检索结果-内蒙古大学图书馆

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Arivalagan, Divya Rubankumar, D. Thushiyanth, K. Suresh Kumar, S. Priyadharsini, V. Vignesh, O. Madras Institute of Technology Anna University Department of Electronics Engineering Chennai India QIS College of Engineering and Technology Department of Electronics and Communication Engineering Ongole India Akshaya College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350371314

The study focuses on developing and refining an advanced object detection model for integration into security systems. It begins with an exploration of challenges during early training epochs, aiming to enhance precision, recall, and overall performance. The training process involves meticulous data preprocessing, augmentation, and iterative optimization of hyperparameters to foster model convergence. Significantly, this project holds implications for security applications, offering an innovative approach to threat detection through advanced computer vision technologies. Notably, the study incorporates the usage of YOLOv3 and YOLOv8 models. The YOLOv8 model showed a mAP50 of 0.936 compared to YOLOv3, which had an mAP50 of 0.719, demonstrating superior performance with YOLOv8. Considerations for real-time processing, integration of temporal information, and collaboration with security professionals are vital for further advancements. © 2024 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

E-waste management in the digital era: A sustainable computing approach

引用

IEEE Potentials 2024年第2期43卷 30-36页

作者： Ramasamy, Rajkumar Gopi, Rahul Sanmugam Palanisamy, Rohini Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai600062 India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India

In the digital era, the proliferation of electronic devices has led to a surge in electronic waste (e-waste), posing significant environmental and health challenges. This article proposes a sustainable computing approach to e-waste management. The article explores the complexities of e-waste management and recycling frameworks within the context of sustainable computing and applications. It delves into the challenges posed by the rapid advancement of technology and the resultant surge in electronic waste. Strategies to mitigate e-waste are discussed, including approaches in product design. The article emphasizes collaborative efforts to address these challenges effectively. By adopting sustainable computing practices and leveraging innovative solutions, the aim is to create a more sustainable approach to managing electronic waste and its environmental impacts. © 1988-2012 IEEE.

关键词： electronic waste Recycling Green computing Metals Maintenance engineering Soil Regulation Water pollution Energy efficiency Resource management

来源：评论

学校读者我要写书评

暂无评论

Evolution and Role of Optimizers in Training Deep Learning Models

引用

IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2039-2042页

作者： XiaoHao Wen MengChu Zhou Guangxi Normal University Guilin 541004China School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China the Helen and John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA IEEE

TO perform well,deep learning(DL)models have to be trained *** optimizer should be adopted?We answer this question by discussing how optimizers have evolved from traditional methods like gradient descent to more advanced techniques to address challenges posed by highdimensional and non-convex problem *** challenges include their hyperparameter sensitivity,balancing between convergence and generalization performance,and improving interpretability of optimization *** continue to seek robust,efficient,and universally applicable optimizers to advance the field of DL across various domains.

关键词： Which Deep continue

来源：评论

学校读者我要写书评

暂无评论

Enhanced Magnetic Resonance Imaging for Accurate Classification of Benign and Malignant Brain Cells 5

Enhanced Magnetic Resonance Imaging for Accurate Classificat...

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Gowda, Dankan V. Kumar, Pullela S.V.V.S.R. Prasad, K.D.V. Ashreetha, B. Kumar, Mekala Bharath Karthikeya, Karanam Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Department of Computer Science & Engineering Aditya College of Engineering Andhra Pradesh Surampalem India Symbiosis Institute of Business Management Hyderabad India Pune India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India Department of Electronics and Communication Engineering Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India

ISBN: (纸本)9798350361155

The difficulty in differentiating between the normal and cancerous cells in the brain through the frequent magnetic resonance imaging approaches is one of the major obstacles to realization of diagnostic precision. The presented work reveals a new MRI image processing technology, which includes an original software that contains complex algorithms and trained machine learning models, as the programs that make the images much better than before. Carefully calibrated computer vision dataset which features brain scans is subjected to the well-defined novel approach, whose performance is compared with the traditional MRI methods by calculating multiple metrics such as classification accuracy, sensitivity, specificity, ROC are and so on. This paper is the climax of a deep cognition of various cell types revealed by the newest MRI method which presents a better contrast than the old techniques. Next, the quality in the detection and recognition of the tumour after, comparison of these modalities displays that the modality of higher resolution, the ability to detect the tumour earlier and better. Such technological improvements in MRI machines will enable the surgeons to identify the growth of tumors at the early stages that will lay the right groundwork for the design of personalized treatment plans and also will have positive impact on the lives of the patients. Through this effect, the level of quality followed by MR imaging has been improved as well as arising new alliances between major imaging companies and machine learning technologies. It can be thought as the border - eraser diagnostics and imaging which will obey medical laws. Therefore, it states that the present hypothetical world should be improved while the advanced and proven diagnostics systems should be developed. The daily clinical applications of these advanced MRIs may well be the beginning of a new era in diagnostic oncology, which will be a very important way forward in improving treatment combined wi

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Integrating Decision Trees with Cloud Computing for Predicting Cognitive Decline in Alzheimer's Patients 2

Integrating Decision Trees with Cloud Computing for Predicti...

