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Simulation Technology in the Development of Mechanics Digital Instructional Resources: A Case Study 6

Simulation Technology in the Development of Mechanics Digita...

引用

6th international conference on Educational Technology Management (ICETM)

作者： Wang, Shang Mao, Xing Zhu, Qingsong Beijing Polytech Sch Automot Engn Beijing 100176 Peoples R China Univ Sci & Technol Beijing Inst Engn Technol Beijing 100083 Peoples R China

ISBN: (纸本)9798400716676

The application of simulation technology in education is an emerging field that has shown promising results in improving the quality of teaching and learning. In this paper, we explore the potential of simulation technology in the development of digital teaching resources for engineering Mechanics (EM) using the metal tensile fracture experiment as an example. Through the development of digitized materials such as colorful stress nephograms, animations, videos, and interactive simulations, students could better understand the fundamental concepts of EM. The effectiveness of the approach was evaluated through a teaching quality evaluation that included student surveys. Results showed that the use of simulation technology significantly improved students' engagement, comprehension of abstract concepts, and overall satisfaction with the course. These findings highlight the potential and advantages of simulation techniques in the development of digital teaching resources for complex courses such as EM, which can benefit from visual and dynamic representations of theoretical concepts.

关键词： Simulation technology Simulation Digital instructional resources teaching

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Prediction of Breast cancer survivability using semi supervised machine learning framework 3

Prediction of Breast cancer survivability using semi supervi...

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3rd international conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2024

作者： Bora, Mousoomi Baruah, Rupam The Assam Kaziranga University Department of Computer Science and Engineering Assam Jorhat India Assam Science and Technology University Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350391565

Artificial intelligence (AI) and machine learning (ML) algorithms are gaining acceptance in the field of health care analytics due to their effectiveness in terms of precision and prediction accuracy. A semi-supervised machine learning framework is proposed in this paper that is based on cluster-and-then-classify approach. The sole purpose of this approach is to enhance the accuracy of breast cancer survivability prediction. Stochastic optimization algorithm has been used for optimal feature selection. Famous standalone clustering algorithms have been implemented in the framework followed by classification step. The clustered output acts as an expert view and can be used in the classification process thereby optimizing the model's performance. The experiments show that blending of clustering results with the optimal features better train most of the standalone supervised machine learning classifiers. Based on the analysis, it is endorsed that compared to the existing cutting-edge ML techniques, the proposed framework using cluster-and-then-classify approach has resulted better performance. The logarithmic - loss has been recorded as low as in the range of [0.9.99E-16 - 0.85]. The area under the ROC curve (ROC-AUC) has been increased up to the range of [0.99 - 1.0]. The prediction accuracy has been enhanced up to the range of [96% -100%]. © 2024 IEEE.

关键词： Self-supervised learning

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Gamification of Business Process Modeling Notation education: an experience report 23

Gamification of Business Process Modeling Notation education...

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27th international conference on Evaluation and Assessment in Software engineering (EASE)

作者： Garaccione, Giacomo Coppola, Riccardo Ardito, Luca Torchiano, Marco Politecn Torino Turin Italy

ISBN: (纸本)9798400700446

Business Process Modeling (BPM) is a skill considered fundamental for computer engineers, with Business Process Modeling Notation (BPMN) being one of the most commonly used notations for this discipline. BPMN modeling is present in different curricula in specific Master's Degree courses related to software engineering, but, in practice, students often underperform on BPMN modeling exercises due to difficulties in learning good modeling practices. In recent years, more and more fields of computer science have employed gamification (the usage of game elements in non-recreational contexts to gain benefits in terms of interest, participation, motivation, and enjoyment) with positive results during both development and teaching processes. Thus, we have developed a platform for BPMN modeling that employs gamification mechanics to facilitate learning good modeling practices with mechanisms such as rewarding good modeling solutions and penalizing less correct ones, with a dedicated feedback mechanism that maps correctly modeled elements to the corresponding concept. A preliminary laboratory experiment has been conducted with students of an Information Systems course to evaluate how students receive the mechanics and if there may be benefits in using a gamified environment for teaching process modeling throughout an entire course.

关键词： Education BPMN Modeling Gamification Process Modeling Computer engineering

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A Hybrid Quantum -classical Fusion Neural Network to Improve Protein-ligand Binding Affinity Predictions for Drug Discovery 5

A Hybrid Quantum -classical Fusion Neural Network to Improve...

