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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Xavier Tan Dai Manna Joyjit Chattoraj Liu Yong Xu Xinxing Dao My Ha Yang Feng Computing & Intelligence Department Institute of High Performance Computing A*STAR Singapore School of Informatics & IT Diploma in Big Data Analytics Temasek Polytechnic Singapore Fluid Dynamics Department Institute of High Performance Computing A*STAR Singapore

ISBN: (纸本)9781665476881

Creating an aerodynamic shape, like an airfoil wing, requires many factors to be considered, especially aerodynamic properties such as its lift-to-drag ratio (L/D). Currently, generating feasible airfoil shapes usually requires computationally expensive tools, such as Computational Fluid Dynamics (CFD). In recent years, increasing work has been directed to utilizing machine learning algorithms to synthesize accurate airfoil shapes while reducing the required computational cost. Generative Adversarial Network (GAN) is one of many algorithms to see success in airfoil shape optimization and is shown to generate good airfoils given a small set of training examples. This paper focuses on implementing a conditional GAN (cGAN) based framework with various filters for airfoil inverse design problem. By labelling the training dataset with aerodynamic characteristics separated by pre-defined thresholds to lift-to-drag ratio (L/D) and shape area, the class labels will be able to guide the network to generate different classes of airfoils influenced by these characteristics. Together with layers of Savitzky-Golay (SG) filter and B-Spline Interpolation, the developed model was shown to achieve good performance in generating new airfoils. In addition, we explored the viability of adding Wasserstein loss from Wasserstein GAN into the network architecture, forming a cWGAN-GP. Testing results showed that cWGAN-GP was able to achieve better performance for a specific airfoil class.

关键词： Training Interpolation Machine learning algorithms Shape Atmospheric modeling Computational fluid dynamics Generative adversarial networks

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Quantifying the Benefits of Implementing Big data analytics on Consumers’ Online Purchase Intentions in e-Commerce

Quantifying the Benefits of Implementing Big Data Analytics ...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Wei, Justin Ang Kang Mansor, Mahayaudin M. Derasit, Zuraidah Bakar, Mohd Aftar Abu Ul-Saufie, Ahmad Zia Hamzah, Firdaus Mohamad Faculty of Business Economics and Accounting HELP University Kuala Lumpur Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute for Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Malaysia Centre for Defence Foundation Studies Universiti Pertahanan Nasional Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798331530303

The digital revolution has reshaped the global economy, especially e-commerce. Innovative technologies such as big data analytics, artificial intelligence, and cloud computing drive this transformation, enabling companies to make informed business decisions and influence consumers’ online purchasing behaviour. This study explores empirical evidence for the benefits of using big data analytics (BDA) to influence consumers’ online purchasing decisions in e-commerce. Primary data was collected via an online survey targeting 300 customers with online shopping experience on platforms that have adopted the digital technologies of BDA. Statistical methods used in this study included questionnaire design, reliability test, descriptive analysis, and multiple regression modelling and diagnostics. The findings indicate that the benefits of implementing BDA in e-commerce for information search, recommendation systems, consumer services, dynamic pricing, security and fraud detection are strongly associated with consumers’ online purchasing intentions. The study underscores the importance of businesses leveraging BDA, as the benefits significantly and positively impact consumers’ purchasing intentions in e-commerce while addressing security and fraud detection concerns that undermine its advantage. Nevertheless, we encourage all companies to leverage BDA implementation in their digital marketplace, which ultimately contributes to achieving e-commerce goals, such as increased market reach, cost reduction, and revenue growth. ©2024 IEEE.

关键词： Marketplaces

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Developing a Strategic data Science Roadmap through Trend Analysis and Digital Maturity Assessment

Developing a Strategic Data Science Roadmap through Trend An...

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IEEE International Technology Management Conference

作者： Kerem Nazlıel Kerem Kayabay Mert Onuralp Gökalp Ebru Gökalp Aydın P. Erhan Eren Altan Koçyiğit Graduate School of Informatics Middle East Technical University Ankara Turkey Converged Computing HLRS University of Stuttgart Stuttgart Germany Data Analytics Center TÜPRAŞ Ankara Turkey Department of Computer Engineering Hacettepe University Ankara Turkey

ISBN: (数字)9798350367355

ISBN: (纸本)9798350367362

The rapid advancements in data Science and Artificial Intelligence require organizations to create strategic roadmaps that align with both technological trends and business objectives. This paper introduces a data-driven approach for developing such strategies, integrating Digital Maturity Assessment, Multivocal Literature Review, Topic Modeling, and SWOT analysis within the data Science Roadmapping (DSR) framework. The proposed approach aims to enhance the accuracy and relevance of roadmap development by combining multiple tools for comprehensive trend identification and organizational capability assessment. Ongoing validation through case studies will assess its practical application and effectiveness.

