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检索条件"任意字段=2020 2nd International Conference on Applied Machine Learning and Data Science, ICAMLDS2020"

共 179 条记录，以下是91-100 订阅

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Human-like Decision Making for Autonomous Lane Changing Using Deep learning 2

Human-like Decision Making for Autonomous Lane Changing Usin...

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2nd IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2021

作者： Wang, Tao Guan, Zhiwei Peng, Tao Li, Da Zhao, Ruoyu School of Automotive and Transportation Tianjin University of Technology and Education Tianjin China School of Automobile and Rail Transportation Tianjin Sino-German University of Applied Sciences Tianjin China

ISBN: (纸本)9781665428767

machine learning technique has been shown to greatly improve the intelligence level of the decision-making system of intelligent vehicle. However, due to the complexity and uncertainty of the real-world traffic environment, its reliability is difficult to guarantee. To address this issue, a method for human-like autonomous lane changing decision-making that adopts hierarchical decision-making is proposed. We built a sample database by carrying out simulation driving experiments, based on the data-driven and deep learning LSTM method to generate lane changing intention, and further verify the rationality of the intention through constraint rules. The simulation experimental results show that this method can safely and effectively realize the autonomous lane changing of intelligent vehicle, which can provide a reference for the research of intelligent vehicle decision-making technology. © 2021 IEEE.

关键词： Decision making

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Acceptance of Chatbot based on Emotional Intelligence through machine learning Algorithm 2

Acceptance of Chatbot based on Emotional Intelligence throug...

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2nd international conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2022

作者： Rokaya, Akter Md Touhidul Islam, Shuvo Zhang, Haotian Sun, Liming Zhu, Ming Zhao, Lianjun School of Computer Science and Technology Shandong University of Technology Zibo China

ISBN: (数字)9781665454766

ISBN: (纸本)9781665454766

In Artificial Intelligence, machine learning involves analyzing data and algorithms to improve accuracy and make human life more comfortable. In recent years, Artificial Intelligence strategies have been successfully applied to a wide range of educational purposes, including chatbots. Chatbot is an Artificial Intelligence tool that helps to improve academic strategies through its automated and intelligent features which enable the educational industry to develop students' learning abilities. Hence, the current study's goal is to combine the technology acceptance model (TAM) and emotional intelligence (EI) theory to evaluate the elements influencing the adoption of chatbot applications by implementing the proposed theoretical model. The qualitative result was evaluated through a questionnaire survey and data was collected from international students who have already experienced using chatbot applications in their university. Here, to examine the data and comprehend the relationship between the study models, machine learning method was used. After the investigation, according to the study's findings, Simple Logistic, Iterative Classifier Optimizer, and LMT classification classifiers had higher accuracy in predicting users' intention to use chatbots than 97.95% of the other classification classifiers. It is believed that the findings of the research will provide direction for students to promote chatbots better within educational activities in the future. © 2022 IEEE.

关键词： Iterative methods

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2nd international conference on applied Technologies, ICAT 2020

2nd International Conference on Applied Technologies, ICAT 2...

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2nd international conference on applied Technologies, ICAT 2020

ISBN: (纸本)9783030715021

The proceedings contain 52 papers. The special focus in this conference is on applied Technologies. The topics include: Gamification Within the learning Evaluation Process Using Ardora at the Salesian Polytechnic University (Guayaquil, Ecuador);storytelling Digital: Experience and Technology in Designs of Tele-Collaborative Projects in Higher Education;Metrics Design of Usability and Behavior Analysis of a Human-Robot-Game Platform: HRG Metrics for LOLY-MIDI;an Electronic Equipment with Face Recognition Capacity Oriented to Measuring the Alcoholic Level in People;A Comparison of Linear and Nonlinear PID Controllers Reset-Based for Nonlinear Chemical Processes with Variable Deadtime;Comparison of Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) Filters in Cardiac Optical Mapping Records;construction of a Low-Cost Semi-automatic machine for Tensile Testing;carwash Station Prototype with Automatic Payment Using Intelligent Control Systems;distribution Networks with Distributed Generation and Electric Vehicles;user Affective Experience into a Scope of Conversational Artificial Intelligence;machine learning Model for the Prediction of Emotions in a Mobile Application;is It Intelligent? A Systematic Review of Intelligence in the Most Cited Papers in IoT;detection and Fault Prediction in Electrolytic Capacitors Using Artificial Neural Networks;constraint Programming for the Pandemic in Peru;detecting Xenophobic Hate Speech in Spanish Tweets Against Venezuelan Immigrants in Ecuador Using Natural Language Processing;sentiment Analysis Related of international Festival of Living Arts Loja-Ecuador Employing Knowledge Discovery in Text;data Mining to Predict COVID-19 Patients’ Recovery on a Balanced dataset;topic Identification from Spanish Unstructured Health Texts;autonomous Intelligent Navigation for Mobile Robots in Closed Environments.

