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2023 International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2023

作者： Manivasagam Kumar, Anshu Murshleen, Mohd. Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350344387

Strategies that may gain insight from both labeled and unlabeled data as well as from their environment are being developed through the use of machine learning. Machine learning is applied in many domains, but it is most commonly used in the healthcare sector, where it offers substantial benefits through suitable decision- and prediction-making techniques. A online system which can continuously monitor the individual's condition and generate an alert when needed is introduced by the proposed work. This suggested methodology uses cloud computing and various machine learning algorithms to store data continuously. To identify risks and provide the right treatment and diagnosis, physicians analyze medical data using machine learning techniques and tools. The range of remote healthcare services includes monitoring elderly patients, premature children, patients with chronic illnesses, and victims of accidents. The current study investigates how machine learning technologies can be used to monitor patients who are located remotely and provide real-time alerts about their condition via a remote system. © 2023 IEEE.

关键词： cloud computing machine learning remote healthcare Remote patient monitoring

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models 16

VisioSLR: A Vision Data-Driven Framework for Sign Language V...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Ismail, Leila Shahin, Nada Tesfaye, Henok Hennebelle, Alain Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Transfer learning

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Industrial Teleassistance with the Help of Augmented Reality 23

Industrial Teleassistance with the Help of Augmented Reality

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Bobeica, Valentin Popescu, Dan Angelescu, Nicoleta Ichim, Loretta National University of Science and Technology Politehnica Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania Valahia University of Targoviste Faculty of Electrical Engineering Electronics and Information Technology Targoviste Romania

ISBN: (纸本)9798331540388

This paper details the integration of augmented reality (AR) in a new factory, focusing on enhancing maintenance efficiency and optimization. The innovative approach involves combining AR technology, an Android application, and Siemens PLCs (Programmable Logic Controller), for efficient automation. The robustness of the PLC enables seamless integration within manufacturing environments, accommodating diverse programming paradigms. The software aspect features Human Machine Interface (HMI) configuration using Siemens' WinCC software, offering dynamic visualizations, alarm handling, and data logging. WinCC's scalability suits applications of varying sizes. The HMI architecture is designed for easy parameter display. The Android application, developed with Java and Python, leverages their respective strengths in Android app development and image processing. Data exchange is facilitated by OPC-UA, supporting multiple programming languages for seamless communication. The AR Teleassistance is highlighted as a revolutionary solution, fostering real-Time communication among global experts, technicians, and operators. Beyond gaming origins, AR teleassistance enhances efficiency in various sectors, minimizing downtime and facilitating global knowledge transfer. The integration of Teleassistance promotes effective collaboration between human expertise and automated systems, crucial for optimal performance in modern industrial settings. Continuous development is suggested through the addition of more 3D mechanical drawings and real-Time stock information for parts in the application, aiming for future optimization and performance enhancement. © 2024 IEEE.

关键词： augmented reality data exchange global knowledge image processing manufacturing environment production processes smart industrial

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A study of adaptive sampling based primitive fidelity methods 16

A study of adaptive sampling based primitive fidelity method...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Xu, Fei Li, Xiaoli Liu, Weiyan Chen, Dong School of Computer Science and Technology Nanjing Tech University Nanjing211816 China School of Information Engineering Jiangsu Open University Nanjing210017 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

The accuracy of images is frequently compromised during the processes of pattern generation and transformation, presenting challenges for existing methodologies to achieve an optimal balance between efficiency and accuracy. To address this issue, a novel primitive fidelity method utilizing adaptive sampling is introduced. This method takes into account the constituent elements of the pattern, designating the primitive as the fundamental unit of vectorization. It initializes the path through adaptive sampling, dynamically regulates the number of segments, and employs a loss function for optimization, thereby enhancing both efficiency and accuracy. Notably, this method is model-free and does not require shape primitive labels, which allows the proposed approach to break through the limitations of specific application domains and escape the challenges of collecting and generalizing vector graphic datasets. The experimental results indicate that the proposed method demonstrates good performance in terms of generation quality and operational efficiency on both public datasets and constructed datasets. © 2025 SPIE.

关键词： Adaptive sampling Model-free Primitive fidelity method Shape primitive labels Vectorization

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Addressing Weight Competition in Long Short-Term Memory (LSTM) Networks for Improving Temperature Prediction 12

Addressing Weight Competition in Long Short-Term Memory (LST...

