检索结果-内蒙古大学图书馆

Hybrid Metaheuristics Feature Selection with Stacked Deep Learning-Enabled Cyber-Attack Detection Model

computer systems science & engineering 2023年第5期45卷 1679-1694页

作者： Mashael M Asiri Heba G.Mohamed Mohamed K Nour Mesfer Al Duhayyim Amira Sayed A.Aziz Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki Department of Computer Science College of Science&Art atMahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Due to exponential increase in smart resource limited devices and high speed communication technologies,Internet of Things(IoT)have received significant attention in different application ***,IoT environment is highly susceptible to cyber-attacks because of memory,processing,and communication *** traditional models are not adequate for accomplishing security in the IoT environment,the recent developments of deep learning(DL)models find *** study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection(HMFS-SDLCAD)*** major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks in the IoT *** the preliminary stage,data pre-processing is carried out to transform the input data into useful *** addition,salp swarm optimization based on particle swarm optimization(SSOPSO)algorithm is used for feature selection ***,stacked bidirectional gated recurrent unit(SBiGRU)model is utilized for the identification and classification of ***,whale optimization algorithm(WOA)is employed for optimal hyperparameter optimization *** experimental analysis of the HMFS-SDLCAD model is validated using benchmark dataset and the results are assessed under several *** simulation outcomes pointed out the improvements of the HMFS-SDLCAD model over recent approaches.

关键词： Cyberattacks security deep learning internet of things feature selection data classification

来源：评论

学校读者我要写书评

暂无评论

Urban Mobility Optimization with IoT and Deep Q Networks for Traffic Density Management 3

Urban Mobility Optimization with IoT and Deep Q Networks for...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Ranganathan, Chitra Sabapathy Jayanthi, L.N. Babu, J. Jagan Manikandan, M. Sivakamy, N. Mphasis Corporation Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Management Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381931

Traffic density and mobility optimization in urban areas are becoming more difficult to manage to improve transportation efficiency. Urban traffic management is complicated, but this research provides a revolutionary technique using the Internet of Things (IoT) and Deep Q Networks (DQN). Traffic lights, sensors, and vehicles may be monitored in real-time using IoT technology to provide a complete picture of urban mobility. As an intelligent decision-making agent, the DQN uses reinforcement learning to adapt to changing traffic circumstances and optimize traffic density management. The model is educated on historical data and refines its tactics via environmental interaction, solving urban transportation problems. A complex data fusion mechanism processes data from different IoT devices. A reinforcement learning framework uses DQN for decision-making, and a feedback loop updates and optimizes traffic management tactics. The research uses simulations and urban case studies to prove that the technique works. The findings show improved traffic flow, congestion, and mobility efficiency. It uses IoT and DQN to develop intelligent urban transport systems that scale and respond to traffic density in complex and dynamic urban environments. © 2024 IEEE.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

引用

computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

来源：评论

学校读者我要写书评

暂无评论

Tumor Infiltration of Microrobot using Magnetic torque and AI Technique 2

Tumor Infiltration of Microrobot using Magnetic torque and A...

引用

2nd IEEE International Conference on Vision Towards Emerging Trends in communication and Networking Technologies, ViTECoN 2023

作者： Shalini, R. Mishra, Laxmi Athulya, S. Chimankar, Abhijeet Gajanan Kandavalli, Sunanda Ratna. Keshav Kumar, K. Selvan, R. Senthamil School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of BCA & IT Chennai India Khwaja Moinuddin Chishti Language University Department of Electronics and Communication Engineering Uttar Pradesh India Christ Deemed to Be University Faculty of Engineering Department of Computer Science and Engineering Bangalore India Chatrapati Shivaji BCA and BCS College Department of B.Sc. Computer Science Maharashtra India Sri Padmavati Mahila Visva Vidyalayam Department of Electronics and Communication Engineering Tirupati India G. Narayanamma Institute of Technology and Science Department of Humanities and Mathematics Hyderabad India Annamacharya Institute of Technology & Sciences Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350347982

Because of their surroundings and lifestyle alternatives, human beings, these days be afflicted by a huge style of illnesses. thus, early contamination prediction will become crucial. on the other hand, primarily based just on signs, docs warfare to make correct forecasts. The most challenging issue is accurately forecasting illnesses, which is why machine learning is essential to accomplish this task. To identify concealed patterns within vast amounts of medical data, disease information is processed using data mining techniques. We evolved a extensive contamination prediction primarily based on the affected person's signs. We rent the device getting to know techniques Convolutional Neural network (CNN) and ANFIS to exactly count on sickness (adaptive community-based totally fuzzy inference machine). For an correct forecast, this trendy illness prediction considers the character's way of life picks and fitness history. ANFIS outperforms CNN's set of rules in phrases of popular infection prediction, with an accuracy price of 96.7%. additionally, CNN consumes extra memory and processing energy than ANFIS because it trains and assessments on facts from the UCI repository. The Anaconda notebook is a suitable tool for implementing Python programming as it contains a range of libraries and header files that enhance the accuracy and precision of the process. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Augmentation of Motor Imagery Data for Brain-Controlled Robot-Assisted Rehabilitation

Augmentation of Motor Imagery Data for Brain-Controlled Robo...

