检索结果-内蒙古大学图书馆

Innovative Practices in technology and Management (ICIPTM), International Conference on

作者： Mary Shiba C Sumit Kumar Mishra S Sandhya K. Vidhya Jaichandran R G. Manjula Department of Computer science R.M.K Engineering College Chennai India Department of Computer Science Chandigarh University Punjab India Department of Master of Business Administration Acharya Institute of Technology Karnataka India Department of Computer Science KPR Institute of Engineering and Technology Chennai India Department of Computer Science Aarupadai Veedu Institute of Technology Chennai India Department of Computer science RMK College of Engineering and Technology Chennai India

Recently, the COVID-19 pandemic has emerged as one of the world's most critical public health concerns. One of the biggest problems in the present COVID-19 outbreak is the difficulty of accurately separating COVID-19 cases from non-COVID-19 cases at an affordable price and in the initial stages. Besides the use of antigen Rapid Test Kit (RTK) and Reverse Transcription Polymerase Chain Reaction (RT-PCR), chest x-rays (CXR) can also be used to identify COVID-19 patients. Unfortunately, manual checks may produce inaccurate results, delay treatment or even be fatal. Because of differences in perception and experience, the manual method can be chaotic and imprecise. technology has progressed to the point where we can solve this problem by training a Deep Learning (DL) model to distinguish the normal and COVID-19 X-rays. In this work, we choose the Convolutional Neural Network (CNN) as our DL model and train it using Kaggle datasets that include both COVID-19 and normal CXR data. The developed CNN model is then deployed on the website after going through a training and validation process. The website layout is straightforward to navigate. A CXR can be uploaded and a prediction made with minimal effort from the patient. The website assists in determining whether they have been exposed to COVID-19 or not.

关键词： COVID-19 Training Pandemics Manuals X-rays Reliability theory Radiology

来源：评论

学校读者我要写书评

暂无评论

Role of Data Mining in Detecting Theft and Making Effective Impact on Performance Management 2nd

Role of Data Mining in Detecting Theft and Making Effective ...

引用

2nd International Conference on Mechanical and Energy Technologies , ICMET 2021

作者： Jain, Vipin Al Ayub Ahmed, Alim Chaudhary, Vijesh Saxena, Divya Subramanian, Muthukumar Mohiddin, Md. Khaja Teerthanker Mahaveer University Uttar Pradesh Moradabad India School of Accounting Jiujiang University 551 Qianjindonglu Jiangxi Jiujiang China Central University of Himachal Pradesh Shahpur India Master of Science in Computer Science University of Texas at Arlington AlingtonTX76019 United States Nagarjuna College of Engineering & Technology Bangalore India Bhilai Institute of Technology Chhattisgarh Raipur India

ISBN: (纸本)9789811901072

This paper analyses the effectiveness of DM (Data Mining) classification techniques for company detection. Different approaches of data mining are accessible for the process of data mining. In many applications, data mining techniques are used to identify and prevent various kinds of theft. While data mining research and possible measures for the detection and identification of different forms of theft are already in progress, there is limited study that reconstructs many elements of theft that employ data mining methods. The use of data mining techniques to detect theft is a responsibility. We are also classifying theft into three groups with respect the use of data mining as an aid for the identification and prevention. This report examines the efficacy in determining false financial statements of policymakers, artificial neural networks, and Bayesian beliefs networks. Management theft, Theft against Customers, and computer-based theft are all three kinds of theft. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Preliminary Staff Collaboration and Artificial Intelligence 3

Preliminary Staff Collaboration and Artificial Intelligence

引用

3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Pandey, Arvind Kumar Mahesh, T.R. Christy, Veena Shaikh, Mohsin Yadav, Preksha Das, Saptorshi Arka Jain University Department of Computer Science Jharkhand Jamshedpur India Department of Computer Science and Engineering Bangalore India Bharat Institute of Higher Education and Research Department of Business Administration India Dr. Vishwanath Karad Mit World Peace University Department of School of Management Pune India Cmr Institute of Technology Master of Business Administration Bangalore India Kiit Deemed to Be University Kiit School of Management India