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12th International Conference on Awareness science and technology, iCAST 2023

作者： Lin, Kun-Wei Li, Yu-Fan Lin, Che-Yu Jiang, Zheng-Han Chen, Wei-Jian dept. Computer Science and Information Engineering Chaoyang University of Technology Taichung Taiwan dept. Information Management Chaoyang University of Technology Taichung Taiwan

ISBN: (纸本)9798350324693

This conference paper presents a study on the application of Long Short-Term Memory (LSTM) networks to predict temperature changes in the upcoming week. During our research, we encountered the issue of weight competition within the LSTM architecture, which can lead to suboptimal predictions. To overcome this challenge, three novel methods including Independent Time Series (ITS) Model, Arithmetic Segmented Time Series (ASTS) Model and Arithmetic Segmented Rolling Time Series (ASRTS) Model, respectively, were used to address weight competition and enhance the accuracy of temperature change forecasts. The effectiveness of these methods were evaluated through extensive experiments and compared against standard LSTM models. The results demonstrate the significance of mitigating weight competition to achieve more reliable and accurate temperature predictions. © 2023 IEEE.

关键词： Long short-term memory

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Alzheimer's Disease Classification via Pre-trained Convolutional Neural Network Variants Utilizing Multi-level Thresholding based on Kapur's Entropy 2

Alzheimer's Disease Classification via Pre-trained Convoluti...

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2nd IEEE International Conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Mahendran, Nivedhitha Durai Raj Vincent, P.M. Srm Institute of Science and Technology School of Computing Technologies Department of Networking and Communications Kattankulathur Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372892

Neurological diseases are the disorders of brain and the nerves that run throughout the human body. There is a range of symptoms that arise as a result of chemical, structural, or electrical modifications in the regions of brain. One such neurological disease is the Alzheimer's Disease (AD), is considered as a slow and progressive disease that majorly affects the cognitive abilities of the individual. It is most common forms of dementia without any proper cure. However, early diagnosis and treatment might slow down the symptoms and improve the lifestyle of the individual. The most common techniques to identify the neurological disorders are imaging of the brain regions, namely, CT, MRI, PET, etc. The learning approaches, such as Deep Learning and Machine Learning are identified to be effective in analyzing these images to provide better accuracies in detecting the AD. In this study, four versions of Convolutional Neural Networks (CNN) are used to classify AD and Control cases. Further, the Kapur's thresholding technique is also implemented to segment the images for improving the classification accuracy of AD. From the results shown in the study, the inference shows that the VGG16 of CNN provides considerable improvement than the other deep learning models before and after the implementation of thresholding. © 2024 IEEE.

关键词： Convolutional neural networks

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Early Detection of Coronary Heart Disease Using Exploration Data Analysis and Gradient Boosting Method

Early Detection of Coronary Heart Disease Using Exploration ...

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2024 International Conference on information technology and Computing, ICITCOM 2024

作者： Riyadi, Slamet Iqbal Al-Habib, Muhammad Alfiana, Fitri Damarjati, Cahya Jusman, Yessi Ramli, Suzaimah Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Information Technology Yogyakarta Indonesia Universiti Pertahanan Nasional Malaysia Faculty of Defense Science and Technology Department of Computer Science Kuala Lumpur Malaysia

ISBN: (纸本)9798350379839

Coronary heart disease is a significant global health issue that contributes to high morbidity and mortality rates, including in Indonesia. Public awareness of risk factors for heart disease is a matter that has an impact on disease burden, prevention, and timely treatment. However, public awareness of coronary heart disease and its risk factors is still low. Many individuals continue to engage in unhealthy lifestyles, lack awareness of effective preventive actions, and neglect regular heart health check-ups. To address these challenges, one of the solutions that can be made is a valuable tool for identifying individuals at risk and enabling early intervention and preventive measures. This study proposes implementing a machine learning model for the early detection of coronary heart disease using Exploration Data Analysis (EDA) and Gradient Boosting methods. Integrates EDA techniques for preprocessing data, such as finding important features and patterns, and then Gradient Boosting techniques to capture complex relationships and make accurate predictions based on a wide range of factors, such as a person's disease history, daily habits, and body characteristics. To evaluate the performance of our model, we split the dataset with an 80:20 ratio for training and testing and achieved an impressive accuracy of 91,39%. These results showed that our model could reliably predict the presence or absence of cardiac disease. With such precise forecasts, early prevention, identification, and treatment might be aided, which would ultimately reduce heart disease-related mortality and morbidity. © 2024 IEEE.