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2024 international conference on Quantum computing and engineering

作者： Domingo, L. Chehimi, M. Banerjee, S. Yuxun, S. He Konakanchi, S. Ogunfowora, L. Roy, S. Selvarajan, S. Djukic, M. Johnson, C. Ingenii Inc New York NY 11580 USA Purdue Univ Data Mine W Lafayette IN 47907 USA

ISBN: (纸本)9798331541378

Drug discovery hinges on the accurate prediction of binding affinity between prospective drug molecules and target proteins that influence disease progression, which is financially and computationally demanding. Although classical and hybrid quantum machine learning models have been employed in previous studies to aid in binding affinity prediction, they encounter several issues related to convergence stability and prediction accuracy. In this regard, this paper introduces a novel hybrid quantum -classical deep learning model tailored for binding affinity prediction in drug discovery. Specifically, the proposed model synergistically integrates 3D and spatial graph convolutional neural networks within an optimized quantum circuit architecture. Simulation results demonstrate a 6% improvement in prediction accuracy relative to existing classical models, as well as a significantly more stable convergence performance compared to previous classical approaches. Moreover, to scalably deploy the proposed framework over today's noisy intermediate-scale quantum (NISQ) devices, a novel quantum error mitigation algorithm is proposed. This algorithm outperforms existing techniques and is capable of mitigating errors with gate noise probabilities, p < 0.05, while resulting in no additional overhead during the training and testing phases.

关键词： Quantum machine learning drug discovery binding affinity quantum fusion model

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Analysing BP Disease Prediction through Transfer learning (machine learning) - A Conceptual Framework and Case Study 4

Analysing BP Disease Prediction through Transfer Learning (m...

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

作者： Bisht, Rajendra Singh Al-Jawahry, Hassan M. Alsailawi, H.A. Lamba, Anil Kumar Dhabliya, Dharmesh Mohsen, Abdul Hussein Al-Khuzaie, Mohammed Y. Mezaal, Ali Abdulkareem Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Bhimtal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq University of Kerbala College of Medicine Department of Chemistry and Biochemistry Kerbala56001 Iraq Geeta University School of Computer Science and Engineering Delhi NCR Panipat India Vishwakarma Institute of Information Technology Pune India Alkunooze University College Computer Engineer Department Basra Iraq National University of Science and Technology College of Technical Engineering DhiQar64001 Iraq Kut University College 'Business Administration Department wasit Al kut52001 Iraq

ISBN: (纸本)9798350360165

In the evolving landscape of the clever healthcare industry, the detection and prognostication of ailments thru technological improvements present each possibility and demanding situations, mainly within the realm of lethal tumors. This paper delves into the application of switch getting to know in the framework of the Internet of Medical Things (IoMT) to enhance the accuracy and reliability of disease predictions. Utilizing the modernA capabilities of the Google Net deep device-learning version, we introduce a relaxed IoMT-based totally technique that substantially outperforms conventional methodologies in predicting lethal most cancers ailments. Our have a study demonstrates a incredible accuracy price of 90 seven.5%, underscoring the ability of switch learning to revolutionize predictive diagnostics within the clever healthcare area. By integrating superior IoMT gadgets and secure information protocols, this method no longer most effective improves illness prediction quotes but moreover addresses important privacy and protection issues inherent in modern scientific information handling. © 2024 IEEE.

关键词： fatal tumors Internet of Medical Things (IoMT) privacy security smart healthcare industry

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An Effective Customer Segmentation Approach to Improve E-Commerce Platform Quality Based on Intensive learning Methodology

An Effective Customer Segmentation Approach to Improve E-Com...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Suresh, G.R. Vinoparkavi, D. Govindasamy, Suresh Rohini, S. Giri, Jayant Sabri, M. Omar Dhanalakshmi Srinivasan College of Engineering and Technology Department of Ece Mamallapuram Chennai India Nandha Engineering College Department of Cse Erode India Sona College of Technology Department of Electrical and Electronics Engineering Salem India R.M.K Engineering College Department of Computer Science Engineering Kavaraipettai Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Economic Department of Business Information Technology Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

To accomplish company goals and reap advantages while satisfying and meeting consumer expectations, segmentation is an essential facilitator. Identifying subgroups of the target market that share common traits, requirements, and priorities is customer segmentation's primary goal. Various dataset dimensionalities are also considered, as are changes across time. In this study, we use time-based segmentation and clustering approaches to the e-commerce industry to conduct predictive neural network analysis using factors such as product reviews, goods, buying trend, watching pattern, and more. In this research, Intensive Neural Classification learning (INCL) is presented as a novel deep learning technique. To assess its efficacy, it is cross-validated with the traditional Neural Network (NN) learning algorithm. We have evaluated unigrams, bigrams, and trigrams as part of the feature extraction process and identified the unique brands per consumer. In addition, the customer's chosen brands may be determined using a neural network, and the projected data can then be utilized for statistical analysis. We all know that sentiment analysis and categorization are huge help when trying to guess how customers feel about certain companies and items. Lastly, using the provided set of input data, we will choose the top brands by assessing the accuracy of the forecasts. © 2024 IEEE.

