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Predictive analytics of train delays using facebook prophet

AIP Conference Proceedings 2024年第1期3128卷

作者： Nawal Nuha Shaharul Nizam Aisyah Mat Jasin Aszila Asmat Norli Anida Abdullah Shuzlina Abdul-Rahman Computer Science Studies College of Computing Informatics and Mathematics Universiti Teknologi MARA(UiTM) Shah Alam Malaysia Computer Science Studies College of Computing Informatics and Mathematics Universiti Teknologi MARA (UiTM) Pahang Branch Raub Campus 27600 Raub Pahang Malaysia Mathematical Sciences Studies College of Computing Informatics and Mathematics Universiti Teknologi MARA (UiTM) Pahang Branch Raub Campus 27600 Raub Pahang Malaysia Mathematics Division Center for Foundation Studies in Science Universiti Malaya 50603 Kuala Lumpur Malaysia Research Initiative Group of Intelligent Systems School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA(UiTM) Shah Alam Malaysia

Train delays can have a significant impact on transport systems, causing passengers inconvenience and operational challenges for train operators. One of the causes of railway delays is seasonality, which includes holidays and weather patterns. ARIMA model is a well-known statistical model for analyzing and forecasting time series data. However, the ARIMA model is unable to deal with seasonal time series data. Furthermore, time series data frequently exhibit stochastic patterns that may include outliers, whereas the traditional ARIMA model fails to produce accurate results when the data contains outliers. This study aims to forecast train delays based on Network Rail dataset using Facebook (FB) Prophet. FB prophet is a flexible time series model that is renowned for its ability to deal with seasonality and outliers. The number of 713 daily delayed occurrences (in minutes) from Edinburgh, Scotland was taken into account and divided at a ratio of 70:30 for model fitting and evaluation, respectively. The testing dataset was fitted with ARIMA, FB Prophet with and without holidays effect. The mean absolute error (MAE) and root mean squared error (RMSE) were used to evaluate the models. The RMSE and MAE of the FB prophet model with holidays feature deliver best performance in term of forecasting accuracy with the lowest rate at 968.446 and 638.558 respectively, as compared to ARIMA and FB prophet without holidays feature. The results are further improved after removing the outliers. This study highlights the potential of FB Prophet model with holidays feature as a predictive analytic tool to enhance train services.

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Modern computing: Vision and Challenges

arXiv

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arXiv 2024年

作者： Gill, Sukhpal Singh Wu, Huaming Patros, Panos Ottaviani, Carlo Arora, Priyansh Pujol, Victor Casamayor Haunschild, David Parlikad, Ajith Kumar Cetinkaya, Oktay Lutfiyya, Hanan Stankovski, Vlado Li, Ruidong Ding, Yuemin Qadir, Junaid Abraham, Ajith Ghosh, Soumya K. Song, Houbing Herbert Sakellariou, Rizos Rana, Omer Rodrigues, Joel J.P.C. Kanhere, Salil S. Dustdar, Schahram Uhlig, Steve Ramamohanarao, Kotagiri Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Center for Applied Mathematics Tianjin University Tianjin China Raygun Performance Monitoring Wellington New Zealand Department of Computer Science York Centre for Quantum Technologies University of York York United Kingdom Microsoft Schiphol Netherlands Distributed Systems Group Vienna University of Technology Vienna Austria Detecon International GmbH Munich Germany Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computer Science University of Western Ontario London Canada Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Institute of Science and Engineering Kanazawa University Japan Tecnun School of Engineering University of Navarra Spain Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Bennett University Greater Noida India Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Baltimore United States Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia COPELABS Lusófona University Lisbon Portugal Sydney Australia The University of Melbourne Victoria Australia Laboratory School of Computing and Information Systems The University of Melbourne Australia

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress. © 2024, CC BY.

