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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Shuja, Mirza Qtaishat, Ahmed Mishra, Hari Mohan Kumar, Mukesh Ahmed, Bilal Lovely Professional University School of Computer Application Phagwara India Sohar University Department of Information Technology Sohar Oman

ISBN: (纸本)9798350346961

People do not have much time to think about their health in today's fast-paced society because of how busy their lives are. The overscheduling of people's lives and a widespread disregard for their health are two factors that contribute to the rise in the incidence of disease. Additionally, the great majority of people have disability that prevents them from operating properly, such as cardiovascular illness. These people are unable to operate normally because of their disabilities. According to the statistics provided by the World Health organization (WHO), cardiovascular disease is responsible for more than one-third of all deaths. Because of this, it is very necessary for anybody working in the medical industry to be able to evaluate the chance that a patient may develop cardiovascular disease. However, because hospitals and the healthcare industry produce such a vast amount of data, it may be difficult to do research owing to the sheer amount of information that is available. The use of methods that are based on Machine Learning (ML) by medical professionals has the potential to cut down on the amount of time and effort spent formulating predictions and organising data. Because of this, we have been talking about the factors that lead to heart disease as well as the methods that are involved in machine learning. We evaluated and compared the efficacy of a wide variety of well-known ML algorithms that were utilised in the experiment, and we made inferences about cardiovascular disease using these techniques. In the end, the purpose of this research is to determine whether a ML system is capable of reliably predicting cardiac illness.. © 2023 IEEE.

关键词： Forecasting

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Future Directions of Artificial Intelligence and Machine Learning in Healthcare: A Systematic Analysis and Mapping Study 6

Future Directions of Artificial Intelligence and Machine Lea...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Mishra, Hari Mohan Ahmed, Bilal Shuja, Mirza Qtaishat, Ahmed Kumar, Mukesh Lovely Professional University School of Computer Application Phagwara India Sohar University Department of Information Technology Sohar Oman

ISBN: (纸本)9798350346961

In order to organise previously conducted research on using machine learning in medical contexts, the authors of this paper employ a method known as systematic mapping. In order to do this, we looked through the abstracts of a variety of publications, including medical journals, healthcare periodicals, and conference proceedings, looking for the phrase 'use of artificial intelligence and machine learning in healthcare.' After doing a search on Google Scholar, which resulted in the retrieval of 500 papers, we classified these studies in accordance with their objectives, approaches, primary concerns, and illnesses. With the use of this technique, we were able to organise our findings into the five categories that are as follows: privacy and security;a framework for privacy and security;interpretable machine learning;medical image assessment;electronic health record processing;and transfer learning. In addition, we found that the evaluation of medical images is the topic that receives the most research, that interpretable machine learning and explainable artificial intelligence are becoming increasingly popular, and that most authors are primarily interested in cancer research. To restate our mission, we want to provide future generations of scholars with an accurate picture of where the field is now and where it is headed.. © 2023 IEEE.

关键词： Machine learning

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Convolutional Neural Networks Aided Reinforcement Learning for Accelerated Optimization of Antenna Topology

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Applied Computational Electromagnetics Society Journal 2025年第1期40卷 35-41页

作者： Dou, Jiangling Gong, Hao Wei, Shuaibing Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming650500 China School of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College Kunming650500 China

A machine learning (ML) framework is proposed to achieve the automatic and rapid optimization of antenna topologies. A convolutional neural network (CNN) is utilized as a surrogate model (SM) and is combined with reinforcement learning (RL) algorithms. Specifically, the RL agent interacts with simulation software to learn. Data accumulated from electromagnetic (EM) simulations are used to train the SM. The CNN-based SM predicts antenna performance based on the topology of the antenna. Subsequently, the SM replaces EM simulations within the RL training environment. The RL agent interacts with the CNN-based SM to search for the optimal topology. This approach significantly reduces dependence on time-consuming EM simulations. To validate the effectiveness of the optimization method, a center-fed microstrip patch antenna is optimized. Simulation results demonstrate that, compared to other optimization methods, impedance bandwidth is improved, while the number of simulation samples and optimization time are significantly reduced. © ACES.

