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Design of a Behavior Classification System for Monitoring Autism Spectrum Disorder using Deep learning 3

Design of a Behavior Classification System for Monitoring Au...

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3rd international Informatics and Software Engineering conference, IISEC 2022

作者： Villanueva, Alonica McCarthy, Sofia Louise Legaspi, Dj Paulo Mendoza, Denelle Jin Venal, Maria Cecilia Templonuevo, Sofia Coleen Rosales, Menchie Technological Institute of the Philippines Computer Engineering Department Quezon City Philippines

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

A developmental disorder known as an autism spectrum disorder (ASD) is characterized by unusual social, communication, and behavioral situations. According to experts, children with ASD ranging from 2-16 years old need constant monitoring because of their unpredictable behavior. Meltdown and seizure are two of the most important aspects that the parents need to monitor because of their aftermath on children with ASD. This paper is aimed at designing a behavior classification system for monitoring children with ASD that classify the activity such as shaking to seizures and self-injurious behavior to meltdown, classifies the audio of the child, such as screaming and crying to meltdown, and designs a mobile application that has recommendations on the proper response of the parent or guardian. The system is built based on the SqueezeNext and Transformer algorithms, which utilize two Raspberry Pi 4B, equipped with a Web Camera responsible for the behavior classification and Re Speaker 2-Mics Pi HAT is responsible for the audio classification of the child with ASD. The audio and video classification system had 98.33% accuracy in classifying the child's behavior, and the device's alert notification and recommendation system had 100% system accuracy. The Behavior Classification System's design has proven to be effective in assisting parents and guardians of children with ASD. © 2022 IEEE.

关键词： Deep learning

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Automatic 3D Teeth Semantic Segmentation with Mesh Augmentation Network 3

Automatic 3D Teeth Semantic Segmentation with Mesh Augmentat...

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3rd international conference on Pattern Recognition and machine learning, PRML 2022

作者： Zhao, Qijun Wu, Xiao Zhu, Feiyu Liu, Jun Wei, Mingying Peng, Jiahan Lu, Yin Sichuan University College of Computer Science Chengdu China Sichuan University National Key Laboratory of Fundamental Science on Synthetic Vision Chengdu China Sichuan University State Key Laboratory of Oral Diseases National Clinical Research Center for Oral Diseases Department of Orthodontics West China Hospital of Stomatology Chengdu China The People's Hospital of Neijiang Department of Stomatology Neijiang China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

In this paper, we propose an approach that improves segmentation networks with automatic augmentation networks for dental mesh data. Since conventional data augmentation is to augment all samples uniformly with predefined parameters, it ignores the unique characteristics of a single tooth sample and cannot make good use of the data set. And the traditional method separates data augmentation and segmentation network training, so the augmented data cannot be well adapted to the network to make it play a good role. We adopt a joint optimization strategy to integrate the augmentation network and the segmentation network, so that the augmented tooth data is the most suitable for the segmentation network. In addition, we design new improved loss functions suitable for augmentation and segmentation networks. Experiments have shown that the automatic augmentation network in our proposed method, named MeshAugNet, can effectively improve the segmentation accuracy after it is used for tooth segmentation. In general, this work achieves a combination of 3D dental data auto- augmentation network and segmentation network, which improves the accuracy of tooth segmentation, and can be used to solve the problem of too few samples in tooth datasets. © 2022 IEEE.

关键词： Semantic Segmentation

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A Comprehensive Review and Current Methods for Classifying Alzheimer's Disease Using Feature Extraction and machine learning Techniques 3rd

A Comprehensive Review and Current Methods for Classifying A...

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3rd international conference on Sustainable Expert Systems, ICSES 2022

作者： Chithra, S. Vijayabhanu, R. Avinashlingam Institute for Home Science and Higher Education for Women Tamil Nadu Coimbatore India Department of Computer Science Avinashilingam Institute for Home Science and Higher Education for Women Tamil Nadu Coimbatore India

ISBN: (纸本)9789811978739

The automatic detection of Alzheimer's disease (AD), particularly in its early stages, is crucial for maintaining human health. Alzheimer's disease appears to have a protracted incubation time due to the fact that it is a neurodegenerative ailment. Consequently, it is crucial to examine Alzheimer's symptoms at various stages. The use of a fusion feature set to classify AD from MRIs is reviewed in this work along with other feature extraction and machine learning (ML) techniques. To extract texture-based information, techniques including the Gray Level Coherence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), and Gray Level Difference Method (GLDM) are used. It is anticipated that the contributions of the extracted spatial domain from these methods would result in a more effective classification-based fusion feature set. According to the study under review, classification frameworks developed using these extracted variables show promise for the customized diagnosis and clinical progression prediction. Finally, this study addressed several future research possibilities and gave a thorough overview of the difficulties in classifying AD. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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application of classification algorithms of machine learning in cybersecurity 4th

Application of classification algorithms of Machine learning...

