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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Amir-Ul-Haque Bhuiyan, T.M. Ahmed, Shakhir Salahin, Md Pollab, Md Jui, Jannatun Nyeem Ahmmed, Md Tanvir Rahman, Anichur Wadud, Md. Anwar Hussen Bangladesh University of Engineering and Technology Department of Computer Science and Engineering Bangladesh Bangladesh University of Business and Technology Department of Computer Science and Engineering Bangladesh Constituent Institute of University of Dhaka Bangladesh

ISBN: (纸本)9798350316001

The Internet of Things (IoT) is revolutionizing healthcare by enabling remote patient monitoring. An IoT-based patient monitoring system is the main focus of this study that uses an electrocardiogram (ECG) sensor, a wire thermometer sensor, and a DHT11 sensor. The objective of the system is to continuously monitor the ECG signals of patients, monitor patients' body temperature and patients' room temperature and humidity, and transmit them wirelessly to a cloud-based server for real-time analysis. The proposed system comprises an ECG sensor, a one-wire thermometer sensor, a DHT11 sensor, a microcontroller, and a wireless communication module. The ECG sensor captures the electrical signals generated by the heart and converts them into digital signals. The DS18B20 sensor measures patients' body temperature, and the DHT11 sensor monitors room temperature and humidity. The microcontroller processes the digital signals and transmits them to the cloud-based server through the wireless communication module. The smartphone Apps take patients' records from the cloud, analyze the ECG signals, and generate necessary suggestions if any abnormalities are detected. © 2023 IEEE.

关键词： Electrocardiograms

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A Survey on Evolution of Video Summarization Techniques in the Era of Deep Learning 3

A Survey on Evolution of Video Summarization Techniques in t...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Vaghela, Priyalba Makwana, Ashwin Charotar University of Science and Technology Department of Computer Science and Engineering Gujarat Changa388421 India Charotar University of Science and Technology Department of Computer Engineering Gujarat Changa388421 India

ISBN: (纸本)9781665462167

By choosing the most instructive segments of the video content, video summarization systems seek to produce a succinct and comprehensive summarization. The most advanced techniques are those that use contemporary Deep Neural Network (DNN) architectures. Several strategies were developed in the past. The most current developments in the field are highlighted while providing a full analysis of current deep-learning-based approaches for broad video summarization. This study reviews the video summary technologies and an examination of key elements of deep-learning-based algorithms. An analysis of deep learning-based algorithms is provided to analyze the development of deep learning-based algorithms for video summarization and to offer suggestions for the pace of future research. The results of these comparisons, as well as some previously discussed challenges regarding the datasets and the applicability of evaluation procedures, will leverage future directions. © 2023 IEEE.

关键词： Long short-term memory

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Adaptive Particle Swarm Optimization of U-net Parameters for Marine Aquaculture Image Segmentation 13

Adaptive Particle Swarm Optimization of U-net Parameters for...

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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Zhao, Jin Wang, Jun Han, Min Fan, Jianchao School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Department of Computer Science Department of Data Science City University of HongKong Hong Kong School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331516147

Marine aquaculture image segmentation plays a crucial role in managing aquatic resources and environmental protection. Traditional deep learning models rely on manual parameter tuning for image segmentation, which limits their efficiency and accuracy. This paper proposes an adaptive Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the U-net model automatically. The algorithm dynamically adjusts the PSO parameters based on the population’s entropy and clustering metrics and employs a hill-climbing algorithm to address the issue of the PSO easily falling into local optima, enhancing the algorithm’s adaptability. The proposed algorithm updates and iterates particles to automatically find suitable model parameters. The remote sensing data used in this experiment were from the Yellow Sea aquaculture area near Dalian, China, with a segmentation accuracy of 91.8%. This method improves segmentation accuracy, reduces the burden of manual parameter tuning, and provides an effective solution for optimizing deep learning models. © 2025 IEEE.

关键词： Aquaculture

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Enhancing remote sensing image analysis: optimization of a hybrid deep network through HHO algorithm

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Multimedia Tools and Applications 2025年 1-21页

作者： Digra, Monia Dhir, Renu Sharma, Nonita Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Punjab Jalandhar144011 India Department of Information Technology Indra Gandhi Delhi Technical University Delhi India

