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24th International Arab Conference on Information Technology, ACIT 2023

作者： Moubayed, Abdallah Injadat, MohammadNoor Alhindawi, Nouh Samara, Ghassan Abuasal, Sara Alazaidah, Raed School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Lebanese American University Electrical and Computer Engineering Department Byblos Lebanon Western University Electrical and Computer Engineering Department LondonON Canada Zarqa University Faculty of Information Technology Data Science and Artificial Intelligence Department Zarqa Jordan Jadara University Faculty of Science and Information Technology Irbid Jordan Zarqa University Faculty of Information Technology Computer Science Department Zarqa Jordan Zarqa University Faculty of Information Technology Software Engineering Department Zarqa Jordan

ISBN: (纸本)9798350384307

Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students' performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset. © 2023 IEEE.

关键词： Deep Learning e-Learning Online Courses Student Performance Prediction

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Noise-Filtering Enhanced Deep Cognitive Diagnosis Model for LatentSkill Discovering

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Intelligent Automation & Soft Computing 2023年第8期37卷 1311-1324页

作者： Jing Geng Huali Yang Shengze Hu National Engineering Research Center of Educational Big Data Central China Normal UniversityWuhan430079China Faculty of Artificial Intelligence in Education Central China Normal UniversityWuhan430079China School of Computer Science and Artificial Intelligence Wuhan Textile UniversityWuhan430079China

Educational data mining based on student cognitive diagnosis analysis can provide an important decision basis for personalized learning tutoring of students,which has attracted extensive attention from scholars at home and abroad and has made a series of important research *** this end,we propose a noise-filtering enhanced deep cognitive diagno-sis method to improve the fitting ability of traditional models and obtain students’skill mastery status by mining the interaction between students and problems nonlinearly through neural ***,modeling complex interactions between students and problems with multidimensional features based on cognitive processing theory can enhance the interpretability of the proposed model;second,the neural network is used to predict students’learning performance,diagnose students’skill mastery and provide immediate feedback;finally,by comparing the proposed model with several baseline models,extensive experimental results on real data sets demonstrate that the proposed Finally,by comparing the proposed model with several baseline models,the extensive experimental results on the actual data set demon-strate that the proposed model not only improves the accuracy of predicting students’learning performance but also enhances the interpretability of the neurocognitive diagnostic model.

关键词： Cognitive diagnosis nonlinear interaction interpretability intelligent education system skill diagnosis

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Quality Assessment of Brain MRI Defacing Using Machine Learning

Quality Assessment of Brain MRI Defacing Using Machine Learn...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Sadeghi, Sina Khodaei, Maryam Hempel, Lars Kirsten, Toralf Department for Medical Data Science Leipzig University Medical Center Leipzig Germany Institute for Medical Informatics Statistics and Epidemiology Leipzig University Leipzig Germany Faculty Applied Computer and Bio Sciences Mittweida University of Applied Sciences Mittweida Germany

ISBN: (纸本)9781643685335

Defacing of brain magnetic resonance imaging (MRI) scans is a crucial process in medical imaging research aimed at preserving patient privacy while maintaining data integrity. However, existing defacing algorithms are prone to errors, potentially compromising patient anonymity. This paper investigates the feasibility and efficacy of automated quality assessment for defaced brain MRIs using machine learning (ML). Our findings demonstrate the promising capability of ML models in accurately distinguishing between properly and inadequately defaced MRI scans. © 2024 The Authors.

关键词： Anonymity

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ML_BrainDetection: An Intelligent Model for Brain Tumor Identification Using Machine Learning 3

ML_BrainDetection: An Intelligent Model for Brain Tumor Iden...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Farag, Mohamed S Gomaa, Ibrahim A Abd-Elghany Zeid, Magdy Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

ISBN: (纸本)9798350306231

Brain tumors are one of the greatest causes of death worldwide. Due to that, early diagnosis and classification of the tumor would surely help increase the chance of survival for patients worldwide. However, classifying and identifying brain tumors requires certain tools, including using Magnetic Resonance Imaging (MRI) to detect brain tumors. Moreover, many researchers in the past have also built up computer-Aided Diagnosis (CAD) systems to aid radiologists in their purpose of detecting brain abnormalities and tumors effectively. In this research, extracted multi-modal features like texture, morphological, entropy-based, Scale Invariant Features are proposed, and consequently, certain robust machine learning techniques, which are k-nearest neighbors (KNN), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes, as well as the Logistic Regression classifier were used to analyze the two data-sets included in the paper, detect tumors found in the brain, not to mention, examining their results, which have then shown the highest accuracy when using the Logistic regression classifier in both data sets, with the Support Vector Machine (SVM) classifier following it, which further support the argument made in the paper. © 2023 IEEE.

