检索结果-内蒙古大学图书馆

11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021

作者： Kho, Sin Hui Mashohor, Syamsiah Hanafi, Marsyita Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9789811681288

High prevalence in diabetes contributed to the rising of the complications such as diabetic retinopathy (DR) which cause the patient to suffer from vision loss and this vision loss may become permanent if it is not well manage and treat. Including other medication non-adherence factor, this rising trend has been made more challenging for physician in keep apace with demand using manual method of retina screening to diagnose DR. Therefore, this project aimed to develop a classifier of deep learning for DR in 5 different level of severity which are normal (no DR), mild, moderate, severe and proliferative DR, to speed up the clinical assessment for early DR detection or DR monitoring progression of disease. The classification was applied using Scottish grading with consider three categories for fundus abnormalities which are hemorrhage, lipid exudates and microaneurysms for a training from scratch model and a pre-trained model using Inception V3. Verified and trustable dataset of retina screening or fundus image available online such as *** used to train and test of the system. Besides, the execution of this project was completed via on cloud training using Google Cloud Platform (GCP). The obtained results are measuring the classifier performance tested in term of accuracy, sensitivity, and specificity. Transfer learning using Inception V3 has been shown a better performance compare to own training model with accuracy 81.2% using transfer learning Inception V3 and 74.7% for own training model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Grading

来源：评论

学校读者我要写书评

暂无评论

Alternating Constrained Minimization Based Approximate Message Passing 48

Alternating Constrained Minimization Based Approximate Messa...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Thomas, Christo Kurisummoottil Slock, Dirk Virginia Tech Wireless@VT Bradley Department of Electrical and Computer Engineering ArlingtonVA United States Eurecom Communication Systems Department France

ISBN: (纸本)9781728163277

Generalized Approximate Message Passing (GAMP) allows for Bayesian inference in linear models with non-identically independently distributed (n.i.i.d.) priors and n.i.i.d. measurements of the linear mixture outputs. It represents an efficient technique for approximate inference, which becomes accurate when both rows and columns of the measurement matrix can be treated as sets of independent vectors and both dimensions become large. It has been shown that the fixed points of GAMP correspond to the extrema of a large system limit of the Bethe Free Energy (LSL-BFE), which represents a meaningful approximation optimization criterion regardless of whether the measurement matrix exhibits the independence properties. However, the convergence of (G)AMP can be notoriously problematic for certain measurement matrices, and the only sure fixes so far are damping (by a difficult-to-determine amount) or perform a double ADMM. In this paper, we revisit the GAMP algorithm (as e.g. for sparse Bayesian learning (SBL)) by more rigorously applying an alternating constrained minimization strategy to an appropriately reparameterized LSL BFE. This guarantees convergence, at least to a local optimum. We furthermore introduce a natural extension of the BFE to integrate the estimation of (the SBL) hyperparameters via Variational Bayes, leading to Variational AMBGAMP or VAM-BGAMP. © 2023 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Secure-JobChain: Revolutionizing Job Recruitment with Blockchain Technology

Secure-JobChain: Revolutionizing Job Recruitment with Blockc...

引用

2024 IEEE International Conference on Intelligent systems and Advanced Applications, ICISAA 2024

作者： Pavithra, S. Geethanjali, S. Vidya, B. Suria, S. Rahul, B.S. Kishore, R. Computer Science and Engineering Rajiv Gandhi College of Engineering and Technology Pondicherry India SRM Institute of Science and Technology Data Science and Business Systems Kattankulathur India SRM Institute of Science and Technology Electronics and Communication Engineering Kattankulathur India

ISBN: (纸本)9798331539948

The traditional job recruitment process is time consuming and also undergoes certain difficulties such as data privacy, lack of transparency, and inefficient credential verification. These difficulties lead to mistrust and also delay the process of hiring. There is a need for improvement in this process that could benefit both the companies and the job applicants. In this paper, a decentralized recruitment platform based on blockchain technology is proposed. This proposed technique will overcome the above-mentioned difficulties by ensuring secure, transparent, and efficient connections between the recruitment companies and the prospective candidates. This solution uses the merits of blockchain i.e., immutability and automation to record job postings, applications, and hiring decisions on an immutable ledger, protecting personal data with cryptographic techniques and automating qualification verification with smart contracts. By implementing the decentralization concepts in Job recruitment, it is proven that the transparency in the interview process is improved by 9 0 and fraud risks are reduced considerably. Moreover, the efficiency of the entire job recruitment process is improved largely. Thus, the decentralized solution proposed in this paper demonstrates its potential in revolutionizing the job recruitment process by providing a secure, transparent and efficient hiring ecosystem. © 2024 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

ECG Signal Feature Extraction and SVM Classifier Based Cardiac Arrhythmia Detection 2

ECG Signal Feature Extraction and SVM Classifier Based Cardi...

