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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是211-220 订阅

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Predictive Maintenance for Hard Disk Drives Using Random Forest 8th

Predictive Maintenance for Hard Disk Drives Using Random For...

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8th IFIP TC 12 international conference on Computer, Communication and Signal Processing, ICCCSP 2024

作者： Baniya, Achal Vinod, Abhay Chinta, Hitesh Surya Manathanathu Pushakara, Bhavith Pati, Peeta Basa Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031736162

As digital data continues to grow exponentially, the reliability of Hard Disk Drives (HDDs) becomes a critical concern for ensuring data integrity and preventing unexpected system downtimes. The research dataset contains historical SMART attribute data collected from HDDs, encompassing parameters such as spin-up time, temperature reallocated sector count and other relevant metrics. The study explored usage of various exploratory data analysis (EDA) & machine learning (ML) techniques for forecasting HDD failures. EDA techniques include Synthetic Minority Over-sampling Technique (SMOTE) algorithm for balancing class representation in training set and Principal Component Analysis (PCA) for dimensionality reduction. Diverse machine learning techniques, such as Random Forest (RF), Decision Tree (DT), K-nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Adaptive Boosting (AdaBoost), XGBoost, Gradient Boosting (GB), Linear Discriminant Analysis (LDA), Logistic Regression (LR), are employed to predict the hard drive failure. Local Interpretable Model-agnostic Explanations (LIME) is employed to identify which features have the greatest influence on a specific prediction, providing insights into the factors driving the model’s decisions. Random Forest exhibited superior performance of accuracy 99.9 and F1 score 0.98 compared to other models. © IFIP international Federation for Information Processing 2025.

关键词： Dimensionality reduction

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File Prefetching Accuracy Enhancement Using Deep learning Approaches 4th

File Prefetching Accuracy Enhancement Using Deep Learning Ap...

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4th international conference on Advanced Network Technologies and Intelligent computing-ANTIC-Annual

作者： Jamadar, Amaan Mulik, Vaishnavi Joshi, Anvay Joshi, Amit D. COEP Technol Univ Dept Comp Sci & Engn Sch Computat Sci Pune Maharashtra India

ISBN: (纸本)9783031837951;9783031837968

Efficient file prefetching is essential for optimizing inputoutput performance in modern operating systems. The readahead mechanism in Linux plays a crucial role in this process by predicting future file access patterns and pre-loading data. However, existing heuristics often struggle to adapt dynamically to varying workloads or other machine learning based approaches might struggle with system-level tasks like readahead because it does not capture temporal features effectively. Current readahead algorithms lack the ability to predict real-time changes in file access patterns, leading to inefficiencies in file system performance, especially under diverse workloads. In this approach, temporal and non-temporal features are utilized, applying models such as Extreme Gradient Boosting and Long Short-Term Memory networks for readahead size prediction. This approach compared the performance of Long Short-Term Memory and Extreme Gradient Boosting for system-level predictive tasks. Long Short-Term Memory outperformed Extreme Gradient Boosting in sequential data handling, achieving higher accuracy but at the cost of greater computational overhead and latency. Extreme Gradient Boosting, though faster and more resource-efficient, struggled with capturing long-term dependencies, still giving an accuracy similar to Long Short-Term Memory. This approach demonstrates the potential for machine learning models to enhance file prefetching mechanisms in Linux, offering a more adaptive and efficient solution for handling diverse file access patterns. Long Short-Term Memory and Extreme Gradient Boosting models have demonstrated impressive performance metrics, with Long Short-Term Memory achieving an R-2 value of 0.9886 and Extreme Gradient Boosting reaching 0.9785. These results indicate that both models significantly outperform Kernel machine learning's accuracy of 95.5%.

关键词： Prefetching Readahead LSTM XGBoost Linux Kernel Deep learning

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Effective Artificial Intelligence Based Diabetic Retinopathy Prediction Model Using Ensemble and Federated learning Technique 12th

Effective Artificial Intelligence Based Diabetic Retinopathy...

