检索结果-内蒙古大学图书馆

Improving Real-Time Data Processing Performance Through Machine Learning

Improving Real-Time Data Processing Performance Through Mach...

Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： S Sathya Chaitali R. Shewale Abhijeet Kar Megha Pandey Srijani Gosh Jagmeet Sohal Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai-127 Department of Information Technology Vishwakarma Institute of Information Technology Pune India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

Actual-time information processing technology have become more and more vital for the efficient operation of diverse enterprise tactics. System gaining knowledge of generation can help improve the overall performance of those information processing structures via allowing them to conform to changing conditions and styles. System gaining knowledge of algorithms may be used to discover tendencies and anomalies in information streams, detect patterns, and make predictions so that it will optimize the overall performance of the records processing gadget. The machine learning algorithms can be skilled and evaluated on ancient facts, or on streaming facts samples after which deployed on a records processing device. The overall performance of the statistics processing system may be progressed via adjusting parameters associated with information sampling frequency, facts granularity and the kind of features to be processed. Further, gadget mastering algorithms can be used to optimize facts preprocessing and publish-processing duties, together with feature extraction and information cleaning. Ultimately, gadget learning techniques can be utilized to optimize the scheduling and execution of records processing jobs, which includes identifying appropriate sources for a selected project and dynamically allocating sources and jobs as in line with the demand.

关键词： Performance evaluation Machine learning algorithms Scalability Information processing Machine learning Prediction algorithms Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Boosting legal case retrieval by query content selection with large language models

arXiv

引用

arXiv 2023年

作者： Zhou, Youchao Huang, Heyan Wu, Zhijing School of Computer Science and Technology Beijing Institute of Technology Southeast Academy of Information Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

Legal case retrieval, which aims to retrieve relevant cases to a given query case, benefits judgment justice and attracts increasing attention. Unlike generic retrieval queries, legal case queries are typically long and the definition of relevance is closely related to legal-specific elements. Therefore, legal case queries may suffer from noise and sparsity of salient content, which hinders retrieval models from perceiving correct information in a query. While previous studies have paid attention to improving retrieval models and understanding relevance judgments, we focus on enhancing legal case retrieval by utilizing the salient content in legal case queries. We first annotate the salient content in queries manually and investigate how sparse and dense retrieval models attend to those content. Then we experiment with various query content selection methods utilizing large language models (LLMs) to extract or summarize salient content and incorporate it into the retrieval models. Experimental results show that reformulating long queries using LLMs improves the performance of both sparse and dense models in legal case retrieval. © 2023, CC BY-NC-SA.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

IoT-Based Smart Home Energy Management System (SHEMS) Using Networking and Automation

IoT-Based Smart Home Energy Management System (SHEMS) Using ...

引用

Data science and Business Systems (ICDSBS), International Conference on

作者： Arokia Martin N Kirubakaran N Senthil Kumar G Selvaganesan C Jegadeeshwari P Vijayaraja V Department of Computer Science and Business Systems Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Dr. MGR Educational and Research Institute (Deemed to be University) Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science RMK College of Engineering and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

Physical energy meter reading concept is outdated and it is not efficient, leading to lots of wastage of manpower and errors, btaining accurate real-time readings is challenging and complex to achieve consistently. Current implementations of IoT technology allow real-time monitoring and energy management capabilities with the integration of IoT ecosystems, Digital Communication technology networks, and Machine Learning algorithms to further analyze data as well as enhance user convenience. However, despite their promising potential, these systems face ongoing challenges, including adaptability, scalability, interoperability and enhanced cyber security resilience, particularly in large-scale deployments. This review examines present SHEMS and their capabilities, from strengths such as real-time tracking of energy usage, optimization and consumption of energy at offpeak hours by the application of predictive analytics using Machine Learning. This also elucidates areas for improvement, such as an improved integration of renewable energy technologies, simplification of the complexities surrounding large-scale deployment and sustainability over the long term. Addressing these deficits, this paper further promotes advancements in the design of SHEMS that are more secure, scalable and inter-operable, increasing communication across IoT ecosystems and by using smart grid technology, which could improve effective management of energy and a potential wider uptake. These suggestions are aimed at fine-tuning existing technologies to be easily integrated into modern, automated energy systems for better energy management.

