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Incorporating Artificial Intelligence Powered Immersive Realities to Improve Learning using Virtual Reality (VR) and Augmented Reality (AR) technology 3

Incorporating Artificial Intelligence Powered Immersive Real...

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3rd International Conference on applied Artificial Intelligence and computing, ICAAIC 2024

作者： Balushi, Jasim Sharki Ghulam Al Jabri, Malak Ibrahim Ahmed Al Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Balakumar, C. Univesity of Technology and Applied Sciences College of Computing and Information Sciences Information Technology Department Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Computer Science Bengaluru India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798350375190

This study analyzes the transformative potential of combining artificial intelligence (AI) with immersive technologies, specifically virtual reality (VR) and augmented reality (AR), to elevate educational outcomes significantly. The synergy of AI and immersive technologies presents a unique opportunity to enhance students' motivation, memory retention, and comprehension of complex subjects, creating a distinct and impactful learning experience. The research comprehensively explores the current understanding of the effectiveness of VR and AR in educational settings, placing special emphasis on their applications in diverse fields such as Science, technology, Engineering, And Mathematics (STEM), medicine, language acquisition, and the development of interpersonal skills. Beyond highlighting the immense potential, the study acknowledges and addresses the obstacles that include the costs associated with equipment, training, and content creation. It underscores the importance of collaborative efforts among technology suppliers, educators, and content producers to overcome these challenges effectively. By recognizing and navigating these hurdles, the study aims to provide practical insights for educational institutions and policymakers, advocating for the integration of AI-powered immersive technology to enhance the overall learning experience for students. The findings underscore the transformative impact of such technologies, emphasizing their role in shaping the future of education and preparing students for the dynamic challenges of the modern world. © 2024 IEEE.

关键词： Augmented reality

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Deep Broad Learning for Emotion Classification in Textual Conversations

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Tsinghua Science and technology 2024年第2期29卷 481-491页

作者： Sancheng Peng Rong Zeng Hongzhan Liu Lihong Cao Guojun Wang Jianguo Xie Center for Linguistics and Applied Linguistics and Laboratory of Language Engineering and ComputingGuangdong University of Foreign StudiesGuangzhou 510006China Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices South China Normal UniversityGuangzhou 510006China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou 510006China Modern Education Technology Center Guangdong University of Foreign StudiesGuangzhou 510006China

Emotion classification in textual conversations focuses on classifying the emotion of each utterance from textual *** is becoming one of the most important tasks for natural language processing in recent ***,it is a challenging task for machines to conduct emotion classification in textual conversations because emotions rely heavily on textual *** address the challenge,we propose a method to classify emotion in textual conversations,by integrating the advantages of deep learning and broad learning,namely *** aims to provide a more effective solution to capture local contextual information(i.e.,utterance-level)in an utterance,as well as global contextual information(i.e.,speaker-level)in a conversation,based on Convolutional Neural Network(CNN),Bidirectional Long Short-Term Memory(Bi-LSTM),and broad *** experiments have been conducted on three public textual conversation datasets,which show that the context in both utterance-level and speaker-level is consistently beneficial to the performance of emotion *** addition,the results show that our proposed method outperforms the baseline methods on most of the testing datasets in weighted-average F1.

关键词： emotion classification textual conversation Convolutional Neural Network(CNN) Bidirectional Long Short-Term Memory(Bi-LSTM) broad learning

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Identifying Fake News in the Russian-Ukrainian Conflict Using Machine Learning 37th

Identifying Fake News in the Russian-Ukrainian Conflict Usin...

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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Darwish, Omar Tashtoush, Yahya Maabreh, Majdi Al-essa, Rana Aln’uman, Ruba Alqublan, Ammar Abualkibash, Munther Elkhodr, Mahmoud Information Security and Applied Computing Eastern Michigan University Ypsilanti United States Computer Science Jordan University of Science and Technology Irbid Jordan Information Technology The Hashemite University Zarqa Jordan Central Queensland University Rockhampton Australia

ISBN: (纸本)9783031286933

The widespread dissemination of false information made possible by social networks' universal accessibility and ease of use may be harmful to both individuals and societies. Regrettably, one of the most popular subjects on social media right now is the crisis between Russia and Ukraine. Spreading fake news about this conflict may cause serious consequences to both countries and their citizens. Therefore, we are motivated to build a fake news detection system similar to those systems already accessible in other fields like healthcare. In this study, we build a dataset of fake and real tweets about the Russian-Ukrainian conflict and evaluate the power of machine learning in this context. The pre-train BERT and five classical machine learning algorithms;namely support vector machine (SVM), Decision Tree (DT), K-nearest neighbor (KNN), logistic regression (LR), and Naïve Bayes (NB) are trained and evaluated through different scenarios of the dataset. The results show that it is possible to develop a system that can discern between real and fake news regarding the Russian-Ukrainian conflict. Support vector machine and logistic regression outperform other learning algorithms and produce comparable prediction models with approximately 76% of accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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A Novel Feature Extraction Technique for ECG Arrhythmia Classification Using ML

A Novel Feature Extraction Technique for ECG Arrhythmia Clas...