ISBN: (纸本)9798350399264

Artificial Intelligence is viewed as by a few contemporary associations all over the planet to foster able labor forces, where the ways of behaving and abilities of the workers are assisting those associations with acquiring upper hands. The administration experts of the contemporary associations are recognizing that the representatives can work cooperatively because of AI and prescient information examination. Utilization of Artificial Intelligence in the representative administration exercises is assisting a few contemporary firms with driving positive representative commitment as a result of the improvement of durable workplace. This exploration work is centered around the positive effect of Artificial Intelligence on representative commitment to this contemporary worldwide business *** models and hypothetical structures on worker commitment are applied in this review. Then again, unique significant hypothetical parts of Artificial Intelligence in forming representative commitment likewise have been examined in this exploration work. The tone and heading of the exploration show that utilization of Artificial Intelligence decidedly affects the contemporary representative commitment exercises. An exploratory review configuration is created and considered for the conduction of this examination. A subjective philosophy has been viewed as in this examination relying upon the exploratory idea of the review to meet the exploration objective. A likelihood inspecting approach has been embraced by the scientist to choose a significant example size and gather expected information. Essential data collection process has been taken on in this review to upgrade legitimate examination result. The discoveries and conversations have demonstrated toward the end that Artificial Intelligence is significant for the contemporary associations to shape representative commitment to a positive way. © 2023 IEEE.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Boosting-Based Machine Learning Approaches for Diabetes Prediction using Indian Demographic and Health Survey Data

Boosting-Based Machine Learning Approaches for Diabetes Pred...

引用

North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Shanta Kallur Suman Yaligar Puneeth N. Thotad Dept. of Computer Science & Engineering KLE Institute of Technology Affiliated to Visvesvaraya Technological University Hubballi India Dept. of Master of Computer Applications KLE Institute of Technology Affiliated to Visvesvaraya Technological University Hubballi India

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364576

Traditional diagnostic techniques are time-consuming and prone to error. Performance is added however time and cost are reduced by using computer-aided diagnostic methods. This study aims to calculate how healthy boosting-based machine learning classifiers perform in automatically identifying diabetes in the Indian context. For this experiment, Indian demographic and health survey 2021 data on women aged 19 to 79 are considered. This data includes clinical, anthropometric, and biochemical components. 12,103 of the 706,504 responders who tested positive for diabetic testing were considered. The predictive exploration-based boosting machine learning models are adopted, such as adaptive boosting, categorical boosting, extreme gradient boosting, gradient boosting, and light gradient boosting models. Kernel Principal Component Analysis is employed for feature extraction. The Extreme Gradient Boosting model demonstrated strong performance on the diabetes dataset both before and after applying Kernel PCA. The metrics including accuracy, F1-score, precision, and recall for the XGBoost model showed improvements: accuracy increased from 81% to 83%, F1-score from 81% to 82%, precision from 81% to 88%, and recall from 81% to 76%.In contrast, Adaptive Boosting performed poorly, while Categorical Boosting, Gradient Boosting, and Light Gradient Boosting models achieved moderate performance levels. Overall, the model attained optimal performance across metrics such as accuracy, precision, Area Under the Curve (AUC), and recall.

关键词： Surveys Measurement Adaptation models Analytical models Accuracy Predictive models Boosting Diabetes Kernel Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Advancing breast ultrasound diagnostics through hybrid deep learning models

引用

computers in Biology and Medicine 2024年 180卷 108962-108962页

作者： Kiran, Ajmeera Ramesh, Janjhyam Venkata Naga Rahat, Irfan Sadiq Khan, Mohammad Aman Ullah Hossain, Anwar Uddin, Roise Department of Computer Science and Engineering MLR Institute of Technology Dundigal Telangana Hyderabad500043 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh 522302 India Department of Computer Science and Engineering Graphic Era Hill University Dehradun248002 India VIT-AP University Andhra Pradesh Amaravati India Department of Computer Science and Engineering BRAC University Bangladesh Master Of Information Science and TechnologyCalifornia State University United States