关键词： Renewable energy sources Machine learning algorithms Automation Smart homes Machine learning Predictive models Real-time systems Smart grids Predictive analytics Optimization

来源：评论

学校读者我要写书评

暂无评论

Signal Quality Prediction for Cognitive Radio Networks using Deep Learning Techniques

Signal Quality Prediction for Cognitive Radio Networks using...

引用

International Conference on Computing and Networking technology (ICCNT)

作者： Sudhanshu Dev M.P. Santhoshkumar Udita Goyal K N Raja Praveen Kishor R. Pathak G. Ravivarman Centre for Interdisciplinary Research for Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Quantum University Research Center Quantum University School of Engineering and Technology Computer Science and Engineering JAIN (Deemed to be University) Bangalore Karnataka Department of Information Technology Vishwakarma Institute of Technology Pune INDIA Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cognitive radio networks (CRNs) are getting increasingly famous due to their potential to deliver excessive-bandwidth offerings to numerous users. Despite this, the signal satisfactory of those networks can be fantastically variable and hard to predict, leading to degraded communication. Currently, deep gaining knowledge of strategies had been proposed as a method to improve the capability of CRNs to correctly expect signal first-rate, allowing multiplied device reliability. On this paper, we gift an assessment of present deep gaining knowledge of tactics to be expecting signal excellent in CRNs, focusing at the structure and application of convolutional neural networks (CNNs), recurrent neural networks (RNNs) and deep reinforcement studying (DRL). The overview additionally examines the overall performance of those processes in terms of accuracy, scalability and generalization to new environments. Subsequently, we provide a survey of capability future guidelines for signal first-rate prediction in CRNs using deep getting to know.

关键词： Deep learning Surveys Performance evaluation Knowledge engineering Accuracy Recurrent neural networks Scalability Cognitive radio Reliability Guidelines

来源：评论

学校读者我要写书评

暂无评论

A Redundancy Free Facial Acne Detection Framework Based on Multi-view Face Images Stitching 14th

A Redundancy Free Facial Acne Detection Framework Based on ...

引用

14th International Conference on Brain Inspired Cognitive Systems, BICS 2024

作者： Luo, Ye Wang, Jianfei Zhang, Linglin Liu, Xinyu Rao, Ji Xu, Wantong Lu, Jianwei Wang, Xiuli School of Computer Science and Technology Tongji University Shanghai201804 China Suzhou Chien-Shiung Institute of Technology Taicang215411 China Institute of Photomedicine Shanghai Skin Disease Hospital Tongji University School of Medicine Shanghai200043 China College of Rehabilitation Science Shanghai University of Traditional Chinese Medicine Shanghai China Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation Ministry of Education Shanghai China

ISBN: (纸本)9789819628841

Facial acne is a prevalent skin disease worldwide. Misjudging the severity of its condition can lead to permanent facial damage or negatively impact patients’ self-esteem. With the rapid advancements in computer vision techniques, automated methods for detecting or grading facial acne in digital images are attracting growing attention. However, in clinical practice, multi-view (left, front, and right) face images used for accurate diagnosis pose a significant challenge due to redundant acne detection on the overlapped image regions that can mislead diagnostic results. In this paper, we propose a novel facial acne detection framework to detect various kinds of acne for accurate acne diagnosis or prognosis prediction. Specifically, multi-view facial images are first stitched by aligning key facial feature points, and then the proposed acne detection network YoloV5-Acne is then applied to the stitched images for patient-level acne detection. Experimental results on a collected multi-view face image dataset (ACNE-Shanghai) and a public acne dataset (ACNE04) validate the effectiveness of the proposed method on various evaluation metrics. The code and the dataset will be released for the research purpose once the paper gets published. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Acne Treatment Face Image Facial Acne Detection Multi-view Image Stitching

来源：评论

学校读者我要写书评

暂无评论

An Effective Implementation and Comparative Analysis of Mobile Robot Path Planning Algorithms

An Effective Implementation and Comparative Analysis of Mobi...

