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检索条件"任意字段=6th Asia Conference on Machine Learning and Computing, ACMLC 2024"

共 606 条记录，以下是91-100 订阅

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Leveraging Deep learning for Multimodal Predictive Maintenance: A Hybrid CNN-LSTM Approach for Enhanced Equipment Failure Prediction in Automated Assembly Lines 6

Leveraging Deep Learning for Multimodal Predictive Maintenan...

引用

6th IEEE International conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Rao, Yelei Southwest Jiaotong University Chengdu China

ISBN: (纸本)9798350374315

the exploration of deep learning techniques for multimodal predictive maintenance in automated production lines is undertaken, presenting a hybrid model that synergizes Convolutional Neural Networks (CNNs) for visual analytics and Long Short-Term Memory (LSTM) networks for temporal sensor data processing. the CNN is designed to pinpoint signs of deterioration and anomalies within visual inputs, while the LSTM extracts predictive patterns from sensor data that indicate equipment failure. Drawing upon a blend of publicly accessible sensor datasets from reputable sources such as the UCI machine learning Repository and Kaggle, enriched by an exclusive collection of imagery from a diverse set of 100 Chinese assembly plants, the integrated analysis from both models results in a comprehensive predictive mechanism for equipment malfunction. this pioneering method is intended to enhance the operational efficiency and precision of automated assembly processes through the empowerment of pre-emptive maintenance actions. © 2024 IEEE.

关键词： Predictive maintenance

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Chronic kidney disease prediction: Optimization of machine learning algorithms 6

Chronic kidney disease prediction: Optimization of machine l...

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6th International conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Kumar, Narinder Singla, Sanjay Department of CSE Chandigarh University India Department of CSE Chandigarh University India

ISBN: (纸本)9798350374629

A wide range of variables, such as more drinking, a growing worldwide pollution trouble caused on by rapid industrialization and warming temperatures, release of toxic gases from polluted water and food, misuse of drugs, and, most importantly, bad habits, are expected to make kidney failure worse. It is one of the deadliest illnesses affecting the world's medical care systems. these factors ultimately lead to a constant rise in the number of kidney anomalies that patients are diagnosed with. Building classification and prediction models for early liver disease diagnosis involves examining the patient's renal datasets. the disease is predicted using machine learning in an attempt to lighten the burden on physicians. this research investigates many past machine-learning algorithms for the diagnosis and categorization of renal disorders. A comparative examination of more than six models suggests the best course of action for the target dataset. For offline challenges, these are the greatest options for improving the accuracy of the model. While there is evidence to show that different ensemble procedures and hyper parameter tuning can lead to improved accuracy, these methods significantly raise computing productivity costs, rendering them impractical for real-world applications. ©2024 IEEE.

关键词： machine learning

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machine learning Based Detection System for Identifying Deepfake Images and Social Bots 6

Machine Learning Based Detection System for Identifying Deep...

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6th International conference on Advancements in computing, ICAC 2024

作者： Liyanage, Chamath Dematgoda, Hirushini Harindi, Dewmini Perera, Nirmal Fernando, Harinda Rathnayake, Samadhi Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

this paper presents a machine learning-based approach to detect two major forms of misinformation on social media platforms: deepfake images and social bots. For deepfake image detection, we propose a novel hybrid model combining DenseNet121 with custom Convolutional Neural Networks, achieving 97% accuracy on a dataset of 140,000 images. the social bot detection component utilizes a dual-branch architecture, processing tweet content through LSTM networks and user metadata through dense layers, attaining 91 % accuracy on the CIC truth seeker dataset. Both models demonstrate balanced performance across classes, addressing the challenges of evolving misinformation tactics. By integrating these detection systems, we aim to enhance the integrity and reliability of information ecosystems in digital spaces. Our approach contributes to the ongoing efforts to combat the spread of misinformation on social media platforms. © 2024 IEEE.

关键词： Bot (Internet)

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Facial Diagnosis Skin Care and Makeup Recommendation Using Advanced Deep learning Techniques 6

Facial Diagnosis Skin Care and Makeup Recommendation Using A...

