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检索条件"任意字段=3rd International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2021"

共 2040 条记录，以下是81-90 订阅

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Research on the Application of College English Teaching System Using Random Forest 3

Research on the Application of College English Teaching Syst...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Huang, Xiaojing Dou, Anqi Hainan Vocational University Of Science And Technology Foreign Language Teaching Department Haikou China

ISBN: (纸本)9798350318609

With the growth of language research and teaching, people realize that the grammatical competence is a significant thing of the communicative competence. Presently, there are various Artificial Intelligence (AI) assisted educational applications in schools as well as universities. This research utilizes an AI approach integrated with the knowledge recommendation to the system as well as develops an online English teaching system. The system of English teaching is helpful in determining the potential as well as internal connections among the estimation results as well as different factors. In this research, the machine learning (ML) approach of Random Forest (RF) is proposed for the College English Teaching System. The proposed approach initially performed the correlation analysis on the developed estimation data. After that, the integration principles were followed to estimate the coordination among indexes in the estimation of the teaching. The proposed RF approach attains the interaction analysis of 98.2 %, 96.3%, 95.2 %, 97.2 % and 96.2 % in the number of students of 20,40, 60, 80 and 1 00 respectively. © 2024 IEEE.

关键词： machine learning

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Leveraging Neural Networks with BiLSTM and CNN Models for Enhanced HR Management Practices 3

Leveraging Neural Networks with BiLSTM and CNN Models for En...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kalva, Yamini Parasar, Anuradha Pujar, Rajlaxmi Pramod Balasubramanian, K. Pasha, Zameer Ramya, S. Sri Ramachandra Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India School of Liberal Education Galgotias University Dankaur India Indira School of Business Studies Pgdm Department of Marketing Pune India Kalasalingam Academy of Research and Education-Deemed to be University Department of Computer Applications Krishnankoil India Department of Management Bengaluru India M.Kumarasamy College of Engineering Department of Artificial Intelligence and Machine Learning Karur India

ISBN: (纸本)9798331508456

The most valuable asset of any business is its employees. Even with technological advances, organizations rely heavily on human resources for system administration. An increasingly competitive sector makes managing and retaining talented workers difficult. An organization's ability to develop, survive, and compete depends on its employees' skills. These challenges can be overcome using preprocessing, feature extraction, and model training. data preparation for machine learning is necessary yet time-consuming. Features selection is critical since it enhances accuracy and reduces overfitting. CNN-BiLSTM trained the model. The CNN- BiLSTM model outperformed BiLSTM and CNN. The model's 90.62 percent accuracy showed it could handle HR tasks. For effective human resource management, this study recommends sophisticated machine learning models. Today's businesses need reliable tools for managing and keeping people, and the CNN-BiLSTM model helps. © 2025 IEEE.

关键词： Personnel selection

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Predicting the Solution Time for Optimization Problems Using machine learning Case of Job Shop Scheduling Problem 3rd

Predicting the Solution Time for Optimization Problems Using...

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3rd international conference on Optimization, learning Algorithms and applications (OL2A)

作者： Pouya, Shaheen Toragay, Oguz Mohammadi, Mehrdad Auburn Univ Ind & Syst Engn Auburn AL 36849 USA Lawrence Technol Univ Mech Robot Ind Engn Southfield MI USA

ISBN: (纸本)9783031530241;9783031530258

In organizations that use optimization and other computer-related problem-solving techniques, a better understanding of the required computational time is essential for efficient decision-making and resource allocation which also directly affects productivity and operational effectiveness. This study proposes the application of various machine learning (ML) methods to predict the computation time needed to solve job shop problems. Specifically, we implemented 11 ML models, including the Deep Neural Network (DNN), which delivered the most accurate results. The proposed approach involves utilizing a DNN algorithm to predict computation time for Integer Programming (IP) job shop problems, trained on synthetically generated data that indicate the gap-time correlation in a branch and bound tree. The developed model in this study estimates the total computation time with an accuracy of 92%. The model development process involves collecting data from a set of solved problems using the branch and bound method and training the ML models to estimate the computational time required to reach the optimal solution in unsolved similar problems.

关键词： machine learning Deep Neural Networks Performance Evaluation Job Shop Scheduling Integer Programming Branch and Bound Method Computation Time Prediction

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Crop Yield Prediction Using Deep learning 1

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3rd international conference on data science, machine learning and applications, icdsmla 2021

作者： Mamatha, K. Samantha, Shantideepa Prasad, Kundan Kumar Department of ISE Don Bosco Institute of Technology Bengaluru India

