检索结果-内蒙古大学图书馆

2023 International Conference on Networking, Electrical engineering, computer science, and technology, IConNECT 2023

作者： Wanto, Anjar Yuhandri, Yuhandri Okfalisa, Okfalisa Universitas Putra Indonesia Yptk Padang Information Technology Faculty of Computer Science Padang Indonesia Universitas Putra Indonesia Yptk Padang Faculty of Computer Science Department of Information Technology Padang Indonesia Universitas Islam Negeri Sultan Syarif Faculty of Science and Technology Department Informatics Engineering Kasim Riau Indonesia

ISBN: (纸本)9798350331172

This study aims to optimize popular Convolutional Neural Network (CNN) models, such as ResNet50, InceptionV4, VGG19, MobileNetV1, MobileNetV2, MobileNetV3 Small, and MobileNetV3 Large, by utilizing the CLAHE (Contrast Limited Adaptive Histogram Equalization) parameter. This research focuses on the problem of classifying retinal fundus images, which have challenges in analyzing the structure and features of the picture. This study compared the performance of CNN models, which were optimized using the CLAHE method as image pre-processing. The fundus retinal image data used is a publicly available dataset. The optimization process is done by changing the parameters in the CNN model and applying the CLAHE technique to improve image quality before the classification process. The results showed that using the CLAHE parameter significantly contributed to improving the performance of the CNN model in classifying retinal fundus images. Some CNN models optimized with CLAHE produce better accuracy than the unoptimized models. There are also variations in performance between the different models, with some models providing better results in classifying retinal fundus images. This study provides new insights regarding using CLAHE parameters in optimizing the CNN model for retinal fundus image classification problems. The results of this research can be the basis for further development in medical image processing and pattern recognition for more accurate and practical diagnostic applications. © 2023 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

RPF-ELD: Regional Prior Fusion using Early and Late Distillation for Breast Cancer Recognition in Ultrasound Images

RPF-ELD: Regional Prior Fusion using Early and Late Distilla...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Haosen Zhang, Gengyuan Zhao, Yingnan Lai, Fang Cui, Wenwei Xue, Jiexiao Wang, Qihang Zhang, Hao Lin, Yi Beijing Jiaotong University School of Computer Science and Information Technology Beijing China Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Harbin Engineering University School of Computer Science and Technology Harbin China University of Denver Department of Computer Science Denver United States University of York Wentworth College York United Kingdom Institute of Heilongjiang The Second Surveying and Mapping Harbin China Southeast University School of Information Science and Engineering Nanjing China Harbin Institute of Technology Faculty of Computing Harbin China Harbin Medical University School of Interdisciplinary Medicine and Engineering Harbin China

ISBN: (纸本)9798350386226

Breast cancer is one of the main factors responsible for the deaths of women worldwide. Ultrasound imaging is a key method for early detection of breast cancer, which can help patients gain valuable treatment time and improve their chances of survival. The computer-aided system of breast cancer recognition has started to receive attention due to the lack of experienced sonographers. Presently, most breast cancer recognition methods typically suffer from uncertain locations and proportions of tumor regions in ultrasound images. In this paper, we propose a novel Regional Prior Fusion framework using Early and Late Distillation (RPF-ELD), inspired by the knowledge distillation of the teacher-student framework, for breast cancer recognition in ultrasound images. Firstly, to enhance the concentration of the tumor regions, a high-performing prior-fused model is trained as the teacher model using ultrasound images with the corresponding regional prior information. Next, a diagnostic model is trained as the student model under the prior-fused model distillation using early and late features to implicitly obtain the regional prior knowledge. Finally, the diagnostic model recognizes the categories of breast cancer from only ultrasound images using the experience from distilled prior knowledge. Two publicly released datasets are used to evaluate the proposed RPF-ELD framework. Experimental results demonstrate that the proposed RPF-ELD surpasses current state-of-the-art methods. © 2024 IEEE.

