检索结果-内蒙古大学图书馆

13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Trivedi, Vijay Kumar Suman, Preetam Sheikh, Javed Khan Maurya, Jay Prakash Pateria, Nikhil School of Computing Science and Engineering Vit Bhopal University Bhopal India School of Computing Science Engineering and Artificial Intelligence Vit Bhopal University Bhopal India

ISBN: (纸本)9798350380583

The aim of the research is to utilize deep learning techniques to support radiologists in enhancing the effectiveness and precision of breast cancer diagnosis. This paper employs a deep neural network (DNN) classifier approach to accurately categorize breast cancer in the MIAS dataset. Firstly, we start by pre-processing the mammography images to remove the digitization noise using a Wiener filter. Afterwards, the k-mean clustering approach is used to identify the Region of Interest (ROI), from which 24 features are extracted using the color and texture feature technique. The next phase based on training a Deep Neural Network (DNN) classifier using the significant features extracted from the training set. The test set is classified the breast cancer tis-sue by using the trained DNN classifier. We used the MAIS dataset for experimental purposes. The experimental results indicate that the average classification accuracy of the proposed deep neural network (DNN) classifier is 94%. The system achieved better performance in comparison to other classifiers, achieving an accuracy of 0.94, recall of 0.87, specificity of 0.94, precision of 0.90, and F-measure of 0.88. After analyzing these measures, it is clear that the proposed DNN classifier performs better than current state-of-the-art methods. © 2024 IEEE.

关键词： Wiener filtering

来源：评论

学校读者我要写书评

暂无评论

A novel feature selection approach with integrated feature sensitivity and feature correlation for improved prediction of heart disease

引用

Journal of Ambient Intelligence and Humanized computing 2023年第9期14卷 12005-12019页

作者： Saranya, G. Pravin, A. Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai600119 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai603203 India

This paper presents a random forest-feature sensitivity and feature correlation (RF-FSFC) technique for enhanced heart disease prediction. The proposed methodology is implemented using the Cleveland heart disease dataset which comprises a total of 120 heart disease patient records. Data imputation was utilized for missing values, and min–max normalization was utilized for data transformation. We attempted to construct an RF-based classifier for coronary heart disease in this paper by combining feature sensitivity and correlation analysis. The sensitivity-based feature selection process ranks features according to their value in assessing CHD risk, and the feature correlation analysis phase analyses if there are any correlations between features. The heart disease prediction accuracy of 81.16% was obtained using the proposed RF-FSFCA technique by omitting five features (sex, hemoglobin, TD, CRF, and cirrhosis). When compared to the Naïve Bayes, decision tree, regression analysis, and support vector machine models, the proposed model offered a higher accuracy of 86.141% without omitting any features. It also offered sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) scores of 87.321%, 87.364%, 91.23, and 91.02 respectively. Experiment findings demonstrated that the proposed RF-FSFC approach significantly improves prediction accuracy as compared to other approaches that do not use the integrated Feature selection method. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

Color Image Restoration via Quaternion-based Hybrid Regularization Method

引用

IAENG International Journal of Applied Mathematics 2024年第11期54卷 2176-2182页

作者： Ma, Mingxi The College of Science Key Laboratory of Engineering Mathematics and Advanced Computing Nanchang Institute of Technology Jiangxi Nanchang330099 China

Color image restoration is a crucial task in imaging science. Many existing methods treat color channels independently, disregarding inherent color structures within the channels. To address this, pure quaternion matrices are used to depict color images. Note that adaptive total variation (ATV) regularization has the advantage of better preserving local features of images than well-known total variation regularization during the restoration of grayscale images. Inspired by this strategy, we propose a novel quaternion-based hybrid regularization model for restoring color images. This model combines the advantages of the ATV and the weighted nuclear norm. Additionally, we design an alternating direction method of multipliers to solve the proposed model efficiently. The experimental results show that the proposed method outperforms several state-of-the-art approaches in both numerical and visual aspects. © (2024), (International Association of Engineers). All rights reserved.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Optimised EfficientNet for multi-class skin cancer diagnosis: integrating chaotic grey wolf algorithms

引用

Current science 2025年第7期128卷 728-736页

作者： Rama, P. Angayarkanni, V. Theresa, W. Gracy Pandiaraj, A. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603 203 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai600 123 India

