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International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Flavia Costi Darian Onchis Codruta Istin Adina Magda Florea Eduard Hogea Department of Computer Science West University of Timisoara Department of Computer and Information Technology Politehnica University of Timisoara Department of Computer Science National University of Science and Technology Politehnica Bucharest

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

This paper aims to provide a comprehensive analysis of the benefits of employing GraphLIME (Local Interpretable Model Explanations for Graph Neural Networks) for reliable diabetes mellitus prediction. Our focus is on highlighting the advantages of integrating GraphLIME with a features attention-mechanism, compared to the standard pairing of deep learning neural networks with the original LIME explainability method. This system enabled us to develop an effective approach for identifying the most relevant features and applying the attention mechanism solely to those features. We conducted a detailed comparison of the performance metrics between the two approaches. By incorporating the attention mechanism, the model reached an accuracy of 92.6% in addressing the problem. The model's performance is thoroughly illustrated, with results further assessed using the Receiver Operating Characteristic (ROC) curve. Applying this technique to a dataset of 768 patients with or without diabetes mellitus, we enhanced the model's performance by over 18%.

关键词： Measurement Attention mechanisms Scientific computing Receivers Predictive models Prediction algorithms Diabetes Numerical models Reliability Standards

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Enhancing Satellite Imagery: Time-Varying Neural Network Approach

Enhancing Satellite Imagery: Time-Varying Neural Network App...

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2024 International Conference on system, Computation, Automation and Networking, ICSCAN 2024

作者： Prema, P. Ramaprabha, J. Venkat Ramana, N. Kalpana, D. Gunasekar, S. Pius Agbulu, G. Panimalar Engineering College Department of Information Technology Chennai India Srm Institute of Science and Technology India Department of Computing Technologies Chennai India Iitm Department of Electrical Engineering Chennai India Rajalakshmi Engineering College Department of Computer Science and Design Chennai India Chinhoyi University of Technology Department of Mechatronics Engineering Zimbabwe

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

Landsat images are essential in geoinformatics and earth observation. However, clouds frequently contaminate satellite images, making cloud removal increasingly necessary due to the growing demand for satellite imagery applications. In order to improve the quality of Landsat imagery, we suggest a novel Time-Varying Features Neural Network (TVFN-N) that consists of several modules: cloud detection, a learning module, feature fusion, and image reconstruction. Metrics including the Structural Similarity Index (SSIM), Root Mean Square Error (RMSE), and Peak Signal Noise Ratio (PSNR) are used to assess how effective the reconstructed images are as individuals. Our tests show that the suggested strategy is quantitatively successful, obtaining an RMSE of less than 0.0179. TVFN-N shows significant improvement in reconstructing Landsat images using multitemporal methods compared to older deep learning techniques. Additionally, we compared our approach using cutting-edge deep learning models and produced more accurate outcomes for jobs involving cloud removal. © 2024 IEEE.

关键词： Landsat

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Hybrid Method for Photovoltaic system Forecasting Based on Wavelet Transform and LSTM 2

Hybrid Method for Photovoltaic System Forecasting Based on W...

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2nd IEEE International Conference on Electrical Engineering, computer and information technology, ICEECIT 2024

作者： Larasati, Devita Ayu Widjonarko Lin, Tzu-Chiao Chen, Chao-Rong Bachroin, Nabil Prakosa, Setya Widyawan Mujiono Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Electrical Engineering University of Jember Jember Indonesia Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Information Technology State Polytechnic of Jember Jember Indonesia

ISBN: (纸本)9798331504373

Photovoltaic (PV) forecasting is done to help in managing planning and knowing PV energy production. However, PV systems are always associated with fluctuating weather parameters, such as solar irradiance, temperature, wind speed, and humidity. In order, to forecast uncertain data, this object over here study proposed a combination method by using a concept of wavelet transform (WT) and the ability of long-short-term memory (LSTM). The WT is used to decompose the output power with the feature extraction process. Then it is used as input to the LSTM to forecast the futuristic value of output power. The forecasting results are shown in a curve and compared with actual data at several time horizons of 1 day, 15 days, 30 days, and 60 days. In addition, the results are also compared with several other ML methods such as Ridge Regression (RR), Linear Regression (LR), Least Absolute Shrinkage and Selection Operator (LASSO), and Elastic Net (ENET). Each of these treatments shows that the forecasting results using the proposed method are superior. This is also supported by evidence on the percent error values of Root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2). © 2024 IEEE.