关键词： Ultrasonic imaging

来源：评论

学校读者我要写书评

暂无评论

Digital-Twin Enabled Time Ahead Resource Allocation for Integrated Fiber-Wireless Connected Vehicular Network

引用

Journal of Communications and information Networks 2024年第3期9卷 296-308页

作者： Akshita Gupta Saurabh Jaiswal Martin Maier Vivek Ashok Bohara Anand Srivastava Wirocomm Research Group Department of Electronics&Communication EngineeringIndraprastha Institute of Information Technology Delhi(IIITD)New Delhi 110020India Department of Computer Science&Applied Mathematics Indraprastha Institute of Information Technology Delhi(IIITD)New Delhi 110020India Optical Zeitgeist Laboratory Institute National de la Recherche ScientifiqueMontreal H5A 1K6Canada

The digital twin(DT)is envisaged as a catalyst for pioneering ecosystems of service provision within an immersive environment born from the convergence of virtual and physical ***,DT could enhance the performance of edge-intelligent connected vehicular networks by allocating network resources efficiently based on the key performance indicators(KPIs)of vehicular data ***,this work addresses the key challenge of computation and spectrum resource allocation for vehicular *** allocate the optimal resource allocation,we subdivided the problem into:traffic classification,collective learning,and resource allocation *** order to do so,this paper concentrates on two crucial vehicular applications:brake application and lane-change *** utilize a random forest model to collectively learn vehicular data traffic in the upcoming time ***,a time-ahead resource allocation algorithm is proposed to improve the quality of service(QoS)by intelligently offloading vehicular data traffic to a DT-based integrated fiber-wireless(Fi-Wi)connected vehicular *** evaluate the performance of the resource allocation strategy in terms of resources required by the network alongside the packet loss *** was observed that there was a 44.74%increase in cost as the total computation resources increased from F=50 to 100 GHz,whereas the PLR of the network decreased by 71.43%.

关键词： connected vehicles digital twin edgeintelligence fiber-wireless machine learning resource allocation

来源：评论

学校读者我要写书评

暂无评论

Data Poisoning: Issues, Challenges, and Needs 7

Data Poisoning: Issues, Challenges, and Needs

引用

7th IET Smart Cities Symposium, SCS 2023

作者： Aljanabi, Mohammad Omran, Alaa Hamza Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Mohammed, Sahar Yousif Ibrahim, Abdelhameed Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science &Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence. © The Institution of engineering & technology 2023.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Prediction of Cardiovascular Disease and Respiratory Disease: A Review

引用

SN computer science 2024年第1期5卷 196页

作者： Parashar, Gaurav Chaudhary, Alka Pandey, Dilkeshwar Amity Institute of Information Technology AMITY University Sector-125 Uttar Pradesh Noida India Department of Computer Science and Engineering KIET Group of Institutions Muradnagar Uttar Pradesh Ghaziabad India

Cardiovascular (CVD) and respiratory diseases (RD) have been in the active domain for machine learning (ML) researchers as these diseases significantly contribute to mortality in humans. Some studies suggest that CVD problems such as cerebrovascular problems, dysrhythmia, inflammatory heart disease, ischemic heart disease (IHD), and RD related problems remain high even after COVID-19 infection clears up. To the best of our knowledge, this is the only study that surveyed these two diseases. This paper’s goal is to explore the existing state of the art in the application of ML in the detection, categorization, and prediction of disorders related to CVD and RD. The review highlights ML algorithms used in prediction of CVD and RD related diseases, datasets used by the articles, technique used for feature selection, features selected for the study, dataset used in the article was unimodal or multimodal, and performance of the algorithm. In CVD category, it was observed that about 15 studies had their performance metrics range between 91% and 100%, 7 studies had between 81% and 90% and about 2 studies had their performance between 70% and 80%. CNN is the most used Feature Selection technique. Only three studies were found in our set that worked on the multimodal dataset and others used the unimodal dataset. In case of RDs, it was observed that about 15 studies had their performance metrics range between 91% and 100%, 7 studies had between 81% and 90% and about 2 studies had their performance between 70% and 80%. CNN is the most used feature selection technique. Only three studies were found in our set that worked on the multimodal dataset and others used the unimodal dataset. The intent of this review is to stimulate the interest of scientists in this challenging field and to acquaint them with current advances in the field. To design a system that predict CVD or RD in a patient using uni or multi modal datasets, approaches such as data cleaning, feature selection, region

