检索结果-内蒙古大学图书馆

5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Abbas, Mohammad Kazim Gupta, Kunal Mudassir, Mohammad Jain, Rishabh Galgotias College Of Engineering And Technology Dept. Of Computer Science And Engineering Greater Noida India Kiet Group Of Institutions Dept. Of Computer Science Delhi-NCR Ghaziabad India

ISBN: (纸本)9798350330861

This review paper investigates the application of audio spectrograms for categorization of musical genres. The use of feature extraction technique in convolutional neural networks (CNNs) for music genre analysis is highlighted in the discussion of these techniques. The relationship between audio spectrograms and musical subgenres is also observed in this paper, as well as how well audio segmentation methods perform in terms of increasing classification precision. The findings of recent research employing various approaches like CNN, PRCNN, LSTM, SVM, etc are used to conduct a comparative analysis of their effectiveness, highlighting the strengths and weaknesses of each approach. The use of spectrogram for feature extraction and multi-layer CNN proved to be ideal for future researches. © 2023 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Quantum-secured cloud communications in 6G networks: an advanced key shifting model with mutual identity authentication for optimal performance in time-constrained environments

引用

Quantum Information Processing 2025年第6期24卷 1-17页

作者： Sundar, K. Rajalakshmi, S. Johnyelma, K. Gunasundari, S. Department of Information Technology Easwari Engineering College Chennai India Department of Computer Science and Engineering (Cyber Security) Velammal Engineering College Chennai India Department of Computer Science and Engineering Velammal Engineering College Chennai India

Numerous computing devices can connect with extremely low latency on next-generation networks, particularly the sixth generation (6G). Inferentially, 6G capabilities offer enormous advantages for the Internet of Things and cloud, taking into account a variety of application fields. However, several issues with authentication and encryption procedures in the cloud are now being looked at. In order to provide better security, methods for incorporating quantum communications in cloud models have been investigated. Although improved quantum key distribution selection algorithms have been created, their performance is limited when taking time requirements into account. The development of an efficient quantum key distribution with mutual identity authentication to ensure security in cloud storage is proposed as a solution to this problem using the advanced key shifting model. In a communication scenario, the model takes into account four distinct objects, including the user, data owner, server, and data storage. By measuring key computation time, processing time, encryption and decryption time, and memory use, the suggested model’s performance is evaluated. The outcomes demonstrate that the suggested model performs better than the current works.

关键词： Cloud model Decryption Encryption Identity authentication Key shifting Quantum key distribution (QKD)

来源：评论

学校读者我要写书评

暂无评论

An Investigation on Spectrum Mobility Mechanisms in Cognitive Network Communication 9

An Investigation on Spectrum Mobility Mechanisms in Cognitiv...

引用

9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Gayathri, N. Anandakumar, H. Sathya, R. Gowri, S. Sri Eshwar College of Engineering Department of Computer Science and Engineering TamilNadu Coimbatore India Kongunadu College of Engineering and Technology Department of Information Technology TamilNadu Trichy India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science TamilNadu Coimbatore India

ISBN: (纸本)9798350397376

In our increasingly digitized environment, the demand for more effective spectrum usage is becoming more and more prominent. By permitting unauthorized individuals to take advantage of the spectrum in an adaptive way, cognitive radio (CR) is a prospective method to increase spectrum efficiency. Spectrum handover, which ensures that Secondary Users (SUs) can leave the available spectrum that is currently in use and identify a suitable target channel to continue the interrupted service, is a critical component of the development of Cognitive Radio Networks (CRN). A complete description and investigation of the current spectrum handoff techniques for CRN are discussed in this paper. Numerous research challenges and constraints are also studied in the development of effective spectrum handoff methods. Also, the comparative study on different spectrum handoff mechanisms is presented to discover the least number of reductions in performance during a spectrum handoff. © 2023 IEEE.

关键词： Cognitive radio

来源：评论

学校读者我要写书评

暂无评论

Research on Improved Quick Sort Algorithm with Duplicate Value Handling 8

Research on Improved Quick Sort Algorithm with Duplicate Val...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Hemin Yadav, Amit Khan, Asif Sah, Abhishek Pratap Chengdu Neusoft University College of Computer and Software Chengdu China Charles Darwin University College of Engineering It and Environment NT Australia Integral University Department of Computer Application Lucknow India University of Electronic Science and Technology of China School of Management Science and Engineering Chengdu China

ISBN: (纸本)9798350376425

An improved algorithm based on Quick Sort algorithm research method is proposed to deal with prevailing duplicate values in the sorting of data. The duplicate values are specially processed, which effectively reduces the number of division and greatly reduces the number of recursions. The experimental results show that the time efficiency is improved by more than 50% when dealing with large data than the traditional Quick Sort algorithm without reducing the space efficiency. © 2024 IEEE.