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2023 IEEE International Conference on Dependable, Autonomic and Secure computing, 2023 International Conference on Pervasive Intelligence and computing, 2023 International Conference on Cloud and Big Data computing, 2023 International Conference on Cyber Science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Rahman, Mohammad Mominur Islam, Ashhadul Charni, Skander Bensmail, Halima Hilbel, Thomas Belhaouari, Samir Brahim College of Science and Engineering Hamad Bin Khalifa University Division of Information and Computing Technology Doha Qatar College of Engineering Qatar University Doha Qatar Doha Qatar Westphalian University of Applied Sciences Department of Electrical Engineering and Applied Sciences Gelsenkirchen Germany

ISBN: (纸本)9798350304602

Feature extraction is the process of transforming raw data into features that are more relevant for machine learning algorithms. The goal of feature extraction is to find a set of features that can be used to accurately predict the target variable. The specific features that are extracted will depend on the specific application. For example, features that are extracted for the purpose of diagnosing arrhythmias will be different from the features that are extracted for the purpose of assessing myocardial infarction. A generalized new algorithm for feature extraction could be helpful for all complex feature extraction data sets. In this paper, we propose a random selection process to generate the required number of new features with the help of existing specific features of the electrocardiogram (ECG) signal. We have named this novel feature extraction method the Random Feature Explorer (RFE). The proposed method was tested and evaluated using Physio Net's MIT-BIH datasets. The results indicate that the suggested method achieved an accuracy of 99.79% in arrhythmia classification. We have made the source code for our proposed method available on GitHub for open access and reproducibility. The code can be accessed at https://***/3NnrH4A © 2023 IEEE.

关键词： Electrocardiograms

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Robust speech perception and classification-driven deep convolutional neural network with natural language processing

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Alexandria Engineering Journal 2025年 123卷 358-368页

作者： Alshammari, Alya Alzaidi, Muhammad Swaileh A. Alrusaini, Othman Alshuhail, Asma Allafi, Randa Yafoz, Ayman Alsini, Raed Khadidos, Alaa O. Department of Applied Linguistics College of Languages Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Engineering and Applied Sciences Applied College Umm Al-Qura University Makkah Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Speech recognition systems use computer algorithms to interpret and process spoken words and translate them into text. Speech-to-text, or Speech detection, is the proficiency of a program or machine to find spoken words aloud and translate them into plain text. Speech detection systems are the most common complex systems that use wide-ranging research in computer engineering, linguistics, and computer science. In today's significant data era, several machine learning (ML) approaches for processing raw, unlabelled speech data still need to be improved. Deep learning (DL) models, on the other hand, with their powerful modelling ability for extensive data, are capable of directly processing unlabelled data and have become a crucial area of research in the domain of automated speech recognition. The DL model has recently gained popularity for voice disorders and automated speech detection. DL is an ML that exploits artificial neural networks (ANN) to gain meaningful insights from data. The DL approach demonstrated great success and achieved significant attention in different fields comprising speech processing, computer vision (CV), and natural language processing (NLP). Therefore, this article develops a Deep Learning with Natural Language Processing based Robust Speech Detection (DLNLP-RSD) technique. The DLNLP-RSD technique derives features from the mel-frequency cepstral coefficient (MFCC) technique. Followed by the DLNLP-RSD technique employs a one-dimensional deep convolutional neural network (CNN) approach for the detection and classification of speech. Moreover, the parameter tuning model uses the grasshopper fractals optimization algorithm (GOA) approach. The performance evaluation of the proposed technique highlights its significant performance over other models concerning distinct measures. An extensive simulation analysis is executed to examine the improved results of the DLNLP-RSD model. The experimental validation of the DLNLP-RSD technique portrayed a sup

关键词： Convolutional neural networks

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An Efficient Detection and Classification of Plant Diseases using Deep Learning Approach

An Efficient Detection and Classification of Plant Diseases ...

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2023 International Conference on Evolutionary Algorithms and Soft computing Techniques, EASCT 2023

作者： Jagadeesan, S. Deepakraj, E. Ramalingam, Venkadeshan Venkatachalam, Ilayaraja Vivekanandan, Manojkumar Manjula, R. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India University of Technology and Applied Sciences - Shinas Branch Information Technology Department Oman Vellore Institute of Technology School of Computer Science and Engineeing Vellore India School of Engineering and Applied Sciences Srm University-AP Department of Computer Science & Engineering Andhra Pradesh Amaravati India Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technology Kattankulathur India

ISBN: (纸本)9798350313413

Crop failure caused by disease-causing pests is an important problem. Farmers struggle with disease management and detection due to inadequate interventions. The goal is to create an automatic system to efficiently identify plant diseases from photos while reducing crop losses and increasing productivity. Machine learning algorithms offer a faster and cheaper alternative to visual inspection by experts. The main goal is to perform image analysis for early diagnosis and effective disease control. The use of CNN architecture for plant disease classification and detection offers a promising solution for plant health monitoring and risk mitigation. Given the threats to crop productivity and global food security, reliable methods for early detection and accurate classification are essential. CNNs enable efficient analysis of vast plant image databases, enabling accurate plant disease identification with speed and accuracy. The multi-layer CNN approach extracts feature and refines the representation, facilitating accurate prediction and accurate diagnosis. Transfer learning methods accelerate system development and allow adaptation to plant disease-specific databases. Combining computer vision algorithms with CNN architecture enables real-time monitoring, early disease detection and targeted intervention, reducing yield losses and improving crop management. This approach uses AI, image analysis, and plant pathology to solve the challenges of sustainable agriculture and plant diseases. System performance is measured by various performance metrics. © 2023 IEEE.

