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2024 International Automatic Control Conference, CACS 2024

作者： Horng, Shih-Cheng Lin, Shieh-Shing Chen, Chih-Yueh Chaoyang University of Technology Department of Computer Science & Information Engineering Taichung Taiwan St. John's University Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798350354904

The emergency department (ED) is one of the busiest regions of a hospital and a high patient volume can impact even the most efficient processes. However, when the ED becomes overcrowded, the ability to deliver timely and effective care is compromised, leading to significant clinical repercussions for health systems. Hospital managers are struggling to balance staffing, safety and care in overcrowded EDs. As an important source of care for safety net populations and those without access to other care, EDs must become more collaborative, adaptable and agile to thrive under these difficult conditions. In this research, an algorithm cooperating driving training-based optimization with ordinal optimization is developed to decide the optimal allocation of emergency medical resources. Driving training-based optimization is a human-based metaheuristic approach that mimics the human activity of driving training. The proposed algorithm is presented to decide the optimal allocation of medical resources for minimizing average patient hospital stays and cost of medical waste simultaneously, and satisfy the specified constraints. The experimental results show that the proposed algorithm can obtain stable and best results on optimal allocation of medical resources problems. © 2024 IEEE.

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Brain Tumor Detection and Classification Using Hybrid VGG Network 26

Brain Tumor Detection and Classification Using Hybrid VGG Ne...

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

作者： Sakib, Adnan Khan, Nisha Jaman Shahrier, Labiba Plabon, Kaushik Al Mahmud, Jubayer Mridha, M.F. Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798350359015

In their highest degree, brain tumors can be extremely deadly. Misdiagnosis can lead to the incorrect course of treatment and lower the likelihood of recovery for patients. To tackle these problems, a hybrid VGG network is extensively researched in the proposed framework to classify brain tumors, including meningioma, pituitary, glioma, and healthy brain MRI slices. In this work, we propose a hybrid model for brain tumor classification. We made use of a publicly available Kaggle brain tumor dataset that included MRI scans showing no brain tumors and three different types of brain tumors: meningiomas, pituitary tumors, and gliomas. We performed preprocessing on the dataset. As our base model, we employed VGG16 and added more CNN layers to it. We trained the additional CNN layers while using transfer learning to freeze the base model layers. The pre-trained VGG16 model is familiar with a variety of textures, forms, and features related to medical imaging. We also added more CNN layers that discovered more complex and distinct task-related brain tumor pattern features that might not have been present in the original ImageNet dataset. We obtained 96.52% test accuracy with our model. © 2023 IEEE.

关键词： Brain tumor CNN Data Augmentation Deep learning Empirical Evaluation Machine Learning MRI

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Broken Rotor Bar Fault Detection in Induction Machines Based on Rectified Three-Phase Stator Currents 10

Broken Rotor Bar Fault Detection in Induction Machines Based...

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10th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Hajnayeb, Saeed Amerian, Fatemeh Marzebali, Mohammad Hoseintabar Abolghasemi, Vahid Seyyedbarzegar, Seyyed Meysam Shahrood University of Technology Faculty of Electrical Engineering Department Shahrood Iran School of Computer Science and Electronic Engineering Department University of ESSEX Colchester United Kingdom

ISBN: (纸本)9798331532543

Induction machines are widely used in industrial and household applications due to their efficiency in electromechanical energy conversion. However, broken rotor bars can significantly disrupt machine performance, typically manifesting as sideband harmonics near the fundamental frequency which can be utilized for fault detection. Nonetheless, frequency leakage can impede fault detection, concealing fault until substantial damage occurs. The proposed method, based on the rectified three-phase stator current (RTPSC), effectively reduces leakage frequency while maintaining the fault characteristic frequency (FCF) at its original amplitude. In addition, generate the fundamental harmonic at a frequency of 6 times. This approach aims to is to enhance diagnostic capabilities in the maintenance and monitoring of induction machines, improving operational efficiency and it also reduces the chance of catastrophic failures. The proposed methodology is tested on cases involving one, two, and three broken rotor bars, with the simulation results validated through experimental verification. © 2024 IEEE.

