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Marine Life Analysis Based on Ocean Water Level Rise and Climate Change Using Underwater Imaging Techniques

Remote Sensing in Earth Systems sciences 2024年第4期7卷 657-669页

作者： Mate, Anuja Dinesh Arulananth, T.S. Sathiya, T. Jyothi, B. Swarna Raja, A. Wasim Yalawar, Mrutyunjaya S. Computer Technology Sou.Venutai Chavan Polytechnic Pune India Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad 500043 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem India Computer Science & amp Engineering Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering CMR Engineering College Hyderabad India

Forecasting sea levels is crucial for harbour operations and coastal structure design. The oceans make up two-thirds of Earth’s surface;therefore, historically, the marine economy has been extremely diversified as well as abundant. In order to explore as well as harness this difficult environment, prominent businesses and ecological groups are developing and deploying novel gadgets backed by state-of-the-art marine mechatronics solutions. This research proposes a novel method of marine life monitoring with ocean water level rise analysis based on climate changes by underwater imaging using machine learning techniques. Here, the input has been collected as underwater images as well as processed for noise removal and normalisation. Then, these images were segmented for monitoring marine life using U-Net graph cut fuzzy clustering with a spatial neural network. Then this segmented image has been classified using multilayer region-based Q-belief vector neural networks for the analysis of sea level rise analysis based on climate change training. Experimental analysis has been carried out for various marine life monitoring datasets and climate change datasets in terms of training accuracy, specificity, recall, AUC, and Kappa-coefficient. Proposed techniques achieved specificity 93%, training accuracy 91%, AUC 95%, recall 96%, and Kappa-coefficient 90%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate changes Machine learning techniques Marine life monitoring Q-belief vector Water level rise analysis

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An Early-Stage Colorectal Cancer Detection from Colonoscopy Images Using Enhanced Res-UNET 1

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8th International Conference on Computers, Management and Mathematical sciences, ICCM 2022

作者： Mahanty, Mohan Vamsi, Bandi Dasari, Madhavi Appaji, Setti Vidya Sagar Department of Computer Science and Engineering Vignan’s Institute of Information Technology-Duvvada Andhra Pradesh Visakhapatnam India Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Computer science and Engineering Department Sridevi Women’s Engineering College Hyderabad India Baba Institute of Technology and Sciences Visakhapatanam India

ISBN: (数字)9783031511677

ISBN: (纸本)9783031511660

The segmentation of the polyps during colonoscopy is one of the most crucial factors for successful colon cancer diagnosis. Due to the wide variety of sizes and shapes of these polyps, this process can be challenging. The introduction of deep learning models in biomedical image analysis has shown a great impact on disease detection and segmentation. Most of the deep learning models concentrated on larger size polyps only, smaller size polyps may not segmented accurately during the procedure and can cause cancer in 5–7 years. This can have a significant impact on the early-stage detection of colon cancer. This chapter proposes an enhanced Res-UNET-based deep learning–based model that can improve the performance of segmentation on small-size polyps. We evaluated the proposed model on ETIS-LaribPolypDB, CVC-ColonDB, CVC-ClinicDB, Kvasir-SEG colonoscopy image datasets. The results show that the proposed enhanced Res-UNET model achieves the top Dice segmentation accuracy and top Hausdorff distance over the recent state-of-the-art models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image enhancement

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Formation Control of Heterogeneous Multi-Agent Systems Under Fixed and Switching Hierarchies

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IEEE Access 2024年 12卷 97868-97882页

作者： Shamrooz Aslam, Muhammad Bilal, Hazrat Chang, Wen-Jer Yahya, Abid Anjum Badruddin, Irfan Kamangar, Sarfaraz Hussien, Mohamed China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221116 China University of Science and Technology of China Department of Automation Hefei230026 China Department of Marine Engineering Keelung202 Taiwan Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana King Khalid University College of Engineering Department of Mechanical Engineering Abha Saudi Arabia King Khalid University Faculty of Science Department of Chemistry Abha61413 Saudi Arabia

Modern information technology is driving the rapid development of the information and automation industry through the combination of computer technology, communication technology, and control technology. Inspired by nature's cluster movement, such as birds, people proposed the concept of Multi-Agent Systems (MASs). This paper deals with the formation control problem for a class of heterogeneous multi-agent systems with time-delays. First, the formation control of heterogeneous multi-agent systems with communication delay in fixed and switching topologies is investigated. A heterogeneous multi-agent system model with input delay and communication delay is established. Furthermore, based on this model, the formation control protocol is designed and the stability of the proposed control protocol in fixed and switched topologies is analyzed. The first- and second-order multi-agent control system was formulated for both fixed and switched topology, respectively. Then, using the new LMI toolbox, the feasibility of the parameters is obtained and the example is created by MATLAB. The simulation results show that the proposed formation control protocol with delay in this paper can make the systems converge to the expected value under fixed and switched hierarchies. © 2013 IEEE.

