检索结果-内蒙古大学图书馆

Strategies for classifying water quality in the Cauvery River using a federated learning technique

International Journal of Cognitive Computing in engineering 2023年第1期4卷 187-193页

作者： J, Vellingiri K, Kalaivanan P, Gopinath M C, Gobinath Subramaniam, Prabhakar Rontala Rangarajan, Sarathkumar School of Information Technology and Engineering Vellore Institute of Technology Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Information System and Technology University of KwaZulu-Natal Durban South Africa Victorian Institute of Technology Melbourne Australia

Artificial intelligence methods are emerging techniques used in the field of environmental protection, especially in the analysis of air, water, and soil quality. AI analyzes vast amounts of environmental data to predict pollution and provide decision-makers with the information they need to develop efficient policies. One of the most important problems in environmental analysis is data security, and many organizations are actively working to ensure the secure collection, storage, and utilization of sensitive environmental data. In addition, organizations are focusing on developing strategies to protect their data from malicious attacks, such as cyber-attacks, as well as from accidental misuses, like unauthorized access. For this purpose, we have introduced a novel water quality prediction using the Federated Learning Technique. Federated learning enables multiple parties to collaborate and train a model on their local data without sharing it with others, thereby preserving data privacy. The proposed method is applied to a Cauvery River dataset of water quality parameters, and the results demonstrate that the PSO-optimized federated learning process achieves better prediction accuracy of 87%, a precision of 85%, a recall of 93%, and an 89% F1 score. © 2023

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

The Laplacian Energy of Hesitancy Fuzzy Graphs in Decision-Making Problems

引用

computer systems science & engineering 2023年第3期44卷 2637-2653页

作者： N.Rajagopal Reddy Mohammad Zubair Khan S.Sharief Basha Abdulrahman Alahmadi Ahmed H.Alahmadi Chiranji Lal Chowdhary School of Advanced Sciences Vellore Institute of TechnologyVellore632014TamilnaduIndia Department of Computer Science and Information Taibah UniversityMedinaSaudi Arabia Department of Mathematics School of Advanced SciencesVellore Institute of TechnologyVellore632014TamilnaduIndia School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

Decision-making(DM)is a process in which several persons concur-rently engage,examine the problems,evaluate potential alternatives,and select an appropriate option to the *** for determining order preference by similarity to the ideal solution(TOPSIS)is an established DM *** objective of this report happens to broaden the approach of TOPSIS to solve the DM issues designed with Hesitancy fuzzy data,in which evaluation evidence given by the experts on possible solutions is presents as Hesitancy fuzzy decision matrices,each of which is defined by Hesitancy fuzzy ***:we represent analytical results,such as designing a satellite communication network and assessing reservoir operation methods,to demonstrate that our suggested thoughts may be used in ***:We studied a new testing method for the arti-ficial communication system to give proof of the future construction of satellite earth *** aim to identify the best one from the different testing *** are alsofinding the best operation schemes in the *** this article,we present the concepts of Laplacian energy(LE)in Hesitancy fuzzy graphs(HFGs),the weight function of LE of HFGs,and the TOPSIS method technique is used to produce the hesitancy fuzzy weighted-average(HFWA).Also,consider practical examples to illustrate the applicability of thefinest design of satellite communication systems and also evaluation of reservoir schemes.

关键词： Hesitancy fuzzy graphs(HFGs) laplacian energy satellite communication system reservoir operation schemes decision-making

来源：评论

学校读者我要写书评

暂无评论

Robust video question answering via contrastive cross-modality representation learning

引用

science China(information sciences) 2024年第10期67卷 211-226页

作者： Xun YANG Jianming ZENG Dan GUO Shanshan WANG Jianfeng DONG Meng WANG School of Information Science and Technology University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center School of Computer Science and Information Engineering Hefei University of Technology Institutes of Physical Science and Information Technology Anhui University School of Computer Science and Technology Zhejiang Gongshang University

Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts, recent studies revealed that current VideoQA models mostly tend to over-rely on the superficial correlations rooted in the dataset bias while overlooking the key video content, thus leading to unreliable results. Effectively understanding and modeling the temporal and semantic characteristics of a given video for robust VideoQA is crucial but, to our knowledge, has not been well investigated. To fill the research gap, we propose a robust VideoQA framework that can effectively model the cross-modality fusion and enforce the model to focus on the temporal and global content of videos when making a QA decision instead of exploiting the shortcuts in datasets. Specifically, we design a self-supervised contrastive learning objective to contrast the positive and negative pairs of multimodal input, where the fused representation of the original multimodal input is enforced to be closer to that of the intervened input based on video perturbation. We expect the fused representation to focus more on the global context of videos rather than some static keyframes. Moreover, we introduce an effective temporal order regularization to enforce the inherent sequential structure of videos for video representation. We also design a Kullback-Leibler divergence-based perturbation invariance regularization of the predicted answer distribution to improve the robustness of the model against temporal content perturbation of videos. Our method is model-agnostic and can be easily compatible with various VideoQA backbones. Extensive experimental results and analyses on several public datasets show the advantage of our method over the state-of-the-art methods in terms of both accuracy and robustness.

