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共 811 条记录，以下是251-260 订阅

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Hierarchical Federated learning Privacy Protection Framework with Enhanced Privacy and Resistance to Byzantine Attacks 7

Hierarchical Federated Learning Privacy Protection Framework...

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7th IEEE international conference on Computer and Communication Engineering Technology, CCET 2024

作者： Guan, Guilin Zhi, Ting Cai, Huimin Cao, Yang Xie, Hongtao CETC Big Data Research Institute Co. Ltd. Guiyang China

ISBN: (纸本)9798350355673

As a new computing paradigm, edge computing provides computing services at the edge of the network. Compared with the traditional cloud computing model, it has the characteristics of high reliability and low latency. Federated learning, as a distributed machine learning method, although it has the characteristics of protecting privacy and data security, still faces problems such as device heterogeneity and data imbalance, resulting in long training time and low training efficiency at some edge ends. To address the aforementioned shortcomings, we propose a federated learning incentive mechanism scheme that enhances privacy in edge scenes and is resistant to Byzantine attacks. Our scheme can encrypt weights through multi-key homomorphic encryption methods to resist data recovery attacks initiated by malicious edge servers and malicious devices. Simultaneously, based on marginal loss technology, detect malicious clients or upload low-quality clients, and evaluate the contribution level of edge devices using Shapley technology as the basis for distributing rewards to participants. To address unauthorized user access, a hybrid password system for identity authentication has been designed, ensuring that only authenticated participants can participate in the training process of federated learning. In addition, extensive experiments were conducted on three datasets to evaluate the performance of our scheme. the results show that our scheme outperforms other advanced federated learning methods in terms of accuracy, fairness, and robustness. © 2024 IEEE.

关键词： Differential privacy

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machine learning for the Classification of Obesity Levels Based on Lifestyle Factors 23

Machine Learning for the Classification of Obesity Levels Ba...

引用

2023 7th international conference on Cloud and Big Data computing, ICCBDC 2023

作者： Khater, Tarek Tawfik, Hissam Singh, Balbir Department of Electrical Engineering University of Sharjah United Arab Emirates School of Built Environment Engineering and Computing Leeds Beckett University United Kingdom

ISBN: (纸本)9798400707339

In recent years, the prevalence of obesity and its related co-morbidities have been increasing significantly. therefore, it is an important challenge to pursue an early prediction of obesity risk that could help in reducing the pace of obesity rise when appropriate interventions are placed, accordingly. the prediction and classification of obesity depend on different factors such as body mass index (BMI) and lifestyle aspects, including eating habits. By focusing on these lifestyles and eating habit factors, we can develop a more holistic approach to weight management and prevention of obesity. the aim of this paper is to propose a machine-learning model that can classify weight levels using lifestyle variables without relying on BMI which enables us to investigate how lifestyle factors affect different levels of weight categorization. Although BMI is the most widely used estimation of obesity, there are other factors that can contribute to gaining weight such as lifestyle factors. the accuracy of our lifestyle-based model reached 75% excluding weight, height, and family history. Our model could serve as a starting point for using an interpretable machine learning model to better understand the effect of lifestyle factors on obesity levels. © 2023 ACM.

关键词： machine learning

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Analyzing Weather Patterns and Mosquito Data to Predict West Nile Virus Outbreaks in Chicago 7

Analyzing Weather Patterns and Mosquito Data to Predict West...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Bachal, Tarush Kumar, Ashwin Shetty, Ansh Pawar, Rajendra School of Computer Engineering & Technology Mit World Peace University Maharashtra Pune India

ISBN: (纸本)9798350304268

this paper describes the problem of predicting the occurrence of West Nile Virus (WNV) in mosquitoes in the city of Chicago. Each year, from late May to early October, mosquito traps are set up across the city, and the mosquitoes collected in the traps are tested for the presence of WNV. the goal of this is to predict where and when different races of mosquitoes will test positive for the WNV based on climate, area, and testing information. A more accurate prediction method could help the city allocate resources more effectively towards preventing transmission of this potentially dangerous infection. this paper includes a literature review of previous studies on the transmission of WNV and the vectorial capacity of mosquitoes. the paper also provides details on the data collection process, including the mosquito trapping, testing, and spray data, as well as the weather data. © 2023 IEEE.

关键词： Climate change Data analysis Disease prevention Disease surveillance machine learning Mosquito behavior Mosquito control Mosquito species Mosquito-borne diseases Precipitation Predictive modeling Public health Temperature West Nile virus

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Augmented Analytics: Modern Business Intelligence and Data Analytics 7

Augmented Analytics: Modern Business Intelligence and Data A...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Ukhalkar, Prakash Bhate, Manasi Hingane, Suraj Hingane, Swati Pimpri Chinchwad College of Engineering Mca Department Pune India MES's Institute of Management and Career Courses Mca Department Pune India Abhinav Education Society's Institute of Management and Research Mca Department Pune India Rmd Sinhgad School of Management Studies Mba Department Pune India