关键词： Duplicate Value Quick Sort Recursion

来源：评论

学校读者我要写书评

暂无评论

Mountain Gazelle Optimization Based Energy Efficient Cluster Head Selection and Routing Protocol for Internet of Things

Mountain Gazelle Optimization Based Energy Efficient Cluster...

引用

International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Aluvala, Srinivas Hussein, Abbas Hameed Abdul Chintapalli, Siva Surya Narayana Singh, Satya Prakash Sarraju, Vijaya Lakshmi School of Computer Science and Artificial Intelligence SR University Warangal India College of Pharmacy Ahl Al Bayt University Karbala Iraq Department of Computer Science and Engineering Birla Institute of Technology Ranchi Mesra India

ISBN: (纸本)9789819778751

Internet of Things (IoT) has become one of the significant principles with the huge acceptance of an intelligent environments. An IoT is a collection of number of sensors combined through an internet to share the communication. In large-scale IoT network, the data is obtained from Wireless Sensor Networks (WSN) and it is forward from sink to the following processing stage of IoT. In this research, the Mountain Gazelle Optimization (MGO) algorithm is proposed for obtaining an energy efficient Cluster Head (CH) selection and routing in IoT. The consumption of energy in IoT is achieved by the optimal routing method by utilizing the optimization algorithm. The optimization-based clustering and routing approach is developed for designing the optimal transmission path by CH to destination. The performance of the proposed MGO method attains better results and it achieves the Packet Loss Rate of 17.6, End-to-End delay of 0.0597, No. of clusters formed of 49, Lifetime of 7 and Throughput of 2.5 with the No. of nodes of 800 when compared to the existing methods like Krill Herd Optimization (KHO), Lightening Search Algorithm (LSA), Lion Optimization Algorithm (LOA) and Particle Swarm Optimization (PSO)-LSA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

Cloud-Enabled Security for Luggage Protection through LSTM Networks and IoT in Contactless Travel 8

Cloud-Enabled Security for Luggage Protection through LSTM N...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sairam, A. Fathima, S. Shafla Prabakaran, T. Murugan, M. Pavithra, B. Mohankumar, N. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Joginpally B.R. Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Srm Institute of Science and Technology Faculty of Management Tamil Nadu Chennai India Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India Symbiosis Institute of Technology Nagpur Campus Maharashtra Pune India

ISBN: (纸本)9798350376425

In contactless travel, luggage security is very crucial. A new luggage protection method is presented using Long Short-Term Memory (LSTM) networks and IoT technologies with cloud-enabled security measures. Real-time monitoring and data analysis improve contactless travel security. In luggage, Internet of Things (IoT) devices communicate with cloud servers efficiently to track luggage. The proposed system relies on LSTM networks for predictive analytics and anomaly detection. LSTM networks can detect abnormal behavior by analyzing past data and learning trends. Cloud infrastructure allows data storage, processing, and centralized management and monitoring, improving security system scalability and dependability. IoT and LSTM networks provide a flexible security architecture that can react to new threats. The technology gives travelers and authorities real-time information to reduce luggage loss, theft, and tampering. In line with travel industry trends, the solution is contactless, making it safer and more convenient for travelers. Cloud-enabled solutions, LSTM networks, and IoT technologies can secure bags in contactless travel. © 2024 IEEE.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Model for Wind Power Forecasting Using Enhanced Multilayer Perceptron

引用

computers, Materials & Continua 2025年第5期83卷 2287-2303页

作者： Ahmed A.Ewees Mohammed A.A.Al-Qaness Ali Alshahrani Mohamed Abd Elaziz Computer Science and Artificial Intelligence Department College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia Department of Computer Damietta UniversityDamietta34517Egypt College of Physics and Electronic Information Engineering Zhejiang Normal UniversityJinhua321004China Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt

Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy *** enhances the efficiency and reliability of renewable energy *** approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy *** primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data *** research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original *** developed FHONO is then employed to optimize the MLP for enhanced wind power *** effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in *** results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R^(2))of 0.967 across all *** findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting.

