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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51-60 订阅

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Non-Invasive Mental Health Prediction using machine learning: An Exploration of Algorithms and Accuracy 7

Non-Invasive Mental Health Prediction using Machine Learning...

引用

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

作者： Bajaj, Vanshika Bathija, Rishabh Megnani, Chandni Sawara, Jasmine Ansari, Nusrat V.E.S. Institute of Technology Computer Engineering Department Mumbai India

ISBN: (纸本)9798350397253

the potential of the machine learning in predicting mental health outcomes is investigated in this study. Two datasets were gathered: one of mental health patient questionnaires and the other of information from MRI scans of Alzheimer's patients. the datasets were pre-processed using techniques such as stop word removal and lemmatization, and the processed data was encoded for increased prediction accuracy. To find the highest performing model, various algorithms such as Logistic Regression, Decision Tree, KNN (K-Nearest Neighbors), Adaboost, Random Forest, and Logistic Regression are examined. the findings indicated that machine learning algorithms can predict mental health outcomes with high accuracy, and that adding demographic, behavioural, and psychological factors can improve prediction accuracy even more. the study emphasizes the significance of creating accessible and accurate mental health prediction tools, as well as the promise of the machine learning in mental health evaluation. © 2023 IEEE.

关键词： Adaptive boosting

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A Novel machine learning based Driving Decision Strategy 7

A Novel Machine Learning based Driving Decision Strategy

引用

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

作者： Sahithi, P. Sravanthi, V. Vanetha, V.G. Krishna Reddy, D. VR Siddhartha Engineering College Dept. of CSE Vijayawada India

ISBN: (纸本)9798350397253

For determining the driving strategy, modern cars are solely dependent on different factors such as people and road conditions. In particular, in the case of self-driving cars that travel autonomously towards the destination, it needs to perform various repetitive identification, judgement, and control operations. To address this challenges, this research study has designed and developed an advanced driving decision strategy by assessing both its internal and exterior components (consumable conditions, Rotation Per Minute levels, and so on) with the help of machine learning models. this will reduce in-vehicle computing and achieve the most effective decision by utilizing evolutionary algorithms, past storage data and huge real-time data generated while driving a car. the data saved in the cloud are assessed by the machine learning algorithms, which then select the best driving strategy. To perform these operations, this study uses gradient boost algorithm. the research result shows that the vehicle features have a substantial influence in making a driving decision. © 2023 IEEE.

关键词： Digital storage

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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...

引用

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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KineFormer: Solving the Inverse Modeling Problem of soft Robots Using Transformers 7th

KineFormer: Solving the Inverse Modeling Problem of Soft Rob...

引用

7th EAI international conference on Robotics and Networks, ROSENET 2023

作者： Alkhodary, Abdelrahman Gur, Berke Bahcesehir University İstanbul Turkey

ISBN: (纸本)9783031644948

soft robotic manipulators provide numerous advantages over conventional rigid manipulators in fragile environments such as the marine environment. However, developing analytic inverse models necessary for shape, motion, and force control of such robots remains a challenging problem. As an alternative to analytic models, numerical models can be learned using powerful machine learning methods. In this paper, the Kinematics Transformer is proposed for developing accurate and precise inverse kinematic models of soft robotic arms. the proposed method recasts the inverse kinematics problem as a sequential prediction problem and is based on the transformer architecture. Numerical simulations reveal that the proposed method can effectively be used in controlling a soft arm. Benchmark studies also reveal that the proposed method has better accuracy and precision compared to the baseline feed-forward neural network. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Inverse kinematics

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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...

引用

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...

引用

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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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

引用

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

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

引用

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...

引用

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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