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Smart Classroom: A Deep Learning Approach towards Attention Assessment through Class Behavior Detection

Smart Classroom: A Deep Learning Approach towards Attention ...

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2022 Advances in science and engineering Technology International Conferences, ASET 2022

作者： Parambil, Medha Mohan Ambali Ali, Luqman Alnajjar, Fady Gochoo, Munkhjargal United Arab Emirates University College of IT Department of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9781665418010

The role of education in shaping future generations is crucial. The rapid advancement in Artificial Intelligence (AI) has prompted a considerable turn of events in the educational field. Advanced AI technologies have the potential to replace traditional classroom practices, giving more importance to students' preferences. Assessing the attention in students is the building block to improve both instructing and learning. Most of the time, it becomes puzzling for an instructor to track every student and know their interest in learning, especially in a vast scenario. In such cases, instructors are often unaware of the most coherent approach while teaching students with different learning capabilities. Therefore, this paper proposed a system that incorporates AI to develop a real-time vision-based smart classroom that autonomously monitors students' attention, as well as, emotions and gives live graphical feedback to the instructor. Our system was able to monitor the presence of a student in the classroom and analyze the students' attention and emotion. The proposed approach was able to assign numerical scores to the student for the concentration level in the class. The stated system can help the instructors evaluate the student's attention instantaneously and more accurately, thus supporting them in conducting the session suitably that may lead to better academic performance. © 2022 IEEE.

关键词： Students

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Classifying Psychiatric Patients Using Machine Learning 25

Classifying Psychiatric Patients Using Machine Learning

引用

25th International Arab Conference on Information Technology, ACIT 2024

作者： Alrjoob, Hmmam M. Alazaidah, Raed Batyha, Radwan Khafajeh, Hayel Elsoud, Esraa Abu Saeb Al-Sherideh, Ala'a Elrashidi, Ali Alqaraleh, Muhyeeddin Amer, Waleed Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Applied Science Private University Faculty of Information Technology Department of Computer Science Amman Jordan Zarqa University Faculty of Information Technology Department Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Cyper Security Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of Information Technology Department of Software Engineering Zarqa Jordan Zarqa University Faculty on Information Technology Department of Cyber Security Zarqa Jordan

ISBN: (纸本)9798331540012

Psychological disorders are considered chronic illnesses that affect a wide range of populations. Some studies in the United States indicate that one in every eight individuals is affected by a psychological disorder. Additionally, individuals with a psychological disorder often have an impact on the nature of the lives of those around them. It is beneficial to detect the illness in its early stages to increase the likelihood of recovery. In this research, thirteen classification algorithms are used. Each algorithm has an interesting approach. It can be used to analyze several datasets where three datasets specific to Psychiatric patients were selected. The datasets have been chosen from UCI Machine Learning Repository. The datasets were re-analyzed after only 50% of the characteristics were approved after being evaluated by the several feature selection techniques. The main objective of this research is to find the best classifier and feature selection strategy in analyzing datasets of Psychiatric patients. After conducting numerous comparisons, it became evident that, the best classifier for the Autism-Adult-Data dataset are Bagging and Random Forest. As for the best learning strategy, it was found to be Functions and Trees, the best feature selection was obtained using Symmetrical Uncourt Attribute Evaluation. Both IBK and KStar classifier proved to be the best classifier for the Parkinson's dataset, Lazy demonstrated to be the most effective learning strategy, Correlation Attribute Evaluation technique yielded the best feature selection. Logistic emerged as the best classifier for planning-relax dataset, the best learning strategy was Bayes, the best feature selection was found to be Symmetrical Uncert Feature Selection. © 2024 IEEE.

关键词： Contrastive Learning

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Empowering Deaf Children with Sinhala Sign Language, Emotion Detection, and Sound Recognition 5

Empowering Deaf Children with Sinhala Sign Language, Emotion...

引用

5th International Conference on Advancements in Computing, ICAC 2023

作者： Fernando, W.H.K. Perera, P.L.D.N. Fernando, M.I.N. Gunasekara, P.T.E.W. Fernando, Harinda De Zoysa, Rivoni Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study describes a broad endeavor to use cutting-edge technologies to empower deaf primary school students in Sri Lanka. Three key elements make up the study: a sound recognition and classification system, an Android software for translating gestures in Sinhala Sign Language (SSL), and a mobile app for emotion recognition and text-tospeech in Sinhala. By promptly alerting caregivers to potential dangers, the sound recognition technology secures the security of deaf children at home, providing an invaluable layer of security. The Android software bridges the communication gap and improves family interactions by translating SSL gestures captured by the device's camera into textual representations. This promotes successful communication between deaf children and their parents. Convolutional Neural Networks (CNNs) are used in the mobile application to detect and understand emotions based on facial expressions. This enables non-verbal youngsters to communicate successfully through Sinhala textto-speech output with adaptive tone modulation. This research aims to build a caring and inclusive atmosphere, enabling increased independence and enhanced communication skills in deaf children by combining these advances. The suggested solutions could significantly improve the lives of deaf kids, providing them with opportunities for better learning, social interaction, and emotional health. © 2023 IEEE.

