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Ensemble learning Approaches to Strategically Shaping Learner Achievement in Thailand Higher Education 3rd

Ensemble Learning Approaches to Strategically Shaping Learne...

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3rd international Semantic Intelligence conference, ISIC 2023

作者： Bussaman, Sittichai Nasa-Ngium, Patchara Nuankaew, Wongpanya S. Sararat, Thapanapong Nuankaew, Pratya Faculty of Science and Technology Rajabhat Maha Sarakham University Maha Sarakham44000 Thailand School of Information and Communication Technology University of Phayao Phayao56000 Thailand

ISBN: (纸本)9789819773558

Thailand faces a severe problem of students dropping out of the higher education system. Therefore, this research has three critical objectives: (1) to study the context of students’ academic achievement in science and technology at the higher education level, (2) to assemble a model to predict the risk of students dropping out of higher education, and (3) to evaluate a model for predicting the risk of a student dropping out from higher education. The population and research sample were 2361 students’ academic achievements from five educational programs of the Faculty of Science and Technology at Rajabhat Maha Sarakham University during the 2010–2022 academic year. The research tool utilized data mining and supervised machine learning techniques: Decision Tree, Naïve Bayes, Neural Networks, Gradient Boosting, Random Forest, and Majority Voting. Model performance was evaluated using the cross-validation approaches and confusion matrix techniques, with four indicators: Accuracy, Precision, Recall, and F1-Score. The results showed that learners’ context in science and technology had various learning achievements. The educational program that needs to be monitored is the Bachelor of Science Program in computer Science. This research successfully developed a predictive model for student dropout risk with an accuracy of 88.14% and an S.D. equal to 1.04. Therefore, this research dramatically benefits the public and stakeholders of Rajabhat Maha Sarakham University, who should be encouraged and encouraged to continue this research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive learning

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A hybrid model using 2D and 3D Convolutional Neural Networks for violence detection in a video dataset 3

A hybrid model using 2D and 3D Convolutional Neural Networks...

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3rd international conference on Communication, Computing and Industry 4.0, C2I4 2022

作者： Jayasimhan, Anusha Pabitha, P. Anna University MIT Campus Department of Computer Technology Chennai India

ISBN: (纸本)9781665474979

In the era of Internet of Things(IoT), the data from edge devices, wearables and other sensors have become available easily and can be efficiently used for knowledge extraction. One such form of streaming data is in the form of videos which maybe captured from CCTV devices installed in various locations. While deep learning algorithms such as Convolutional Neural Networks(CNN) are not only effective for image classification but their application can also be extended for classifying videos. Two dimensional(2D) CNN classify images by extracting the spatial features from an image. However, when they are applied to videos, the temporal features of a video may get suppressed leading to a poor accuracy. In this work, we implement a hybrid CNN model, by combining a three dimensional convolutional neural network(3D CNN) and 2D CNN for identifying if the video footage contains violence related activities. Results prove that the hybrid CNN model attains a maximum training accuracy upto 99.93% and validation accuracy of 84.5% for violence recognition from video footages. © 2022 IEEE.

关键词： 3D CNN deep learning video classification violence detection

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Exploring fusion model for the detection and localization of colorectal polyps 3

Exploring fusion model for the detection and localization of...

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3rd IEEE international Power and Renewable Energy conference (IPRECON)

作者： Geetha, S. Gopakumar, C. Chengannur Coll Engn Coll Engn Dept Comp Sci & Engn Karunagappally India APJ Abdul Kalam Technol Univ Thiruvananthapuram Kerala India Coll Engn Dept Elect & Commun Engn Karunagappally India

