检索结果-内蒙古大学图书馆

Spectral Algorithms for Streaming Graph Analysis: A Survey

Annals of Data Science 2021年第4期8卷 667-681页

作者： Sarkar, Braznev Bhattacharyya, Malay Department of Information Technology Indian Institute of Engineering Science and Technology Shibpur Howrah711103 India Centre for Artificial Intelligence and Machine Learning Indian Statistical Institute 203 B. T. Road Kolkata700108 India Machine Intelligence Unit Indian Statistical Institute 203 B. T. Road Kolkata700108 India

Streaming data models refer to some constrained settings through which continuous flow of information regarding updates on the data becomes available. Graphs can also be represented in a streaming setting where interaction information turns out to be accessible as a stream of inclusion or exclusion of interactions. Analysis of streaming graphs helps to understand extreme-scale and dynamic real-life interactions in different forms. The growth of world wide web has drastically changed the way we look at various real-life evolving gigantic networks. This has motivated the development of streaming algorithms to be applied on graphs at scale. To achieve this scalability, sketching and sampling strategies are generally adopted to realize the different attributes of graphs. Spectrum of a graph, being one of the most appreciated characteristics, has lead to the evolution of an entire class of spectral algorithms. In this paper, we touch upon the state-of-the-art progress in streaming graph analysis with spectral algorithms. We mainly cover the latest developments in the areas like sampling, sparsification, singular value decomposition, counting problems related to local structures, analysis of global structures, partitioning, labeling, mesh processing, discovery of patterns, anomalous hotspot discovery, detection of communities, etc. on the subject of streaming graphs. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Singular value decomposition

来源：评论

学校读者我要写书评

暂无评论

Revolutioning IIot: A Smart Appliance of an Internet of Things over Industrial Manufacturing Unit to Make Error-Free Circumstances

Revolutioning IIot: A Smart Appliance of an Internet of Thin...

引用

2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Ravi, Kiran Chand Vaishnavi, T. Princy, Anni Kandaswamy, Vijay Anand Dinesh, M. Lakshmisridevi, S. MLR Institute of Technology Department of ECE Hyderabad India Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of ECE Tamil Nadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha University Department of VLSI Microelectronics Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331542344

A future where cyberspace and the real world are inseparable is the goal of the Internet of Things (IoT). The potential for the development of robust industrial services and applications, colloquially known as the Industrial Internet of Things (IIoT), is enormous. From the acquisition of raw materials to the provision of customer service and support, the Internet of Things (IoT) was utilized across numerous sectors by industries. The Internet of Things (IoT) offers a great opportunity to develop significant manufacturing-related services and applications, and it is widely regarded as a crucial technology of the industrial revolution. In addition, it facilitates communication between intelligent devices, allowing them to exchange information and data-crucial for complex systems to make decisions in the here-And-now. In this paper, a new method called Error-Free Smart Industrial Control System (EFSICS) is introduced to improve the efficiency of industrial operations through the use of IIoT. To test how well this method works, it is cross-validated with the traditional model called Classical Industrial Control System (CICS). Regarding the manufacturing aspects in particular, the suggested method is well-suited to the effects of the Internet of Things on sustainable development. Researchers are now at the forefront of cyber integrated manufacturing research, which will further illuminate the present state of the internet of things (IoT) in manufacturing. © 2024 IEEE.

关键词： Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

DBROA based Convolutional Auto Encoder for European Credit Card Fraud Detection

DBROA based Convolutional Auto Encoder for European Credit C...

引用

Data Science and Information System (ICDSIS), IEEE International Conference on

作者： Piyush Kumar Pareek B R Mohan E V Vidya Pankaj Zanke K S Navyashree Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India Senior Technical Business Analyst Sapiens International Corporation USA

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Numerous studies using bad neuronic nets (DNNs) for the objective of detecting recognition card fraud have concentrated on enhancing point prediction accuracy and limiting unwelcome biases by developing various network designs or learning models. It is crucial to reckon uncertainty together with point estimate of the training data has a significant impact on how well machine learning algorithms function, therefore data imbalance is not a minor problem. In general, the data only shows a tiny portion of false communications. The effectiveness of simple machine acquiring classifiers is significantly impacted by this. This research studies several data augmentation strategies to handle the unbalanced data problem for credit card fraud detection in order to deal with the shortage of false events. The balanced data is then sent to a Convolutional Auto Encoder (CAE) for classification, and a Developed Battle Royale optimisation algorithm (DBROA) is used to determine the model’s weight in the best possible way. For the purpose of recognising credit card fraud involving European cardholders, each model includes stratified K-fold cross-validation and a real-world dataset. The results show that the suggested model is more accurate than current models.

