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检索条件"任意字段=6th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2023"

共 265 条记录，以下是51-60 订阅

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Using machine learning to Find Dependencies in data to Improve Working Conditions 6th

Using Machine Learning to Find Dependencies in Data to Impro...

引用

6th international conference on Recent Innovations in Computing, ICRIC 2023

作者： Kukartsev, V.V. Zamolotskii, S.A. Glinscaya, A.R. Mikhalev, A.S. Bauman Moscow State Technical University Moscow105005 Russia Siberian Federal University Krasnoyarsk660041 Russia Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia

ISBN: (纸本)9789819734412

this paper discusses the benefits and potential of machine learning in data analysis and its ability to discover hidden dependencies. the paper also discusses a case study on the application of machine learning in medical research to identify factors affecting heart failure mortality. An experiment was conducted investigating predictions using a decision tree, which showed higher accuracy after feature selection. After that, an experiment was conducted investigating the predictions using artificial neural network—multilayer perceptron, which also showed higher accuracy after feature selection. As a result, the most significant influencing factors are identified. Two classifiers were also proposed that can be used to predict mortality, which will improve the working conditions in manufacturing. the results of the study emphasize the importance of using machine learning in modern data analytics and their potential to improve predictive and analytical ability. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Economic development Economy Socio-economic development Sustainable economic development

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Sentiment Analysis of Audio Files Using machine learning and Textual Classification of Audio data 6

Sentiment Analysis of Audio Files Using Machine Learning and...

引用

6th international conference on Information Systems and Computer Networks, ISCON 2023

作者： Saraswat, Shipra Bhardwaj, Shubham Vashistha, Saksham Kumar, Rishabh Abes Institute of Technology Ghaziabad Affiliated to Aktu Dept. of Computer Science and Enggineering UP Lucknow India

ISBN: (纸本)9798350346961

Sentiment Analysis has an increasing implication in solving Human-machine collaboration issue. It's a tough task to be able to know how an individual feels but it seems even worse to recognize these sentiments using a machine. As we all know in today's world with day-to-day advancement in technologies people are searching for more and more easy and convenient ways to operate, with subsequent growth in the applications of Artificial Intelligence (AI), it now has generated a need to spontaneously identify the sentiments of the person involved in the Human Computer Interaction (HCI). the demand for sentiment analysis is increasing and it now is applied in various parts of industry. this research paper discusses the methods to identify various sentiments from human conversation using textual classification of audio data.. © 2023 IEEE.

关键词： Sentiment analysis

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Recent Advances in data Preprocessing and machine learning Approaches for Battery's State of Charge and State of Health Estimation: A Review 6

Recent Advances in Data Preprocessing and Machine Learning A...

引用

6th international Future Energy Electronics conference, IFEEC 2023

作者： Gu, Xinyu See, Khay Zhou, Xiuze Wang, Yunpeng Zang, Caiyun AIIM|ISEM Facility University of Wollongong Innovation Campus Wollongong Australia Ai Research Institute Hithink RoyalFlush Information Network Co. Ltd Hangzhou China Sydney Australia Shanghai China

ISBN: (纸本)9798350339888

this work provides a comprehensive review of data preprocessing and machine learning approaches applied to estimate a battery's state of charge (SOC) and state of health (SOH) over the past five years. the standard procedure for preprocessing battery time series data and the associated techniques to address inherent challenges are described. Dominant machine learning architectures and their applications in SOC and SOH estimation are explored. Additionally, potential directions for future research are highlighted. © 2023 IEEE.

关键词： Battery management systems

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Time estimation in Port Terminals: the Key Role of Automation Revealed by data Analysis with Deep learning 6

Time estimation in Port Terminals: The Key Role of Automatio...

引用

6th international conference on Advanced Communication Technologies and Networking, CommNet 2023

作者： Idrissi, Ayoub El Haidine, Abdelfatteh University Chouaib Doukkali National School of Applied Sciences El Jadida Morocco

ISBN: (纸本)9798350329391

Accurate estimation of the time spent by ships in port terminals plays a vital role in operational efficiency. this paper highlights the significant impact of automation through in-depth data analysis with Deep learning. the study is based on observations of the time spent by ships as a function of their characteristics, integrating the level of terminal automation and the development ranking of the countries where these ports are located. the deep learning model trained can be used to analyze historical and real-time data, identify patterns, and predict time spent in terminal port accurately. the research findings highlight the variables that have the greatest impact on port performance and discuss how managing these variables effectively can optimize port processes and reduce vessel turnaround times. the study also emphasizes the role of automation, and data analytics in making informed decisions for optimizing port terminal delay. the results demonstrate the tangible benefits of automation in seaport operations, offering valuable insights to decision-makers and port managers to enhance operational efficiency and deliver higher quality services. Leveraging automation can unlock opportunities to improve competitiveness, sustainability, and overall operational performance in the dynamic maritime industry. © 2023 IEEE.