Remote sensing (RS) image interpretation includes multi-label picture classification, a key problem. The complicated and diverse character of many remote sensing scenes, which are produced by the spatial combination and association of various objects, makes classification difficult. In this research, an innovative approach that combines deep neural networks with bio-inspired optimization techniques to handle the challenges of classifying remote sensing images. First, the efficacy of proposed model was evaluated using the publicly accessible remote sensing scene dataset for multi-label classification. Second, this work employs the HHO (Harris Hawk optimization) model to improve classification performance by efficiently choosing features and also reduces the time complexity by 78%. The DenseNet-169 and VGGNet16 models extract the necessary properties to enhance remote sensing classification. Finally, validate the data by integrating additional validation data in the training set, which leads to more reliable and consistent accuracy and loss. The proposed VGGNet16 with Bi-LSTM and DenseNet169 with Bi-LSTM achieves accuracy on four distinct datasets 98.83% and 99.69% on UCM, 97.77% and 97.98% on AID, 95.06% and 96.84% on NWPU, whereas RSI-CB256 dataset achieved 98.5% and 98.91% accuracy. The hybrid fusion model distinguishes important from irrelevant data. To demonstrate its efficacy, authors conducted extensive tests on various distinct datasets such as UCM, AID, NWPU, RSICB256 and have ascertained that this approach exhibits superiority over existing state-of-the-art techniques currently employed in the field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep neural networks

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3D CNN with Attention Modules for Enhanced COVID-19 and Pneumonia Screening from CT Scans 2

3D CNN with Attention Modules for Enhanced COVID-19 and Pneu...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Rahman, Taifur Faruk, Md. Farukuzzaman Khansur, Md. Nasif Srizon, Azmain Yakin Hasan, S. M. Mahedy Al Mamun, Md. Hossain, Md. Faruk Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Electrical & Electronic Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331517984

During the COVID-19 outbreak, deep-learning techniques have been extensively studied for computer-assisted diagnosis. However, distinguishing COVID-19 from other types of pneumonia remains challenging. This research describes an automated chest CT imaging technique for COVID-19 pneumonia screening. CT scans provide valuable three-dimensional information, aiding in detecting COVID-19 symptoms. The limited availability of comprehensive 3D datasets has slowed the use of attention-based models in this field. To address this, we propose a novel 3D Convolutional Neural Network (CNN) with a residual block incorporating self-attention and spatial attention modules. Our primary goal is accurately classifying COVID-19 and pneumonia cases based on CT scans. Integrating attention modules enhances spatial correlation analysis across CT slices, enabling dynamic re-calibration of the CNN output to identify complex patterns. Training our 3D CNN on CT scan images from the CC-CCII dataset achieved an impressive accuracy rate of 99.42%. Subsequently, we used the CNN's prediction probabilities to construct feature vectors for each sample. By training various machine learning classifiers independently and combining them through soft voting, we achieved a remarkable 98.66% accuracy. This study highlights how our approach can improve pulmonary patient care and diagnostic processes, leveraging state-of-the-art deep learning techniques for accurate and efficient COVID-19 pneumonia screening. © 2024 IEEE.

关键词： COVID-19

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Scheduling of Low-Latency Medical Services in Healthcare Cloud with Deep Reinforcement Learning

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Tsinghua science and technology 2025年第1期30卷 100-111页

作者： Hongfei Du Ming Liu Nianbo Liu Deying Li Wenzhong Li Lifeng Xu School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610054China School of Information Renmin University of ChinaBeijing 100872China Department of Computer Science and Technology Nanjing UniversityNanjing 210093China People’s Hospital of Quzhou City Wenzhou Medical UniversityQuzhou 324000China

In the current landscape of online data services,data transmission and cloud computing are often controlled separately by Internet Service Providers(ISPs)and cloud providers,resulting in significant cooperation challenges and suboptimal global data service *** this study,we propose an end-to-end scheduling method aimed at supporting low-latency and computation-intensive medical services within local wireless networks and healthcare *** approach serves as a practical paradigm for achieving low-latency data services in local private cloud *** meet the low-latency requirement while minimizing communication and computation resource usage,we leverage Deep Reinforcement Learning(DRL)algorithms to learn a policy for automatically regulating the transmission rate of medical services and the computation speed of cloud ***,we utilize a two-stage tandem queue to address this problem *** experiments are conducted to validate the effectiveness for our proposed method under various arrival rates of medical services.

关键词： medical service tandem queue cloud computing Deep Reinforcement Learning(DRL) resource allocation

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Machine Learning Algorithms on COVID-19 Prediction Using CpG Island and AT-CG Feature on Human Genomic Data 1st

Machine Learning Algorithms on COVID-19 Prediction Using CpG...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Manik, Md. Motaleb Hossen Habib, Md.Ahsan Ahmed, Tanim Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna Bangladesh

ISBN: (纸本)9783031346217

A pandemic has broken out throughout the world since December 2019 and later it has been named COVID-19. The flow of normal life has collapsed due to this pandemic, especially in the economic, public health, and education sectors. The number of infected people is increasing daily. So, the identification of COVID-19 patients is a crying need in the health sector to stop the spread of this virus. Recently, Machine Learning algorithms have shown fascinating results in the prediction of medical data. In this study, we have proposed an approach to predict COVID-19 on human genomic data using the CpG Island feature and the newly introduced AT-CG feature by Machine Learning algorithms. Our proposed system produces an impressive result of the highest 99.91% accuracy on the Naïve Bayes algorithm. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Coronavirus