关键词： Adaptive boosting

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WU-Net++: A novel enhanced Weighted U-Net++ model for brain tumor detection and segmentation from multi-parametric magnetic resonance scans

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Multimedia Tools and Applications 2024年第28期83卷 71885-71908页

作者： Das, Suchismita Dubey, Rajni Jena, Biswajit Tsai, Lung-Wen Saxena, Sanjay Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India Division of Cardiology Department of Internal Medicine Taipei Medical University Hospital Taipei111031 Taiwan Department of Computer Science and Engineering Institute of Technical Education and Research SOA Deemed to Be University Bhubaneswar India Department of Medicine Research Taipei Medical University Hospital Taipei111031 Taiwan Department of Information Technology Office Taipei Medical University Hospital Taipei11031 Taiwan Graduate Institute of Data Science College of Management Taipei Medical University Taipei11031 Taiwan

Brain tumor detection and segmentation from multi-parametric magnetic resonance (MR) scans are crucial for the prognosis and treatment planning of brain tumor patients in current clinical practice. With recent technological advancements, artificial intelligence-based deep learning has proven its indispensable image analysis capability in the most challenging tasks. This study proposes an automated WU-Net + + deep learning model for brain tumor segmentation using multiparametric structural MR scans obtained from the BraTS 2018 dataset. The model was validated through cross-dataset testing for intracranial hemorrhage (ICH) classification (ATLAS V2.1 dataset) and multi-organ segmentation from abdominal images of TCIA and BTCV datasets. The WU-Net++ model, a novel version of U-Net, was developed by adjusting its pooling operation as a weighted function of max and average pooling for brain tumor segmentation. The proposed model (WU-Net + +) achieved an F1 score of 0.94 ± 0.124, a dice score of 0.91 ± 0.132, and an AUC value of 0.915 for whole tumor segmentation. The model also achieved a high accuracy of 0.9949 ± 0.121 in ICH classification and dice scores of 0.912 ± 0.21, 0.844 ± 0.25, and 0.893 ± 0.17 for spleen, esophagus, and portal and splenic vein segmentation, respectively. Our study revealed that WU-Net + + has significant potential to improve the accuracy of segmentation and could be an effective method in the era of precision medicine. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetic resonance imaging

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UAV-based edge computing system used in energy-saving offloading method

UAV-based edge computing system used in energy-saving offloa...

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International Conference on Emerging Electronic and Automation technology 2022, ICEEAT 2022

作者： Abdulazeez, Hussein Amer Shaker, Ahmed Maher School of Communication and Information Engineering Huazhing University of Science and Technology Hubei Wuhan China School of Computer Science Univeirty of Wuhan Technology Wuhan China

This paper proposes an offloading strategy based on unmanned aerial vehicle (UAV) managed computer resources to provide dependable communication and intensive processing power in complicated terrain. In order to reduce compute and offload user terminal energy consumption, a UAV-assisted edge computing model is built, taking into account the computing requirements of user terminals, the delay restrictions of computing jobs, and the energy limits of UAVs. The trajectory can be used to reduce the user terminal's energy usage in the allotted amount of time. The simulation results demonstrate that the suggested approach is appropriate for various channel circumstances, allowing the user terminal to accomplish the work while outperforming existing benchmark systems in terms of energy usage. Optimizing the flight trajectory of the UAV will have a substantial impact when the signal-to-noise ratio SNR=20dB. The results demonstrate that, at T=5s, the UAV's flying trajectory optimization strategy reduces energy consumption by 56.26%. © Published under licence by IOP Publishing Ltd.

关键词： Unmanned aerial vehicles (UAV)

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Liquidt: stock market analysis using liquid time-constant neural networks

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International Journal of information technology (Singapore) 2024年第2期16卷 909-920页

作者： Gajjar, Pranshav Saxena, Aayush Acharya, Kairav Shah, Pooja Bhatt, Chintan Nguyen, Thanh Thi College of Engineering and Computing George Mason University Fairfax VA United States Electronics and Computer Science University of Southampton Southampton United Kingdom Department of Computer Science & Engineering School of Technology Pandit Deendayal Energy University Gandhinagar India School of Information Technology Deakin University Geelong Australia Institute of Technology Nirma University Ahmedabad India

Accurate and efficient predictions concerning stock prices are an intriguing and sought-after task in the field of computational financial analysis. This paper aims to leverage and validate novel deep learning pipelines for predicting NSE stock prices of Adani Ports, Reliance, and Tata Steel using recurrent neural architectures. The scope of this paper pertains to the utility of Liquid Time-Constant Networks (LTCs) and the development of a novel enhanced computing paradigm centric on LTCs. By assessing the use of bi-directionality for time-series prediction and the existing modality of architectures with LSTM and BiLSTM implications, the authors functioned on developing an LTC-based architecture that is capable of predicting stock market trends in a superlative fashion. The paper thoroughly explains the temporal characteristics and predictive trends and completes a comparative analysis with simple RNNs, GRUs, LSTMs, and their bidirectional counterparts, emphasizing the potential substitutes of the frequently used LSTMs. After sufficient experiments, the paper presents a comparative study for gauging a computational efficiency and accuracy trade-off for the aforementioned task. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Deep learning Liquid neural networks Machine learning Stock price prediction Time series data

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