关键词： Cardiovascular disease Heart disease prediction Lung disease prediction Machine learning Respiratory disease

来源：评论

学校读者我要写书评

暂无评论

MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning 39

MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning

引用

39th IEEE International Conference on Data engineering, ICDE 2023

作者： Zheng, Shangfei Wang, Weiqing Qu, Jianfeng Yin, Hongzhi Chen, Wei Zhao, Lei Soochow University School of Computer Science and Technology China Monash University Department of Data Science and Ai Australia The University of Queensland School of Information Technology and Electrical Engineering Australia

ISBN: (纸本)9798350322279

Multi-modal knowledge graphs (MKGs) include not only the relation triplets, but also related multi-modal auxiliary data (i.e., texts and images), which enhance the diversity of knowledge. However, the natural incompleteness has significantly hindered the applications of MKGs. To tackle the problem, existing studies employ the embedding-based reasoning models to infer the missing knowledge after fusing the multi-modal features. However, the reasoning performance of these methods is limited due to the following problems: (1) ineffective fusion of multi-modal auxiliary features;(2) lack of complex reasoning ability as well as inability to conduct the multi-hop reasoning which is able to infer more missing knowledge. To overcome these problems, we propose a novel model entitled MMKGR (Multi-hop Multi-modal Knowledge Graph Reasoning). Specifically, the model contains the following two components: (1) a unified gate-attention network which is designed to generate effective multi-modal complementary features through sufficient attention interaction and noise reduction;(2) a complementary feature-aware reinforcement learning method which is proposed to predict missing elements by performing the multi-hop reasoning process, based on the features obtained in component (1). The experimental results demonstrate that MMKGR outperforms the state-of-the-art approaches in the MKG reasoning task. © 2023 IEEE.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Prediction of Early Stage Breast Cancer by Injection of Gold Nano Particles and Analyzing Images using Data Analytics 2

Prediction of Early Stage Breast Cancer by Injection of Gold...

引用

2nd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2022

作者： Nelli, Sreevidya Kezia Rani, B. Sreenidhi Institute of Science and Technology Department of Information Technology Hyderabad India University College of Engineering Department of Computer Science and Engineering Rajamahendravaram India

ISBN: (纸本)9781665491112

According to recent studies, breast cancer is the most common cancer among women, and its prevalence is steadily increasing. An early diagnosis and treatment of this condition can greatly increase the chances of a cure. Further, Long-term mortality rates from breast cancer dramatically lowered by early cancer identification. Finding cancer cells in their early stages is the most important step for the best prognosis. Researchers have examined a variety of breast diagnostic techniques, including mammography, ultrasound, positron emission tomography, and biopsy. These methods have some drawbacks, though, such the fact that they are expensive, time-consuming, and unsuitable for young women. Therefore, it is vital to create a quick and sensitive early-stage breast cancer diagnosis tool. Recently, the rapid advancement of nanotechnology has increased research on nanoparticles, especially those that can be applied to the medical field. Gold nanoparticles (AuNPs) are continuously being studied and used as tools for further research resulting in ever-expanding knowledge that constantly challenges the frontiers of nanotechnology. An increasing interest is being manifested in the use of AuNPs to treat cancer, from diagnosis and monitoring through to treatment. Cancer is a multifactorial disease that requires multifaceted treatments, so these efforts aim to revolutionize current methods of treating it. In this review, the current applications of AuNPs in early breast cancer diagnosis and the importance of biosensors in predicting early cancer occurrence are discussed. © 2022 IEEE.