ISBN: (纸本)9781665491754

computer Vision (CV) and Artificial Intelligence (AI) have reported unprecedented growth in recent years and demonstrated potential in almost all domains. In the medical realm machine learning and deep learning techniques are widely used for detection as well as classification of a nomalies and segmentation of anomaly regions. Salient feature extraction is a challenging task in traditional machine learning techniques but deep learning models successfully extract better features with convolutional neural networks (CNN) and report better performance when the training dataset is tremendous. In the medical domain, the main challenge is the limited availability of annotated data to perform training. Colorectal polyp detection and localization is an arduous task due to the close resemblance of polyps with the surrounding colon regions and the irregular shapes of the polyps. But detection, localization, and segmentation of colon polyps are of great importance in the screening and removal of polyp regions for the prevention of colorectal cancer. Currently used U-Net and Mask-RCNN-based architectures for polyp segmentation are computationally expensive. In this paper, we explore a fusion model to overcome the downsides of both machine learning and deep learning techniques. The fusion model serves the similar purpose of segmentation with less computational overhead and can be used to assist clinicians in screening tests. In the proposed work, pre-trained deep CNNs are explored to extract salient features from image patches followed by ensemble machine learning techniques for the detection of colon polyps. A localization algorithm is applied on the detected image patches for localizing the position of the colon polyps.

关键词： fusion models colorectal polyp detection and localization ensemble machine learning techniques Random Forest XGBoost elliptical contour

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Using EEG Brain Signals to Predict Children's learning Performance During Technology-assisted Language learning 3

Using EEG Brain Signals to Predict Children's Learning Perfo...

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3rd IEEE international conference on Human-machine Systems, ICHMS 2022

作者： Nakov, Nikolas Alimardani, Maryam Tilburg University Department of Cognitive Science and Artificial Intelligence Tilburg Netherlands

ISBN: (数字)9781665452380

ISBN: (纸本)9781665452380

Brain-computer interface (BCI) systems can provide an objective evaluation of a learner's performance based on their brain signals. Having an accurate assessment of the learning process can facilitate the adaptation of the educational setting to improve the effectiveness of learning. The current study aimed at developing BCI classifiers that can accurately predict the learning performance of children by investigating temporal and spectral characteristics of their brain signals. EEG brain activity collected from children while learning a second language was analyzed by extracting two types of features: frequency-domain features using power spectral density (PSD) analysis and time-domain features using detrended fluctuation analysis (DFA). Two machine learning models (i.e., SVM and KNN) were trained with either PSD features or both PSD and DFA features. It was expected that adding a time-domain feature would improve the prediction accuracy of the models. However, the models with only frequency-domain features performed significantly better, suggesting that EEG temporal correlations may not reflect learning processes. Despite this, both approaches yielded high prediction performance, with SVM reaching 84% accuracy in predicting the learning performance of children. These findings highlight the potential of neurotechnology to be employed in learning analytics to refine traditional methods of academic performance measurement and ultimately achieve personalized learning. © 2022 IEEE.

关键词： Electroencephalography

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SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions 3

SynthBA: Reliable Brain Age Estimation Across Multiple MRI S...

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3rd IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Puglisi, Lemuel Rondinella, Alessia De Meo, Linda Guarnera, Francesco Battiato, Sebastiano Ravi, Daniele University of Catania Dept. of Math and Computer Science Catania Italy University College London Department of Neuroinflammation London United Kingdom School of Physics Engineering and Cs University of Hertfordshire Hatfield United Kingdom

ISBN: (纸本)9798350378009

Brain age is a critical measure that reflects the biological ageing process of the brain. The gap between brain age and chronological age, referred to as brain PAD (Predicted Age Difference), has been utilized to investigate neurodegenerative conditions. Brain age can be predicted using MRIs and machine learning techniques. However, existing methods are often sensitive to acquisition-related variabilities, such as differences in acquisition protocols, scanners, MRI sequences, and resolutions, significantly limiting their application in highly heterogeneous clinical settings. In this study, we introduce Synthetic Brain Age (SynthBA), a robust deep-learning model designed for predicting brain age. SynthBA utilizes an advanced domain randomization technique, ensuring effective operation across a wide array of acquisition-related variabilities. To assess the effectiveness and robustness of SynthBA, we evaluate its predictive capabilities on internal and external datasets, encompassing various MRI sequences and resolutions, and compare it with state-of-the-art techniques. Additionally, we calculate the brain PAD in a large cohort of subjects with Alzheimer's Disease (AD), demonstrating a significant correlation with AD-related measures of cognitive dysfunction. SynthBA holds the potential to facilitate the broader adoption of brain age prediction in clinical settings, where re-training or fine-tuning is often unfeasible. The SynthBA source code and pre-trained models are publicly available at https://***/LemuelPuglisi/SynthBA. © 2024 IEEE.