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4th international conference on Innovative Data Communication Technologies and application, ICIDCA 2022

作者： Abdiyeva-Aliyeva, Gunay Aliyev, Jeyhun Sadigov, Ulfat Baku Higher Oil School Institute of Control Systems Azerbaijan National Academy of Sciences Yeni Salyan Highway 3rd km 25 Sabail district Bibiheybat settlementBakhtiyar Vahabzadeh street 68 Baku Azerbaijan Kharkiv National University of Radio Electronics Nauky Ave. 14 Kharkiv Ukraine Azerbaijan Technical University H.Javid 25 Baku Azerbaijan

In the contemporary period, the cyber-crime activities have been one of the significant problems in the both private and public organizations. Building real-time checking systems to prevent digital crime activities is challenging but very useful in the detection of cyber-attacks both in time and effective. machine learning (ML) is a part of the data science where these mentioned problems could be solved with automated processes. Variety of algorithms in ML can be applied in cybersecurity to prevent attacks and minimize the security risks. Extreme Gradient Boosting (XGBoost) is one of those algorithms can be used effectively in the cybercrime detection activities. Due to the huge number of manual jobs in the investigation process and rule-based prevention methods, XGboost is thought to be automate those processes. In the paper, how ML algorithms can be applied in detection of cybercrime activities, the mathematical background in XGBoost and the main features selected to this problem are discussed. © 2023 The Authors. Published by Elsevier B.V.

关键词： Decision trees

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Adaptive Control of Data Center Cooling using Deep Reinforcement learning 3

Adaptive Control of Data Center Cooling using Deep Reinforce...

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3rd IEEE international conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

作者： Heimerson, Albin Sjolund, Johannes Brannvall, Rickard Gustafsson, Jonas Eker, Johan Lund Univ Lund Sweden Res Inst Sweden RISE Gothenburg Sweden Lund Univ Ericsson Res Lund Sweden

ISBN: (数字)9781665451420

ISBN: (纸本)9781665451420

In this paper, we explore the use of Reinforcement learning (RL) to improve the control of cooling equipment in Data Centers (DCs). DCs are inherently complex systems, and thus challenging to model from first principles. machine learning offers a way to address this by instead training a model to capture the thermal dynamics of a DC. In RL, an agent learns to control a system through trial-and-error. However, for systems such as DCs, an interactive trial-and-error approach is not possible, and instead, a high-fidelity model is needed. In this paper, we develop a DC model using Computational Fluid Dynamics (CFD) based on the Lattice Boltzmann Method (LBM) Bhatnagar-Gross-Krook (BGK) algorithm. The model features transient boundary conditions for simulating the DC room, heat-generating servers, and computer Room Air Handlers (CRAHs) as well as rejection components outside the server room such as heat exchangers, compressors, and dry coolers. This model is used to train an RL agent to control the cooling equipment. Evaluations show that the RL agent can outperform traditional controllers and also can adapt to changes in the environment, such as equipment breaking down.

关键词： Reinforcement learning CFD Modeling Datacenter Cooling Adaptive Control

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Using machine learning classification methods to detect COVID-19 Status in Selangor District 3

Using Machine Learning classification methods to detect COVI...

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3rd international conference on Artificial Intelligence and Data Sciences, AiDAS 2022

作者： Razi, Nor Faezah Mohamad Sarkam, Nor Aslily Mohammad, Nor Hazlina Zulkifli, Ilya Zulaikha Jamidin, Jaaida Najihah Faculty Computer and Mathematical Sciences Department of Statistics Tapah Road Malaysia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

Recently, Covid 19 pandemic has been recognized as a public health emergency of international concern. According to global COVID-19 infection data, the total number of cases is over 147 million, with over 3 million fatalities. Common model that use to predict binary outcome is Logistic regression model. However, the majority of the models have not been implemented widely by using ML approaches. Thus, the interest of this study has been coined to the Covid 19 cases prediction model that influence the extend of risk to urge Covid 19 infection. Therefore, this study addressed how to use four ML algorithms offered by Rapid Miner software tools to identify the optimum classification model. The results show that Decision Tree has been very promising to produce a high percentage of accuracy rate of 75.47% compared to other models. Further research on the data structure is necessary to be conducted in order to address problems like bias and an unbalanced dataset. In addition, new factors like vaccination status should be incorporated into the model to determine whether the respondent is at risk of contracting COVID 19 or not. © 2022 IEEE.

关键词： COVID-19

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Relating machine learning to the Real-World: Analogies to Enhance learning Comprehension 3rd

Relating Machine Learning to the Real-World: Analogies to En...