引用

17th International Joint Conference on Biomedical engineering systems and Technologies, BIOSTEC 2024

作者： Mouček, Roman Kodera, Jakub Mautner, Pavel Průcha, Jaroslav Department of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Univerzitní 8 Plzen Czech Republic Department of Information and Communication Technologies in Medicine Faculty of Biomedical Engineering Czech Technical University nám. Sítná 3105 Kladno Czech Republic

Brain-controlled robot-assisted rehabilitation is a promising approach in healthcare that can potentially and in parallel improve and partly automate the rehabilitation of motor apparatus and related brain structures responsible for movement. However, building a real-world rehabilitation system has many challenges and limitations. One of these challenges is the small size of the data that can be collected from the target group of people recovering from injured motor functions to train deep learning models recognizing motor imagery patterns. Therefore, the primary experiments with data augmentation and classification results over the collected and augmented dataset are presented. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Hadiyyissa Automatic Speech Recognition Using Deep Learning Approach 5

Hadiyyissa Automatic Speech Recognition Using Deep Learning ...

引用

5th International Conference on Information and communication Technology for Development for Africa, ICT4DA 2023

作者： Shamore, Selamu Gure, Amin Tuni Yimer, Mohammed Abebe Wachemo University Department of Computer Science Hossana Ethiopia Arsi University Department of Information System Arsi Ethiopia Arba Minch University Faculty of Computing and Software Engineering Arba Minch Ethiopia

ISBN: (纸本)9798350328486

Speech Recognition focuses on developing advanced technologies that can recognize and translate spoken language into text. Automatic Speech Recognition (ASR) is an Artificial Intelligence (AI) technology that captures human voice from a mi-crophone and analyzes it based on spoken words to comprehend natural language. In Ethiopia, ASR models have been developed for specific local languages such as Amharic, Tigrigna, Wolaytta, and Afaan Oromo, but there has been a lack of research on ASR models for the Hadiyyisa language, which is also spoken in Ethiopia. The research study designed and developed an ASR model for the Hadiyyisa language. The researchers collected data from native speakers of the Hadiyyisa language and used Python programming to design and develop the deep learning model for the Hadiyissa ASR model. Different methods and techniques were used in the study to achieve the research objectives and complete the entire research process. The proposed model utilized deep learning algorithms including RNN, LSTM, BiLSTM, and CNN, along with suitable feature extraction techniques such as Mel Frequency Cepstral Coefficients (MFCC). The accuracy of the proposed ASR model was evaluated through experimentation, which demonstrated that the BiLSTM model outperformed the other deep learning models with an overall accuracy of 90.95 % Based on the experimental evidence, the proposed model was found to be better suited for ASR for the Hadiyyisa language. © 2023 IEEE.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Two-Stage Robust Optimization Under Decision Dependent Uncertainty

引用

IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

来源：评论

学校读者我要写书评

暂无评论

Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13847-13867页

作者： Balasubramanian, E. Elangovan, E. Tamilarasan, P. Kanagachidambaresan, G.R. Chutia, Dibyajyoti Department of Mechanical Engineering Head-Centre for Autonomous System Research Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi Tamilnadu Chennai600062 India Department of Electronics and Communication Engineering Centre for Autonomous System Research Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi Tamilnadu Chennai600062 India Department of Aeronautical Engineering Centre for Autonomous System Research Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi Tamilnadu Chennai600062 India Department of Computer Science and Engineering and Associate Editor Wireless Networks Springer Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi Tamilnadu Chennai600062 India Department of Space North Eastern Space Applications Centre ISRO Meghalaya Umiam793103 India