关键词： Convolutional neural networks

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Double-Edged Agriculture 4.0: Hodiernal Expedient Technologies and Cyber-Security Challenges 6

Double-Edged Agriculture 4.0: Hodiernal Expedient Technologi...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Arya, Swati Tripathi, Shubham Srivastava, Archana Aggarwal, Shruti Soni, Nupur Ansar, Syed Anas School of Computer Application Babu Banarasi das University Lucknow India Thapar Institute of Engineering and Technology Punjab India

ISBN: (纸本)9798350304480

Digitalization and intelligent systems have engulfed all dimensions and vicinity of existence. The intense fusion of hodiernal information technology and archaic agriculture has ushered in the epoch of agriculture 4.0, invariably reputed as smart agriculture. Variegated operational or forthcoming technologies reckon in agriculture 4.0, e.g., algae feedstock, automation, hydroponics, nanotechnology, genome editing, cognitive computing, blockchain, and machine learning, may have far-reaching implications for future agriculture and food systems and radical rudimentary potential. Although the profusion benefits of this Fourth Revolution abound, many unresolved issues and stumbling blocks must be conquered to make this paradigm shift feasible. Future solutions will coerce data availability and accuracy as vital factors to assist farmers, also security will become indispensable for constructing vigorous and efficacious systems. Security deals with difficulties like compatibility, scarce funds, and gargantuan data as smart agriculture incorporate a huge gamut and volume of resources. In this frame of reference, the authors opine that the inclusion and exclusion impact of Agriculture 4.0 technologies require ancillary scrutiny. This paper articulates the core technologies of agriculture 4.0 with an explicit focus on its applications in veritable contexts whilst delving into security issues (chiefly cyber-security). This research is envisaged to have a pragmatic influence on agriculture 4.0, emphasizing paradigms and security measures to aid farm stakeholders to navigate the firm's transition to digitization. © 2023 IEEE.

关键词： Agriculture 4.0 Cloud Computing Cyber Security Data Analytics Internet of Things Sensor

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Enhanced Electrocardiogram Data Compression and Classification Using 2D Convolutional Neural Networks 5

Enhanced Electrocardiogram Data Compression and Classificati...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Yadav, Amrendra Singh Mehta, Preeti Mishra, Deepti Barik, Rabindra Kumar AVM-Indian Institute of Information Technology and Management Department of Computer Science and Engineering Gwalior India National Institutes of Technology Delhi Department of Computer Science and Engineering Delhi India Kiit Deemed to be University School of Economics and Commerce Bhubaneshwar India Kiit Deemed to be University School of Computer Application Bhubaneshwar India

ISBN: (纸本)9798331522100

The task of electrocardiogram (ECG) classification is a combination of data compression and classification of signals based on the patient's health. This paper introduces a novel technique for compressing electrocardiogram (ECG) images and utilizing these compressed images for accurate classification. We transform ECG signals into images and compress them to reduce their size while preserving classification accuracy significantly. Our approach employs a 2-dimensional Convolutional Neural Network (2-D CNN) model and achieves an impressive accuracy of 97% even after compressing the images by up to 76%. The proposed technique was tested on the widely-used MIT-BIH Arrhythmia Dataset, demonstrating its robustness and efficacy. Maintaining high classification accuracy despite significant image compression is crucial for efficient storage and transmission in resource-constrained environments. This method holds substantial potential for real-time monitoring and diagnosis in telemedicine applications, emphasizing the importance of our contribution to the field. © 2025 IEEE.

关键词： Electrocardiography

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Research on Model Predictive Control-Based Feedback Strategies for Autonomous Driving 6

Research on Model Predictive Control-Based Feedback Strategi...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Li, Ting Xu, Xianxia Liu, Xuejuan Liu, Jie Guangxi Vocational Normal University School of Computer and Information Engineering Nanning China Key Laboratory of Application Technology of Intelligent Connected Vehicle Nanning China

ISBN: (纸本)9798350316520

To address the issues of control stability and safety in autonomous vehicles, this study focuses on research related to Model Predictive Control (MPC) algorithms and algorithm optimization. By leveraging MPC control algorithms and system dynamics techniques, we have achieved stable autonomous driving. Simulation experiments based on Model Predictive Control have been conducted to validate the feasibility and effectiveness of MPC algorithms for feedback control in autonomous driving. Two distinct simulation scenarios have been designed to verify the MPC algorithm's performance under different road conditions. The results indicate that the MPC algorithm effectively enhances the control stability and safety of autonomous driving vehicles. © 2024 IEEE.