引用

2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Vigenesh, M. Taneja, Madhur Jayasree, S. Samrat, Bhavuk Padmapriya, G. Kulkarni, Prutha P Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of S&h Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Sciences Chemistry and Biochemistry Karnataka Bangalore India Vishwakarma Institute of Technology Entc Pune India

ISBN: (纸本)9798350388602

Among the most pressing and intricate challenges in control and computer engineering today is the navigation of autonomous mobile robots. People are now more motivated than ever to use innovative methods, thanks to the development of cutting-edge computational engineering technology. In order to address complicated engineering challenges in the real world, researchers have mostly looked to natural occurrences and their methods for inspiration. In challenging and dense environments, path planning is the best way of defining a safe route in which a mobile robot can navigate to achieve a certain goal from another location. Mobile robots thus require smart and efficient route planning algorithms for proper navigation and self-execution capabilities. In this study, four well-known algorithms for route planning were investigated: Hybrid DA+CS algorithm: The proposed methods DA, CS and PSO using a non-holonomic mobile robot. To test these methods, a test environment has been created with static impediments which will remain constant throughout the experiment. This is because it is where the intended route of the mobile robot is followed and where the availability of the map acquired for path planning algorithms allows assessing and testing them. A comparative study of the following algorithms has been carried out in an effort to understand the trade off between them based on the conventional performance indicators being used. © 2024 IEEE.

关键词： Robot programming

来源：评论

学校读者我要写书评

暂无评论

Attack-Agnostic Deep Face Anti-Spoofing

Attack-Agnostic Deep Face Anti-Spoofing

引用

IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Ajian Liu Zichang Tan Yanyan Liang Jun Wan MAIS Institute of Automation Chinese Academy of Sciences Beijing China Institute of Deep Learning Baidu Research Beijing China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macau

The task of face anti-spoofing (FAS) is to determine whether the captured face from a face recognition system is live or fake. Current methods which are trained with existing fake faces ignore the generalization and perform poorly on unseen attacks. To tackle this problem, a novel Attack-agnostic Face Anti-spoofing framework is proposed. Different from previous methods that can be treated as a defense system, we regard face anti-spoofing as a unified framework with the attack and defense systems and optimize the defense system against unseen attacks via adversarial training with the attack system. Concretely, the attack system consists of two modules: an Adversarial learning-based Attack Pattern Generation (Adv-APG) module and a Supervised learning-based Attack Pattern Drift (Sup-APD) module. The Adv-APG module generates a series of spoofing samples by recombining a live face with known attack patterns in a generative way. The Sup-APD module pulls the generated spoofing samples in a supervised way to an unknown domain that makes the defense system ineffective. The defense system is free to choose and compatible with our attack system. Extensive experiments are conducted by using three different defense architectures to verify that the proposed attack system can improve the performance on both seen- and unseen attacks on multiple datasets.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator

arXiv

引用

arXiv 2025年

作者： Kou, Wei-Bin Zhu, Guangxu Ye, Rongguang Wang, Shuai Tang, Ming Wu, Yik-Chung Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen International Center For Industrial And Applied Mathematics Shenzhen Research Institute of Big Data Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Learning-based street scene semantic understanding in autonomous driving (AD) has advanced significantly recently, but the performance of the AD model is heavily dependent on the quantity and quality of the annotated training data. However, traditional manual labeling involves high cost to annotate the vast amount of required data for training robust model. To mitigate this cost of manual labeling, we propose a Label Anything Model (denoted as LAM), serving as an interpretable, high-fidelity, and prompt-free data annotator. Specifically, we firstly incorporate a pretrained Vision Transformer (ViT) to extract the latent features. On top of ViT, we propose a semantic class adapter (SCA) and an optimization-oriented unrolling algorithm (OptOU), both with a quite small number of trainable parameters. SCA is proposed to fuse ViT-extracted features to consolidate the basis of the subsequent automatic annotation. OptOU consists of multiple cascading layers and each layer contains an optimization formulation to align its output with the ground truth as closely as possible, though which OptOU acts as being interpretable rather than learning-based blackbox nature. In addition, training SCA and OptOU requires only a single pre-annotated RGB seed image, owing to their small volume of learnable parameters. Extensive experiments clearly demonstrate that the proposed LAM can generate high-fidelity annotations (almost 100% in mIoU) for multiple real-world datasets (i.e., Camvid, Cityscapes, and Apolloscapes) and CARLA simulation dataset. © 2025, CC BY-NC-ND.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Identification of Critical Coupled Nodes Based on Electrical-Topological Compound Centrality Index for Smart Grid 10