Today, doctors rely heavily on medical imaging to identify abnormalities. Proper classification of these abnormalities enables them to take informed actions, leading to early diagnosis and treatment. This paper introduces the "EfficientKNN" model, a novel hybrid deep learning approach that combines the advanced feature extraction capabilities of EfficientNetB3 with the simplicity and effectiveness of the k-Nearest Neighbors (k-NN) algorithm. Initially, EfficientNetB3, pre-trained on ImageNet, is repurposed to serve as a feature extractor. Subsequently, a GlobalAveragePooling2D layer is applied, followed by an optional Principal Component Analysis (PCA) to reduce dimensionality while preserving critical information. PCA is used selectively when deemed necessary. The extracted features are then classified using an optimized k-NN algorithm, fine-tuned through meticulous *** model underwent rigorous training using a curated dataset containing benign, malignant, and normal medical images. Data augmentation techniques, including rotations, shifts, flips, and zooms, were employed to help the model generalize and efficiently handle new, unseen data. To enhance the model's ability to identify the important features necessary for accurate predictions, the dataset was refined using segmentation and overlay techniques. The training utilized an ensemble of optimization algorithms—SGD, Adam, and RMSprop—with hyperparameters set at a learning rate of 0.00045, a batch size of 32, and up to 120 epochs, facilitated by early stopping to prevent *** results demonstrate that the EfficientKNN model outperforms traditional models such as VGG16, AlexNet, and VGG19 in terms of accuracy, precision, and F1-score. Additionally, the model showed better results compared to EfficientNetB3 alone. Achieving a 100 % accuracy rate on multiple tests, the EfficientKNN model has significant potential for real-world diagnostic applications. This study highlights the model's s

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

OHMBC: Optimized Hybrid deep learning Model for Classification of Breast Cancer

OHMBC: Optimized Hybrid deep learning Model for Classificati...

引用

Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Binju Saju V Asha Neethu Tressa Pooja B Kondikoppa Pooja Pavan Kumar V J Department of Computer Science and Engineering (Artificial Intelligence) Adi Shankara Institute of Engineering and Technology Kalady India Department of Master of Computer Applications New Horiznon College of Engineering Bengaluru India

Men are much less likely than women to develop breast cancer. Breast lumps, bloody nipple discharge, and changes in the nipple's or breast's shape or texture are all indications of breast cancer. The study proposes a hybrid model to classify Diabetic retinopathy images. Initially, images are pre-processed which includes Image resizing, Gray scale conversion and noise removal using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is compared with other studies in literature. The proposed model has obtained 96% accuracy, 98.1% sensitivity, 98.2% specificity, 98% precision, and 98.18% F1 Score.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Outline of Data Encryption Standard (DES) Tools and Its Facets within the Realm of Cloud Computing

An Outline of Data Encryption Standard (DES) Tools and Its F...

引用

Advances in Computation, Communication and Information technology (ICAICCIT), International Conference on

作者： Riya Kukreti Kavitha R Apoorva Joshi Department of Computer Science and Engineering Tula’s Institute Dehradun India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Master of Computer Applications Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India

The development of Discrete Event Simulation (DES) systems for enhancing decision-making and research in cloud computing has experienced a substantial growth. The growing diversity of cloud systems makes it extremely challenging to effectively manage cloud resources and services inside data centers. The increasing prevalence of Internet of Things (IoT) gadgets, smartphone apps, and cloud services, together with the introduction of concepts like Edge and Fog Computing, have made this system more complex. A significant amount of time and effort must be invested in these tasks. In intricate hyperscale computer environments, decision assistance is provided by the safe and dependable DES technology. This study aims to provide a comprehensive evaluation of DES systems made especially for cloud computing environments. The study carefully evaluates these instruments in a number of categories and is based on a thorough investigation of recent literature. This study goes beyond conventional stand-alone simulation systems by incorporating custom extensions and add-ons designed for certain cloud computing tasks. In addition to highlighting CloudSim’s increasing significance as the main platform for simulated research, this also draws attention to the lack of tools made expressly for distributed execution.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Robust MAES Encryption And Decryption Algorithm using Ankaru Model Through Fog Computing

A Robust MAES Encryption And Decryption Algorithm using Anka...

引用

Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Karunya S Belina V.J. Sara Angayarkkani K Sowmiya J Sonia A Jeganraj P Department of Information Technology & BCA Dwaraka Doss Goverdhan Doss Vaishnav College Chennai India Department of Computer Applications SRM Institute of Science & Technology Chennai India Department of CSE Aalim Muhammed Salegh College of Engineering Chennai India Master of Computer Applications SRM Institute of Science & Technology Chennai India Department of BCA-DS SRM Institute of Science & Technology Chennai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

The world as a whole now depends on the data that people have stored online and how these developments are tracked in the future. To satisfy the requirements of safeguarding the information of an individual, a company, or a rapidly growing sector, there is only one standard. The first phase, encryption, and the second, decryption, are absolutely necessary to complete. Data is encrypted for protection, then it is decrypted for retrieval. The Advanced Encryption Standard provides the highest level of security for data protection, ensuring that its processing remains hidden. Data on edge devices is encrypted using the AES method by Fog. Since AES has never experienced a data breach, safety and security are the most fundamental truths that may be discovered through its use. Data from Edge devices is encrypted and then modified using fog computing to secure it using the AES technique. The data is then recovered by decryption. The secret key is primarily concerned with data entering and returning to the source, which transforms the proper propagation outcome.

