检索结果-内蒙古大学图书馆

2024 International Conference on engineering and Computing, ICECT 2024

作者： Ahmed, Muhammad Liaquat, Umer Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Mir, Azka University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Science and Technology Department of Computer Science Kuwait

ISBN: (纸本)9798350349719

Brain stroke is the world's leading cause of death, impacting numerous lives annually. The chances of having a stroke have increased by 50% over one's lifetime, impacting one in four people worldwide. Machine learning may assist in the prior detection of strokes, allowing for more timely diagnosis which can save a person's life and disability. However, existing methods face challenges in integrating ML models with clinical applications to predict patient stroke status with optimal accuracy. This study conducts a comprehensive investigation into brain stroke risk prediction using numerous ML algorithms, including Random Forest, Logistic Regression, KNN, Naive Bayes, XGBoost, and LightGBM. Our main contribution is the development of a web app that predicts stroke based on user inputs with optimal accuracy. The selected dataset contains 5110 records of normal people and 249 records of people having strokes which is highly imbalanced. To ensure model robustness, the dataset undergoes balancing through SMOTE oversampling and KNN Imputation for handling missing values. Custom thresholds for classifiers optimize training, with comprehensive evaluations utilizing key statistics such as confusion matrices, F1-Score, recall, accuracy, and precision. Cross-validation is employed to assess potential overfitting and underfitting risks in machine learning models. Random Forest ranked as the most imposing, reaching a remarkable 99.85% accuracy on the test set with a custom threshold of 0.6 and a random state at 42. Additionally, a user-friendly Web App has been developed to predict stroke status based on user input, enhancing practical usability and accessibility for individuals seeking risk assessments. © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Optimizing Stand-Alone PV Systems:A Metaheuristic-Enhanced Fuzzy Approach for Adaptive MPPT

引用

China Communications 2025年第1期22卷 61-74页

作者： Tina Samavat Mostafa Nazari Lin Fuhong Lei Yang Department of Computer Science and Engineering University of NevadaRenoNVUSA Faculty of Mechanical Engineering Shahrood University of TechnologyShahroodIran School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

This paper introduces a simple yet effective approach for developing fuzzy logic controllers(FLCs)to identify the maximum power point(MPP)and optimize the photovoltaic(PV)system to extract the maximum power in different environmental *** propose a robust FLC with low computational complexity by reducing the number of membership functions and *** optimize the performance of the FLC,metaheuristic algorithms are employed to determine the parameters of the *** evaluate the proposed FLC in various panel configurations under different environmental *** results indicate that the proposed FLC can easily adapt to various panel configurations and perform better than other benchmarks in terms of enhanced stability,responsiveness,and power transfer under various scenarios.

关键词： genetic algorithm imperialist competitive algorithm invasive weed algorithm maximum power point tracking

来源：评论

学校读者我要写书评

暂无评论

Improvisation of Node Mobility Using Cluster Routing-based Group Adaptive in MANET

引用

computer Systems science & engineering 2023年第3期44卷 2619-2636页

作者： J.Shanthini P.Punitha S.Karthik Department of Computer Science&Engineering Dr.N.G.P.Institute of EngineeringCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

In today's Internet routing infrastructure,designers have addressed scal-ing concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary *** tactical Mobile Ad-hoc Network(MANET),hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out,self-mending and *** in the routing process is one of the key aspects to increase MANET performance by coordinat-ing the pathways using multiple criteria and *** present a Group Adaptive Hybrid Routing Algorithm(GAHRA)for gathering portability,which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile *** on this aspect,the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach,with the objectives of enhancing the out-put of MANET routing computation in each *** analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.

关键词： Diplomatic mobile Ad-hoc network grouping mobility interior stable hybrid routing scheme adaptive switch structure clustering communication

来源：评论

学校读者我要写书评

暂无评论

Advancing emotion recognition via EEG signals using a deep learning approach with ensemble model

引用

Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 143-154页

作者： Immanuel, Rajeswari Rajesh Sangeetha, S.K.B. Department of Computer Science and Engineering SRM Institute of Science and Technology Faculty of Engineering and Technology Vadapalani Campus Tamil Nadu Chennai India

