检索结果-内蒙古大学图书馆

An efficient signed SSL/TLS-based data security in the cloud using LTT-DDBM and TECC

International Journal of Information technology (Singapore) 2024年 1-16页

作者： Tiwari, Chandra Sekhar Jha, Vijay Kumar Department of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi India

Cloud Data Security (CDS) is a set of strategies for securing data from security threats. However, the previous works ignored the prevention of multiple cloud security attacks. To tackle this problem, an efficient security framework using Signed Secure Socket Layer/Transport Layer Security (SSL/TLS) with Log Triangular Transfer function-based Deep Drop Boltzmann Machines (LTT-DDBM) and Rivest-Shamir-Adleman Session Key-centric Twisted Edwards Curve Cryptography (RSASK-TECC) techniques is proposed in this paper. The process begins with the web owner who requests an SSL/TLS certificate from the Certificate Authority Centre (CAC) for establishing a website. After meeting CAC requirements, the web owner uploads files to the server. The Data Owner (DO) logs in and the signed SSL/TLS certificate is sent to the CAC for verification using LTT-DDBM. After the successful verification, a public key and session key are generated to encrypt the data. The data is further encrypted within 1124 ms using Advanced Encryption Standard Session key-based Twisted Edwards Curve Cryptography (AESS-TECC) for enhanced security. Before downloading, the data user checks the hash codes for integrity. If the codes match, then the access is granted;or else, it is denied. Hence, the proposed work outperformed the traditional methodologies. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Advanced encryption standard session key based twisted Edwards curve cryptography (AESS-TECC) Cloud data security Digital signature algorithm (DSA) Hashing algorithm Log triangular transfer function based deep drop Boltzmann machines (LTT-DDBM) Secure socket layer/transport layer security (SSL/TLS)

来源：评论

学校读者我要写书评

暂无评论

Unguided Machine Learning-Based Computation Offloading for Near-Memory Processing 38

Unguided Machine Learning-Based Computation Offloading for N...

引用

38th International Conference on VLSI Design, VLSID 2025

作者： Maity, Satanu Ghose, Manojit Kumar, Avinash Chakraborty, Anol Chakraborty, Ankit Indian Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India Jorhat Engineering College Department of Computer Science and Engineering Assam India

ISBN: (纸本)9798331522445

Recent data-intensive applications encounter memory wall bottlenecks in the traditional processor-centric computing architecture due to the need for frequent and extensive off-chip data movement. The emerging 3D-enabled near-memory processing (NMP) is a potential solution where computation can be offloaded and performed near memory, eliminating the need for expansive off-chip data transfer. Recently, some crucial works in this context have been proposed that use a supervised machine learning approach that requires a well-labeled dataset, which necessitates creating a time-consuming simulation process. Furthermore, their major focus was on profiling and offloading the entire application to the NMP side, causing a sub-optimal performance because of the low processing power on the NMP cores. This paper presents an unguided machine-learning-based approach (UCO) for computation offloading in NMP architecture that offloads only the memory-intensive regions of an application. The approach extracts several application performance characteristics and creates two clusters using them - one for the CPU side and another for the NMP side. The proposed approach's effectiveness is demonstrated through a series of comprehensive experiments with standard simulators and dataintensive benchmark applications. The results indicate that UCO improves performance (1.31x and 1.10x), saves energy (16% and 12%), and reduces off-chip data transfer (78% and 73%) compared to conventional and state-of-the-art approaches. © 2025 IEEE.

关键词： Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Image-based Plant Diseases Detection using Deep Learning

引用

Materials Today: Proceedings 2023年 80卷 3500-3506页

作者： Panchal, Adesh V. Patel, Subhash Chandra Bagyalakshmi, K. Kumar, Pankaj Khan, Ihtiram Raza Soni, Mukesh Department of Computer Science & Engineering GEC Patan India School of Computer Science and Engineering VIT Bhopal University Sehore MP Bhopal India Electrical and Electronics Engineering SRI Ranganathar Institute of Engineering and Technology Coimbatore India Department of Computer Science & Engineering Institute of Engineering & Technology Uttar Pradesh Greater Noida India Jamia Hamdard Computer Science Department Delhi India Jagran Lakecity University Bhopal India

Agriculture plays a major role in developing countries like India, however the food security still remains a vital issue. Most of the crops get wasted due to lack of storage facility, transportation, and plant diseases. More than 15% of the crops get wasted in India due to diseases and hence it has become one of the major concern to be resolved. There is a need of automatic system that can identify these diseases and help farmers to take appropriate steps to get rid of crop loss. Farmers have followed the conventional method of identifying the plant disease with their naked eyes, and it not possible for all the farmers to identify these diseases the same way. With the advance in Artificial Intelligence, there is a need to incorporate the facilities of the computer vision in the field of agriculture. Deep Learning rich libraries and user as well as developer friendly environment to work with, all these qualities make Deep Learning as the favorable method to get started with this problem. In this paper we have used Deep Learning because of the advantages it offers to work with images especially in image classification to get improvised results. The methodology includes taking leaves of infected crops and label them as per the disease pattern. The images of infected leaves are processed pixel based operations are applied to improve the information from the image. As a next step feature extraction is done followed by image segmentation and at the last classification of crop diseases based on the patterns extracted from the diseased leaves. The CNN (Convolutional Neural Network) is used for the classification of diseases, for the demonstration purpose the public dataset is used consisting of around 87 K images (RGB type images) including healthy as well as diseased leaves. © 2021