关键词： Mean square error

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Harnessing Deep Learning for Activity Recognition in Seniors' Daily Routines with Wearable Sensors 47

Harnessing Deep Learning for Activity Recognition in Seniors...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Department of Applied Statistics Faculty of Applied Science Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350365597

Wearable sensors are increasingly popular for monitoring the daily activities of older individuals, sparking significant interest in human activity recognition (HAR). Accurate and timely identification of these activities is crucial for providing personalized care and timely interventions to maintain the well-being of elderly individuals. However, existing HAR methods often need support with complex activity patterns, limited labeled data, and the need for real-time performance. To address these challenges, this study proposes a novel deep-learning approach called ResBiGRU for precisely identifying daily activities in older adults using wearable sensors. The ResBiGRU architecture combines residual connections with BiGRU networks to capture short-term and long-term dependencies in sensor data effectively. We also propose a data augmentation strategy to improve generalization performance. Evaluation of the HAR70+ dataset, consisting of activities performed by 18 elderly individuals, demonstrates superior performance of our ResBiGRU model with an accuracy of 97.39% and an F1-score of 97.74%. Furthermore, our model supports real-time inference, making it suitable for practical applications. Implementing the ResBiGRU approach could significantly enhance monitoring and care for older adults by accurately identifying their daily activities. This research advances HAR methods tailored for elderly individuals, opening new avenues for personalized healthcare and assisted living technologies. © 2024 IEEE.

关键词： Wearable sensors

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Mobile lung cancer early warning based on Windows Azure cloud computing

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International Journal of Wireless and Mobile Computing 2021年第1期20卷 9-19页

作者： Lv, Jun Wang, Pan Zhao, Zijuan College of Information and Computer Taiyuan University of Technology Taiyuan Shanxi China Department of Computer Science and Technology Lvliang University Shanxi China

In view of the repeated investment in the current medical lung cancer early warning system, 'information island' and the low rate of lung cancer early warning, this paper proposes a mobile lung cancer early warning system based on Windows Azure cloud computing with the application requirements of medical information system. The system is applied to the smart phones of Windows system, and the Windows Azure Microsoft cloud platform is accessed through wireless network, using SQL Azure to store medical data (such as lung CT image, ECG curve and high-resolution colour ultrasound image), embed the medical lung cancer early warning model based on deep learning in the cloud computing platform to process and analyse the patient's medical data, and realise the real-time synchronous update of doctors, patients and cloud platform data. The accuracy of lung cancer early-warning system on LIDC-IDRI is high, and the detection results are uploaded to doctors and patients' smart phones in real time. The results show that the mobile lung cancer early warning system based on Windows Azure cloud computing not only effectively integrates medical resources, but also provides patients with medical services and consultation at home, which integrates the advantages of all aspects and adapts to the development of the times. © 2021 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Biological organs

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Investigating the Use of Artificial Intelligence for Secure and Transparent Record Keeping in Education 2

Investigating the Use of Artificial Intelligence for Secure ...

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2nd International Conference on Informatics, ICI 2023

作者： Ravindar, B. Naik, Darshana A. Jain, Neetu Lourens, Melanie Tiwari, Mohit Kunekar, Pankaj SR University School of Sciences Telangana Warangal India Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India Institute of Management and Research New Delhi India Durban University of Technology Faculty of Management Sciences South Africa Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India

ISBN: (纸本)9798350343830

Numerous businesses, particularly education, are being rapidly transformed by artificial intelligence (AI). AI is being utilised in school management to enhance student results, the learning experience, and administrative chores. This study intends to investigate the use of AI in educational management, comprising advantages and disadvantages. The study effort examines the literature on AI in school management using a systematic review method. According to the research, AI has several benefits, such as increased student engagement, personalised instruction, and cost effectiveness. But AI also presents several difficulties, including ethical issues, possible biases, and the necessity to reskill the workforce. The study finds that AI may significantly enhance educational management, but its application must be done so carefully and cautiously. © 2023 IEEE.

关键词： Students

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Detecting Face-Touching Gestures with Smartwatches and Deep Learning Networks 47

Detecting Face-Touching Gestures with Smartwatches and Deep ...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Faculty of Applied Science Department of Applied Statistics Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350365597

Everyday actions like scratching one's nose or resting the chin on one's hand may facilitate the spread of germs and diseases. Detecting these gestures holds the potential for innovative health monitoring and disease prevention applications. However, the identification of face-touching poses challenges due to the variety of human gestures and the limitations in accuracy associated with wearable sensors. This study introduces an original deep-learning system to identify face-touching gestures using standard smartwatches' inertial measurement unit sensors. The system on the smartwatch captures and pre-processes multi-channel time-series data from the accelerometer to generate robust input features. We utilize a benchmark, the Face Touching dataset, to evaluate the recognition performance of deep learning networks, including our proposed network. Our approach proposes a hybrid deep residual network architecture tailored for sequence-based gesture classification using signals from the smartwatch sensors. In our experiments, the developed deep learning framework achieves an impressive F1-score of 97.53% in detecting face-touch gestures. The suggested system takes a step forward in advancing the practical applications of face-touch gesture detection to smartwatch-based health sensing and disease prevention. © 2024 IEEE.

