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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

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Client dependability evaluation in Federated learning framework 3

Client dependability evaluation in Federated Learning framew...

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3rd international conference on Communications, Information System, and data Science, CISDS 2024

作者： Han, Kexiu Zhang, Guoyue Yang, Liangbin Bai, Jing University of International Relations HaiDian BeiJing100080 China

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

Federated learning (FL) is a widely adopted distributed machine learning paradigm, individual clients train local models by using their private datasets and then send model updates to a central server. While its decentralized training process can protect data privacy, it is vulnerable to attacks such as model poisoning attack and backdoor attack. The effect of malicious clients can be mitigated by applying robust FL methods. However, most existing solutions ignored the client dependability. This paper explores a method for quantitatively assessing the client dependability in FL framework. Firstly, based on semi-Markov process (SMP), we build a multi-dimensional evaluation model for understanding how the client's behaviors under attack and its recovery behaviors affect the client dependability. Then, we deduce the formulas of calculating the availability, security risk and reliability in order to analyze the quantitative relationship between different factors and the client dependability from these three perspectives. Furthermore, we perform numerical analysis to investigate how different system parameters impact the client dependability. © 2025 SPIE.

关键词： Federated learning

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Role of Artificial Intelligence and machine learning in scalable Internet of Things Applications 3

Role of Artificial Intelligence and Machine Learning in scal...

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3rd international conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Singla, Neeraj Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350330724

Anything non-living that can take decisions on its own is called artificially intelligent. It is a technique designed to mimic human behaviour. On the other hand machine learning if a subset of artificial intelligence. It is a statistic method used to enable machine to improve with experience. They both go hand in hand. Now IoT has been there in existence for an enormous amount of time but during recent times due to large scale good connectivity for efficient data transfer, machine learning and analytics and Conversional Artificial Intelligence has made IoT possible. © 2023 IEEE.

关键词： Internet of things

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Computer Aided Image Recognition Technology for Food Microbial Detection 3

Computer Aided Image Recognition Technology for Food Microbi...

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3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Ruan, Yanchun Jiangsu Vocational College Tourism Jiangsu Yangzhou225000 China

ISBN: (纸本)9798331519032

This article aims to explore the application potential of computer-aided image recognition technology in the field of food microbial detection, in order to overcome the limitations of traditional detection methods, such as long time-consuming and cumbersome operation. In order to achieve this goal, an image recognition system of food microorganisms based on advanced image processing algorithm and machine learning model is designed and constructed. In terms of methods, this article uses high-resolution microscope and digital camera to collect images, and uses image preprocessing, segmentation, feature extraction and other technologies to process images;Then, DL (Deep learning) model is applied to learn and classify the extracted microbial characteristics. The experimental results show that the recognition accuracy of the system for Escherichia coli, Salmonella and other food microorganisms is over 95%, and the detection time is significantly shorter than that of the decision tree method. This achievement proves the effectiveness and practicability of computer-aided image recognition technology in food microbial detection. It has a certain contribution to improving the detection ability of food microorganisms. © 2024 IEEE.

关键词： Computer aided image recognition Deep learning Food microbial detection Food safety

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Predictive Maintenance Analysis using Multivariate machine Performance data for Industrial Downtime Reduction 3

Predictive Maintenance Analysis using Multivariate Machine P...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Saddala, Rajeswara Reddy Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita school of computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita school of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Tool downtime prediction requires efficient monitoring because it helps maximize industrial operation quality while maintaining uninterrupted plant operations. The analysis utilizes machine learning algorithms with Decision Trees to identify machine downtime events through operational measurements which include hydraulic pressure and coolant temperature and spindle speed and vibration levels. data normalization alongside Forward Selection feature selection methods were used on a balanced dataset to boost model performance for prediction purposes. The Decision Tree model functioned to differentiate machine defects into two discrete categories—failure and no failure—thus proving its competence in such predictive classification activities. The study conducted a comparative evaluation between Decision Trees and Random Forest along with other algorithms which included SVM and Logistic Regression and Adaboost in addition to CatBoost and XGBoost and Naive Bayes and KNN using KD-Tree and Ball-Tree variations. The experimental findings show that Decision Trees provide clear predictions coupled with high precision and fair runtimes to support predictive maintenance implementations. The research delivers fundamental findings that help improve downtime prediction methods to boost operational effectiveness. © 2025 IEEE.