关键词： Performance evaluation Hash functions Computational modeling Transforms Data models Encryption Discrete wavelet transforms Protection Standards Edge computing

来源：评论

学校读者我要写书评

暂无评论

Security Challenges and Elucidations in Cloud Storage and File Systems: An Advanced Investigation Review

Security Challenges and Elucidations in Cloud Storage and Fi...

引用

Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： S. P. Santhoshkumar S. Hariharasudhan S. Poorna Prakash Jim Mathew Philip S. Haritha T. Nalini Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai India Artificial Intelligence & Data Science Akshaya College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore Tamil Nadu India Master of Computer Application Rathinam Technical Campus. Coimbatore India Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

With the widespread adoption of cloud storage and file systems, security concerns have become paramount. This paper presents an advanced investigation into the security challenges encountered in cloud storage and file systems. We explore the vulnerabilities and threats that these systems face, ranging from unauthorized access to data breaches and insider attacks. Additionally, we examine the evolving landscape of security solutions, including encryption techniques, access control mechanisms, and intrusion detection systems. Through a comprehensive review of existing literature and case studies, we identify emerging trends and best practices for mitigating security risks in cloud storage and file systems. Our analysis provides valuable insights for researchers, practitioners, and organizations seeking to enhance the security posture of their cloud-based infrastructure. Cloud storage and file systems have revolutionized data management, offering scalability, flexibility, and accessibility. However, alongside their benefits, they present formidable security challenges. This paper conducts an advanced investigation into the security landscape of cloud storage and file systems, aiming to interpret key challenges and propose effective solutions. Through a thorough examination of prevalent security threats, including unauthorized access, data breaches, and insider attacks, this study identifies vulnerabilities inherent in cloud environments. Moreover, it scrutinizes emerging security technologies such as encryption, access control mechanisms, and intrusion detection systems, assessing their efficacy in mitigating risks. By synthesizing insights from existing literature and real-world case studies, this investigation provides valuable guidance for enhancing the security posture of cloud-based infrastructure. Ultimately, this paper contributes to the ongoing discourse on cloud security by offering nuanced analyses and actionable recommendations to researchers, practitioners,

关键词： Cloud computing File systems Reviews Scalability Cloud computing security Intrusion detection Data breach Market research Encryption Security

来源：评论

学校读者我要写书评

暂无评论

Block Chain technology for Securing IoT Devices and Data 9

Block Chain Technology for Securing IoT Devices and Data

引用

9th International Conference on science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Aancy, H. Mickle Kodgire, Shilpa Prashant Mohitha, MaddulaRatna Manimegalai, V. Vijay, G. Dhotre, Sunita Panimalar Engineering College Department of Master of Business Administration Tamil Nadu Chennai600123 India Maharashtra Institute of Technology Aurangabad Department of Electronics And Telecommunication Engineering Maharashtra India Aditya Institute of Technology And Management Department of Computer Science And Engineering Andhra Pradesh Tekkali India Sri Krishna College of Technology Department of Electrical And Electronics Engineering Tamil Nadu Coimbatore641042 India Department of English Tamil Nadu Dindigul624622 India College of Engineering Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350365092

IoT detector data can be covered and authenticated using blockchain armature to avoid data duplication. It also enables secure device data transfer. IoT can handle scalability and trust challenges with blockchain, keeping data private and secure. Blockchain technology improves authentication, fraud resistance, and decentralized data security. It can polarize device authentication and communication, barring a central authority. The processing power demanded for blockchain mining and the vulnerability of present blockchains to mining monopolization are obstacles. Hyundai has patronized IoT security blockchain startups. Blockchain technology could secure IoT networks and sensitive data. Blockchain and IoT give a strong answer to linked device security issues. Blockchain's decentralized and inflexible tally can secure IoT bias and their data, according to this abstract. Blockchain technology allows secure, peer- to- peer connectivity and data sharing, guarding IoT bias from manipulation and unauthorised access. Blockchain's cryptographic algorithms guarantee data integrity, while smart contracts automate device relations to misbehave with security rules. Blockchain's practical counteraccusations in IoT security could alter how we guard our decreasingly linked world, as this exploration shows. © 2024 IEEE.

关键词： Blockchain

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：