关键词： Emotion Recognition

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Explainable Artificial Intelligence computer-Aided Diagnosis for Heart Myocardial Infarction 7

Explainable Artificial Intelligence Computer-Aided Diagnosis...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Saleh, Radhwan A. A. Abualkebash, Humam Mohammed, Alaa Aldeen Addo, Daniel Bütün, Ertan Al-Antari, Mugahed A. Kocaeli University Mechatronics Engineering Department Kocaeli Turkey University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Firat University Department of Computer Engineering Elaziǧ Turkey Sejong University Department of Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350383065

Myocardial infarction (MI) is a significant cardiovascular condition known for its detrimental impact on the heart. The rapid and accurate diagnosis of MI is crucial for reducing mortality rates. In the realm of medical imaging interpretation, explainable artificial intelligence (XAI) has emerged as a promising research field. XAI offers a valuable tool to create interpretable saliency maps for each MRI slice, providing insights into the decision-making processes employed by AI models. Our proposed framework begins with the enhancement of MI images using Contrast Limited Adaptive Histogram Equalization. We integrate and adopt five deep learning models: InceptionV3, InceptionResNetV2, ResNet50V2, VGG16, and Xception, along with the Grad-CAM technique, to demonstrate the robustness of these models in MI detection. Among these models, InceptionResNetV2 achieved the highest accuracy with a score of 83.33%, and an impressive F-score of 88.10%. However, based on the Grad-CAM results, the most trustable and robust model is ResNet50V2, which consistently demonstrated its focus on regions of interest in both normal and abnormal predictions. These compelling findings should motivate researchers and stakeholders in the medical industry to consider implementing this framework in practice, ultimately contributing to the early and precise diagnosis of MI and reducing MI-related mortality rates. © 2023 IEEE.

关键词： Deep learning

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Day-ahead PV Output Power Forecasting Utilizing Boosting Recursive LightGBM-LSTM Framework

Day-ahead PV Output Power Forecasting Utilizing Boosting Rec...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Hokmabad, Hossein Nourollahi Husev, Oleksandr Vinnikov, Dmitri Belikov, Juri Petlenkov, Eduard Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia

ISBN: (纸本)9798350396782

Recently, global photovoltaic (PV) system installations have surged. Precise forecasting is vital for their grid integration and carbon emission cuts. However, due to fluctuating solar radiation, predicting PV output is difficult. Machine learning models, notably Long Short-Term Memory (LSTM) networks, offer a solution. This study presents a novel framework using a boosted recursive Light Gradient Boosting Machine (LightGBM)-LSTM network to forecast daily PV output at hourly intervals. Conducted in Northern Europe—a region with significant solar radiation variability—the models trained on meteorological and historical data showed a 12% improvement in RMSE, a 13% reduction in MAPE, and a 5% increase in the R2 score compared to standalone LSTM models. © 2023 IEEE.

关键词： boosting ensemble method LightGBM LSTM numerical weather prediction Solar power forecasting

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Artificial Intelligence computer-Aided Diagnosis to automatically predict the Pediatric Wrist Trauma using Medical X-ray Images 7

Artificial Intelligence Computer-Aided Diagnosis to automati...

引用

7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Erzen, Elif Merve Bütün, Ertan Al-Antari, Mugahed A. Saleh, Radhwan A. A. Addo, Daniel Firat University Department of Computer Engineering Elaziǧ Turkey Sejong University Department of Artificial Intelligence Seoul Korea Republic of Kocaeli University Department of Mechatronics Engineering Kocaeli Turkey University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350383065

Pediatric wrist trauma (PWT) is a common injury that occurs in hospital emergency departments, with fractures being among the most frequent cases. The traditional diagnostic process for these injuries involves the collaboration of radiologists and surgeons. However, recent advancements in deep learning-based computer vision algorithms have shown potential in automating and expediting this diagnosis. In this paper, we propose an automatic end-to-end computer-aided diagnosis (CAD) system that can simultaneously detect and classify various types of pediatric wrist injuries, including bone anomalies, bone lesions, foreign bodies, fractures, metallic artifacts, periosteal reactions, pronator signs, soft tissue abnormalities, textual elements, and the axis. To train and evaluate the proposed CAD system, we utilize the public GRAZPEDWRI-DX dataset. The design of our CAD system involves careful steps such as medical X-ray data collection, data labeling, preprocessing, prediction AI model optimization, and model evaluation. The core component used for the detection and classification task in our system is YOLOv8, the advanced and state-of-the-art version of YOLO for object detection. Experimental results demonstrate that our proposed CAD system achieves promising evaluation results in terms of precision (77.80%), recall (54.60%), mAP@50 (59.10%), and mAP50@95 (37.20%). These encouraging evaluation results can serve as a future direction to provide practical solutions for improving the capabilities of the healthcare system in rapidly and accurately predicting pediatric wrist injuries. © 2023 IEEE.

关键词： Forecasting

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Improving performance in collaborative games through personality-based matchmaking 1

Improving performance in collaborative games through persona...

