检索结果-内蒙古大学图书馆

Informatica (Slovenia) 2025年第16期49卷 77-86页

作者： Dhanalakshmi, B. Tamije Selvy, P. Department of Computer Science and Engineering Dr.N.G. P Institute of technology India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology India

The rapid advancement and proliferation of Cyber-Physical Systems (CPS) have led to an exponential increase in the volume of data generated continuously. Efficient classification of this streaming data is crucial for predicting system behaviors and enabling proactive decision-making. This research aims to extract actionable knowledge from the continuous data streams of CPS and predict their behavior using advanced supervised learning algorithms. The predictions facilitate timely interventions and necessary actions within the interconnected physical network. The background of this work lies in the intersection of CPS, machine learning, and data stream mining. Traditional batch processing methods are inadequate for real-time analysis of CPS data due to their inherent latency and computational inefficiency. This research employs state-of-the-art techniques for real-time data processing, including incremental learning, sliding window models, and ensemble methods tailored for streaming data. Our approach differs from existing works by focusing on a comprehensive framework that integrates real-time data ingestion, preprocessing, feature extraction, and model updating in a seamless pipeline. Unlike previous studies that often rely on static datasets and offline analysis, our method ensures continuous learning and adaptation to evolving data patterns. Comparative analysis with existing techniques demonstrates superior performance in terms of accuracy, latency, and scalability. Specifically, our models achieved an average classification accuracy of 92%, with a precision of 90%, recall of 89%, and an F1 score of 89.5%. These metrics indicate significant improvements over traditional batch processing methods, which typically lag in responsiveness and adaptability. This research provides a robust and efficient solution for the real-time classification of streaming data from CPS, enhancing the system's ability to predict behaviors and take necessary actions promptly. © 2025 Slov

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

OpBench: an operator-level GPU benchmark for deep learning

引用

science China(Information sciences) 2024年第9期67卷 76-87页

作者： Qingwen GU Bo FAN Zhengning LIU Kaicheng CAO Songhai ZHANG Shimin HU Department of Computer Science and Technology Tsinghua University National Innovation Institute of Defense Technology Academy of Military Science Beijing Fitten Technology Co. Ltd.

Operators(such as Conv and ReLU) play an important role in deep neural networks. Every neural network is composed of a series of differentiable operators. However, existing AI benchmarks mainly focus on accessing model training and inference performance of deep learning systems on specific models. To help GPU hardware find computing bottlenecks and intuitively evaluate GPU performance on specific deep learning tasks, this paper focuses on evaluating GPU performance at the operator level. We statistically analyze the information of operators on 12 representative deep learning models from six prominent AI tasks and provide an operator dataset to show the different importance of various types of operators in different networks. An operator-level benchmark, OpBench, is proposed on the basis of this dataset, allowing users to choose from a given range of models and set the input sizes according to their demands. This benchmark offers a detailed operator-level performance report for AI and hardware developers. We also evaluate four GPU models on OpBench and find that their performances differ on various types of operators and are not fully consistent with the performance metric FLOPS(floating point operations per second).

关键词： deep learning operator benchmark model benchmark GPU performance analysis deep neural networks

来源：评论

学校读者我要写书评

暂无评论

UVM++: A Large-Scale Benchmark for Beverage Recognition in Intelligent Vending Machine

引用

IEEE Transactions on Consumer Electronics 2024年第1期70卷 249-262页

作者： Zhang, Haijun Ouyang, Chao Zuo, Dingding Zhou, Haibin Li, Guojian Huang, Zhe Yang, Kai Harbin Institute of Technology Department of Computer Science Shenzhen518055 China

In recent years, artificial intelligence (AI)-driven computer technology has become popular in many practical applications in the retail industry. In particular, the development of face recognition, mobile payment, cloud computing and other technologies has given birth to three new forms of consumption related to 'unmanned retail', i.e., unmanned convenience stores, unmanned vending machines and unmanned vending racks. Due to the sudden outbreak of COVID-19, the characteristics of unmanned retail such as intelligent operation, convenience and a lack of contact have allowed the public to gradually become used to this approach. An unmanned vending machine (UVM) can be reconfigured from a refrigerated cabinet for selling commodities such as beverages. It can take photos of the goods purchased by customers, and can recognize these commodities based on information from the collected images. However, some commercial UVMs are subject to strong restrictions on the types and placement of goods, making them difficult to achieve data collection and convenient operation. In this research, we therefore design a system for automated data collection and annotation. The mechanical structure of our system is designed to mimic the placement of drinks and the scene of each drink with other different types of drinks around it. We construct a large-scale benchmark dataset called UVM++, which is composed of several sub-datasets collected from different scenes. Extensive experimental results are presented and the performance of popular object detection methods on this task is examined. In particular, we perform an empirical study of cross-scene detection by various object detectors. Our findings suggest that the proposed data annotation system and the constructed benchmark dataset, UVM++, can be useful for performing various tasks in the environment of a UVM, thus driving development in the field of new retail. © 1975-2011 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Hybrid optimization assisted deep ensemble classification framework for skin cancer detection

