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检索条件"任意字段=International Conference on Teaching and Learning in Computing and Engineering"

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Machine learning-Based Optimization for Identifying Effective Drugs in Breast Cancer on an in Vitro Platform 4

Machine Learning-Based Optimization for Identifying Effectiv...

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4th IEEE international conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Saxena, Mahima Rao, P.V.V.S. Eswara Choudhary, Amar Reddy, P. Srinivas Sharma, Vipin Kumar Kumar, Vinish Ajay Kumar Garg Engineering College Department of Computer Science Ghaziabad India Sasi Institute of Technology and Engineering Department of Computer Science Tadepalligudem India Department Electronics and Communication Engineering Bengaluru India Cvr College of Engineering Mechanical Engineering Department Rangareddy India Ims Engineering College Department of Mca India Ghaziabad India

ISBN: (纸本)9798350360165

In this innovative quest for more efficient irrigation, we investigate the combined use of sensor technology and machine intelligence to optimize agricultural water usage. We collect precise data on essential variables, including water flow, temperature, humidity, soil moisture, and water level, every 15 days by promptly deploying sensors in the field to monitor them in real-time. We use the vast amount of data available to us to train machine learning models, including Recurrent Neural Networks (RNN), K-Nearest Neighbours (KNN), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), specifically for streams that are anticipated to have a certain distribution. CNN has exceptional precision with a decoding accuracy of 94.5% in visual signal interpretation, surpassing ANN, KNN, and RNN which achieve lower accuracies of 91.1%, 88.7%, and 83.6% respectively. Our investigation uncovers a fluid interaction between sensor data and model training, leading to each model exhibiting unique characteristics. These findings not only showcase the accuracy with which machine learning can distribute water, but they also signify a noteworthy progress in resource-efficient and sustainable agriculture. In addition to generating mathematical discoveries, our research establishes the foundation for advanced irrigation systems that smoothly integrate mechanical and natural processes in agricultural fields. © 2024 IEEE.

关键词： Soil moisture

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EmoRoom: Unveiling Academic Emotions Through Interactive Visual Analytics in Classroom Videos 9th

EmoRoom: Unveiling Academic Emotions Through Interactive Vis...

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9th Future Technologies conference, FTC 2024

作者： Yuvaraj, Rajamanickam Faizal, Sahil Ratnavel, Rajalakshmi Yang, Wang Science of Learning in Education Centre Office of Education Research National Institute of Education Nanyang Technological University Singapore637616 Singapore Department of Computer Science and Engineering Tandon School of Engineering New York University New YorkNY11201 United States School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computing and Information Systems Singapore Management University Singapore188065 Singapore

ISBN: (纸本)9783031731211

Measuring emotions in educational settings can provide important information in predicting and explaining student learning outcomes. Knowledge of student’s classroom emotions can also help teachers understand their students’ learning behaviors, improve their teaching methods, and optimize students’ learning and development. However, it can be highly challenging for teachers to monitor and accurately understand student emotions within classroom or group contexts, especially while they are actively teaching and attending to many students simultaneously. Video recording classroom activity can address that issue as high-definition cameras can be used to continuously record groups of students, enabling online or offline analyses of student emotions and supplementing teacher monitoring, retrieval, and interpretation of those processes. Teachers find it difficult to use existing emotion recognition methods to analyze student behaviors in videos due to a lack of user-friendly interfaces that facilitate automatic analysis. To address this challenge, we developed EmoRoom, an open-source tool designed to simplify the annotation and analysis of videos from an emotional perspective. EmoRoom tool offers a practical solution for quantifying and visualizing the frequency of emotions for individuals of interest. Furthermore, it can assist teachers in refining their teaching methods by offering a comprehensive overview of students’ emotional experiences during learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Students

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

12th International Conference on Frontiers of Intelligent Co...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