关键词： Stators

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Grocery Purchase through Blockchain System 7

Grocery Purchase through Blockchain System

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Kumar, Boddu Manoj Rajeswari, G. Thanush, B.V. Krishna, Vamsi Sree, E. Divija Balakrishnan, Arunkumar Sathyabama Institute of Science and Technology School of Computer Science Engineering with Blockchain Technology Tamil Nadu Chennai600119 India Sathyabama Institute of Science and Technology School of Computer Science Engineering Tamil Nadu Chennai600119 India VIT-AP University School of Computer Science Engineering India Crm Institute of Technology School of Computer Science Engineering Karnataka Bengaluru India Sree Vidyanikethan Engineering College Rangampeta School of Computer Science Engineering with Artificial Intelligence Tirupati517102 India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India

ISBN: (纸本)9798331527822

The integration of blockchain technology into the grocery purchasing process offers a transformative approach to enhancing transparency, security, and efficiency. This paper presents a comprehensive framework for a blockchain-based grocery system, detailing each step from user registration and identity verification to product listing, smart contract creation, and real-time inventory management. Consumers can place orders with confidence, knowing that payments are securely processed and supply chain logistics are fully transparent. The use of IoT devices ensures quality control during transportation, while smart contracts automate delivery and receipt confirmation. Additionally, the system supports consumer feedback and dispute resolution through immutable records and predefined arbitration mechanisms. This innovative application of blockchain technology aims to revolutionize the grocery sector, fostering trust and accountability among all stakeholders involved. Future research will explore the potential for AI integration and address scalability and interoperability challenges. © 2024 IEEE.

关键词： Smart contract

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Early-Stage Cervical Cancerous Cell Detection from Cervix Images Using YOLOv5

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computers, Materials & Continua 2023年第2期74卷 3727-3741页

作者： Md Zahid Hasan Ontor Md Mamun Ali Kawsar Ahmed Francis M.Bui Fahad Ahmed Al-Zahrani S.M.Hasan Mahmud Sami Azam Department of Software Engineering(SWE) Daffodil International University(DIU)SukrabadDhaka1207Bangladesh Group of Biophotomatiχ Department of Information and Communication TechnologyMawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh Department of Electrical and Computer Engineering University of Saskatchewan57 Campus DriveSaskatoonS7N5A9SKCanada Computer Engineering Department Umm Al-Qura UniversityMecca24381Saudi Arabia Department of Computer Science American International University-Bangladesh(AIUB)KuratoliDhaka1229Bangladesh College of Engineering IT and EnvironmentCharles Darwin UniversityCasuarina0909NTAustralia

Cervical Cancer(CC)is a rapidly growing disease among women throughout the world,especially in developed and developing *** this many women have ***,it is curable if it can be diagnosed and detected at an early stage and taken proper *** the high cost,awareness,highly equipped diagnosis environment,and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early *** solve this issue,the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix cell *** system is designed using the YOLOv5(You Only Look Once Version 5)model,which is a deep learning *** build the model,cervical cancer pap-smear test image datasets were collected from an open-source repository and these were labeled and *** the YOLOv5 models were applied to the labeled dataset to train the *** versions of the YOLOv5 model were applied in this study to find the best fit model for building the automated system to diagnose CC at an early *** of the model’s variations performed *** model can effectively detect cervical cancerous cell,according to the findings of the *** the medical field,our study will be quite *** can be a good option for radiologists and help them make the best selections possible.

关键词： Cervical cancer pap-smear deep learning cancerous cell YOLOv5 model

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Application of Fuzzy Theory to Parameter Design in Wafer Stealth Laser Dicing

Application of Fuzzy Theory to Parameter Design in Wafer Ste...

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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Su, Te-Jen Chiu, Chien-Liang Su, Chun-Hsien Tian, Mau-Yi Cheng, Jui-Chuan Wang, Shih-Ming Department of Electronic Engineering National Kaohsiung University of Sciences and Technology Kaohsiung Taiwan Department of Computer Science and Information Engineering Cheng Shiu University Kaohsiung Taiwan

