检索结果-内蒙古大学图书馆

Dermo classify: A dermatologist skin disease detection and classification using DCNN

research on Biomedical engineering 2025年第1期41卷 1-18页

作者： Sudar, K. Muthamil Nagaraj, P. Muneeswaran, V. Panda, Baidyanath Bhoi, Akash Kumar Department of Computer Science and Engineering Mepco Schlenk Engineering College Tamilnadu Sivakasi India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India LTIMindtree 1 American Row 3 Floor HartfordCT06103 United States Directorate of Research Sikkim Manipal University Gangtok Sikkim737102 India

Purpose: In current circumstances, investigators who are doing exploration in various regions remote from their old neighborhood are experiencing different skin issues like dermatitis, dry skin, and psoriasis because of environment or weather condition changes, food sensitivities, and water changes. This results in itching and dry patching on their skin. An affected individual should investigate and analyze the skin sickness, take appropriate precautions to keep the illness from spreading and seek therapy from a skin expert as quickly as time permits. Since the investigators come from different parts of the country, they are unfamiliar with hospitals and places, so they avoid such skin diseases without realizing the severity of the sickness. To address this, we propose a computer-aided system that detects and classifies skin diseases early, helping individuals to identify their condition and seek appropriate treatment quickly. Methods: To detect skin disease at an early stage, this work epitomizes a strategy using a computer-aided model according to sophisticated deep learning neural network techniques such as convolutional neural network (CNN), Residual neural networks, and Xception neural network to detect skin disease. We use the HAM-10000 data set, which contains data on seven different types of skin diseases. Results: The CNN model produced the most significant performance accuracy of 98.3718% in the experiments. Conclusion: The paper presents a promising approach for skin disease detection and classification using DCNNs. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Empirical Analysis of Developing EC Based Algorithms for Various WSN Network Applications 4

Empirical Analysis of Developing EC Based Algorithms for Var...

引用

4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Bansal, Saloni Reddy, R. Venkateswara Aggarwal, Saurabh Adnan, Myasar Mundher Kumar, Devender Angadi, Sanjeevkumar Gla University Department of Computer Engineering and Applications Mathura India Cmr College of Engineering & Technology Department of Cse India Uttaranchal Institute of Technology Uttaranchal University Department of Mechanical Engineering Dehradun248007 India The Islamic University Najaf Iraq Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Nutan College of Engineering and Research Computer Science and Engineering Pune Maharashtra Talegaon Dabhade India

ISBN: (纸本)9798350360165

Wi-Fi Sensor Networks (WSNs) have emerged as a transformative era for several applications, from environmental monitoring to protection systems, due to improvements in wi-fi verbal exchange and sensor technologies. The deployment of Mobile Wireless Sensor Networks (MWSNs) introduces the task of maximizing community lifetime at the equal time as making sure green facts series and processing. This test proposes a novel technique to extend the lifetime of MWSNs by using way of leveraging Evolutionary Computing (EC) techniques. Through a comparative assessment of five EC algorithms-Genetic Algorithms (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Neighborhood Field Optimization (NFO)-the research highlights the strengths and limitations of every method in optimizing the energy performance of MWSNs. The results show the functionality of EC strategies in enhancing the sustainability and operational basic overall performance of WSNs, presenting treasured insights for destiny studies and realistic programs in the subject. © 2024 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

E-voting system using cloud-based hybrid blockchain technology

引用

Journal of Safety science and Resilience 2024年第1期5卷 102-109页

作者： Beulah Jayakumari S Lilly Sheeba Maya Eapen Jani Anbarasi Vinayakumar Ravi A.Suganya Malathy Jawahar Department of Information Technology Tagore Engineering College Chennai India School of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram ChennaiIndia Department of Information Technology Jerusalem College of Engineering ChennaiIndia School of Computer Science and Engineering Vellore Institute of Technology Chennai India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Leather Process Technology Division CSIR-Central Leather Research Institute ChennaiIndia

With the invention of Internet-enabled devices,cloud and blockchain-based technologies,an online voting system can smoothly carry out election *** pandemic situations,citizens tend to develop panic about mass gatherings,which may influence the decrease in the number of *** urges a reliable,flexible,transparent,secure,and cost-effective voting *** proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system,and it is carried out in three phases:the registration phase,vote casting phase and vote counting phase.A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting *** smart contracts,third-party interventions are eliminated,and the transactions are secured in the blockchain ***,to provide accurate voting results,the practical Byzantine fault tolerance(PBFT)consensus mechanism is adopted to ensure that the vote has not been modified or ***,the overall performance of the proposed system is significantly better than that of the existing *** performance was analyzed based on authentication delay,vote alteration,response time,and latency.

