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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 787 条记录，以下是91-100 订阅

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Sparse-learning-based High-order Dynamic Functional Connectivity Networks for Brain Disease Classification 23

Sparse-learning-based High-order Dynamic Functional Connecti...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Wang, Jianhui Jie, Biao Zhang, Xingyu Li, Wen Wu, Zhaoxiang Yang, Yang School of Computer and Information Anhui Normal University Anhui Wuhu China

ISBN: (纸本)9781450398633

Dynamic functional connectivity network (DFCN) derived from resting-state functional magnetic resonance imaging (rs-fMRI), which characterizes the dynamic interaction between brain regions, has been applied to classification of brain diseases. However, existing studies usually focus on dynamic changes of low-order (i.e., pairwise) correlation of brain regions, thus neglecting their high-order dynamic information that could be important for brain disease diagnosis. therefore, in this paper, we first propose a novel sparse learning based high-order DFCNs construction method, and then build a novel learning framework to extract high-level and high-order temporal features from the constructed high-order DFCNs for brain disease classification. the experimental results on 174 subjects from from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of our proposed method in comparison with state-of-the-art methods. © 2023 ACM.

关键词： Magnetic resonance imaging

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Neural Potential learning for Tweets Classification and Interpretation 7

Neural Potential Learning for Tweets Classification and Inte...

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7th international conference of soft computing and Pattern Recognition (SoCPaR)

作者： Kitajima, Ryozo Kamimura, Ryotaro Uchida, Osamu Toriumi, Fujio Tokai Univ Grad Sch Sci & Technol Kanagawa Japan Tokai Univ Grad Sch Engn IT Educ Ctr Kanagawa Japan Tokai Univ Grad Sch Engn Dept Human & Informat Sci Kanagawa Japan Univ Tokyo Grad Sch Engn Tokyo Japan

ISBN: (纸本)9781467393607

the present paper aims to apply a new neural learning method called "Neural Potential learning, NPL" to the classification and interpretation of tweets. It has been well known that social media such as the twitter play crucial roles in transmitting important information at the time of natural disasters. In particular, since the Great East Japan Earthquake in 2011, the twitter has been considered as one of the most efficient and convenient communication tools. However, because much redundant information is contained in the tweets, it is usually difficult to obtain important information from the flows of the tweets. thus, it is urgently needed to develop some methods to extract the important and useful information from redundant tweets. To cope with complex and redundant data, a new neural potential learning has been developed to extract the important information. the method aims to find some highly potential neurons and enhance those neurons as much as possible to reduce redundant information and to focus on important information. the method was applied to the real tweets data collected in the earthquake and it was found that the method could classify the tweets as important and unimportant ones more accurately than the other conventional machine learning methods. In addition, the method made it possible to interpret how the tweets could be classified, based on the examination of highly potential neurons.

关键词： twitter classification interpretation neural network potentiality

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Applications and Challenges of machine learning Techniques for Smart Manufacturing in Industry 4.0 7

Applications and Challenges of Machine Learning Techniques f...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Kolhe, Kartik Somatkar, Avinash. A. Bhandarkar, Mrunalini S. Kotangale, Kapil B Ayane, Swapnil S. Shirke, Sonal I. Vishwakarma Institute of Information Technology Department of Mechanical Engineering Pune India Pimpri Chinchwad College of Engineering Department of Electronics & Telecommunication Engineering Pune Nigdi India

ISBN: (纸本)9798350304268

the fourth industrial revolution, Industry 4.0, is transforming the manufacturing industry by integrating advanced technologies such as IoT, AI, and ML into the manufacturing process. In particular, the application of machine learning techniques in smart manufacturing has the potential to revolutionize the way manufacturers approach production. this paper reviews the various applications of machine learning techniques in smart manufacturing, including predictive maintenance, quality control, and supply chain management. We also discuss the challenges associated with implementing machine learning techniques in manufacturing, such as data management and privacy concerns, lack of skilled personnel, and the need for effective collaboration between manufacturers and technology providers. Additionally, we provide insights into the future of machine learning in manufacturing, including the potential for increased automation, improved operational efficiency, and the development of new business models. Overall, this paper highlights the potential benefits and challenges associated with the application of machine learning techniques in smart manufacturing, providing valuable insights for manufacturers looking to integrate these technologies into their operations. © 2023 IEEE.

