检索结果-内蒙古大学图书馆

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sunil Kumar, V. Jyothi, A.P. Sumanth, S. Nagamani, H.S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Peenya Bengaluru India Department of Computer Science and Computer Applications Kolar India Department of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350382952

For this leaf disease study, apple and sugarcane plants are identified and classified using a mix of character features and in-depth automated coding. Farm sugar cane leaves were copied for the online apple Plant Village Dataset. Pre-process images to reduce size and improve HLG filters. Image segmentation using DWT and GLCM feature extraction follows. Like the Low-Low (LL) band, DWT images produce 4-orthogonal subbands. Deep Auto Encoder and SVM algorithms retrieved subgroup functions and identified diseases from training data. If classification findings are ignored, the division stage receives data with HL, LH, and HH bands to explain approach consistency. Deep Auto encoder classifies illness using training data after extracting sub-band features. The Auto Encoder and SVM classifier demonstrated that the proposed model outperformed alternatives in LL band. Other subbands performed worse than the LL band. This study found that LL sub band data provided fine image data for better sickness categorization than HH, HL, and LH sub band data. © 2024 IEEE.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Senti Comment: Event Success Prediction through Sentiment Analysis of User Feedback 5

Senti Comment: Event Success Prediction through Sentiment An...

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Varma, Nadimpalli Madana Kailash Vidya, Rakapu Sri SriSaiteja, Thullimilli Teja, Chintakunta Pranay Manchala, Yugandhar Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Telangana Hyderabad501286 India Department of Computer Science & Engineering Vardhaman College of Engineering Telanagana Hyderabad501286 India Department of Information Technology Vardhaman College of Engineering Telangana Hyderabad501286 India

ISBN: (纸本)9798350361155

Sentiment analysis has emerged as a pivotal tool in extracting valuable insights from user-generated content, with its applications spanning various *** paper presents a novel approach to event evaluation using sentiment analysis techniques within the domain of Natural Language Processing (NLP). By analyzing user comments from diverse events such as parties, college fests, and conferences, we develop a robust NLP model to assess event success based on positive sentiment. Our model offers real-time insights into event reception and provides interpretative analysis of user feedback, including suggestions and expressions of appreciation. This research contributes to advancing sentiment analysis methodologies and equips event organizers with a valuable tool for enhancing future event planning strategies. © 2024 IEEE.

关键词： Data visualization

来源：评论

学校读者我要写书评

暂无评论

A queuing theory based delay-efficient packet scheduler for machine type communication

引用

International Journal of Vehicle Information and Communication Systems 2022年第2期7卷 121-132页

作者： Sunita, T.N. Malakreddy, A. Bharathi Department of Computer Science and Engineering Rajiv Gandhi Institute of Technology Karnataka Bengaluru560032 India Department of Artificial Intelligence and Machine Learning Bms Institute of Technology and Management Karnataka Bengaluru560064 India

Lately, there is a drastic change in the telecommunication industry due to emergence of Internet of Things (IoT). machine to machine (M2M) plays a very important role in enabling IoT. As the LTE is mainly suitable for Human-to-Human (H2H) communication, MTC faces some challenges in LTE like radio resource management and uplink-scheduling algorithms are not suitable for MTC. In this paper, we use a queuing theory and propose a packet scheduler, which would model the regular and event-driven traffic and schedule the jobs in queue for request processing. Then using the Markov Chain process, we calculate the queue length and determine the blocking probability. If there is high blocking probability and still there are some requests in queue, then those requests will be processed in virtual server for maximum network usage. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： machine-to-machine communication

来源：评论

学校读者我要写书评

暂无评论

Incorporation of Deep learning (DL) Techniques to make Intelligent and Adaptable Wireless Communication Systems

Incorporation of Deep Learning (DL) Techniques to make Intel...

引用

2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Tiwari, Anamika Kumar, Sandeep Rengarajan, A. Noida Institute of Engineering and Technology Department of Artificial Intelligence & Machine Learning Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350344387

The emergence of Deep learning (DL) was a response to the widespread availability of mobile data and the growing complexity of wireless networks. It is a subfield of machine learning that has been proposed in academic literature. This technology is regarded as a breakthrough in the management of mobile data traffic. Wireless systems generate a significant amount of varied data, which presents computational issues. In order to tackle this problem, a variety of optimization methodologies are being investigated. DL increases wireless communication by enhancing its intelligence and adaptability. This paper offers an overview of the DL platform and several methodologies employed to enable deep learning in wireless communications. Wireless networking involves various disciplines, including networking, routing, and scheduling, all of which incorporate DL. This text encompasses a comprehensive range of topics. The topic encompassed several optimization methodologies and the latest breakthroughs in this domain. © 2023 IEEE.

