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Investigating students’ online learning behavior with a learning analytic approach: field dependence/independence vs. holism/serialism

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Interactive learning Environments 2023年第2期31卷 1041-1059页

作者： Yang, Tzu-Chi Chen, Sherry Y. Big Data Research Center & Institute of Education National Chiao Tung University Hsinchu Taiwan Department of Mathematics and Information Education National Taipei University of Education Taipei Taiwan Graduate Institute of Network Learning Technology National Central University JhongLi Taiwan

Individual differences exist among learners. Among various individual differences, cognitive styles can strongly predict learners’ learning behavior. Therefore, cognitive styles are essential for the design of online learning. There are a variety of cognitive style dimensions and overlaps exist among these dimensions. In particular, Witkin’s field dependence/independence and Pask’s Holism/Serialism share some similarities. To this end, it is necessary to develop a framework to show overlapped behavior between these two cognitive style dimensions. To address this issue, this study used the Lag Sequential Analysis to examine the overlaps between these two cognitive style dimensions from the aspect of online learning behavior. The results from this study indicated that the overlaps mainly appear in comprehensive/local and dynamic/fixed approaches. Based on the findings of this study, we develop a framework that can support the improvement of instruction design so that the needs of different cognitive style groups can be accommodated. Accordingly, this study is an interdisciplinary work, which makes scientific contributions to three communities, i.e. human–computer interaction, digital learning and learning analytic. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： cognitive style learning analytics learning behavior self-regulated learning

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Deep learning Application for Images Augmentation in Electrical Component Classification System 3rd

Deep Learning Application for Images Augmentation in Electri...

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3rd International Conference on Advances in Information and Communication technology, ICTA 2024

作者： Long, Nguyen The Huong, Nguyen Thu Lien, Pham Thi Gen, Anna Shmeleva Viet, Tran Tuan Department of Informatics Institute of Cybersecurity and Digital Technologies MIREA –Russian Technological University Moscow119454 Russia Laboratory of Artificial Intelligence and Machine Learning Institute of Information Technology and Data Science Irkutsk National Research Technical University Irkutsk664074 Russia University of Information and Communication Technology Thai Nguyen University Thai Nguyen70000 Viet Nam

ISBN: (纸本)9783031809422

The possibility of application a convolutional neural network to assess the augmentation of electrical images is proposed. We studied various conditions for sample preparation, optimizer algorithms, the number of pixels in the samples, the size of the training sample, color schemes, compression quality, and other photometric parameters in view of effect on training the neural network. Due to the proposed preliminary data preparation, the optimum of the architecture and hyperparameters of the neural network we achieved a classification accuracy of at least 98%. This paper outlines measurements of several devices and a process for simulating large amounts of annotated data based on those measurements. A convolutional neural net (CNN) algorithm for classifying signals is described. Performance of the CNN is compared to that of dynamic time warping (DTW) and correlation algorithms, with respect to varying training data set size and noise level. The CNN has the best performance for almost all cases considered. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Deep multimodal predictome for studying mental disorders (vol 44, pg 509, 2023)

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HUMAN BRAIN MAPPING 2023年第14期44卷 4967-4967页

作者： Rahaman, M. A. Chen, J. Fu, Z. Lewis, N. Iraji, A. van Erp, T. G. M. Calhoun, V. D. Department of Computational Science and Engineering Georgia Institute of Technology Atlanta Georgia USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia USA Clinical Translational Neuroscience Laboratory Department of Psychiatry and Human Behavior University of California Irvine Irvine California USA Center for the Neurobiology of Learning and Memory University of California Irvine Irvine California USA