关键词： Efficiency

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Supervised machine learning for Cloud Security 6

Supervised Machine Learning for Cloud Security

引用

6th international conference on Information Systems and Computer Networks, ISCON 2023

作者： Singhal, Saurabh Srivastava, Rishabh Shyam, R. Mangal, Deepak GLA University Department of Cea Uttar Pradesh Mathura India Bengaluru India Jain University Department of Cs and It Bengaluru India

ISBN: (纸本)9798350346961

Although there is a lot of interest in cloud computing, security concerns have prevented it from becoming mainstream. Users of cloud services frequently worry about the loss of data, the compromise of security, and the unavailability of the services at important moments. Security applications that employ learning-based solutions are gaining attraction in the literature thanks to recent advancements. However, the most challenging aspect of these approaches is the objective datasets. Numerous internal datasets are off-limits for public usage for various reasons, including privacy and the possibility of missing statistical information. Even though there is some lacking, researchers are using these datasets for training and testing in experimental settings. Using a single dataset to train a machine learning model often produces misleading findings. How well these models perform when applied to data from a variety of sources and contexts is an open question, although it hasn't been thoroughly explored in the literature. As, cloud problems are unique, therefore it is crucial to evaluate the performance of these models over a wide range of circumstances. To train the supervised machine learning models used in this research, we make use of the dataset made available by UNSW. For evaluating the performance of these models, we have used the ISOT dataset.. © 2023 IEEE.

关键词： Supervised learning

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Research on Semantic Verification Method of Control Instruction Based on Ensemble learning 6

Research on Semantic Verification Method of Control Instruct...

引用

6th international conference on Artificial Intelligence and Big data, ICAIBD 2023

作者： Jiang, Peiyuan Chen, Kuanming Wang, Teng Li, Yukun Pan, Weijun Civil Aviation Flight University of China College of Air Traffic Management Guanghan China

ISBN: (纸本)9781665491259

For flight safety, the semantic verification of radiotelephony communication is of great significance, which can avoid aviation accidents caused by communication errors between pilots and air traffic controllers. To achieve this goal, we used an ensemble learning strategy in machine learning and applied it to deep learning to build an ensemble learning model for semantic verification. Firstly, we used three different pre-trained language models based on the Transformer architecture for fine-tuning on the control instruction semantic verification dataset. then, we used the Bagging ensemble classification method to integrate the trained base models, and finally used a voting method based on accuracy weights to output the ensemble model. the experimental results show that our ensemble model can significantly improve the discriminative performance of the model without increasing its complexity. Compared with the best-performing base model, the discriminative accuracy has increased by 1.4%. © 2023 IEEE.

关键词： learning systems

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Exploratory data Analysis & data Mining on NBA Match Prediction 6

Exploratory Data Analysis & Data Mining on NBA Match Predict...

引用

6th international conference on Computing and Big data, ICCBD 2023

作者： Yao, Yuheng School of Mathematical Sciences Beihang University Beijing China

ISBN: (数字)9798350317008

ISBN: (纸本)9798350317008

Basketball has been a popular domain for researchers to collect and analyse vast amount of data. With the data of players, teams, games, and seasons, researchers can gain useful insights into players and matches. Nowadays, machine learning is an important tool for data analysis and prediction and has achieved powerful effect and inspiring accuracy in existing research of NBA data. In this paper, we are motivated to research on the factors that are essential for NBA match outcome prediction and how to build the most effective data set and most powerful models to make the best prediction. Multi- variate regression analysis was used to analyse the importance of factors and how these factors influence the outcome of a match. then machine learning models, like naive bayes, random forest, Logistic regression, K nearest neighbors and LDA are applied. Finally, an experiment was conducted in search of the best data setting. It turns out that the most significant factor is the recent win rate, while the best model in accuracy is logistic model with average accuracy of 67% over all seasons and the best strategy is generating data from last season. © 2023 IEEE.