关键词： Support vector machines

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A Review on data Quality Dimensions for Big data 7

A Review on Data Quality Dimensions for Big Data

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7th Information Systems International Conference, ISICO 2023

作者： Ridzuan, Fakhitah Zainon, Wan Mohd Nazmee Wan Faculty of Data Science and Computing Universiti Malaysia Kelantan Pengkalan Chepa Kelantan Kota Bharu16100 Malaysia Institute for Artificial Intelligence & Big Data Universiti Malaysia Kelantan Pengkalan Chepa Kelantan Kota Bharu16100 Malaysia School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia

Big data wave has led to a rapid increase in the amount of data being collected by organizations. While the accuracy and reliability of prediction models are often prioritized, the quality of the collected data is frequently overlooked. Poor data quality can result in the common problem of ‘garbage in, garbage out'. Traditional measures of data quality, such as accuracy, consistency, completeness, and timeliness, are no longer adequate in the era of Big data. Therefore, this paper proposes a taxonomy of data quality dimensions specifically for Big data, addressing emerging challenges by formulating 20 dimensions and categorizing them into four distinct categories. © 2023 The Authors. Published by Elsevier B.V.

关键词： Information management

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From User Preferences to Accurate Predictions: Enhancing Movie Recommendation Systems with Neural Collaborative Filtering and Sentiment Analysis

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SN computer science 2025年第3期6卷 1-26页

作者： Bsoul, Qusay Zawaideh, Firas Alqadi, Basma S. Almusfar, Latifa Abdullah Khalaf, Osamah Ibrahim Alattas, Ahmed Saleh Alali, Muath AbdElminaam, Diaa Salama Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia College of Business Administration Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam 31441 Saudi Arabia Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Baghdad Iraq Information Science Department Faculty of Arts and Humanities King Abdulaziz University Jeddah Saudi Arabia Data Science and Artificial Intelligence Faculty of Information Technology Applied Science Private University Amman Jordan Jadara Research Center Jadara University Irbid 21110 Jordan MEU Research Unit Middle East University Amman 11831 Jordan

This paper introduces an advanced movie recommendation system that combines neural collaborative filtering (NCF) with sentiment analysis to improve personalization, accuracy, and robustness in recommending movies. By integrating user preferences with the sentiment polarity of reviews, the system refines recommendations, effectively addressing challenges such as the cold start problem, data sparsity, and limited semantic understanding. Unlike traditional collaborative and content-based filtering methods, which often struggle with diversity and accuracy, our approach utilizes deep learning within NCF to reveal hidden patterns in user behavior, enhancing recommendations’ relevance and precision. The proposed system is evaluated using standard performance metrics, including RMSE, MSE, and MAE, demonstrating its superior performance over conventional filtering techniques. Its enhanced scalability and adaptability position it as a promising tool for personalized content delivery in digital entertainment, with considerable potential for large-scale, dynamic recommendation environments. This research contributes to the existing knowledge on recommendation systems and offers new insights into improving content personalization and user satisfaction. The novelty and scientific value of this work lie in applying deep learning to tackle the challenges of accurate content recommendation in the rapidly evolving digital media landscape. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative filtering Content-based filtering Cosine and person similarities Deep learning Evaluation metrics MAE Movie recommendation system NCF RMSE Surprise library

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Utilization of a Full Convolutional Autoencoder for the Task of Anomaly Detection in Hyperspectral Imagery

Utilization of a Full Convolutional Autoencoder for the Task...

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2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024

作者： Wang, Jingwen Yu, Yize Zhao, Rui Li, Minyi School of Mathematics and Statistics Ningbo University 315211 China Business School Ningbo University 315211 China Faculty of Electrical Engineering and Computer Science Ningbo University 315211 China Deqing Data Co. Ltd. 313200 China

The advancement of artificial intelligence has significantly improved the capability to capture background features in hyperspectral images (HSI), thereby demonstrating commendable performance in the domain of hyperspectral anomaly detection (HAD). The existing approaches, however, still exhibit certain limitations: (1) The deep feature learning process lacks contextual, anomaly constraints, and prior information. (2) The priority reconstruction of the background cannot be ensured by traditional HSI anomaly detectors based on self-supervised deep learning. (3) The utilization of spatial information in hyperspectral images is limited by the fully connected deep network structure of the HSI anomaly detector. The performance of many hyperspectral anomaly detectors is limited by assumptions or presumptions regarding background and anomaly distributions, as these detectors cannot accurately account for the complex real-world distributions. The paper proposes a self-supervised full convolutional autoencoder as a solution to address these issues. The effectiveness and performance of the method were confirmed through evaluation on two real hyperspectral datasets, demonstrating superiority over nine other state-of-the-art methods. © 2024 J. Wang, Y. Yu, R. Zhao & M. Li.