ISBN: (纸本)9798350389210

The application of fuzzy theory in stealth laser dicing technology for wafers aims to enhance the precision and efficiency of cutting parameters. With technological advancements and the growth of the electric vehicle industry, the demand for semiconductor chips has rapidly increased, driving wafer cutting technology towards greater efficiency and precision. Compared to traditional blade cutting and laser cutting techniques, stealth laser dicing technology can avoid the chipping caused by blade cuts and the thermal damage caused by laser cuts, providing a stress-free and thermal damage-free solution. This study optimizes cutting parameters through a fuzzy control system using a layered setting method. The first layer determines the most suitable laser power based on the thickness of the workpiece and the metal layer;the second layer uses the final wafer thickness and the laser power determined in the first layer as inputs to calculate the optimal scribing height. These parameter settings aim to enhance cutting quality and efficiency while also verifying the practical application effects of the fuzzy control system. Experimental results show that the fuzzy control system significantly improves cutting precision and efficiency, achieving a 100% cutting success rate, a 66% reduction in material consumption, a 4% reduction in energy usage, and a 50% reduction in parameter adjustment labor time. The precision of the fuzzy control system in controlling laser power surpasses manual settings, and the optimization of scribing height is also significantly improved. The study demonstrates that fuzzy theory has extensive potential applications in stealth laser dicing of wafers. Future research may integrate artificial intelligence technology to further enhance the automation of cutting parameter settings, thereby improving production efficiency and product quality. © 2024 IEEE.

关键词： Fuzzy control

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CRAFT: Criticality-Aware Fault-Tolerance Enhancement Techniques for Emerging Memories-Based Deep Neural Networks

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2023年第10期42卷 3289-3300页

作者： Nguyen, Thai-Hoang Imran, Muhammad Choi, Jaehyuk Yang, Joon-Sung Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical Engineering School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad44000 Pakistan Department of Semiconductor Systems Engineering Sungkyunkwan University Suwon16419 Korea Republic of School of Electrical and Electronic Engineering Department of System Semiconductor Engineering Yonsei University Seoul03722 Korea Republic of

Deep neural networks (DNNs) have emerged as the most effective programming paradigm for computer vision and natural language processing applications. With the rapid development of DNNs, efficient hardware architectures for deploying DNN-based applications on edge devices have been extensively studied. Emerging nonvolatile memories (NVMs), with their better scalability, nonvolatility, and good read performance, are found to be promising candidates for deploying DNNs. However, despite the promise, emerging NVMs often suffer from reliability issues, such as stuck-at faults, which decrease the chip yield/memory lifetime and severely impact the accuracy of DNNs. A stuck-at cell can be read but not reprogrammed, thus, stuck-at faults in NVMs may or may not result in errors depending on the data to be stored. By reducing the number of errors caused by stuck-at faults, the reliability of a DNN-based system can be enhanced. This article proposes CRAFT, i.e., criticality-aware fault-tolerance enhancement techniques to enhance the reliability of NVM-based DNNs in the presence of stuck-at faults. A data block remapping technique is used to reduce the impact of stuck-at faults on DNNs accuracy. Additionally, by performing bit-level criticality analysis on various DNNs, the critical-bit positions in network parameters that can significantly impact the accuracy are identified. Based on this analysis, we propose an encoding method which effectively swaps the critical bit positions with that of noncritical bits when more errors (due to stuck-at faults) are present in the critical bits. Experiments of CRAFT architecture with various DNN models indicate that the robustness of a DNN against stuck-at faults can be enhanced by up to 105 times on the CIFAR-10 dataset and up to 29 times on ImageNet dataset with only a minimal amount of storage overhead, i.e., 1.17%. Being orthogonal, CRAFT can be integrated with existing fault-tolerance schemes to further enhance the robustness of DNNs aga

关键词： Fault tolerance

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A Review on existing IoT Architecture and Communication Protocols used in Healthcare Monitoring System

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Journal of The Institution of Engineers (India): Series B 2022年第1期103卷 245-257页

作者： Verma, Navneet Singh, Sukhdip Prasad, Devendra Computer Science and Engineering Department Deenbandhu Chhotu Ram University of Science & Technology Haryana Sonipat India Institute of Engineering and Technology Chitkara University Punjab 140401 India