关键词： video question answering cross-modality fusion contrastive learning cross-media reasoning

来源：评论

学校读者我要写书评

暂无评论

Attenuate Class Imbalance Problem for Pneumonia Diagnosis Using Ensemble Parallel Stacked Pre-Trained Models

引用

computers, Materials & Continua 2023年第4期75卷 891-909页

作者： Aswathy Ravikumar Harini Sriraman School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this ***-aided diagnosis of pneumonia using deep learning techniques iswidely used due to its effectiveness and performance. In the proposed method,the Synthetic Minority Oversampling Technique (SMOTE) approach is usedto eliminate the class imbalance in the X-ray dataset. To compensate forthe paucity of accessible data, pre-trained transfer learning is used, and anensemble Convolutional Neural Network (CNN) model is developed. Theensemble model consists of all possible combinations of the MobileNetv2,Visual Geometry Group (VGG16), and DenseNet169 models. MobileNetV2and DenseNet169 performed well in the Single classifier model, with anaccuracy of 94%, while the ensemble model (MobileNetV2+DenseNet169)achieved an accuracy of 96.9%. Using the data synchronous parallel modelin Distributed Tensorflow, the training process accelerated performance by98.6% and outperformed other conventional approaches.

关键词： Pneumonia prediction distributed deep learning data parallel model ensemble deep learning class imbalance skewed data

来源：评论

学校读者我要写书评

暂无评论

Efficient Gait Analysis Using Deep Learning Techniques

引用

computers, Materials & Continua 2023年第3期74卷 6229-6249页

作者： K.M.Monica R.Parvathi School of Computer Science and Engineering Vellore Institute of TechnologyChennai600117India

Human Activity Recognition(HAR)has always been a difficult task to *** is mainly used in security surveillance,human-computer interaction,and health care as an assistive or diagnostic technology in combination with other technologies such as the Internet of Things(IoT).Human Activity Recognition data can be recorded with the help of sensors,images,or *** daily routine-based human activities such as walking,standing,sitting,etc.,could be a difficult statistical task to classify into categories and hence 2-dimensional Convolutional Neural Network(2D CNN)MODEL,Long Short Term Memory(LSTM)Model,Bidirectional long short-term memory(Bi-LSTM)are used for the *** has been demonstrated that recognizing the daily routine-based on human activities can be extremely accurate,with almost all activities accurately getting recognized over 90%of the ***,because all the examples are generated from only 20 s of data,these actions can be recognised *** from classification,the work extended to verify and investigate the need for wearable sensing devices in individually walking patients with Cerebral Palsy(CP)for the evaluation of chosen Spatio-temporal features based on 3D foot ***-control research was conducted with 35 persons with CP ranging in weight from 25 to 65 *** Motion Capture(OMC)equipment was used as the referral method to assess the functionality and quality of the foot-worn *** average accuracy±precision for stride length,cadence,and step length was 3.5±4.3,4.1±3.8,and 0.6±2.7 cm *** cadence,stride length,swing,and step length,people with CP had considerably high inter-stride ***-worn sensing devices made it easier to examine Gait Spatio-temporal data even without a laboratory set up with high accuracy and precision about gait abnormalities in people who have CP during linear walking.

关键词： Human activity recognition neural networks gait analysis wearable sensors kinetic analysis cerebral palsy

来源：评论

学校读者我要写书评

暂无评论

An Energy Efficient Hybrid Communication Protocol for Large Area Wireless Sensor Networks 4

An Energy Efficient Hybrid Communication Protocol for Large ...

引用

4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karthigeyan, Shreyas Bhol, Sampark Jain, Akshat Yadav, Karthik Sinha, Akash Padma Priya, R. School of Computer Science and Information Systems Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India Department of Computer Science and Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal462003 India

Energy conservation is an indispensable aspect of the protocols designed for Wireless Sensor Networks (WSNs). The communication protocols for WSN fall mainly under two categories: centralized and distributed. Centralized protocols are not feasible for real-world WSNs as the network can span across large areas where most of the nodes cannot directly communicate with the base station (BS). The routing issues in such networks can be addressed using multi-hop protocols. However, the process of BS guided clustering in such methods cannot be performed. Distributed clustering methods that minimize the intervention of BS in clustering can provide feasible solutions in such cases, but the energy expenditure is considerably high. This paper, hence, proposes a hybrid clustering based routing protocol for large WSN that employs certain aspects of centralized as well as that of distributed protocols. Experimental evaluation of the proposed protocol in terms of network lifetime gives promising results that clearly indicate the energy efficiency and consistency of the proposed work in different scenarios. The proposed work can prove beneficial in real-life scenarios where the deployed nodes are far away from the BS. © 2025 The Authors. Published by Elsevier B.V.

