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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7094 条记录，以下是51-60 订阅

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Preliminary Investigation on Machine learning and Deep learning Models for Change of Direction Classification in Running 25th

Preliminary Investigation on Machine Learning and Deep Learn...

引用

25th international conference on intelligent data engineering and automated learning

作者： Jaiswal, Pranay Kaushik, Abhishek Lawless, Fiona Malaquias, Tiago McCaffery, Fergal Dundalk Inst Technol Dundalk Ireland STATSports Newry North Ireland

ISBN: (纸本)9783031777301;9783031777318

the ability to detect, define, and classify Change of Direction (COD) movements during running plays a crucial role in sports science, as it has been widely used to assess athlete performance. Automating the process of COD classification during live games or training can provide real-time feedback. In this study, we evaluated Machine learning (ML) and Deep learning (DL) models for the classification of COD using accelerometers and gyroscope sensor data, and speed data were calculated from the Global Positioning System (GPS) sensor data. We hypothesized that DL algorithms classify COD better than ML classification algorithms. Comparative analysis showed that the best-performing DL and ML models showed similar behavior. Similarly, the statistical analysis observed no significant difference. this emphasized the importance of accurate model selection.

关键词： Change of Direction Machine learning Deep learning Sports Running Classification

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Ensemble learning to Assess Dynamics of Affective Experience Ratings and Physiological Change 11

Ensemble Learning to Assess Dynamics of Affective Experience...

引用

11th international conference on Affective Computing and intelligent Interaction (ACIIW)

作者： Dollack, Felix Kiyokawa, Kiyoshi Liu, Huakun Perusquia-Hernandez, Monica Raman, Chirag Uchiyama, Hideaki Wei, Xin Nara Inst Sci & Technol Nara Japan Delft Univ Technol Delft Netherlands

ISBN: (纸本)9798350327458

the congruence between affective experiences and physiological changes has been a debated topic for centuries. Recent technological advances in measurement and data analysis provide hope to solve this epic challenge. Open science and open data practices, together with data analysis challenges open to the academic community, are also promising tools for solving this problem. In this entry to the Emotion Physiology and Experience Collaboration (EPiC) challenge, we propose a data analysis solution that combines theoretical assumptions with data-driven methodologies. We used feature engineering and ensemble selection. Each predictor was trained on subsets of the training data that would maximize the information available for training. Late fusion was used with an averaging step. We chose to average considering a "wisdom of crowds" strategy. this strategy yielded an overall RMSE of 1.19 in the test set. Future work should carefully explore if our assumptions are correct and the potential of weighted fusion.

关键词： affective computing continuous ratings biosignal processing machine learning data analysis challenge

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Joint Power and Flexible Numerology Allocation in 5G Networks Using Deep Reinforcement learning 11

Joint Power and Flexible Numerology Allocation in 5G Network...

引用

11th international conference on Wireless Networks and Mobile Communications (WINCOM) - Inclusive and intelligent Connectivity

作者： Topcu, Alican Lawey, Ahmed Q. Zaidi, Syed Ali Raza Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT W Yorkshire England

ISBN: (纸本)9798350377873;9798350377866

this study presents a novel power allocation optimisation strategy that does not rely on traditional power constraints. Unlike previous works in the literature, we introduce a ratio between the allocated and requested data rates and incorporate this ratio into the reward function of our deep reinforcement learning algorithm. the highest reward of 1 is achieved when the allocated and requested data rates are equal. Additionally, we jointly optimise power and numerology allocation, considering the users' delay and data rate requirements. Any numerology can be allocated to users as long as their requirements are satisfied. this approach enables users to be allocated optimum numerology and transmit power. By addressing the challenge posed by greedy users, our approach enhances the flexibility and performance of the power and numerology allocation process.

关键词： Numerology allocation power allocation reinforcement learning

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Deep learning-based Visual Recognition System for Fruit-Picking Robots 11

Deep Learning-based Visual Recognition System for Fruit-Pick...

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11th IEEE international conference on Cybernetics and intelligent Systems (CIS) / 11th IEEE international conference on Robotics, Automation and Mechatronics (RAM)

作者： Ma, Rui Liu, Yihan Wu, Qike Zhang, Shaoru Wang, Nan Zhang, Jielu Luo, Fang Lin Hebei Normal Univ Coll Engn Shijiazhuang 050024 Hebei Peoples R China Jiangsu Tailong Reducer Co LTD Hebei Prov Innovat Ctr Wireless Sensor Network Da Taizhou 225400 Peoples R China Jiangsu Tailong Reducer Co LTD Taizhou 225400 Jiangsu Peoples R China Jiangsu Tailong Reducer Co LTD Taizhou 050024 Peoples R China Nanyang Technol Univ NTU Sch Elect & Elect Engn Nanyang Ave Singapore 639798 Singapore

