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

共 6841 条记录，以下是171-180 订阅

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Optimizing Mental Health Status Prediction Models Using machine learning Algorithms 3

Optimizing Mental Health Status Prediction Models Using Mach...

引用

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

作者： Liu, Dong Guangdong Polytechnic of Science and Technology Guangdong Zhuhai519090 China

ISBN: (纸本)9798331519032

In contemporary society, mental health concerns are gaining heightened attention, and accurately forecasting individuals' mental well-being has become a pivotal research focus. Traditional predictive models for mental health often struggle with issues like the complexity of data processing and insufficient prediction precision. However, the application of machine learning algorithms introduces new approaches to address these challenges. In the age of artificial intelligence, creative works are high-quality data sources for machine learning, raising crucial questions about copyright regulations. Balancing these rules to foster innovation in both cultural and technological sectors is a pressing issue. This study employs various machine learning techniques, such as support vector machines, decision trees, and deep learning algorithms, to thoroughly examine and evaluate mental health data. As artificial intelligence technology evolves rapidly and reshapes its applications, new research themes and methodologies continuously emerge within machine learning, with advances in deep learning and reinforcement learning driving the field forward. This paper not only reviews the essential techniques in the optimization process but also explores future research avenues and potential applications, aiming to provide valuable insights for ongoing research and practical efforts in mental health prediction. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on ventilation and noise reduction technology for substations based on deep learning 3

Research on ventilation and noise reduction technology for s...

引用

3rd international conference on Energy and Power engineering, Control engineering (EPECE)

作者： Zhu, Li Zhu, Zixuan Jiang, Wenxiu State Grid Shanghai Econ Res Inst Shanghai 200000 Peoples R China Power China Shanghai Elect Power Engn Co Ltd Shanghai Peoples R China

ISBN: (纸本)9798350366105;9798350366099

With the accelerated urbanization process and the continuous growth of power demand, substations are a critical component of the power system. However, the main noise sources in substations, such as main transformers, reactors, and cooling fans, have a significant impact on the surrounding environment. Therefore, this paper proposes an optimization design method for ventilation and noise reduction in substations based on deep learning. Firstly, the finite element method is used to simulate the ventilation and noise data of substations under multiple operating conditions to obtain sufficient samples. Secondly, the construction of the Convolutional Neural Network (CNN) model is completed, and the obtained data is used for model training. Finally, the optimization solution of parameter design is achieved using the SAC algorithm. The results show that the designed parameters for substations using this method can effectively reduce noise and comply with national standards.

关键词： transformer station deep learning ventilation and noise reduction

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Sentiment Analysis of User Reviews on Bert-Based E-Commerce Platforms 3

Sentiment Analysis of User Reviews on Bert-Based E-Commerce ...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Huiyang, Yin Jin, Hou Hongwen, Li Lanxin, Liu Hao, Zhou Sichuan University of Science & Engineering Yibin China

ISBN: (纸本)9798350380989

The task of e-commerce platform user review sentiment analysis is to collect user reviews on various e-commerce platforms to make a dataset, and then manually annotate them with labels to perform sentiment analysis using deep learning methods. In deep learning-based sentiment analysis, embedding generation of text and feature extraction are the core parts, and this paper chooses to study these two parts and proposes a deep learning model based on bert-bigru-textcnn. © 2024 IEEE.

关键词： bert bigru deep learning emotional analysis user review

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Enhancing Grid Resilience: machine learning-Based Electrical Fault Detection in Power Systems 3

Enhancing Grid Resilience: Machine Learning-Based Electrical...

引用

3rd IEEE international conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2024

作者： Bhandari, Tejanshu Payne, Prasun Gourisaria, Mahendra Kumar Das, Suchismita Singh, Vinayak Mishra, Soumya Ranjan School of Computer Engineering Kiit Deemed to be University Odisha Bhubaneswar India Ge Healthcare Karnataka Bengaluru India

ISBN: (纸本)9798350387193

Electrical fault detection is one of the most critical aspects of maintaining grid resilience and reducing downtime in power systems. However there is a scarcity of adequet systems to detect these anomalies and therefore this research employs modern tools, including machine learning and data analytics, to develop a system capable of distinguishing between anomalies such as equipment breakdowns or short circuits and normal functioning through the analysis of various electrical parameters. In this study, a dataset consisting of Line Current and Line Voltage of different phases was analyzed by applying feature scaling post which different machine learning algorithms such as Decision Tree, Random Forest, MLP, Logistic Regression, and SVM were used to detect and classify the electrical faults that may occur. From the research it was found that KNN outperformed all the other algorithms and managed to achieve the highest overall accuracy as well as highest class-based accuracies as well. © 2024 IEEE.