Autonomous mission capabilities with optimal path are stringent requirements for Unmanned Aerial Vehicle (UAV) navigation in diverse applications. The proposed research framework is to identify an energy-efficient optimal path to achieve the designated missions for the navigation of UAVs in various constrained and denser obstacle prone regions. Hence, the present work is aimed to develop an optimal energy-efficient path planning algorithm through combining well known modified ant colony optimization algorithm (MACO) and a variant of A*, namely the memory-efficient A* algorithm (MEA*) for avoiding the obstacles in three dimensional (3D) environment and arrive at an optimal path with minimal energy consumption. The novelty of the proposed method relies on integrating the above two efficient algorithms to optimize the UAV path planning task. The basic design of this study is, that by utilizing an improved version of the pheromone strategy in MACO, the local trap and premature convergence are minimized, and also an optimal path is found by means of reward and penalty mechanism. The sole notion of integrating the MEA* algorithm arises from the fact that it is essential to overcome the stringent memory requirement of conventional A* algorithm and to resolve the issue of tracking only the edges of the grids. Combining the competencies of MACO and MEA*, a hybrid algorithm is proposed to avoid obstacles and find an efficient path. Simulation studies are performed by varying the number of obstacles in a 3D domain. The real-time flight trials are conducted experimentally using a UAV by implementing the attained optimal path. A comparison of the total energy consumption of UAV with theoretical analysis is accomplished. The significant finding of this study is that, the MACO-MEA* algorithm achieved 21% less energy consumption and 55% shorter execution time than the MACO-A*. moreover, the path traversed in both simulation and experimental methods is 99% coherent with each other.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Cloud-Powered Virtual Doctor Robots for Enhancing Elderly Care and Chronic Disease Management with Recurrent Neural Networks 3

Cloud-Powered Virtual Doctor Robots for Enhancing Elderly Ca...

引用

3rd International Conference on Electrical, Electronics, Information and communication Technologies, ICEEICT 2024

作者： Ramesh, S. Mohankumar, N. Kovarasan, Rajesh Kambattan Selvamanikandan, R. Yuvaraj, S. Sujatha, S. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Chennai Tamil Nadu Kattankulathur India Maharashtra Nagpur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Trichy India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350369083

This research introduces cloud-powered virtual doctor robots to improve elderly care and chronic illness management. This system uses recurrent neural networks (RNNs) to give personalized medical care to elderly chronic disease patients. These virtual doctor robots may integrate with cloud computing infrastructure to access huge medical databases for real-time patient data analysis and individualized treatment and care suggestions. Healthcare technology has advanced by using RNNs to learn from patient data, adapt to changing health situations, and predict prospective health issues. RNNs can interpret sequential data, thus virtual doctor robots may identify trends in patient health parameters, forecast illness development, and suggest risk mitigation strategies. Cloud-powered virtual doctor robots provide a scalable solution to conventional healthcare systems rising issues. These robots can work effectively in resource-constrained areas by offloading computational chores to distant servers, providing continuous medical expertise and lowering healthcare staff reliance. This technique might improve aged care and reduce load on overworked healthcare systems. Using proactive monitoring, timely intervention, and personalized guidance, virtual doctor robots with RNNs can improve health outcomes, empower patients to manage chronic conditions, and create a more sustainable healthcare model. © 2024 IEEE.

关键词： Medical robotics

来源：评论

学校读者我要写书评

暂无评论

TRACING THE MOTION IN HIGH-SPEED VIDEO FRAMES USING DEEP LEARNING

TRACING THE MOTION IN HIGH-SPEED VIDEO FRAMES USING DEEP LEA...

引用

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Gayathri, R. Harsha, B.K. Sharmiladevi, D. Sajitha, L.P. Rao, Vuda Sreenivasa Nirmala, G. Department of ECE Rajalakshmi Engineering College Tamilnadu Chennai India Department of ECE CMR Institute of Technology Karnataka Bengaluru India Department of Electronics and Communication Engineering Sona College of Technology Tamilnadu Salem India Department of Computer Science and Business System R. M. K Engineering College Tamilnadu Kavaraipettai India Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Computer Science and Engineering R.M.D Engineering College Chennai India

ISBN: (纸本)9798350305708

One of the most important jobs of a self-driving vehicle's visual system is to identify and categorize obstacles along the route. Cars need to be detected, categorized, localized, and linked across frames if they are to be tracked. With the low cost and high utility of visual information, vision-based technologies have gained popularity for this purpose. In this paper, we develop a deep learning-based motion tracking from high-speed video frames. The model is developed using deep convolutional neural network that makes it to be applicable for classification of objects in the video frames. The deep convolutional neural network classifies the obstacles present in the pathway and offers a better classification pattern than the conventional models. © 2023 IEEE.

关键词： Application Convolutional Neural Network Deep Learning High-Speed Video Motion Vehicle Video Frames

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：