关键词： Gold nanoparticles

来源：评论

学校读者我要写书评

暂无评论

Parallel and Batch Multiple Replica Auditing Protocol for Edge Computing 21

Parallel and Batch Multiple Replica Auditing Protocol for Ed...

引用

21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Yi Zheng, Wenying Pandi, Vijayakumar Bhuiyan, Md Zakirul Alam Thamilarasi, C. Nanjing University of Information Science and Technology School of Computer Science Nanjing China Zhejiang Sci-Tech University School of Computer Science and Technology Hangzhou China University College of Engineering Tindivanam Anna University Chennai Department of Computer Science and Engineering Melpakkam India Fordham University Department of Computer and Information Sciences BronxNY United States P.S.V College of Enfgineering and Technoogy Department of Electronics and Communication Engineering Krishnagiri India

ISBN: (纸本)9798350329223

The edge computing architecture exposes data transmission and storage to unauthorized access, compromising data integrity. Users typically employ multiple data backups to ensure data reliability and availability, enhancing data redundancy and recovery capabilities. Nevertheless, verifying the integrity of multiple backup data under edge computing entails computational and communication overhead. In response, we present a parallel and batch multiple replica auditing protocol tailored for edge computing. Contributions include an efficient multiple replica construction method based on RSA, minimizing computational overhead, and a parallel data integrity verification approach that reduces communication overhead. A prototype implementation is evaluated in a real edge computing environment and compared to existing multiple replica auditing approaches. © 2023 IEEE.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Investigating and Implementing the Efficiency of Image Restoration Techniques in Digital Image Processing 3

Investigating and Implementing the Efficiency of Image Resto...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Acharjya, Kalyan Yuvaraj, S. Jadhav, Varsha D. Sidhu, Amritpal Sinha, Deepak Kumar Kaneria, Ojasvee Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350319125

This look at explores the efficiency of diverse photo recovery strategies, inclusive of noise reduction, histogram change, facet enhancement, polishing, and smoothing, in digital picture processing. An extensive evaluation of the techniques becomes conducted to assess their effectiveness in restoring degraded virtual pictures. Numerous objective metrics, together with Structural Similarity (SSIM) Index, peak sign-to-Noise Ratio (PSNR), and mean Squared blunders (MSE), have been used to quantitatively examine the first-rate of the restored photographs. Effects indicated that noise discount and area enhancement furnished the finest improvements within the snap shots. Histogram changes and sprucing also stepped forward the high-quality of the pictures, at the same time as the smoothing technique ended in a moderate lower in pleasant. This examines offers a detailed insight into the efficiency of various photo healing techniques and can serve as a reference for in addition investigations. © 2023 IEEE.

关键词： Investigations Smoothing Strategies Structural Techniques

来源：评论

学校读者我要写书评

暂无评论

Patients' Health Analysis using Machine Learning 5

Patients' Health Analysis using Machine Learning

引用

5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming Chopra, Shakti Raj National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan Lovely Professional University Department of Electronics and Communication Engineering Punjab Phagwara India

ISBN: (纸本)9781665475242

The main aim of this study was to analyze patient health using Machine Learning (ML). To do this, we used the Extreme Gradient Boost (XGBoost) classifier and auto-ML-Pycaret techniques. The sequential procedure we followed for the XGBoost model is data analysis, feature engineering, and model building, which are discussed in this paper. For these tasks, we used data science tools such as the Jupyter notebook and Google Colab (GC). Subsequently, we discuss the auto-ML-Pycaret model, which is an excellent tool for ML tasks. Finally, a performance comparison is performed between the two models based on their accuracy levels. The accuracy rate for the first ML model was 87%, and for the auto ML Pycaret model, we achieved 88% accuracy. Based on the accuracy percentages and time factor, we observed that the auto-ML Pycaret model performed better than the XGBoost model. © 2023 IEEE.

关键词： Machine 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：