检索结果-内蒙古大学图书馆

A Blockchain-Assisted Trusted Federated Learning for Smart Agriculture

SN computer science 2025年第3期6卷 1-26页

作者： Manoj, T. Makkithaya, Krishnamoorthi Narendra, V.G. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal 576104 India

Smart agriculture promises to alleviate the burden of climate risks on crop production by leveraging machine learning tasks. These tasks act as a decision support instrument for making well-informed choices by stakeholders in the agricultural value chain. Currently, predictive models in smart agriculture demand a centralized collection of diverse data, fragmented across multiple information systems leading to a single point of failure. The application of the Federated Learning (FL) technique restricts the movement of raw data and trains the model at the data source. However, the FL approach does not ensure trust factors like privacy, authentication, data provenance, transparency and traceability. To address this, a decentralized federated learning framework built on blockchain can be a potential solution. In this study, we introduce a blockchain-based framework called AgriFLChain for trusted federated learning in the context of smart agriculture. We focus on crop yield prediction as an illustrative use case, initially discussing centralized deep learning models (ResNet-16, ResNet-28, CNN-DNN, and CNN-LSTM). We then detail the authentication and data provenance mechanisms for federated learning participants, utilizing blockchain-based Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). We implement these models using vanilla federated learning and Differential Privacy (DP) federated learning approaches, achieving transparency and traceability through smart contracts by recording metadata of model updates into the blockchain. Finally, detailed evaluation demonstrates that AgriFLChain achieves comparable efficiency to centralized models while maintaining scalability in blockchain transactions for higher data volumes. © The Author(s) 2025.

关键词： Blockchain Crop yield prediction Deep learning Federated learning Smart agriculture Smart contracts

来源：评论

学校读者我要写书评

暂无评论

Predicting Student Smoking Likelihood: A Machine Learning Regression Analysis 2

Predicting Student Smoking Likelihood: A Machine Learning Re...

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kocher, Sheen Kumar, Horesh Jain, Tarun Kumar, Ashish Manipal University Jaipur Department of Computer Science & Engineering Jaipur India G.L. Bajaj Institute of Technology and Management Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350382952

Smoking stands as a prominent preventable hazard that contributes to untimely mortality. Smoking remains a concern for health among students as it has enduring impacts on both personal well being and society, at large. For the purpose of creating focused treatments and preventive programs, it is essential to identify the variables that affect students' propensity to start smoking. This study uses machine learning regression approaches to give a thorough analysis of predicting student smoking propensity. We acquired a variety of data from a sizable number of students who were enrolled in educational institutions. Details about their demographics, socioeconomic situation, academic accomplishments, peer influences, and various facets of their lifestyle are included in this data. We utilized this dataset to make predictions on the probability of students engaging in smoking. In our research we employed regression models such, as Linear regression, Ridge regression, Lasso regression, Decision tree regression etc. Keywords - PCOS, Machine Learning, Classification, SVM, KNN, XGBoost, Logistic Regression, Random Forest. © 2024 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

The Smart and Secured Communication Using Applied Cryptography Algorithms 5th

The Smart and Secured Communication Using Applied Cryptograp...

引用

5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Agarwal, Trapty Jebakumari, S. Adlin Hemlata Ayesha, Amreen Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This paper describes the implementation of a at ease communication device the usage of an implemented cryptography algorithm. The gadget is designed to provide comfy statistics and voice communique between endpoints using techniques consisting of encryption and digital signature. The applied cryptography set of rules includes primary components, the symmetric and the uneven algorithms. The symmetric algorithms, along with AES and DES, are used for statistics encryption, even as the uneven algorithms, which include RSA and DSA, are used for virtual signature. The proposed gadget additionally focuses on security of physical places to defend the important thing exchange by means of the use of physical access control measures which include biometric identification. The system additionally employs advanced safety strategies together with message authentication codes and protection protocols like SSL/TLS. The device is examined for robustness and safety using real communique experiments. The results confirm that the proposed machine is cozy and green in supplying comfortable communication among endpoints. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Tools for Covid-19 Pneumonia Classification

Deep Learning Tools for Covid-19 Pneumonia Classification

引用

International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Kanyangarara, Ngonidzashe Mathew Soumya, D.R. Sahana, Subrata Das, Sanjoy Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Computer Science Indira Gandhi National Tribal University RCM Manipur India