关键词： Obsolescence

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Unity makes strength: Coalition Formation-based Group-buying for Timely UAV Data Collection

Unity makes strength: Coalition Formation-based Group-buying...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nan Qi Yeting Huang Wen Sun Shi Jin Theodoros A. Tsiftsis Qihui Wu Xiang Su National Mobile Communications Research Laboratory Southeast University Nanjing China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China School of Intelligent Systems Science & Engineering Jinan University Zhuhai China Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway Center for Ubiquitous Computing University of Oulu Oulu Finland

ISBN: (纸本)9781665435413

With their high mobility, unmanned aerial vehicles (UAVs) become appealing data collectors in hard-to-reach wide-area distributed sensor networks. Different from existing works focusing on the perspective of UAVs for service order optimization and UAV utility maximization, we consider the utilities of both sensors and UAVs, and innovatively model the competition among sensors (buyers) for the service of UAVs (sellers) as an auction game. A “unity makes strength” strategy is exploited. That is, to strengthen the bidding competitiveness, a group-buying coalition auction method that encourages sensors to form coalitions to bid for UAV service is proposed. Besides, we propose a parallel variable neighborhood ascent search algorithm, we can quickly determine the approximately optimal group-buying coalition structure. Numerical results show that the proposed method outperforms the joint trajectory design-task scheduling (TDTS) UAV-to-community method and the single coalition formation game (CFG) method.

关键词： Wireless sensor networks Games Data collection Autonomous aerial vehicles Search problems Information age Trajectory

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Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

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Journal of Healthcare Engineering 2022年第1期2022卷 3861161页

作者： Alzu'Bi, Dalia Abdullah, Malak Hmeidi, Ismail Alazab, Rami Gharaibeh, Maha El-Heis, Mwaffaq Almotairi, Khaled H. Forestiero, Agostino Hussein, Ahmad Mohdaziz Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid2210 Jordan Department of General Surgery and Urology University of Science and Technology Irbid22110 Jordan Department of Diagnostic and Interventional Radiology Faculty of Medicine Jordan University of Science and Technology Irbid2210 Jordan Computer Engineering Department Computer and Information Systems College Umm Al-Qura University Makkah21955 Saudi Arabia Institute for High Performance Computing and Networking CNR CS Rende Italy Deanship of E-Learning and Distance Education Umm Al-Qura University Makkah21955 Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Faculty of Information Technology Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models;three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results;they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life. © 2022 Dalia Alzu'bi et al.

关键词： Convolutional neural networks

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Anti-periodic switching dynamical system with application to chaotic system: A fixed-time fractional-order sliding mode control approach

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Partial Differential Equations in Applied Mathematics 2025年 14卷

作者： Ahmed, Saim Khan, Hasib Azar, Ahmad Taher Alzabut, Jehad College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Automated Systems and Computing Lab (ASCL) Prince Sultan University Riyadh 11586 Saudi Arabia Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Department of Mathematics Shaheed Benazir Bhutto University Sheringal PO Box 18000 Dir Upper Khyber Pakhtunkhwa Pakistan Department of Industrial Engineering OSTİM Technical University Ankara 06374 Turkey Center for Research and Innovation Asia International University Yangiobod MFY G'ijduvon street Bukhara House 74 Uzbekistan

The paper presents an analysis of a new class anti-periodic switching dynamical system in chaotic power systems, incorporating a fixed-time control strategy. Theoretical insights, based on fixed point theorems (FPTs) and stability principles, are coupled with computational techniques to address system complexities. A robust control strategy utilizing fixed-time fractional-order sliding mode control is formulated to efficiently manage nonlinear dynamics. The Lyapunov proposition is employed to assess stability properties. The comparative analysis demonstrates the effectiveness of the approach in achieving trajectory tracking and fixed-time convergence goals. This interdisciplinary study bridges insights from chaotic power systems with control strategies, offering a valuable contribution to understanding and application in complex dynamical systems. © 2025 The Authors

关键词： Anti-periodic switching dynamical system Fixed-time sliding mode control Fractional order control Positive solutions Power system Riemann–Liouville derivative

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Performance and energy-aware bi-objective tasks scheduling for cloud data centers

arXiv

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arXiv 2021年

作者： Materwala, Huned Ismail, Leila Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in a cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to tradeoff the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm. © 2021, CC BY.

关键词： Energy efficiency

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Security-aware data offloading and resource allocation for MEC systems: A deep reinforcement learning

TechRxiv

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TechRxiv 2021年

作者： Elgendy, Ibrahim A. Muthanna, Ammar Hammoudeh, Mohammad Shaiba, Hadil Ahmed Unal, Devrim Khayyat, Mashael The School of Computer Science and Technology Harbin Institute of Technology China Department of Computer Science Faculty of Computers and Information Menoufia University Egypt The St. Petersburg State University of Telecommunication St. Petersburg193232 Russia Moscow117198 Russia Department of Computing Manchester Metropolitan University ManchesterM15 6BH United Kingdom The College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Qatar University College of Engineering KINDI Center for Computing Research Doha Qatar The University of Jeddah College of Computer Science and Engineering Department of Information Systems and Technology Jeddah Saudi Arabia