关键词： Adversarial machine learning

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Robust machine learning Approach to Plant Species Classification for Sustainable Agriculture 3

Robust Machine Learning Approach to Plant Species Classifica...

引用

3rd international conference on Emerging Trends in Electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Mumtaz, Sara Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331504861

Sustaining resilient agricultural practices is essential for the overall yield because it guarantees both economic stability and food supplies. By incorporating Zero-Shot learning into a strong leaf classification architecture, our method meets this demand. We optimized an Al-powered four-step categorization process using the Bee Colony Algorithm. To pre-process, segment, extract features from, and classify leaf image data, zero-shot learning is employed. Pre-processing, which includes histogram equalization and contrast stretching, is followed by morphological segmentation. In feature extraction, two methods are used: Harris corner detection and SURF (Speeded Up Robust Features). Zero-Shot learning for classification is optimized using the Bee Colony Algorithm. At 92.73% accuracy, the suggested method surpasses out previous methods like CNN and SVM, suggesting that it has the potential to enhance crop quality and disease control in agriculture. ©2024 IEEE.

关键词： Zero-shot learning

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Developing a Hybrid machine learning System for Parkinson's Disease Detection Using a Combination of Freezing of Gait Video and Inertial Sensor data 3

Developing a Hybrid Machine Learning System for Parkinson's ...

引用

3rd international conference on Computing and machine Intelligence (ICMI)

作者： Zhou, Caroline Harker Sch San Jose CA 95129 USA

ISBN: (纸本)9798350372977;9798350372984

Detection of Parkinson's disease remains challenging due to the complexity and cost of diagnosis. Recently, different machine learning models have been proposed to detect Parkinson's. This paper proposes a novel hybrid machine learning detection system to detect Parkinson's disease using a combination of both videos of freezing of gait and numerical sensor data. Three machine learning models (sensor model, video model, and a hybrid model using a combination of the data) have been developed and evaluated. Four machine learning algorithms were used for model development, which were Logistic Regression, K-neighbors, Bayesian Regression, and Random Forest. All the models were evaluated using standard performance metrics of accuracy and precision based on the prediction of 12 commonly used Parkinson's rating scales (Mini-Mental, NFo-GQ, H&Y, UPDRS-II, UPDRS-III, PIGD, Dyskinesia, HADS, HADS-A, HADS-D, FES-I, mini-BESTest). This is the first time to use all 12 rating scales as output of a machine learning model for Parkinson's disease detection. The results indicate that the hybrid model has a significantly better performance than the video or sensor models. The hybrid model achieved an average accuracy of approximately 94% for all twelve rating scales, while the sensor model had an accuracy of approximately 91% and the video model had an accuracy of approximately 89%. This research indicates that the hybrid model is accurate and reliable because of its ability to fully use two sets of clinical data to make a final decision.

关键词： Parkinson's disease machine learning model hybrid turning-in-place inertial sensor freezing of gait

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Transformer Models for Bitcoin Price Prediction 24

Transformer Models for Bitcoin Price Prediction

引用

3rd international conference on Frontiers of Artificial Intelligence and machine learning (FAIML)

作者： Mai, Tai Cavazza, Marc Prendinger, Helmut Hanoi Univ Sci & Technol Hanoi Vietnam Univ Stirling Stirling Scotland Natl Inst Informat Tokyo Japan