关键词： CNN-Based Approach Error Rate Plant disease Precision ROC

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Sliding Mode Control Strategy for State-of-Charge (SoC) Balancing in Lithium-ion Battery Packs 13

Sliding Mode Control Strategy for State-of-Charge (SoC) Bala...

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13th International Conference on Control, Automation and Information Sciences, ICCAIS 2024

作者： Feng, Yong Han, Fengling Yang, Rebecca Wang, Yanmin Bai, Fan Truong, Nguyen-Vu School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia School of Science Rmit University Melbourne Australia School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China National Institute of Applied Mechanics and Informatics Vietnam Academy of Science and Technology Ho Chi Minh City Viet Nam

ISBN: (纸本)9798331542047

This paper proposes a sliding mode control strategy for balancing the State of Charge (SoC) in lithium-ion battery packs. By thoroughly analyzing the battery balancing circuit topology, a bidirectional Cuk circuit balancing topology for a series-connected battery pack is established, and its mathematical model is developed. Utilizing sliding mode control technology, a balancing strategy is designed to equalize the SoC of all cells in a battery pack composed of multiple series-connected battery cells. The effectiveness of the proposed method is verified through experiments, demonstrating that the developed battery balancing system can successfully balance the SoC of each cell in the battery pack. © 2024 IEEE.

关键词： State of charge

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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Computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Optimized Vaccine Selection Using Machine Learning and Genetic Algorithms: A Study on Side Effects of COVID-19 Vaccines 12th

Optimized Vaccine Selection Using Machine Learning and Genet...

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12th International Conference on Soft computing for Problem Solving, SocProS 2023

作者： Soni, Vishal Joshi, Shubham Deep, Kusum Pant, Millie Department of Mechatronics Indian Institute of Information Technology Bihar Bhagalpur813210 India Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India Department of Mathematics Indian Institute of Technology Uttarakhand Roorkee247667 India Mehta Family School for Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India

ISBN: (纸本)9789819732913

The COVID-19 pandemic has necessitated large-scale vaccination campaigns to control the virus's spread. Given the scale of these efforts, it's crucial to scrutinize potential adverse effects, especially in vulnerable population groups. This study combines machine learning and Genetic Algorithms (GA) to analyze the patterns of patient recovery post-vaccination. We use a range of classifiers, including the Gradient Boosting Decision Tree (GBDT), to identify key features and symptom categories that influence patient recovery. We also use Principal Component Analysis (PCA) to reduce dimensionality and uncover patterns within high-dimensional data. The data used in this study have taken from the Vaccine Adverse Event Reporting System (VAERS). With the help of GA, we aim to guide optimal vaccine selection for susceptible demographics, minimizing the risk and severity of side effects. Our research paves the way for more personalized vaccination strategies, promoting healthier outcomes and reducing risks associated with mass vaccination. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Exploring Incremental Classifiers For Predicting Student Performance Across Semesters

Exploring Incremental Classifiers For Predicting Student Per...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： E. Ben George R. Senthilkumar Fatma Al-Junaibi Zakariya Al-Shuaibi Faculty College of Computing and Information Sciences University of Technology and Applied Sciences – Muscat Muscat Sultanate of Oman

ISBN: (数字)9798331540104

ISBN: (纸本)9798331540111

Accurately predicting student performance in advance has become crucial in educational systems to ensure timely interventions and enhance academic support. This research investigates the application of incremental learning algorithms to predict student grades across multiple semesters for a course. The traditional classifiers may not suit the student’s academic prediction since the data is being added frequently, so there is a necessity of using classifiers that support the incremental data. Various incremental classifier categories, such as Naive Bayes, linear models, tree-based models, ensemble methods, and neighbor-based models, were used to assess their efficacy in real-time student data. Student performance data for four semesters are used to study the dynamic learning process of the classifier. Each semester’s data is trained and updated with the previous semester’s model. This allows each model to adapt when new data is introduced every semester. The study will examine how well these incremental models perform on incremental data by tracking performance measures such as accuracy, precision, recall, F1 score, and training time for each semester. The findings will highlight the suitable incremental classifier to predict the student’s grade over various periods and will provide key insights for educators.

关键词： Training Adaptation models Accuracy Incremental learning Predictive models Nearest neighbor methods Prediction algorithms Data models Time measurement Real-time systems

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