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An innovative model for capturing seasonal patterns of train passenger movement using exogenous variables and fuzzy time series hybridization

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Journal of Open Innovation: Technology, Market, and Complexity 2024年第1期10卷

作者： Devianto, Dodi Permana, Dony Arif, Erman Afrimayani, Afrimayani Yanuar, Ferra Maiyastri, Maiyastri Yollanda, Mutia Department of Mathematics and Data Science Universitas Andalas Padang 25156 Indonesia Department of Statistics Universitas Negeri Padang Padang 25132 Indonesia Information System Study Program Universitas Terbuka Tangerang Selatan 15418 Indonesia

Train is a popular mode of ground transportation due to the ability to accommodate a large number of passenger, save time, avoid traffic congestion, offer cost-effective fares, and provide a relatively high level of safety. These benefits contribute to an annual increase in passenger numbers, particularly during holidays and the year-end period. Consequently, it is essential for management to anticipate potential capacity constraints faced by train operators. Detecting this challenge encompasses observing train passenger count trends at the end of each year, which can be effectively analyzed using Seasonal Autoregressive Integrated Moving Average (SARIMA) model to account for seasonal effects. Train passenger surges also align with the Eid Al-Fitr holiday in Indonesia, an event that varies annually according to the Hijri calendar. To address this issue, SARIMA was adapted to include exogenous effects in the form of calendar variations, producing Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX). Furthermore, a novel numerical model was proposed using Fuzzy Time Series Markov Chain (FTSMC). Consequently, it is vital for management to anticipate potential capacity constraints that train operators might face. This innovation was introduced through hybrid SARIMA-FTSMC and hybrid SARIMAX-FTSMC. The results showed that hybrid SARIMA-FTSMC delivered the highest accuracy level with the smallest error value, providing more precise insights into train passenger movement patterns. These modeling results also offered valuable recommendations for risk management to address capacity limitations during the Eid Al-Fitr holiday and the year-end period, enabling train operators to optimize the services effectively. © 2024 The Authors

关键词： Accuracy level Hybrid SARIMA-FTSMC Hybrid SARIMAX-FTSMC SARIMA SARIMAX Train passenger

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What affects the performance of POD for the simulation of heat transfer through building component? 8

What affects the performance of POD for the simulation of he...

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8th International Building Physics Conference, IBPC 2021

作者： Hou, Tianfeng Roels, Staf Janssen, Hans KU Leuven Department of Civil Engineering Building Physics Section Leuven3001 Belgium Data Assimilation Research Team RIKEN Center for Computational Science Kobe Japan RIKEN Prediction Science Laboratory Kobe Japan RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program Kobe Japan

The capability of the proper orthogonal decomposition for the simulation of heat transfer in building components is investigated via three applications: linear heat transfer (not coupled to mass transfer), mildly non-linear heat transfer (coupled to air and moisture transfer (hygroscopic)) and highly non-linear heat transfer (coupled to moisture transfer (capillary)). It is shown that increasing non-linearity leads to an increasing number of required construction modes. To further investigate the reason for this degrading performance of POD, the singular values' decay progress from the different training snapshots is addressed in this paper. The results confirm that a fast decay of the singular values implies a high interrelation of the snapshots and a better performance of the POD method. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Heat transfer

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Optimized Security Mechanism for publicly Secret Key Sharing over Cloud using Blockchain

Optimized Security Mechanism for publicly Secret Key Sharing...

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2024 International Symposium on Securing Next Generation Systems Using Future Artificial Intelligence Technologies, SNSFAIT 2024

作者： Byeon, Haewon Quraishi, Aadam Thuniki, Praveen Keshta, Ismail Soni, Mukesh Shabaz, Mohammad Department of AI and Software Inje University Gimhae50834 Korea Republic of Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of Intervention Treatment Institute HoustonTX United States Sr Program Analyst Georgia CummingGA30040 United States Computer Science and Information Systems Department College of Applied Sciences Al Maarefa University Riyadh Saudi Arabia Dr. D. Y. Patii Vidyapeeth Pune Dr. D. Y. Patii School of Science & Technology Tathawade Pune India Model Institute of Engineering and Technology J&K Jammu India

A publicly verifiable key sharing mechanism based on threshold key sharing is provided to explore the security of users' private keys on the blockchain. Participating nodes check the key fragment after receiving it, effectively preventing it from being abused. The crucial sections of the nodes that participated in the critical splicing are made public during the critical recovery stage to prevent them from performing harmful things during the critical recovery stage. Add IDs to the nodes that participated in the crucial splicing during the key distribution stage;a dynamic threshold system is intended to track and update the status of malicious nodes in real time. When the node that possesses the crucial component fails, the owner of the critical component and the main node relocate a key element to the new participating nodes. To safeguard sensitive information. The experimental results show that this system has a key recovery rate of 80% and threshold qualities such as traceability, enforceability, and recoverability. © 2024 Copyright for this paper by its authors.