关键词： Deep learning Fog computing Mobile network Reinforcement learning Wireless network

来源：评论

学校读者我要写书评

暂无评论

Edge Computing Devices Authentication using Quantum Computing 15

Edge Computing Devices Authentication using Quantum Computin...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Telsang, Vinayak A. Kakkasageri, Mahabaleshwar S. Devangavi, Anil D. Dept. of Computer Science and Engineering Biluru Gurubasava Mahaswamiji Institute of Technology Mudhol India Dept. Electronics and Communication Engineering Basaveshwar Engineering College Bagalkote India Dept. of Artificial Intelligence and Machine Learning Basaveshwar Engineering College Bagalkote India

ISBN: (纸本)9798350370249

Edge computing allows different technologies such as Internet of Things (IoT), vehicle-to-vehicle communications(VV), Industrial IOT (IIOT) to connect to the cloud computing to facilitate end users. The edge networks uses low bandwidth, and enhances the processing speed. With more number of IoT devices connected in the network the privacy of data stored at the edge server also under threat. Authentication is a major challenge which is be addressed to provide security and privacy to IoT devices connected in the network. The existing works uses cryptographic algorithms to solve the authentication problems, but with increase in growth of quantum computers and quantum computing, breaking the complex cryptographic algorithm is not an issue. Hence one should adopt the quantum approach to provide authentication in edge network. The proposed work uses quantum key and context information of IoT devices to store in the edge server using blockchain, which proves to be more secure. The performance analysis shows that it is better with respect to registration, encryption, decryption and authentication of IoT devices. ©2024 IEEE.

关键词： Authentication Blockchain Cryptography Edge computing Quantum key

来源：评论

学校读者我要写书评

暂无评论

machine learning Approach: Consumer Buying Behavior Analysis

Machine Learning Approach: Consumer Buying Behavior Analysis

引用

2022 IEEE Pune Section International Conference, PuneCon 2022

作者： Sharma, Anjali Pratap, Aradhana Vyas, Kishan Mishra, Sashikala Symbiosis Institute Technology Of Artificial Intelligence and Machine Learning Pune India University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9781665498975

The rise of multiple company competitors during the COVID-19 outbreak resulted in fierce competition among competing firms for new clients and the retention of current ones. As a result of the foregoing, exceptional customer service is required, regardless of the size of the organization. Furthermore, any company's ability to know each of its customers' desires will provide it an advantage when it comes to providing specialized customer care and establishing customized marketing plans for them. The term 'Consumer Buying Behavior Analysis' refers to a comprehensive assessment of the company's ideal clients/customers. In this project, we're utilizing the K-Means Algorithm to divide clients into two groups: 'Highly Active Customers' and 'Least Active Customers.' Then, utilizing the Apriori Algorithm, we use Association Rule Mining to recommend the best goods to clients based on their purchasing history and associations. We take one step further and use Logistic Regression to validate our Clustering operation by doing Binary Classification with our clusters as the label, resulting in accuracy and an F1 score of 91%. © 2022 IEEE.

关键词： K-means clustering

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Review on Probable Measures for Addressing Security Limitations in Internet of Drones

A Comprehensive Review on Probable Measures for Addressing S...

引用

2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Gupta, Gaurav Menaka, C. Padmanabhan, P. Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Artificial Intelligence & Machine Learning Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350344387

The Internet of Drones (IoD) has garnered significant interest in recent years due to its portable and automated nature and is being utilized across various domains such as the military, search and rescue operations, and entertainment. However, due to the inherently unsecured radio transmission paths in the Internet of Drones (IoD), there are numerous security concerns associated with the data collected, generated, or processed by drones. This paper aims to address the prominent challenges of security and privacy in the IoD. It undertakes a thorough examination of security concerns and corresponding solutions for IoD security. The discussion encompasses the security requirements associated with IoD and highlights the most recent advancements in research pertaining to IoD security. Authentication methods and Blockchain-based schemes are highlighted in this overview of numerous significant security technologies. Through an extensive analysis, we hereby present an examination of the challenges encountered by existing methodologies and propose potential avenues for future research in the field of IoT security. This review highlights the necessity of implementing suitable security measures to tackle IoT security concerns. Furthermore, it emphasizes the importance of developing security solutions that carefully consider the trade-off among the level of security provided and the cost efficiency of implementation. © 2023 IEEE.