关键词： Wind power forecasting multilayer perceptron fire hawk optimizer non-monopolize search

来源：评论

学校读者我要写书评

暂无评论

Optimizing Wind Turbine Placement for Enhanced Energy Production Using Modified Genetic Algorithm 7

Optimizing Wind Turbine Placement for Enhanced Energy Produc...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Sudarshan, T.A. Srinivasulu, Ch. Thethi, H. Pal Bhutani, Vijay Subhashini, P. Ramesh, G. New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad50043 India Lovely Professional University Phagwara India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Griet Department of Computer Science and Engineering Bachupally Hyderabad India

ISBN: (纸本)9798350350067

The optimal site of wind farms is determined using an updated genetic algorithm approach, which maximizes output capacity while reducing the amount of turbines built and the area used by each farm. In particular, two examples are examined: One kind of wind is a homogeneous wind that blows in just one direction, while the other two are also possible. In the first scenario (a), 599 individuals were first split into 25 subpopulations and then evolved three thousand times. The evolution of case (b) takes place over three thousand generations, beginning with 600 individuals distributed over 19 subpopulations. Every design's fitness, overall power generation, power output efficiency, and ideal configurations are all part of the findings. The results show that the modified genetic algorithm provides much better results compared to the conventional Genetic Algorithm method. © 2024 IEEE.

关键词： Windmill

来源：评论

学校读者我要写书评

暂无评论

Software Bug Prediction using Machine Learning 15

Software Bug Prediction using Machine Learning

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sathiyaraj, A. Angel, T.G. Ruby Turukmane, Anil V. Ramkumar, D. Guravaiah, Y. Department of Information Technology School of Computing Sathyabama Institute of Science and Technology Tamil Nadu Chennai600 119 India School of Computer Science and Engineering VIT-AP University Inavolu Andhra Pradesh Amaravati522237 India Department of Computer Science and Engineering MJR College of Engineering and Technology Diguvapokulavaripalli Pulicherla Andra Pradesh Chittoor District517214 India

ISBN: (纸本)9798331300579

The entire success of software is impacted by software bug prediction (SBP), a crucial part of the software development and maintenance life cycles. It is necessary to anticipate issues in order to increase the software's dependability, efficiency, and cost. In spite of the fact that a number of methods have been put out in the literature, it is difficult to create a reliable bug prediction model. In this paper, a machine learning (ML) based prediction method for software issues is introduced. Algorithms for guided machine learning that are based on historical data have been used to predict software errors. Two of the classifiers are Support Vector Machine and Naive Bayes (NB). How accurate and useful ML methods can be employed was demonstrated through the evaluation process. Included is the utilization of a comparative measure. © Grenze Scientific Society, 2024.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Enhancement of Level of Security using Wireshark Through Continuous Monitoring and Detection System 4

Enhancement of Level of Security using Wireshark Through Con...

引用

4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Bhat, C.Rohith Mane, Sulakshana Bhausaheb Bhatt, Chandradeep Verma, Garima Naser, Sarmad Jaafar Jweeg, Muhsin Department of Computer Science and Engineering India Bharati Vidyapeeth College of Engineering Department of Computer Engineering Navi Mumbai India Graphic Era Hill University Computer Science and Engineering Dehardun248002 India Graphic Era Deemed to Be University Computer Science& Engineering Uttarakhand Dehradun248002 India National University of Science and Technology Collage of Nursing Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

We suggest using edge computing to install an intrusion detection system (IDS) at the network edge in order to address the problem of intrusion detection in wireless sensor networks (WSNs). This IDS functions as a proactive defence system designed specifically with WSNs in mind. Our method combines an arithmetic optimisation algorithm (AOA) with Wireshark and machine learning to create an edge intelligence framework for identifying denial-of-service (DoS) assaults in wireless sensor networks (WSNs). We use the wireshark for optimisation adjustment and a parallel strategy to promote communication between populations in order to increase model accuracy. Our model achieves 98.9% accuracy (ACC) through simulated studies utilising the IDS dataset in Matlab2018b. The effectiveness and practical significance of our suggested intrusion detection approach are highlighted by these experimental results. © 2024 IEEE.

关键词： Network intrusion

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：