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Methodological Implementation for Predicting Student Performance Using Data Mining Classifiers and machine learning 4

Methodological Implementation for Predicting Student Perform...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Agarwal, Mini Agarwal, Bharat Bhushan IFTM University Moradabad India

ISBN: (纸本)9798350306118

Each engineering university's major goal is to provide students with a world-class education. However, following COVID-19, when schools reopened, there was a large announcement that pupil overall performance was deteriorating daily because everything had changed from online to offline. Predicting negative grades among college students at the beginning level is critical so that their professors and management can assist and encourage them to change the negative grade into a top grade. My research area comes and provides strategies to reduce academic troubles among college students. the key goal of this research study is to become acquainted with the many aspects that aid in forecasting learner success. the second purpose is to examine and analyze the various facts. Data Mining classifiers and Artificial Neural Networks on a dataset including 689 student records of IP University for prediction of student performance in Python programming language. this work then entails calculating the Accuracy, Fl-score, Recall value, Precision value, ROC curve, and the Confusion matrix of several algorithms and determining the best set of rules based on the following metric scores. A Random Forest was executed with 98% accuracy. © 2023 IEEE.

关键词： Students

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Comparison of logistic regression and random forest algorithms for airport's runway assignment 4

Comparison of logistic regression and random forest algorith...

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4th international conference on Electronics Communication Technologies, ICECT 2022

作者： Kanjanasurat, I. Jungsuwadee, W. Lasakul, A. Benjangkaprasert, C. KOSEN-KMITL Bangkok Thailand Computer Engineering Department School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok10520 Thailand

Various automation systems are currently developed using machine learning techniques. It is used to predict and decide on numerous complex tasks in order to reduce the likelihood of human error. Logistic regression is one of the most widely employed machine learning (ML) algorithm. In this study, the accuracy of logistic regression was compared to that of random forest for the assignment of Suvarnabhumi Airport runways to arriving aircraft. the accuracy of the logistic regression model was determined to be 82%, while the accuracy of the random forest model was 77%. Logistic regression was found to be more precise for predicting the appropriate runway to assign to arriving aircraft. © Published under licence by IOP Publishing Ltd.

关键词： machine learning

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Precision Agriculture: Federated learning CNNs Aloe Vera Leaf Disease Analysis 4

Precision Agriculture: Federated Learning CNNs Aloe Vera Lea...

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4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Sharma, Vikrant Kukreja, Vinay Mehta, Shiva Garg, Ashish Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Chitkara University Institute Of Engineering And Technology Chitkara University Punjab India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350384628

In order to identify and classify illnesses of aloe vera leaves more effectively, this research is the first to use Federated learning (FL) in conjunction with Convolutional Neural Networks (CNN). It encompasses a holistic strategy, tackling four tiers of leaf diseases using datasets from six separate customers. the novel approach makes use of Federated learning's collaborative power to combine different client ideas, making the model more resilient and flexible while protecting client data. To skillfully use a convolutional neural network (CNN) architecture to analyze the datasets and identify patterns that correspond to the four stages of illness severity: mild (1-25%), moderate (26-50%), advanced (51-75%), and severe (76-100%). Metrics like accuracy, precision, recall, F1-score, and support were used to assess the model's performance. the results showed that Client yp-6 was very good at illness categorization;she achieved an F1-score of 96.42 and recall and precision of an incredible 96.49% and 96.37%, respectively. the support value of 1462.50 and the excellent accuracy of 0.98 provided additional proof of its effectiveness. Clients yp-4 and yp-5 also showed impressive skills;they averaged 88.50 on the macro and 88.63 on the micro levels, respectively. Metrics like these demonstrated how well they dealt with diverse data and how well they balanced performance across different severity *** the other hand, clients yp-1 to yp-3, while competent, pointed out areas that might be improved upon with somewhat lower averages, which allowed us to spot chances for refining the model. this study proves without a reasonable doubt that convolutional neural networks (CNNs) and a Federated learning strategy work wonders for the fast, accurate, and privacy-preserving diagnosis of illnesses affecting aloe vera leaves. this innovative application tackles a pressing problem in agriculture and provides a collaborative, scalable solution that might completely change th

关键词： Diseases

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Impact of COVID-19 Lockdowns on Air Quality in Bangladesh: Analysis and AQI Forecasting with Support Vector Regression 4

Impact of COVID-19 Lockdowns on Air Quality in Bangladesh: A...

