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Advancing AI-Driven BETC Models for Transformative Educational Data Mining and learning Analytics 24

Advancing AI-Driven BETC Models for Transformative Education...

引用

2024 international Symposium on Artificial Intelligence for Education, ISAIE 2024

作者： Chen, Zhiqiang Chen, Yanru Jinhua University of Vocational Technology Zhejiang Jinhua China Jinhua No.1 High School Zhejiang Jinhua China

ISBN: (纸本)9798400707100

This research paper presents an innovative integration of Artificial Intelligence (AI) with the British Technology Education Council (BETC) method, specifically tailored for civil engineering education. The study introduces an AI-driven BETC model that integrates real-world engineering data, industry standards, and virtual construction technologies to significantly enhance learning efficiency and teaching feedback precision. The model was rigorously evaluated through quantitative metrics, demonstrating a substantial increase in pre-class task completion rates from 30% to 87%, accuracy of achievements from 38% to 82%, and student satisfaction from 56% to 97%. These results not only validate the effectiveness of the AI-BETC model but also highlight the transformative potential of AI in educational data mining and learning analytics within the civil engineering sector. The study's findings underscore the importance of adopting advanced technologies in educational practices to improve outcomes and prepare students for the challenges of the future. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Students

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Deep learning and Dynamic BPSO for Road Accident Severity Prediction using Twitter Data 15

Deep Learning and Dynamic BPSO for Road Accident Severity Pr...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Raul, Sanjib Kumar Rout, Rashmi Ranjan Somayajulu, Dvln National Institute of Technology Department of Computer Science and Engineering Telangana Warangal506004 India

ISBN: (纸本)9798350370249

The traffic and medical authorities utilize accident severity prediction through social media postings for better administration and robust emergency response. Deep learning-based models can be used for accident severity prediction using social media data. However, most of the deep learning methods can only perform well when trained with an optimal set of hyper-parameters. This necessitates the use of meta-heuristics in conjunction with deep learning. Moreover, the increase in depth of the networks warrants partial training of the network to make the training process efficient. In this work, we propose a novel partial training-based hybrid deep neural network on convolutional neural networks (CNN) and long short-term memory (LSTM), which is partially trained, and the hyperparameters are optimized using a dynamic version of binary particle swarm optimization (DBPSO) known as accident tweet hybrid deep dynamic BPSO (AT-HDBPSO). Experiments show that the proposed AT-HDBPSO turns out to be efficient in training compared to traditional deep learning-based training. Finally, the proposed method outperforms other baseline and state-of-the-art techniques in prediction accuracy. © 2024 IEEE.

关键词： Tweets

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Prediction of Ayurveda Herbs and its Benefits using CNN and MobileNet Algorithms in Deep learning 4

Prediction of Ayurveda Herbs and its Benefits using CNN and ...

引用

4th international conference on Intelligent Technologies, CONIT 2024

作者： Ghosh, Sambhram Thenmozhi, M. School of Computing College of Engineering and Technology Department of Networking and Communications SRM Nagar Kattankulathur Tamil Nadu Chengalpattu District 603203 India

ISBN: (纸本)9798350349900

Ayurveda has been an integral part of ancient Indian medicine and it still plays an important role in the creation of Homeopathic medicines. In this paper we have introduced a comprehensive system for detecting, grading, and classifying Ayurveda Herbs by taking images as input and analyzing those images using two Deep learning Algorithms CNN and MobileNet. The proposed system employs advanced technology to accurately identify different categories and classes of Ayurvedic Herbs, utilizing image processing and machine learning techniques for classification and gradation using Deep learning Algorithms like CNN and MobileNet. Additionally, it stores a pre-trained uses and benefits data for each and every Ayurvedic Herbs Class and displays the information based on the class of Ayurveda Herbs detected. © 2024 IEEE.

关键词： Adversarial machine learning

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Use of Generative AI for Audio Speech Recognition: Methods and Selection Criteria 6

Use of Generative AI for Audio Speech Recognition: Methods a...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Parmanand, Satish L. Chandankhede, Pankaj H. Deshmukh, Atul R. Electronics and Telecommunication Engineering Department G H Raisoni College of Engineering Nagpur India

ISBN: (纸本)9798331522667

The generative AI has transformed human speech recognition systems audio-wise it was impossible prior. In this paper, various generative AI methods for speech recognition like End-to-end deep learning models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) and Transformer Models are described. A comprehensive evaluation framework is introduced in it to aid in the selection of appropriate generative AI models with respect to their performance and computational efficiency as well as language/dialect neutrality. Results indicate that, though generative AI substantially improves the performance of speech recognition in relation to accuracy and robustness, some guidelines need to be defined for an effective selection procedure aimed at maximizing system efficiency. Furthermore, the study highlights a call for further work to improve integration of these powerful AI models in practice-oriented solutions and establish their effectiveness and scalability for practical deployment. Improving these guidelines will allow for more accurate and trustworthy speech recognition systems going forward. © 2025 IEEE.

