检索结果-内蒙古大学图书馆

1st International Conference on data, Computation and Communication, ICDCC 2024

作者： Kumaresan, T. Geetha, A. Rajeshkumar, G. Benita Gracia Thangam, J. Eswaran, Malathi Rajkumar, Thivya Bannari Amman Institute of Technology Dept. of Computer Science and Engineering Sathyamangalam India Presidency University Dept. of Computer Science and Engineering Bangalore India Velalar college of Engineering and Technology Dept. of Artificial Intelligence and Data science Erode India Kongu engineering College Dept. of Computer Technology Perundurai India

ISBN: (纸本)9798331532956

Blind people face many difficulties while engaging with their surroundings. This project is all about creating innovative solutions for the blind people by providing the information in the format of audio. Image-to-Audio transformation system specifically designed for natural language understanding using Large Language Model (LLM).The system will capture a picture or video of the situation present in front of the blind person and then provide descriptions about the images. The objective is to provide guidelines for blind people by understanding their surroundings through an LLM model and provide audio response. The CLIP model analyzes the objects, scenes, text etc.. and provides relevant information. The extracted visual data is then passed through GTTs (Google-Text-To-Speech) to create rich audio descriptions. It provides accurate and detailed representation of the visual content, allowing blind people to understand their surroundings. The project involves integrating convolutional neural networks and Large Language Model (e.g GPT-4) technologies to create a real-time image-to-text system for blind individuals. The CNN model is used to calculate the distance of the object from the user. We are integrating the Google Assistants to open the app automatically by the blind people with help of their voice. © 2024 IEEE.

关键词： Image retrieval

来源：评论

学校读者我要写书评

暂无评论

ML-Based Fault Detection Strategies for Power Electronics 9

ML-Based Fault Detection Strategies for Power Electronics

引用

9th IEEE India International Conference on Power Electronics, IICPE 2023

作者： Jain, Aarushi Lathwal, Rani Data Science Jagannath University New Delhi India Amity University Department of Computer Science & Engineering Noida India

ISBN: (纸本)9798350307252

Power electronics plays a pivotal role in modern energy systems, contributing to improved efficiency, reduced emissions, and enhanced control in various applications such as renewable energy integration, electric vehicles, and smart grids. This vital domain within electrical engineering, has undergone a remarkable transformation through the integration of Machine Learning, as the power electronics industry has witnessed significant advancements in optimizing operations, improving reliability, and enabling sustainable energy solutions. This research paper presents various strategies of Machine learning used in power electronics. It focuses on using Machine Learning based models to examine and train various voltage and current record units obtained by simulated malfunctioning transmission line model created with Matlab /Simulink. This yielded a dataset consisting of 12,001 fault data samples. Machine Learning models SVM, KNN and Decision Tree were trained on the same data and were employed to detect the faults. Next, the model was chosen for its strong fault detection capabilities and potential, which were evaluated based on accuracy, precision, and the F1- score. Effective fault detection techniques play a crucial role in optimizing power system control, reducing energy wastage, and ensuring top-notch performance. © 2023 IEEE.

关键词： Fault detection

来源：评论

学校读者我要写书评

暂无评论

Scheduling with Obligatory Tests 32

Scheduling with Obligatory Tests

引用

32nd Annual European Symposium on Algorithms, ESA 2024

作者： Dogeas, Konstantinos Erlebach, Thomas Liang, Ya-Chun Department of Computer Science Durham University United Kingdom Data Science Institute Columbia University New YorkNY United States Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9783959773386

Motivated by settings such as medical treatments or aircraft maintenance, we consider a scheduling problem with jobs that consist of two operations, a test and a processing part. The time required to execute the test is known in advance while the time required to execute the processing part becomes known only upon completion of the test. We use competitive analysis to study algorithms for minimizing the sum of completion times for n given jobs on a single machine. As our main result, we prove using a novel analysis technique that the natural 1-SORT algorithm has competitive ratio at most 1.861. For the special case of uniform test times, we show that a simple threshold-based algorithm has competitive ratio at most 1.585. We also prove a lower bound that shows that no deterministic algorithm can be better than √2-competitive even in the case of uniform test times. © Konstantinos Dogeas, Thomas Erlebach, and Ya-Chun Liang;licensed under Creative Commons License CC-BY 4.0.

