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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

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Iran Journal of Computer Science 2024年第3期7卷 699-721页

作者： Nurjahan Mahbub-Or-Rashid, Md. Satu, Md. Shahriare Tammim, Sanjana Ruhani Sunny, Farhana Akter Moni, Mohammad Ali Department of IoT and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakair Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur Bangladesh Department of Management Information Systems Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Software Engineering Auburn University Auburn AL United States Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Faculty of Health and Behavioural Sciences School of Health and Rehabilitation Sciences The University of Queensland St Lucia Australia

The public’s health is seriously at risk from the coronavirus pandemic. Millions of people have already died as a result of this devastating illness, which affects countless people daily worldwide. Unfortunately, no specific therapeutic drugs or vaccines are available to cure patients completely. Therefore, early identification of infected individuals and their isolation can help reduce community transmission of COVID-19. Despite being commonly used, the reverse transcription polymerase chain reaction (RT-PCR) test is costly, time-consuming, and requires suitable kits, which are not always readily available. An alternative solution for the traditional RT-PCR test is machine learning and deep learning-based COVID-19 detection that utilizes clinical features of chest X-rays and computed tomography (CT) images. In this study, we conducted a detailed review of more than 100 recently published works to detect COVID-19-infected patients. Thus, different data preprocessing, data augmentation, image enhancement, feature extraction, machine learning, deep learning, Explainable Artificial Intelligence (AI) methods, etc. were explored to understand how these techniques were used to process images for identifying COVID-19. Additionally, we identified the current challenges in this field and suggested further research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Chest X-ray Computed tomography COVID-19 Deep learning Machine learning

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Physical Adversarial Attack Scheme on Object Detectors using 3D Adversarial Object 2

Physical Adversarial Attack Scheme on Object Detectors using...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Toheed, Abeer Yousaf, Muhammad Haroon Rabnawaz Javed, Ali Uet Taxila Swarm Robotics Lab-NCRA Pakistan Uet Taxila Department of Computer Engineering Pakistan Cust VisPRS Research Group Pakistan Uet Taxila Department of Software Engineering Pakistan

ISBN: (纸本)9781665498197

Adversarial attacks are being frequently used these days to exploit different machine learning models including the deep neural networks (DNN) either during the training or testing stage. DNN under such attacks make the false predictions. Digital adversarial attacks are not applicable in physical world. Adversarial attack on object detection is more difficult as compared to the adversarial attack on image classification. This paper presents a physical adversarial attack on object detection using 3D adversarial objects. The proposed methodology overcome the constraint of 2D adversarial patches as they only work for certain viewpoints only. We have mapped an adversarial texture onto a mesh to create the 3D adversarial object. These objects are of various shapes and sizes. Unlike adversarial patch attacks, these adversarial objects are movable from one place to another. Moreover, application of 2D patch is limited to confined viewpoints. Experimentation results show that our 3D adversarial objects are free from such constraints and perform a successful attack on object detection. We used the ShapeNet dataset for different vehicle models. 3D objects are created using Blender 2.93 [1]. Different HDR images are incorporated to create the virtual physical environment. Moreover, we targeted the FasterRCNN and YOLO pre-trained models on the COCO dataset as our target DNN. Experimental results demonstrate that our proposed model successfully fooled these object detectors. © 2022 IEEE.

关键词： Object detection

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Experimental Analysis of Plant Disease Identification Methodology Based on Improved Classification and Deep Learning Principle

Experimental Analysis of Plant Disease Identification Method...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Shibu, S. Priyadharshini, S. Madhini, M. Radha, K. Giri, Pallavi Samhan, Ahmad A. Ali Panimalar Engineering College Department of Ece Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Robotics and Automation Tamil Nadu Chengalpattu India S.A. Engineering College Department of Ece Chennai India Dr. Mahalingam College of Engineering and Technology Department of Cse Pollachi Coimbatore India Laxminarayan Innovation Technological University Department of Civil engineering Nagpur India Zarqa University Faculty of Information Technology Deoartment of Software Engineering Zarqa Jordan Universityof Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The agriculture industry is essential for providing excellent food and significantly contributes to the growth of economies and people. Plant diseases can result in substantial reductions in food output and diminish species diversity. The prompt identification of plant diseases by precise or automated detection methods can improve food production quality and save economic losses. In recent years, deep learning has significantly enhanced the identification accuracy of image categorization and object detection systems. In this paper, a novel method termed Learning Enabled Disease Detection Logic (LDDL) is introduced for the efficient identification of plant diseases, which is cross-validated against the traditional Convolutional Neural Network (CNN) to assess the efficacy of the proposed scheme. The report delineates the specific crop illnesses addressed, the models utilized, the data sources referenced, and the overall efficacy as measured by the performance criteria applied in each study for plant disease diagnosis. The findings demonstrate that the proposed model, LDDL, achieves superior accuracy compared to prevalent illness detection methods and identifies the primary parameters influencing the efficacy of deep learning-based tools. The research offers significant insights for researchers, practitioners, and industry professionals in plant disease detection by addressing challenges and limitations, presenting a thorough understanding of the current research landscape, elucidating the advantages and drawbacks of various methods, and suggesting potential solutions to facilitate their implementation. © 2024 IEEE.