The detection of skin cancer holds paramount importance worldwide due to its impact on global health. While deep convolutional neural networks (DCNNs) have shown potential in this domain, current approaches often struggle with fine-grained variability in skin lesion features, imbalanced datasets and inadequate augmentation. The objectives of our study encompass building a model that not only enhances accuracy but also reduces training time and cost, improves dimensionality reduction during classification and segmentation, and is evaluated on larger datasets for robustness. We enhance the learning process using an improved nature-inspired optimisation algorithm tailored specifically for skin cancer classification tasks. This algorithm optimises model parameters to maximise classification accuracy while minimising computational overhead. Our proposed architecture leverages a gold standard dermatological image dataset meticulously curated and validated by experts. This dataset spans eight distinct classes, including actinic keratosis, basal cell carcinoma, melanoma, nevus, and others, ensuring a diverse representation of skin conditions. The balanced class distribution in the training and test sets enables fair evaluation and robustness assessment of our deep learning models. The developed algorithm showcases novelty in its approach, contributing to advancements in early skin cancer detection technologies. This study introduces an optimised deep learning model designed for the early detection of skin cancer, achieving a notable accuracy of 97.48%. Our study aims to contribute significantly to the medical field by providing a reliable and efficient tool for dermatologists and healthcare practitioners to assist in diagnosing skin conditions accurately and promptly, ultimately improving patient outcomes and healthcare efficiency. © (2025), (Indian Academy of sciences). All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Amazon Web Services Cloud Compliance Automation with Open Policy Agent 4

Amazon Web Services Cloud Compliance Automation with Open Po...

引用

4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Paul, Alen Manoj, Rishi Udhayakumar, S. Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350385793

The security challenges posed by Infrastructure as code (IaC) are outgrowing established security procedures in an era of rapidly adopting cloud computing and DevOps methodologies. Using security principles to be integrated into the CI/CD pipeline for continuous validation and remediation, this research work proposes a DevSecOps approach to enable cloud environment IaC security. The proposed architecture evaluates CloudFormation Template file to the specified security policy by utilizing Open Policy Agent (OPA). Any violations are flagged by OPA, which may force the deployment process to stop. By incorporating security and compliance considerations into the development pipeline, this method guarantees that vulnerabilities are kept out of production systems. Organizations can improve the overall security posture of their cloud systems by proactively identifying and remediating security problems by implementing a DevSecOps approach. By ensuring that IaC deployments adhere to specified security policies based on the compliance requirements, OPA's continuous security validation reduces the possibility of misconfigurations and security flaws. © 2024 IEEE.

关键词： Cloud security

来源：评论

学校读者我要写书评

暂无评论

A review on network function virtualisation security challenges and design considerations

引用

International Journal of Networking and Virtual Organisations 2024年第4期31卷 368-393页

作者： Ahmad, Naveed Rathore, Muhammad Siraj Ngadi, Md Asri bin Faculty of Engineering University Teknologi Malaysia 81110 Malaysia Faculty of Computing Department of Computer Science Capital University of Science and Technology Islamabad46000 Pakistan

Network function virtualisation (NFV) offers several benefits to both network operators and end users. It is a more programmable and low-cost solution as compared to a traditional network. Since the network functions are implemented in software, there is a great opportunity of extending a network according to the changing requirements of the customers. However, at the same time, there are many security threats since the same commodity hardware is shared among multiple virtualised network functions. In literature, various NFV security threats are reported. In this work, our aim is to consolidate them in a more structured and organised manners. We highlight the different building blocks of a NFV framework and then explore how a particular component may become the victim of an attacker. Finally, we summarise our lesson learned and present open research issues which might be useful for NFV practitioners and researchers. © 2024 Inderscience Enterprises Ltd.