关键词： E-voting Cloud Blockchain Smart contract Consensus mechanism

来源：评论

学校读者我要写书评

暂无评论

Traffic Detection and Enhancing Traffic Safety: YOLO V8 Framework and OCR for Violation Detection Using Deep Learning Techniques

Traffic Detection and Enhancing Traffic Safety: YOLO V8 Fram...

引用

2024 International Conference on science, Technology, engineering and Management, ICSTEM 2024

作者： Nagaraj, P. Muthamil Sudar, K. Gurusigaamani, A.M. Sai Vasanth, B. Saranya, V.S.S. Reddy, P. Girish Kumar Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India Department of Computer Science and Engineering Mepco Schlenk Engineering College Virudhunagar Sivakasi India

ISBN: (纸本)9798350376913

Making sure traffic is safe and well-managed has become a top priority in the world of contemporary transportation. Using the robust YOLO (You Only Look Once) v8 model in conjunction with Optical Character Recognition (OCR) technology, this research explores the creation and deployment of a state-of-the-art traffic detection system. The main goal is to make roads safer by detecting traffic in real-time and automatically identifying violations. This research is built around the YOLO v8 framework, which is known for its fast and accurate object detection. With YOLO v8 and OCR technology integrated, the system's capabilities are greatly enhanced. Extracting textual information from licence plates allows the system to automatically detect violations like speeding. In order to make it work in a wide variety of international contexts, the OCR component is strong and can handle different fonts, sizes, and languages. To sum up, intelligent traffic management systems have come a long way using the YOLO v8 Framework and optical character recognition (OCR) for violation detection and traffic detection and enhancement. © 2024 IEEE.

关键词： Optical character recognition

来源：评论

学校读者我要写书评

暂无评论

A Novel NSGA-II Approaches for Combating Advanced Persistent Threats with Machine Learning 12th

A Novel NSGA-II Approaches for Combating Advanced Persistent...

引用

12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Soni, Monika Pawar, Avinash M. Khubalkar, Deepti Tanjeja, Ankit Kumar Mishra, V.A. Waykole, Tushar Satish Auricle Global Society of Education and Research Bikaner India Bharati Vidyapeeth’s College of Engineering for Women Maharashtra Pune India Pune India Information Technology Arya College of Engineering Jaipur India Computer Engineering VIIT Pune India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India

ISBN: (纸本)9789819601387

Advanced persistent threats (APTs) are becoming more advanced, requiring the creation of strong cybersecurity measures. This study suggests a new method to address advanced persistent threats (APT) by utilizing machine learning, particularly by implementing the "Non-dominated Sorting Genetic Algorithm" II (NSGA-II). The proposed model attains a true-positive rate (TPR) of 98.62% and a precision of 97.59%. Extensive experiments were conducted using a diverse dataset containing APT and benign data samples to assess the effectiveness of NSGA-II compared to other popular machine learning algorithms such as random forest, isolation forest, and LightGBM. The NSGA-II model consistently surpassed other algorithms in terms of true-positive rate (TPR) and precision, demonstrating its superiority in detecting and mitigating advanced persistent threats (APT). The study explores the structure and functionality of NSGA-II, highlighting its ability to enhance the efficiency of machine learning models for APT detection. The algorithm's efficiency in exploring the solution space, balancing multiple objectives, and identifying nondominated solutions greatly contributes to its success in dealing with the complex nature of APT. This study emphasizes the practical implications of using NSGA-II for APT detection in real-world situations. The model shows a high level of accuracy in differentiating malicious activities from normal network behavior, offering an effective defense against APT. The suggested method employing NSGA-II for APT detection provides a promising solution to the changing cybersecurity threat environment. The high true-positive rate (TPR) and precision rates of NSGA-II exceed those of random forest, isolation forest, and LightGBM, demonstrating its potential as a strong and effective tool in addressing advanced persistent threats (APT) and improving cybersecurity resilience. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

引用

computer Modeling in engineering & sciences 2024年第3期138卷 2029-2059页

作者： Sheetal Sharma Kamali Gupta DeepaliGupta Shalli Rani Gaurav Dhiman Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India PG Department of Information Technology Goswami Ganesh Dutta Sanatan Dharma CollegeChandigarh160030India Department of Computer Science Government Bikram College of CommercePatialaIndia Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityMohali140413India Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India Division of Research and Development Lovely Professional UniversityPunjab144001India

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart *** address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed *** analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