Characterizing neuropsychiatric disorders is challenging due to heterogeneity in the population. We propose combining structural and functional neuroimaging and genomic data in a multimodal classification framework to leverage their complementary information. Our objectives are two-fold (i) to improve the classification of disorders and (ii) to introspect the concepts learned to explore underlying neural and biological mechanisms linked to mental disorders. Previous multimodal studies have focused on naïve neural networks, mostly perceptron, to learn modality-wise features and often assume equal contribution from each modality. Our focus is on the development of neural networks for feature learning and implementing an adaptive control unit for the fusion phase. Our mid fusion with attention model includes a multilayer feed-forward network, an autoencoder, a bi-directional long short-term memory unit with attention as the features extractor, and a linear attention module for controlling modality-specific influence. The proposed model acquired 92% (p < .0001) accuracy in schizophrenia prediction, outperforming several other state-of-the-art models applied to unimodal or multimodal data. Post hoc feature analyses uncovered critical neural features and genes/biological pathways associated with schizophrenia. The proposed model effectively combines multimodal neuroimaging and genomics data for predicting mental disorders. Interpreting salient features identified by the model may advance our understanding of their underlying etiological mechanisms.

关键词： functional network connectivity multimodal deep learning resting-state functional and structural MRI saliency schizophrenia classification single nucleotide polymorphism

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Human AI conversational systems: when humans and machines start to chat

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Personal and Ubiquitous Computing 2024年第6期28卷 857-860页

作者： Borsci, Simone Chamberlain, Alan Nichele, Elena Bødker, Mads Turchi, Tommaso Department of Learning Data and Technology Human Factors CODE group University of Twente Enschede Netherlands Mixed Reality Lab School of Computer Science University of Nottingham Nottingham United Kingdom Lincoln International Business School College of Arts Social Sciences and Humanities University of Lincoln Lincoln United Kingdom Department of Digitalization at Copenhagen Business School Frederiksberg Denmark Department of Computer Science University of Pisa Pisa Italy Department of Surgery and Cancer Imperial College of London London United Kingdom

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Thai Conversational Chatbot Classification Using BiLSTM and data Augmentation 1st

Thai Conversational Chatbot Classification Using BiLSTM and ...

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1st International Conference on data Science and Artificial Intelligence, DSAI 2023

作者： Lhasiw, Nunthawat Tanantong, Tanatorn Sanglerdsinlapachai, Nuttapong Thammasat Research Unit in Data Innovation and Artificial Intelligence Department of Computer Science Faculty of Science and Technology Thammasat University Pathum Thani Thailand Strategic Analytics Networks with Machine Learning and AI Research Team National Electronics and Computer Technology Center Pathum Thani Thailand

ISBN: (纸本)9789819979684

Chatbot platforms, e.g., Facebook and Line, have revolutionized human interaction in the digital age. In order to develop an automatic chatbot classification, there are several challenges especially for Thai chat messages. Conversational messages are usually short and ambiguous. Therefore, it is difficult to find a dataset for constructing an effective classification model. To address the limited size of the dataset, data augmentation techniques can be possibly applied. data augmentation involves generating synthetic messages by applying various transformations to existing data samples while preserving their original meaning. In this study, the size and diversity of the dataset is increased by two methods, i.e., text augmentation using word2vec from Thai2Fit and English-Thai machine translation models proposed by VISTEC. Based on the augmented messages, a Deep learning technique, BiLSTM, is used to construct a chatbot classification model. The experimental obtained results demonstrate that data augmentation can help to increase the classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Classification (of information)

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Optimizing Explanations by Network Canonization and Hyperparameter Search

Optimizing Explanations by Network Canonization and Hyperpar...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Pahde, Frederik Yolcu, Galip Ümit Binder, Alexander Samek, Wojciech Lapuschkin, Sebastian Fraunhofer Heinrich-Hertz-Institute Department of Artificial Intelligence Germany Technische Universität Berlin Germany Singapore Institute of Technology ICT Cluster Singapore University of Oslo Norway BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