关键词： Scheduling algorithms

来源：评论

学校读者我要写书评

暂无评论

Oral Cancer Detections and Classification Using Region Based Convolutional Neural Network 2

Oral Cancer Detections and Classification Using Region Based...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Kumar, M Prema Ashfaq Ahmed, K. Subash, K. Aeron, Anurag Department of Ece Andhra Pradesh Bhimavaram India Department of Emerging Technologies in Computer Science Andhra Pradesh Kurnool India Department of Data Science Trichy India Miet Meerut Department of Computer Science and Engineering Meerut India

ISBN: (纸本)9798350384277

Early detection dramatically increases the survival rate of oral cancer (OC). Artificial intelligence (AI) technology has garnered more attention in the field of diagnostic medicine in present periods. This study set out to assess the available data regarding AI's efficacy in OC diagnosis critically. Artificial intelligence diagnostic accuracy and capacity to detect early phases of OC were highlighted. In this project, performance indicators will be measured and oral cancer will be divided and classified using intelligent computing techniques. The role of oral cancer classification and detection to achieve a high recognition rate while leveraging the best theoretical components of oral cancer images, a newly established region-based Convolutional Neural Network (RCNN-COA) and the Chimp Optimization Algorithm were used to improve a Deep Learning Method. Then, using the recommended Chimp Optimization Algorithm (COA), a region-based convolutional neural network (R-CNN) classifier was trained using the acquired theoretical properties and the innovative image. A comparison of many deep learning and machine learning models' performances has been reported in a study. The findings imply that the deep learning model is capable of managing the problematic task of early oral malignant tumor detection. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

A Credible and Fair Federated Learning Framework Based on Blockchain

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第2期6卷 301-316页

作者： Chen, Leiming Zhao, Dehai Tao, Liping Wang, Kai Qiao, Sibo Zeng, Xingjie Tan, Chee Wei China University of Petroleum School of Computer Science and Technology East China Qingdao266580 China CSIRO's Data61 Sydney2015 Australia Hefei University of Technology Anhui230009 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore Tiangong University School of Software Tianjin300387 China Southwest Petroleum University School of Computer Science Chengdu610500 China

Federated learning (FL) enables cooperative computation between multiple participants while protecting user privacy. Currently, FL algorithms assume that all participants are trustworthy and their systems are secure. However, the following problems arise in real-world scenarios: 1) Malicious clients disrupt FL through model poisoning and data poisoning attacks. Although some research has proposed secure aggregation methods to solve this problem, most methods have limitations. 2) The current method cannot fairly evaluate client contribution in some scenarios. Some clients exhibit free-rider behavior, seeking to cheat the reward system and manipulate global models. Evaluating client contribution and distributing rewards also present challenges. To address these challenges, we design a trustworthy federated framework to ensure secure computing throughout the federated task process. First, we propose a method of detecting malicious models to guarantee secure model aggregation. Then, we propose a fair method of assessing contribution to identify client-side free-riding behavior. Finally, we implement a computation process based on blockchain and smart contracts to guarantee the trustworthiness and fairness of federated tasks. To validate the performance of our framework, we simulate different types of client attacks and contribution evaluation scenarios on several open-source datasets. The experiments show that our framework ensures the credibility of federated tasks and achieves a fair evaluation of client contributions. © 2020 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

引用

3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Analyzing Classroom Behavior to Enhance Student Engagement and Learning Outcomes 2

Analyzing Classroom Behavior to Enhance Student Engagement a...