引用

2nd International Conference on Electrical, Electronics, Information and communication Technologies, ICEEICT 2023

作者： Venkatesan, C. Thamaraimanalan, T. Ramkumar, M. Sivaramakrishnan, A. Marimuthu, M. HKBK College of Engineering Department of Electronics and Communication Engineering Bangalore India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Knowledge Institute of Technology Department of Computer Science and Business Systems Salem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India Sona College of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350397635

In a variety of medical applications, electrocardiogram (ECG) records are essential for the identification of cardiovascular disorders. Cardiovascular issues such cardiac arrhythmias and coronary heart disease (CHD) are among the most prevalent and can result in cardiac arrest or sudden cardiac death. This study employs ECG signal preprocessing and feature extraction to identify cardiac arrhythmias and evaluate CHD risk. This research stresses the use of a Support Vector Machine (SVM) classifier for cardiac arrhythmia identification after ECG signal preprocessing. The preprocessed ECG signal is then subjected to arrhythmic beat classification to find anomalies. Extracted R-peaks from the ECG signal are divided into normal and arrhythmic risk subjects using the SVM classification-based approach for abnormality identification. When compared to other similar classifiers, the K-Nearest Neighbor (KNN) classifier offers the highest classification accuracy of 97.5%. © 2023 IEEE.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

Optimal Resource Allocation for NOMA Wireless Networks

引用

computers, Materials & Continua 2023年第2期74卷 3249-3261页

作者： Fahad R.Albogamy M.A.Aiyashi Fazirul Hisyam Hashim Imran Khan Bong Jun Choi Turabah University College Computer Sciences ProgramTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Jazan UniversityJazanP.O.Box 218Saudi Arabia Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra Malaysia(UPM)43400SerdangMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarP.O.Box 814Pakistan School of Computer Science and Engineering Soongsil UniversitySeoulKorea

The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user ***-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user *** its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power *** contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum ***,it will increase the complexity of its *** power allocation techniques will have a direct impact on the system’s *** a result,in order to boost the system capacity,an efficient power allocation mechanism must be *** research developed an efficient technique based on conjugate gradient to solve the problem of downlink power *** major goal is to maximize the users’maximum weighted sum *** suggested algorithm’s most notable feature is that it converges to the global optimal *** compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.

关键词： Non-orthogonalmultiple access wireless networks power domain 5G networks

来源：评论

学校读者我要写书评

暂无评论

Investigating Routing Protocol Attacks on Low Power and Lossy IoT Networks

引用

SN computer Science 2024年第4期5卷 393页

作者： Kiran, Usha Maurya, Poonam Sharma, Himanshu Department of Computer Science Indian Institute of Technology Bhilai Bhilai India Department of Electronics Systems Aalborg University Aalborg Denmark Department of Electronics and Communication Noida Institute of Engineering and Technology Greater Noida India

Internet-of-things (IoT) networks are distinguished by nodes with limited computational power and storage capacity, making Low Power and Lossy Networks (LLNs) protocols essential for effective communication in resource-constrained environments. One such protocol is the Routing Protocol for Low-Power and Lossy networks (RPL), which establishes and manages routes in RPL-based networks. RPL contributes to optimized routing and reduced network overhead in LLNs. However, the RPL-based protocol is susceptible to various internal and external vulnerabilities that require thorough exploration and mitigation. Experimental results illustrate the impact of several RPL attacks, including the DODAG Information Solicitation (DIS) attack, version number attack, decreased rank attack, and worst parent selection (WPS) attack. For simulation purposes, we employed the Contiki Cooja network simulator. Further, we conduct a comparative analysis of these RPL-based attacks, revealing that the WPS attack has a significant impact on the network performance compared to other attacks mentioned in the paper. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Decreased rank attack DIS attack Intrusion detection system RPL attacks Version number attack WPS attack

来源：评论

学校读者我要写书评

暂无评论

Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm

引用

computers, Materials & Continua 2025年第3期82卷 4339-4369页

作者： Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Hussain Alsalman Taha Alfakih School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeedsLS13HEUK Department of Computer Science and Engineering Chennai Institute of TechnologyChennai602109India Centre for Research Impact & Outcome Chitkara UniversityChandigarh140401India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Self-Development Skill Common First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.