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Actas del I Congreso Espanol de Videojuegos, CEV 2022 - 1st Spanish Congress on Videogames, CEV 2022

作者： Villar, Alejandro León, Carlos Department of Software Engineering and Artificial Intelligence Computer Science Faculty Universidad Complutense de Madrid Madrid28040 Spain

Automatic player matchmaking in video games is usually focused on balancing players by taking into account their past performance. The objectives of video games in which the winning condition is clear and specifically defined by their design make this approach widely used and effective. However, this approach is not necessarily optimal when the main objective is not only winning but also obtaining other rewards like creative achievements, enjoyment, socialization or world construction. This paper describes a formal approach for automatic matchmaking that takes into account the players' personality traits. This form of matchmaking is oriented towards game genres in which competition is not necessarily the general dynamic. An implementation of the system, evaluation in a virtual escape room and results are provided. Preliminary results suggest that the approach is potentially effective. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Interactive computer graphics

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COVID-19 Detection Through Deep Learning Algorithms using Chest X-ray Images 3

COVID-19 Detection Through Deep Learning Algorithms using Ch...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Hossain, Tanzim Jahan, Nusrat Mazumder, Md. Sakib Ali Islam, Rakibul Javed Mehedi Shamrat, F.M. Khater, Ankit Daffodil International University Department of Software Engineering Dhaka Bangladesh Daffodil International University Department of Information Technology & Management Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Jadavpur University Department of Computer Science and Engineering India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

COVID-19 became a pandemic affecting the lives of every human globally by the end of 2019. The disease impaired the lungs of infected patients. Precise prediction and diagnosis of COVID-19 disease are challenging due to its resemblance to viral pneumonia. Using multiple deep learning approaches, the researchers used chest X-ray (CXR) imaging to diagnose COVID-19. The X-ray image dataset from Kaggle is used for the study by selecting the COVID-19 and normal class. InceptionV3, MobileNetV2, VGG19,VGG16 and ResNet50 are the five neural networks used for binary classification of COVID-19. The accuracy of MobileNetV2 surpasses that of the remainder of the model by 93.02%. However, it has a compilation time of 1836 seconds per epoch. Besides, VGG16 has an accuracy of 92.37%, with a compilation time of 603 seconds per epoch. Compared to these models, Inceptonv3, Resnet50 and VGG19 perform with an accuracy score of 86.42%, 68.34% and 91.79%. Applying deep learning techniques to COVID-19 radiological imaging holds great promise for enhancing the accuracy of diagnosis when in comparison to the gold standard RT-PCR test and assisting healthcare professionals in making decisions quickly © 2022 IEEE.

关键词： COVID-19

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Multi-View Incongruity Learning for Multimodal Sarcasm Detection 31

Multi-View Incongruity Learning for Multimodal Sarcasm Detec...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Guo, Diandian Cao, Cong Yuan, Fangfang Liu, Yanbing Zeng, Guangjie Yu, Xiaoyan Peng, Hao Yu, Philip S. Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798891761964

Multimodal sarcasm detection (MSD) is essential for various downstream tasks. Existing MSD methods tend to rely on spurious correlations. These methods often mistakenly prioritize non-essential features yet still make correct predictions, demonstrating poor generalizability beyond training environments. Regarding this phenomenon, this paper undertakes several initiatives. Firstly, we identify two primary causes that lead to the reliance of spurious correlations. Secondly, we address these challenges by proposing a novel method that integrate Multimodal Incongruities via Contrastive Learning (MICL) for multimodal sarcasm detection. Specifically, we first leverage incongruity to drive multi-view learning from three views: token-patch, entity-object, and sentiment. Then, we introduce extensive data augmentation to mitigate the biased learning of the textual modality. Additionally, we construct a test set, SPMSD, which consists potential spurious correlations to evaluate the the model's generalizability. Experimental results demonstrate the superiority of MICL on benchmark datasets, along with the analyses showcasing MICL's advancement in mitigating the effect of spurious correlation. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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Toward Effective and Efficient DevOps using Blockchain 22

Toward Effective and Efficient DevOps using Blockchain

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26th ACM International Conference on Evaluation and Assessment in software engineering, EASE 2022

作者： Akbar, Muhammad Azeem Mahmood, Sajjad Siemon, Dominik Software Engineering LUT University Finland Information and Computer Science Department King Fahd University of Petroleum and Minerals Australia

ISBN: (纸本)9781450396134

Recently, blockchain and DevOps have received attention from the software industry as both offer number of benefits individually. Automation, measurement, and sharing are the core pillars of DevOps, and to securely manage these;blockchain can play an important role. This study aims to understand the role of blockchain technology in the DevOps paradigm. This study presents factors that influence the adoption of blockchain in DevOps. Furthermore, this study suggests a framework that assists in merging the characteristics of blockchain in the DevOps paradigm. The findings of this study give a knowledge base for industry experts and the research community to develop the roadmap and guidelines for the adoption of blockchain in the DevOps paradigm. © 2022 ACM.

关键词： Framework Factors blockchain DevOps

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