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12th international conference on Recent Trends in computing, ICRTC 2024

作者： Meenakshi, G. Sudharsan, M. Prakash, K. Department of Computer Science Justice Basheer Ahmed Sayeed College for Women Chennai India Department of Computer Applications Dayananda Sagar University Karnataka Bengaluru India

ISBN: (纸本)9789819788354

The advancement of artificial intelligence helps to predict and analyze huge quantities of data based on the supplies. Diabetic Retinopathy (DR) prediction and classification is an important research topic because, affording to a recent survey, around 422 million individuals worldwide are affected and are losing their vision. A plethora of studies and techniques have been developed to predict and analyze DR. However, until the main research gap is filled through the development of better models and the addition of new features, the advancement of machine and deep learning in terms of predictions and classifications changes many things in society. But the main problem in machine and deep learning is that the data is centralized, so the prediction and classification are performed only based on the central dataset, and the changes in the different places are not considered for prediction and classification. In this effort, we proposed a typical to sort out the mentioned issues such as central data storage-based training and training imbalanced problems. The proposed model is used in ensemble, convolutional neural network (CNN), and federated learning for prediction and classification in a distributed way. In the proposed work, ensemble learning is used to forecast the DR, the CNN algorithm is used to categorize the stages of DR, and federated learning is used to share the model with privacy and predict the result. The FedCS and Federated average are used for cleint selection and model averaging. The proposed work is implemented in the MESSIDOR dataset and checked for accuracy in the different scenarios of client selection and aggregation. Different scenarios are controlled with the help of automated privacy and information protection controls. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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A Predictive Maintenance Platform for a Conveyor Motor Sensor System Using Recurrent Neural Networks 5th

A Predictive Maintenance Platform for a Conveyor Motor Senso...

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5th international conference on Neural computing for Advanced Applications (NCAA)

作者： Kiangala, Kahiomba Sonia Wang, Zenghui Univ South Africa UNISA Dept Elect & Smart Syst Engn ZA-1709 Johannesburg Gauteng South Africa

ISBN: (纸本)9789819770007;9789819770014

Since the effusion of Industry 4.0 (I40) and smart manufacturing, predictive maintenance (PdM) has become critical to prevent severe system breakdowns and costly production downtime in various industries. Several state-of-the-art Artificial Intelligence (AI) approaches, such as machine learning models (ML), empower the PdM design concept to produce more accurate outcomes. In this study, we propose the development of a PdM platform that effectively detects future possible warnings and failures in the vibration speed sensor of a conveyor motor. We build the platform backend using recurrent neural networks (RNN) regression ML models to predict vibration speed values and their trends. We train our RNN models using univariate time-series historical vibration sensor data recorded over the years. The experimental results demonstrate the robustness of our platform built on RNN models compared to other traditional regression machine learning models such as Extreme Gradient Boosting (XGboost). We compare the effectiveness of the PdM platform with two RNN models: a basic RNN model and a long short-term memory (LSTM) model. Unlike other regression PdM systems focusing on predicting devices Remaining Useful Life (RUL), our PdM platform forecasts and generates new vibration sensor data based on the desired future time range. It also pinpoints set warning and failure values and indicates when these faults are more likely to occur. Factories can utilize this feature for analytics purposes.

关键词： Conveyor Motor Sensor Predictive Maintenance (PdM) Recurrent Neural Networks (RNN) Regression Models time-series data

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Texture-Based Classification of Geo-Fossils 26th

Texture-Based Classification of Geo-Fossils

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26th international conference on Information Integration and Web Intelligence, iiWAS 2024

作者： Saif, Amal Alnagi, Eman Ahmad, Ashraf Princess Sumaya University for Technology Amman11941 Jordan

ISBN: (纸本)9783031780929

Classification of fossil images is a crucial task in paleontology, aiding in understanding ancient life forms and their evolutionary history. This study explores the effectiveness of statistical features in representing the texture of six distinct fossil classes: Ammonites, Belemnites, Corals, Crinoids, Leaf Fossils, and Trilobites. This approach will lessen the computing power needed to categorize the fossils and the enormous volume of data usually required to train Artificial Intelligence (AI) models. Various statistical texture features were extracted from the images. Several machine learning (ML) classifiers were trained and assessed using these features. Each model’s performance was evaluated using accuracy, precision, recall, and F1−score metrics. This approach has been compared with feature extraction conducted by Convolution Neural Network (CNN). The results demonstrate the effectiveness of the proposed feature extraction approach in classifying fossil images, outperforming the CNN model with 10% accuracy and thus providing insights into the most suitable approaches for automated fossil identification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Exploring Ransomware Detection Based on Artificial Intelligence and machine learning 4

Exploring Ransomware Detection Based on Artificial Intellige...