引用

2nd IEEE International Conference on Integrated Circuits and communication Systems, ICICACS 2024

作者： Meenakshi, K. Ravinder, Bathini Pasha, Mohammed Rehaman Thinakaran, K. Jayanayudu, Dabbara Maniraj, S.P. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Vadapalani India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India Siddartha Institute of Science and Technology Department of Electronics and Communication Engineering Andhra Pradesh Puttur India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350317558

The proposed research work suggests a combined technique based on decision trees and cloud computing for predicting cognitive decline in Alzheimer's patients. Medical records and data from wearable devices are among the many sources that perform preprocessing and feature engineering. Decision tree models can make the most efficient use of computing resources. Decision tree topologies are tested for performance criteria, including accuracy and F1 score. The system's predictive powers are rather encouraging since it frequently achieves accuracy rates over 90%. However, limitations in data quality and the intricacy of decision tree interpretation provide challenges. The prediction of cognitive deterioration in Alzheimer's patients may be made more scalable and interpretable by integrating decision trees with cloud computing. This method has promise for early identification and intervention despite its limitations. To further improve prediction accuracy and clinical value, future research should increase data quality and make it easier to understand. © 2024 IEEE.

关键词： Alzheimer's disease Cloud computing Decision trees Early detection Healthcare

来源：评论

学校读者我要写书评

暂无评论

A sparse attack method on skeleton-based human action recognition for intelligent metaverse application

引用

Future Generation computer Systems 2023年第1期143卷 51-60页

作者： Dai, Cheng Huang, Yinqin Chien, Wei-Che College of Computer Science Sichuan University Sichuan Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Department of Computer Science and Information Engineering National Dong Hwa University Hualien City Taiwan

Graph convolutional networks (GCNs) based skeleton human action recognition has been one of the hottest techniques in intelligent Metaverse system applications in recent years. However, the robustness of GCN model based skeleton action recognition, has not been widely discussed due to the complex spatio-temporal nature. Base on solving this problem, we propose a sparse attack method with dynamic attention which can generate adversarial samples with lower deviation under the condition of keeping attack success rate in this paper. Firstly, we develop spatial temporal consistency loss which can not only preserve spatial integrity between reference sample and adversarial sample but also keep temporal coherence between consecutive frames across adversarial sample. Then, we develop an interaction-based perturbation contribution analyze method to discard unnecessary perturbations. Besides that, we design a dynamic attention approach to tilt the perturbations towards some joints that have higher dynamics which can decrease overall deviations. Finally, we conduct extensive experiments to evaluate our proposal and the experimental results show that our method contributes to discover potential causes of model fragility and provides material in adversarial training which will increase the robustness of GCN based skeleton human action recognition models. © 2022

关键词： Musculoskeletal system

来源：评论

学校读者我要写书评

暂无评论

Improved Deep Matrix Factorization for Signal Recovery in Random Frequency Diversity Array Radar 2

Improved Deep Matrix Factorization for Signal Recovery in Ra...

引用

2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Guo, Dayan Lin, Zijian Huang, Tianyao Qi, Yue He, Jie Zhang, Xiao-Ping Wang, Xueqian Li, Gang University of Science and Technology Beijing School of Computer & Communication Engineering Beijing China Tsinghua Shenzhen International Graduate School Institute of Data and Information Shenzhen China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798331515669

Motivated by the success of recent deep learning researches on radar, we consider a deep learning based matrix factorization method for suppressing the high range-angle side-lobes in random frequency diversity array (FDA) radars. While the existing deep matrix factorization approach is designed for real-valued situations and signals of FDA radars are complex-valued cases, we propose two methods to extend the existing approach into complex-valued framework: 1) adjusting the neural network structures and activation functions for complex-valued operations, and 2) constructing a real-valued representation of the original complex-valued input. Simulation results demonstrate that the proposed methods outperform traditional ones in multi-target scenarios. © 2024 IEEE.