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3rd international conference on Soft Computing and its Engineering applications, icSoftComp 2021

作者： Pendyala, Vishnu S. San Jose State University San JoseCA95192 United States

ISBN: (纸本)9783031057663

machine learning is an exciting field for many, but the rigor, math, and its rapid evolution are often found to be formidable, keeping them away from studying and pursuing a career in this area. Similarity has been substantially explored in machine learning algorithms such as in the K-nearest neighbors, Kernel methods, Support Vector machines, but not so much in human learning, particularly when it comes to teaching machine learning. In the course of teaching the subject to undergraduate, graduate, and general pool of students, the author found that relating the concepts to real-world examples greatly enhances student comprehension and makes the topics much more approachable despite the math and the methods involved. This paper relates some of the concepts, artifacts, and algorithms in machine learning such as overfitting, regularization, and Generative Adversarial Networks to the real world using illustrative examples. Most of the analogies included in the paper were well appreciated by the students in the course of the author’s teaching and acknowledged as enhancing comprehension. It is hoped that the material presented in this paper will benefit larger audiences, drawing more learners to the field, resulting in enhanced contributions to the area. The paper concludes by suggesting deep learning for automatically generating similarities and analogies as a future direction. © 2022, Springer Nature Switzerland AG.

关键词： Students

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The application of Graph Neural Network in Natural Language Processing and computer Vision 3

The Application of Graph Neural Network in Natural Language ...

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3rd international conference on machine learning, Big Data and Business Intelligence (MLBDBI)

作者： Liu, Xiaochen Su, Yang Xu, Bingjie Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China Univ Waterloo Cheriton Sch Comp Sci Waterloo ON Canada Southwest Jiaotong Univ Sch Comp Sci & Artificial Intelligence Chengdu Peoples R China

ISBN: (纸本)9781665417907

Graphs can represent information transformation via geometrical relations, which have been well studied and applied in various research areas. A graph-based learning network named Graph Neural Network (GNN) arose with the vast development of deep learning in recent years. Unlike traditional deep learning networks such as CNN and RNN, GNN is superior in dealing with non-Euclidean graph data. This survey focuses on two widespread application fields of GNN, natural language processing (NLP) and computer vision (CV). Firstly, based on the tasks they perform, we categorize the most popular research sub-domains of NLP and CV, purpose a detailed review on the application of GNN in these areas. Secondly, we thoroughly analyzed the benchmark datasets applied in the GNN models while comparing them with different evaluation metrics. Finally, we briefly discuss the potential future direction of GNN according to its model building procedure and related application branches.

关键词： deep learning graph neural network natural language processing computer vision

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Research on Tobacco Leaf Color Classification Based on machine learning 3

Research on Tobacco Leaf Color Classification Based on Machi...

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3rd international conference on Image Processing, computer Vision and machine learning, ICICML 2024

作者： Zhang, Qianzi Hou, Kaihu Deng, Shaowen Li, Xiangwei Zhang, Yuling Song, Cheng Kunming University of Science and Technology School of Mechanical and Electrical Engineering Kunming650550 China Co. Ltd. Yuxi653100 China

ISBN: (纸本)9798350355413

Objective: This study explores the application of machine learning algorithms in tobacco leaf color classification to improve classification ***: Images of tobacco leaves of different grades were collected, and RGB color data was extracted. Six machine learning algorithms were employed for classification prediction, and the best algorithm and features were ***: After denoising, algorithm optimization, and feature introduction, the classification accuracy of the random forest algorithm improved from 75% to 90.77%.Conclusion: machine learning effectively achieves automatic classification of tobacco leaf colors, with data quality, algorithm selection, and feature engineering significantly impacting model performance. This research provides technical support for intelligent grading of tobacco leaves, though attention must be given to the model's generalization ability and processing speed. © 2024 IEEE.

关键词： Random forests

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Research on the application of New Intelligent Sensor Electronic Equipment in Automobile

Research on the Application of New Intelligent Sensor Electr...

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Data Science and computer application (ICDSCA), IEEE international conference on

作者： Shaoxuan Zhou Intelligent Manufacturing and Information Engineering College Sichuan Technology & Business College Chengdu China

There is great randomness in multi-sensor acquisition of intelligent unmanned vehicles. There are great differences in the characteristics of the collected information. This paper presents an adaptive multi-sensor data fusion algorithm. The learning machine based on support vector machine regression is applied to the intelligent sensor system of single chip microcomputer. With STC12LE5410AD single chip as the core controller and nRF905 wireless transceiver control chip, the data collected by the sensor is transmitted wirelessly. Experiments show that this algorithm can greatly improve the fusion rate of multi-sensor differential information, increase the amount of effective information, and achieve good results.

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