ISBN: (纸本)9789819986606

The outbreak of Covid-19 has triggered a worldwide problem, especially in Asia and America. The World Health Organization (WHO) declared the sickness a pandemic on March 20, 2020. It arrived in waves, and most countries worldwide have now experienced two waves and are on the approach of experiencing the third. The goal of this study is to build up and certify a computer-Aided Diagnosis (CADx) system for distinguishing between COVID-19-positive patients and non-COVID Patients people. Chest X-ray (CXR) images will be used to accomplish this. From public datasets which we got on GitHub 2295 CXR images were obtained which included 712 COVID-19 positive and 1583 normal. The proposed CADx system utilized a Conventional Neural Network (CNN) model for data argumentation and CNN was built, compiled, and trained with the help of TensorFlow and Keras. For the sake of appraisal, our datasets were separated into three categories: Train/Test and Validation. The three sets’ accuracy was evaluated and the results for Training, Validation, and Test were 97.77%, 97.81%, and 97.72% respectively. In the end, this study was able to create a precise computer-Aided Diagnosis system for the two categories of classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

AI Enhanced Phishing Detection System 3

AI Enhanced Phishing Detection System

引用

3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Chinnasamy, P. Krishnamoorthy, P. Alankruthi, K. Mohanraj, T. Kumar, B. Santhosh Chandran, Likha Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Thiruthangal Nadar College Pg Department of Computer Science Chennai51 India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Ibrahimpatnam501506 India Mlr Institute of Technology Department of Science and Humanities Hyderabad India

ISBN: (纸本)9798350361186

It attempts to create a sophisticated system that uses artificial intelligence (AI) methods to prevent and detect phishing assaults in advance. Cybersecurity is seriously threatened by phishing attempts, which take advantage of human weakness to trick people into disclosing private information. In order to precisely identify and categorize any phishing attempts, the suggested method makes use of machine learning algorithms and natural language processing (NLP) to examine a variety of characteristics of emails, websites, and digital *** project uses AI-driven methods to improve cybersecurity defenses already in place by offering a more reliable and effective way to detect and stop phishing assaults, protecting people and businesses from the negative effects of cyberattacks. © 2024 IEEE.

关键词： Denial-of-service attack

来源：评论

学校读者我要写书评

暂无评论

Edge Computational Integrated Improved Data Model for Adjustment and Restoration Strategy for Smart Grid Environment 1

Edge Computational Integrated Improved Data Model for Adjust...

引用

1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Premalatha, J. Sarojini Abirami, G. Poornima, D. Sivakumar, Gayathri Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

In the current context, integrated distribution networks of the electricity grid to provide reliability during blackouts. Self-healing systems may keep functioning and reconfigure themselves in response to external breakdowns or attacks by using the efficient strategy of restoration. An intelligent decision-making framework is necessary to ensure the system as a whole can self-reconfigure in the face of external failures and attacks. Incorrect grid modifications can pose a significant threat to the power grid's security and cause delays in network traffic and packet loss. So, in this research, Edge Computational Integrated Improved Data paradigm (EC-IDM) to deal with failures and attacks in real time. By mitigating the consequences of security worries, packet loss, latency, and overcrowded networks, edge computing has greatly increased the smart grid's capacity for efficient operation and communication. Smart Grid's posterior intelligent model (SSPIM) integration enhances the system's functionality and productivity. This research introduces an EC-IDM that can detect blackout scenarios and automatically construct a restoration strategy in real time. In EC-IDM, an enhanced hierarchical coordination mechanism and a local object optimization method are combined to optimize failures and attacks in real time over many time and space axes. Differentiating features of EC-IDM include real-time data analysis, networked simulation, smart decision support, and a wealth of other useful features. The simulation results indicate that the recommended strategy achieves best in security concerns optimizes network congestion delay, and packet loss security, increases efficiency and performance compared to the other standard ways. © 2023 IEEE.

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

Applications of Blockchain technology in Modern Education System - Systematic Review

Applications of Blockchain Technology in Modern Education Sy...