关键词： Analytical models Systematics Technology management data science Strategic planning Market research data models Reproducibility of results Artificial intelligence Systematic literature review

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A Hybrid Neural Network with Smart Skip Connections for High-Precision, Low-Latency EMG-Based Hand Gesture Recognition

arXiv

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arXiv 2025年

作者： Wazir, Hafsa Ahmad, Jawad Khan, Muazzam A. Jan, Sana Ullah Khan, Fadia Ali Khan, Muhammad Shahbaz ICESCO Chair for Big Data Analytics and Edge Computing Quaid-i-Azam University Islamabad Pakistan Cybersecurity Center Prince Mohammad Bin Fahd University Alkhobar Saudi Arabia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Department of Computer Science HITEC University Taxila Pakistan

lectromyography (EMG) is extensively used in key biomedical areas, such as prosthetics, and assistive and interactive technologies. EMG signals measure the electrical activity of muscles during different motions. EMG signals play a key role in gesture recognition studies, such as hand gesture recognition. This paper presents a new hybrid neural network named ConSGruNet for precise and efficient hand gesture recognition. The proposed model comprises convolutional neural networks with smart skip connections in conjunction with a Gated Recurrent Unit (GRU). The proposed model is trained on the complete Ninapro DBl dataset. The proposed model boasts an accuracy of 99.7% in classifying 53 classes in just 25 milliseconds. In addition to being fast, the proposed model is lightweight with just 3,946 KB in size. The fast inference time and lightweight architecture of the proposed model makes it suitable for resource constrained loT devices. Moreover, the proposed model has also been evaluated for the reliability parameters, i.e., Cohen's kappa coefficient, Matthew's correlation coefficient, and confidence intervals. The close to ideal results of these parameters validate the models performance on unseen data. © 2025, CC BY-NC-ND.

关键词： Convolutional neural networks

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Long Short-Term Memory (LSTM) model for Indian sign language recognition

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 11185-11203页

作者： Nihalani, Rahul Chouhan, Siddharth Singh Mittal, Devansh Vadula, Jai Thakur, Shwetank Chakraborty, Sandeepan Patel, Rajneesh Kumar Singh, Uday Pratap Ghosh, Rajdeep Singh, Pritpal Saxena, Akash School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh Sehore India Department of Mathematics Central University of Jammu UT of JK India Department of Data Science and Analytics Central University of Rajasthan Rajasthan Ajmer India School of Engineering and Technology Central University of Haryana Haryana Mahendragarh India

The human-computer interaction process is a vital task in attaining artificial intelligence, especially for a person suffering from hearing or speaking disabilities. Recognizing actions more traditionally known as sign language is a common way for them to interact. Computer vision and Deep learning models are capable of understanding these actions and can simulate them to build up a sustainable learning process. This sign language mechanism will be helpful for both the persons with disabilities and the machines to unbound the gap to achieve intelligence. Therefore, in the proposed work, a real-Time sign language system is introduced that is capable of identifying numbers ranging from 0 to 9. The database is acquired from the 8 different subjects respectively and processed to achieve approximately 200k amount of data. Further, a deep learning model named LSTM is used for sign recognition. The results were compared with different approaches and on distinct databases proving the supremacy of the proposed work with 91.50% accuracy. Collection of daily life useful signs and further improving the efficiency of the LSTM model is the research direction for future work. The code and data will be available at https://***/rahuln2002/Sign-Language-Recognition-using-LSTM-model. © 2024-IOS Press. All rights reserved.

关键词： Long short-term memory

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Nonblocking execution in GraphBLAS

Nonblocking execution in GraphBLAS

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Aristeidis Mastoras Sotiris Anagnostidis Albert– Jan N. Yzelman Computing Systems Laboratory Zurich Research Center Huawei Technologies Switzerland Data Analytics Laboratory Department of Computer Science ETH Zurich Switzerland

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

GraphBLAS is a recent standard that allows the expression of graph algorithms in the language of linear algebra and enables automatic code parallelization and optimization. GraphBLAS operations are executed either in blocking or in non-blocking mode. Although there exist multiple implementations of GraphBLAS for efficient blocking execution on both shared-and distributed-memory systems, none of these implementations supports full nonblocking execution to improve data locality. In this paper, we present a preliminary evaluation for two algorithms, Pagerank and Conjugate Gradient, that confirms the importance of nonblocking execution, by showing promising speedups over the corresponding blocking execution.

关键词： Analytical models Distributed processing Heuristic algorithms Instruction sets Distributed databases Manuals Linear algebra

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Predictive Insight of Global Warming-A Sustainable Control System

Predictive Insight of Global Warming-A Sustainable Control S...