关键词： Topology

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Generative AI in Focus : A Comprehensive Review of Leading Models Across Modalities 4

Generative AI in Focus : A Comprehensive Review of Leading M...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Aishwarya, S. Selvamurugan, C. Parthiban, K.G. Nathan, B. Manoj Prabhakar, J. Mohamed Noordeen, A. Sri Eshwar college of Engineering Department of Computer Science and Engineering Coimbatore India Dhaanish Ahmed Institute of Technology Department of Science and Humanities Coimbatore India Dhaanish Ahmed Institute of Technology Department of Biomedical Engineering Coimbatore India Dhaanish Ahmed Institute of Technology Department of Computer Science and Engineering Coimbatore India Dhaanish Ahmed Institute of Technology Department of Artificial Intelligence & Data Science Coimbatore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

GenAI has revolutionized the generation of realistic and imaginative data in ways that were previously beyond the capabilities of other machine learning algorithms. This area is rapidly gaining traction, with extensive research currently underway to advance it further. The most recent EY Reimagining Industry Futures Study highlights the generative AI (GenAI) to be a pivotal technology. The surge in GenAI's prominence can largely be attributed to advancements in deep neural networks, hardware technology such as GPUs and increased availability of data. This research study reviews the most popular GenAI models, spanning text, image, and audio modalities. This study provides a comparative analysis of various models, serving as a comprehensive resource for understanding the functionality and efficiency of the top GenAI models available today. © 2024 IEEE.

关键词： Generative adversarial networks

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Optimizing Purchase Intention Prediction in E-Commerce 1

Optimizing Purchase Intention Prediction in E-Commerce

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Jayanthi, S. Rajeshwari, D. Goud, Naresh M. Geetha, R. Franklin, S. Benjamin Rajyalakshmi, Pannangi Department of Artificial Intelligence &data Science Telangana India Sri Indu Institute of Engineering and Technology Department of CSE Telangana Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Telangana Hyderabad India Bengaluru India Faculty of Behavioural and Social Sciences Department of Social Work Chennai India TKR College of Engineering and Technology Department of CSE Hyderabad India

ISBN: (纸本)9798331518943

This study investigates the effectiveness of deep learning models - Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM - in predicting purchase intention on e-commerce platforms using session-level data. The dataset, sourced from the UCI Machine Learning Repository, comprises 12,330 sessions with features such as 'PageValues,' 'BounceRates,' 'ExitRates,' and user behavior attributes like 'Month' and 'VisitorType.' After preprocessing categorical variables using Label Encoding and standardizing numerical features, the data was split into an 80-20% training and testing set. Each of the five models was then trained and assessed using metrics such as accuracy, ROC-AUC, and other classification measures. Among the models, the CNN achieved the highest performance with an accuracy of 90.34% and a ROC-AUC score of 0.9156, followed by the LSTM model with an accuracy of 89.72% and a ROC-AUC score of 0.9102. Although the RNN model performed well, it showed relatively lower metrics with an accuracy of 88.40% and a ROC-AUC of 0.8934. 'PageValues' exhibited the strongest positive correlation with purchase intention (Pearson correlation: 0.4926, Spearman correlation: 0.6264), while 'ExitRates' and 'BounceRates' had significant negative correlations of -0.2071 and -0.1507, respectively. This study identifies CNN and LSTM as robust models for predicting purchase intentions in e-commerce, with 'PageValues' being the most influential predictor. The results provide valuable insights for optimizing e-commerce platforms to improve conversion rates. Future research will focus on external validation and expanding the dataset to ensure broader applicability across various e-commerce contexts. © 2024 IEEE.

关键词： Convolutional neural networks

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Fault Tolerant Property of Interdependent Networks 12

Fault Tolerant Property of Interdependent Networks

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12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2022

作者： Li, Xifeng Bi, Dongjie Peng, Libiao Tang, Yu Xie, Yongle Xu, Juan School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Artificial Intelligence and Big Data Yibin University Yibin China

ISBN: (纸本)9781839538360

Recent years have witnessed a surge of extensive study upon the robustness of a system consisting of interdependent networks. Both the mechanism of cascading failures and the corresponding recovery strategies, which means to heal malfunction of a system before it runs completely into collapse, are two key issues in the field of robustness analysis of interdependent networks. An essential question that has always been ignored is how to design a fault tolerance scheme to buffer against cascading failures. In order to deeply understand the relationship between the fault-tolerance assignment and the robustness of interdependent networks, we propose a cascading failure model with the consideration of the fault tolerance property of the nodes in interdependent networks, where the node are specified different fault tolerant capacity according to their degree values. By theoretical and numerical studies, it is found that the robustness of a system can be improved significantly if the high-degree nodes are assigned high fault tolerant capacity. However, robustness of a system can be hampered if the high degree nodes are assigned low fault tolerant capacity. Our results prove that the fault tolerance assignment scheme of the nodes has great impacts on the robustness of interdependent networks and provide important thoughts to reinforce reliability of the interdependent systems by the proper allocation of fault tolerant capacity. © 2022 IEEE.

