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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Bhaskar, Nuthanakanti Aelgani, Vivekanand Kumar, Sheo Devi, Bigul Sunitha Kumar, Voruganti Naresh Divya, G. CMR Technical Campus Department of Computer Science and Engineering Hyderabad India CMR College of Engineering & Technology Department of Computer Science and Engineering Hyderabad India CMR Engineering College Department of Computer Science and Engineering Hyderabad India CMR Institute of Technology Department of Computer Science and Engineering Hyderabad India CMR Technical Campus Department of Information Technology Hyderabad India

ISBN: (纸本)9798331519056

Globally, heart disorders, often recognized as cardiovascular diseases, are among the major causes of death. Additional lives might be protected the earlier they are identified and anticipated. Cardiovascular disease can be identified with an electrocardiogram (ECG), a simple, low-cost, non-invasive method of detecting the electrical functioning of the heart. Employing a public dataset of ECG images from heart patients, deep learning algorithms were utilized in this study to calculate the four main cardiac abnormalities: abnormal heartbeat, myocardial infarction history, coronary artery disease, and typical individual categories. Initially, the transfer method for learning was studied using the previously learned deep neural networks, VGG16, RESNET50, and InceptionV3. Classical methods of machine learning such as Support Vector Machine, K-nearest relatives, Random Forest, and Naïve Bayes were able to identify features employing the models that were already trained previously stated. For the study, ECG images from people who had or did not have a diagnosis of CVD were collected. Using these preprocessed pictures, relevant features including time-domain, frequency-domain, including nonlinear features were extracted. The collected features were then used to train various machine learning computations, which helped identify the prevalence of CVD. The efficiency of each algorithm was evaluated using metrics such as precision, specificity, and consciousness. In addition, the research employed Flask technology in Python to create an online user experience for the model, facilitating patients' and healthcare practitioners' easy access. ECG pictures can be uploaded through the interface, and the model analyzes them to determine whether CVD is present. These algorithms can also offer a reliable diagnosis, which lowers the possibility of human error. © 2024 IEEE.

关键词： Deep neural networks

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Functional Pattern-Related Anomaly Detection Approach Collaborating Binary Segmentation with Finite State Machine

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computers, Materials & Continua 2023年第12期77卷 3573-3592页

作者： Ming Wan Minglei Hao Jiawei Li Jiangyuan Yao Yan Song School of Information Liaoning UniversityShenyang110036China School of Computer Science and Technology Hainan UniversityHaikou570228China School of Physics Liaoning UniversityShenyang110036China

The process control-oriented threat,which can exploit OT(Operational technology)vulnerabilities to forcibly insert abnormal control commands or status information,has become one of the most devastating cyber attacks in industrial automation *** effectively detect this threat,this paper proposes one functional pattern-related anomaly detection approach,which skillfully collaborates the BinSeg(Binary Segmentation)algorithm with FSM(Finite State Machine)to identify anomalies between measuring data and control *** detecting the change points of measuring data,the BinSeg algorithm is introduced to generate some initial sequence segments,which can be further classified and merged into different functional patterns due to their backward difference means and *** analyzing the pattern association according to the Bayesian network,one functional state transition model based on FSM,which accurately describes the whole control and monitoring process,is constructed as one feasible detection ***,we use the typical SWaT(Secure Water Treatment)dataset to evaluate the proposed approach,and the experimental results show that:for one thing,compared with other change-point detection approaches,the BinSeg algorithm can be more suitable for the optimal sequence segmentation of measuring data due to its highest detection accuracy and least consuming time;for another,the proposed approach exhibits relatively excellent detection ability,because the average detection precision,recall rate and F1-score to identify 10 different attacks can reach 0.872,0.982 and 0.896,respectively.