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FPGA based ANN classifier for Bengali Handwritten Digit Recognition 26

FPGA based ANN classifier for Bengali Handwritten Digit Reco...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Hossain, S M Riad Zinnurine, Khaled Sadad, Nahin Ul Mondal, Md. Nazrul Islam Rajshahi University of Engineering & Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

In order to accurately and quickly recognize Bengali handwritten digits and characters, this paper suggests an FPGA-based hardware accelerator design of ANN for handwritten Bengali character recognition applications. To discover the best FPGA design for the various categorization rates, a study of network designs is first undertaken. Sigmoid as well as ReLU neurons integrated by Xilinx Vivado workspace and an axi DMA controller created in Verilog Hardware Description Language (HDL) make up the majority of its components. It is clear that sigmoid-based function implementation exceeds ReLU-based implementation for very tiny data widths (such as 4 and 8 bits). The maximum detection accuracy for Sigmoid implementation is 94.00%, and the maximum for ReLU is 87.00%. Finally, this hardware upgrade considerably increased processing speed by 4 times compared to software implementation and about 27 times faster in terms of existing CPU-based hardware implementation, in addition to maintaining recognition accuracy the same as software equivalence. Also, the power consumption is reduced by more than half of its existing solution as discussed before. As a result, the fast and accurate recognition of Bengali handwritten numbers with low power consumption was made possible by our specialized ANN models with FPGA capabilities. © 2023 IEEE.

关键词： and ReLU Artificial Neural Network (ANN) Bengali Digit Field Programmable Gate Array Hardware Accelerator HDL Recognition Sigmoid

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HQF-CC: hybrid framework for automated respiratory disease detection based on quantum feature extractor and custom classifier model using chest X-rays

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International Journal of Information technology (Singapore) 2024年第2期16卷 1145-1153页

作者： Eswara Rao, G.V. Rajitha, B. Computer Science and Engineering Department Motilal Nehru National Institute of Technology Allahabad UP Prayagraj 211004 India

In recent years, humans have been affected by a variety of respiratory diseases, such as dry cough, fever, pneumonia, and COVID-19. Respiratory diseases may severely damage the respiratory system of humans. The early diagnosis of it can help enable appropriate treatment immediately to reduce the abnormalities in the healthcare monitoring systems. A chest X-ray could be used as one of the medical tools to assess the severity of respiratory diseases. In order to predict or classify respiratory diseases, many researchers have proposed different deep learning-based models. But they consume more computational resources. This article proposes a hybrid framework for respiratory disease detection based on a quantum feature extractor and custom classifier model (HQF-CC) from Chest X-rays. Here, we propose a quantum machine learning algorithm named MMS (Multi-Multi-Single) for feature extraction and a custom classifier for classification. The experiments were performed on the COVID-19 Radiography Dataset (CRD), which includes 15,153 Chest X-ray images of COVID-19, viral, and Normal diseases, respectively. The proposed model had the highest training and testing accuracy of 97.2% and 98.8% on the CRD dataset and a training and testing loss of 0.02, 0.01. Upon various experiments, the proposed model has proven to be more accurate, portable, and memory-efficient than other deep and machine learning models for respiratory disease detection. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Classification Deep learning model Feature extraction Quantum transfer learning Qubit Respiratory disease

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Review on Malware Classification and Malware Detection Using Transfer Learning Approach 5

Review on Malware Classification and Malware Detection Using...

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5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Priya, V. Sofia, A. Sathya Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9781665474672

The amount of malware infecting computers and other communication devices and migrating across the internet has greatly increased over the years. Numerous methods and procedures have been put forth up to this point to find and eliminate these hostile agents. However, a lot of malwares is still being developed, which can get past some cutting-edge malware detection algorithms, as new and automated malware production techniques emerge. Consequently, there is a need for the classification and detection of these antagonistic agents that have the potential to compromise the security of individuals, businesses, and a wide range of other digital assets. To develop a new improved approach for efficient zero-day malware detection, there is a compelling need to reduce bias and objectively assess these methods. This study focuses on investigating transfer learning strategies for malware picture classification, including AlexNet, VGG16, VGG19, GoogLeNet, and ResNet. Here, malware binaries are turned into grayscale images before being processed using models for classification based on transfer learning. As transfer learning uses pre-trained models, the primary objective is to save training time. In addition, an effective model for malware classification will be built in order to obtain performance that is unbiased. © 2023 IEEE.

关键词： Malware

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