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6th International conference on Advancements in computing, ICAC 2024

作者： Senevirathna, Oshadi Rathnayake, Samadhi thilakarthna, thusithanjana Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

this research presents specifically an enhanced model of skin condition identification, skin color analysis, and makeup suggestion based on machine learning. the system combines a Custom Convolutional Neural Network (CNN), XGBoost, and Support Vector machine (SVM) to help in determining skin conditions including;acne, pimples, and dark spots. the Custom CNN proves to yield higher accuracy in comparison with other models such as VGG and ResNet. Further, the skin tone, which is an important characteristic of a face mask, is formulated by paying much attention to individual facial areas or specifically the cheeks to determine the most popular color that is then compared to certain categories of skin tone. Recommendations for makeup are made using both content based and collaborative filtering approach depending on the color of the skin preference of the user. According to the results of the experimental studies, the proposed model based on the Custom CNN, XGBoost, and SVM has the high accuracy of predictions;at the same time, the use of the hybrid recommendation system allows improving the individualization of makeup recommendations. the benefit of this approach is that there is a general enhancement in the field of personalised skincare and beauty solutions. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on Logistics Sorting Volume Prediction Based on machine learning 6

Research on Logistics Sorting Volume Prediction Based on Mac...

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6th IEEE International conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Tian, Zhiwei Qian, Ruliang Sun, Boyuan Qinghai Minzu University Qinghai China

ISBN: (纸本)9798350374315

In modern logistics, accurate forecasting of sorting center cargo volumes is crucial for resource allocation and process optimization. Traditional time series forecasting models, such as ARIMA and SARIMA, perform well in short-term predictions but encounter diminishing performance in long-term scenarios. this study aims to overcome these limitations by employing non-traditional time series forecasting techniques, specifically machine learning algorithms. We constructed a predictive model that includes a variety of features. By developing a comprehensive set of features that includes lagged cargo volumes, time series characteristics, and trend indicators, we trained individual predictive models for each sorting center. During the model selection process, we evaluated multiple machine learning models, including random forests, neural networks, support vector machines, and linear regression. the evaluation was based on the models' fit to the training set and their performance on the validation set. By analyzing residual plots and calculating scores on the test set, we determined the models' predictive accuracy and bias to select a reliable and precise forecasting model. the model evaluation utilized MAE, MSE, and RMSE metrics, with the random forest outperforming the other algorithms. Ultimately, we used the random forest model to provide a 30-day cargo volume forecast for sorting centers, enhancing predictive accuracy with hourly data granularity. © 2024 IEEE.

关键词： Prediction models

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machine learning-Driven Paradigms in VLSI Design: Exploring the Synergy with Pruning for Optimal Circuit Efficiency 6

Machine Learning-Driven Paradigms in VLSI Design: Exploring ...

引用

6th Parul International conference on Engineering and Technology, PICET 2024

作者： Parameswari, P. Divya Ananthalakshmi, A.V. Puducherry Technological University Puducherry India

ISBN: (纸本)9798350369748

this survey paper explores the intersection of machine learning and VLSI design, shedding light on the evolving landscape of innovative methodologies. the initial focus is on papers discussing the pivotal role of machine learning(ML) in shaping VLSI design paradigms. Subsequently, the paper delves into the realm of approximate computing, identifying pruning as a standout concept for its efficiency. Building on this, the paper advocates for a synergistic approach, proposing the integration of machine learning and pruning for optimal circuit design. Two illustrative papers are discussed to underscore the practical implications of this combined methodology, providing a holistic perspective on the advancements in VLSI design. © 2024 IEEE.

关键词： Adversarial machine learning

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the effect of Data Augmentation Using SMOTE: Diabetes Prediction by machine learning Techniques 6

The effect of Data Augmentation Using SMOTE: Diabetes Predic...

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6th Artificial Intelligence and Cloud computing conference (AICCC) / 5th asia Digital Image Processing conference

作者： Al-Qerem, A. Ali, A. M. Alauthman, M. Al Khaldy, M. Aldweesh, A. Zarqa Univ Fac Informat Technol Dept Comp Sci Zarqa 13110 Jordan Al Ahliyya Amman Univ Fac Engn Commun & Comp Engn Dept Amman 19328 Jordan Univ Petra Fac Informat Technol Dept Informat Secur Amman Jordan Univ Petra Business Inteligence & Data Analyt Amman Jordan Shaqra Univ Coll Comp & Informat Technol Shaqra 11911 Saudi Arabia

