检索结果-内蒙古大学图书馆

Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

Computer Systems science & engineering 2023年第5期45卷 1469-1482页

作者： Doaa Sami Khafaga Amel Ali Alhussan Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mohamed Saber El-Sayed M.El-kenawy Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansouraEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Arrhythmia has been classified using a variety of *** of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more *** with cardiac arrhythmias can benefit from competent monitoring to save their *** arrhythmia classification and prediction have greatly improved in recent *** are a category of conditions in which the heart's electrical activity is abnormally rapid or *** year,it is one of the main reasons of mortality for both men and women,*** the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)*** proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia *** proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s *** purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia *** method of comprehensive arrhythmia classification outperforms earlier methods presented in the *** achieved classification accuracy using the proposed approach is 99.8%.

关键词： Feature selection electrocardiogram metaheuristics dipper throated algorithm

来源：评论

学校读者我要写书评

暂无评论

Metaheuristic Optimization for Mobile Robot Navigation Based on Path Planning

引用

Computers, Materials & Continua 2022年第11期73卷 2241-2255页

作者： El-Sayed M.El-kenawy Zeeshan Shafi Khan Abdelhameed Ibrahim Bandar Abdullah Aloyaydi Hesham Arafat Ali Ali E.Takieldeen Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Science Department College of Engineering and Computer ScienceMustaqbal UniversityBuraidahSaudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Mechanical Engineering Qassim UniversityBuraidah51452Saudi Arabia

Recently,the path planning problem may be considered one of the most interesting researched topics in autonomous *** is why finding a safe path in a cluttered environment for a mobile robot is a significant requisite.A promising route planning for mobile robots on one side saves time and,on the other side,reduces the wear and tear on the robot,saving the capital *** route planning methods for the mobile robot have been developed and *** to our best knowledge,no method offers an optimum solution among the existing *** Swarm Optimization(PSO),a numerical optimization method based on the mobility of virtual particles in a multidimensional space,is considered one of the best algorithms for route planning under constantly changing environmental *** the researchers,reactive methods are increasingly common and extensively used for the training of neural networks in order to have efficient route planning for mobile *** paper proposes a PSO Weighted Grey Wolf Optimization(PSOWGWO)*** is a hybrid algorithm based on enhanced Grey Wolf Optimization(GWO)with *** order to measure the statistical efficiency of the proposed algorithm,Wilcoxon rank-sum and ANOVA statistical tests are *** experimental results demonstrate a 25%to 45%enhancement in terms of Area Under Curve(AUC).Moreover,superior performance in terms of data size,path planning time,and accuracy is demonstrated over other state-of-the-art techniques.

关键词： Mobile robot swarm optimization robot route planning neural networks

来源：评论

学校读者我要写书评

暂无评论

A Hybrid GAN-CNN Framework for Low-Light Image Enhancement Combining Structural Noise Reduction and Perceptual Quality

A Hybrid GAN-CNN Framework for Low-Light Image Enhancement C...

引用

2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Gobinath, R. Boopathi, R. Vijayalakshmi, S. Disney, D. Anu Priyadharshini, P. Nirmala Maheshwari, K. E.G.S. Pillay Engineering College Nagapattinam611002 India Karpaga Vinayaga College of Engineering and Technology Department of Electrical and Electronic Engineering Tamilnadu 603308 India Sona College of Technology Department of Electronics and communication Engineering Tamilnadu Salem636005 India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai600119 India Adithya Institute of Technology Department of Information Technology Coimbatore641107 India Sir Issac Newton College of Engineering and Technology Department of Artificial Intelligence and Data Science Nagapattinam611 102 India

ISBN: (纸本)9798331509828

Low light region is one of the difficult scenarios for surveillance system, especially noisy, low-quality images may lead to poor object detection and scene analysis. To tackle this problem, we introduce a hybrid GAN - CNN framework for low light image denoising and quality enhancement. We develop a framework that combines the strengths of Generative Adversarial Networks (GANs) for photorealistic image generation and Convolutional Neural Networks (CNNs) for robust noise reduction. First denoising and feature extraction is done by the CNN module, and the GAN part further refines the denoised output to improve the perceptual quality of the generated images. We train and validate the proposed model in standard low light image dataset and show superior PSNR, SSIM, and visual clarity compared to state of art methods. We show that the hybrid GAN-CNN model successfully trades between image denoising accuracy and visual realism, thereby providing a viable alternative for enhancing surveillance performance in lowlight conditions. Keywords: Image denoising, GAN-CNN hybrid, low-light enhancement, PSNR, SSIM, surveillance systems. © 2025 IEEE.

关键词： Gallium nitride

来源：评论

学校读者我要写书评

暂无评论

Innovative AgTech: A Predictive Machine Learning-Driven Precision Farming Solution for Enhancing Agricultural Productivity in Wardha

Innovative AgTech: A Predictive Machine Learning-Driven Prec...