关键词： Deep learning

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Intelligent Flower Recognition System Based on Deep learning 3

Intelligent Flower Recognition System Based on Deep Learning

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3rd international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2022

作者： Gu, Fenfei Dept. of Big Data and Artificial Intelligence Anhui Xinhua University Anhui Hefei China

ISBN: (纸本)9781510657281

With the continuous expansion of the application fields of deep learning, human life is becoming more and more wonderful. This paper mainly uses the application of deep learning in the field of image recognition and uses deep learning algorithm to recognition the problem of flowers. Due to the huge number of flowering species, How to automatically recognition flowers through the system gives people more understanding of flowers while appreciating them, which is a topic with practical application value. This paper constructs a deep learning multi-classification model based on ResNet, and conducts training based on PyTorch framework on the data set of more than 3000 pictures of five kinds of flowers. After 50 rounds of iterative training, it finally achieves 91% average accuracy on the test dataset. Finally, a recognition system based on this model is realized by visualization technology, so as to facilitate users to effectively recognize the kind of flowers through this system. © 2022 SPIE.

关键词： Image recognition

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application of Deep Belief Network for Sustainable Development via Deep learning to Export Credit Risk Assessment Under the Belt and Road Strategy 3

Application of Deep Belief Network for Sustainable Developme...

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3rd international conference on Applied Mathematics, Modeling and computer Simulation, AMMCS 2023

作者： Li, Zheng Pu, Maomin Song, Jinlong Li, Qing College of Finance and Economics Shanghai Lida University Shanghai China

ISBN: (纸本)9781643684581

The purpose of the study is to reduce the export credit risk of enterprises, and the export credit risk assessment of enterprises under the belt and road strategy based on deep learning is discussed. First, the research background and deep belief networks are introduced. Second, the contrast divergence algorithm based on the deep belief model is improved on the restricted Boltzmann machine. Finally, the deep belief network of classification and partition is constructed and simulated. The results show that the test accuracy of the classification and partition of the restricted Boltzmann machine (CPRBM) constructed is higher than that of the restricted Boltzmann machine (RBM). When the accuracy of the algorithm is verified under the condition of unbalanced two classification samples in a relatively small amount of datasets, the accuracy of the CPRBM algorithm is 93.71%, and the accuracy of the RBM algorithm is 89.86%. In the optimization stage of the deep belief networks, the convergence rate of the CPRBM is slower than that of the RBM. Since the optimized system increases the penalty term in the first training stage of the deep belief networks, the penalty term is canceled in the second stage of optimization. At three time points, the algorithm accuracy of the CPRBM is higher than that of the RBM. The simulation results are consistent with the previous experimental results. Although the accuracy is not high at the third time point, the CPRBM algorithm still has some advantages. Compared with the accuracy of the support vector machine (SVM) and the deep extreme learning machine (DELM), the CPRBM algorithm based on deep belief networks has the highest accuracy at any time point. The CPRBM algorithm constructed has obvious advantages compared with common models, and the overall performance of the algorithm is better. The conclusions provide the support for the sustainable development of the economy under the Belt and Road strategy. © 2023 The Authors.

关键词： Classification (of information)

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Time-Weighted Frequency Domain Audio Representation with GMM Estimator for Anomalous Sound Detection 48

Time-Weighted Frequency Domain Audio Representation with GMM...