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4th international conference for Emerging Technology, INCET 2023

作者： Hossain, Mohammed Tahmid Hossain, Afra Meem, Sabrina Masum Monir, Md Fahad Ullah Miah, Md Saef Bin Sarwar, Talha Independent University Bangladesh Department of CSC Dhaka Bangladesh Independent University Bangladesh Department of CSE Dhaka Bangladesh American International University-Bangladesh Department of Computer Science FST Dhaka Bangladesh Universiti Malaysia Pahang Faculty of Computing Pekan Malaysia

ISBN: (纸本)9798350335750

Over the past few decades, air pollution has emerged as a significant environmental hazard, causing premature deaths in Southeast Asia. the proliferation of industrialization and deforestation has resulted in an alarming increase in pollution levels. However, the COVID-19 pandemic has significantly reduced the amount of volatile organic compounds and toxic gases in the air due to the decrease in human activity caused by lockdowns and restrictions. this study aims to investigate the air quality in various geographical areas of Bangladesh, comparing the air quality index (AQI) during different lockdown periods to equivalent eight-year time spans in 10 of the country's busiest cities. this study demonstrates a strong correlation between the rapid and widespread dispersion of COVID-19 and air pollution reduction in Bangladesh. In addition, we evaluated the performance of Support Vector Regression (SVR) in AQI forecasting using the time series dataset. the results can help improve machine learning and deep learning models for accurate AQI forecasting. this study contributes to developing effective policies and strategies for reducing air pollution in Bangladesh and other countries facing similar challenges. © 2023 IEEE.

关键词： COVID-19

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NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition 40

NeuralStagger: Accelerating Physics-constrained Neural PDE S...

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40th international conference on machine learning

作者： Huang, Xinquan Shi, Wenlei Meng, Qi Wang, Yue Gao, Xiaotian Zhang, Jia Liu, Tie-Yan King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Microsoft Res AI4Sci Beijing Peoples R China

Neural networks have shown great potential in accelerating the solution of partial differential equations (PDEs). Recently, there has been a growing interest in introducing physics constraints into training neural PDE solvers to reduce the use of costly data and improve the generalization ability. However, these physics constraints, based on certain finite dimensional approximations over the function space, must resolve the smallest scaled physics to ensure the accuracy and stability of the simulation, resulting in high computational costs from large input, output, and neural networks. this paper proposes a general acceleration methodology called NeuralStagger by spatially and temporally decomposing the original learning tasks into several coarser-resolution subtasks. We define a coarse-resolution neural solver for each subtask, which requires fewer computational resources, and jointly train them with the vanilla physics-constrained loss by simply arranging their outputs to reconstruct the original solution. Due to the perfect parallelism between them, the solution is achieved as fast as a coarse-resolution neural solver. In addition, the trained solvers bring the flexibility of simulating with multiple levels of resolution. We demonstrate the successful application of NeuralStagger on 2D and 3D fluid dynamics simulations, which leads to an additional 10 similar to 100x speed-up. Moreover, the experiment also shows that the learned model could be well used for optimal control.

关键词： machine learning

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Analysis and Prediction of Wear Characteristics of Sustainable Metal Matrix Composites Using machine learning with Decision Making Algorithm 4

Analysis and Prediction of Wear Characteristics of Sustainab...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Agme, Vaishali N. Ashish Pant, Rushali Sridevi, Mulagundla Suman, A. Babu, M. Vijaya Sekhar Bharati Vidyapeeth College of Engineering Department of Applied Science Maharashtra Navi Mumbai410210 India Kl Deemed University Department of Cse Andhra Pradesh Vaddeswaram India Graphic Era Deemed to Be University Me Dehradun R/S Dehradun248002 India R R District Telangana 501510 India Audisankara College of Engineering and Technology Department of Mathematics Gudur Andhra Pradesh Tirupati524101 India Giet Engineering College Department of Mechanical Engineering Andhra Pradesh Rajamahendravaram India

ISBN: (纸本)9798350300888

the goal of this research was to analyse and predict the wear characteristics of sustainable metal matrix composites using machine learning with a decision-making algorithm. the study used Al 7072 alloy with nano-particle SiC as an alloying element, which was added in three different compositions (0.3 %, 0.6%, and 0.9%) through stir casting. the experiment was based on the Taguchi L27 orthogonal array and an Artificial Neural Network (ANN) was utilised as a machine learning approach to predict the responses with 100% accuracy. the input parameters were composition (C), load (R), and rotation speed (S), while the responses were the Specific wear rate (Spr) and friction coefficient (Fc), which were optimised using the Taguchi combined optimisation technique. the results showed that the ANN approach was effective in predicting the wear characteristics of the sustainable metal matrix composites and the Taguchi combined optimisation technique improved the wear resistance and friction coefficient. this study provides a valuable contribution to the field of sustainable materials, as the findings can be used to improve the design and production of sustainable metal matrix composites with improved wear characteristics, which can lead to a reduction in energy consumption and environmental impact. © 2023 IEEE.

关键词： Wear resistance

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Research on Deep learning Based-carbon Measurement Model in UHVDC System 4

Research on Deep Learning Based-carbon Measurement Model in ...