ISBN: (纸本)9798350304268

Analytics that define the most valuable and possible automation are referred to as augmented analytics. Analytics is the name given to the scientific method of identifying and communicating critical patterns in data. To improve decision-making, gain understanding, prepare data, and exchange data, augmented analytics focused on turning raw data into information. Measurement and insights of data are obtained through augmented analytics. It employs operations research, computer programming, and statistics. It is functional in places with an enormous amount of information. When it comes to analytics and decision-making, augmented analytics can be supplemented and eventually fully automated. Augmented analytics is the most recent method for considering data and analytics. Artificial intelligence (AI) and analytics are combined. It involves combining artificial intelligence (AI) with conventional analytics, the most popular machine learning (ML), and natural language processing (NLP). It differs from typical analytics or business intelligence (BI) tools in that ML technology is constantly learning and improving outcomes behind the scenes. Augmented analytics, in particular, allows for faster access to insights gained from vast volumes of structured and unstructured data to provide ML-based recommendations. this intelligence contributes to discovering hidden patterns and deviations in data, as well as the removal of human bias and the development of forecasting capabilities that can help an organization decide the future course of action. © 2023 IEEE.

关键词： Artificial Intelligence Augmented Analytics Business Intelligence machine learning Natural Language Processing

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Cybersmish: A Proactive Approach for Smishing Detection and Prevention using machine learning 7

Cybersmish: A Proactive Approach for Smishing Detection and ...

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7th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Kumarasiri, W.L.T.T.N. Siriwardhana, M.K.J.C. Suraweera, S.A.D.S.L. Senarathne, A.N. Harshanath, S.M.B. Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350341485

Social engineering attacks pose a significant risk to user data confidentiality, including credit card details and social network passwords. To combat the rise of smishing attacks targeting digital devices, this research proposes Cybersmish, which utilizes machine learning and NLP Transformer-based models. Cybersmish's three main components include a message content analyzer, URL analyzer and HTML content analyzer. Leveraging pre-trained models such as MobileBERT, DistilBERT, XGBoost and TF-BERT, Cybersmish achieves accuracy levels exceeding 90% in detecting and analyzing SMSs, classifying URLs, and evaluating HTML web application authenticity. this research underscores the effectiveness of deploying advanced technologies to enhance mobile device security, protecting users from social engineering attacks. It addresses the need for robust solutions against evolving cyber threats, emphasizing the critical role of technology in safeguarding sensitive information in the digital age. © 2023 IEEE.

关键词： Natural language processing systems

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An Enriched Employee Retention Analysis System with a Combination Strategy of Feature Selection and machine learning Techniques 7

An Enriched Employee Retention Analysis System with a Combin...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Silpa, N. Maheswara Rao, V.V.R. Subbarao, M. Venkata Kurada, Ramachandra Rao Reddy, Shiva Shankar Uppalapati, Padma Jyothi Shri Vishnu Engineering College for Women Department of C.S.E. Bhimavaram India Shri Vishnu Engineering College for Women Department of E.C.E. Bhimavaram India S.R.K.R. Engineering College Department of C.S.E. Bhimavaram India Vishnu Institute of Tehnology Department of C.S.E. Bhimavaram India

ISBN: (纸本)9798350397253

In today's competitive business environment, retaining regular employees is a major challenge for all organizations. Retention of employees is an essential management strategy that hopes to create positive work environment and achieve lasting success. thus, researchers turned their attention to machine learning framework to quickly find the reasons of lowering attrition and estimate staff turnover. In reality, many factors influence employees' decisions to remain with the organization. the high dimensionality of employee data makes it difficult for data analysts to comprehend and analyses in any meaningful way within time. As a consequence, feature selection got a special focus in the research in order to improve the quality of the data analysis and discover the pertinent data. In this study, the authors propose an Enriched Employee Retention Analysis System (EERAS) that uses a novel approach with a right combination of feature selection model and machine learning techniques. the double goal of the proposed EERAS is to (1) identify the factors that influence employee turnover and (2) recommend key factors for employee retention. To achieve the aimed goals, the authors undertake an empirical analysis on a large employee dataset with four feature selection strategies: MRMR Chi-2, ANOVA and Kruskal on five different classification learning algorithms - Decision Tree, Linear Regression, Naive Bayes, Support Vector machine, and XGBoost. the results demonstrated that feature selection improves classification accuracy while decreasing training time. In addition, the system recommends critical elements that might be incorporated into HR policy to ensure long-term staff retention. © 2023 IEEE.

关键词： Feature Selection

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Survey of Deep learning Models for Image-Based Disease Detection in Plants 7th

Survey of Deep Learning Models for Image-Based Disease Detec...

