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5th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2019

作者： Azizah, D.M. Permatasari, E.O. Statistics Department Faculty of Mathematics Computing and Data Science InstitutTeknologiSepuluhNopember GebangKidul Road number 66 Surabaya60111 Indonesia

Stunting is a condition of failure to thrive in children under five (babies under five years old) as a result of chronic malnutrition so that the child is too short for his age. Malnutrition occurs since the baby is in the womb and in the early period after the baby is born. However, the condition of stunting only appears after the baby is 2 years old. Based on Basic Health Research in 2017 the province with the highest prevalence of short and very short toddlers in Indonesia is East Nusa Tenggara which is equal to 40.3 percent. This shows that the prevalence of stunting toddlers in NTT Province is still far from the overall prevalence of stunting toddlers in Indonesia. Cases of toddler stunting can be analyzed using the Multivariate Adaptive Regression Splines (MARS) method because the data used are high-dimensional data and do not show clear patterns of relationships between response variables and predictor variables. The data used is the percentage of stunting toddlers in NTT in 2017 with the district / city research unit. Based on the analysis, it was found that the district in NTT with the highest percentage of stunting children was South East Timor and the district with the lowest percentage was East Manggarai. The best MARS model is formed from 3 basis functions with 2 predictor variables included in the model, namely the percentage of pregnant women at risk of chronic lack of energy (X1) and the percentage of infants with low birth weight (X6). And after the classification results obtained the accuracy of the classification of 77.27% with an error of 22.72%. © Published under licence by IOP Publishing Ltd.

关键词： Clustering algorithms

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

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Multi-Channel Hypergraph Convolution Group Recommendation with Member Information Enhancement 25

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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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： Chen, Tianhao Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach. © 2023 IEEE.

关键词： Recommender systems

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A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

arXiv

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arXiv 2024年

作者： Moubayed, Abdallah Injadat, MohammadNoor Alhindawi, Nouh Samara, Ghassan Abuasal, Sara Alazaidah, Raed School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Electrical and Computer Engineering Department Western University LondonON Canada Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students’ progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students’ performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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A New Interactive Gaming Approach for Enhancing Math Learning Among Marginalized Communities

A New Interactive Gaming Approach for Enhancing Math Learnin...

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Smart Technology (ICE-SMARTec), IEEE International Conference on

作者： Hapini Awang Nur Suhaili Mansor Nor Hazlyna Harun Juhaida Abu Bakar Abdulrazak F. Shahatha Al-Mashhadani Mohamad Ghozali Hassan Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Sintok Malaysia Data Science Research Lab School of Computing Universiti Utara Malaysia Sintok Malaysia Faculty of Business Sohar University Sohar Oman Disaster Management Institute School of Technology Management and Logistics Universiti Utara Malaysia Sintok Malaysia

Mathematics is often considered a boring and challenging subject, which makes it difficult for educators to attract marginalized children to learn and embrace it. Therefore, this article discusses the development and evaluation of a mobile game application called Math-Hero, designed to enhance marginalized children's mathematical skills through an interactive, engaging, and heartfelt learning environment. To examine the effectiveness of Math-Hero, an online survey method was employed to collect data from 32 respondents. The evaluation model was developed following the Technology Acceptance Model (TAM). Three constructs - Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Intention to Adopt (ITA) - were examined quantitatively using descriptive statistics and correlation analysis. Additionally, qualitative data were analyzed using thematic and sentiment analyses. The results of quantitative analyses indicate that Math-Hero is easy to use, beneficial, and the users intend to use it in the future. Its ease of use has a positive correlation with usefulness and intention to adopt it. The sentiment analysis also supports the findings, as most of the users have a positive and neutral perception of Math-Hero. Although Math-Hero has shown promise in improving marginalized children's mathematical skills, there is still room for improvement to make it even more robust and effective in the future. Moreover, further research is necessary to empirically demonstrate the effectiveness of Math-Hero among diverse marginalized communities, beyond just Semai youngsters.