Nowadays, due to modernization or advancement in the Internet of Things (IoT) especially in the Healthcare area, we want to take care of our elders with some monitoring equipment, and the Internet of Things can play a significant role in it. The motivation of writing this paper is to collect the information of various existing Internet of Things Architecture and Communication Techniques used in Healthcare Monitoring System to observe that how efficiently, different researchers have used it. So we have studied different real-time health monitoring system based on diseases which are common in elderly people like diabetes, blood pressure, heart disease, sleep apnea, and cancer, etc. In this real-time health monitoring system, researchers introduced many new measures, communication techniques like ZigBee, Long-Range Wide Area Network (LoRawan), Radio Frequency Identification (RFID). Apart from this, it was also observed that remote monitoring system in Healthcare is incomplete without data processing and early prediction in such diseases. Though, Machine learning provides efficient techniques to extract knowledge from diagnostic medical datasets collected from the patients. That is why we highlighted the current role of various Machine Learning algorithms like Support Vector Machine, K-Nearest Neighbor, Random Forest, etc., for processing of Healthcare data and also helpful to predict the output more precisely. © 2021, The Institution of Engineers (India).

关键词： Internet of things

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Accurate detection of fresh and old vertebral compression fractures on CT images using ensemble YOLOR

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Multimedia Tools and Applications 2024年第41期83卷 89375-89391页

作者： Hsieh, Min-Hong Chang, Chuan-Yu Hsu, Shao-Min Department of Orthopedics Dalin Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation Chiayi Taiwan School of Medicine Tzu Chi University Hualien Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

Vertebral compression fractures resulting from osteoporosis contribute to pain and disability among older people, necessitating early detection and treatment. While MRI provides effective diagnosis, its higher cost poses a challenge. Conversely, CT imaging offers a more cost-effective alternative. However, CT-based detection of vertebral fractures may lack the accuracy of MRI. This paper presents a novel YOLO-based object detection method for localizing old and fresh fractures on spine CT images to expedite diagnosis and identify the optimal treatment window. We propose replacing the CSPDarknet53 backbone in the native YOLOR model with MobileViT and EfficientNet_NS, training three separate YOLOR models with different backbones. Subsequently, an ensemble approach is employed to leverage the enhanced feature extraction capabilities of the three models. Experimental results show individual model accuracies of 89%, 89.8%, and 89.2%, respectively. Furthermore, by replacing the convolution layer with the Involution layer and integrating the models using the ensemble method, we achieve a remarkable accuracy rate of 93.4%. The proposed Ensemble YOLOR model surpasses other state-of-the-art approaches, offering a fast and precise solution. This advancement provides valuable insights and guidance for physicians in diagnosing compression between old and new fractures. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： computerized tomography

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Enhancing Medical Image Segmentation with Recurrent Neural Network Architectures 2

Enhancing Medical Image Segmentation with Recurrent Neural N...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Masson, Palak Sharma, Deepak Yadav, Kanchan Sethi, Tarun Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Srm Institute of Science and Technology Department of Computer Applications UP Modinagar India Gla University Department of Mechanical Engineering UP Mathura India Iamr Department of Computer Applications UP Ghaziabad India

ISBN: (纸本)9798350371055

Scientific imaging is a swiftly evolving and developing vicinity of healthcare. Greater accurate segmentation of clinical images is paramount for correct diagnosis. Recent breakthroughs in profound mastering studies have allowed researchers to faucet into a powerful but unstructured information form - images. With the usage of recurrent neural network architectures (RNNs), researchers are exploring the capacity of this shape of facts to acquire better consequences from scientific photograph segmentation. RNNs are regarded for their flexibility in encoding temporal relationships and processing established statistics. In medical image segmentation, this flexibility aids in recognizing complicated styles in photographs and helps the model to localize features inside the image better. Furthermore, the networks can run on images of various sizes, ensuring that models can handle photos created using unique imaging modalities. Additionally, RNNs can examine large annotated datasets and, consequently, help supply meaningful segmentation outcomes. Recent advances in using RNNs for medical photo segmentation have shown promising effects. RNNs have been used to phase colon anatomy, mind tumors, and lymph nodes. U-internet-primarily based segmentation fashions that include RNNs and feature-achieved high segmentation accuracy for medical photograph segmentation responsibilities have also evolved. Within destiny, RNN-based total models will remain superior and could be implemented in various medical imaging segmentation tasks. As such, RNNs will make scientific imaging segmentation extra green and accurate, allowing faster and more accurate analysis and treatment. © 2024 IEEE.

关键词： Achieved Recognizing Relationships Segmentation

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