关键词： Measurement Accuracy Uncertainty machine learning algorithms Europe Training data Predictive models

来源：评论

学校读者我要写书评

暂无评论

An Efficient Feature Selection in Diabetes Prediction Using Clustering Greedy Stepwise Search Algorithm 15

An Efficient Feature Selection in Diabetes Prediction Using ...

引用

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

作者： Rajeswarappa, G. Vignesh, R. Ratha, Ashis Kumar Devi, S. Nachiya, R.A.M. Jainaf Selvakumar, M. G Pulla Reddy Engineering College Department of Cse Kurnool518007 India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Karunya Nagar Coimbatore India Iilm University School of Computer Science and Engineering Department of Computing and Security Uttar Pradesh Greater Noida India Nandha Engineering College Department of Mca Tamilnadu Erode India B.S. Abdur Rahman Crescent Institute of Science and Technology Crescent School of Pharmacy Chennai Vandalur600048 India Bannari Amman Institute of Technology Department of Information Technology India

ISBN: (纸本)9798350370249

Diabetes is a metabolic disease that is common across the world and has a high yearly risk of complications that can be life-threatening. These problems have the potential to kill several important organs if left unchecked. Preventing severe consequences can be achieved by guaranteeing prompt treatment, contingent upon early identification. Refining datasets for efficient data mining operations necessitates data pretreatment, which frequently entails eliminating extra functions that may jeopardize predicted precision. In statistical machine learning, feature selection is a well-established topic that uses methods like K-means clustering and the greedy stepwise search algorithm. Even with limited capabilities, empirical testing of the SVM classifier on the Pima Indian Diabetes (PID) dataset repeatedly shows that it is more accurate than other approaches. The PID dataset is a popular benchmark for evaluating data mining techniques, demonstrating the usefulness of this dataset for research and development projects. © 2024 IEEE.

关键词： Diabetes Feature Selection Glucose Greedy Stepwise SVM Classifier

来源：评论

学校读者我要写书评

暂无评论

Autonomous Vehicles: Steering the Future of Transportation with Self-Driving technology and Advanced AI Systems 5

Autonomous Vehicles: Steering the Future of Transportation w...

引用

5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Daniel Nareshkumar, M. Reddy, Desidi Narsimha Sowmiya, A. Pandi, V. Samuthira Sateesh, Rayala Hindumathy, N. Simats University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Tamil Nadu Chennai India Soniks Consulting Llc TX United States Rajalakshmi Engineering College Department of Eee Thandalam Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Mlr Institute of Technology Department of Ece Hyderabad India Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Department of Artificial Intelligence and Machine Learning Avadi Chennai India

ISBN: (纸本)9798350376685

Autonomous vehicles (AVs) are a game-changer in the transportation industry;they will alter cityscapes, cut down on accidents, and reimagine how people experience mobility. This study investigates the complex relationship between autonomous vehicles and high-tech AI systems, looking at how these two technologies can influence transportation in the future. The paper highlights crucial developments in sensor fusion, machine learning algorithms, and real-time data processing that are essential for the development of fully autonomous systems by analyzing the present status of AV technology. In the first part of the study, the basic technologies that support AVs are described. These technologies include LiDAR, radar, cameras, and GPS. When taken together, they allow AVs to perceive their surroundings and locate themselves. After that, it delves into how AI is helping AVs understand sensor data, make judgments in real-time, and carry out complicated maneuvers, showcasing how advancements in deep learning, neural networks, and predictive analytics are powering these capabilities. Integrating AVs into current transportation infrastructures presents both potential and obstacles, which the paper delves into. Critical situation safety, legal frameworks, cybersecurity risks, and AI decision-making ethics are all covered. In addition to the environmental advantages of improved traffic flow and decreased emissions, the study delves into the possible socioeconomic implications, including shifts in urban design, public transit, and the labor market. The research delves into the existing deployment tactics and the path for scaling AV operations through a thorough analysis of case studies, pilot projects, and industry reports. It determines how prepared different groups are to accept this revolutionary technology, including the general public, lawmakers, and manufacturers. To sum up, the study highlights how important it is for academic institutions, business executives, and governme

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Wireless Capsule Endoscopy for Crohn's Disease Automated Image Classification using Deep learning 2

Wireless Capsule Endoscopy for Crohn's Disease Automated Ima...