关键词： Blockchain Encryption Key sharing mechanism Privacy Secret key Security

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Big data in healthcare:Conceptual network structure,key challenges and opportunities

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Digital Communications and Networks 2023年第4期9卷 856-868页

作者： Leonardo B.Furstenaua Pedro Leivas Michele Kremer Sott Michael S.Dohan Jose Ricardo Lopez-Robles Manuel J.Cobo Nicola Luigi Bragazzi Kim-Kwang Raymond Choo Post-Graduate Program in Industrial Engineering Federal University of Rio Grande Do SulPorto Alegre90035-190Brazil University of Santa Cruz Do Sul Av.Independencia 2293Santa Cruz Do SulRSBrazil Business School Unisinos UniversityAv.Dr.Nilo Peçanha 160091330-002Porto AlegreBrazil Faculty of Business Administration Lakehead UniversityThunder BayOntarioCanada Autonomous University of Zacatecas Av.Ramon Lopez Velarde Col.CentroZacatecas98000Mexico Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain York University ON M3J 1P3Canada Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249USA

Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources *** data has attracted wide attention from academia,for example,in supporting patients and health professionals by improving the accuracy of decision-making,diagnosis and disease *** research aimed to perform a Bibliometric Performance and Network Analysis(BPNA)supported by a Scoping Review(SR)to depict the strategic themes,thematic evolution structure,main challenges and opportunities related to the concept of big data applied in the healthcare *** this goal in mind,4857 documents from the Web of science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT *** bibliometric performance showed the number of publications and citations over time,scientific productivity and the geographic distribution of publications and research *** strategic diagram yielded 20 clusters and their relative importance in terms of centrality and *** thematic evolution structure presented the most important themes and how it changes over ***,we presented the main challenges and future opportunities of big data in healthcare.

关键词： Big data Healthcare digitalization Bibliometric Strategic intelligence Co-word analysis SciMAT

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DIVERSITREE: A NEW METHOD TO EFFICIENTLY COMPUTE DIVERSE SETS OF NEAR-OPTIMAL SOLUTIONS TO MIXED-INTEGER OPTIMIZATION PROBLEMS

arXiv

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arXiv 2022年

作者： Ahanor, Izuwa Medal, Hugh Trapp, Andrew C. Department of Industrial and Systems Engineering University of Tennessee-Knoxville United States Business School Data Science Program Worcester Polytechnic Institute United States

While most methods for solving mixed-integer optimization problems compute a single optimal solution, a diverse set of near-optimal solutions can often lead to improved outcomes. We present a new method for finding a set of diverse solutions by emphasizing diversity within the search for near-optimal solutions. Specifically, within a branch-and-bound framework, we investigated parameterized node selection rules that explicitly consider diversity. Our results indicate that our approach significantly increases the diversity of the final solution set. When compared with two existing methods, our method runs with similar runtime as regular node selection methods and gives a diversity improvement between 12% and 190%. In contrast, popular node selection rules, such as best-first search, in some instances performed worse than state-of-the-art methods by more than 35% and gave an improvement of no more than 130%. Further, we find that our method is most effective when diversity in node selection is continuously emphasized after reaching a minimal depth in the tree and when the solution set has grown sufficiently large. Our method can be easily incorporated into integer programming solvers and has the potential to significantly increase the diversity of solution sets. Copyright © 2022, The Authors. All rights reserved.

关键词： Integer programming

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Tweets Sentiment Analysis during COVID-19 Pandemic

Tweets Sentiment Analysis during COVID-19 Pandemic

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2020 International Conference on data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Alanezi, Maha A. Hewahi, Nabil M. University of Bahrain Big Data Science Analytics Master Program Collage of Science Sakhir Bahrain University of Bahrain Collage of Information Technology Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781728196756

The aim of this paper is to investigate the impact of social distance on people during COVID-19 pandemic using twitter sentiment analysis through a comparison between the k-means clustering and Mini-Batch k-means clustering approaches. To find the most common frequent words, two datasets have been investigated (WHO and Bahrain ministry of health datasets) to be as data preparation and exploration. Another two datasets (English and Arabic datasets) are used in the clustering of k-means. In this paper, a comparison between k-means and Mini-Batch k-means is performed to find a pattern. The word frequency shows that there are several words related to the pandemic. The sentiment analysis result show that in USA, Australia, Nigeria, Canada, and England, most tweets are neutral. However, the majority of tweets are positive tweets from both Italy and India. In addition, the k-means cluster in the English dataset reveals several cluster trends where COVID-19 pandemic procedures are addressed in cluster 1, and health workers are encouraged in cluster 3. © 2020 IEEE.

关键词： COVID-19

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Prediction of Stock Price and Direction Using Neural Networks: datasets Hybrid Modeling Approach

Prediction of Stock Price and Direction Using Neural Network...