关键词： Process control-oriented threat anomaly detection binary segmentation FSM

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Deep learning-based classification,detection,and segmentation of tomato leaf diseases:A state-of-the-art review

Artificial Intelligence in Agriculture

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Artificial Intelligence in Agriculture 2025年第2期15卷 192-220页

作者： Aritra Das Fahad Pathan Jamin Rahman Jim Md Mohsin Kabir M.F.Mridha Department of Computer Science and Engineering American International University-BangladeshDhaka 1229Bangladesh Institute of Information and Communication Technology Shahjalal University of Science and TechnologySylhet 3114Bangladesh Advanced Machine Intelligence Research Lab Dhaka 1207Bangladesh Bangladesh University of Business and Technology DhakaBangladesh

The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity,efficiency and *** by the farmers poses the risk of inadequate treatments,harming both tomato plants and *** of disease diagnosis is essential,necessitating a swift and accurate response to misdiagnosis for early *** regions are ideal for tomato plants,but there are inherent concerns,such as weather-related *** diseases largely cause financial losses in crop *** slow detection periods of conventional approaches are insufficient for the timely detection of tomato *** learning has emerged as a promising avenue for early disease *** study comprehensively analyzed techniques for classifying and detecting tomato leaf diseases and evaluating their strengths and *** study delves into various diagnostic procedures,including image pre-processing,localization and *** conclusion,applying deep learning algorithms holds great promise for enhancing the accuracy and efficiency of tomato leaf disease diagnosis by offering faster and more effective results.

关键词： Deep learning Tomato leaf disease Classification Detection Segmentation Imaging

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Visual Computing Unified Application Using Deep Learning and computer Vision Techniques

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第1期18卷 59-74页

作者： Sowmya, B.J. Meeradevi Seema, S. Dayananda, P. Supreeth, S. Shruthi, G. Rohith, S. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Karnataka Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Karnataka Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Karnataka Bengaluru India

Vision Studio aims to utilize a diverse range of modern deep learning and computer vision principles and techniques to provide a broad array of functionalities in image and video processing. Deep learning is a distinct class of machine learning algorithms that utilize multiple layers to gradually extract more advanced features from raw input. This is beneficial when using a matrix as input for pixels in a photo or frames in a video. computer vision is a field of artificial intelligence that teaches computers to interpret and comprehend the visual domain. The main functions implemented include deepfake creation, digital ageing (de-ageing), image animation, and deepfake detection. Deepfake creation allows users to utilize deep learning methods, particularly autoencoders, to overlay source images onto a target video. This creates a video of the source person imitating or saying things that the target person does. Digital aging utilizes generative adversarial networks (GANs) to digitally simulate the aging process of an individual. Image animation utilizes first-order motion models to create highly realistic animations from a source image and driving video. Deepfake detection is achieved by using advanced and highly efficient convolutional neural networks (CNNs), primarily employing the EfficientNet family of models. © 2024 by the authors of this article. Published under CC-BY.

关键词： Animation

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Effectiveness of Precursor Emission Reductions for the Control of Summertime Ozone and PM2.5 in the Beijing–Tianjin–Hebei Region under Different Meteorological Conditions

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Advances in Atmospheric sciences 2025年第4期42卷 653-669页

作者： Jing QIAN Hong LIAO Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science&TechnologyNanjing 210044JiangsuChina

We used observed concentrations of air pollutants,reanalyzed meteorological parameters,and results from the Goddard Earth Observing System Chemical Transport Model to examine the relationships between concentrations of maximum daily 8-h average ozone(MDA8 O_(3)),PM_(2.5)(particulate matter with diameter of 2.5μm or less),and PM_(2.5)components and 2-m temperature(T2)or relative humidity(RH),as well as the effectiveness of precursor emission reductions on the control of O_(3) and PM_(2.5) in Beijing–Tianjin–Hebei(BTH)under different summertime temperature and humidity *** observed(simulated)MDA8 O_(3) and PM_(2.5) concentrations increased as T2 went up,with linear trends of 4.8(3.2)ppb℃^(−1) and 1.9(1.5)μg m^(−3)℃^(−1),*** results showed that the decreases in MDA8 O_(3) from precursor emission reductions were more sensitive to T2 than to *** a larger proportion of volatile organic compound(VOC)emissions at higher T2 was more effective for the control of summertime O_(3) in *** the control of summertime PM_(2.5) in BTH,reducing nitrogen oxides(NOx)combined with a small proportion of VOCs was the best *** magnitude of reduction in PM_(2.5) from reducing precursor emissions was more sensitive to RH than to T2,with the best efficiency at high *** from this study are helpful for formulating effective policies to tackle O_(3) and PM_(2.5) pollution in BTH.