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7th international conference on soft computing: theories and Applications, SoCTA 2023

作者： Mathur, Abhishek Shrivastava, Shailendra Ku. Department of Information Technology Samrat Ashok Technological Institute Vidisha India Samrat Ashok Technological Institute Vidisha India

ISBN: (纸本)9789819720880

Crop diseases pose a severe risk to global food supplies, but accurate and timely diagnosis is hampered by a lack of resources in many regions. As the number of people with access to smartphones continues to rise throughout the world, and as recent advancements in computer vision allowed by deep learning pave the way, we may soon be able to use them to aid in the detection of diseases. Acquiring pictures, preprocessing, segmenting, and extracting features are all part of the image processing pipeline for disease detection in plants. In this review study, we take a look back at some of the most popular methods for classifying plant diseases: "Convolutional Neural Network (CNN)", "Support Vector machine (SVM)", "K-Nearest Neighbors", and "Artificial Neural Network (ANN)". the results of the study show that the convolutional neural network method outperforms the more conventional techniques in terms of accuracy. this review study discusses the use of DL models to depict different plant diseases and gives a detailed description of their operation. Furthermore, several research gaps are noted from which better clarity may be obtained in regard with early detection of plant diseases. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial neural network Convolution neural network Deep learning Image processing Plant disease detection Recurrent neural network

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Explainable software Defect Prediction from Cross Company Project Metrics using machine learning 7

Explainable Software Defect Prediction from Cross Company Pr...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Haldar, Susmita Capretz, Luiz Fernando Fanshawe College School of Information Technology London Canada Western University Department of Electrical and Computer Engineering London Canada

ISBN: (纸本)9798350397253

Predicting the number of defects in a project is critical for project test managers to allocate budget, resources, and schedule for testing, support and maintenance efforts. software Defect Prediction models predict the number of defects in given projects after training the model with historical defect related information. the majority of defect prediction studies focused on predicting defect-prone modules from methods, and class-level static information, whereas this study predicts defects from project-level information based on a cross-company project dataset. this study utilizes software sizing metrics, effort metrics, and defect density information, and focuses on developing defect prediction models that apply various machine learning algorithms. One notable issue in existing defect prediction studies is the lack of transparency in the developed models. Consequently, the explain-ability of the developed model has been demonstrated using the state-of-the-art post-hoc model-agnostic method called Shapley Additive exPlanations (SHAP). Finally, important features for predicting defects from cross-company project information were identified. © 2023 IEEE.

关键词： Defect density

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Preserving Users' Sensitive Data in Physical and Virtual World Using machine learning: A Strategy 23

Preserving Users' Sensitive Data in Physical and Virtual Wor...

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7th international conference on Information System and Data Mining, ICISDM 2023

作者： Gill, Nasib Singh Gulia, Preeti Sagu, Amit Goyal, Bharti Department of Computer Science & Applications Maharshi Dayanand University Rohtak India

ISBN: (纸本)9798400700637

the impact of computing on society is intricate and far-reaching both physically and virtually. Computers have led to an increase in productivity since they have made a wide variety of operations simpler and more efficient. Specifically, Internet of things has made maintaining infrastructure faster, cheaper, and greener. When it comes to the relevance of the virtual world, technology has completely altered the way we engage with one another. Communication has been streamlined and quickened thanks to the internet and social media platforms. But along with the rising use of technology, new societal challenges are being witnessed including security, freedom, and privacy. Preserving these values is crucial for users, and machine learning may play a key role in mitigating these risks and challenges. this paper attempts to provide a machine learning strategy for safeguarding users' personal and sensitive data against intrusion, fraud, malware, spam, and fake profile, etc. Furthermore, the paper also presents the fruitful and encouraging results of various experiments based on proposed machine learning strategies while utilizing two separate datasets. © 2023 ACM.

关键词： Sensitive data

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7th international conference on Smart Trends in computing and Communications, SmartCom 2023

7th International Conference on Smart Trends in Computing an...

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7th international conference on Smart Trends in computing and Communications, SmartCom 2023

ISBN: (纸本)9789819907687

the proceedings contain 144 papers. the special focus in this conference is on Smart Trends in computing and Communications. the topics include: Semi-Supervised Medical Image Segmentation on Data from Different Distributions;profiling Network Traffic by Using Classification Techniques in machine learning;identification of Cracks in High-Rise Buildings Using Drones and Deep learning;wine Quality Prediction Using machine learning Techniques;wearable Technology with Location Tracking, Health Monitoring, and Attendance Tracking for Employees;analysis of Path Loss of Wireless Channel with Hata Model;a Study on Cross Site Scripting Attacks;Solving Text-to-SQL Task through machine Translation;Deep learning Framework for Liver CT Image Segmentation and Risk Prediction;evolution, Trends, and Future Developments of Business Intelligence;Face Recognition Using Convolutional Neural Network (CNN);Design and Development of a Cross-Platform Application: MSCW App;improving Naturalness in Speech Synthesis Using Fuzzy Logic;online Handwriting Recognition in Multiple Languages;playful thinking as a Strategy to Assess Mathematical Skills in Primary School;anomaly Detection in Medical IoT Devices Using Federated learning;comparative Analysis of Image Augmentation and Data Deduplication Techniques;Identification of Spoofing URLs Using Hybrid Algorithms;Role of PRI Functionaries in Adaptation of e-Governance at Gram Panchayat: A Study of Two States;a Discrete Firefly Algorithm Applied to Structural Bridge Truss Optimization;a Secure Protocol for Authentication and Data Storage for Healthcare System;implementation of Real Time Face Mask Recognition Technique Using Keras, TensorFlow;Factors Affecting the Integration of Educational Technologies in Academic Delivery by Management Teachers During COVID-19 Times;face Recognition at Various Angles.

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