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Chinnasamy, P. Ramani, D. Roja Ayyasamy, Ramesh Kumar Jebamani, B. Jency A Dhanasekaran, S. Praveena, V. MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Faculty of Information and Communication Technology Kampar Malaysia KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Department of Information Technology TamilNadu Srivilliputtur India Dr.N.G.P. Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350348217

The development of new technologies and also the provision of great connections are becoming increasingly important in all aspects of our everyday situations. This advanced technology also brings a variety of flaws, most importantly security-related problems. Distributed ledger technology, amongst the most prominent technologies in the previous few years, is gaining traction due to its ability to just provide privacy in a variety of domains, including logistics management, transportation, as well as other industries. The educational industry should also make use of the important features blockchain technology offers. This technology is now being considered by schools and universities, particularly universities, as an approach to enhance the teaching-learning process and encourage the participation of all stakeholders including such undergraduates, professors, and even family members. E-transcripts, digitized certificates and credentials, data storage, plus access control will all benefit from it. This detailed research analysis discusses the adaptability of blockchain technology in the education sector. © 2023 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Ahead of the Curve: Predictive Maintenance Solutions for Modern Automobiles

Ahead of the Curve: Predictive Maintenance Solutions for Mod...

引用

International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Aishwarya Barath, Ananya Satish Kumar, T. Khushi, L. Lavanya, N. Department of Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India

ISBN: (纸本)9789819613472

Predictive maintenance in the automotive sector has received a lot of attention because of its potential to improve operating efficiency and lower maintenance costs. This paper presents an approach for predictive maintenance that focuses on engine condition prediction and battery remaining usable life (RUL) predictions. The work intends to develop precise predictive models to detect anomalies in engine health and anticipate battery degeneration, allowing for proactive maintenance interventions. The research entails gathering and analyzing data from engine sensors and battery characteristics in order to train and test various machine learning models. XGBoost was the best algorithm for forecasting engine health, with an accuracy of 0.66, while Bagging Regressor was the best regression technique for engine prediction, with an MSE of 4443.04 and RMSE of 66.66. These models are highly accurate and efficient, demonstrating their potential for real-world use in automobile maintenance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

An Optimal Deep Learning for Cooperative Intelligent Transportation System

引用

computers, Materials & Continua 2022年第7期72卷 19-35页

作者： K.Lakshmi Srinivas Nagineni E.Laxmi Lydia A.Francis Saviour Devaraj Sachi Nandan Mohanty Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Periyar Maniammai Institute of Science&TechnologyThanjavur613403India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyTelangana500075India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Science and Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Engineering College of Engineering PunePuneMaharashtra411005India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related *** the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS *** the same time,prediction of short-term traffic flow is highly essential to manage the traffic *** to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow *** paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so *** presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow *** IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count *** addition,deep belief network(DBN)model is applied for the prediction of short-term traffic *** further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of *** results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.

关键词： Cooperative intelligent transportation systems traffic flow prediction deep belief network deep learning vehicle counting

来源：评论

学校读者我要写书评

暂无评论

Fast face detection using a unified architecture for unconstrained and infrared face images

引用

Cognitive Systems Research 2022年 74卷 18-38页

作者： Dash, Priyabrata Ranjan Kisku, Dakshina Gupta, Phalguni Kanta Sing, Jamuna Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur721302 India Department of Computer Science and Engineering National Institute of Technology Durgapur West Bengal Durgapur713209 India Department of Computer Science and Engineering Institute of Engineering & Technology GLA University Uttar Pradesh Mathura281406 India Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata700032 India

Human face detection along with its localization is a difficult task when the face is presented in the cluttered scene in an unconstrained scenario that might be with arbitrary pose variations, occlusions, random backgrounds and infrared (IR) environment. This paper proposes a novel face detection method which can address some of these issues and challenges quite successfully during face detection in unconstrained as well as infrared environments. It makes use of Fast Successive Mean Quantization Transform (FastSMQT) features for image enhancement and feature representation to deal with illumination and sensor insensitiveness. A split up Sparse Network of Winnows (SNoW) with Winnow updating rule is then exploited to speed up the original SNoW classifier. Finally, the features and classifiers are combined together with skin detection algorithm for face detection in crowd image and head orientation correction of near infrared faces. The proposed face detector is robust in handling pose, occlusion, illumination, blur and low image resolution. The experiment is performed on six challenging and publicly available databases, viz. BIOID, LFW, FDDB, UFI, WIDER FACE and IIT Delhi near infrared. The experimental results depict that the proposed method outperforms traditional as well as some advanced methods in detecting unconstrained and infrared faces under challenging situations. © 2022 Elsevier B.V.

关键词： Face recognition

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：