关键词： Convolutional neural networks

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On the Predefined, Prescribed and Arbitrary Time Convergence 48

On the Predefined, Prescribed and Arbitrary Time Convergence

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Kumar Pal, Anil Kamal, Shyam Bandyopadhyay, Bijnan Fridman, Leonid University of Calgary Department of Electrical and Software Engineering Calgary Canada Varanasi Department of Electrical Engineering Varanasi India Iit Jodhpur Department of Electrical Engineering Jodhpur India Department of Control Engineering and Robotics Coyoacán D.F.04510 Mexico

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Over the past few years, rated convergence has gained importance and has acquired tremendous interest from the research community. In this regard, some nomenclatures have emerged, broadly falling under the spectrum of finite time stability. However, these notions have certain aspects which differentiate them. Therefore it becomes imperative to bring out a comparative study among these notions. Moreover, such recent concepts are being applied to several application problems. Thus it is essential to understand even the small details associated with them. The purpose of the present paper is to address these objectives. In addition, the convergence of states and boundedness of the control in case of arbitrary time convergence has also been discussed. © 2022 IEEE.

关键词： Industrial electronics Convergence

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Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

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Operations Research Forum 2024年第3期5卷 58页

作者： Wei, Li Solihin, Mahmud Iwan Saruchi, Sarah ‘Atifah Astuti, Winda Hong, Lim Wei Kit, Ang Chun School of Computer Science and Technology (School of Software) Guangxi University of Science and Technology Guangxi 545006 China Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur 56000 Malaysia Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan 26600 Malaysia Computer Engineering Department Automotive and Robotics Engineering Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Anomaly detection Computer vision Deep learning Defect detection High-precision cylindrical parts Image processing Optical illumination

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An Efficient Interactive Multilayered Approach for Segmentation of Complex Images 3

An Efficient Interactive Multilayered Approach for Segmentat...

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3rd International Conference on Communication Technologies, ComTech 2021

作者： Naeem, Juwairiyah Javed, Kashif Rubab, Saddaf Jawad Khan, M. SMME Department of Robotics and Intelligent Machines Engineering Islamabad Pakistan MCS Department of Computer Software Engineering Islamabad Pakistan RIME Department of Robotics and Intelligent Machines Engineering Islamabad Pakistan

ISBN: (纸本)9781665419017

This paper presents an interactive algorithm that is fast and accurate for segmentation of multiclass images called GMMLC (Gaussian Mixture Model in Laplacian Coordinates). The proposed framework has an imperative feature of classification of complex images with similar background and foreground features. This paper refers to such images as Indistinct and Indistinguishable Boundary images. GMMLC combines the prevalent features of the multivariate Gaussian Mixture Model and the weighted valency graph based approach of Laplacian Coordinates. Commendable results are obtained by combining an efficient but suboptimal solution defined in probability space and the average neighborhood preservation properties of LC. This work is inspired by Casca's seed based image segmentation approach. However, this technique presents significantly improved results in terms of boundary fitting and efficiency with only a small amount of seeds due to the homogenous regions obtained from GMM preprocessing. The proposed framework outperforms all existing advanced techniques by means of greater efficiency and better boundary fitting with minimum user intervention. The algorithm is applied on 500 images from the Berkeley datasets (BSD500 and BSD300) and 50 images from the Grab cut dataset (PASCAL VOC 2007). The paper culminates with a qualitative boundary and efficiency evaluation in comparison with five prominent algorithms BPT, SRG, GC, SIOX and LC. © 2021 IEEE.

关键词： Image segmentation

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Detection of Alzheimer's Disease using Deep Learning: An Optimized Approach

Detection of Alzheimer's Disease using Deep Learning: An Opt...

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International Conference on Intelligent Autonomous Systems (ICoIAS)

作者： Gulnaz Ahmed Meng Joo Er Shahid Zikria Muhammad Sadiq Fareed Institute of Artificial Intelligence and Marine Robotics Dalian Maritime University Dalian China Department of Software Engineering Software Engineering Institute of Guangzhou Guangzhou China

This paper suggests a new CNN that requires just a few parameters to diagnose AD and is perfect for training on smaller datasets. Compared to existing state-of-the-art models, the proposed model uses a more optimized technique to identify the early stages of Alzheimer's disease. The image is sharpened and normalized as part of the preparation phase to help the model learn more quickly and succinctly. We used a bespoke CNN model that makes precise predictions with minimum parameters. Five Convolutional layers and three fully connected layers, which have equally learned samples from each class, make up the proposed model. The approach was compared using a variety of accuracy metrics, such as accuracy, AUC, precision, recall, and Fl-Score, as well as categorical cross-entropy loss, self, and other comparisons. To accurately forecast the method, several trials were carried out. The values for the evaluation measures are as follows: accuracy, AUC, precision, recall, fl-score, and loss are 99.95%, 99.99%, 99.97%, 99.95%, and 0.0016 respectively. According to simulation results, the proposed model performs better than existing cutting-edge models in all evaluation metrics.