关键词： Matrix factorization

来源：评论

学校读者我要写书评

暂无评论

IoT based Smart ID Card for Working Woman Safety 7

IoT based Smart ID Card for Working Woman Safety

引用

7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Gowrishankar, V. Prabhakaran, G. Tamilselvan, K.S. Judgi, T. Parimala Devi, M. Murugesan, A. Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode India Nandha Engineering College Department of Electronics and Communication Engineering Erode India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering India K S R Institute for Engineering and Technology Department of Electrical and Electronics Engineering Tiruchengode India

ISBN: (纸本)9798350397253

Working women must be protected from assault in many parts of the world. A smart ID card based on the Internet of Things (IoT) could make female workers safer and be one solution to this problem. Because of the built-in sensors and connectivity components, this ID card will be able to sense and respond to its surroundings. If the sensors detect something unusual, such as a sudden movement or a loud noise, they will send a signal to the system. Following the completion of the data analysis, the system takes any necessary actions, such as sending alerts to security personnel or notifying on-call personnel. The proposed system will provide the user with access to an emergency panic button. The system's GPS component will track the user's location and send alerts to the appropriate parties. There will also be a cloud-based monitoring system with the proposed approach. This allows the employer or loved ones of the worker to track the worker's whereabouts and activities in real time. The system will analyze the data using machine learning techniques, and the results will reveal the worker's behavior and level of safety. A smart ID card linked to the Internet of Things (IoT) could help women work in safer environments. The proposed technology would enable real-time monitoring, predictive alarms, and data analysis. All of these precautions will help ensure female employees' safety and avoid unpleasant situations. © 2023 IEEE.

关键词： Personnel

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Diagnosis System for Liver Cancer Through Multimodal Determination for Early Treatment

Deep Learning-Based Diagnosis System for Liver Cancer Throug...

引用

2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Fatima, N. Sabiyath Singh, Ramendra Swathi, Akuthota Choudhary, Amar Ahmed, Azhar Sharma, Chhaya B.S. Abdur Rahman Crescent Institute of Science & Technology Department of Computer Science and Engineering Chennai India Ghaziabad India Cvr College of Engineering Department of Computer Science and Engineering Mangalpalli Rangareddy India Alliance University Department Electronics and Communication Engineering Bengaluru India Kcc Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350379716

The present research provides an analysis of the development and implementation of a deep learning-based diagnosis system for liver cancer. The provided system was developed using a dataset encompassing 2300 sensor readings, including imaging, clinical, and genomic data. A range of deep learning features was implemented to improve the early detection of liver cancer such as CNN, VGG 16, VGG 19, and Inception V3. The data were divided into two categories: one is for training, and another used for testing. The results of the data analysis have shown that VGG 19 provides the highest diagnostic accuracy of 98.76%, with VGG 16, Inception V3 and CNN displaying the levels of 94.5%, 93.4%, and 90.25%, respectively. The Model performance analysis was also augmented at the application of precision, recall, and F1 score indicators, as well as using AUC-ROC values which highlighted VGG 19 as the most effective algorithm. Another method of evaluation was the application of confusion matrices for plotting the results based on the number of true positive, true negative, false positive, and false negative detections, where all the samples from the dataset were added to each model calculation. The obtained results have shown the importance of multimodal data for the increase of diagnosis accuracy and revealed the promising potential of advanced computation techniques in medical analytics. In such a way, it can be stated that the developed deep learning models contribute to the ability of healthcare specialists in the definition of liver cancer at early stages, which allows timely interventions and facilitates the development of a range of personalized treatment protocols. The application of such algorithms has the potential to reshape liver cancer diagnostics, where major tasks are associated with further improvement and validation of the developed algorithms based on clinical case studies. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Enhanced Archimedes Optimization Algorithm for Clustered Wireless Sensor Networks

引用

computers, Materials & Continua 2022年第10期73卷 477-492页

作者： E.Laxmi Lydia T.M.Nithya K.Vijayalakshmi Jeya Prakash Kadambaajan Gyanendra Prasad Joshi Sung Won Kim Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Andhra Pradesh530049India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Electronics&Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600025India Department of Electronics&Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

Wireless sensor networks(WSN)encompass a set of inexpensive and battery powered sensor nodes,commonly employed for data gathering and tracking *** energy utilization of the nodes in WSN is essential to capture data effectively and transmit them to *** latest developments of energy efficient clustering techniques can be widely applied to accomplish energy efficiency in the *** this aspect,this paper presents an enhanced Archimedes optimization based cluster head selection(EAOA-CHS)approach for *** goal of the EAOA-CHS method is to optimally choose the CHs from the available nodes in WSN and then organize the nodes into a set of ***,the EAOA is derived by the incorporation of the chaotic map and pseudo-random ***,the EAOA-CHS technique determines a fitness function involving total energy consumption and lifetime of *** design of EAOA for CH election in the WSN depicts the novelty of *** order to exhibit the enhanced efficiency of EAOA-CHS technique,a set of simulations are applied on 3 distinct conditions dependent upon the place of base station(BS).The simulation results pointed out the better outcomes of the EAOA-CHS technique over the recent methods under all scenarios.

关键词： Wireless sensor network CH selection energy efficiency clustering lifetime

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：