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4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Rele, Mayur Samuel, John Patil, Dipti Krishnan, Udaya Parachute Health PrincetonNJ07054 United States CGS Inc New YorkNY10285 United States University of Cumberlands WilliamsburgKY United States Cognizant Technology Solutions Tamil Nadu India

Ransomware is an increasingly prevalent cybersecurity hazard due to its ability to encrypt data and request payment for its decryption. The threat's dynamic nature generally renders conventional ransomware detection methods ineffective. This paper suggests an innovative method for detecting ransomware that capitalizes on artificial intelligence (AI) and machine learning (ML). A novel technique has been developed that integrates robust anomaly detection and classification algorithms with advanced feature extraction from system logs, network traffic, and file metadata. This technique achieves high accuracy with minimal false-positive rates by employing autoencoders, isolated forests for anomaly detection, random forests, and support vector machines for classification. The method's ability to substantially improve ransomware defenses has been demonstrated through extensive testing on a large dataset, revealing that it outperforms current approaches. The study establishes a firm foundation for proactive ransomware detection and mitigation by demonstrating the advantages of integrating AI and ML in cybersecurity. © 2025 The Authors. Published by Elsevier B.V.

关键词： Cybersecurity

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Identification of Rumination Patterns in Cattle Through Optical Flow Analysis and machine learning Techniques 16th

Identification of Rumination Patterns in Cattle Through Opti...

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16th international conference on Genetic and Evolutionary computing, ICGEC 2024

作者： Ishikawa, Taichi Zin, Thi Thi Aikawa, Masaru Kobayashi, Ikuo Graduate School of Engineering University of Miyazaki Miyazaki Japan Organization for Learning and Student Development University of Miyazaki Miyazaki Japan Sumiyoshi Livestock Science Station Field Science Center Faculty of Agriculture University of Miyazaki Japan 1-1 Gakuen Kibanadai-Nishi Miyazaki889-2192 Japan

ISBN: (纸本)9789819615346

Rumination is a critical indicator of a cow’s physiological state, making it a valuable metric for managing cow health and predicting calving. Traditional human observation of rumination behaviour is time consuming and impractical for continuous monitoring, and sensor-based identification can be stressful for the cows due to the need for attachment. To address these challenges, this study introduces a non-contact method for identifying cow rumination. The proposed approach involves capturing video footage of multiple cows from above, recognizing cow regions, and validating the method’s effectiveness through experiments. Specifically, we utilize optical flow and frame-to-frame subtraction methods to extract moving cow regions from the recorded videos. From this data, we derive 11 features and employ a Support Vector machine (SVM) for classification. Training the SVM with label features resulted in a test data identification accuracy of approximately 60%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optical flows

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Improving Imbalanced data Classification in Bank Marketing Using Cluster PCA-Based Weighted Random Forest 4th

Improving Imbalanced Data Classification in Bank Marketing U...

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4th international conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Atif, Dalia Univ Tipaza Econ Tipasa 42000 Algeria

ISBN: (纸本)9783031821493;9783031821509

Telemarketing is currently the most effective method for banks to sell long-term deposits and enhance profitability. Leveraging machine learning techniques can optimize profits by accurately targeting the most promising potential customers, particularly addressing the challenge of imbalanced classification inherent in this domain. This paper proposes an algorithm to address the imbalance problem by using the minority instance's contribution to the variance explained by principal components to train a weighted random forest after clustering the minority class with K-means. Using the UCI bank marketing dataset with an 80:20 training-evaluation split, the proposed algorithm achieves the best compromise between sensitivity and precision without altering the data distribution. This performance is benchmarked against three classifiers-Random Forest (RF), Logistic Regression (LR), and Support Vector machine (SVM)-and oversampling techniques: SMOTE, K-means SMOTE, and ADASYN.

