes understanding requirements engineering principles via interactive exercises and hands-on examples. The approach involves ChatGPT assisting in collecting user stories, creating a use case and class diagrams, and formulating sequence diagrams. This method employs an agile strategy focusing on select user stories and encourages student interaction with ChatGPT for a deeper understanding of the subject. The goal is to demonstrate the potential of AI tools to transform software engineering education by providing practical applications and real-life scenarios. The research outlines a comprehensive plan for integrating ChatGPT into a software engineering syllabus, focusing on requirements engineering. The findings could significantly influence the teaching and understanding of software engineering principles, benefiting educators and students. © 2023 IEEE.

关键词： Requirements engineering

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Prediction of student performance using machine learning techniques 5

Prediction of student performance using machine learning tec...

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5th International IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2023

作者： Elsaid Khoudier, Mohamed Mohsen Mahmoud Abdelnaby, Raneem Hany Eldamnhoury, Zeyad Mohamed Ali Abouzeid, Salma Raafat El-Monayer, Ghada K. Enan, Nada M. El-Ghamry, Amir Fouad, Khaled Moawad, Ibrahim New Mansoura University Faculty of Computer Science and Engineering Egypt Mansoura University Faculty of Computer Science and Engineering New Mansoura University School of Engineering and Computer Science University of Hertfordshire Hosted Global Academic Foundation Faculty of Computers and Information Egypt Benha University Faculty of Computer Science and Engineering New Mansoura University Faculty of Computers and Artificial Intelligence Egypt Ain Shams University Faculty of Computer Science and Engineering New Mansoura University Faculty of Computer and Information Sciences Egypt

ISBN: (纸本)9798350381030

The ever-increasing importance of education has driven researchers and educators to seek innovative methods for enhancing student performance and understanding the factors that contribute to academic success. This paper presents a methodology for predicting student performance (SPP) that leverages machine learning techniques to forecast students' academic achievements based on a variety of features, such as demographic information, academic history, and behavioral patterns. The proposed SPP method utilizes a real-world collected dataset from multiple educational institutions to ensure an accurate and comprehensive *** proposed methodology starts with a data preparation stage, where the data is cleansed and organized for analysis. This process encompasses tasks such as handling missing values, scaling the data, and transforming variables if necessary. The RFE selection technique was used to select the most important features for the SPP model. A number of machine learning classifiers were tested, and the linear regression algorithm was found to be the best performer. The evaluation results showed that the linear regression algorithm can be used to predict the student performance with a mean absolute error (MAE) of 0.23 and a root mean square error (RMSE) of *** results of this study demonstrate the potential of machine learning algorithms in predicting student performance and identifying key factors that influence academic success. This information can be leveraged by educators and academic institutions to develop targeted intervention strategies, tailored learning experiences, and personalized recommendations for students, ultimately fostering a more effective learning environment and improving overall educational outcomes. © 2023 IEEE.

关键词： Forecasting

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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike Information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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Adaptive isomap feature extractive gradient deep belief network classifier for diabetic retinopathy identification

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Multimedia Tools and Applications 2025年第9期84卷 6349-6370页

作者： Singh, Alka Kumar, Rakesh Gandomi, Amir H. Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology Gorakhpur India Faculty of Engineering & Information Technology University of Technology Sydney SydneyNSW Australia Óbuda University Budapest1034 Hungary

Diabetic retinopathy (DR) is an infection that bases eternal visualization loss in patients with diabetes mellitus. With DR, the glucose level in the blood increases, as well as its viscosity, this results in fluid leakage into surrounding tissues in the retina. In other words, DR represents the pathology of capillaries and venules in the retina with leakage effects, being the main acute retinal disorder caused by diabetes. Many DR detection methods have been previously discussed by different researchers;however, accurate DR detection with a reduced execution time has not been achieved by existing methods. The proposed method, the Shape Adaptive box linear filtering-based Gradient Deep Belief network classifier (SAGDEB) Model, is performed to enhance the accuracy of DR detection. The objective of the SAGDEB Model is to perform an efficient DR identification with a higher accuracy and lower execution time. This model comprises three phases: pre-processing, feature extraction, and classification. The shape adaptive box linear filtering image pre-processing is carried out to reduce the image noise without removing significant parts of image content. Then, an isomap geometric feature extraction is performed to compute features of different natures, like shape, texture, and color, from the pre-processed images. After that, the Adaptive gradient Tversky Deep belief network classifier is to perform classification. The deep belief network is probabilistic and generative graphical model that consists of multiple layers such as one input unit, three hidden units, and one output unit. The extracted image featuresare considered in the input layer and these images are sent to hidden layers. Tversky similarity index is applied in hidden layer 1 to analyze the extracted features with testing features. Regarding the similarity value, the sigmoid activation function is determined in hidden layer 2 so different levels of DR can be identified. Finally, the adaptive gradient method is