ISBN: (纸本)9798350364200;9798350364194

Fruit recognition plays an important role in automated picking, in order to improve the accuracy and real-time picking, this study uses deep learning methods to design a fruit-picking robot visual recognition system. First, multiple preprocessing methods are used to expand the sample data, and the images are proportionally cropped and scaled to make the image dataset more complete;subsequently, to improve the accuracy of image recognition, a multilayer Convolutional Neural Networks (CNN) is established;and the Adam algorithm is utilized to train the model parameters several times to determine the optimal hyperparameters, thus overcoming the defects of the multilayer neural network that has a local optimal search. the experimental results show that compared with the random forest method, this system has the characteristics of high-speed recognition and high accuracy in fruit picking, and can quickly and accurately recognize fruit images, with the recognition speed of a single image taking only 0.081 seconds, and the recognition accuracy reaching more than 97.35%. the method has important theoretical and application value and provides an effective means for automatic fruit recognition.

关键词： Fruit-picking robot Deep learning Object recognition Image detection

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Integrating Vision Localization and Deep Reinforcement learning for High-Precision, Low-Cost Peg-in-Hole Assembly 11

Integrating Vision Localization and Deep Reinforcement Learn...

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11th IEEE international conference on Cybernetics and intelligent Systems (CIS) / 11th IEEE international conference on Robotics, Automation and Mechatronics (RAM)

作者： Lin, Ze Wang, Chuang Xie, Longhan Zeng, Miaosheng Wu, Sihan South China Univ Technol Shien Ming Wu Sch Intelligent Engn Guangzhou Peoples R China

ISBN: (纸本)9798350364200;9798350364194

Combining vision and force assembly strategies constitutes a crucial component of industrial automation production. However, there are two challenges. First, the positioning error of existing vision systems often fails to meet the demands of precision assembly, necessitating the design of assembly strategies based on force sensors tailored to the characteristics of the assembly process. Second, the extensive data required to train vision positioning systems represents a significant burden. therefore, we propose an assembly method based on object location with a relatively low deployment cost and fine-tuning with reinforcement learning. this method reduces the training cost with few training samples through data augmentation and the incorporation of attention modules. Furthermore, by integrating a deep reinforcement learning module, it performs high-precision hole searching and insertion operations learned by itself, mitigating the impact of visual positioning errors. the experimental results indicate that by improving the vision model, the required labeled samples for training can be reduced to 10. Furthermore, the method proposed in this paper achieves a faster average assembly time by 7.8 seconds and reduces the average number of assembly steps by 28 compared to traditional methods.

关键词： Reinforcement learning Attention mechanism data augmentation Peg-in-hole assembly

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A Machine learning Approach to Residential Energy Prediction using Large-Scale datasets in MongoDB 11

A Machine Learning Approach to Residential Energy Prediction...

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11th international conference on Wireless Networks and Mobile Communications (WINCOM) - Inclusive and intelligent Connectivity

作者： Moumen, Idriss Rafalia, Najat Abouchabaka, Jaafar Ibn Tofail Univ Fac Sci Dept Comp Sci Kenitra Morocco

ISBN: (纸本)9798350377873;9798350377866

this paper investigates the efficacy of machine learning algorithms for forecasting energy consumption within residential environments. A comprehensive dataset containing over 2.07 million observations of individual household electric power consumption, housed within a MongoDB database, serves as the foundation for this analysis. three wellestablished regression models, Linear Regression, Gradient Boosting, and XGBoost, are evaluated. their performance is assessed using Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared metrics. the primary objective is to identify the optimal approach for household energy management by evaluating the effectiveness of each model in accurately predicting energy consumption patterns. By harnessing the predictive capabilities of machine learning and MongoDB's efficient data management functionalities, this research reveals that Gradient Boosting emerged as the bestperforming model. this finding suggests that Gradient Boosting offers valuable insights for optimizing energy consumption and promoting sustainable practices. this work contributes to the expanding body of knowledge concerning the application of machine learning for energy prediction, particularly within the context of readily scalable databases like MongoDB. the identified superiority of Gradient Boosting, applied to a large dataset, has the potential to serve as a cornerstone for the development of intelligent energy management systems, ultimately promoting efficient and sustainable household energy consumption.

关键词： Machine learning Energy Consumption Forecasting MongoDB data Analysis Smart grids.

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An Ontology-Lexicon-Driven Approach for Refining Sentiment Analysis Processes 25th

An Ontology-Lexicon-Driven Approach for Refining Sentiment A...