Human emotions are the mind's responses to external stimuli, and due to their dynamic and unpredictable nature, research in this field has become increasingly important. There is a growing trend in utilizing deep learning and machine learning techniques for emotion recognition through EEG (electroencephalogram) signals. This paper presents an investigation based on a real-time dataset that comprises 15 subjects, consisting of 7 males and 8 females. The EEG signals of these subjects were recorded during exposure to video stimuli. The collected real-time data underwent preprocessing, followed by the extraction of features using various methods tailored for this purpose. The study includes an evaluation of model performance by comparing the accuracy and loss metrics between models applied to both raw and preprocessed data. The paper introduces the EEGEM (Electroencephalogram Ensemble Model), which represents an ensemble model combining LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) to achieve the desired outcomes. The results demonstrate the effectiveness of the EEGEM model, achieving an impressive accuracy rate of 95.56%. This model has proven to surpass the performance of other established machine learning and deep learning techniques in the field of emotion recognition, making it a promising and superior tool for this application. © 2024 - IOS Press. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Towards an intrusion detection system for detecting web attacks based on an ensemble of filter feature selection techniques

引用

Cyber-Physical Systems 2023年第3期9卷 244-259页

作者： Kshirsagar, Deepak Kumar, Sandeep Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India Department of Computer Engineering and IT College of Engineering Pune Pune India

The use of machine learning models in intrusion detection systems (IDSs) takes more time to build the model with many features and degrade the performance. The present paper proposes an ensemble of filter feature selection techniques (EFFST) to obtain a significant feature subset for web attack detection by selecting one-fourth split of the ranked features. The experimentation on the CICIDS 2017 dataset shows that the proposed EFFST method provides a detection rate of 99.9909%, with J48 using 24 features. The system’s performance is compared to the original features and traditional relevant feature selection methods employed in IDSs. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Automated Diabetic Retinopathy detection and classification using stochastic coordinate descent deep learning architectures

引用

Materials Today: Proceedings 2023年 80卷 3333-3345页

作者： Rosline Mary, A. Kavitha, P. Department of Computer Science and Engineering Vemana Institute of Technology Bengaluru India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India

A new stochastic coordinate descent deep learning architectures optimization is proposed for Automated Diabetic Retinopathy Detection and Classification from different data sets and convolution networks. Initially, the layer-by-layer comparison of convolution matrix, pooling, transition, and dense of each network along with their matrix order is examined. Loss minimization after the testing data from the prediction analysis is considered as objective function for every stage of convolution networks. Similarity among the networks is identified for optimizing the matrix order of each layer and the minimization is performed for classifying the Diabetic Retinopathy level. The performance of the proposed optimization architectures confirmed through confusion matrix for every data set taking training accuracy and data loss as measures. The result of the proposed optimized schemes shows that for different the datasets different in amount data displays the dominance than existing schemes. The numerical analysis of different training accuracy and data loss is presented for validating the proposed work. © 2021

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

A Hybrid AI Approach for Fault Detection in Induction Motors Under Dynamic Speed and Load Operations

引用

IEEE Access 2025年 13卷 102869-102898页

作者： Irfan Ishaq, Muhammad Adnan, Muhammad Akbar, Muhammad Ali Bermak, Amine Saeed, Nimra Ansar, Maaz FAST National University of Computer and Emerging Sciences Department of Electrical Engineering Faisalabad-Chiniot Campus Chiniot38000 Pakistan HBKU Computer Science and Engineering Department Doha Qatar

Faults in an Induction Motor (IM) can lead to unexpected downtime, resulting in considerable economic and productivity losses. From existing literature, conventional fault diagnosis approaches in an IM struggle to reliably identify fault patterns at different speeds, particularly under variable speed and changing load conditions. To resolve this issue, this paper presents a unique hybrid Convolutional Neural Network (CNN) along with the Long Short Term Memory (LSTM) topology for diagnosing faulty patterns in an IM under loaded and unloaded variable speed settings. The proposed method can identify faults such as rotor imbalances, misalignment, stator winding issues, voltage imbalances, broken rotor bars, and broken bearings. Experiments performed using the University of Ottawa Electric Motor Dataset – Vibration and Acoustic Faults under Constant and Variable Speed Conditions (UOEMD-VAFCVS) dataset reveals that all three accelerometers are 99.93% accurate at constant speed and 99.96% at variable speed under both loaded and unloaded conditions. In terms of fault diagnostic accuracy in an IM operating at different speeds and load conditions, this methodology outperforms cutting-edge methodologies in the literature. Moreover, using the publicly available CWRU dataset, this study validates the robustness of the proposed methodology in terms of operational issues in an IM. Finally, the proposed method achieves incredible results at varying speeds, stressing the need to improve industrial equipment reliability and maintenance methods. © 2013 IEEE.