关键词： Model predictive control

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A novel approach for calculating single-source shortest paths of weighted digraphs based on rough sets theory

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Mathematical Biosciences and Engineering 2024年第2期21卷 2626-2645页

作者： Hua, Mingfeng Xu, Taihua Yang, Xibei Chen, Jianjun Yang, Jie School of Computer Jiangsu University of Science and Technology Zhenjiang212100 China Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China School of Physics and Electronic Science Zunyi Normal University Zunyi563002 China

Calculating single-source shortest paths (SSSPs) rapidly and precisely from weighted digraphs is a crucial problem in graph theory. As a mathematical model of processing uncertain tasks, rough sets theory (RST) has been proven to possess the ability of investigating graph theory problems. Recently, some efficient RST approaches for discovering different subgraphs (e.g. strongly connected components) have been presented. This work was devoted to discovering SSSPs of weighted digraphs by aid of RST. First, SSSPs problem was probed by RST, which aimed at supporting the fundamental theory for taking RST approach to calculate SSSPs from weighted digraphs. Second, a heuristic search strategy was designed. The weights of edges can be served as heuristic information to optimize the search way of k-step R-related set, which is an RST operator. By using heuristic search strategy, some invalid searches can be avoided, thereby the efficiency of discovering SSSPs was promoted. Finally, the W3SP@R algorithm based on RST was presented to calculate SSSPs of weighted digraphs. Related experiments were implemented to verify the W3SP@R algorithm. The result exhibited that W3SP@R can precisely calculate SSSPs with competitive efficiency. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Heuristic algorithms

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Malaria and Pneumonia Disease Prediction Using Deep Learning 2

Malaria and Pneumonia Disease Prediction Using Deep Learning

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Vijendra Singhal, Manoj Dwivedy, Bhoopendra Jain, Pallavi Ritika Abes Institute of Technology Department of Information Technology Ghaziabad India Gl Bajaj Institute of Technology and Management Department of Information Technology Greater Noida India School of Computer Science and Engineering Galgotia's University Greater Noida India School of Computer Application and Technology Galgotia's University Greater Noida India

ISBN: (纸本)9798350371055

Early identification, intervention, and efficient treatment planning all rely heavily on disease prediction. This study examines the methods for disease prediction based on deep learning, concentrating on pneumonia and malaria in particular. Pneumonia and malaria are two common, sometimes fatal infections, especially in underdeveloped areas. Traditional diagnostic techniques for these disorders can be time-consuming and frequently need for specialized knowledge. Therefore, it is necessary to investigate automated and reliable illness prediction methods. This study's goal is to assess how well deep learning algorithms can foresee malaria and pneumonia from medical imaging data. The study makes use of a broad dataset that includes chest X-ray pictures for the identification of pneumonia and blood smear images with malaria infection. To improve the dataset's quality and variety, preprocessing procedures including picture normalization and augmentation are used. The results of this research demonstrate the potential of deep learning algorithms for malaria and pneumonia prediction, which advances the field of medical diagnostics. These automated prediction algorithms could help medical personnel diagnose patients quickly, allowing for quick treatment to start and better patient outcomes. © 2024 IEEE.

关键词： Backpropagation CNN Deep Learning Inspection V3 Model Jupiter Python Transfer Learning

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Construction of PCOS Prediction Model based on BP Neural Network 6

Construction of PCOS Prediction Model based on BP Neural Net...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Hu, Xianqing Yadav, Amit Khan, Asif Sah, Abhishek Pratap Azam, Sami School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP Neural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients. © 2025 IEEE.

关键词： Prediction models

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The impact and prospects of computer-aided technology for promoting universal breast cancer screening 2

The impact and prospects of computer-aided technology for pr...

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2nd International Conference on Health Big Data and Intelligent Healthcare, ICHIH 2023

作者： Zhu, Tianyuan Fu, Xiaoyang Huang, Yucheng Zhuhai College of Science and Technology Alibaba Cloud Big Data Application School Zhuhai China Zhuhai College of Science and Technology School of Computer Science Zhuhai China

ISBN: (纸本)9798350309324

Since breast cancer is difficult to prevent, detecting and treating it early may improve patient outcomes. Universal breast cancer screening is an important method for early detection and treatment. Our work surveyed different populations about their awareness of breast cancer and their willingness to be screened. Then, we summarized the potential barriers to universal breast cancer screening through a comparison of current breast cancer screening methods and models. We propose using computer-aided technology for promoting universal breast cancer screening, including predicting disease risk, improving diagnostic efficiency, and achieving individualized screening goals. Finally, we analyze the problems that need to be solved, outline the basic steps to implement improvements in the integration of computer-aided technology into the health care field represented by breast cancer screening and describe the prospects for future development. © 2023 IEEE.

关键词： Diseases

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