关键词： Error fault detection techniques sensor faults outliers Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Zero Trust Networks: Evolution and Application from Concept to Practice

引用

computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

来源：评论

学校读者我要写书评

暂无评论

A smart recommender model based on learning method for sentiment classification

引用

Multimedia Tools and Applications 2024年 1-21页

作者： Chiranjeevi, Phaneendra Rajaram, A. Research Scholar Department of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam611002 India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam611002 India

The problem of classifying the text is not only the topic since it contains the sentiment. Large amount of information are availed but determining the positive and negative thoughts is a major task. The existing method uses various classification technique and machine learning techniques for data processing. Naïve Bayes, entropy classification, linear regression, etc., are the existing algorithm. These algorithms are not performing well with time-consuming analysis. The proposed method uses the Structured Support Vector Machine Learning Algorithm for classification. Here the multifaceted text classification dataset is taken for the analysis. This proposed research work recognizes the favorable and unfavorable sentiments towards specific subjects. The techniques for giving the sentences and terms in a document their semantic significance so they can be used more effectively than existing possibilities. Based on the sentiment keyword from the user review, we can rank the positive and negative weights. By this application, the user will get to know which is best and suitable related to their requirement since they can access their requirement in efficient way. Entire survey should be model in MATLAB 2018a suite. The accuracy of this proposed analysis been increased when compared to the existing analysis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Twin attention based multi-task convolutional bidirectional long short term memory for facial expression recognition

引用

Multimedia Tools and Applications 2025年第10期84卷 8037-8070页

作者： Sreenivas, Velagapudi Sivaneasan, B. Vani, K. Suvarna Chakrabarti, Prasun Singapore Institute of Technology Singapore138683 Singapore Engineering Cluster Singapore Institute of Technology Singapore138683 Singapore Department of Computer Science and Engineering AI/ML Research Group Head VR Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur313601 India

Facial Expression Recognition (FER) aims to detect the emotional state of facial images. It is playing an increasingly important role in several application areas, including human–computer interaction (HCI), video transcriptions, and social communications. This article provides an adequate attention-based multi-task deep learning method for facial expression recognition. First, the input videos are collected from the RAVDESS and MELD datasets. Then, the input videos are converted using a threshold-based keyframe extraction algorithm. Next, the input data is pre-processed using the Adaptive Pixel Density Median Filtering (A-PDMF) method. Key features such as shape, color and texture are extracted from the pre-processed images. Finally, the facial expressions are recognized by proposing a novel twin attention-based multi-task convolutional bidirectional long-short-term memory method (TA-MC-BiLSTM). In addition, the classification parameters are optimally tuned using the EX-AHA method (Extended Artificial Hummingbird Algorithm). The proposed model reduces the size of facial features while accurately identifying the wide range of facial expressions. For simulation, the proposed method prefers python tool and results are analyzed using RAVDESS and MELD datasets. The simulation results show that the proposed model provides better results than other existing models in terms of accuracy of 99.6% for RAVEDESS and 99.4% for MELD. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Fake and propaganda images detection using automated adaptive gaining sharing knowledge algorithm with DenseNet121

引用

Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3519-3531页

作者： Muthukumar, A. Raj, M. Thanga Ramalakshmi, R. Meena, A. Kaleeswari, P. Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India Department of Electronics and Communication Engineering KLN College of Engineering Tamil Nadu Pottapalayam India

An additional tool for swaying public opinion on social media is to present recent developments in the creation of natural language. The term "Deep fake" originates from deep learning technology, which effortlessly and seamlessly steers someone toward digital media. Artificial Intelligence (AI) techniques are a crucial component of deep fakes. The generative powers of generative capabilities greatly reinforce the advancements in language modeling for content generation. Due to low-cost computing infrastructure, sophisticated tools, and readily available content on social media, deep fakes propagate misinformation and hoaxes. These technologies make it simple to produce misinformation that spreads fear and confusion to everyone. As such, distinguishing between authentic and fraudulent content can be challenging. This study presents a novel automated approach for the identification of deep fakes, based on Adaptive Gaining Sharing Knowledge (AGSK) and using DenseNet121 architecture. During pre-processing, the image’s sensitive data variance or noise is eliminated. Following that, CapsuleNet is used to extract the feature vectors. The deep fake is identified from the resulting feature vectors by an AGSK with DenseNet121 architecture, together with the hyper-parameter that has been optimized using the AGSK model. Propaganda and defamation pose less of a concern, and the results of the suggested deepfake image recognition model show how reliable and successful the model is. The accuracy of detection is 98% higher than other cutting-edge models. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Digital storage

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：