检索结果-内蒙古大学图书馆

Characterization of Cyclic Diagnosability of Regular Diagnosable Networks

IEEE Transactions on Reliability 2024年第1期73卷 270-278页

作者： Zhang, Hong Zhou, Shuming Cheng, Eddie Hsieh, Sun-Yuan Fujian Normal University School of Mathematics and Statistics Key Laboratory of Analytical Mathematics and Applications Ministry of Education Fuzhou350117 China Oakland University Department of Mathematics and Statistics RochesterMI48309 United States National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan

The reliability of interconnection network ordinarily is measured by two significant indexes, namely, connectivity and diagnosability. The qualitative and quantitative reliability analysis relies on the choice of an appropriate mathematical modeling and assumptions consistent with the actual situation. The cyclic connectivity is a well-established index to evaluate the reliability of interconnection network. For a networkG, we use kc(G) to denote cyclic connectivity of G, which is the minimum size of the node cut setDsuch thatG - Dis disconnected and at least two of its components have cycles. Based on the cyclic connectivity, cyclic diagnosability (ct(G)) is proposed to measure the self-diagnostic capability of the networks. Up to this day, the cyclic connectivity of some special networks has been determined successfully, but the cyclic diagnosability of a great deal of networks is still up in the air. In this work, we investigate the measurable relationship between cyclic connectivity and 2-good connectivity under certain restrictions. Furthermore, we characterize the cyclic diagnosability of a class of networks in terms of character commonality of the networks. To be more specific, we show that ct(G) = kc(G) + (l - k) under the PMC model (PMC-M) and the MM∗ model (MM∗-M), where l is the regular degree of network and l ≥ 3, 1 ≤ k © 1963-2012 IEEE.

关键词： Program processors

来源：评论

学校读者我要写书评

暂无评论

Intelligent traffic controller

引用

International Journal of Information Technology (Singapore) 2022年第4期14卷 2141-2153页

作者： Kumar, Sachin Baliyan, Anupam Tiwari, Anurag Tripathi, Aniket Kumar Jaiswal, Balram Department of Computer Science & Engineering Ajay Kumar Garg Engineering College Ghaziabad India Bharati Vidyapeeth’s Institute of Computer Applications and Management New Delhi India

This paper explores the application of dynamic traffic control timings using predefined input parameters. The method is a dynamic traffic algorithm that takes the rate of inflow, rate of outflow and queue length as input parameters to estimate the green-time that must be allocated to each road. The basic idea is to efficiently distribute the green-time based on traffic congestion in contrast to traditional methods of fixed time for traffic lights irrespective of their traffic status. The paper also compares the differences between these two methods based on some efficiency parameters using a simulator. © 2019, Bharati Vidyapeeth's Institute of computer applications and Management.

关键词： Average waiting time Congestion Control IOT Queue length

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Techniques for Fault Detection in Industrial Machinery 5

Deep Learning Techniques for Fault Detection in Industrial M...

引用

5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Panigrahi, Bhawani Sankar Thiyagarajan, T. Tamilselvi, M. Tilak Babu, S.B.G. Pavithra, G. Shaik, Bazani Vardhaman College of Engineering Department of IT Telangana Hyderabad India Nehru Institute of Information Technology and Management Department of Computer Applications Tamilnadu Coimbatore India Roever Engineering College Computer Science And Engineering Tamilnadu Perambalur India Aditya Engineering College Department of ECE India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India Ramachandra College of Engineering Department of Mechanical Engineering Andhra Pradesh Eluru India

ISBN: (纸本)9798350351378

The use of deep learning techniques for the purpose of improving fault detection in industrial machinery. It is of the utmost importance to have defect detection mechanisms that are both reliable and effective, since the complexity of industrial processes continues to increase. In this paper, the implementation of deep learning algorithms is investigated. These algorithms make use of neural networks to understand complex patterns and anomalies that are present in data coming from machinery. There are many different models that are being researched to see whether or not they are effective in detecting defects at early stages, limiting downtime, and eliminating costly interruptions. These models include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). For the purpose of this , the performance of various methodologies is evaluated over a wide range of industrial situations, taking into consideration issues such as the variability of sensor data and noise. The findings demonstrate the promise of deep learning as a significant tool for enhancing defect detection skills, thereby paving the way for industrial equipment systems that are more reliable and resilient. © 2024 IEEE.