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2020 International Conference on data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Aradi, Mohamed Al Hewahi, Nabil Univestity of Bahain Sakhir Big Data Science Analytics Master Program Collage of Science Bahrain Univestity of Bahain Collage of Information Technology Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781728196756

Stock prices prediction is one of the most daunting tasks to achieve for day traders, investors, and data scientists. They are complex functions of a wide array of contributing factors that affects the movement dynamics. Politics, social outlook, the company's sales performance, and socio-economic factors all have an influence of how the stock is perceived globally and thus affecting the price movement based on the supply/demand balance. In this work a Framework is proposed to predict the price and movement direction by using multiple datasets that includes news sentiment, social sentiment, company earnings announcement, and technical indicators. A case study was applied on the Apple Inc. stock using Long short-term Memory Neural Networks (LSTM) and Deep Neural Networks (DNN). Results showed superiority of the LSTM model in both predicting stock value [75.4 MSE, +-2.52 PE] and the direction with [70.1% classification accuracy] over the DNN model which suffered from prediction lag and dependency on trend indicators achieving [53.1%] in predicting the direction of the stock. © 2020 IEEE.

关键词： Fruits

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Sample Complexity of Kernel-Based Q-Learning

arXiv

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arXiv 2023年

作者： Yeh, Sing-Yuan Chang, Fu-Chieh Yueh, Chang-Wei Wu, Pei-Yuan Bernacchia, Alberto Vakili, Sattar MediaTek Research Graduate Program of Data Science National Taiwan University Academia Sinica Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan

Modern reinforcement learning (RL) often faces an enormous state-action space. Existing analytical results are typically for settings with a small number of state-actions, or simple models such as linearly modeled Q-functions. To derive statistically efficient RL policies handling large state-action spaces, with more general Q-functions, some recent works have considered nonlinear function approximation using kernel ridge regression. In this work, we derive sample complexities for kernel based Q-learning when a generative model exists. We propose a nonparametric Q-learning algorithm which finds an ǫ-optimal policy in an arbitrarily large scale discounted MDP. The sample complexity of the proposed algorithm is order optimal with respect to ǫ and the complexity of the kernel (in terms of its information gain). To the best of our knowledge, this is the first result showing a finite sample complexity under such a general model. © 2023, CC BY.

关键词： Reinforcement learning

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Graph Laplacian Learning with Exponential Family Noise

arXiv

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arXiv 2023年

作者： Shi, Changhao Mishne, Gal The Electrical and Computer Engineering Department UC San Diego CA92093 United States The Halıcıoglu Data Science Institute The Neurosciences Graduate Program UC San Diego CA92093 United States

Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matrix to reveal the spectral decomposition of signals in the graph frequency domain. However, a common challenge in applying GSP methods is that in many scenarios the underlying graph of a system is unknown. A solution in such cases is to construct the unobserved graph from available data, which is commonly referred to as graph or network inference. Although different graph inference methods exist, these are restricted to learning from either smooth graph signals or simple additive Gaussian noise. Other types of noisy data, such as discrete counts or binary digits, are rather common in real-world applications, yet are underexplored in graph inference. In this paper, we propose a versatile graph inference framework for learning from graph signals corrupted by exponential family noise. Our framework generalizes previous methods from continuous smooth graph signals to various data types. We propose an alternating algorithm that jointly estimates the graph Laplacian and the unobserved smooth representation from the noisy signals. We also extend our approach to a variational form to account for the inherent stochasticity of the latent smooth representation. Finally, since real-world graph signals are frequently non-independent and temporally correlated, we further adapt our original setting to a time-vertex formulation. We demonstrate on synthetic and real-world data that our new algorithms outperform competing Laplacian estimation methods that suffer from noise model mismatch. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal processing

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Transfer Learning for Emulating Ocean Climate Variability across CO2 forcing

arXiv

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arXiv 2024年

作者： Dheeshjith, Surya Subel, Adam Gupta, Shubham Adcroft, Alistair Fernandez-Granda, Carlos Busecke, Julius Zanna, Laure Courant Institute of Mathematical Sciences New York University United States Program in Atmospheric and Oceanic Sciences Princeton University United States Center for Data Science New York University United States Lamont Doherty Earth Observatory Columbia University United States

With the success of machine learning (ML) applied to climate reaching further every day, emulators have begun to show promise not only for weather but for multi-year time scales in the atmosphere. Similar work for the ocean remains nascent, with state-of-the-art limited to models running for shorter time scales or only for regions of the globe. In this work, we demonstrate high-skill global emulation for surface ocean fields over 5-8 years of model rollout, accurately representing modes of variability for two different ML architectures (ConvNext and Transformers). In addition, we address the outstanding question of generalization, an essential consideration if the end-use of emulation is to model warming scenarios outside of the model training data. We show that 1) generalization is not an intrinsic feature of a data-driven emulator, 2) fine-tuning the emulator on only small amounts of additional data from a distribution similar to the test set can enable the emulator to perform well in a warmed climate, and 3) the forced emulators are robust to noise in the forcing. © 2024, CC BY.

关键词： Transfer learning

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