ISBN: (数字)9789811959363

ISBN: (纸本)9789811959356

Agriculture provides a living for around 58% of India’s population. Agriculture, forestry, and fisheries were expected to generate ₹19.48 lakh crore in FY20. Given the significance of agriculture in India, farmers might benefit from early forecasting of agricultural yields. The study focuses on predicting agricultural yield, for Karnataka state using the regression with neural network model. The final constructed dataset takes parameters like agricultural area, crop, taluka, year, season, district wise annual rainfall (mm), district wise maximum and minimum temperature (°C) and harvest or yield for the time period of 1997–2017. The underlying model is built utilizing a Multilayer Perceptron Neural Network, a ReLu Activation function, an Adam Optimizer, and 50 epochs with a batch size of 200. The end of the training gained 96.43% accuracy on test data. Several additional well-known regression algorithms such as Multinomial Linear Regression, Random Forest Regression, and Support Vector machine are also constructed and trained using the same dataset so as to compare their performance to the base model. From the final comparison results it was found that neural network model has outperformed classic machine models for crop yield prediction in terms of both mean absolute error and accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Development of data Driven Model for Proton Exchange Membrane Fuel Cell Using machine learning Approaches 3

Development of Data Driven Model for Proton Exchange Membran...

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3rd IEEE international conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Dhaked, Dheeraj Kumar Kumar, Purushottam Ganguly, Sanjib Iit Guwahati Eee Department Assam Guwahati India

ISBN: (纸本)9798350313703

The world is moving towards a sustainable source of transportation and energy. Proton exchange membrane fuel cells (PEMFCs) are the major key for that. A multi-input data-based predictive model of PEM fuel cell is built using machine learning approach, such as, linear regression approach, support vector machine (SVM), decision tree (DT) method, random forest (RF) approach etc. Since fuel cell deterioration is very sensitive to operating circumstances, the transfer learning approach is used to accurately forecast fuel cell deterioration for varied activities, particularly in the early stages. The results illustrate the effectiveness and repeatability of the intended strategy. The created voltage prediction digital model is cross-validated using fuel cell real-time voltage collected during operation. This article's goal is to create an early-stage fuel cell voltage prediction digital model that can predict deterioration in real-time and is tolerant to various prediction patterns. The developed models are able to predict the voltage, and RF has the more accurate data model among all. © 2024 IEEE.

关键词： Support vector machines

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A Novel Approach for Visualizing Medical Big data Using Variational Autoencoders 3rd

A Novel Approach for Visualizing Medical Big Data Using Var...

引用

3rd international conference on data science, machine learning and applications, icdsmla 2021

作者： Madhukar Rao, G. Ramesh, Dharavath Jharkhand Dhanbad India

ISBN: (纸本)9789811959356

Big data are enormous amounts of information that may accomplish marvels. This has been a topic of special interest over the past two decades due to its tremendous development. In the medical sector, a different large data resource include medical files, patient hospital information, and clinical exam reports and Internet-based equipment. data visualization may utilize the human vision system fully to lead people throughout statistical analyses and intuitively and easily relay the message buried while behind the data. The Big data Medical Care Platform is designed based on the system needs and operational position, from collecting and exchange of health information, analysis, and knowledge management level. In this study, the VAE utilized to visualize big data for the administration of large data in the health sector is analyzed. Experimental findings show that in comparison to traditional methods, VAE can provide outstanding prediction performance, along with PCA speedily ICA, feature analysis, nonnegative matrix factor, latent Dirich allocation in terms of AUROC and accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data visualization

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Identification of Web-based Attacks on Android Devices using Classification Model 3

Identification of Web-based Attacks on Android Devices using...

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3rd international conference on Power Electronics and IoT applications in Renewable Energy and its Control, PARC 2024

作者： Bagla, Piyush Mishra, Amit Kumar Pandey, Neeraj Kumar Sharma, Ravi National Institute of Technology Department of Computer Science and Engineering Punjab Jalandhar India Graphic Era Hill University Department of Computer Scienece and Engineering Dehradun India Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350309409

Nowadays machine learning is increasing its usage in multiple domains such as bio medical, information security, firewall protection, medical science, internet of things and mobile application security. Hence the need of machine learning is increased more and more for several applications and in this current article we try to use machine learning for identifying the web or online attacks on mobile devices. Limiting the personal information leakage and privacy has become a difficult challenge as smartphone usage has increased quickly. Impersonation is a main consequence of this type of leakage. Since current defensive measures (such as passcodes and fingerprinting) are unable to regularly monitor usage and determine whether a user is authorized, it is practically impossible to stop this kind of unlawful usage. In general, once the user got authorized by the device then complete control will be under the user and no protection mechanism can stop the complete access of device data. Therefore, a multi-view bagging that is an approach based on machine learning is used to gather sequential tapping information on the keyboard of smart phone. To continually authenticate the user while they type, we build a sequential tapping biometrics approach. By conducting various experiments on our proposed model by taking sample dataset CLaMP (Classification of Malware with Portable headers) from Kaggle website and tested the ensemble classification model on that dataset. According to empirical and theoretical studies that compare our model to widely used shallow machine techniques we conclude that an 8.42% equal error rate, a 94.41% H-mean, and a 94.24% accuracy can be achieved by our system with the use of accelerometer and only five keyboard taps. © 2024 IEEE.

关键词： Internet of things

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Intelligent machine learning Framework for Classification of Vehicular Traffic in Internet of Vehicles 3

Intelligent Machine Learning Framework for Classification of...