关键词： ozone PM_(2.5) emission reductions meteorological conditions

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IoT-Aided Charity-Redistribution of Excess Food 2

IoT-Aided Charity-Redistribution of Excess Food

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2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Yogananth, V.S. Balasubramani, M. Vellore Institute of Technology Department of Computer Applications Tamil Nadu Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350328202

The utilization of the Internet of Things (IOT) has shown significant potential in various aspects of daily life, yet its application in addressing social issues remains underdeveloped. India, with a substantial number of orphanages, faces challenges in efficiently distributing crucial information, hindering donors from obtaining accurate data on food reserves. This project proposes an IOT-based system to tackle this issue, using Node MCU IOT devices connected to sensors that relay data through a network, accessible and monitorable via web applications. The proposed system architecture aims to bridge the gap between those who waste food and those in poverty who desperately need it. We intend to facilitate food donations by implementing smart containers equipped with IOT technology. These containers can detect excess food waste and share this information via a GSM module with trusted charitable organizations. This application is designed to serve three main stakeholders: service providers, donors, and food recipients in need. By creating smart containers, we ensure that donated food reaches the right individuals. We prioritize collaborating with trusted charities to oversee the distribution process. Only authorized charity members can access and distribute the food stored in these containers, ensuring that it reaches those who need it most. © 2024 IEEE.

关键词： Containers

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Multi-Label Feature Selection for High-Dimensional Biological Data via Global Relevance and Redundancy Optimization Based on JS Divergence 14

Multi-Label Feature Selection for High-Dimensional Biologica...

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14th International Conference on information science and technology, ICIST 2024

作者： Yang, Man Wang, Yibo Wang, Yadi Guo, Xiaoding Mu, Huiyu Che, Hangjun Institute of Data and Knowledge Engineering School of Computer and Information Engineering Henan University Kaifeng475004 China Computer Science and Technology School of International Business Henan University Zhengzhou450018 China College of Electronic and Information Engineering Southwest University Chongqing400715 China

ISBN: (纸本)9798350353334

In recent years, multi-label feature selection has been widely used in fields such as bioinformatics, information retrieval, and multimedia annotation. Most of the previous multi-label feature selection methods are directly transformed from the traditional single-label feature selection methods, or they cannot make full use of label information. As a result, the selected feature subset involves features that are redundant or irrelevant to label information. Moreover, most algorithms do not use discretization when processing continuous data sets, so they cannot effectively eliminate the interference of abnormal data. In order to solve these problems, based on the GRRO (Global Relevance and Redundancy Optimization) algorithm, the JS divergence is used to better measure the overall similarity between two distributions and introduces data discretization preprocessing operations for continuous data sets. The experimental results after experimental verification on ten typical high-dimensional biological data sets show that the GRRO-JS algorithm is superior to the traditional multi-label feature selection method and the GRRO algorithm in terms of accuracy and efficiency and has high practical value. © 2024 IEEE.

关键词： Data accuracy

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Relationship Between the Long-Term Practice of Tai Chi Stance with Neural Patterns of the Brain: An Exploratory Study

Relationship Between the Long-Term Practice of Tai Chi Stanc...