ISBN: (纸本)9798350302493

Explainable AI (XAI) is slowly becoming a key component for many AI applications. Rule-based and modified backpropagation XAI approaches however often face challenges when being applied to modern model architectures including innovative layer building blocks, which is caused by two reasons. Firstly, the high flexibility of rule-based XAI methods leads to numerous potential parameterizations. Secondly, many XAI methods break the implementation-invariance axiom because they struggle with certain model components, e.g., BatchNorm layers. The latter can be addressed with model canonization, which is the process of restructuring the model to disregard problematic components without changing the underlying function. While model canonization is straightforward for simple architectures (e.g., VGG, ResNet), it can be challenging for more complex and highly interconnected models (e.g., DenseNet). Moreover, there is only little quantifiable evidence that model canonization is beneficial for XAI. In this work, we propose canonizations for currently relevant model blocks applicable to popular deep neural network architectures, including VGG, ResNet, EfficientNet, DenseNets, as well as Relation Networks. We further suggest a XAI evaluation framework with which we quantify and compare the effects of model canonization for various XAI methods in image classification tasks on the Pascal VOC and ILSVRC2017 datasets, as well as for Visual Question Answering using CLEVR-XAI. Moreover, addressing the former issue outlined above, we demonstrate how our evaluation framework can be applied to perform hyperparameter search for XAI methods to optimize the quality of explanations. Code is available on https://***/frederikpahde/xai-canonization. © 2023 IEEE.

关键词： Deep neural networks

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Ciao AI: the Italian adaptation and validation of the Chatbot Usability Scale

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Personal and Ubiquitous Computing 2023年第6期27卷 2161-2170页

作者： Borsci, Simone Prati, Elisa Malizia, Alessio Schmettow, Martin Chamberlain, Alan Federici, Stefano Faculty of Behavioural Management and Social Sciences Department of Learning Data Analysis and Technology Group of Cognition Data and Education University of Twente Enschede Netherlands Department of Surgery and Cancer Faculty of Medicine NIHR London IVD Imperial College of London London United Kingdom Department of Engineering "Enzo Ferrari" University of Modena and Reggio Emilia Modena Italy Computer Science Department University of Pisa Pisa Italy Molde University College Molde Norway School of Computer Science University of Nottingham Nottingham United Kingdom Department of Philosophy Social and Human Sciences and Education University of Perugia Perugia Italy

Chatbot-based tools are becoming pervasive in multiple domains from commercial websites to rehabilitation applications. Only recently, an eleven-item satisfaction inventory was developed (the ChatBot Usability Scale, BUS-11) to help designers in the assessment process of their systems. The BUS-11 has been validated in multiple contexts and languages, i.e., English, German, Dutch, and Spanish. This scale forms a solid platform enabling designers to rapidly assess chatbots both during and after the design process. The present work aims to adapt and validate the BUS-11 inventory in Italian. A total of 1360 questionnaires were collected which related to a total of 10 Italian chatbot-based systems using the BUS-11 inventory and also using the lite version of the Usability Metrics for User eXperience for convergent validity purposes. The Italian version of the BUS-11 was adapted in terms of the wording of one item, and a Multi-Group Confirmatory Factorial Analysis was performed to establish the factorial structure of the scale and compare the effects of the wording adaptation. Results indicate that the adapted Italian version of the scale matches the expected factorial structure of the original scale. The Italian BUS-11 is highly reliable (Cronbach alpha: 0.921), and it correlates to other measures of satisfaction (e.g., UMUX-Lite, τb = 0.67;p © 2023, The Author(s).