引用

2nd IEEE International Conference on Integrated intelligence and Communication Systems, ICIICS 2024

作者： Nalayini, P. Jayasankari, S. Gunasundari, C. Elumalai, G. Kumar, Bharathi Ramesh Balamurugan, E. School of Computing Sastra Deemed to be University Thanjavur India School of Computing CSE Sastra Deemed to be University Thanjavur India School of Computing SRM Institute of Science and Technology Tiruchirappalli India Department of ECE Panimalar Engineering College Chennai India Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial intelligence and Machine Learning Bannari Amman Institute of Technology Sathyamangalam India

ISBN: (纸本)9798331504960

Despite the diagnosis of Crohn's Disease now transformed through wireless capsule endoscopy (WCE), traditional manual interpretation by gastroenterologists contains substantial restrictions such as high inter-observer inconsistency, time dedication, and potential oversight of important clinical signs. The paper proposes an automated image classification system founded on deep learning methods specifically, Convolutional Neural Network (CNN) for feature extraction and Long Short-Term Memory networks (LSTM) for temporal sequence examination. The technique applies Transfer learning to improve model performance with minimal labeled data, thus facilitating precise identification of CD-related abnormalities. Strong testing on an endoscopist-annotated dataset indicates the proposed system substantially surpasses existing systems about accuracy of 94.5%, sensitivity of 92.0%, and specificity of 96.0%. Promising enhanced patient outcomes in the therapy of Crohn's Disease, the automated method significantly decreases interpretation time, minimizes inter-observer inconsistency, and increases diagnostic accuracy. © 2024 IEEE.

关键词： Endoscopy

来源：评论

学校读者我要写书评

暂无评论

Scheduling the Tasks in Cloud Computing using Adaptive Adjustment in Whale Optimization Algorithm

Scheduling the Tasks in Cloud Computing using Adaptive Adjus...

引用

Data Science and Information System (ICDSIS), IEEE International Conference on

作者： Piyush Kumar Pareek G D Nithyashree S A Rajashekhar Pankaj Zanke K S Navyashree Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India Senior Technical Business Analyst Sapiens International Corporation USA

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

One of the most popular infrastructures for employing virtual machines, also known as processing units, is cloud computing. Task scheduling is fundamental problems of cloud computing. A number of methods to handle the nondeterministic polynomial-time (NP)-complete optimisation issue of choosing the best scheduling criteria in cloud computing have been proposed by academics in the past. An effective optimisation approach is suggested in this research, based on the whale optimisation algorithm (WOA), to address the common problems of solution algorithms, which include poor global optimisation ability, ease of sliding into local optimisation, and sluggish convergence speed. We provide an adaptive adjustment method that may dynamically alter search behaviour during the whale optimisation algorithm’s iterative operation. The technique described to further update the position of search individuals also uses the evolutionary approach of differential variation. The algorithm’s exploration and development capacities are then balanced, and a restart approach is included to keep the system from entering local optimisation. The approach simultaneously resolves the work planning issue in the situation of rain cloud processing. When compared to other algorithms, this technique uses more resources and converges more quickly. Additionally, the evaluation function seeks for the optimal answers by taking the makespan, resource cost, and load balancing level into account.

关键词： Cloud computing Scheduling algorithms Heuristic algorithms Scalability Whales Whale optimization algorithms Scheduling

来源：评论

学校读者我要写书评

暂无评论

Context Gating in Spiking Neural Networks: Achieving Lifelong learning through Integration of Local and Global Plasticity

arXiv

引用

arXiv 2024年

作者： Shen, Jiangrong Ni, Wenyao Xu, Qi Pan, Gang Tang, Huajin The State Key Lab of Brain Machine Intelligence College of Computer Science and Technology Zhejiang University Zhejiang310027 China The Faculty of Electronic Information and Electrical Engineering School of Artificial Intelligence Dalian University of Technology Dalian116024 China