ISBN: (纸本)9789819601387

The proceedings contain 49 papers. The special focus in this conference is on Frontiers of Intelligent computing: Theory and Applications. The topics include: Nepali Word Spelling Correction Using Ensemble learning Technique;the Impact of Information Security Policies and Innovations in Digital Technology on the Transformation of Healthcare in Developing Nations: A Literature Review;integrating Multi-omics and Clinical Narratives for Predictive Modeling: Genomics, Transcriptomics, Proteomics, and Medical Texts in Disease Analysis;a Comprehensive Survey of Fake Review Detection Technology;a Hybrid Intelligent Decision Support System for Automating Financial Credit Evaluation;Advancing Taxation in the New Era: Enhancing Tax Ratios with the Core Tax Administration System (CTAS);Enabling Grid Stability: Harnessing μPMU Data for Data-Driven Analysis of Grid Frequency Events;machine learning Techniques to Detect Fake News on Social Media: A Systematic Review;cutting-Edge Technology-Based Social Enterprises in India for Sustainable, Inclusive Healthcare;enhancing Telugu Sarcasm Classification Models with Word Embeddings in Imbalanced Datasets;capital Punishment: Analyzing Trends in the United States (1976–2016);exploring the Technostress Issue Among Indonesian Young Entrepreneurs;teaching Computer Science Using Cloud-Based IDE with Perspectives in Inclusivity;Comparison of Data Encryption Standard (DES) and Advanced Encryption Standard (AES) in Security Issues of Cloud computing: A Literature Review;enhancing Corporate and Factory Training Through Game Development: A Comparative Review;usability of Intelligent System in Implementing Tutoring Lesson in Education: A Literature Review;performance Efficiency of Cloud computing—A Literature Review;decentralized Finance (DeFi) Wallets: A Review of its Efficiency, Usability, and Effectiveness;Social Networks on WEB 3.0.

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Cloud computing for Drug Discovery and Telemedicine in QSAR Analysis 13

Cloud Computing for Drug Discovery and Telemedicine in QSAR ...

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13th international conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Ahmad, Javed Ahmad, Shahnawaz Arif, Mohd Nazim, Mohd Sharda University Department of Cse Greater Noida India School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computing Science and Engineering Galgotias University Department of Cse Greater Noida India Noida Institute of Engineering and Technology Department of Cse Noida Greater India

ISBN: (纸本)9798350380583

This research is focused on developing drug discovery and telemedicine through advanced Quantitative Structure-Activity Relationship (QSAR) analysis. The performance of state-of-the-art machine learning (ML) algorithms like Random Forest (RF), DNN-QSAR, Support Vector Machine (SVM), was evaluated and the potential of Chemoinformatic based feature selection methods were explored for increasing the accuracy of developed QSAR models. However, not much innovation has been proposed in managing large chemical data and developing highly generalizable and more accurate QSAR models specific to telemedicine with low computational cost. Therefore, to fill this gap, we intended to use cloud computing (CC) as a downscale solution to host large chemical data and develop highly generalizable QSAR models that are more appropriate for telemedicine. This new way integrating cloud with QSAR for telemedicine is compatible with the world of current scientific research in pharmaceutical industries in which constant evolution and updating is a routine process. In addition to all these features integration of cloud also overcomes several inherited limitations of being non-performing with respect to speed up/QSAR analysis time or limited number or size chemicals database limitation that are used only in academic research studies. © 2024 IEEE.

关键词： Chemoin-formatic Cloud computing Drug Discovery Machine learning QSAR Analysis

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Classification of COVID-19 Utilizing CT Scan Images Employing the EfficientNet Model

Classification of COVID-19 Utilizing CT Scan Images Employin...

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2024 international conference on Advances in computing Research on Science engineering and Technology, ACROSET 2024

作者： Sharma, Priyanka Sharma, Varsha Computer Science &engineering Rajiv Gandhi Proudyogiki Vishwavidhyalaya Bhopal India

ISBN: (纸本)9798350388800

In the realm of medical imaging, particularly for the critical task of distinguishing between COVID-19 and non-COVID cases, the analysis of CT scans has emerged as a formidable challenge. The primary concern is achieving high accuracy and low loss in predictive models to support diagnostic decisions. To tackle this, various deep learning architectures, including ResNet50, Inception, and VGG19, were employed, yet none matched the proficiency of the Proposed EfficientNet model. Utilizing a dataset enriched through augmentation techniques to yield 17,099 images, the models were trained and tested on a uniform framework of 15 epochs, a consistent learning rate of 0.00001, and an image resolution of 224 × 224. In this standardized environment, the proposed EfficientNet model, with a strategic batch size of 64 and 100 steps per epoch, showcased a substantial performance improvement, attaining a near-perfect training accuracy of 99.54% and a test accuracy of 98.24%, with impressively low training and test losses of 0.015 and 0.145, respectively. Compared to other models, this translates to an accuracy improvement of over 4% on training and approximately 14% on testing. Conclusively, the Proposed EfficientNet model marked an improvement of roughly 5% over the closest competitor, presenting a significant advancement in the field of medical image classification for the detection of COVID-19. © 2024 IEEE.