关键词： Neural networks

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machine learning-Based Study on Airbnb Housing Price Prediction Evidence from Hong Kong 7

Machine Learning-Based Study on Airbnb Housing Price Predict...

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7th international conference on Electronic Information Technology and Computer Engineering (EITCE)

作者： Wang, Qingxiao Huang, Lihua Henan Univ Sci & Technol Business Sch Luoyang Peoples R China

ISBN: (纸本)9798400708305

the emergence of the sharing economy has brought about transformative changes across various industries. A prominent example, the Airbnb platform, has introduced a new business model to the hospitality sector, enabling numerous microenterprises to thrive through unique pricing strategies. A comprehensive understanding of these pricing strategies is crucial for exploring the essence of this innovative business model. this paper analyzes a dataset of 6,481 property listings from *** in the Hong Kong region as of June 24, 2023. Leveraging natural language processing and machine learning techniques, a price prediction model is developed. the study employs three distinct prediction methods: Linear Regression, Random Forest, and XGBoost. By comparing their predictive performances, the Random Forest method demonstrates the strongest predictive capability. Furthermore, an attribution analysis is conducted using the Random Forest model, aiding hosts in setting appropriate prices and creating a stable and equitable rental environment for tenants.

关键词： machine learning Price Forecast Airbnb

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A Proposal for an Explainable Fuzzy-based Deep learning System for Skin Cancer Prediction 7

A Proposal for an Explainable Fuzzy-based Deep Learning Syst...

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7th international conference on eDemocracy and eGovernment (ICEDEG)

作者： Lima, Servio Teran, Luis Portmann, Edy Univ Fribourg Human IST Inst Blvd Perolles 90 Fribourg Switzerland Univ Fuerzas Armadas ESPE Comp Sci Dept Sangolqui Ecuador

ISBN: (纸本)9781728158822

Explainable deep learning (XDL) is a research field that aims to make deep learning predictions or classifications more understandable for humans. Literature shows that deep learning (DL) algorithms are more precise in terms of their prediction than traditional machine learning (ML) algorithms. Nevertheless, they lack the interpretability and explainability that less complex algorithms are more fitted. Nowadays, one of the main goals of XDL research is to make these algorithms highly precise and explainable. there exist different systems and techniques, but only limited research on explaining deep neural networks using soft computing approaches. the purpose of this research is to propose the theoretical fundamentals of an explainable fuzzy-based deep learning (EFBDL) system that is both precise and explainable. the system comprises two main parts. First, the deep network part composed of a convolutional neural network (CNN) based on Inception V4 for image classification, a transfer learning mechanism, and a feature extraction algorithm based on neuron perturbation. Second, a soft computing part comprised of a fuzzy rule-based system (FRBS), a hierarchical network for natural language generation named granular linguistic model of a phenomenon (GLMP), and a human-machine integration methodology for linguistic rules named highly interpretable linguistic knowledge (HILK). the output of the overall system is an explanation of the neural network decision using natural language. this system focuses on preventing skin cancer rather than healing it. thus, governments could use this kind of system for implementing policies focused on prevention and save in overall treatment costs of the disease.

关键词： Explainable deep learning artificial intelligence skin cancer prediction

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Malware Detection and Analysis using machine learning 7

Malware Detection and Analysis using Machine Learning

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Sirigiri, Manoj Sirigiri, Divya Aishwarya, R. Yogitha, R. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Brillio Technologies Bangalore India

ISBN: (纸本)9781665464086

Online privacy for people is getting worse every day. Computer malware is tainting the data records of some well-known companies. Hackers can gain access to a network and change data, once inside. this work discusses several types of malware and communication strategies, such as Trojans, keyloggers, port forwarding, source code obfuscation, application format converters, and social engineering techniques. three different machine learning algorithms are applied in this work to derive meaningful insights from the data. As a result of the work proposed, given malware can be categorized as a malicious or a non-malicious application. this is executed by analyzing the classification report, accuracy and f1-score metrics. the Random Forest is selected as a champion model based on the highest f1-score for the validation dataset. © 2023 IEEE.