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Suresh Kumar, S. Sivaprakasam, Anantha Joseph, R Edwin Sandeep, Atmakuru Siva Buvankalyan, P. Rajalakshmi Engineering College Department of Artificial Intelligence and Data Science Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Automobile Engineering Chennai India

ISBN: (纸本)9798331534950

This research presents a high-tech smart system that utilizes video records to monitor student behavior in classroom settings and identify specific activities like raising hands and dozing. The uneven frequency of certain behaviors along with variances in student posture, seating arrangements, and video quality make it extremely difficult to identify these behaviors in a live classroom. While some behaviors, like raising one's hand, may happen frequently and in a variety of ways, others, like napping, may happen less frequently but are just as crucial to comprehending student engagement. We use a sizable annotated dataset, in which bounding boxes are constructed around significant objects of interest in the video frames, to get over these difficulties. Our system's core is a convolutional neural network (CNN), which we have specially tuned for behavior identification. The CNN model is made to quickly evaluate the visual data and recognize characteristics and patterns connected to certain student behaviors in real time. We make sure the model can accurately identify actions like raising one's hand and falling asleep even when there are variations in lighting, seating configurations, and video quality by training it with a large dataset. Our model is specifically designed to achieve high accuracy in identifying minute cues that distinguish one movement from another, in contrast to conventional methods that might find it difficult to comprehend the intricacy of these actions. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

引用

computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Based Autonomous Optimization of Unattended Wireless Sensor Networks Through Multi-Cause Reinforcement Learning

引用

SN computer science 2025年第2期6卷 1-11页

作者： Chaudhary, Jitendra Kumar Dudeja, Deepak Verma, Neeraj Kumar Saini, Arun Kumar Kapila, Dhiraj Mohapatra, Smaranika School of Computing Graphic Era Hill University Bhimtal Campus Uttrakhand Bhimtal India Department of Computer Science and Engineering M.M. Engineering College Maharishi Markandeshwar (Deemed to Be University) Mullana Haryana Ambala India Department of Data Science and Engineering Manipal University Jaipur Jaipur India Department of IcfaiTech The ICFAI University Jaipur India Department of Computer Science & Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India

Self-configuration refers to a node's ability to dynamically adjust resource allocation based on changing network conditions, either autonomously or with minimal human input. Alongside this, self-optimization is aimed at maintaining optimal network performance despite fluctuating conditions. Protocols like MRL-SCSO, designed for Unattended Wireless Sensor Networks (UWSNs), ensure robust coverage, connectivity, and energy efficiency under varying loads. MRL-SCSO employs learning-based mechanisms, such as Reinforcing Multiple Agents, incorporating features like boundary construction, state scheduling, topology control, and data dissemination to enhance network management. When compared to the Collect Tree Protocol (CTP), MRL-SCSO exhibits faster packet transmission due to backbone nodes and traffic load prediction, reducing delays by 2–5 ms, which is a significant improvement over existing algorithms. Additionally, MRL-SCSO delivers a better packet delivery ratio, dropping only by 5–12% with shorter data transmission intervals, while CTP experiences a 20–40% drop when all nodes are active. Furthermore, MRL-SCSO improves throughput by 20–30 kb/s compared to CTP, demonstrating its superior ability to optimize network performance without sacrificing reliability. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： CTP MRL-SCSO PDR Reliability UWSN

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach for Heart Disease Classification using Cascaded XGBoost Algorithm 3

A Novel Approach for Heart Disease Classification using Casc...

引用

3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Rajitha, M. Megala, S. Santhana School of Computer Studies RVS College of Arts and Science Sulur Coimbatore India Artificial Intelligence and Data Science SNS College of Engineering Coimbatore India Department of Computer technology Hindustan College of Arts and Science Coimbatore India

ISBN: (纸本)9798331532420

Heart disease remains a leading cause of mortality worldwide. Accurate and timely diagnosis is crucial for effective treatment and prevention. This research proposes a novel approach using a cascaded XGBoost model to improve the accuracy and efficiency of heart disease diagnosis. With gradient boosting, the cascaded XGBoost model effectively handles complex interactions within the dataset, leading to superior performance compared to traditional machine learning techniques like logistic regression, support vector machines, and decision trees. The proposed model was evaluated on a standard heart disease dataset, demonstrating significant improvements in terms of accuracy, precision, and recall. Furthermore, the model's ability to identify key factors contributing to heart disease provides valuable insights for clinical decision-making. This research highlights the potential of machine learning techniques, particularly cascaded XGBoost, in advancing the field of heart disease diagnosis and improving patient outcomes. © 2024 IEEE.

关键词： Contrastive 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：