关键词： Predictive maintenance

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Misinformation Detection Using a Hybrid Deep learning model with Transformer Based Pre-Trained ALBERT Model 3

Misinformation Detection Using a Hybrid Deep Learning model ...

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3rd IEEE international conference on data, Decision and Systems, ICDDS 2024

ISBN: (纸本)9798350363890

Rise of social media platforms has made the spread of information easier. This also raises the issue of spreading misinformation which creates serious problems for society and governments and has a negative impact on many facets of daily life. One of the difficult problems in natural language processing is misinformation detection, which involves separating misinformation from genuine (or truthful) information. This study proposes a transformer-based pre-trained Albert model and integrated deep learning model which provides enhanced feature learning on dataset collected from different sources to provide a solution to the problem of misinformation detection. The dataset used for the task is a combination of Liar, genuine and misleading information which leads to Combined Corpus dataset. The deep learning model used are Bi-directional Gated Recurrent Unit (Bi-GRU) and a Convolutional Neural Network (CNN) to provide better feature engineering to categorize genuine and misinformation. A base Albert model has also been trained on the Combined Corpus (CC) dataset to evaluate the effect of enhanced feature learning using Bi-GRU and CNN with Albert. The results obtained reflect 1.30% and 0.76% increment in accuracy on Combined Corpus (CC) dataset from the base Albert model. © 2024 IEEE.

关键词： Contrastive learning

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machine learning Based Automatic Pricing and Replenishment Decision for Vegetable Products 3

Machine Learning Based Automatic Pricing and Replenishment D...

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3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Liu, Honglei Shanghai Lixin University of Accounting and Finance Shanghai China

ISBN: (纸本)9798350376173

In a fresh food superstore, vegetable items have a short shelf life due to their short shelf life. Supermarkets usually replenish the goods on a daily basis based on the historical sales and demand of each item. Therefore, in this paper, automatic pricing and replenishment decisions for vegetable items are related to the research. data preprocessing is carried out first, which contains operations such as dimensionality reduction, outliers, and missing values. Then a correlation analysis model is established to analyze the relationship of vegetables between different categories and single items through correlation coefficients, and it is found that cauliflower and foliage may promote each other or grow synergistically in many aspects, and may be similar in terms of soil requirements, nutrient uptake, or growth cycle, which leads to a strong positive correlation between them in the data. Finally, future vegetable pricing and replenishment decisions were predicted by constructing an LSTM neural network. © 2024 IEEE.

关键词： Shelf life

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IoT-Enabled Predictive Healthcare Monitoring Using machine learning Models 3rd

IoT-Enabled Predictive Healthcare Monitoring Using Machine L...

引用

3rd international conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Patil, Nitin Jagannath Sharma, Avinash Prasad, K.D.V. Yogi, Kottala Sri Dankan Gowda, V. Department of Instrumentation Engineering D. N. Patel College of Engineering Shahada Maharashtra Untavad India Professor Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Punjab Mohali140307 India Pune India Pune India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bengaluru India

ISBN: (纸本)9789819751457

This study explores the capabilities of integrating the Internet of Things (IoT) with machine learning to monitor health in real time. Using IoT-powered devices like heart rate monitors, glucose meters, and blood pressure devices, health metrics were consistently gathered and sent to a cloud-based system. After refining the data for better quality, machine learning techniques were used to create health prediction models. The findings showed significant precision, especially when using neural networks, in predicting health-related events. This combined system can identify and notify healthcare experts about potential health concerns immediately. However, the promise of ongoing health tracking and early detection comes with concerns regarding data protection, data handling, and model reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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A Comparative Analysis on Deep learning Approaches for Patient data Privacy Using Electronic Health Record 3

A Comparative Analysis on Deep Learning Approaches for Patie...