关键词： Condition monitoring

来源：评论

学校读者我要写书评

暂无评论

ANN-Based Scalable Video Encoding Method for Crime Surveillance-Intelligence of Things applications

ANN-Based Scalable Video Encoding Method for Crime Surveilla...

引用

2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Agarwal, Rupanshi Pathak, Paresh Tipu, Rupesh Kumar Singh, Digvijay Kalnawat, Aarti Dhabliya, Dharmesh Invertis University Department of Computer Science and Engineering Bareilly India School of Computer Science and Applications Iimt University Meerut India K. R. Mangalam University Department of Civil Engineering Gurugram India School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand India Pune India Viit Department of It Pune India

ISBN: (纸本)9798350301595

The proliferation of high-quality, networked CCTV cameras in public areas has raised the volume of constantly generated video data significantly in recent years. The need for scalable, distributed video processing infrastructure is growing as a result. In order to make unstructured videos supplied to the platform searchable and comprehensible by both humans and machines, an intelligent video analytics platform runs them through a series of trans-disciplinary algorithms. The use of video analytics extends well beyond just surveillance to include things like video asset management. Many different commercial and academic approaches exist here. Most current systems for face and object recognition, however, have a time-honored client-server architecture that leaves out support for increasingly intricate use cases. Furthermore, distributed computing is seldom used to manage such frameworks at a scale. Furthermore, no current works provide any kind of assistance with low-level networked video processing APIs. Not only did they ignore the expanding needs of customers, academics, and developers, but they also neglected to address a comprehensive service-oriented environment. In this work, we offer an Artificial Neural Network based Scalable Video Coding (ANN-SVC) framework for intelligent video surveillance as a means of resolving these problems. The suggested system can handle both live video feeds and video analytics in batches. Similarly, the batch processing data for each realtime stream is available. This work therefore has a connection to the idea of symmetry. We also provide Spark's first distributed video processing library. To guarantee scalability, efficacy, and fault-tolerance, SIAT takes use of cutting-edge distributed computing technology. Finally, in an effort to back up our assertions, we implement and test our suggested framework. The simulation shows better results in throughput, gooodput and PSNR. © 2023 IEEE.

关键词： Scalable video coding

来源：评论

学校读者我要写书评

暂无评论

Social-Aware Group Display Configuration in VR Conference 38

Social-Aware Group Display Configuration in VR Conference

引用

38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Hsu, Bay-Yuan Shen, Chih-Ya Yuan, Hao Shan Lee, Wang-Chien Yang, De-Nian Department of Industrial Engineering and Engineering Management National Tsing Hua University China Department of Computer Science National Tsing Hua University China Institute of Information Systems and Applications National Tsing Hua University China Department of Computer Science and Engineering Pennsylvania State University United States Institute of Information Science Academia Sinica Taiwan Research Center of Information Technology Innovation Academia Sinica Taiwan

ISBN: (纸本)1577358872

Virtual Reality (VR) has emerged due to advancements in hardware and computer graphics. During the pandemic, conferences and exhibitions leveraging VR have gained attention. However, large-scale VR conferences, face a significant problem not yet studied in the literature - displaying too many irrelevant users on the screen which may negatively impact the user experience. To address this issue, we formulate a new research problem, Social-Aware VR Conference Group Display Configuration (SVGD). Accordingly, we design the Social Utility-Aware VR Conference Group Formation (SVC) algorithm, which is a 2-approximation algorithm to SVGD. SVC iteratively selects either the P-Configuration or S-Configuration based on their effective ratios. This ensures that in each iteration, SVC identifies and chooses the solution with the highest current effectiveness. Experiments on real metaverse datasets show that the proposed SVC outperforms 11 baselines by 75% in terms of solution quality. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Virtual reality