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3rd international conference on Power Electronics and IoT applications in Renewable Energy and its Control, PARC 2024

作者： Tripathy, Biswajit Tripathy, Subhranshu Sekhar Bebortta, Sujit Mukherjee, Tanmay Ravenshaw University Departmenet of Computer Science Cuttack75303 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India University Department of Computer Science and Engineering Bhubaneswar751030 India

ISBN: (纸本)9798350309409

In a world that is always changing due to population growth, it is difficult to forecast how routes will behave in the future, especially with regard to traffic. In this work, Traci was used to generate traffic-related data in a real-world setting, including waiting periods, coordinates, speed, and displacement. After that, cars were categorized according to how long they took to wait, and performance was examined using a variety of machine learning methods, such as Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector machine (SVM), and K-means. The RF algorithm fared better than other algorithms, according to empirical studies, in estimating waiting times and evaluating traffic conditions. After that, the study used the Rapidly Exploring Random Tree (RRT) algorithm, a path-planning and simplification technique, to find vehicle travel paths that would not cause collisions. By decreasing wait times and improving customer satisfaction, this strategy aims to optimize journey routes and cut related expenses. Drones are used in the proposed system, and base stations and ad hoc networks work together to improve overall performance. © 2024 IEEE.

关键词： Vehicles

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Analysis and Performance Evaluation of Credit Card Fraud by Multi-model ML 3

Analysis and Performance Evaluation of Credit Card Fraud by ...

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3rd international conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Islam, Md Aminul Asif Imran, A.T.M. Rahman, Md Habibur Pabel, Md Amran Hossen Mishra, Bhupesh Kumar Basu, Kashinath University of Gloucestershire Data Science School of Engineering and Technology Cheltenham United Kingdom Dhaka Bangladesh University of Chittagong Department of Mathematics Chittagong Bangladesh Wright State University Master of Science in Business Analytics United States United Kingdom Oxford Brookes University Data Science School of Engineering Computing and Mathematics United Kingdom

ISBN: (纸本)9798350388282

Using credit cards is a convenient and efficient payment mechanism. Credit card fraud significantly impacts financial loss, mental health, and the reputation of financial institutions. This study would incorporate an analysis of the preceding statement about eliminating several obstacles associated with the availability of public data, the presence of unbalanced data, the dynamic nature of fraud tendencies, and the prevalence of false alarms. The authors discuss various machine-learning techniques used to detect credit card fraud. Extreme learning, Decision Trees, Random Forests, Support Vector machines, Logistic Regression, and XG Boost are among these techniques on the European card benchmark dataset. A comparative evaluation of the effectiveness of machine learning has also been conducted, and precision is increased by incorporating multiple layers. The study's findings indicate significant improvements in several crucial metrics, including accuracy, f1-score, precision, and AUC curves. XGBoost model achieves optimized values of 99.9\% accuracy. The proposed model outperforms contemporary machine learning approaches in credit card fraud detection. It has been observed that the combination of data balancing techniques significantly reduces the occurrence of false negatives. This study has substantial potential for credit card fraud detection applications in the real world, providing an effective and efficient method for addressing this ongoing critical issue. © 2024 IEEE.

关键词： Decision trees

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Breast cancer diagnosis using machine learning Techniques 3

Breast cancer diagnosis using machine learning Techniques

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3rd international conference on Optics, Computer applications, and Materials science, CMSD 2023

作者： Muhamediyeva, D.K. Shaazizova, M.E. Doshanova, M. Yu. Tashkent University of Information Technologies Named after Muhammad Al-Kharezmy Tashkent Uzbekistan Digital Technologies and Artificial Intelligence Development Research Institute Tashkent Uzbekistan

The goal of this research is to diagnose breast cancer using machine learning methods including decision trees, support vector machines (SVM), and na39;ve Bayesian classifiers. The properties of cell nuclei taken f... 详细信息

ISBN: (纸本)9781510674486

The goal of this research is to diagnose breast cancer using machine learning methods including decision trees, support vector machines (SVM), and naïve Bayesian classifiers. The properties of cell nuclei taken from breast biopsies are included in the Breast Cancer Wisconsin dataset, which is used in this study. Three different machine learning algorithms-naive Bayesian classifier, SVM and decision tree-are used to develop predictive models aimed at classifying samples as malignant or benign tumours. The study involves training the models on evaluation data and then evaluating their performance on test data. In order to compare the effectiveness of each method, many metrics like accuracy, sensitivity, and specificity are used in the evaluation of the results. The outcomes demonstrate the efficacy of every technique examined in this research. This work can be used as a springboard for future research into refining and customizing these techniques to intricate clinical situations, in addition to offering a useful comparison of the efficacy of three distinct machine learning approaches for breast cancer diagnosis. The results may facilitate the application of machine learning techniques in clinical settings, hence facilitating early detection and bettering the prognosis of patients with breast cancer. © 2024 SPIE. All rights reserved.

关键词： Computer vision

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