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3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Mishra, Ritunsa Satpathy, Rabinarayan Pati, Bibudhendu Sri Sri University Faculty of Engineering & Technology Odisha Cuttack India Rama Devi Women's University Dept. of Computer Science Odisha Bhubaneswar India

ISBN: (纸本)9798350354379

The widespread adoption of electronic health records (EHRs) across virtually all U.S. hospitals has facilitated the accumulation of vast amounts of unstructured medical data, which, while invaluable for advancing healthcare, also pose significant privacy risks. This paper presents a comparative analysis of various deep learning strategies aimed at bolstering the privacy of EHR data before it is made publicly accessible. We introduce and evaluate two innovative deep learning models on the MIMIC_III dataset, measuring their performance in terms of accuracy and F1 scores. Our research employs these models within a framework that emphasizes 'patient privacy preservation' (P3) and explores the application of differential privacy techniques using Recurrent Neural Networks (RNNs) and Artificial Neural Networks (ANNs) to secure the sharing of training data. The results demonstrate the efficacy of these advanced methodologies in enhancing the privacy protections of EHR data, thereby offering crucial insights for medical research. Our findings underscore the critical role of privacy-preserving measures in the dissemination of EHR data and validate the potential of sophisticated deep learning approaches to ensure secure data exchange. This study contributes to the broader discourse on reconciling the needs of medical research with the imperative to protect patient confidentiality. © 2024 IEEE.

关键词： Electronic health record

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A Comparative Analysis of Alzheimer's Disease Detection using Deep learning 3

A Comparative Analysis of Alzheimer's Disease Detection usin...

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3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Sharma, Garvit Gupta, Dinki Bhardwaj, Priyanshu Ramneet Verma, Pawan Sharda University Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798331528201

Alzheimer's is one of the progressively debilitating conditions, and it currently affects millions of people worldwide with no definitive medication for treatment. Understanding the nature of this disease is very important and stands to achieve early intervention and management. The development of models to recognize individuals having Alzheimer's based on neuroimaging data, particularly MRI scans, has thus become accessible through recently advanced machine learning and deep learning algorithms. This paper does a comparison of some of the leading algorithms. Models used for research in this study are Support Vector machines, Convolutional Neural Networks, 3D Convolutional Neural Networks, and hybrid pre-trained models. There's model differentiation which resulted in pointing out the gap in the system and weaknesses of each approach. Apart from this, the discovery of the Explainable AI (XAI) concept regarding insight in the predictions of the algorithms has also been emphasized recently. As a result, XAI can improve the interpretability of AI systems. With this perspective, the degree of insight could lead toward better outcomes as far as diagnosis and responses in clinical environments are concerned. Given the importance of the paper, it will suffice to contribute to further refinements of diagnostic methodologies for AD and inform future studies on the subject. © 2024 IEEE.

关键词： Convolutional neural networks

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Newborn Weight Prediction And Interpretation Utilizing Explainable machine learning 3

Newborn Weight Prediction And Interpretation Utilizing Expla...

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3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Chowdhury, Safiul Haque Mamun, Mohammad Hossain, Muhammad Minoar Hossain, Mohammad Ibrahim Iqbal, Md. Sadiq Kashem, Mohammod Abul Bangladesh University Department of Computer Science and Engineering Dhaka Bangladesh Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering and Technology Department of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350388282

Newborn weight refers to the weight of a baby at the time of birth. It is typically measured in units such as pounds and ounces or kilograms. The weight of a newborn is an essential metric used by healthcare professionals to assess the health of an infant and its development. This research emphasizes the prediction of newborn weight using machine learning (ML) and Explainable Artificial Intelligence (XAI). The proposed approach involves preprocessing a dataset encompassing various parameters of newborns and their mothers during pregnancy. The primary objective is to develop an automated system capable of accurately predicting the weight of a newborn. Employing a range of regression ensemble ML models, namely Bootstrap Aggregating regression (BAGGINGR), Random Forest Regression (RFR), Ensemble Voting Regression (VOTIINGR), Extreme Gradient Boosting Regression (XGBOOSTR), Stacked Generalization Regression (STACKINGR), and Gradient Boosting Decision Trees Regression (GBDTR). This research develops a cross-validation approach, and notably, the GBDT model emerges as the top performer, yielding impressive results with average metrics Mean Squared Error (MSE) of 247.26, Mean Absolute Error (MAE) of 12.29, R-squared (R2) of 0.23, Peak Signal-to-Noise Ratio (PSNR) at 20.99 dB, and Signal-to-Noise Ratio (SNR) at 48.65 dB. The outcomes of the research are interpreted through Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) in XAI, emphasizing the significance of interpreting these parameters. The proposed research has valuable insights to enhance the long-term health conditions of newborns, reduce mortality rates, and provide crucial support to healthcare professionals. © 2024 IEEE.

关键词： machine learning

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