Identification of Critical Coupled Nodes Based on Electrical...

引用

10th IEEE Joint International Information technology and Artificial Intelligence Conference, ITAIC 2022

作者： Yao, Shiying Yu, Jiawei Dong, Jiuling Yang, Xiaolong Luo, Jingtang Song, Ge Zheng, Yuanshuo State Grid Sichuan Economic Research Institute Sichuan China State Grid Chengdu Electric Power Supply Company Sichuan China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

With rapid application communication technology in the power system, the interdependencies between communication network and power network is drastically increasing, which greatly improve and optimize the operation and control efficiency of the smart grid. Meanwhile, the growing interdependencies also dramatically make the smart grid more vulnerable, which have become one of the biggest reasons of cascading failures. Thus, the electrical-topological compound centrality (ETCC) index is proposed to identify critical coupled nodes and adopt protective measures for improving operational reliability of the smart grid. Firstly, an unweighted and undirected graph is abstracted from the cyber-physical power system for quantitative analysis. Secondly, on the basis of the coupled network model, each node importance is calculated and sorted according to the ETCC algorithm. By sorting results, critical nodes are identified. Finally, the IEEE 39-Bus system is simulated to verify the effectiveness of the proposed algorithm. The results demonstrate that the proposed ETCC not only consider the interaction between information flow and energy flow, but also include the network topology in coupled networks and have higher effectiveness compared with other algorithms. © 2022 IEEE.

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

Beyond Entanglement Routing: Source Assignment and All-Optical Switching-Based Distribution

引用

IEEE/ACM Transactions on Networking 2024年

作者： Zhu, Qiucheng Zhao, Yangming Xu, Hongli Huang, Liusheng Qiao, Chunming State Key Laboratory for Novel Software Technology Nanjing University Nanjing210008 China School of Intelligent Software and Engineering Nanjing University Suzhou Campus Suzhou215163 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China Department of Computer Science and Engineering University at Buffalo State University of New York BuffaloNY14260 United States

Entanglement routing plays a vital role in distributed quantum computing and quantum networks. Previous works on entanglement routing have addressed several design challenges due to limited quantum resources, failures to establish entanglement, and decoherence of established entanglement, but considered neither the limitations imposed by having a limited number of entangled photon sources (EPSes), nor the benefits of using all-optical (or quantum) switching to distribute entangled photons. In this paper, we first explore the problem of jointly optimizing entanglement routing and EPS assignment, assuming no all-optical switching capability. In other words, a pair of entangled photons generated by one EPS can be distributed to two neighboring quantum nodes. We then relax the above assumption so as to allow a pair of entangled photons generated by one EPS to be distributed to non-adjacent nodes using all-optical switching. We propose two corresponding solutions, namely, entanglement routing and EPS assignment (or ERSA), and ERSAwith all-optical switching-based distribution (or ERSA+D) that aim to maximize the number of entanglement connections between the given set of source-destination (SD) pairs while avoiding starvation and achieving fairness among the SD pairs. In order to obtain efficient solutions in a large discrete solution space in a timely manner, we first formulate each optimization problem as an Integer Linear Programming (ILP), and then propose efficient algorithms to derive near-optimal solutions based on relaxation, Lagrangian decomposition, duality iteration, and rounding techniques. Extensive simulations show that ERSA+Dcan increase network throughput by up to 1652% and 113%, respectively, thanks to EPS assignment optimization, and all-optical switching based entanglement distribution. © 1993-2012 IEEE.

关键词： Quantum computers

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：