关键词： Feature extraction

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A Robust Framework for Epileptic Seizure Diagnosis: Utilizing GRU-CNN Architectures in EEG Signal Analysis 20

A Robust Framework for Epileptic Seizure Diagnosis: Utilizin...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Nikoupour, Maryam Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Alzahra University Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran Iranian Research Organization for Science and Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350383942

Seizures that take place repeatedly and without provocation are referred to as epilepsy. Epilepsy can be diagnosed with electroencephalography (EEG). One of the most influential challenges of the past few years has been the use of deep learning algorithms to replace manual inspection of medical signals by specialists, such as epilepsy signal classification. This paper presents a multi-label classification approach for epileptic seizures using deep learning. UCI machine learning repository's epileptic seizure dataset has been used to classify epileptic seizure patients. 178 features are present in each of the 11500 samples in the dataset. Based on a variety of criteria, the proposed method may have a positive impact on epilepsy diagnosis, in most cases by approximately 6% compared with existing methods utilizing long short-term memory (LSTM) and autoencoder. It is possible thus to develop and apply gated recurrent unit-based methods with good potential for categorizing EEG signals for epilepsy diagnosis based on gated recurrent unit (GRU)-CNN based methods. © 2024 IEEE.

关键词： Electroencephalography

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Multi-Site Aggregate Production Planning Using Particle Swarm Optimization

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Journal of engineering, Project, and Production Management 2022年第1期12卷 62-69页

作者： Wibawa, Aji Prasetya Mahmudy, Wayan F. Rizki, Agung Mustika Yuliastuti, Gusti Eka Tama, Ishardita Pambudi Electrical Engineering Department Universitas Negeri Malang Malang Indonesia Computer Science Faculty of Computer Science Brawijaya University Malang Indonesia Industrial Engineering Faculty of Engineering Brawijaya University Malang Indonesia

Aggregate planning is a crucial stage in the production process because it supports other processes. Careless production planning may cause production costs to spike sharply that hurts the company financially. This study explores the novel usage of particle swarm optimization (PSO) to discover a set of solutions among the objective of a multioptimization problem in aggregate production planning. The study uses a small home textile industry with complex production processes of school uniforms as a case study. The results show that the production cost difference between actual data and the proposed method is IDR330,670,000. Thus, PSO can solve the multi-site aggregate planning by reducing the company production cost. Copyright © Journal of engineering, Project, and Production Management (EPPM-Journal).

关键词： Aggregates

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Novel Framework of Segmentation 3D MRI of Brain Tumors

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computers, Materials & Continua 2023年第2期74卷 3489-3502页

作者： Ibrahim Mahmoud El-Henawy Mostafa Elbaz Zainab H.Ali Noha Sakr Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt Department of Computer Science Faculty of Computers and InformaticsKafrelsheikh UniversityKafrelsheikhEgypt Department of Embedded Network Sys.and Tec. Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Medical image segmentation is a crucial process for computer-aided diagnosis and *** image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis and *** and speckle noise are different types of noise in magnetic resonance imaging(MRI)that affect the accuracy of the segmentation process ***,image enhancement has a significant role in MRI *** paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free *** proposed techniques consider the values of Peak Signal to Noise Ratio(PSNR)and the level of noise as inputs to the attention-U-Net model for segmentation of the *** framework has been divided into three stages:removing speckle and Rician noise,the segmentation stage,and the feature extraction *** framework presents solutions for each problem at a different stage of the *** the first stage,the framework uses Vibrational Mode Decomposition(VMD)along with Block-matching and 3D filtering(Bm3D)algorithms to remove the ***,the most significant Rician noise-free images are passed to the three different methods:Deep Residual Network(DeRNet),Dilated Convolution Auto-encoder Denoising Network(Di-Conv-AE-Net),andDenoising Generative Adversarial Network(DGAN-Net)for removing the speckle *** Bm3D have achieved PSNR values for levels of noise(0,0.25,0.5,0.75)for reducing the Rician noise by(35.243,32.135,28.214,24.124)and(36.11,31.212,26.215,24.123)*** framework also achieved PSNR values for removing the speckle noise process for each level as follows:(34.146,30.313,28.125,24.001),(33.112,29.103,27.110,24.194),and(32.113,28.017,26.193,23.121)forDeRNet,Di-Conv-AE-Net,and DGAN-Net,*** experiments that have been conducted have proved the efficiency of the proposed framework a