ISBN: (纸本)9798400709777

Time Series forecasting has been approached by a multiplicity of techniques including deep learning methods of various degrees of sophistication, showcasing notable advancements and improved performance over the past few years. More recently, there has been a sustained interest in the study of Transformers, a class of models renowned for their remarkable capacity to capture intricate long-range dependencies and interactions. This ability is perceived as particularly relevant and impactful in the context of time series modeling, reflecting a growing recognition of their potential in enhancing forecasting accuracy and understanding of complex temporal patterns. However, taking advantage of this principle to deploy successful forecasting methods is not yet clearly understood, and requires significant experimentation or engineering. Therefore, in this paper, we compare multiple variations of the Transformer model (standard Transformer, Autoformer, Informer), coupled with diverse combinations of embedding data. In particular, as the emphasis of our work is on forecasting, we investigate the relationship between Transformers' input segment length and prediction performance in a multi-step time intervals framework. Our results suggest that the Autoformer outperforms both standard Transformer and Informer across various prediction steps. We also observe that shorter input lengths and shorter prediction lengths generally produce better model performance.

关键词： Deep learning Transformers Time Series Forecasting Bitcoin

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Bangla Sentiment Analysis On Highly Imbalanced data Using Hybrid CNN-LSTM & Bangla BERT 3

Bangla Sentiment Analysis On Highly Imbalanced Data Using Hy...

引用

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

作者： Khanam, Fahmida Chakraborty, Anik Habib, Md. Ahsan Iqbal, Md. Sadiq Bangladesh University Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

Sentiment analysis is a technique that combines machine learning and natural language processing to identify the emotional attitude of a text. This is a very active research area in recent years. Bengali is the fifth most spoken Indo-European language in the world. Many people in Bangladesh use news portals and social media to gather information on various topics. We used a publicly available dataset from Kaggle. This data set consists of more negative reviews than positive reviews. We try to experiment with this dataset with different models, such as traditional ML models and deep learning models like CNN, LSTM, and the transformer model (Bangla-BERT-base). The Bangla-BERT-base achieved a notable 96% accuracy through 10-fold cross-validation. Several other performance measures are also used to evaluate our model. © 2024 IEEE.

关键词： Sentiment analysis

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Leveraging Federated learning for Weather Classification in the Era of Smart Cities 3

Leveraging Federated Learning for Weather Classification in ...

引用

3rd international conference on Computing and machine Intelligence (ICMI)

作者： Gargees, Rasha S. Cent Michigan Univ Dept Comp Sci Mt Pleasant MI 48859 USA

ISBN: (纸本)9798350372977;9798350372984

The advent of smart cities has led to a significant increase in the collection and utilization of diverse data streams, including imagery datasets capturing various aspects of urban environments. This paper presents a novel approach to weather classification in the era of smart cities through the integration of federated learning (FL) techniques. This study aims to leverage decentralized computing to enhance the accuracy and efficiency of weather classification models. The federated model exhibits robustness and adaptability to the dynamic and heterogeneous nature of smart city environments. The findings of this research contribute to the advancement of weather monitoring and prediction capabilities in smart cities, fostering a more resilient and responsive urban infrastructure. Additionally, the federated learning approach demonstrated its effectiveness in managing decentralized datasets, ensuring the model training while maintaining data privacy in the context of smart city deployments. The study leverages the pre-trained ResNet50 architecture to achieve a commendable training accuracy rate of 87% and a validation accuracy rate of 86%. The implications of this research extend beyond weather classification, laying the groundwork for the broader integration of federated learning techniques in diverse applications within smart city ecosystems.

关键词： federated learning smart cities weather classification urban infrastructure imagery data

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Adopting Ensemble learning and machine learning Techniques for Predictive Modeling in Traffic data Analysis 3

Adopting Ensemble Learning and Machine Learning Techniques f...

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3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Santhosh Narayanan, B. Shyamala, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

In this study, the trends of traffic data are analyzed using predictive analysis techniques applied to the NYC taxi dataset. Various models were tested for forecasting travel time, fare, and speed. Notably, the Decision Tree Regression model demonstrated outstanding performance with an impressive R2 score of 0.9995 for travel time prediction. Additionally, an ensemble model consisting of Linear Regression and Decision Tree Regression achieved an accuracy of 0.93 for fare prediction. Furthermore, another ensemble model combining LSTM and DBN attained an R2 score of 0.90 for speed prediction. This study highlights the effectiveness of diverse predictive modeling techniques in accurately forecasting these features. © 2024 IEEE.

关键词： Long short-term memory

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