引用

artificial intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Aishwarya Kadu K. T. V Reddy Ujwalla Gawande Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha IT Dept YCCE Nagpur Maharastra

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Agricultural productivity is crucial for economic growth of a nation, especially for countries like India which is highly dependent on agriculture. This study addresses the need for cutting-edge technology to enhance crop productivity by proposing a Mobile/Web-based precision farming system. Leveraging Machine Learning (ML) algorithms, the proposed system recommends optimal crops based on several factors such as environmental parameters (Nitrogen (N), Phosphorus (P), Potassium (K)), pH value, Temperature, Rainfall, Humidity and Soil Nutrients., The current study applied five ML algorithms Naïve Bayes, Random Forest, on a dataset comprising of 2200 records and 7 variables to create a predictive ML model. Among the five algorithms, the Naïve Bayes exhibits superior performance, achieving a precision rate of 99.50%. Despite challenges like data scarcity and the need for field validation, the research aims to provide Wardha farmers with a free and open-source precision farming platform. By facilitating informed crop management decisions, the platform seeks to augment agricultural productivity and foster sustainable economic growth in the region. The robustness of the proposed system is further validated by the identified algorithmic accuracy, with Random Forest emerging as the most efficient option for crop recommendation within the specified environmental context. This study under-scores the potential of precision farming to revolutionize agriculture and contribute to long-term economic development in emerging regions. The validation process involves a train-test split, cross-validation, and field validation to ensure the reliability and accuracy of the machine learning models. Additionally, the impact assessment highlights potential benefits such as improved crop yields, resource optimization, sustainable economic growth, and long-term environmental benefits for the Wardha region.

关键词： Economics Productivity Machine learning algorithms Accuracy Temperature Prediction algorithms Agriculture Bayes methods Random forests Farming

来源：评论

学校读者我要写书评

暂无评论

Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

引用

Computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

来源：评论

学校读者我要写书评

暂无评论

Guarding Voices, Protecting Homes: A Comprehensive Case Study on Voice Assistant Security in Smart Living 12

Guarding Voices, Protecting Homes: A Comprehensive Case Stud...

引用

12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Rehman, Fazalur Hashmi, Joudat Abdullah, Moiz Zaman, Hafsah School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan College of Engineering Georgia Tech University Department of Analytics United States Air University Faculty of Computing and Artificial Intelligence Department of Cyber Security Islamabad Pakistan

ISBN: (纸本)9798350330366

The evolution of technologies in the last decades has transformed the whole human interaction experience with the digital world. Integrated into users' home voice assistant devices has rationalized personal interaction with the technology however convenience of intelligent voice assistant presents security and privacy risks. This paper investigates the security vulnerabilities of smart voice assistant devices specifically focusing on Google Home Mini. Through a series of simulated attacks and comprehensive analysis, this study uncovers potential risks ranging from unauthorized access to user data privacy compromise within home networks. The objective is to raise awareness about these vulnerabilities and their implication urging the need for robust security measures and providing valuable observation for future research. As voice devices are becoming an integral part of daily life addressing these challenges is paramount to ensuring user privacy data integrity and overall security of the smart home ecosystem. © 2024 IEEE.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Detecting Driver Fatigue with Python and OpenCV 10

Detecting Driver Fatigue with Python and OpenCV

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Pandi, C. Anish, T.P. Selvanayaki, S. Jagadeesan, P. Vedaraj, M. Sanjay, R. Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Computer Science and Engineering Avadi India R.M.K College of Engineering and Technology Computer Science and Engineering Tamil nadu India Saveetha Engineering College Artificial Intelligence and Data Science Tamil nadu Thandalam India R.M.D Engineering College Computer Science and Engineering Tamil nadu India

ISBN: (纸本)9798350353068

Utilizing eye aspect ratio analysis and facial landmarks, the sleep detection tool is intended to detect indicators of sleepiness or drowsiness in people. By leveraging the face recognition library, the program detects and extracts facial landmarks, with a particular focus on locating the positions of the eyes. The software analyzes the eye aspect ratio (EAR), which takes into account the relative distances between particular eye landmarks, to ascertain if an individual has fully or partially closed their *** video frame capture is performed from a chosen video source, typically a webcam, allowing real-time monitoring of the user. Each frame is processed to detect and analyze the eye regions, computing the EAR values for both the left and right eyes. If the EAR falls below a predefined threshold for a consecutive number of frames, it indicates that the person's eyes are closed for an extended duration, suggesting potential drowsiness or *** enhance user safety, an alarm sound is played when the program detects drowsiness, providing an immediate alert to the individual. The alarm sound serves as an auditory cue, helping the person regain alertness and avoid potential accidents or undesired consequences. Additionally, the program displays the computed EAR on the video feed, allowing for visual feedback and monitoring of the eye aspect ratio values for debugging purposes and fine-tuning the system's *** employing this sleep detection program, various applications can benefit from its capabilities. For instance, it can be utilized in the context of drowsy driving detection systems, alerting drivers when they exhibit signs of fatigue and helping prevent accidents due to impaired driving. Moreover, the program holds potential for use in other scenarios where monitoring individuals' alertness and detecting sleep-related patterns is essential, such as workplace safety or medical *** summary, the sleep detection program employs faci

关键词： Video signal processing

来源：评论

学校读者我要写书评

暂无评论

Consensus Reaching Model for 2-Rank Group Decision Making with Personalized Individual Semantics