关键词： COVID-19

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Heterogeneous Induction Motor Current Dataset Fusion for Efficient Generalised MCSA-Based Fault Classification

Heterogeneous Induction Motor Current Dataset Fusion for Eff...

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2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big Data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Elhalwagy, Ayman Kalganova, Tatiana Brunel University London Department of Electronic and Electrical Engineering London United Kingdom

ISBN: (纸本)9798350304602

Advances in Machine learning (ML) have paved the way for smarter and more efficient predictive maintenance strategies in industrial settings. However, the deployment of ML algorithms for fault detection and diagnostics in motors often faces challenges due to varying operating conditions and motor characteristics. This paper introduces the Order Domain Transformer (ODT), a pre-processing algorithm designed to standardise and align the frequency components of signals from different motors, thereby enhancing the learning capability of Neural Networks. The study explores the computational efficiency of the algorithm, highlighting its suitability for real-time applications. Experimental results indicate that using ODT maintains performance on data from the same motors trained on, but results in a substantial improvement in cross-motor generalisation and model performance, with an average accuracy of 0.67 using ODT in comparison to 0.35 without ODT, validating the efficacy and efficiency of the ODT approach. The results indicate a future potential to train a single model for multiple motors, thereby optimising the utilisation of available labelled data, and reinforcing the principles of Green AI by significantly reducing the computational resources required for training. © 2023 IEEE.

关键词： Induction motors

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Theoretical Models of Adoption and Acceptance of e-learning Prior and During Covid19 4

Theoretical Models of Adoption and Acceptance of e-Learning ...

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4th international Multidisciplinary Information Technology and engineering conference, IMITEC 2024

作者： Kolo, Ditlalane Irene Zuva, Tranos Applied Computer Sciences Vaal University of Technology Vanderbijlpark South Africa

ISBN: (纸本)9798350387988

Prior to the Covid19 pandemic, the educational methods used were physical face to face teaching, e-learning, and blended learning, which is a combination of physical teaching and e-learning. During the Covid 19 pandemic, physical education was not possible, leading to e-learning becoming the favorable method of administering education. While e-learning became the compulsory method during the Covid 19 pandemic, there was a limited time for adoption and acceptance of e-learning. This study reviewed the models and/or frameworks for adoption and acceptance prior and during Covid19 in e-learning to identify necessary variables for a pandemic period. The inclusion and exclusion criteria were used to select the papers assessed. The assessment acknowledged 18 related studies published prior and during Covid19. The results showed that prior to the pandemic period, there were variables used which were not included during the pandemic period in adopting and accepting the e-learning system. Out of 52 variables discovered in this study, 7 variables are confirmed to be common/core variables which are exogenous and/or endogenous. The 7 common variables are Social Influence, Facilitating Condition, Behavioral Intention, Perceived Usefulness, Perceived Ease of Use, Attitude and Intension to use. It further showed that Perceived Ease of Use and Perceived Usefulness were the most crucial common variables used during the pandemic. The core variables are indicators of adoption and acceptance of technologies in any environment. Therefore, it is concluded that the research studies are influenced by outbreak of disease pandemic or any natural disaster that can occur, as seen during the Covid19 pandemic the new variables were initiated. © 2024 IEEE.

关键词： Contrastive learning

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Methodology for Virtual Education of Clinical engineering through the Implementation of Digital Tools for Simulated Scenarios and international Collaboration 7

Methodology for Virtual Education of Clinical Engineering th...