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

PneumonXNet - A deep learning model for pneumonia detection

PneumonXNet - A deep learning model for pneumonia detection

引用

2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Aditya, Harshit Ramani, Anchita Kumar, Nilesh Kasturi Bhattacharjee, Vandana Sikkim Manipal Institute of Technology Department of Computer Science and Engineering Majitar India Birla Institute of Technology Department of Computer Science and Engineering Mesra Ranchi India

ISBN: (纸本)9798350385199

Standard chest X-rays are used to diagnose pneumonia, an infection of the lungs that can be highly contagious due to being caused by viruses and bacteria. Nevertheless, this method is more time-consuming and routine-dependent with a bias in outcome. So to overcome these problem we developed PneumonXNet, a bespoke CNN model for the detection of pneumonia. We evaluated the performance of our method with respect to pre-trained models such as VGG16, EfficientNetB3 and ResNet50 on a X-ray image dataset comprising 5,856 images. The best result we have reached is a test accuracy of 97% with dropout value of 0.4 which is better than the pretrained models. The pre-trained model EfficientNetB3 showed the highest accuracy, which points on possible applications of transfer learning. This research signifies the capabilities of CNN models to streamline and improve detection on pneumonia, which in turn paves way for technological sophistication in health care sectors. The findings of this study carry significant impact on pneumonia detection, emphasizing the importance of deep learning model in pneumonia detection and how it can help in saving a lot of time for doctors and patients from the traditional X rays collecting systems. This can also reduce the overall treatment cost for patients. This research therefore offers new avenues for developing strong techniques for pneumonia identification, which hold promise for improving healthcare technology and patient outcomes in general. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Early Diabetics Prediction Using Multi Model Approaches in Machine Learning 9

Early Diabetics Prediction Using Multi Model Approaches in M...

引用

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

作者： Renugadevi, G. Sasi Kala Rani, K. Oswalt Manoj, S. Saranya, N. Goudhaman, M. Sri Krishna College of Engineering And Technology Department of Computer Science And Engineering Chennai India Dr Ngp Institute of Technology Department of Computer Science And Engineering Chennai India Jeppiaar Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365092

Diabetics is the maximum common non-transmissible disease and the deadliest disease worldwide, affecting five hundred and thirty-seven mountain individuals. Diabetics container be carried on by a variety of sources, with an unhealthy diet, high BP, abnormal cholesterol, a personal antiquity of the disease, and inactivity. Early diabetes prediction using IoT can be done by monitoring various health parameters and identifying patterns that may indicate the development of diabetes. In Proposed work, Mendeley Data Repository is a real-time data have been taken for early diabetes prediction. The Proposed hybrid Backward elimination method with Maximum Relevance, Minimum Redundancy (BE-MRMR) algorithm When integrating backward elimination and the MRMR algorithm, you can use the strengths of both strategies to identify a small but informative set of features for early diabetic prediction, potentially enhancing model interpretability and generalisation. Finally, after eliminating features, left with a subdivision of structures that subsidize most to the replica's performance. This feature given to various machine learning classifiers RF, ANN, SVM to predict early diabetics. These models produce performance matrices is calculated in terms of accuracy, specificity, precision and sensitivity. © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Clinical deep model to analyse medical multivariate time-series data for health diagnosis

引用

Cyber-Physical Systems 2025年第2期11卷 139-164页

作者： Almahadeen, Layth Vijay, Richa Shabaz, Mohammad Soni, Mukesh Singh, Pavitar Parkash Patel, Pavan Byeon, Haewon Department of Financial and Administrative Sciences Al- Balqa’Applied University Al-Salt Jordan Department of computer science Amity University UP Noida India Department of Computer Science Engineering Model Institute of Engineering and Technology Jammu Jammu and Kashmir India Department of Computer Science Engineering University Centre for Research & Development Chandigarh University Mohali India Department of Management Lovely Professional University Phagwara India Department of Computer Science Engineering Ahmedabad Institute of Technology Ahmedabad India Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of

Clinical auxiliary decision-making is related to life and health of patients, so the deep model needs to extract the personalised representation of patients to ensure high analysis and prediction accuracy;and provide a basis for prediction conclusions. In this context, a clinical deep model proposed an interpretable assessment method of patient health status based on contextual learning of medical features, encoding the time-series features of each variable separately, and using a multi-head de-coordination self-attention mechanism for learning Relationships between different features;feature skip-connection encoding based on a compressed excitation mechanism is proposed to improve the sensitivity of the model. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Integration of Blockchain and IPFS: healthcare data management & sharing for IoT Environment