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25th international conference on intelligent data engineering and automated learning

作者： Toldy, Francisco Belo, Orlando Univ Minho ALGORITMI Res Ctr LASI P-4710059 Braga Portugal

ISBN: (纸本)9783031777370;9783031777387

the high number of sentiment analysis systems and applications developed over the last few years provided companies with very sophisticated analysis tools, allowing them to establish preferences, trends and patterns of customer behavior. this is quite important for companies intending to change their way of being, promoting work actions aimed at specific customer segments, to obtain business advantages and improve their image and performance in the market in which they work. In this paper, we present and describe a sentiment analysis system that combine techniques based on ontologies and domain lexicons, to provide relevant indicators to support the evaluation of the degree of user satisfaction and know the influence of each ontological element incorporated in opinion texts in sentiment classification.

关键词： Sentiment Analysis Opinion Analysis Ontologies Lexical Classification data Preparation Tagging Texts Marketing Applications

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intelligent Information System for Simulation and Management Decision 11th

Intelligent Information System for Simulation and Management...

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11th international conference on Smart Education and E-learning

作者： Glukhova, Lyudmila V. Filippova, Olga A. Syrotyuk, Svetlana D. Gudkova, Svetlana A. Munirova, Yuliya S. Volga Reg State Univ Serv Tolyatti Russia Togliatti State Univ Tolyatti Russia

ISBN: (纸本)9789819749560;9789819749546;9789819749539

Currently, intelligent decision support systems (IDSS) are widely used. their main component is a knowledge base. Its adequacy determines the ability of the system to justify the recommended decisions, especially in conditions of diversity and incompleteness of input data. Research Methods: the methods of classification and systematization, fuzzy logic was used to solve the problems. the paper presents: a model of knowledge base structure, a model of intelligent information system for assessing the adequacy of the knowledge base, a set of rules for making managerial decisions. the results were tested in the Higher School of intelligent Systems and Cybertechnologies.

关键词： intelligent information system Knowledge bases Neuro-fuzzy models

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A Deep-learning Approach for the Identification of New Subtypes of Lung Cancer 25th

A Deep-Learning Approach for the Identification of New Subty...

引用

25th international conference on intelligent data engineering and automated learning

作者： Banerjee, Tuhin Corradini, Andrea Canon EMEA Amstelveen Netherlands MCI Innsbruck Austria

ISBN: (纸本)9783031777301;9783031777318

Lung cancer is the deadliest cancer in the world. It is caused by unchecked cell division of damaged cells in the lungs forming tumors that eventually prevent the lung from functioning properly. Identification of novel unsupervised subtypes of lung cancer is critical to reveal new insights into the underlying biology of cancer and ensure that patients receive specialized precision treatment based on the subtype of cancer they are suffering from. the ability of modern sequencing tools to produce patient-specific RNA sequencing (RNA-seq) gene expression data has transformed cancer research by offering in-depth understanding of the molecular landscape of cancer. this paper reports on a pipeline that comprise of a Deep Autoencoder (DAE) model coupled with hierarchical agglomerative clustering (H-Clust). It aims to identify new unsupervised lung cancer subtypes from RNA-seq expression samples collected from a publicly available dataset. Further, a deep learning (DL) model, Artificial Neural Network (ANN) is used to classify a patient's data into one of the newly identified subtypes.

关键词： lung cancer subtype TCGA RNA-seq deep learning DAE encoder-decoder agglomerative clustering ANN

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Machine learning in Breast Cancer Diagnosis: A Comprehensive Analysis and Prediction Methodology 11

Machine Learning in Breast Cancer Diagnosis: A Comprehensive...

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11th international conference on Wireless Networks and Mobile Communications (WINCOM) - Inclusive and intelligent Connectivity

作者： Derraz, Yasmin Korchiyne, Redouan Laraqui, Abderrahmane Benassila, Mehdi Ergouyeg, Mouad Cherrab, Hicham Benzyane, Zakaria Sbai, Meriem El Houssaini, Zineb Squalli Ibn Tofail Univ Fac Sci Kenitra Morocco

ISBN: (纸本)9798350377873;9798350377866

Breast cancer remains a significant global health concern, necessitating effective early detection and treatment strategies. In this paper, we conduct a detailed analysis of the application of machine learning techniques in healthcare, with a specific focus on breast cancer diagnosis. through a systematic review of existing literature, we explore various approaches and methodologies used by researchers to leverage medical data for breast cancer prediction. Additionally, we present a comprehensive methodology for data collection, preprocessing, and analysis, employing machine learning algorithms such as logistic regression, random forests, and neural networks. Our results demonstrate the efficacy of machine learning models in accurately predicting breast cancer, with promising performances in terms of sensitivity, specificity, and precision. this study highlights the transformative potential of machine learning in enhancing breast cancer diagnosis and underscores its role in improving clinical outcomes and patient care.

关键词： Machine learning Breast Cancer Diagnosis Healthcare Prediction Methodology Logistic Regression Random Forests Neural Networks Clinical Outcomes.

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