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作者： Yan, Jinliang Zheng, Lianghong Lu, Fuqiang Zhang, Qiongyue Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry College of Mathematics and Computer Fujian354300 China Department of Information and Computer Technology No.1 Middle School of Nanping Fujian353000 China Changzhou Institute of Technology Changzhou213032 China

In this paper, we present two novel energy-preserving schemes for the Schrödinger-Boussinesq system based on the second-order Adams prediction-correction method. We first introduce supplementary variables to the initial model simultaneous its invariants to obtain a consistent and structural stable system. Then, we discretize the extended system by directly using the Fourier pseudo-spectral method in space and second-order Adams prediction-correction method in time to construct conservative schemes. Numerical results show that the proposed schemes can reach second-order accuracy and can conserve the discrete invariants very well. © 2022 John Wiley & Sons, Ltd.

关键词： Forecasting

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Framework for classifying gait disorders and fall prevention

Framework for classifying gait disorders and fall prevention

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International Conference on computer, Electrical & Communication Engineering (ICCECE)

作者： Rupa Saha Koushik Karmakar Suparna Biswas Department of Computer Application Narula Institute of Technology Department of Computer Science and Technology Narula Institute of Technology Department of Computer Science & Engineering MAKAUT W B

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

One of the crucial approaches that use tracking and analyzing human movement for the identification of functional disorders and illnesses of the human body is known as the gait analysis method. This method helps diagnose neurological and musculoskeletal conditions. It also tracks the effectiveness of rehabilitation. However, conventional techniques for analyzing gait frequently depend on costly items and methods like fixed lab apparatus, force plates, and motion capture devices. But new developments in wearable sensor technology and machine learning provide new methods that are cheaper, more affordable, portable, and easily accessible. It continuously tracks gait in real-world settings. In this work, the use of wearable sensors for gait analysis, which includes devices like magnetometers, gyroscopes, and accelerometers, is examined. They are integrated into gadgets like smartphones, insoles, and smartwatches and offer real-time kinematic data that machine learning algorithms can analyze. Some of the important gait parameters 1that are extracted from sensor data are stride length, cadence, and gait symmetry. Machine learning is used to process this data and use it for categorization and gait analysis. The suggested method improves patient monitoring outside of clinical settings. It supports individualized rehabilitation plans and also identifies early indicators of gait abnormalities. The use of the wearable sensor-based gait analysis may emerge as a game-changing medical tool. It offers constant, discrete monitoring and prompts intervention in gait-related disorders. This works explains it thoroughly. This report offers insights for upcoming researchers in the field by surveying the state of the art in wearable sensor-based gait analysis.

关键词： Legged locomotion Machine learning algorithms Accuracy Tracking Reviews Real-time systems Biomedical monitoring Gait recognition Wearable sensors Smart phones

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Investigating the Impact of Public-Key Infrastructure in Information Security Models for Networked applications 5th

Investigating the Impact of Public-Key Infrastructure in Inf...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Shree, Ritu Saxena, Kumud Mehta, Deepak Pandeya, Megha Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Computer Science Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Public-key infrastructure (PKI) is poised to play a crucial function in the use of secure data technology systems. With the increasing complexity of these dais’s networks and packages, PKI gives a reliable way to electronically affirm and securely exchange critical records. This paper investigates the effect of PKI on modern-day facts security models for networked applications. An assessment of the literature is conducted to discover the limitations of various PKI‐enabled architectures and packages. The research seems at the usage of cryptography and digital certificates inside the implementation of PKI, and evaluates the different varieties of authentication, authorization, and encryption strategies in shielding networked structures. Furthermore, the paper discusses the advantages and demanding situations associated with the deployment of PKI for distinctive use instances. The findings exhibit that during order for PKI to efficiently defend networked structures, a sound authentication system need to be blended with sturdy and consumer-friendly encryption strategies. Moreover, the paper shows strategies for overcoming the demanding situations related to enforcing PKI in a relaxed and efficient way. Public-key infrastructure (PKI) is an important device in data security fashions for networked packages. PKI is a security architecture that entails the use of public and private interchangeable cryptographic keys to ensure authenticated and comfortable verbal exchange among entities. Through developing a hierarchical accept as true with device, virtual certificate are used to cozy communications and the sender and authenticity and integrity of records. This architecture gives a at ease foundation for at ease dispensed computing across multiple sites located in numerous geographical places, as it enhances confidentiality, integrity, and authentication of exchanged information. In addition, PKI can facilitate cozy statistics sharing, alleviate potential privateness issues,