关键词： Fault tolerance

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Functional Verification of Extended Kalman Filter on FPGA for Railway Wheelset Parameters Estimation

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IEEE Access 2025年 13卷 96646-96659页

作者： Mal, Khakoo Memon, Tayab Din Kalwar, Imtiaz Hussain Junejo, Allah Rakhio Memon, Majid Hussain Memon, Tarique Rafique Memon, Sufyan Ali Chowdhry, Bhawani Shankar Sukkur IBA University Department of Computer Science Sukkur65200 Pakistan Mehran University of Engineering & Technology NCRA-Condition Monitoring Systems Laboratory Jamshoro76062 Pakistan Torrens University Center for Artificial Intelligence Research and Optimization Faculty of Design and Creative Technology MelbourneVIC3000 Australia Iqra University Faculty of Engineering Sciences and Technology Karachi75500 Pakistan GC University Hyderabad Department of Information Technology Hyderabad71000 Pakistan Quaid-e-Awam University of Engineering Science and Technology Department of Electronic Engineering Nawabshah67450 Pakistan Sejong University Department of Defense Systems Engineering Seoul05006 Korea Republic of

The forces generated at the wheel-rail contact patch are important and crucial to the railway vehicle’s operation because they control the dynamic behavior of the entire rolling stock. The transfer of the tractive force applied by the locomotive’s traction and braking system requires a certain amount of adhesion force at the wheel-rail contact area. The wheel may skid while decelerating and slip when accelerating due to the maximal adhesion force at the wheel-rail interface may be less than the applied tractive force. A major difficulty is accurately estimating adhesion conditions and implementing them in real time because the contact forces between the wheel and rail are complicated and are highly non-linear. The fast-processing speed and hardware flexibility of field programmable gate arrays (FPGAs) make them ideal for the implementation of nonlinear condition monitoring systems. The goal of our work is to estimate various railway wheelset characteristics relating to wheel-rail contact forces under variable track-contact situations by designing and implementing the extended Kalman filter (EKF) model utilizing MATLAB and FPGA. The onboard estimation of wheel-rail interaction parameters is verified using the Xilinx (Registered trademark) System-On-Chip (SoC) Zynq and Xilinx Spartan-3 FPGA devices, built-in with the National Instruments (NI) myRIO® development board and sbRIO (Registered trademark) single-board controller. Two FPGA platforms are used to test the dataset generated by the MATLAB railway nonlinear wheelset model under different track circumstances, which include the vehicle’s acceleration and deceleration modes. The 95% of the simulation results achieved. The proposed model’s hardware synthesizes on FPGA are validated, and the area-performance is analyzed. Both FPGA devices take 52 cycles per sample and hence throughput becomes 769 kilo samples per second. Comparing the Xilinx System-On-Chip Zynq FPGA device to the Spartan-3, it is found that the latter

关键词： Extended Kalman filters

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Reliable and Fair Trustworthiness Evaluation Protocol for Platoon Service Recommendation System

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Chinese Journal of Electronics 2025年第2期34卷 563-575页

作者： Cheng Hongyuan Liu Yining Zhou Fei Tan Zhiyuan Zhang Xianchao School of Computer Science & Engineering Linyi University Linyi 273300 China School of Data Science and Artificial Intelligence Wenzhou University of Electronic Technology Wenzhou 325027 China Guangxi Key Laboratory of Digital Infrastructure Nanning 530023 China School of Computing Engineering & the Built Environment Edinburgh Napier University Edinburgh EH10 5DT UK College of Information Science and Engineering Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing 314001 China

Aiming at the problems of the communication inefficiency and high energy consumption in vehicular networks, the platoon service recommendation systems (PSRS) are presented. Many schemes for evaluating the reputation of platoon head vehicles have been proposed to obtain and recommend reliable platoon head vehicles. These trustworthiness evaluation protocols for PSRS fail to achieve both reliability and fairness. We first provide a reliable trustworthiness evaluation method to ensure that the reputation level of platoon head vehicle can be calculated by cloud service provider (CSP) with the help of key agreement mechanism and truth discovery technology. The semi-trusted entity CSP may maliciously tamper with the reputation level of the platoon head vehicle. We also provide a reputation level confirmation method to ensure the fairness of trustworthiness evaluation. Formal security proof and security analysis are provided to show that our trustworthiness evaluation protocol can achieve the goals of privacy protection, reliability, fairness and resistance to several security attacks. Experiments demonstrate that this protocol can save execution time and achieve reliable and fair trustworthiness evaluation for PSRS.