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Drift Detection Analytics for IoT Sensors 2

Drift Detection Analytics for IoT Sensors

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2nd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Munirathinam, Sathyan ASML Holding San Diego CA 92127 USA

The Industrial Internet of Things (IoT) has an unique opportunity to have a greater impact on the manufacturing sector. Monitoring the health of the expensive equipments in the factory is critical for the business and the opportunities where IoT can be truly transformational. Most often the industries operates on a primitive way of monitoring these equipments on a Statistical Process Control (SPC) Limits. The major flaw in this monitoring system is unable to detect drifts within the static limit and upon triggering of the limits, it is usually too late for the team in the manufacturing floor to take preventive actions before the system goes down. In this paper, we developed a generic model for detecting drifts and identifying potential outliers. The model uses a double linear regression method to identify both aggressive and progressive drift, as well as adjusted boxplot method to detect outliers in both symmetric and skewed distributions. Unlike conventional drift detection approaches, this model has low computational complexity and can be applied to both batch and stream data. This paper will also introduce the infrastructure and architecture on enabling near real-time analytics using the IoT platform and streaming cluster, which reduces the data latency available for analysis to 10 minutes. Enabling real-time monitoring allows the end users to react to the alarms in a timely manner This system has proven that it is able to provide early detection before the impact was observed as compared to the existing system. Manufacturing operation team could establish a new business process to respond to the early drift alarms by using quality shift left approach. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： IoT Drift Analytics Big data Statistical Control Limits Sensor machine learning Concept Drift

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Predicting Employee Voluntary Departure: Leveraging machine learning Algorithms for Improved Retention Strategies 2

Predicting Employee Voluntary Departure: Leveraging Machine ...

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2nd international conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Sarmento, Gonçalo Mota, Mickael Abbasi, Maryam Martins, Pedro Department of Informatics Polytechnic of Viseu Viseu Portugal Polytechnic Institute of Coimbra Applied Research Institute Coimbra Portugal Research Centre in Digital Services Polytechnic of Viseu Viseu Portugal

ISBN: (纸本)9798350305418

Employee voluntary departure presents significant challenges for organizations, affecting productivity and incurring substantial costs related to recruitment and training. This study aims to tackle this crucial issue by utilizing a comprehensive human resources dataset, which includes 14,999 records across ten columns. Its objective is to predict whether an employee will remain with the company or leave. To achieve this, seven distinct machine learning algorithms were employed: Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector machines, Logistic Regression, and Multi-Layer Perceptron. The performance of each algorithm was assessed through a series of experiments involving different percentages of test data and variations in dataset processing. Notably, the Random Forest classifier emerged as the most accurate model, achieving an impressive accuracy rate of 98.47%. © 2023 IEEE.

关键词： Algorithm selection Comparative analysis Human resources machine learning algorithms Performance evaluation Predictive modeling

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Study on Individual Behavior of Dairy Cows Based on Activity data and Clustering 20

Study on Individual Behavior of Dairy Cows Based on Activity...

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2nd international conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2020

作者： Hou, Shuai Cheng, Xiaodong Shi, Lei Zhang, Shiqiang College of Electronic Information Engineering Inner Mongolia University Hohhot Inner Mongolia China

ISBN: (纸本)9781450388306

This study uses the untagged multi-dimensional activity data as the data set, based on K-means and PCA clustering algorithm, in order to complete the behavior study of individual dairy cows. This paper introduces the development background and general situation of machine learning in animal husbandry, expounds the hardware design and composition of the self-developed activity data acquisition system, describes the characteristics of clustering algorithms including K-means and PCA, compares the advantages and disadvantages of different models and completes clustering, and obtains the data with different category tags, which can be used as the preprocessing of unlabeled data for classification problems. And it provides some ideas and basis for individual identification through the analysis of behavioral characteristics. © 2020 ACM.

关键词： K-means clustering

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Regression analysis and prediction using LSTM model and machine learning methods 2

Regression analysis and prediction using LSTM model and mach...