关键词： Authentication

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models 3

Hyperspectral Images for Crop Prediction using Machine and D...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 IEEE.

关键词： Classification Deep Learning(DL) Hyperspectral Images(HSI) Machine Learning(ML)

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Hybrid Model for Enhancing Fault Detection System in Nuclear Power Plant using Support Vector Machine and Multivariate State Estimation Techniques

Hybrid Model for Enhancing Fault Detection System in Nuclear...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Rahul Rajendhiran Ayyanathan N Department of Computer Application Hindustan Institute of Technology and Science Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Nuclear power plants (NPPs) are vital for sustainable energy production but require meticulous monitoring to ensure safety and operational stability. This study proposes a hybrid framework integrating Support Vector Machines (SVM) and Multivariate State Estimation (MSE) to enhance real-time fault detection and system reliability in NPPs. While SVMs effectively classify operational anomalies, challenges persist in optimizing kernel selection and managing high-dimensional sensor data. Our approach combines SVM’s predictive capabilities with MSE’s dynamic state estimation, enabling early detection of deviations and improved decision-making. By addressing limitations in real-time data processing and complex nuclear environments, this method supports predictive maintenance and reduces operational risks. The study reviews existing techniques and demonstrates how merging these tools advances monitoring accuracy, safety, and efficiency in NPPs. This paper explored critical gaps in the application of Support Vector Machines (SVM) for real-time monitoring in nuclear power plants (Nuclear Power Plants) and proposed a hybrid approach that combines SVM with the Multivariate State Estimation Technique (MSET).

关键词： Support vector machines Fault detection Real-time systems Hybrid power systems Safety Reliability State estimation Kernel Power generation Monitoring

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Disease Detection Using Ensemble Model in Machine Learning

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SN computer Science 2025年第3期6卷 1-15页

作者： Mohapatra, Rojalin Giri, Parimala Kumar Sayyad, Irfan Sahu, Amaresh Brahma, Biswajit Kamila, Nilayam Kumar Department of Computer Science and Engineering Gandhi Institute for Technological Advancement Odisha Bhubaneswar India Department of Information Technology and Computer Application Vignan’s Foundation for Science Technology and Research (Deemed to be University) Andhra Pradesh Vadlamudi India Department of MCA Ajay Binay Institute of Technology Odisha Cuttack India McKesson Corporation San Francisco CA United States Capital One Wilmington DE United States

Disease detection is a vital issue in the healthcare system. Few diseases need to be tested early if any patient discovers any disease-related symptoms in their body. Although various methods are used to test several diseases based on the symptoms, it is necessary to improve the experimental testing for disease detection through various machine learning (ML) methods. Thus, the ensemble ML Model is proposed to enhance the experimental performance per the methods. Different machine learning methods such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Gaussian Naive Bayes (GNB) and Logistic Regression (LR) are used to make ensemble models based on the combination of two or three methods. The experiment is demonstrated on the proposed ensemble model using a diabetes data set and performed with various evaluation metrics. As per the performance of the ensemble method, (SVM + RF) performed well than other methods with an accuracy of 99%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ensemble model Logistic regression Machine learning Naïve Bayes Random forest SVM Voting classifier

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Integration of Chemometric Analysis and Machine Learning for Predictive Classification of Raisin Quality