关键词： Sales

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Evaluation of the New Electrical engineering Program Qualification Mix (PQM) in an Open Distance learning (ODeL) Environment

Evaluation of the New Electrical Engineering Program Qualifi...

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2023 IEEE international conference on Industrial engineering and engineering Management, IEEM 2023

作者： Hlalele, T.S. University of South Africa Department of Electrical Engineering Johannesburg South Africa

ISBN: (纸本)9798350323153

The ODeL education has emerged to be the fastest growing method of teaching in the world today. In South Africa, it has been adopted by several universities and the universities of technology. The University of South Africa (UNISA) is the largest full accredited institution of higher learning to offer ODeL in South Africa. The new programme qualification mixture (PQM) in engineering, which has been implemented requires change in teaching methods, study materials and improved virtual learning environments. The engineering Council of South Africa (ECSA) standards and requirements need to be met by this new curriculum. This paper evaluates the new PQM based on ECSA requirements. A comparison is made between the module of the same content in the new PQM and the old PQM. We conclude by paying attention to new approaches to be utilized and further identify the challenges and possible solutions to this new PQM. © 2023 IEEE.

关键词： new curricula OdeL education Programme qualification mix

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Problems of Building Machine learning Models in teaching Students 5

Problems of Building Machine Learning Models in Teaching Stu...

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5th international conference on Control in Technical Systems, CTS 2023

作者： Zhukova, Nataly A. Subbotin, Alexey N. Saint-Petersburg Federal Research Centre of the Russian Academy of Sciences St. Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' St. Petersburg Russia

ISBN: (纸本)9798350304664

The article deals with the problems that arise in the process of teaching students in courses involving the development of machine learning models. The subject area is analyzed. The main problems are the low speed of building models and their verification. A solution method based on the use of fog and cloud computing environments is proposed. The conducted experimental studies showed that the time for building models decreased by 22.17 times, the speed of the teacher's work increased by 47 %. The presented method of increasing the speed of building models can be applied in laboratory work, in the management of final qualifying works. Also, the method is applicable for building machine learning models in solving practical problems in the field of production, urban economy, in the provision of transport services, etc. © 2023 IEEE.

关键词： Machine learning

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Breath Sound Acquisition in Different Environments 11

Breath Sound Acquisition in Different Environments

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11th international conference on Electrical, Electronic and computing engineering, IcETRAN 2024

作者： Ciric, Dejan Bogunovic, Durad Jankovic, Marko Miletic, Miljan University of Niš Faculty of Electronic Engineering in Niš Niš Serbia Academy of Applied Preschool Teaching and Health Studies Serbia

ISBN: (纸本)9798350386998

Breathing is one of the vital functions of a human. This is why the monitoring of breathing represents an important tool for diagnosing a medical condition in a patient, that is, for assessing health status. Apart from medical diagnostics, breath monitoring can be used for other purposes such as managing stress, optimizing sports performance, security applications, or human-computer interaction. Breath detection employs diverse sensors and approaches. The initial step in this process involves acquiring breath sound. This paper explores the potential for acquiring breath sound using several types of microphones in different environments. To this end, four microphones of varying types (digital MEMS, electret, studio condenser, and smartphone microphone) are utilized. Acquisition is conducted in two distinct environments or scenarios: with the microphone in the air in close proximity to the nose and mouth, and with the microphone in a military gas mask. The recorded signals are subsequently analyzed in both the time and spectral domains. © 2024 IEEE.

关键词： Microphones

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Application of Decision Tree Algorithm in teaching Quality Analysis of Physical Education 2nd

Application of Decision Tree Algorithm in Teaching Quality A...

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2nd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2022

作者： Zhang, Chengliang Yang, Hui Qujing Medical College Yunnan Qujing655000 China

ISBN: (纸本)9783031239496

learning situation analysis is not only the basic link of teaching activities, but also one of the basic ways to improve effective teaching. However, only on the premise of ensuring the effectiveness of learning situation analysis can we improve the effectiveness of teaching. Compared with other teaching, physical education teaching has more urgent needs and stricter requirements for learning situation analysis. Therefore, this paper uses data analysis technology to elaborate what teachers should pay attention to and how to do when doing learning situation analysis of physical education teaching from the current situation of learning situation analysis of physical education teaching, the mode of learning situation analysis of physical education teaching and the method of learning situation analysis of physical education teaching, In order to provide theoretical guidance for physical education teaching practice. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Data handling

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