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Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs

arXiv

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arXiv 2023年

作者： Chen, Zhaoliang Wu, Zhihao Zhong, Luying Plant, Claudia Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Faculty of Computer Science University of Vienna Vienna1090 Austria ds:UniVie Vienna1090 Austria

Heterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks. One challenge of heterogeneous graph learning is the design of learnable meta-paths, which significantly influences the quality of learned embeddings. Thus, in this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically studies meta-paths containing multi-hop neighbors from an adaptive aggregation of multi-order adjacency matrices. The proposed model first builds different orders of adjacency matrices from manually designed node connections. After that, an intact multi-order adjacency matrix is attached from the automatic fusion of various orders of adjacency matrices. This process is supervised by the node semantic information, which is extracted from the node homophily evaluated by attributes. Eventually, we utilize a one-layer simplifying graph convolutional network with the learned multi-order adjacency matrix, which is equivalent to the cross-hop node information propagation with multi-layer graph neural networks. Substantial experiments reveal that AMOGCN gains superior semi-supervised classification performance compared with state-of-the-art competitors. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Characterizing Human Collective Behaviors During COVID-19 — Hong Kong SAR, China, 2020

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China CDC weekly 2023年第4期5卷 71-75,I0001-I0003页

作者： Zhanwei Du Xiao Zhang Lin Wang Sidan Yao Yuan Bai Qi Tan Xiaoke Xu Sen Pei Jingyi Xiao Tim K.Tsang Qiuyan Liao Eric H.YLau Peng Wu Chao Gao Benjamin J.Cowling WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina Laboratory of Data Discovery for Health Hong Kong Science and Technology ParkNew TerritoriesHong Kong Special Administrative RegionChina Department of Genetics University of CambridgeCambridgeCB23EHUK Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)Singapore College of Information and Communication Engineering Dalian Minzu UniversityDalian CityLiaoning ProvinceChina Department of Environmental Health Sciences Mailman School of Public HealthColumbia UniversityNew York CityNYUSA School of Artificial Intelligence Opticsand Electronics(iOpen)Northwestern Polytechnical UniversityXi’an CityShaanxi ProvinceChina

Summary What is already known about this topic?People are likely to engage in collective behaviors online during extreme events,such as the coronavirus disease 2019(COVID-19)crisis,to express awareness,take action,and... 详细信息

关键词： collective likely extreme

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Iterative pseudo-Labeling with deep feature annotation and confidence-based sampling

arXiv

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arXiv 2021年

作者： Benato, Bárbara C. Telea, Alexandru C. Falcão, Alexandre X. Laboratory of Image Data Science Institute of Computing University of Campinas Campinas Brazil Department of Information and Computing Sciences Faculty of Science Utrecht University Utrecht Netherlands

Training deep neural networks is challenging when large and annotated datasets are unavailable. Extensive manual annotation of data samples is time-consuming, expensive, and error-prone, notably when it needs to be done by experts. To address this issue, increased attention has been devoted to techniques that propagate uncertain labels (also called pseudo labels) to large amounts of unsupervised samples and use them for training the model. However, these techniques still need hundreds of supervised samples per class in the training set and a validation set with extra supervised samples to tune the model. We improve a recent iterative pseudo-labeling technique, Deep Feature Annotation (DeepFA), by selecting the most confident unsupervised samples to iteratively train a deep neural network. Our confidence-based sampling strategy relies on only dozens of annotated training samples per class with no validation set, considerably reducing user effort in data annotation. We first ascertain the best configuration for the baseline- a self-trained deep neural network - and then evaluate our confidence DeepFA for different confidence thresholds. Experiments on six datasets show that DeepFA already outperforms the self-trained baseline, but confidence DeepFA can considerably outperform the original DeepFA and the baseline. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

Longitudinal Segmentation of MS Lesions via Temporal Diffe...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, Stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031845246

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/Longitudinal-Difference-Weighting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Social-enhanced recommendation using graph-based contrastive learning 25

Social-enhanced recommendation using graph-based contrastive...

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作者： Xue, Peng Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models. © 2023 IEEE.

关键词： Recommender systems

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