关键词： Authentication Blockchain Internet of Drones (IoD) Privacy Security

来源：评论

学校读者我要写书评

暂无评论

Improving Accuracy in Network Intrusion Detection via machine learning Algorithms: An In-Depth Examination

Improving Accuracy in Network Intrusion Detection via Machin...

引用

2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Suriya Prakash, J. Trishlaa, S. Soniya, R. Lakshmanan, V. Kiran, S. Department of Cse Ramnagara India Rajarajeshwari College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Nitte Meenakshi Institute of Technology Department of Mathematics Bengaluru India

ISBN: (纸本)9798350375442

Intrusion detection is essential for securing computer networks by monitoring and analyzing activity to detect and respond to unauthorized access and malicious behavior. By examining network traffic and system logs, intrusion detection systems (IDS) help protect data integrity and availability, enabling swift responses to potential security incidents. In an ever-evolving cyber threat landscape, IDS plays a critical role in maintaining network security. This research investigates the performance of various machine learning algorithms for classification tasks using a dataset from Kyoto collected on December 31st, 2015. The algorithms explored include Naive Bayes, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), AdaBoost, and CatBoost. Training and testing the model with different data splits (80/20, 70/30, 60/40) revealed that AdaBoost, LGBM, XGBoost, Decision Tree, Gradient Boosting, and Random Forest consistently delivered the highest accuracy. The study concludes that selecting the appropriate algorithms and data splits is crucial for enhancing IDS effectiveness, providing valuable insights for future research. Explore the code behind this study at https://***/Trishlaa07/Kyoto_2015_Dec_day31 © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

A Neuro-Fuzzy Predictor Trained by an Elitism artificial Electric Field Algorithm for Estimation of Compressive Strength of Concrete Structures

引用

Informatica (Slovenia) 2023年第5期47卷 41-48页

作者： Kumar Dash, Ch Sanjeev Nayak, Sarat Chandra Behera, Ajit Kumar Dehuri, Satchidananda Department of Computer Science and Engineering Silicon Institute of Technology Silicon Hills Patia Bhubaneswar751024 India Department of Artificial Intelligence Engineering and Machine Learning CMR College of Engineering and Technology Hyderabad501401 India Department of Information and Communication Technology Fakir Mohan University Vyasa Vihar Odisha Balasore756019 India

Meta-heuristic learning-based forecasting is widely acceptable vis-à-vis manual and statistical methods in estimating the compressive strength of concrete structures. However, there is always a scope for exploring an effective, automated, and accurate predictor for this domain. This article proposes an elitism artificial electric field algorithm-based neuro-fuzzy network (eAEFA+NFN) for the prediction of compressive strength of concrete structures. The elitism method helps AEFA preserve the best individuals from iteration to iteration, by directly placing best fit particles into the population for the next generation and thus strengthening the optimization capacity of AEFA. A single hidden layer neural network (SHNN) is used as the base model and its inputs are fuzzified using the Gaussian triangular membership function with a degree of membership to different classes. The optimal number of input data, hidden neurons, bias, and weights for the hidden layer are decided by eAEFA. The model is evaluated on samples from a publicly available dataset with curing ages at 3, 7, 14, and 28 days. Considering four sample series, the eAEFA+NFN produced an average MAPE of 0.092073 and ARV of 0.139731 which are better compared to others. The experimental outcomes and analysis are in favor of the eAEFA+NFN-based forecasting. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Electric fields

来源：评论

学校读者我要写书评

暂无评论

A Study on Integrating machine learning Techniques for Waste Management 7

A Study on Integrating Machine Learning Techniques for Waste...

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Vaidya, Ruchika Dattangire, Rahul Biradar, Divya Verma, Prateek Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Data Engineering Publicis Sapient HoustonTX77002 United States University of Texas Department of Computer Science ArlingtonTX76019 United States

ISBN: (纸本)9798350372816

The current study focuses on how various machine learning (ML) algorithms can be used in waste management and emphasizes the significance of intelligent technologies in addressing contemporary issues. By leading a broad writing survey, the paper features the meaning of incorporating these advances into waste management practices. The system segment subtleties the information assortment, preprocessing, model determination, and assessment measurements utilized in the review. The accuracy and performance metrics of various classifiers, including K Nearest Neighbors, Linear Support Vector Classifier, Logistic Regression, Decision Tree, Multi-Layer Perceptron, and Random Forest, are demonstrated by the results of experiments with them. Especially noteworthy is the MLP classifier's 91.81% accuracy. The discoveries highlight the viability of ML approaches in ssmart waste management. In addition, a comparison table that summarizes the classifiers' accuracy scores provides insight into their suitability for applications in waste management. © 2024 IEEE.

关键词： Adversarial machine learning

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：