关键词： Hybrid power

来源：评论

学校读者我要写书评

暂无评论

Modified MMS: Minimization Approach for Model Subset Selection

引用

computers, Materials & Continua 2023年第10期77卷 733-756页

作者： C.Rajathi P.Rukmani School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Considering the recent developments in the digital environment,ensuring a higher level of security for networking systems is *** security approaches are being constantly developed to protect against evolving *** ensemble model for the intrusion classification system yielded promising results based on the knowledge of many prior *** research work aimed to create a more diverse and effective ensemble *** this end,selected six classification models,Logistic Regression(LR),Naive Bayes(NB),K-Nearest Neighbor(KNN),Decision Tree(DT),Support Vector Machine(SVM),and Random Forest(RF)from existing study to run as independent *** the individual models were trained,a Correlation-Based Diversity Matrix(CDM)was created by determining their *** models for the ensemble were chosen by the proposed Modified Minimization Approach for Model Subset Selection(Modified-MMS)from Lower triangular-CDM(L-CDM)as *** proposed algorithm performance was assessed using the Network Security Laboratory—Knowledge Discovery in Databases(NSL-KDD)dataset,and several performance metrics,including accuracy,precision,recall,and *** selecting a diverse set of models,the proposed system enhances the performance of an ensemble by reducing overfitting and increasing prediction *** proposed work achieved an impressive accuracy of 99.26%,using only two classification models in an ensemble,which surpasses the performance of a larger ensemble that employs six classification models.

关键词： Ensemble learning intrusion detection minimization model diversity

来源：评论

学校读者我要写书评

暂无评论

Tropical Cyclone Intensity Prediction Using Deep Learning Techniques- A Survey 4

Tropical Cyclone Intensity Prediction Using Deep Learning Te...

引用

4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Ilayaraja, V. Rajamohana, S.P. Jagadeesan, S. Aishwarya, V. Rakesh, M. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632 014 India School of Engineering & Technology Pondicherry University Department of Computer Science Puducherry Karaikal India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Tamil Nadu Vellore632 014 India

ISBN: (纸本)9798331528171

Tropical cyclones, characterized by strong winds and heavy rainfall, threaten human life in coastal regions crucial to the economy, including fisheries, agriculture, tourism, and infrastructure. Their frequent occurrence impacts communities reliant on these industries for livelihoods. Accurate estimation of tropical cyclone intensity is vital for disaster preparedness, risk assessment, and timely evacuations. Recent advancements in machine learning and deep learning have been applied to predict cyclone intensity from satellite images, providing insights into cyclone dynamics and enhancing disaster response. This paper analyzes recent research on intensity estimation using various machine learning algorithms and discusses future prospects for improving accuracy and reliability. © 2024 IEEE.

关键词： Tropical cyclone

来源：评论

学校读者我要写书评

暂无评论

Heart Disease Prediction Using Machine Learning Algorithms

Heart Disease Prediction Using Machine Learning Algorithms

引用

2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Priya, M. Anitha, A. Nallakaruppan, M.K. Raju, J. Raghavan, R. Srivastava, Divyansh School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India School of Electrical Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350325188

Heart disease is one of the destructive infections that an enormous populace of individuals all over the planet grieves. The prediction of heart disease is very much urged because of increasing death rates. The conventional method of prediction isn't adequate for such a disease. Evolving a clinical determination framework based on machine learning concepts gives more exact conclusions than the conventional way for the prediction of such illness. In this paper, we have used different machine learning algorithms to accurately foresee whether an individual has a heart illness or not. The concepts of machine learning like linear prediction KNN, SVM, and, ANN to predict the probability of heart disease in an individual are used. The medical and general details of the person like BMI, alcohol and tobacco consumption, etc. are used to make the prediction. © 2023 IEEE.

关键词： ANN Heart Disease KNN Machine Learning Neural Network SVM

来源：评论

学校读者我要写书评

暂无评论

Optimized Neural Network for Automated Cardiovascular Disease Classification: Meta-heuristic Enabled Model

引用

SN computer science 2025年第1期6卷 1-14页

作者： Kusuma, S. Jothi, K.R. Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) signals. The model consists of two main steps: feature extraction using a refined Lion Algorithm with New Mutated evaluation (LA-NM) and classification through an ensemble approach combined with a Neural Network (NN). The proposed model significantly outperforms traditional classifiers. At 80% training data, it achieves accuracy improvements of 8.4% to 3.11% over Neural Network (NN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and ensemble models. At 90% training data, it shows precision improvements ranging from 1.03% to 0.31% better than the comparison models and specificity improvements ranging from 13.92% to 0.52%. These results underscore the effectiveness of the model and suggest its potential for further development in personalized treatment recommendations based on patient history. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ensemble models Feature extraction Heart disease classification Neural network Optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：