关键词： Generative adversarial networks

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A Multi-Objective Multi-Agent RL-Based Approach for Task Offloading in IoT-Enabled Classrooms 25

A Multi-Objective Multi-Agent RL-Based Approach for Task Off...

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25th IEEE international conferences on High Performance computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhang, Juli Huang, Shuo Niu, Zhenxing Lin, Yishuai Wang, Quan Xidian University Academy of Advanced Interdisciplinary Research Xian China College of Computer Science and Technology Xidian University Xian China

ISBN: (纸本)9798350330014

With the widespread deployment of smart sensors and edge computing, Internet of Things (IoT) has become an im-portant enabler to facilitate many conventional systems, such as e-learning, e-health, and assisted driving. Among these systems, one of the most impactful, IoT-enabled practices is e-learning systems, which aim to capture real-time behaviors of students via a multitude of sensors in accordance with course materials, and to further provide adaptive teaching effectiveness. However, e-learning system, specifically an IoT-enabled classroom, faces three important challenges: (1) how to capture the real-time sensing information;(2) how to collect and differentiate jobs with delay constraints, such as face recognition, multi-modal attention detection, etc;and (3) how to allocate computing resources of edge nodes to satisfy the delay requirements of jobs that consist of multiple tasks. To tackle the first two challenges, we design and implement a novel IoT-enabled, e-learning system, named Yo-Yo, with edge computing to collect real-time jobs and tasks for further data analysis. In Yo-Yo system, both job completion rate and energy consumption are considered as quality-of-service (QoS) metrics. Therefore, for the third challenge, we propose a new multi-objective, multi-agent reinforcement learning method to jointly optimize these two objectives. Experiments results indicate that for each device, around 4,200 jobs are generated, with varying tasks associated with each job. The average job completion rate can achieve up to 94% over 10 devices;for dealing with complex task dependency, our approach can improve by 24 %, while reducing the ineffective energy consumption by 70 % against to single-agent based methods. © 2023 IEEE.

关键词： Edge computing

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Federated learning for Enhanced ECG Signal Classification with Privacy Awareness 46

Federated Learning for Enhanced ECG Signal Classification wi...

引用

46th Annual international conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Phan, Quoc Bao Nguyen, Linh Bui, Ngoc Thang Nguyen, Dinh C. Zhang, Lan Nguyen, Tuy Tan Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ86011 United States Mayo Clinic Department of Radiology RochesterMN55905 United States The University of Alabama in Huntsville Department of Electrical and Computer Engineering HuntsvilleAL35899 United States

ISBN: (纸本)9798350371499

This paper presents a novel approach for classifying electrocardiogram (ECG) signals in healthcare applications using federated learning and stacked convolutional neural networks (CNNs). Our innovative technique leverages the distributed nature of federated learning to collaboratively train a high-performance model while preserving data privacy on local devices. We propose a stacked CNN architecture tailored for ECG data, effectively extracting discriminative features across different temporal scales. The evaluation confirms the strength of our approach, culminating in a final model accuracy of 98.6% after 100 communication rounds, significantly exceeding baseline performance. This promising result paves the way for accurate and privacy-preserving ECG classification in diverse healthcare settings, potentially leading to improved diagnosis and patient monitoring. © 2024 IEEE.

关键词： Federated learning

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Compact, Optimized, and Effective: The CNNSpectra Approach to Deep learning in Spectral Data Analysis 4

Compact, Optimized, and Effective: The CNNSpectra Approach t...

引用

4th international conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Asharindavida, Fayas Liu, Jun Uhomoibhi, James Nibouche, Omar Ulster University School of Computing Belfast United Kingdom Ulster University School of Engineering Belfast United Kingdom

ISBN: (纸本)9798350394375

Deep learning has been instrumental in advancing various fields, including spectral data analysis. However, it is essential to recognize that deep layers and complex networks are not always prerequisites for achieving superior performance results. In this context, we introduce CNNSpectra, a simplified yet highly optimized deep learning model tailored for spectral data analysis, particularly for assessing food quality from portable spectroscopic devices. CNNSpectra's performance is comprehensively evaluated on diverse datasets, including flour powders, spices, and chocolate, collected across multiple sessions. The results demonstrate that CNNSpectra consistently achieves competitive performance levels across various spectral datasets. While it may not outperform the best machine learning models in all cases, its simplicity and optimization render it a promising approach for real-world spectral data analysis, offering a valuable tool for food quality assessment from portable devices. This work underscores the importance of exploring more efficient and effective deep learning models in spectral data analysis and opens avenues for future research and improvements in this domain. © 2024 IEEE.