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2024 China Automation Congress, CAC 2024

作者： Li, Mengmeng Zhang, Jianbo Wan, Hong School of Electrical and Information Engineering Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350368604

Research has shown that practicing Tai Chi can alleviate brain tension and enhance the activity of the autonomic nervous system in the human body. However, there is limited research on the effects of Tai Chi Stance on brain function. This study aims to analyze the electroencephalogram (EEG) characteristics of long-term Tai Chi Zhuang practitioners and investigate the changes in brain dynamic neural patterns during different stages of Tai Chi Stance, together with the differences between long-term practitioners and a control group. In this study, we first analyzed the changes in EEG spectral characteristics during different stages of Tai Chi Stance practice of long-term practitioners. We found that the alpha (8-13 Hz) power significantly increased during the stance, indicating a state of deep relaxation during the Tai Chi practice. The experimental group also exhibited significant changes in brain functional network parameters in the alpha band during the stance, including increased clustering coefficient and global efficiency. Finally, inter-group differences in EEG characteristic parameters were analyzed between the experimental group and control group. The results showed significant differences in relative power and functional connectivity in the alpha band between the two groups, indicating that long-term Tai Chi Stance practice can effectively enhance brain network efficiency and improve information integration capabilities. This study provides scientific evidence for Tai Chi Stance training to enhance brain health. © 2024 IEEE.

关键词： Electroencephalography

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A Secure and Efficient information Authentication Scheme for E-Healthcare System

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computers, Materials & Continua 2023年第9期76卷 3877-3896页

作者： Naveed Khan Jianbiao Zhang Ghulam Ali Mallah Shehzad Ashraf Chaudhry Faculty of Information Technology Beijing University of TechnologyBeijing100124China Department of Computer Science Shah Abdul Latif UniversityKhairpur66111Pakistan Department of Computer Engineering Faculty of Engineering ArchitectureNisantasi UniversityIstanbul34398Turkey

The mobile cellular network provides internet connectivity for heterogeneous Internet of Things(IoT)*** cellular network consists of several towers installed at appropriate locations within a smart *** cellular towers can be utilized for various tasks,such as e-healthcare systems,smart city surveillance,traffic monitoring,infrastructure surveillance,or sidewalk *** is a primary concern in data broadcasting,particularly authentication,because the strength of a cellular network’s signal is much higher frequency than the associated one,and their frequencies can sometimes be aligned,posing a significant *** a result,that requires attention,and without information authentication,such a barrier cannot be ***,we design a secure and efficient information authentication scheme for IoT-enabled devices tomitigate the flaws in the e-healthcare *** proposed protocol security shall check formally using the Real-or-Random(ROR)model,simulated using ProVerif2.03,and informally using pragmatic *** comparison,the performance phenomenon shall tackle by the already result available in the MIRACL cryptographic lab.

关键词： IoT-enable device e-healthcare authentication edge computing

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A New Cluster Decision Analysis Study based on EWM and Combined Regression Analysis 1

A New Cluster Decision Analysis Study based on EWM and Combi...

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1st International Conference on Software, Systems and information technology, SSITCON 2024

作者： Feng, Zikan Zhao, Yixuan Dalian University of Technology Dalian International School of Information Science and Engineering China

ISBN: (纸本)9798350352931

In the context of the development of data science and big data technology, decision analysis has become quite important in many fields. In particular, in business management, financial market analysis, and the formulation of public policies, how to effectively analyze and make use of a large amount of data to make scientific and rational decisions has become an urgent problem. In these regards, cluster analysis is an important method of statistical analysis that helps a decision-maker get a better understanding of the structure of the data or patterns by organizing them into clusters in which objects that belong to the same cluster are similar to each other and much different from those that form other clusters. However, traditional clustering methods basically rely on single statistical or machine learning models, which often results in limitations and instability in the outcomes. Therefore, combining multiple methods to improve accuracy and reliability in decision analysis is a current research focus. In this respect, a new cluster decision analysis method is proposed in the present study, one that combines the Entropy Weight Method with combined regression analysis. The Entropy Weight Method is an objective weighting method aimed at determining the weights of indicators;it is very effective in reflecting variability and information content in every indicator. Combined regression analysis joins powers of these various regression models to be more pervasive and accurate in understanding relationships between variables. One can, therefore, fine-tune the precision of cluster analysis by such combined methodology and at the same time, enhance the interpretability and practicality of results. It will build up this new approach and apply it to the deep data analysis of certain fields, verifying the effectiveness and superiority of the application, and providing more scientific and reliable analytical tools for related decision-makers to make decisions in complex environ

关键词： Data accuracy

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