关键词： MRI Rician noise speckle noise segmentation deep learning

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A Systematic Review of Federated Learning Mechanism for The Internet of Medical Things 1

A Systematic Review of Federated Learning Mechanism for The ...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Aravind, P. Jerlin, M Asha Devarajan, Ganesh Gopal Nagarajan, Senthil Murugan School of Computer Science and Engineering Vellore Institute of Technology Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Ghaziabad201204 India Medicine University of Luxembourg Faculty of Science Technology Department of Mathematics Luxembourg

ISBN: (纸本)9798350353648

The rapid evolution of healthcare technology, no-tably the integration of the Internet of Medical Things (loMT), has revolutionized patient care, diagnosis, and treatment method-ologies. However, this progress introduces substantial challenges, primarily concerning the security, privacy, and interoperability of sensitive medical data. Federated Learning, a decentralized ma-chine learning approach, emerges as a possible solution to address these difficulties since it allows for collaborative model training without requiring raw patient data exchange. This systematic review explores and evaluates various FL mechanisms tailored for 10MT applications. The review examines existing literature, encompassing the amalgamation of loT and healthcare, the challenges faced in traditional health care systems, and the role of FL in mitigating these challenges. It highlights applications, privacy-preserving techniques, security measures, advantages, limitations, and potential enhancements in utilizing FL within 10MT environments. The review also presents a structured methodology for evaluating FL paradigms, encompassing performance metrics, privacy/security analysis, advantages/limitations assessment, and comparative analysis. Moreover, the paper pro-poses an innovative framework for federated learning in 10MT, detailing its architecture, modules, algorithms, functionalities, ethical considerations, and expected impact. Through a comprehensive analysis and synthesis of existing literature and proposed strategies, this review aims to provide insights for health care stakeholders in selecting and implementing optimal FL strategies for collaborative medical advancements while upholding stringent data privacy and security standards in 10MT. © 2024 IEEE.

关键词： Federated learning

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Improvised Prediction of Rainfall using Random Forest Classifier over Taiwan 4

Improvised Prediction of Rainfall using Random Forest Classi...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Puttanapura, Jagadeshwari Monika Singh, T. Kishor Kumar Reddy, C. Doss, Srinath Stanley College of Engineering & Technology for Women Department of Computer Science and Engineering Hyderabad India Botho University Faculty of Engineering and Technology Botswana

ISBN: (纸本)9798331515720

Inadequate rainfall causes reduced harvesting, reduced availability of water, economic losses, environmental degradation, public health problems, decreased power generation, social unrest, and infrastructure destruction. Improved Rainfall prediction helps in better management of farming, resource allocation, scheduling, and disaster prevention. This leads to enhanced farming strategies, as well as more efficient water resource distribution and disaster preparedness. The dataset contains six weather variables including humidity, temperature, pressure, wind direction and speed. Numerous techniques, such as long short-Term memory (LSTM) model, bidirectional long short-Term memory (Bi-LSTM), Deep Learning Neural Networks (DLNN), Random Forest (RF), Artificial Neural Networks (ANN), K-Nearest Neighbor, Support Vector Machines (SVM), Logistic Regression were compared. The RF model's precision and accuracy were 86% and 96% respectively. In comparison with other models' performance, RF had the best outcomes. The results demonstrate that the RF algorithm can supply dependable meteorological forecasts. Such dependability is of use to farmers and other disaster response stakeholders, since it enhances planning and preparedness efforts through provision of reliable predictions. © 2024 IEEE.

关键词： Rain

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