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2021 2nd international conference on Artificial Intelligence and Information Systems, ICAIIS 2021

作者： Tan, Fangbi Zhongnan University of Economics and Law Wuhan China

In this paper, the LSTM model in deep learning is applied to regression analysis, and the LSTM model is used to solve the problems of nonlinearity and data interdependence in regression analysis, so as to improve the traditional regression analysis model. Through the actual modeling application experiment, on the one hand, the prediction accuracy of different model parameters is compared and analyzed, on the other hand, the effectiveness and practicability of LSTM model in multiple regression analysis and prediction are confirmed. © Published under licence by IOP Publishing Ltd.

关键词： Regression analysis

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Large scale predictability analysis of process variables from injection molding machines 2

Large scale predictability analysis of process variables fro...

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2nd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Tripathi, Shailesh Mittermayr, Christian Muhr, David Jodlbauer, Herbert Univ Appl Sci Upper Austria Prod & Operat Management Wels Austria Greiner Packaging Int GmbH Greinerstr 70 A-4550 Kremsmuenster Austria

This paper analyzes the process variable data from injection molding processes to identify the key process variables, which can be predicted by other process variables, which highlights the interdependence among different process variables in various production scenarios. The available data from injection molding machines provide information for the run-time, setup parameters of machines, and measurements of different process variables through sensors. For predictive modeling, we employed different linear regression models with the recursive backward feature selection and SVM regression models using a radial kernel to predict nonlinear process variables. We also applied the linear and SVM regression models for outlier data in the process variables assuming that the upper bound outliers represent the perturbed state of process variables during production. These perturbations are affected by material type, machine type (age and performance), regime changes, or other external effects and subsequently affect the predictability of process variables and production output. Such cases are different compared to the normal or controlled range of process variables. Our analysis shows that the predictability varies for different material types due to the interdependence of the associated process variables. We further highlight that various process variables exhibit nonlinear relationships and cannot be predicted using linear models. We additionally look for the interdependence of process variables used previously by three studies as input features to predict product quality. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Injection molding Process Parameter Setting Manufacturing Model Selection Regression Analysis machine learning Industry 4.0

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Prediction of Covid-19 Infection in Indonesia Using machine learning Methods 2

Prediction of Covid-19 Infection in Indonesia Using Machine ...

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2020 2nd international conference on science and Technology, ICoST 2020

作者： Zakiyyah, H. Suyanto, S. School of Computing Telkom University Bandung Indonesia

Currently, the world is experiencing a prolonged pandemic known as Covid-19. Many prediction models of Covid-19 have been developed by the governments to make the right decisions to control the outbreak. In Indonesia, there is also much research on the prediction of Covid-19 using machine learning methods, which provide the statistics to predict the total cases, the total deaths, the peak and the end of the pandemic. This paper investigates three prediction models: Gaussian Naive Bayes (GNB), and Support Vector machine (SVM), and Decision Tree (DT) in predicting total cases and total deaths of Covid-19 in Indonesia. First, a preprocessing is applied to change the string data to the numerical dataset using a label encoder. Second, the models are trained using the Covid-19 Indonesia Time Series All dataset (CITSAD) with 90% and 10% train/test split. The three models are then investigated to predict new cases and new deaths. The evaluation using the CITSAD of ten provinces in Indonesia shows that DT gives the highest accuracy of 93% and provides the fastest processing time of 48.4 seconds. © Published under licence by IOP Publishing Ltd.

关键词： Decision trees

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Gender Detection from Handwritten Documents Using Concept of Transfer-learning 2nd

Gender Detection from Handwritten Documents Using Concept of...

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2nd international conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： AL-Qawasmeh, Najla Suen, Ching Y. Department of Computer Science and Software Engineering Concordia University CENPARMI Montreal Canada

ISBN: (纸本)9783030598297

Offline gender detection from Arabic handwritten documents is a very challenging task because of the high similarity between an individual’s writings and the complexity of the Arabic language as well. In this paper, we propose a new way to detect the writer gender from scanned handwritten documents that mainly based on the concept of transfer-learning. We used a pre-trained knowledge from two convolution neural networks (CNN): GoogleNet, and ResNet, then we applied it on our data-set. We use this two CNN architectures as fixed feature extractors. For the analysis and the classification stage, we used a support vector machine (SVM). The performance of the two CNN architectures concerning accuracy is 80.05% for GoogleNet, 83.32% for ResNet. © 2020, Springer Nature Switzerland AG.

关键词： Support vector machines

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