引用

Multimedia Tools and Applications 2025年第1期84卷 63-94页

作者： Pukhta, Irfan Rashid Rout, Ranjeet Kumar Department of Computer Science and Engineering National Institute of Technology Srinagar190006 India

In 2018, there were 1 million occurrences of non-melanoma cancer and 288,000 occurrences of malignant skin cancer (MM) recorded worldwide. Given the aging of the population and limited resources for medical care, a complete strategy to prevent skin cancer must include both practical detection and accurate diagnosis. Dermatologists and family physicians may find it challenging to identify skin cancer, particularly MM, at an early phase. The purpose of this paper is to suggest a paradigm for skin cancer detection, where pre-processing is the initial step for which median filter (MF) is employed. The pre-processed image undergoes segmentation, which is done by an Improved DBSCAN approach. Once the segmentation is over, features like "GLCM, Local Binary pattern (LBP), Local Vector Pattern (LVP), and Improved Center Symmetric Local Ternary Pattern (CSLTP)" are extracted. This is the final phase, where an ensemble classifier (EC) that combines the models like "Convolutional Neural network (CNN), Long Short Term Memory (LSTM), and Bidirectional Gated Recurrent Unit (Bi-GRU)" is deployed. Further, weights of CNN, Bi-GRU, and LSTM are tuned by Hawks Updated Blue Monkey Algorithm with Gaussian Map Randomness (HUBM-GMR) based hybrid optimization. The study will ultimately validate the HUBM-GMR model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

引用

Multimedia Tools and Applications 2024年 1-21页

作者： Gorai, Joy Shaw, Dilip Kumar Department of Computer Science and Engineering National Institute of Technology Jamshedpur India

As internet use in communication networks has grown, fake news has become a big problem. The misleading heading of the news loses the trust of the reader. Many techniques have emerged, but they fail because fraudsters or exploiters find new ways to deceive them. Semantic analysis and machine learning techniques play a significant role in fake news detection. We must semantically assess terms used in the headline and main content before filtration because words can have different meanings in different contexts. In the paper, a method for determining fake news is introduced by calculating the dissimilarity between the title and content of the news. Vector distance calculators are used to extract semantic dissimilarities, which were then utilized as an additional feature. Initially, Term frequency-inverse document frequency (Tf-Idf) and Mutual Information (MI) are employed for text Feature Extraction (FE) on the ‘title’ and ’content’ of the news articles. Subsequently, four different vector distance calculators are used to extract vector distance-based features. The resulting distance values are used to train various machine learning classifiers, achieving the highest accuracy of 99%. Our method provides a comprehensive analysis by capturing diverse aspects of semantic dissimilarity from various distance calculators. The proposed method is then compared with previous techniques to demonstrate its effectiveness. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Antenna Optimization using Machine Learning Algorithms and their Applications: A Review

引用

Journal of Engineering science and technology Review 2024年第2期17卷 128-144页

作者： Pandey, Anil Kumar Singh, M.P. Department Of Computer Science & Engineering National Institute Of Technology Patna India

Antenna optimization using machine learning is a rapidly evolving field that leverages the power of artificial intelligence to design and improve antenna systems. Antenna optimization is a process of modifying antenna parameters to achieve desired performance metrics, such as gain, bandwidth, radiation pattern, and impedance matching. This paper presents a review of the most advanced development in antenna design and optimization by using machine learning techniques. The aim of this survey is to focus on different machine learning optimization techniques and their optimization capability with efficiency challenges. A deep outline from literature survey on optimization of antennas using machine learning are presented and listing various optimization algorithms and procedures that are applied to produce desired antenna characteristics and specifications. Firstly, a brief introduction of machine learning and its algorithms, later a quick explanation of antenna optimization process followed by an arranged introduction of different types of printed antenna designs using machine learning algorithm are reported. The methods emphasized in this survey have probably an effect on the imminent advancement of antennas for a variety of wireless applications. © (2024), (International Hellenic University – School of science). All Rights Reserved.