关键词： Personal computing

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Exploring the Data Science 7

Exploring The Data Science

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Kumar, Amodh Kapil, Akshat Ahlawat, Deepak Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350304268

Data Science is a rapidly expanding field in technology that has attracted a lot of concentration recently due to the enormous influence it has on many different companies and domains. the field encompasses a wide range of techniques and tools aimed at extracting meaningful insights and knowledge from large and complex datasets. this study seeks to offer a thorough review of the field of Data Science by exploring its history, fundamental concepts, and current trends. It also highlights the current trends and developments in the field, such as big data, cloud computing, and the Internet of things (IoT). the paper concludes by discussing the future of Data Science and the impact it will have on various industries, including healthcare, finance, and retail. © 2023 IEEE.

关键词： Artificial Intelligence Deep learning machine learning Natural Language Processing

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iOverbook: Intelligent Resource-Overbooking to Support soft Real-time Applications in the Cloud 7

iOverbook: Intelligent Resource-Overbooking to Support Soft ...

引用

7th IEEE international conference on Cloud computing (CLOUD)

作者： Caglar, Faruk Gokhale, Aniruddha Vanderbilt Univ Dept Elect Engn & Comp Sci Nashville TN 37235 USA

ISBN: (纸本)9781479950638

Cloud service providers (CSPs) often overbook their resources with user applications despite having to maintain service-level agreements with their customers. Overbooking is attractive to CSPs because it helps to reduce power consumption in the data center by packing more user jobs in less number of resources while improving their profits. Overbooking becomes feasible because user applications tend to overestimate their resource requirements utilizing only a fraction of the allocated resources. Arbitrary resource overbooking ratios, however, may be detrimental to soft real-time applications, such as airline reservations or Netflix video streaming, which are increasingly hosted in the cloud. the changing dynamics of the cloud preclude an offline determination of overbooking ratios. To address these concerns, this paper presents iOverbook, which uses a machine learning approach to make systematic and online determination of overbooking ratios such that the quality of service needs of soft real-time systems can be met while still benefiting from overbooking. Specifically, iOverbook utilizes historic data of tasks and host machines in the cloud to extract their resource usage patterns and predict future resource usage along with the expected mean performance of host machines. To evaluate our approach, we have used a large usage trace made available by Google of one of its production data centers. In the context of the traces, our experiments show that iOverbook can help CSPs improve their resource utilization by an average of 12.5% and save 32% power in the data center.

关键词： resource overbooking cloud computing soft real-time performance

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Air Quality Prediction Using machine learning and Deep learning: An Exploratory Study 7

Air Quality Prediction Using Machine Learning and Deep Learn...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Bhushan, Megha Dawar, Ishaan Sharma, Shantanu Bawaniya, Tushar Kumar Anand, Umang Negi, Arun School of Computing Dit University Dehradun India Delloite Usi Gurgaon India

ISBN: (纸本)9798350304268

Over the past few decades, urbanization and industrialization have expanded rapidly in both emerging and developed countries. there is growing concern between the government and the public regarding the adverse effects of air pollution on human health, which has led to an increased emphasis on sustainable development as a solution to this problem. Modern industries release waste products into the atmosphere in the form of liquid droplets, solid particles, and gas molecules, leading to a high concentration of particulate matter. In turn, this can lead to harmful impacts on human health. On the other hand, forecasting techniques aid in predicting the future levels of pollution in particular locations and may even recommend helpful preventative measures. Air pollution forecasting has been topic of research in numerous studies, the majority of which are based on sequence models (generate predictions after being trained with raw pollution data). this study examined the machine learning and deep learning techniques used to predict air pollution and air quality. this information would help researchers provide in-depth knowledge about the latest techniques and future challenges related to the prediction of air pollution. © 2023 IEEE.

关键词： air pollution air quality deep learning machine learning

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Soil Nutrient Analysis for the Cultivation of Plants using machine learning Algorithms 7

Soil Nutrient Analysis for the Cultivation of Plants using M...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Vidhya, S. Rajalakshmi, R. Mahalingamuthuram, M. Mukesh, C. Mohit, K. Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India

ISBN: (纸本)9798350397253

the machine learning algorithm is used to analyze the minerals of the soil to identify which plants to be cultivated on the specific land. In proposed work, Combining the Random Forest and Support Vector machine algorithms namely, multi-model approach. this approach helps to get an adequate information about which plant can be grown on basis of nature of the soil. the multi-model approach improved the prediction accuracy, especially when certain categories of nutrients were selected for classification. the proposed method is going to explore the different levels of nutrient characterization, ranging from 3 to 13 levels to get more precise nutritional information. this approach is highly beneficial for farmers to develop better harvests. © 2023 IEEE.

关键词： Forecasting

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