Humans learn multiple tasks in succession with minimal mutual interference, through the context gating mechanism in the prefrontal cortex (PFC). The brain-inspired models of spiking neural networks (SNN) have drawn massive attention for their energy efficiency and biological plausibility. To overcome catastrophic forgetting when learning multiple tasks in sequence, current SNN models for lifelong learning focus on memory reserving or regularization-based modification, while lacking SNN to replicate human experimental behavior. Inspired by biological context-dependent gating mechanisms found in PFC, we propose SNN with context gating trained by the local plasticity rule (CG-SNN) for lifelong learning. The iterative training between global and local plasticity for task units is designed to strengthen the connections between task neurons and hidden neurons and preserve the multi-task relevant information. The experiments show that the proposed model is effective in maintaining the past learning experience and has better task-selectivity than other methods during lifelong learning. Our results provide new insights that the CG-SNN model can extend context gating with good scalability on different SNN architectures with different spike-firing mechanisms. Thus, our models have good potential for parallel implementation on neuromorphic hardware and model human's behavior. Copyright © 2024, The Authors. All rights reserved.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

machine learning in Healthcare: A Review of Current Applications and Future Trends

Machine Learning in Healthcare: A Review of Current Applicat...

引用

machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： Prajyot Yesankar Chetan Puri Aditya Barahate Pradanywant M. Gote Janhavi Hajbe Adesh Pawar Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Computer Science & Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

machine learning (ML) has been a main force behind important breakthroughs in patient tracking, personalized medicine, medical tests, and operating efficiency in the healthcare business in recent years. machine learning algorithms provide unique insights into early disease identification, picture analysis, and prediction analytics, improving diagnosis accuracy and treatment results. These programs are able to examine big and complex information. By predicting treatment effectiveness and genetic risks, machine learning (ML) allows personalized medicine by allowing customized care through tailored therapy approaches. Furthermore, preventative health management is backed by ML-driven prediction analytics and online patient tracking, especially for chronic illnesses. Operationally, machine learning increases process efficiency and cuts costs, allowing for data- driven decision-making and individualized patient care. However, there are important ethics issues with machine learning acceptance in the healthcare business as well, such as data protection, computer bias, and legal problems. This study offers a full analysis of the state-of-the-art machine learning applications in healthcare, stressing their revolutionary potential while discussing the ethics issues and hurdles that must be addressed to ensure execution that is both responsible and fair.

关键词： Ethics machine learning algorithms Law Hospitals Precision medicine Data protection machine learning Medical services Predictive analytics Computer security

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Evaluation of Fake Face Recognition Scheme using artificial intelligence Oriented learning Scheme 1

A Comprehensive Evaluation of Fake Face Recognition Scheme u...

引用

1st International Conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Subashree, S. Rose S, Remya Valarmathy, A.S. Joseph, Princy Dennis, P. Jasmin Ravi, S. E.G.S. Pillay Engineering College Department of Computer Science and Engineering Tamil Nadu Nagapattinam India R.M.D. Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India CMR College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Vi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India Saveetha University Department of VLSI Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350365016

Fake face recognition has emerged as a critical area of research due to the proliferation of synthetic media and its potential misuse in various domains. This study presents a thorough evaluation of a novel Fake Face Recognition Scheme utilizing artificial intelligence Oriented learning Scheme (AIOLS) coupled with Generative Adversarial Network (GAN)-GoogleNet integration. The aim is to discern authentic facial images from synthetic or tampered ones with high accuracy. In this research, we employed a dataset comprising a diverse range of authentic and synthetic facial images. The proposed scheme incorporates the power of GANs for generating synthetic facial images and leverages GoogleNet, a state-of-the-art deep convolutional neural network, for discriminative feature extraction. The integration within the AIOLS framework facilitates efficient learning and adaptation to complex patterns present in fake facial images. The evaluation of our scheme was conducted through rigorous experimentation on the dataset, employing various performance metrics. Notably, the accuracy obtained was measured at an impressive 96.7%, signifying the effectiveness of our approach in distinguishing between real and fake facial images. Additionally, other metrics such as precision, recall, and F1-score were also computed to provide a comprehensive assessment of the scheme's performance. © 2024 IEEE.

关键词： Generative adversarial networks

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：