关键词： Network function virtualization

来源：评论

学校读者我要写书评

暂无评论

Emotion detection: Facial emotion detection using ML 4th

Emotion detection: Facial emotion detection using ML

引用

4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Sharma, Sharad Chauhan, Ritik Vajpayee, Pooja School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781032911571

Cognitive perception of images is an intense task, like guessing the truth of a thought or a mystery. In this process, we use different methods to solve the need to know the job. In recent years, emotional intelligence has received widespread attention due to its important role in improving human-computer interaction and emotional analysis. Because of its processes for extracting visual features, deep neural networks-particularly convolutional neural network (CNN)-are frequently employed in FER. A lot of research has been done on CNN, but only a few areas have solved the facial recognition (FER) problem. After many reports and studies, the shortcoming of most existing models is that they often consider the front profile and ignore the side profile of the current image, although different angular views are also important for the perspective model. This helps you make informed decisions about personal needs, providing support, or security threats. However, this proved to be too complex for computers, and feature extraction needed a wide range of image processing methods. Numerous machine learning techniques are appropriate for this particular purpose. To use machine learning for any kind of detection, an algorithm must first be trained and then tested on relevant data. This article examines some machine learning algorithms and strategies for extraction that aid in the analysis of human emotion. This review contains a comprehensive review of a variety of materials, methods, and measures commonly used in psychology research. It also highlights the challenges and limitations of current models, including dealing with uncertainties and cultural differences. Face recognition in large datasets poses additional challenges due to the diversity of images as well as the differences between side and front views. © 2025 the Author(s).

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Advanced Congestion Management System 1

Advanced Congestion Management System

引用

1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Rajakumar, P. Baliyan, Akshay Chaudhary, Deepanshi Panwar, Sachin Kumari, Richa Galgotias University School of Computing Science & Engineering UP India

ISBN: (纸本)9798350356816

Congestion is poised to become a significant problem in numerous Asian cities and other countries. The causes of traffic congestion include signal malfunctions, inadequate law enforcement, and risky traffic management. The limited expansion possibilities of existing infrastructure in Indian cities present a major challenge, making effective traffic management the only viable option. The negative impact of congestion on the economy, environment, and overall quality of life necessitates prompt action. Various traffic management technologies are available, such as video investigation, infrared, inducible loop detection, and sensor networks, all of which contribute to efficient traffic management. Nevertheless, these systems are costly to install and maintain and take considerable time to implement. In this regard, a cost-effective alternative technology called radio frequency identification (RFID) is introduced, offering real-time traffic control capabilities comparable to the current signal system. Compared to other congestion management methods, this innovative technology requires less time and cost for deployment. By identifying bottlenecks early on, this groundbreaking technology enables proactive measures, saving drivers time and money while alleviating traffic congestion. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

来源：评论

学校读者我要写书评

暂无评论

Automatic Sentimental Analysis by Firefly with Levy and Multilayer Perceptron

引用

Computer Systems science & engineering 2023年第9期46卷 2797-2808页

作者： D.Elangovan V.Subedha School of Computing Computer Science and EngineeringSathyabama Institute of Science and TechnologyChennai600118India Computer Science and Engineering Panimalar Institute of TechnologyChennai600069India

The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and *** people share their views and ideas around the world through social media like Facebook and *** goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s *** on if they provide a positive or negative perspective on a given topic,text documents or sentences can be *** compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature *** firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of *** account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy).

关键词： Firefly algorithm feature selection feature extraction multi-layer perceptron automatic sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

The real estate time-stamping and registration system based on Ethereum blockchain

引用

Blockchain(Research and Applications) 2024年第1期5卷 119-132页

作者： Liyuan Zhang Limian Ci Yonghong Wu Benchawan Wiwatanapataphee School of Electrical Engineering Computing&Mathematical ScienceCurtin UniversityKent StreetPerth6102WAAustralia

In recent years,there has been a growing interest in real estate investments that utilize blockchain *** real estate investments usually involve third-party intermediaries for verifying and recording informal real estate *** paper proposes a blockchain-based real estate investment model and presents a detailed description of the real estate register authentication aspect of the *** model uses blockchain technology to create tamper-evident records of real estate transactions and provide secure authentication and verification for informal real estate ***,each real estate transaction is recorded in a block,and all transaction records are kept on the *** means that inventors can access these transaction records and verify their authenticity and *** system can also use smart contracts to automate the process of real estate transactions,which further improves transaction efficiency and reduces ***,the model’s timestamp and authentication mechanism can eliminate third-party intermediaries and ensure the authenticity and validity of real estate transactions through distributed ledgers and verification ***,blockchain-based real estate systems offer advantages of security,transparency,efficiency,and cost *** ongoing blockchain advancements,these systems are expected to play a crucial role in future real estate investment transactions.

关键词： Blockchian technology Smart contract Real estate investment Time-stamping and registration system

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：