来源：评论

学校读者我要写书评

暂无评论

Adaptive Weighted Low-Rank Sparse Representation for Multi-View Clustering

引用

IEEE Access 2023年 11卷 60681-60692页

作者： Khan, Mohammad Ahmar Khan, Ghufran Ahmad Khan, Jalaluddin Anwar, Taushif Ashraf, Zubair Atoum, Ibrahim A. Ahmad, Naved Shahid, Mohammad Ishrat, Mohammad Alghamdi, Abdulrahman Abdullah Dhofar University College of Commerce and Business Administration Salalah211 Oman Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur522502 India Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China GLA University Department of Computer Engineering and Applications Uttar Pradesh Mathura281406 India AlMaarefa University College of Applied Sciences Department of Computer Science and Information Systems Riyadh13731 Saudi Arabia National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei10607 Taiwan Shaqra University College of Computing and Information Technology Shaqra11961 Saudi Arabia

Ongoing researches on multiple view data are showing competitive behavior in the machine learning field. Multi-view clustering has gained widespread acceptance for managing multi-view data and improves clustering efficiency. Large dimensionality in data from various views has recently drawn a lot of interest from researchers. How to efficiently learns the appropriate lower dimensional subspace which can manage the valuable information from the diverse views is challenging and considerable issue. To concentrate on the mentioned issue, we asserted a novel clustering approach for multiple view data through low-rank representation. We consider the importance of each view by assigning the weight control factor. We combine consensus representation with the degree of disagreement among lower rank matrices. The single objective function unifies all factors. Furthermore, we give the efficient solution to update the variable and to optimized the objective function through the Augmented Lagrange's Multiplier strategy. Real-world datasets are utilized in this study to exemplify the efficiency of the introduced technique, and it is contemplated to preceding algorithms to demonstrate its superiority. © 2013 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Investigation on Nail Denting or Crumbling from Nail Psoriasis Patient Ailment Data using the Machine Learning Algorithm and Data Mining Techniques 8

Investigation on Nail Denting or Crumbling from Nail Psorias...

引用

8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Janani, K. Santhoshkumar, S.P. Vikram, D. Priya, S. Vijayaraj, M. Priyanga, M. Manipal Institute of Technology Department of Data Science and Computer Applications Manipal India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Department of Computer Science and Engineering Chennai India Rathinam Technical Campus Department of Artificial Intelligence and Data Science Coimbatore India Rathinam Technical Campus Department of Information Technology Coimbatore India

ISBN: (纸本)9798350367904

Nail denting or crumbling may result from nail psoriasis. A nail condition known as psoriatic onychodystrophy or psoriatic nails. Psoriasis sufferers frequently experience it;reported occurrences range from 10% to 78%. Psoriatic nails are more common in the elderly and in people with psoriatic arthritis. By examining the nails on a human hand, numerous illnesses can be found early in the diagnosing process. The nail color of an individual can help in the diagnosis of specific medical disorders. In this case, the recommended method results in the decision-making process for sickness diagnosis. The system is fed by human nail art. The technique recognizes nail features unique to a given condition by analyzing images of nails. The suggested technique makes advantage of the various distinctive characteristics of human nails to detect illness by changing the colour of the nails. The first training set data, which is derived from an image of a patient's nails with a particular ailment, is handled by the Weka tool. To get the desired results, the feature discoveries of the picture are compared to the training dataset. The distortion of the nail unit is known as nail disease. Nail units have their own disease class due to their own indications, symptoms, causes, and consequences, which may or may not be related to other medical disorders. Nail problems are still difficult and ambiguous to identify. A novel machine learning method for recognising and classifying nail problems from photographs is presented in this study. This framework combines CNN models with data mining techniques to extract characteristics. This research was also contrasted with certain other province algorithms (Support vector, ANN, K - nearest neighbors, and RF) evaluated on datasets and showed positive results. The accuracy numbers show how well the model can distinguish nail conditions. This ground-breaking discovery lays the groundwork for precise diagnosis in dermatology, medical image processing, an

关键词： Medical image processing

来源：评论

学校读者我要写书评

暂无评论

Analysis and Classification of Restaurants Based on Rating with XGBoost Model 3

Analysis and Classification of Restaurants Based on Rating w...