ISBN: (纸本)9798400716225

Diabetes mellitus, a severe and enduring condition characterized by impaired glucose metabolism, poses a substantial threat to public health. Its pervasive impact continues to escalate globally, with a rising incidence that challenges preventive measures. Despite earnest efforts, individuals struggle to evade the clutches of diabetes, necessitating innovative approaches for disease management. Traditional methodologies in diabetes health monitoring exhibit limitations, prompting the exploration of advanced techniques. this study employs machine learning (ML) methods to delve into diabetes, aiming to enhance diagnostic accuracy. the primary objective is to develop a method capable of precise diabetes diagnoses with a heightened level of precision. the investigation incorporates machine learning algorithms, specifically Random Forest (RF), K Nearest Neighbor (KNN), and Logistic Regression. the inclusion of these algorithms seeks to streamline data processing ***, this study incorporates the Synthetic Minority Over-sampling Technique (SMOTE) as a data augmentation strategy. SMOTE addresses imbalances in the dataset, contributing to a more robust and representative sample. the research evaluates the effectiveness and accuracy of diabetes prediction using these algorithms both before and after SMOTE implementation. By considering the impact of SMOTE, the study aims to determine the optimal algorithm for assessing diabetes development. the comparative analysis sheds light on how SMOTE enhances the overall performance of machine learning models. this nuanced approach not only refines diabetes diagnostic protocols but also underscores the significance of addressing data imbalances in predictive modeling for enhanced precision in disease prediction.

关键词： Classification Data augmentation Diabetes

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Detecting Bitcoin CoinJoin Transactions Using machine learning 6

Detecting Bitcoin CoinJoin Transactions Using Machine Learni...

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6th International conference on Blockchain computing and Applications, BCCA 2024

作者： Dekhil, Oumayma Tonga-Naha, Rodrigue Mayeli-Feridani, Mikaeil Najjar, Fatma Zhang, Kaiwen Department of Software and IT Engineering ÉTS Montréal QC Canada

ISBN: (纸本)9798350351538

Anonymity is one of the characteristics that makes Bitcoin mainstream. However, various approaches have been proposed to deanonymize Bitcoin to reveal the identities of the people behind the addresses. In order to enhance anonymity, Bitcoin mixing services were developed to obscure links between Bitcoin addresses. CoinJoin transactions method is one of the most used mixing approaches. the CoinJoin technique is a concept where multiple users merge their transactions into one larger transaction [16]. In this article, we propose a new system of detecting CoinJoin transactions using machine learning. Firstly, we developed a new algorithm to extract connected transactions and trace them back to the Coinbase transaction. Afterwards, we conducted a rigorous analysis of the data and performed an ablation study to identify the most relevant features of our machine learning models. then, we implemented and fine-tuned them using an automated tool called OPTUNA, after which we trained the models and evaluated their performances, including accuracy, precision, and F1 score. © 2024 IEEE.

关键词： Anonymity

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Optimizing User Experience through machine learning: A Review of Key Tools and Technologies 6

Optimizing User Experience Through Machine Learning: A Revie...

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6th International conference on Advancements in computing, ICAC 2024

作者： Abhayasundara, Kulesh thilakarathna, Gihan Nagahawatta, Diliru thilakarathna, Lasitha Wickramarathne, Jagath Sri Lanka Institute of Information Technology Faculty of Graduate Studies Malabe Sri Lanka

ISBN: (纸本)9798331517878

the application of machine learning (ML) to user experience (UX) design is the subject of this study, with a particular focus on privacy concerns, bias mitigation, and the development of best practices for ethical ML implementation. It evaluates several ML tools, including PyTorch, TensorFlow, Azure, BigQuery, alongside design platforms like Figma, can be assessed for their effectiveness in optimizing UX workflows, enhancing user satisfaction, and improving system adaptability. A comprehensive literature review identifies gaps in the current research, such as the need for more robust, user-centered frameworks and a lack of detailed analysis regarding the long-term impacts of ML-driven UX systems. the study emphasizes the critical importance of collaboration between ML experts and UX designers to create more efficient, inclusive, and user-friendly systems while maintaining high ethical standards in data use. Furthermore, it explores emerging technologies, such as augmented and virtual reality, that are expected to influence the future of ML-UX integration. these findings offer valuable guidance for both practitioners and researchers aiming to leverage ML's potential in UX design. Ultimately, this review contributes to the broader discourse by providing recommendations that address innovation, ethical challenges, and responsible implementation across diverse digital design contexts. © 2024 IEEE.

关键词： Adversarial machine learning

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Leveraging Smart Video-Based learning to Enhance Student Engagement and Collaboration 6

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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6th International conference on Advancements in computing, ICAC 2024

作者： Serasinghe, Chanukya Perera, Nashali Samarasinghe, Devindu Muthusinghe, Madusha Wijesundara, Malitha Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Humanities and Sciences Malabe Sri Lanka

ISBN: (纸本)9798331517878

the COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. this research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. the system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. the methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. the results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. the efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. this innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience. © 2024 IEEE.

关键词： Federated learning

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