Integration of Chemometric Analysis and Machine Learning for...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Gokul Padmanaban Princy Suganthi Bai S Department of Computer Application Hindustan Institute of Technology & Science Chennai India Department of Computer Science Hindustan Institute of Technology & Science Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The integration of chemometric analysis and machine learning techniques to enhance the predictive classification of raisin quality, a vital task for agricultural and food processing industries. The study employs chemometrics to analyze physical and chemical characteristics, selecting critical features such as color, texture, and chemical composition to reduce data redundancy and optimize model accuracy. Various machine learning algorithms, including Support Vector Machines (SVM), and Artificial Neural Networks (ANN), are applied and compared to predict raisin quality. By merging chemometric approaches with advanced machine learning methods, the proposed framework demonstrates improved classification accuracy over traditional techniques, showcasing its potential for real-world applications in quality control and automated sorting systems. Future efforts will aim to expand the dataset, refine model performance, and explore new feature selection techniques to further boost classification accuracy.

关键词： Support vector machines Accuracy Visual analytics Machine learning Quality control Artificial neural networks Real-time systems Data models Chemicals Sorting

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Integration of Chemometric Analysis and Machine Learning for Predictive Classification of Raisin Quality

Integration of Chemometric Analysis and Machine Learning for...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Padmanaban, Gokul Princy Suganthi Bai, S. Hindustan Institute of Technology & Science Department of Computer Application Chennai India Hindustan Institute of Technology & Science Department of Computer Science Chennai India

ISBN: (纸本)9798331521394

关键词： Support vector machines

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Automated Anomaly Detection in Medical Imaging Using Support Vector Machines 5th

Automated Anomaly Detection in Medical Imaging Using Support...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Goyal, Manish Kumar Shahid, Mohammad Karthikeyan, M.P. Kumar, Ritesh Department of Computer Science & Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Clinical imaging techniques such as X-ray, Magnetic Resonance Imaging (MRI), and laptop Tomography (CT) scanners are used to diagnose, compare, and deal with sufferers. Automatic Anomaly Detection is an automatic technique for identifying and diagnosing abnormalities in clinical imaging. It has been diagnosed that Support Vector Machines (SVM) can be effectively used to generate correct automatic anomaly detection for scientific imaging. SVM is an approach that uses supervised getting-to-know to create a choice boundary between classes, with the decision primarily based on the information to be had. The gain of SVM is that it plays an automatic function extracting from the medical imaging facts and uses those extracted capabilities for anomaly detection. It combines photograph segmentation and classification to classify the abnormal regions in an image. So one can enhance the accuracy of SVM;it's miles viable to use an expansion of kernel capabilities and unique parameter tuning. Moreover, introducing an automatic pipeline from training to deploying SVM reduces the efforts of manual feature extraction from photos. Consequently, aid Vector Machines is a feasible approach for automatic anomaly detection in scientific imaging. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Improving Medical Image Classification Through Transfer Learning and Meta Learning 5th

Improving Medical Image Classification Through Transfer Lear...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Arora, Sonia Jebakumari, S. Adlin Singh, Vaishali Kharbas, Vikas Kumar Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

This paper presents an observation of improving clinical picture classification via transfer getting to know and meta-studying. Switching getting-to-know and meta-learning methods are usually employed in medical imaging class tasks. Transfer studying objectives to switch information and illustration from a properly-trained version and mildew that expertise to higher healthy the problem at hand, while meta-gaining knowledge is a version of switch gaining knowledge of which targets to seize and adapt the generalizable expertise of a project domain, and to allow a progressed impact for the goal tasks. Inside the clinical imaging scenario, the two procedures can dig deeper into the several layers which might be involved in gaining deep knowledge of algorithms. The authors compare the effectiveness of the proposed strategies in five publicly available medical imaging datasets. Effects have shown that the proposed techniques notably advanced the usual type accuracy for a few datasets. Furthermore, it was found that the 2-step technique showed higher performance than the one-step technique. This study presents a higher understanding of the efficacy of transfer learning and meta-mastering techniques for clinical photo-type tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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