关键词： Anomaly Detection Full Convolutional Autoencoder Hyperspectral Image Analysis Self-Supervised Learning

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A Report on Sentiment Analysis of Requirements Engineering Artifacts created in University Course 12

A Report on Sentiment Analysis of Requirements Engineering A...

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12th International Workshop on Quantitative Approaches to Software Quality, QuASoQ 2024

作者： Katsuie, Takumi Ogata, Shinpei Okano, Kozo Iimura, Yukako Saito, Shinobu Shinshu University Faculty of Engineering 4–17–1 Wakasato Nagano-shi Nagano380–8553 Japan NTT Computer & Data Science Laboratories 3–9–11 Midori-Cho Musashino-shi Tokyo180–8585 Japan

This technical report introduces the results of sentiment analysis of artifacts in requirements engineering phase. These artifacts contain descriptions of requirements and functions for the development target such as software product and solution. The descriptions of requirements reflect user needs and problems are described based on the analysis of users’ dissatisfaction with the current situation and their expectations. On the other hand, the description of functions describes the behavior of the system and the interaction between the system and humans. Therefore, we apply sentiment analysis to requirements artifacts which are created in an exercise for university students. We, then, investigate how the sentiment of the descriptions in the artifacts are changed. Sentiment analysis is performed using Google Cloud’s Natural Language API on the descriptions included in the artifacts such as customer journey maps and user story mappings. From the results of the application, we confirmed that the sentiment score of each artifact was different. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Requirements engineering

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Facial Emotion Detection Using Haar-Cascade Classifier and Convolutional Neural Networks 2

Facial Emotion Detection Using Haar-Cascade Classifier and C...

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2020 2nd International Conference on science and Technology, ICoST 2020

作者： Riyantoko, P.A. Sugiarto Hindrayani, K.M. Data Science Department Faculty of Computer Science UPN "veteran" Jawa Timur Indonesia Informatics Department Faculty of Computer Science UPN "veteran" Jawa Timur Indonesia

computer vision has the challenge to detect the facial emotions of humans. Recently, in computer vision and machine learning, it's possible to detect emotion from video or image accurate. In our research will propose to classify facial emotion using Haar-Cascade Classifier and Convolutional Neural Networks. The experiment uses the FER2013 dataset which was collected for the facial expression recognition dataset, and we proposed seven classified facial expression. The CNN model gain MSE and accuracy value based on epoch variety. The results showed that with the increase in the epoch value, the smaller MSE value would be obtained, likewise, the accuracy value would be increased. Thus, the proposed algorithm of CNN is proven to be effective for facial emotion detection. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Emotion Recognition

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MLCardiovascularPrediction: Machine Learning-Based Cardiovascular Disease Prediction 3

MLCardiovascularPrediction: Machine Learning-Based Cardiovas...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Zeid, Magdy Abd-Elghany Elatif Gomaa, Ibrahim Abd Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pinang Pulau11800 Malaysia

ISBN: (纸本)9798350306231

Like most diseases that affect a human's life, car-diovascular disease is dangerous and unwanted for anyone who has heard of it as it brings a high risk of death. In today's world, most deaths are caused by heart disease as it could come from age, heritage, smoking, and many factors that could seem normal, and it doesn't matter what part of the world you are in. There must be a way to know or predict if this person is at high risk of cardiovascular disease. This paper and project talk about six algorithms: K-Nearest-Neighbour (KNN), Decision Tree (DT), Naive Bayes, Neural Network, Logistic Regression, and Support Vector Machine (SVM). The results came between 60% to 70% in one dataset and from 80 % to 90 % in the second. Still, they mostly favor the Neural Network algorithm as it can learn and model around complex relationships. The SVM showed different outputs between the two datasets as one is significantly larger than the other, possibly due to the outliers in both datasets being different and more complex in the larger one. Cardiovascular disease can be very dangerous, but finding and discovering it soon can be a lifesaver. These algorithms also can help find and predict it, as it was shown with not just decent, but good and respectable success rates for each one. © 2023 IEEE.

关键词： Decision trees

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