关键词： Electric fault location

来源：评论

学校读者我要写书评

暂无评论

Medicine and Disease Association Prediction via Attention-Based Medical Heterogeneous Information Network Representation Learning

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2022年第1期49卷 69-78页

作者： Zhang, Bin Yang, Dan Lin, Zhihuang Master of Computer Science and Software Engineering Department University of Science and Technology Liao Ning Anshan China School of Computer Science and Software Engineering University of Science and Technology Liao Ning Anshan China

Knowledge of medication and disease has been rapidly accumulated. Also, an increasing number of researchers have paid more attention to predicting medicine-disease associations by machine learning methods. The associations of entities in the medical heterogeneous information network involve different association types. In this paper, we propose MHIN-MD, a representation learning of medical heterogeneous network model to solve the association between medicines and diseases in medical heterogeneous information network. Specifically, we first construct a medical heterogeneous information network contains diseases, medicines, diagnosis, etc. Next, we design a neural network model to learn the feature vector representation of medical nodes information. We try to find the association between medicines and diseases through the characteristics of medicines structure and the interaction between medicines. Using the Euclidean distance to calculate medicine similarity and disease similarity, generates a set of similar nodes between medicines and diseases to prediction association between medicines and diseases. Finally, our extensive experiments on MIMIC- III datasets and DrugBank datasets, the result demonstrates that MHIN-MD can outperform baselines in the association prediction. © 2022. All Rights Reserved.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Analyzing the Effectiveness of Voice-Based User Interfaces in Enhancing Accessibility in Human-computer Interaction 13

Analyzing the Effectiveness of Voice-Based User Interfaces i...

引用

13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Jha, Roshan Fahim, Md Forkan Hossain Hassan, Md Al Mahedi Rai, Chulindra Islam, Md Minhazul Sah, Rahul Kumar Department of Computer Science and Engineering Bengaluru India Department of Computer Science and Engineering Chattogram Bangladesh

ISBN: (纸本)9798350305463

Voice-based user interfaces (VUIs) represent a promising avenue for enhancing accessibility in humancomputer interaction (HCI). This research paper investigates the effectiveness of VUIs in addressing accessibility challenges for individuals with disabilities. We examine the benefits and challenges associated with VUIs, considering their impact on diverse user groups. Through an exploration of relevant literature and empirical studies, we assess the potential of VUIs to improve accessibility and inclusivity in technology. The research contributes insights into the strengths and limitations of VUIs, highlighting key considerations for designers, developers, and policymakers striving to create more accessible HCI solutions. © 2024 IEEE.

关键词： User interfaces

来源：评论

学校读者我要写书评

暂无评论

A Study on Identifying, Categorizing and Reporting Usability Bugs and Challenges 4

A Study on Identifying, Categorizing and Reporting Usability...

引用

4th International Conference on Communication Technologies, ComTech 2023

作者： Ain, Qurat Ul Rana, Tauseef Aamana Department of Computer Science Islamabad Pakistan NUST Military College of Signals Department of Computer Software Engineering Islamabad Pakistan Department of Software Engineering Rawalpindi Pakistan

ISBN: (纸本)9798350337211

To assess the quality, acceptability and user experience of interactive applications, usability is one of the most integral quality attributes. However, significant number of usability bugs are being experienced by the end users of interactive applications thus creating a gap between users and technology. To bridge this gap and to overcome those usability bugs and challenges, it is significantly important to clearly identify the those usability bugs along with the gaps and challenges causing them. This research study aims to identify the usability bugs being experienced by users of interactive applications and proposes a categorization taxonomy for the identified bugs. Moreover, to reach the cause of emergence of usability bugs, the study attempts to identify and report the usability gaps and challenges in existing usability practices. Furthermore, this study performs a mapping between the identified gaps and challenges in existing practices to that of the recommendations to address the identified gaps. Index Terms - Usability, interactive applications, usability bugs, user experience, user satisfaction, usability testing, user interface. © 2023 IEEE.

关键词： User interfaces

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

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

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：