关键词： Bank deposits Telemarketing Imbalance Weighted Random Forest

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Navigating the Edge: A Comprehensive Comparison of machine learning Algorithms on Prominent Edge Platforms 4th

Navigating the Edge: A Comprehensive Comparison of Machine L...

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4th international conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Sayed, Aya Nabil Al-Hasan, Tamim Mahmud Bensaali, Faycal Nhlabatsi, Armstrong Qatar Univ Dept Elect Engn Coll Engn Doha Qatar Qatar Univ Coll Engn KINDI Ctr Comp Res Doha Qatar

ISBN: (纸本)9783031821493;9783031821509

As the demand for deploying machine learning (ML) and Deep learning (DL) models at the network's edge continues to grow, it becomes imperative to evaluate and compare the performance of various edge platforms. This study introduces an innovative edge-based pipeline for analyzing home energy and environmental data and addressing challenges in managing imbalanced label distributions. The comparison between random under-sampling and over-sampling strategies reveals the superiority of over-sampling, significantly improving accuracy and f1-score. More specifically, over-sampling using the Decision Tree (DT) technique enhanced accuracy and f1-score by 4.97% and 9.24%, respectively, when compared to the original results. The paper expands its contribution by conducting a thorough performance evaluation of various edge platforms, emphasizing critical metrics. The evaluated edge platforms include a Raspberry Pi 4b+, Jetson Nano Developer Kit, Odroid XU4, and Coral Dev Board. The study conducted an exhaustive comparison of the runtime of these prominent edge platforms when running ML/DL algorithms for tabular and image datasets during the testing phase. The Jetson Nano, particularly with its built-in GPU, demonstrates superior performance in ML/DL tasks, showcasing accelerated processing times for Convolutional Neural Network (CNN)-based algorithms. These findings emphasize the efficacy of the Jetson Nano in handling computationally intensive tasks for edge-based applications.

关键词： Edge computing machine learning Deep learning Energy Efficiency

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7th international Workshop on Predictive Intelligence in Medicine, PRIME 2024, held in conjunction with the 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

7th International Workshop on Predictive Intelligence in Med...

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7th international Workshop on Predictive Intelligence in Medicine, PRIME 2024, held in conjunction with the 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031745607

The proceedings contain 17 papers. The special focus in this conference is on Predictive Intelligence in Medicine. The topics include: Spectral Graph Sample Weighting for Interpretable Sub-cohort Analysis in Predictive Models for Neuroimaging;RCT: Relational Connectivity Transformer for Enhanced Prediction of Absolute and Residual Intelligence;gene-to-Image: Decoding Brain Images from Genetics via Latent Diffusion Models;physics-Guided Multi-view Graph Neural Network for Schizophrenia Classification via Structural-Functional Coupling;automated Patient-Specific Pneumoperitoneum Model Reconstruction for Surgical Navigation Systems in Distal Gastrectomy;MNA-net: Multimodal Neuroimaging Attention-Based Architecture for Cognitive Decline Prediction;Improving Brain MRI Segmentation with Multi-Stage Deep Domain Unlearning;DynGNN: Dynamic Memory-Enhanced Generative GNNs for Predicting Temporal Brain Connectivity;Strongly Topology-Preserving GNNs for Brain Graph Super-Resolution;generative Hypergraph Neural Network for Multiview Brain Connectivity Fusion;identifying Brain Ageing Trajectories Using Variational Autoencoders with Regression Model in Neuroimaging data Stratified by Sex and Validated Against Dementia-Related Risk Factors;integrating Deep learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction;self-Supervised Contrastive learning for Consistent Few-Shot Image Representations;Neurocognitive Latent Space Regularization for Multi-label Diagnosis from MRI;Segmentation of Brain Metastases in MRI: A Two-Stage Deep learning Approach with Modality Impact Study.

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