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A Survey of Human Action Recognition and Posture Prediction

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Tsinghua Science and Technology 2022年第6期27卷 973-1001页

作者： Nan Ma Zhixuan Wu Yiu-ming Cheung Yuchen Guo Yue Gao Jiahong Li Beiyan Jiang Beijing Key Laboratory of Information Service Engineering the College of RoboticsBeijing Union UniversityBeijing 100101China Department of Computer Science Hong Kong Baptist UniversityHong Kong 999077China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China School of Software Tsinghua UniversityBeijing 100084China College of Robotics Beijing Union UniversityBeijing 100101China

Human action recognition and posture prediction aim to recognize and predict respectively the action and postures of persons in *** are both active research topics in computer vision community,which have attracted considerable attention from academia and *** are also the precondition for intelligent interaction and human-computer cooperation,and they help the machine perceive the external *** the past decade,tremendous progress has been made in the field,especially after the emergence of deep learning ***,it is necessary to make a comprehensive review of recent *** this paper,firstly,we attempt to present the background,and then discuss research ***,we introduce datasets,various typical feature representation methods,and explore advanced human action recognition and posture prediction ***,facing the challenges in the field,this paper puts forward the research focus,and introduces the importance of action recognition and posture prediction by taking interactive cognition in self-driving vehicle as an example.

关键词： human action recognition posture prediction computer vision human-computer cooperation interactive cognition

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Improved Sequencing Heuristic DSDV Protocol Using Nomadic Mobility Model for FANETS

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Computers, Materials & Continua 2022年第2期70卷 3653-3666页

作者： Inam Ullah Khan Muhammad Abul Hassan Muhammad Fayaz Jeonghwan Gwak Muhammad Adnan Aziz Department of Electronic Engineering Isra University(SEAS)Islamabad44000Pakistan Department of Computing and Technology Abasyn University Peshawar25000Pakistan Deparment of Computer Engineering Jeju National UniversityJejuKorea Department of Software Korea National University of TransportationChungju27469Korea Department of Biomedical Engineering Korea National University of TransportationChungju27469Korea Department of AI Robotics Engineering Korea National University of TransportationChungju27469Korea Department of IT&Energy Convergence(BK21 FOUR) Korea National University of TransportationChungju27469Korea

Most interesting area is the growing demand of flying-IoT mergers with smart ***,aerial vehicles,especially unmanned aerial vehicles(UAVs),have limited capabilities for maintaining node energy *** order to communicate effectively,IoT is a key element for smart *** improving network performance,routing protocols can be deployed in flying-IoT to improve latency,packet drop rate,packet delivery,power utilization,and average-end-to-end ***,in literature,proposed techniques are verymuch complex which cannot be easily implemented in realworld *** issue leads to the development of lightweight energyefficient routing in flying-IoT *** paper addresses the energy conservation problem in *** paper presents a novel approach for the internet of flying vehicles using DSDV ***-DSDV gives the notion of bellman-ford algorithm consisting of routing updates,information broadcasting,and stale *** shows optimal results in comparison with other contemporary routing *** mobility model is utilized in the scenario of flying networks to check the performance of routing protocols.

关键词： Flying-IoT DSR smart cities UAV’s FANETS

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Multi-Objective Optimization Design for DC-Link Capacitor of Motor Driver

Multi-Objective Optimization Design for DC-Link Capacitor of...

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2024 CPSS and IEEE International Symposium on Energy Storage and Conversion, CPSS and ISESC 2024

作者： Liu, Qian Huang, Kangxing Wei, Xueliang Zheng, Yihong Xiang, Hao School of Automation Wuhan University of Technology Wuhan China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong College of Artificial Intelligence China University of Petroleum-Beijing Beijing China Wuhan IActuator Robotics Co. Ltd Software Development Department Wuhan China

ISBN: (纸本)9798350380514

The capacitor is a common weak point in motor drive system design, necessitating targeted selection, design, and optimization of capacitor banks within these systems. Traditional capacitor design often relies on engineering experience, usually meeting specific capacitance and voltage requirements through simple parallel or series connections of capacitors. This approach often fails to fully optimize volume, cost, and reliability. This paper uses comprehensive theoretical modeling to optimize capacitor module design, effectively balancing capacitor lifespan, cost, and volume. For the design and selection of DC-link capacitors in a 1kW permanent magnet synchronous motor drive system, a multi-objective optimization design method for capacitor banks is proposed. The study treats the individual capacitance values of parallel capacitors as variables, significantly enhancing the overall performance of the capacitor module through precise optimization and the introduction of necessary constraints in the selection design. The proposed method is validated with a 1kW permanent magnet synchronous motor drive system. The optimization results show that under the guidance of a multi-objective optimization framework, the designed DC-link capacitor module meets physical size requirements while achieving overall optimization in terms of volume, cost, and lifespan, significantly improving system reliability. © 2024 IEEE.

关键词： Permanent magnets

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