关键词： Reliability Fairness Privacy protection Trustworthiness evaluation Vehicle platoon

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Explainable artificial intelligence for wind power forecasting model based on long short-term memory

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Neural Computing and Applications 2025年第19期37卷 14589-14611页

作者： Yassen, Mona Ahmed El-Kenawy, El-Sayed M. Abdel-Fattah, Mohamed Gamal Ismail, Islam Mostafa, Hossam El-Deen Salah Faculty of Artificial Intelligence Hours University Damietta Egypt Department of Electronics and Communications Engineering Faculty of Engineering Mansoura University Mansoura35516 Egypt PO Box 33349 Bahrain Polytechnic Isa Town Bahrain Applied Science Research Center Applied Science Private University Amman Jordan Jadara University Research Center Jadara University Irbid Jordan Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura35111 Egypt

Expanding the world’s economy leads to higher requirements for energy storage. Traditional energy resources decline at the same time that environmental contamination levels increase in the world. Wind power is the most potential energy resource supported by its status as a significant renewable energy system. Wind power generation has become a popular and exciting method among nations worldwide for generating renewable energy. High wind power generation unpredictability results in unavoidable errors throughout the wind power prediction process, creating substantial difficulties in the optimal management of power systems. Wind power prediction errors remain unavoidable, but appropriate wind power uncertainty models help power system operators reduce their adverse impact on operational decision-making performance. In this paper, developed an appropriate machine learning model that efficiently forecasted wind power data through time series analysis. The long Short-Term Memory (LSTM), Gated Reference Unit (GRU), and Autoregressive Integrated Moving Average (ARIMA) are the machine algorithms used in this investigation. This paper proposed an X-double LSTM, which integrates explainable artificial intelligence (XAI) and long short-term memory (LSTM). The XAI-Shapley Additive Explanations (SHAP) model is modified to pinpoint the crucial elements affecting the power generation forecasting model’s accuracy in cutting-edge solar systems. Nine metrics are used to assess the efficacy of the proposed X-double LSTM model: root mean square error, mean bias error, correlation coefficient, relative root mean square error, Nash–Sutcliffe efficiency, mean square error, mean absolute deviation, coefficient of multiple determination, and Willmott index of agreement. For MSE, RMSE, MAE, MBE, r, R2, RRMSE, NSE, and WI, the suggested model improves by 0.000 11, 0.011, 0.008, 0.008, 0.99, 0.98, 2.5, 0.98, and 0.98, respectively. Other machine learning methods, including the Transformer model

关键词： Windmill

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Sensor Based Smart Wearable Devices for Women: A Review 2

Sensor Based Smart Wearable Devices for Women: A Review

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Khadse, Chinmaya Hajare, Shital Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Natural organic menstrual cycles stay in large part taboo during many regions of India mainly rural areas hence threatening teenagers with useless menstrual hygiene requirements mixed with primary sanitation issues and network disapproval. The fitness problems and psychological misery get up from these subjects. The World Health Organization (WHO) establishes that inadequate menstrual hygiene ends in urinary tract infections and infections of the reproductive device. The importance of India's menstrual population now stands at 355 million girls and ladies which needs immediately reaction through powerful answers. This observe investigates how IA and IoT-driven smart wearable generation can increase menstrual health control (MHM) for teenage Indian women. A proposed low cost wearable tool collects basal body temperature (BBT) and heart charge variability (HRV) information and bodily activity information while retaining cultural appropriateness. AI chatbots and academic guide and actual-time comments capabilities exist inside this device. The system features complete menstrual health tracking competencies with the aid of using cycle prediction competencies and implements in-tool heating structures for ache discount together with strain reduction software for emotional aid. The system uses techniques that start from person desires even as imposing LSTM networks together with Random Forest Regression machines for forecasting menstrual cycles and symptom reviews. Validations run on the tool arise in practical conditions which observe both its performance accuracy along person pleasure quotes and reliability through the years. The observe shows wearable era has the capacity to revolutionize menstruation take care of Indian teens via presenting them with a powerful approach to address their public health needs. © 2025 IEEE.

关键词： mHealth

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