关键词： Microstrip antennas

来源：评论

学校读者我要写书评

暂无评论

Integration of IoT-enabled UAVs in Smart Agriculture: Types, Applications, Trends, challenges 7

Integration of IoT-enabled UAVs in Smart Agriculture: Types,...

引用

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

作者： Prathyusha, Gajawada Krishnan, M.B.Mukesh Deepak, S.Prem Kumar SRM Institute of Science and Technology School of Computing Department of Networking and Communications India B V Raju Institute of Technology Department of Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350372816

Unmanned aerial vehicles (UAVs) have garnered increasing attention in recent years due to their utilization of artificial intelligence (AI) technologies and automation processes. These vehicles are being developed for various applications, including the military, smart agriculture, rescue operations, entertainment, monitoring, and medicine delivery. In the current era, UAVs extensively employ a wide range of technologies to transmit data to base stations. The collected data undergoes processing, producing remarkable outcomes through the integration of machine learning and deep learning technologies. This paper reviews disease identification and object detection techniques and their associated applications & challenges. Additionally, the research explores recent trends and technologies in the field of smart agriculture. This research emphasizes the use of sensors in unmanned aerial vehicles (UAVs) by analyzing the methods utilized for recognizing diseased areas and things while identifying insects and sick crops. This paper also addresses the challenges encountered by existing methodologies and proposes directions for future research and development. Furthermore, this review delves into suitable techniques for heterogeneous UAVs and examines IoT-related strategies. © 2024 IEEE.

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

Online Convex Optimization with Switching Cost and Delayed Gradients

引用

Performance Evaluation Review 2024年第4期51卷 22-23页

作者： Senapati, Spandan Vaze, Rahul Department of Computer Science & Engineering Indian Institute of Technology Kanpur India School of Technology and Computer Science Tata Institute of Fundamental Research India

We consider the online convex optimization (OCO) problem with quadratic and linear switching cost when at time t only gradient information for functions fτ, τ 16(Lµ+5) for the quadratic switching cost, and also show the bound to be order-wise tight in terms of L, µ. In addition, we show that the competitive ratio of any online algorithm is at least max{Ω(L), Ω(pLµ )} when the switching cost is quadratic. For the linear switching cost, the competitive ratio of the OMGD algorithm is shown to depend on both the path length and the squared path length of the problem instance, in addition to L, µ, and is shown to be order-wise, the best competitive ratio any online algorithm can achieve. Copyright is held by author/owner(s).

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

A threefold chaotic image encryption technique for efficacious image data protection

引用

International Journal of computers and Applications 2024年第11期46卷 974-982页

作者： Anwar, Shamama Srivastava, Anshuman Department of Computer Science and Engineering Birla Institute of Technology Ranchi India

With the profound use of digital contents in education and social media, multimedia content have become a prevalent means of communication and with such rapid increase, information security is still a major concern. This paper provides a summary of the techniques of securing image data focusing primarily on encryption. This paper additionally also explores the contribution of chaos theory in the domain of image encryption and applies a threefold chaotic map sequence to design an image encryption technique. This chaos-based encryption technique renders characteristics like sensitivity, determinacy, and ergodicity, and hence provides a better trade-off between security and computational speed. The efficacy of proposed algorithm is also evaluated based on well-known statistical metrics like Number of Changing Pixel Rate (NPCR), Unified Average Changing Intensity (UACI), Information Entropy, local Entropy, Correlation analysis, key sensitivity analysis, and NIST Randomness test. Furthermore, the key sensitivity analysis is also discussed which is a major part of any encryption algorithm analysis. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

A BERT-encoded ensembled CNN model for suicide risk identification in social media posts

引用

Neural Computing and Applications 2024年第18期36卷 10955-10970页

作者： Gorai, Joy Shaw, Dilip Kumar Department of Computer Science and Engineering National Institute of Technology Jamshedpur India

Suicide is a significant public health issue that devastates individuals and society. Early warning systems are crucial in preventing suicide. The purpose of this research is to create a deep learning model to identify suicide risk using a combination of bidirectional encoder representations from transformers (BERT) and an ensemble of multiple convolutional neural networks (CNN). BERT is used to encode the text data into numerical representations that capture the context-aware meaning of words and phrases, while ensemble CNN is used to analyze the encoded text data to identify patterns and relationships relevant to suicide risk. The model is trained on a large corpus of text data from social media and suicide notes and evaluated on a validation set of labeled data. The results of the proposed model perform better than the recent approaches in detecting suicide risk and can potentially be a valuable tool for suicide prevention efforts. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural 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：