Consensus Reaching Model for 2-Rank Group Decision Making wi...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhang, Zhen Yu, Wenyu Li, Ke Institute of Systems Engineering School of Economics and Management Dalian University of Technology Dalian116024 China School of Data Science and Artificial Intelligence Dongbei University of Finance and Economics Dalian116025 China Business School Sichuan University Chengdu610065 China

ISBN: (纸本)9781665410205

Traditional group decision-making problems focus on obtaining a complete ranking of all alternatives from best to worst. However, in many real-life scenarios, there are instances where it is necessary to assign only two rank levels to alternatives, creating a ranking where one subset of alternatives is prioritized above another subset. These scenarios are referred to as 2-rank group decision-making problems. Linguistic preference relations serve as an effective tool for expressing decision-makers preferences, as they allow comparisons between two alternatives at a time using linguistic terms. Nonetheless, in 2-rank group decision-making problems with linguistic preference relations, it is common for the same linguistic term to hold different meanings for different decision-makers, a phenomenon known as personalized individual semantics (PISs). Addressing how to model PISs in 2-rank group decision-making problems presents a significant challenge. In this paper, we develop a consensus-reaching model for 2-rank linguistic group decision-making problems, incorporating PISs and consistency control for decision-makers. Specifically, we first employ consistency-driven models to evaluate and improve the consistency of each decision-makers linguistic preference relations. Based on this foundation, we determine the 2-rank preference vectors for both individuals and the group. Subsequently, we propose a 2-rank consensus measurement method and design a 2-rank consensus-reaching process to help decision-makers enhance their consensus level. This involves the development of a PIS-based consensus level maximization model and a PIS-based minimum adjustment model. Furthermore, we introduce an algorithm to implement the consensus-reaching framework. Ultimately, numerical experiments and simulation results are provided to demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Extended Finite State Machine based Fault Tolerance in WSN 7

Extended Finite State Machine based Fault Tolerance in WSN

引用

7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Lekha, D. Ahsan Shariff, M. Lakshmi, S. Venkata Anwar Basha, H. Department of Computer Science and Engineering RMK College of Engineering and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Aalim Muhammed Salegh College of Engineering Tamil Nadu Chennai India Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore India School of Computer Science and Engineering REVA University Karnataka Bengaluru India

ISBN: (纸本)9781665464086

Wireless Sensor Network (WSN) suffers from different malware attacks. Several traditional approaches are proposed for detecting fault nodes in WSN. It is necessary to redistribute the nodes and detect the fault nodes while modifying the WSN parameters. Faults can occur in the nodes interrupting the continuous communication process of the nodes in the WSN. The main reason for a fault is also the induction of the energy drain in a node to the maximum possible level, failure of links in communicating nodes due to bandwidth constraints, and attacks induced by the malicious nodes. To solve this problems, Extended Finite State Machine based Fault Tolerance (EFSM_FT) in the WSN is used to detect the fault sensor nodes in the WSN. In this approach, called the finite state machine (FSM), state prompted is modified while a set of situations are true. Every node can be absolutely to be faulty or not establishing on the sensor node states. Using sensor energy, drop rate, forward sensor rate, sensor obtained rate, bandwidth, and transmission delay, this method finds the node with the fault. As a result, this approach accurately detects the faulty nodes in the WSN. The simulation outcomes illustrate the proposed method has a better fault detection ratio and minimizes the false negative ratio in the WSN. © 2023 IEEE.

关键词： Finite automata

来源：评论

学校读者我要写书评

暂无评论

Research on two-stage V2G optimal scheduling strategy for electric vehicles considering scheduling priority 8

Research on two-stage V2G optimal scheduling strategy for el...

引用

8th International Conference on Energy System, Electricity, and Power, ESEP 2023

作者： Jie, Liu Bingyu, Li Ruixiong, Jing Xingchen, Guo Lianbing, Li Research Institute of Electric Power Science State Grid Power Co. of Hebei Province Ltd. Hebei Shijiazhuang050021 China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300130 China School of Electrical Engineering Hebei University of Technology Tianjin300130 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

Disorderly charging of large-scale electric vehicles will exacerbate the peak-to-valley load difference in the station area, increase the load pressure of grid equipment, and threaten the safe and economic operation of the grid. How to utilize EV V2G technology to reduce the load peak-valley difference to realize the heavy overload management of the station area is a key concern in the future. This paper proposes a two-stage optimal scheduling strategy for EVs in V2G mode counting and prioritizing, establishes a two-layer optimal scheduling model with the objective function of minimizing the standard deviation of loads, minimizing the peak-to-valley difference, and maximizing the revenue of the users' side and the agents, develops its charging and discharging prioritized scheduling plan through the optimization of the charging and discharging distribution time period and the dynamic time-sharing tariff, and employs the quadratic variational difference evolution (SVDE) algorithm to solve the model, and the results are shown in the following figure. The model is solved, and the results show that the proposed model can effectively reduce the load peak-valley difference and realize the win-win benefits for the station governance, user side and charging pile agents. © 2024 SPIE.

关键词： Landforms

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：