关键词： Complex networks

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Reservation Systems Cancellation Determination Using Machine learning 1

Reservation Systems Cancellation Determination Using Machine...

引用

1st international conference for Women in computing, InCoWoCo 2024

作者： Kumar, Abhishek Kumari, Archana Burman, Ravi Kumar Pandey, Muskan Kumar, Nishant Kamaldeep, Kamaldeep Jharkhand University of Technology Computer Science Engineering Ranchi India Ramgarh Engineering College Computer Science Engineering Jharkhand India Electronics & Communication Engineering BIT Sindri Jharkhand India St. Xavier's College Computer Science Engineering Ranchi India

ISBN: (纸本)9798331518943

The increasing reliance on online reservation systems across various industries, including hospitality, airlines, and e-commerce, has heightened concerns over last-minute cancellations, may lead to monetary losses and ineffective operations. This study examines the application of machine learning techniques, particularly the Random Forest classifier (a bagging ensemble method), to predict reservation cancellations. In order to enable businesses to put proactive measures in place to reduce these risks, the research attempts to identify important variables that affect cancellations, such as customer demographics, booking lead times, and cancellation policies. The model was trained using 80% of the datasets, with the remaining 20% being utilized for testing. The model was assessed using key performance metrics such F1-Score, precision, recall, and accuracy. The model obtained a 0.37 F1-Score, recall of 0.38, precision of 0.37, and accuracy of 42%, indicating reasonable performance. The model's ROC-AUC score of 0.42 indicates that it has to be improved in order to better differentiate between canceled and non-canceled reservations. Overall, the findings show that Random Forest and other machine learning techniques, can be useful for predicting reservation cancellations, though further improvements are needed. Future research could concentrate on improving the model through hyper parameter tuning, feature engineering, and exploring more advanced methods to enhance predictive performance across different industries. © 2024 IEEE.

关键词： Predictive analytics

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Detection and classification of brain tumor

Detection and classification of brain tumor

引用

international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Preethi, P. Jawahar Aravind, G. Praveen Kumar, G. Yogesh Raj, S. Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9781032669663

The emerging topic in many medical diagnostic applications is automated flaw identification in medical imaging. The automated diagnosis of tumours in MRI scans is particularly important since it offers details about aberrant tissues required for treatment planning. Human examination is the traditional approach for finding flaws in magnetic resonance brain imaging. The vast volume of data makes this strategy unfeasible. In order to reduce the death rate of humans, reliable and automated categorization techniques are necessary. Because it would free up radiologist time and have proven accuracy, automated tumour detection approaches have been developed. The intricacy and variety of tumours make it difficult to detect brain cancers with an MRI. In this study, we provide machine learning techniques to address the shortcomings of conventional. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Magnetic resonance

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AI-Driven Diagnostics: Comparative Analysis of Machine learning and Deep learning Approaches in Multiple Sclerosis Diagnostics 11

AI-Driven Diagnostics: Comparative Analysis of Machine Learn...

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11th IEEE international conference on Advances in computing and Communications, ICACC 2024

作者： Anitha, L. Narmadha, D. Naveen Sundar, G. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350379136

Multiple Sclerosis (MS) is a chronic autoimmune disorder that targets the central nervous system, resulting in considerable physical and cognitive disabilities. Timely and precise diagnosis is essential for the effective management and treatment of Multiple Sclerosis (MS). Predicting MS involves using various types of data to assess the risk and diagnose the disease. The data commonly used are clinical, imaging, speech and gene data. This work presents a review of machine learning and deep learning methods, encompassing a broad spectrum of algorithms such as Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Convolutional Neural Networks (CNNs), and Artificial Neural Networks (ANNs). Furthermore, the survey gives a comparative analysis of the dataset used, algorithms employed and the performance metrics. The survey also compares the implemented results with DL using the gene data. It also identifies current challenges in MS prediction, such as heterogeneity of MS, integration of multimodal data, validation and clinical implementation. Our goal is to offer a valuable resource for researchers and practitioners. © 2024 IEEE.

关键词： Convolutional neural networks

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