关键词： Buses

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Advancing Alzheimer's Nodule Detection Through a Comprehensive Multi-Scene Deep learning Framework 10

Advancing Alzheimer's Nodule Detection Through a Comprehensi...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Premkumar, C. Rajeshwari, R. Remya, R. Cholaathiraj, M.S. Reethika, A. Bharathidasan, S. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Sathyamangalam India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Vel Tech Rangarajan Dr Sagunthala R&d institute of science and Technology Department of Electronics and Communication Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350384369

Determining the precise location of Alzheimer's nodules is essential for estimating the risk of brain cancer. Conventional CAD modules, including MRI, PET, and CT, struggle with feature extraction and segmentation due to time limits and complexity. This study proposes an efficient brain nodule detection technique based on the Multi-Scene Deep learning Framework (MSDLF) that makes use of the vesselness filter. A four-channel CNN model is developed using information from two image sequences to enhance radiologists' ability to recognise four-stage nodules. This adaptable model may be used in two different classes. The outcomes demonstrate how well the MSDLF performs while processing substantial volumes of image data for brain nodule recognition, improving with accuracy 98.86% and 98.45% of sensitivity by using decreasing false positives. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Food-Health-Chain: A Food Supply Chain for Internet of Health Things Using Blockchain

Food-Health-Chain: A Food Supply Chain for Internet of Healt...

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International Conference on Network Security and Blockchain technology, ICNSBT 2023

作者： Das, Puja Haldar, Amrita Singh, Moutushi Pise, Anil Audumbar Guha Roy, Deepsubhra Department of Computer Science HMM College for Women Dakshineswar Kolkata700035 India Department of Computer Science and Business Studies IEM Kolkata700091 India Department of Information Technology IEM Salt Lake Kolkata700091 India Department of Data Science and Machine Learning Computer Science University of the Witwatersrand Johannesburg South Africa Institute of Engineering and Management Kolkata700091 India

ISBN: (纸本)9789819944323

Foodborne disease is still a major public health concern in both advanced and emerging economies. In terms of adopting new technology, the health industry continues to lag behind all other sectors. India has traditionally relied on food farming to make money but which food item is good for whom we have no proper idea. Especially when it comes to healthcare sectors, the Internet of health things (IoHT) and blockchain can play an important role. It can enhance the quality of care for its users and reduce costs by allocating the correct food chain to the targeted users. We are currently employing blockchain in supply chain management to cluster the patients based on their health care type. For example, diabetic patients prefer a particular type of food. Segregating the food type within the supply chain can reduce a lot of effort and cost. Blockchain can provide a solution to the issues that arise in the healthcare-based supply chain system because of its decentralized and immutable nature. The service's execution has been depicted in pictures and accompanied by detailed descriptions. The ultimate result of this study was to demonstrate the immutability, availability, and transparency security of blockchain in the healthcare-based supply chain. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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AI-Optimized Placement and Routing for PCB Design 9

AI-Optimized Placement and Routing for PCB Design

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9th International Conference on Science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Sathyaraj, P. Arulkumar, S. Ajina, A. Beshir, Kedir Umasankar, L. Arularasan, A.N. Rmk College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Puduvoyal601206 India Wachemo University Department of Electrical And Computer Engineering Ethiopia M S Ramaiah Institute of Technology Department of Artificial Intelligence And Machine Learning Bangalore560054 India S.A. Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Tiruvallur600077 India Panimalar Engineering College Department of Artificial Intelligence And Data Science Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

Rapid electronic device development requires more complicated and densely packed PCB designs. These systems need properly placed and connected electrical components for best performance and reliability. Complexity and time restrictions are rendering manual methods ineffective. Therefore, AI-based PCB insertion and routing automation methods have been developed. This research suggests using AI to optimize PCB component arrangement and routing. The system uses machine learning and optimization to improve design efficiency. The AI system creates layouts that match performance requirements, decrease signal interference, and meet manufacturing limits using previous design data and user preferences. A learning module that updates its knowledge base, an algorithm that chooses between design objectives, and real-time user input are examples. Signal integrity, thermal issues, and manufacturability are considered while modifying design requirements. Evaluation of the suggested methodology shows considerable gains in design efficiency, time-to-market, and electronic system performance. This research improves AI-automated PCB design, enabling more complex and effective electrical systems. AI in PCB design will revolutionize the industry by solving complicated design problems and speeding up the development of cutting-edge electronics. © 2024 IEEE.

关键词： Reinforcement learning

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