引用

3rd International Conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2022

作者： Dixit, Anuj Kumar Nair, Rekha R Babu, Tina School of Engineering Dayananda Sagar University Department of Computer Applications Bengaluru India School of Engineering Dayananda Sagar University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665482684

The restaurant business is one of the most competitive and the need for restaurants is growing daily. Bangalore is a foodie's paradise, boasting cuisines from all over the world. Hence, this research work focuses the classification of restaurants on the basis of rate with XGBoost model. The Exploratory Data Analysis(EDA) with different graphs provides an analysis of the data before classification. Prior to EDA the data sets are cleaned with various steps to increase the accuracy of the visualization and classification. The research was performed using Zomato data set for restaurants in a specific locality(Bangalore). Data Visualization techniques helped to analyze food culture, trends and patterns. This research describes a model for understanding the elements that influence restaurant ratings. Predictive analytics and machine learning together with a variety of tools and methodologies, help in predicting restaurant ratings. The model in this research is developed using multiple supervised techniques, with the most efficient algorithm being evaluated. The classification XGBoost model provided an accuracy of 98.07 %. The outcome of the work assists new restaurants in selecting on their menu, cuisine, theme, prices, geographic location, and so on, consequently enhancing business. The rate and location details helps people to select the restaurants for the dining. © 2022 IEEE.

关键词： Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Approach for Predicting Air Quality Index in Smart Cities 5

A Machine Learning Approach for Predicting Air Quality Index...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Swamynathan, S. Sneha, N. Ramesh, S.P. Niranjana, R. Ponkumar, D. David Neels Saravanakumar, R. Karpagam Academy of Higher Education Department of Computer Applications Tamil Nadu Coimbatore India School of Computer Science & Engineering Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India Sri Krishna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331518097

One essential natural resource, the air quality index has been deteriorated by economic activities. Many studies have been conducted about the forecasting of periods of unsafe atmospheric conditions;however, the majority of these studies are limited in their capacity to include seasonal and other factors due to insufficient longitudinal data. India noise pollution has compiled a 6-year dataset that has been used to build several prediction models. There are encouraging outcomes when AQI levels are predicted using machine learning methodologies such as support vector machines (SVMs) artificial neural networks (ANNs), and Naive Bayes (NB). However, in several tests involving datasets from three different locations, NB, ANN, and SVM performed well to maximize prediction performance. When it comes to mean absolute error (MAE), R-squared (R2), and Root-mean-square deviation (RMSE), the combined performance of stacks is unsurpassed. In the related work section, summarize the limitations of existing models, such as inadequate data preprocessing, low spatialtemporal resolution, limited feature selection, or overfitting in complex models. Highlight gaps in scalability, real-time adaptability, and generalization across regions, emphasizing the need for robust, efficient models tailored to air quality prediction. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Sugar Learning: Deep Learning for Rapid Detection and Classification of Sugarcane Diseases

Sugar Learning: Deep Learning for Rapid Detection and Classi...

引用

2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Brar, Simrat Kaur Sharma, Rishabh Vats, Satvik Kukreja, Vinay Chandigarh Group of Colleges Department of Computer Applications Landran India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350311204

Sugarcane is a widely cultivated crop due to its high demand and supply in various industries. However, the increase in production levels has led to an increase in the number of diseases that affect the crop. One of the most devastating diseases is sugarcane red rot (SRR) disease. A multi-layer perceptron (MP) based deep learning (DL) model was built for the identification and classification of SRR illness using a dataset of 20,000 photos of sugarcane leaves in order to address this problem. This model was trained on 20,000 photographs of sugarcane leaves. The dataset was classified into 5 different disease severity levels. The proposed model achieved an accuracy rate of 97.97% for binary classification and an accuracy rate of 98.03% for overall multi-classification. Furthermore, a comparison of the different SRR illness stages was carried out, and it was demonstrated that the proposed model is an effective tool for accurately categorizing sugarcane images based on the severity of SRR disease. This study contributes to the development of an efficient and accurate model for the early detection and diagnosis of SRR disease in sugarcane crops, which is essential for improving crop yield and preventing economic losses. © 2023 IEEE.

关键词： Diseases

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：