引用

Multimedia Tools and Applications 2024年 1-22页

作者： Mishra, Rajiv Kumar Yadav, Rajesh Kumar Nath, Prem Department of Computer Science & amp Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & amp Engineering Delhi Technological University Delhi India Department of Computer Science HNB Garhwal University Srinagar India

The immense volume of data generated and collected by smart devices has significantly enhanced various aspects of our daily lives. However, safeguarding the sensitive information shared among these devices is crucial. Ensuring the security of the Internet of Things (IoT) ecosystem from unauthorized access is imperative. Blockchain technology emerges as a promising solution to address these security concerns. Nevertheless, the effectiveness of Blockchain in handling the extensive data generated by smart devices is challenged by the rapid pace of IoT data generation and the slower transaction validation speed within Blockchain networks. This research aims to resolve these issues by integrating Blockchain with the Inter-Planetary File System (IPFS), creating a robust framework for secure data recording on a distributed storage network while enabling authorized access to the stored data. The proposed mechanism involves defining and recording access policies and cryptographic hash content on the Blockchain network, while storing the actual IoT-generated data on IPFS to enhance the confidentiality, integrity, and availability (CIA) triad. Performance assessments of the proposed scheme demonstrate its security and practicality, validating its potential for real-world application. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Assisted Trust Management in Vehicular Ad-Hoc Network 15

Blockchain-Assisted Trust Management in Vehicular Ad-Hoc Net...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Banerjee, Suman Kuila, Pratyay Naik, Banavath Balaji National Institute of Technology Department of Computer Science and Engineering Sikkim737139 India National Institute of Technology Department of Computer Science and Engineering Patna800005 India

ISBN: (纸本)9798350370249

Vehicle-to-everything (V2X) communication is crucial in Intelligent Transportation Systems (ITSs) for enhancing road safety, traffic efficiency, and convenience. To address the need for trust in V2X communications, a trust management system for Vehicular Ad Hoc Networks (VANETs) using blockchain technology has been proposed. This system calculates dynamic trust scores for vehicles based on direct communication, recommendations from other vehicles, and proximity to roadside units (RSUs). The trust scores are stored in a transparent and tamperproof blockchain, ensuring the immutability and transparency of trust scores. Simulations demonstrate that this blockchain-based trust management system significantly improves the security and reliability of V2X communications. This system is essential for the widespread acceptance of VANETs by fostering a trusted communication environment. © 2024 IEEE.

关键词： and Edge computing Blockchain Trust V2X VANET

来源：评论

学校读者我要写书评

暂无评论

A comprehensive analysis of threat vectors in software-defined networking

引用

Multimedia Tools and Applications 2024年第39期83卷 86791-86832页

作者： Dahiya, Seema Sehrawat, Harkesh Kharb, Seema Siwach, Vikas University Institute of Engineering and Technology Maharshi Dayanand University Rohtak Rohtak India Department of Computer Science and Engineering SRM University Sonepat Sonipat India

In recent years, cyber-attacks have become more frequent and advanced, targeting critical infrastructure, businesses, homes, and government agencies. Detecting and preventing these attacks at the earliest stage possible is crucial to avoid serious harm, including data breaches. Researchers and experts in cybersecurity are looking to Software-Defined Networking (SDN) technologies as a solution to enhance real-time defense against cyber-attacks. SDN revolutionizes traditional networking by offering unprecedented flexibility and control over network resources, making it possible to adapt quickly to emerging threats. SDN provides logically centralized network control by separating the control plane from the data plane. This enables network programming and can block network activity when malicious movement is spotted. This paper presents a comprehensive analysis of threat vectors in SDN. It examines the various ways in which SDN networks are vulnerable to cyber-attacks, including network infrastructure, application layer, and SDN controller. The paper also evaluates the effectiveness of existing security measures and proposes future research directions to enhance SDN security. Overall, the paper highlights the potential of SDN as a powerful tool in the fight against cybercrime and emphasizes the importance of continued research and development to improve SDN security. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Multi-factor Authentication and Data Integrity for WBAN Using Hash-Based Techniques 5th

Multi-factor Authentication and Data Integrity for WBAN Usin...

引用

5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Pabitha, B. Vani, V. Sanshi, Shridhar Karthik, N. Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India Department of Computer Science and Engineering National Institute of Technology Karnataka Mangalore India

ISBN: (纸本)9789819767250

In recent days, a wireless body area network (WBAN) has been developed as part of the Internet of Things (IoT) with sensors and actuators in three different modes, building its network, i.e., in-body sensors, wearable sensors, and on-body sensors. The doctor’s access the data recorded and monitored by the sensor embedded in the patient to treat critical situations immediately. Maintaining data integrity and guarding against threats is necessary to secure sensitive patient information. Several people have proposed schemes for authenticating data access through formal and informal verification. In this research work, we carry out multi-factor authentication extensively using zero-knowledge proofs. The anomaly detection of the sensors is detected using machine learning algorithms, which help tune the sensors to their correct working conditions. The work aims to concentrate on sensor working conditions promptly and to handle attacks like masquerade, forgery, and key escrow attacks. To assess whether performance metrics are superior in computing cost, storage overhead, and communication overhead, utilize the BAN logic tool. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wearable sensors

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：