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International Conference on computing Communication Control and Automation (ICCUBEA)

作者： Soumit Roy Hafizullah Mahmudi Rik Das Jaya H. Dewan Sudeep D. Thepade Analytics Presales and Solution PracticeLead Jade Global Chicago USA Data Management and Analytics Kamyab ITS Kabul Afghanistan Association for Computing Machinery Ranchi India Department of Information Technology Pimpri Chinchwad College of Engineering Pune India Computer Engineering Department Pimpri Chinchwad College of Engineering Pune India

Global warming is one of the major issues for decades. Implementation of integrated solution to get sustainable control system has significant challenges. Acceptance of the key precautions and maintenance of sustainability became a bottleneck. The consequence of human social activities increases the consumption of fossil fuels as power resources which indirectly increased the concentration of greenhouse gases and water vapor, in the environment. This causes the earth's average surface temperature to increase. Water vapor is responsible for two-thirds of this issue; however, C02 is proven to be the key factor of global warming. The global temperature could have increased to 3.8 o C in case C02 concentration was doubled. The trend and association between rising temperatures and C02 emissions are shown in this work, along with an explanation of the many methods behind ideal mitigation. One of the goals of this research is to identify the issue area and integrate the climatic data. This will assist in obtaining a dependable control system capable of addressing global warming concerns. This paper outlines how data engineering and analytics may be used to regulate global warming and provides an architecture for the Green Warming Model (GWD). Therefore, the primary contribution of this study effort is environment preservation using data engineering and machine learning (ML).

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ADAPTIVE data COLLECTION FOR INTRA-INDIVIDUAL STUDIES AFFECTED BY ADHERENCE

arXiv

引用

arXiv 2022年

作者： Monacelli, Greta Zhang, Lili Schlee, Winfried Langguth, Berthold Ward, Tomás E. Murphy, Thomas B. School of Computing Dublin City University Insight SFI Research Centre for Data Analytics Ireland Department of Psychiatry and Psychotherapy University of Regensburg Germany School of Mathematics and Statistics University College Dublin Insight SFI Research Centre for Data Analytics Ireland

Recently the use of mobile technologies in Ecological Momentary Assessments (EMA) and Interventions (EMI) has made it easier to collect data suitable for intra-individual variability studies in the medical field. Nevertheless, especially when self-reports are used during the data collection process, there are difficulties in balancing data quality and the burden placed on the subjects. In this paper, we address this problem for a specific EMA setting which aims to submit a demanding task to subjects at high/low values of a self-reported variable. We adopt a dynamic approach inspired by control chart methods and design optimization techniques to obtain an EMA triggering mechanism for data collection which takes into account both the individual variability of the self-reported variable and of the adherence rate. We test the algorithm in both a simulation setting and with real, large-scale data from a tinnitus longitudinal study. A Wilcoxon-Mann-Whitney Rank Sum Test shows that the algorithm tends to have both a higher F1 score and utility than a random schedule and a rule-based algorithm with static thresholds, which are the current state-of-the-art approaches. In conclusion, the algorithm is proven effective in balancing data quality and the burden placed on the participants, especially, as the analysis performed suggest, in studies where data collection is impacted by adherence. Copyright © 2022, The Authors. All rights reserved.

关键词： data acquisition

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A General Framework for Clustering and Distribution Matching with Bandit Feedback

arXiv

引用

arXiv 2024年

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y.F. Scarlett, Jonathan Descartes Program CNRS@CREATE 138602 Singapore Department of Information Systems and Analytics School of Computing National University of Singapore 119077 Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Department of Computer Science Department of Mathematics Institute of Data Science National University of Singapore 119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank–Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ *** Codes 68T05 Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Comparing deep learning models for volatility prediction using multivariate data

arXiv

引用

arXiv 2023年

作者： Ge, Wenbo Lalbakhsh, Pooia Isai, Leigh Lensky, Artem Suominen, Hanna The Australian National University School of Computing CanberraACT Australia Monash University Department of Data Science and Artificial Intelligence ClaytonVIC Australia Euler Capital Drysdale VIC Australia Lensky Analytics BraddonACT Australia The Australian National University School of Computing Canberra ACT Australia University of Turku Department of Computing Turku Finland

This study aims at comparing several deep learning-based forecasters in the task of volatility prediction using multivariate data, proceeding from simpler or shallower to deeper and more complex models and compare them to the naive prediction and variations of classical GARCH models. Specifically, the volatility of five assets (i.e., S&P500, NASDAQ100, gold, silver, and oil) was predicted with the GARCH models, Multi-Layer Perceptrons, recurrent neural networks, Temporal Convolutional Networks, and the Temporal Fusion Transformer. In most cases the Temporal Fusion Transformer followed by variants of Temporal Convolutional Network outperformed classical approaches and shallow networks. These experiments were repeated, and the difference between competing models was shown to be statistically significant, therefore encouraging their use in practice. © 2023, CC BY.

关键词： Predictive analytics

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