关键词： data accuracy

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AI-Enabled Predictive analytics for Proactive Maintenance in IoT Systems 6

AI-Enabled Predictive Analytics for Proactive Maintenance in...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Chawla, Harpreet Singh Veerasamy, K. Patil, Sunilkumar R Sivakumar, V. Shunmugapriya, B. Balarama Krishna, C. IFTM University Department of Computer Science & Engineering U.P Moradabad India Karpagam Academy of Higher Education Coimbatore Department of Computer Science Tamil Nadu India Bharati Vidyapeeth's College of Engineering Department of Mechanical Engineering Pune India Department of Information Technology Virudhunagar Dist TN Krishnankoil India National Engineering College Department of Computer Science and Engineering Tamil Nadu Kovilpatti India School of Science SR University Telangana Warangal India

ISBN: (纸本)9798350304480

Wind turbine operations and maintenance expenditures have always been a significant burden, with the majority of money spent on unforeseen unorganized failures, fixes, and downtime expenses. Commercial Internet of things (IoTs) and artificial intelligence (AI) technological advances have achieved breakthroughs and are truly revolutionary innovations with the ability to monitor, forecast, and avoid catastrophic breakdowns. A Sensor-oriented IoT system (SOIS) will aid in the monitoring of critical characteristics of wind turbines that govern their operating circumstances, such as wind pace, motion, temperature, and power generated. For proactive maintenance of wind turbines, an integrated strategy using machine learning (ML) approaches for SOIS was applied. the system's diagnostic component receives sensor data and does predictive analytics in cloud environment. Five ML techniques are used, and the outcomes are summarized for prediction. the techniques'outputs are contrasted for the accuracy of the detected statistics, and it is discovered that the Optimized Artificial Neural Network performs better for proactive maintenance and predictive analytics of the power of wind turbines. © 2023 IEEE.

关键词： Artificial Intelligence machine learning Predictive analytics Proactive maintenance Wind turbine

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machine learning Algorithms for Diabetes Diagnosis Prediction 24

Machine Learning Algorithms for Diabetes Diagnosis Predictio...

引用

6th international conference on Image, Video and Signal Processing, IVSP 2024

作者： Zambrana, Amanda Fanek, Loreen Carrera, Pablo Salar Ali, Md Liakat Narasareddygari, Mourya Reddy Dept. Of Computer Science And Physic Rider University NJ United States

ISBN: (纸本)9798400716829

Diabetes is one of the most common health conditions in the United States, affecting more than 37 million U.S. adults. Despite how many people are affected by this disease, a permanent cure does not currently exist. therefore, it is very important for patients to get the proper diagnosis and treatment. If diabetes is not diagnosed and properly treated to control blood sugar levels, serious health problems can occur. though undiagnosed cases are very dangerous, still 1 in 5 U.S. adults with diabetes do not know that they have it. therefore, a method for early detection or prediction of the disease is imperative for healthcare workers to provide timely distribution of treatment. Presently, hospitals collect large volumes of patient data with various tests and provide appropriate treatment based on diagnoses. With big data analytics, hidden patterns can be found in the data that help to predict diagnosis outcomes. Such predictions can help lower the rate of undiagnosed patients. In our paper, we propose a prediction model for classification of diabetes based on various features of patient data. We developed and applied six machine learning algorithms on two diabetes data sets to predict diagnoses. We found that the best algorithms for prediction with our two data sets were Ridge Classifier with an accuracy of 83.12%, along with Random Forest and Decision Tree classifiers, both achieving accuracies of 95%. © 2024 ACM.

关键词： Forecasting

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Vital Sense analytics—For Maximizing the Performance of Football Players Using Wearables 6th

Vital Sense Analytics—For Maximizing the Performance of Foo...

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6th international conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： thomas Leonid, T. Abilaash, S. Gopal, Adith V. HariBabu, E. Department of Electronics and Communication Engineering KCG College of Technology Chennai600097 India

ISBN: (纸本)9789819986279

Wearable devices are increasingly utilized in the realm of sports, including football, to track players’ performance during training and games. these devices incorporate various sensors such as GPS trackers, Pulse oximeters, Node MCU, and ECG sensors, to capture biometric data, including speed, distance covered, and heart rate. the collected data is then transmitted to a central system for analysis (Ash and Stults-Kolehmainen in Establishing a global standard for wearable devices in sport and fitness: perspectives from the New England chapter of the American College of Sports Medicine Members, 2020 [1]). the analyzed and processed data is fed into a machine learning algorithm, which generates output that is received by an application. the model is trained with 10,000 samples and tested with almost 300 test sets. Multiple algorithms have been used out of which, random forest classifies efficiently with an accuracy of 95%. To evaluate the player with performance metrics, the random forest algorithm has been found to be the best when compared among the other algorithms. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： machine learning

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