关键词： Machine learning power systems security smart grid

来源：评论

学校读者我要写书评

暂无评论

Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

引用

computer systems Science & engineering 2022年第10期43卷 275-289页

作者： V.Ulagamuthalvi G.Kulanthaivel A.Balasundaram Arun Kumar Sivaraman Department of Computer Science and Engineering Sathyabama UniversityChennai600119India Department of Electrical Electronics and Communication EngineeringNITTTRChennai600113India Centre for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India

One of the fast-growing disease affecting women’s health seriously is breast *** is highly essential to identify and detect breast cancer in the earlier *** paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer *** learning algorithms are fully automatic in learning,extracting,and classifying the features and are highly suitable for any image,from natural to medical *** methods focused on using various conventional and machine learning methods for processing natural and medical *** is inadequate for the image where the coarse structure matters *** of the input images are downscaled,where it is impossible to fetch all the hidden details to reach accuracy in *** deep learning algorithms are high efficiency,fully automatic,have more learning capability using more hidden layers,fetch as much as possible hidden information from the input images,and provide an accurate *** this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram *** performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.

关键词： Medical image processing deep learning convolution neural network breast cancer feature extraction classification

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Diagnosis Using Elephant Herding Optimization and Sparse Autoencoder Through Gene Expression Analysis 15th

Breast Cancer Diagnosis Using Elephant Herding Optimization ...

引用

15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Bharanidharan, N. Chakravarthy, S. R. Sannasi Kumar, V. Vinoth Kavya, B. Meghana, G. Rajaguru, Harikumar School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering Dayananda Sagar University Bangalore India Department of Computer Science and Engineering PES University RR Campus Bangalore India

ISBN: (纸本)9783031810794

Gene expression analysis is an efficient modality to detect breast cancer. But analysis of the gene expression data is a complicated task due to the involvement of large number of numerical data for each subject. This research work focuses on the development of computerized diagnosis systems for analyzing the gene expression data and classifying the subjects as either normal or breast adenocarcinoma. Sparse autoencoder is used for dimensionality reduction and Elephant Herding Optimization is used for transforming the features. Four different supervised classifiers namely support vector machine, random forest classifier, decision trees, and naive bayes classifier are examined for classification task. Among them, random forest provides the superior balanced accuracy of 86% while it was further enhanced to 92% with the help of sparse autoencoders and elephant herding optimization algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Transcription

来源：评论

学校读者我要写书评

暂无评论

Selective feature-based ovarian cancer prediction using MobileNet and explainable AI to manage women healthcare

引用

Multimedia Tools and Applications 2024年 1-22页

作者： Almujally, Nouf Abdullah Alzahrani, Abdulrahman Hakeem, Abeer M. Attiah, Afraa Umer, Muhammad Alsubai, Shtwai Polsinelli, Matteo Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Ovarian cancer is a global health concern due to the unavailability of an effective screening strategy and is often diagnosed at a late stage with approximately 70% of the case which reduces the survival chances of patients. Initial diagnosis is challenging due to inconspicuous symptoms in its initial stages which complicate its timely diagnosis. Regular screenings, such as pelvic exams, ultrasounds, and blood tests targeting specific biomarkers can be helpful for early diagnosis. In addition, the use of machine learning models can help automate this process thereby assisting medical experts for accurate diagnosis. This study aims at timely and accurate detection of ovarian cancer using a transfer learning approach that uses the MobileNet model. Moreover, the Chi-square technique is used to extract the most impactful features for better accuracy. For experiments, the Soochow University ovarian cancer dataset is employed. Extensive experiments are performed using all features from the dataset, as well as, using Chi-square-based selective features. The best accuracy of 90.88% is achieved with the Xception model when all features are used. Experimental results show a substantial increase when selective features are used indicating a 98.49% accuracy using the MobileNet model with 20 most important features. In addition, precision, recall, and F1 scores of 99.13%, 99.27%, and 99.20% are obtained showing the model’s robustness and generalization. This study also employs Shapley additive explanations to explain the importance of various features toward the model’s output thereby providing transparency for the model’s decision-making process. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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：