关键词： Network architecture

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Prediction based Auto-Pilot Interface for Drone to Object Chasing using Historical TSPI Data 23

Prediction based Auto-Pilot Interface for Drone to Object Ch...

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23rd International Conference on Control, Automation and systems, ICCAS 2023

作者： Jang, Shinhyoung Park, Byeonghwi Jeong, Juheon Mahedy, Jack Gebreslassie, Nebey Lee, Minji Matson, Eric T. Chungnam National University Department of Computer Science and Engineering Daejeon34134 Korea Republic of Purdue University Department of Computer and Information Technology West LafayetteIN47906 United States Purdue University Department of Technology West LafayetteIN47906 United States

ISBN: (纸本)9788993215274

Replacing military aircraft with Unmanned Aerial Vehicles (UAV) offers advantages such as reducing costs and risks associated with conventional military aircraft. To defend against enemy UAV attacks, anti-drone systems are used. While most technologies focus on drone detection, this paper proposes a process of tracking targets using arbitrary UAV to enhance camera data quality or directly engage and neutralize targets. The system comprises drone detection, movement prediction, and chasing. Devices such as radar are used to detect drones, and they provide Time-Space-Position information (TSPI) using suitable data formats. Using Machine Learning (ML) techniques make it feasible to predict an object's future location, represented with equations. Utilizing trained machine learning models, the Auto-pilot Application Programming Interface (API) of the drone is employed to track based on the predicted results. Through experiments, the feasibility of predicting an object's future location using various ML techniques has been confirmed. Furthermore, verification has been conducted to establish the attainability of implementing the Auto-pilot API through the utilization of DJI's API. © 2023 ICROS.

关键词： Drones

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Towards Electronic Voting Using Face Verification technology 19

Towards Electronic Voting Using Face Verification Technology

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19th International Conference on Emerging Technologies, ICET 2024

作者： Hashmi, Huma Ahmed, Hamza Ahmar, Rana Ali Syed, Muhammad Ibrahim Khan, Khurram Mahmood, Zahid Comsats University Islamabad Department of Electrical and Computer Engineering Abbottabad Pakistan Ziel Global Limited Department of Product Design Islamabad Pakistan Gik Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering KPK Topi Pakistan

ISBN: (纸本)9798350379587

This study presents an electronic voting system using the face detection and verification methods. The proposed system consists of face detection and face identification module to detect and authenticate voters' face. Initially, voter's face is detected using facial land mark estimation module. While face verification module uses linear discriminant analysis integrated with the adaptive boosting method. Face detection and verification modules work perfectly up to pose variation up to ±15°. Detailed experiments show that proposed system achieves a mean accuracy of 94.15% and requires nearly 50 seconds to cast a single vote. The study also contributes to the development of an excellent alternative to conventional electoral system. © 2024 IEEE.

关键词： Photointerpretation

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Playing the video games during COVID-19 pandemic and its effects on player's well-being 2

Playing the video games during COVID-19 pandemic and its eff...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Noaman, Samar Billi Ibrahim, Amer Ali, Liaqat Iqbal, M.W. Ashraf, Asma Haseeb, Usama Muneer, Salman Almajed, Rasah Hamid, K. Canadian University Dubai Faculty of Management Dubai United Arab Emirates American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates University of Science and Technology of Fujairah College of Engineering and Technology Fujairah United Arab Emirates Superior University Department of Software Engineering Lahore54000 Pakistan Superior University Department of Computer Science Lahore54000 Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan American University in the Dubai College of Computer and Information Technology United Arab Emirates

ISBN: (纸本)9798350335644

COVID-19 affected our lives intensly. That state of affairs made humans helpless. They had been depressed and experienced loneliness. At that time many human beings were determined to play video games just to loosen up their minds. Many games changed into an additional source of revenue wherein during COVID-19 they were playing while earning money. With these advantages, there were also some poor effects was accrued. A number of players remained playing video games post COVID-19. The carried out survey is based on the social video games results on players' well-being and additionally on the effects of gamers' health and their sensible lifestyles. We are going to investigate the behavior of gamers engaged with video games during the COVID-19 lockdown and the video games affects on their well-being, the time they served in playing video games, and the consequential effect on their behavior and social and mental well-being. The results provide a start line for empirically grounded discussions on video games at some stage in the pandemic, their use, and potential outcomes. Different agegroups of players have been investigated. Most players are between 18 and 30 years. A number of the gamers during lock down played a few hours but most of players were males who spent most of their day playing video games. However, now the ratio of playing video games is reduced as examined with past circumstances. Roughly we can say that the condition as a whole is better, the reason why players enforced video games in their post COVID-19 practical life1. © 2023 IEEE.

关键词： Coronavirus

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