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48th IEEE international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Guan, Jian Liu, Youde Zhu, Qiaoxi Zheng, Tieran Han, Jiqing Wang, Wenwu Harbin Engineering University Group of Intelligent Signal Processing College of Computer Science and Technology China Harbin Institute of Technology School of Computer Science and Technology China University of Technology Sydney Centre for Audio Acoustics and Vibration Australia University of Surrey Centre for Vision Speech and Signal Processing United Kingdom

ISBN: (纸本)9781728163277

Although deep learning is the mainstream method in unsupervised anomalous sound detection, Gaussian Mixture Model (GMM) with statistical audio frequency representation as input can achieve comparable results with much lower model complexity and fewer parameters. Existing statistical frequency representations, e.g. the log-Mel spectrogram's average or maximum over time, do not always work well for different machines. This paper presents Time-Weighted Frequency Domain Representation (TWFR) with the GMM method (TWFR-GMM) for anomalous sound detection. The TWFR is a generalized statistical frequency domain representation that can adapt to different machine types, using the global weighted ranking pooling over time-domain. This allows GMM estimator to recognize anomalies, even under domain-shift conditions, as visualized with a Mahalanobis distance-based metric. Experiments on DCASE 2022 Challenge Task2 dataset show that our method has better detection performance than recent deep learning methods. TWFR-GMM is the core of our submission that achieved the 3rd place in DCASE 2022 Challenge Task2. © 2023 IEEE.

关键词： Deep learning

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A Review on Speech Synthesis Based on machine learning 3rd

A Review on Speech Synthesis Based on Machine Learning

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3rd international conference on Artificial Intelligence and Speech Technology, AIST 2021

作者： Kumari, Ruchika Dev, Amita Kumar, Ashwni Indira Gandhi Delhi Technical University for Women Delhi India

ISBN: (纸本)9783030957100

Recently, Speech synthesis is one of the growing techniques in the research domain that takes input as text and provides output as acoustical form. The speech synthesis system is more advantageous to physically impaired people. In execution process, there arise some complications by surrounding noises and communication style. To neglect such unnecessary noises various machine learning techniques are employed. In this paper, we described various techniques adopted to improve the naturalness and quality of synthesized speech. The main contribution of this paper is to elaborate and compare the characteristics of techniques utilized in speech synthesis for different languages. The techniques such as support vector machine, Artificial Neural Network, Gaussian mixture modeling, Generative adversarial network, Deep Neural Network and Hidden Markov Model are employed in this work to enhance the speech naturalness and quality of synthesized speech signals. © 2022, Springer Nature Switzerland AG.

关键词： Deep neural networks

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machine learning for Plastic Waste Detection: State-of-the-art, Challenges, and Solutions 3

Machine Learning for Plastic Waste Detection: State-of-the-a...

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3rd international conference on Communications, Information, Electronic and Energy Systems, CIEES 2022

作者： Tamin, Owen Moung, Ervin Gubin Ahmad Dargham, Jamal Yahya, Farashazillah Omatu, Sigeru Angeline, Lorita Universiti Malaysia Sabah Faculty of Computing And Informatics Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia Education Research Centre Higashi-Hiroshima739-0046 Japan Taylor's University School of Computer Science Faculty of Innovation And Technology Selangor Malaysia

ISBN: (数字)9781665491495

ISBN: (纸本)9781665491495

machine learning has advanced rapidly in recent technologies of artificial intelligence. The abundant and cheap computation in machine learning has created more knowledge and reliability of this method. Therefore, more researchers have relied on machine learning to solve plastic waste pollution, which contributes to global greenhouse gas emissions. Datasets are fundamental requirements to build the foundation of the plastic waste detection system. This paper provides an overview of several state-of-the-art deep learning models, focusing on the dataset used for waste classification and identification. The performance of each deep learning model in classification accuracy is also discussed. The challenges of implementing deep learning in plastic waste detection are identified;(i) Data deficiency for training, (ii) Quality of the existing plastic waste dataset, and (iii) Color spaces. Lastly, the proposed solution to each challenge is presented. © 2022 IEEE.

关键词： Object detection

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