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7th IEEE World engineering Education conference (EDUNINE) - Reimaging engineering - Toward the Next Generation of engineering Education, Merging Technologies in a Connected World

作者： Cuchca, Katherin Mileny Zumaeta Mendoza, Diana Naomi Guevara Perez, Diego Alexander Perez Pontificia Univ Catolica Peru Univ Peruana Cayetano Heredia Biomed Engn Program Lima Peru QSyst SAS Res & Dev Medellin Colombia

ISBN: (纸本)9798350320503

The Covid-19 global pandemic has represented a challenge for education, which had to migrate to virtual environments. Universities adopted different teaching methods to keep contributing to the growth of the professionals in various fields. In this context, the Biomedical engineering program of the Pontificia Universidad Catolica del Peru and the Universidad Peruana Cayetano Heredia had to change or adapt the methodology of the courses included in its curriculum in order to reach the learning objectives. This paper presents a methodology for an innovative approach of simulated scenarios using digital tools for the virtual teaching of Clinical engineering. The learning results achieved in two semesters of implementation of the methodology, during 2020 and 2021, were measured by means of a survey applied to the students at the end of the course. Obtaining achievement results above 76 % and improvement opportunities that would be useful for the next version of this course and for the replication of the methodology in other universities.

关键词： Pandemic Biomedical engineering virtually education methodology learning achievements

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Improving Active learning on Imbalanced Datasets by Features Mixing

Improving Active Learning on Imbalanced Datasets by Features...

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2023 international conference on Cloud computing, Performance computing, and Deep learning, CCPCDL 2023

作者： Chen, Yuzhen Ye, Haibo School of computer science and technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China

ISBN: (纸本)9781510666474

When resources are limited, active learning seeks to optimize the dataset labeling process by choosing the most valuable unlabeled samples through sampling strategy. The majority of active learning techniques in use today are intended for balanced datasets. Machine learning, however, frequently deals with unbalanced datasets in the actual world. We think that unbalanced datasets make active learning more difficult. Here, we suggest an innovative active learning strategy for unbalanced datasets called the imbalanced feature-mixing K-center method (IFMK). In order to lessen the imbalance in the unlabeled samples, it develops interpolation between the representation of unlabeled samples by learning the more pertinent generic properties of the class. We employ a regularization term to enhance the proximity and diversity of unlabeled and labeled samples during sample querying. Results from two datasets with a wide range of imbalance variables demonstrate the superiority of our strategy over more current active learning techniques. On a dataset with more imbalance, these improvements are more noticeable. © 2023 SPIE.

关键词： learning algorithms

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Application of Clustering Algorithms to enhance Personalized learning through Recommendation Model

Application of Clustering Algorithms to enhance Personalized...

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2024 international conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Sharif, Mhd Saeed Tamang, Madhav Theeng Mani, Dyuthi Elmedany, Wael Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom College of Information Technology University of Bahrain Bahrain

ISBN: (纸本)9798331533137

Technology and education have recently changed student involvement with learning resources and educational experiences. It is very important to have a platform which can provide personalized learning resources and group students by learning patterns. The research develops recommendation systems and grouping algorithms to help students with their learning patterns and performance measures. To record multidimensional student learning experiences, demographics, learning activities, problem-solving behaviours, and performance measures are extracted and processed. This system makes personalised student suggestions via collaborative filtering, especially Alternating Least Squares (ALS). The model predicts learning materials and problem sets based on student preferences and ability levels by analysing historical student interactions with learning resources. The research also uses clustering methods like K-means clustering to group students with similar learning and performance patterns. Clustering analysis lets instructors discover student group traits and tailor interventions and support techniques. This research also examines temporal relationships in student learning sequences using sequential models. Sequential learning activities and problem-solving behaviour of students help recurrent neural networks (RNNs), or sequential pattern mining algorithms predict their next activities. This research used a huge dataset of over sixteen million exercise logs and applied collaborative filtering (ALS) and K-means clustering to find learning patterns. The ALS-based recommendation system achieved a Mean Squared Error (MSE) of 0.23, showed excellent predictive accuracy. Clustering grouped students by learning behaviours, enabling targeted interventions. This research improves personalised learning using machine learning and data analytics in education. The recommendation system can help instructors tailor training, give individualised support, and boost academic success for various

关键词： Federated learning

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