引用

4th international conference on Smart Grid and Energy Engineering, SGEE 2023

作者： Lou, Lan Wang, Jingping Xie, Changjun Bian, Yonghao University of Toronto The Department of Computer and Mathematical Sciences TorontoM1C 1A4 Canada Wuhan University of Technology College of Automation Wuhan430070 China Cash Processing Solutions Limited Shenzhen518034 China

ISBN: (纸本)9798350359619

With the increasing application of the Ultra-High Voltage Direct Current (UHVDC) system, carbon emissions produced by these systems are growing in proportion within the power industry. However, several challenges exist in reducing carbon emissions for the UHVDC system, including difficulties in processing high-dimensional and strongly coupled data and establishing electrical-to-carbon conversion models. Considering this, this paper investigates the carbon measurement issue in the UHVDC system as follows: First, the loss mechanism is analyzed to determine its distribution. then, considering the characteristics of loss, a factor accounting algorithm is selected to calculate the electrical-to-carbon conversion for the system. Finally, a carbon measurement model is constructed, combining deep learning models. To address the challenges of feature extraction and fusion in the UHVDC system, a model fusion strategy based on Residual Network and Long Short-Term Memory (LSTM) is proposed. Experimental results demonstrate that the evaluation indexes of the fusion carbon measurement model are superior to other deep learning models. Compared to the LSTM, the fusion algorithm model reduces mean squared error, root mean squared error, mean absolute error, and mean absolute percentage error by 30.51%, 20%, 16.67%, and 2.17% respectively. © 2023 IEEE.

关键词： Carbon

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Mobile application of Convolutional Neural Networks for Melanoma Classification 4

Mobile Application of Convolutional Neural Networks for Mela...

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4th IEEE international conference on Smart Information Systems and Technologies, SIST 2024

作者： Omirgaliyev, Ruslan Arystanbekova, Asylai Alzhanov, Arman Makatov, Arstan Department of Computer Engineering Astana IT University Astana Kazakhstan Department of Creative Industries Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350374865

Skin cancer is one of the most common types of cancer in terms of incidence around the world. Melanoma, in particular, manifests as visible pigmented skin lesions, often a result of sun overexposure. Despite the fact of rapid development of dermatology, delays in diagnosing skin diseases and poor access to their treatment are occurring in modern medicine. Several studies have demonstrated the potential of CNN in developing accurate and noninvasive screening methods for skin cancer. Nowadays, a limited number of mobile applications are identified that involve deep learning for the scanning and screening of skin diseases. this study simultaneously proposes a CNN architecture with a non-standard activation function, as well as presents the results of the proposed CNN model in terms of various metrics, such as accuracy, recall, specificity, precision, and so on. the software development is implemented in the Flutter framework, focusing on the Waterfall model. the key components of the mobile application, including the splash screen, main page, navigation, health-related features, and the "About Us" section, are described. the results of this study demonstrate that the proposed CNN model achieved 81.53% in terms of accuracy. the image augmentation techniques employed contribute to improved model generalization and robustness. By combining the capabilities of CNNs with user-friendly mobile interfaces, individuals can gain easy access to melanoma assessment, promoting proactive healthcare practices. © 2024 IEEE.

关键词： Mobile applications

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Research on Job Shop Scheduling Problem Based on Multi-Agent Reinforcement learning 4

Research on Job Shop Scheduling Problem Based on Multi-Agent...

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4th international conference on Artificial Intelligence and computer Engineering, ICAICE 2023

作者： Hu, Zhikun Chen, Yimin Li, Hao Deng, Xuegang Yunnan University School of Information Science and Engineering Yunnan Kunming650504 China Yunnan Development and Reform Commission Yunnan Kunming650051 China

ISBN: (纸本)9798400708831

the job shop scheduling problem is an abstraction of the job processing process in the manufacturing workshop, which has important economic value in real life. At present, most of the research on job shop scheduling problems based on reinforcement learning is based on a single agent architecture, which makes the number of interactions required in the solving process directly related to the size of the problem. there are difficulties in training the model on large-scale instances. Propose an end-to-end method based on multi-agent reinforcement learning, treating each machine as an agent and making processing decisions for multiple machines in one interaction, while training problem scale independent strategies. Using disjunctive graphs as a representation of states in problem modeling to model the execution order relationships between operations. In network design, a graph neural network based on induced point attention is further proposed for feature extraction to overcome the problems of high computational complexity and attention degradation. the experiments on the Taillard dataset show that the performance of our model is superior to multiple traditional methods and a recent single agent reinforcement learning method. © 2023 ACM.

关键词： Reinforcement learning

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Video Insights application, A machine learning Approach 5

Video Insights Application, A Machine Learning Approach

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5th IEEE international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Singh, Nishant Rathaur, Prashant Singh, Ajay Patel, Nitesh Kumar Galgotias College Of Engineering & Technology Information Technology Department Greater Noida India

ISBN: (纸本)9798350330861

Unsupervised learning has emerged as a potential method for summarizing films without the requirement for human annotations, which is an important study field in computer vision. this thesis provides a novel method for automatically identifying and extracting sample frames from videos using unsupervised learning and deep learning approaches.. the suggested method learns a low-dimensional representation of the video frames using clustering and dimensionality reduction techniques, which is then applied to provide a summary of the film. We compare our method to current cutting-edge unsupervised video summarization techniques using a number of benchmark datasets, and the results demonstrate that it performs better in terms of both objective metrics and subjective quality. Our approach also exhibits good generalization performance across different types of videos, indicating its potential for real-world applications. this thesis represents a significant leap forward in the field of video summarization, introducing innovative viewpoints on leveraging unsupervised learning techniques to tackle this complex task. © 2023 IEEE.

关键词： Object detection

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