The Internet of Things (IoT) is permeating our daily lives where it can provide data collection tools and important measurement to inform our decisions. In addition, they are continually generating massive amounts of data and exchanging essential messages over networks for further analysis. The promise of low communication latency, security enhancement and the efficient utilization of bandwidth leads to the new shift change from Mobile Cloud computing (MCC) towards Mobile Edge computing (MEC). In this study, we propose an advanced deep reinforcement resource allocation and security-aware data offloading model that considers the computation and radio resources of industrial IoT devices to guarantee that shared resources between multiple users are utilized in an efficient way. This model is formulated as an optimization problem with the goal of decreasing the consumption of energy and computation delay. This type of problem is NP-hard, due to the curse-of-dimensionality challenge, thus, a deep learning optimization approach is presented to find an optimal solution. Additionally, an AES-based cryptographic approach is implemented as a security layer to satisfy data security requirements. experimental evaluation results show that the proposed model can reduce offloading overhead by up to 13.2% and 64.7% in comparison with full offloading and local execution while scaling well for large-scale devices. © 2021, CC BY.

关键词： Reinforcement learning

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A cluster-based trusted routing method using fire hawk optimizer (FHO) in wireless sensor networks (WSNs)

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Scientific Reports 2023年第1期13卷 1-20页

作者： Hosseinzadeh, Mehdi Yoo, Joon Ali, Saqib Lansky, Jan Mildeova, Stanislava Yousefpoor, Mohammad Sadegh Ahmed, Omed Hassan Rahmani, Amir Masoud Tightiz, Lilia Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam School of Computing Gachon University 1342 Seongnamdaero Seongnam 13120 South Korea Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Technology University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan

Today, wireless sensor networks (WSNs) are growing rapidly and provide a lot of comfort to human life. Due to the use of WSNs in various areas, like health care and battlefield, security is an important concern in the data transfer procedure to prevent data manipulation. Trust management is an affective scheme to solve these problems by building trust relationships between sensor nodes. In this paper, a cluster-based trusted routing technique using fire hawk optimizer called CTRF is presented to improve network security by considering the limited energy of nodes in WSNs. It includes a weighted trust mechanism (WTM) designed based on interactive behavior between sensor nodes. The main feature of this trust mechanism is to consider the exponential coefficients for the trust parameters, namely weighted reception rate, weighted redundancy rate, and energy state so that the trust level of sensor nodes is exponentially reduced or increased based on their hostile or friendly behaviors. Moreover, the proposed approach creates a fire hawk optimizer-based clustering mechanism to select cluster heads from a candidate set, which includes sensor nodes whose remaining energy and trust levels are greater than the average remaining energy and the average trust level of all network nodes, respectively. In this clustering method, a new cost function is proposed based on four objectives, including cluster head location, cluster head energy, distance from the cluster head to the base station, and cluster size. Finally, CTRF decides on inter-cluster routing paths through a trusted routing algorithm and uses these routes to transmit data from cluster heads to the base station. In the route construction process, CTRF regards various parameters such as energy of the route, quality of the route, reliability of the route, and number of hops. CTRF runs on the network simulator version 2 (NS2), and its performance is compared with other secure routing approaches with regard to energy, throughp

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A fuzzy logic-based secure hierarchical routing scheme using firefly algorithm in Internet of Things for healthcare

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Scientific Reports 2023年第1期13卷 1-23页

The Internet of Things (IoT) is a universal network to supervise the physical world through sensors installed on different devices. The network can improve many areas, including healthcare because IoT technology has the potential to reduce pressure caused by aging and chronic diseases on healthcare systems. For this reason, researchers attempt to solve the challenges of this technology in healthcare. In this paper, a fuzzy logic-based secure hierarchical routing scheme using the firefly algorithm (FSRF) is presented for IoT-based healthcare systems. FSRF comprises three main frameworks: fuzzy trust framework, firefly algorithm-based clustering framework, and inter-cluster routing framework. A fuzzy logic-based trust framework is responsible for evaluating the trust of IoT devices on the network. This framework identifies and prevents routing attacks like black hole, flooding, wormhole, sinkhole, and selective forwarding. Moreover, FSRF supports a clustering framework based on the firefly algorithm. It presents a fitness function that evaluates the chance of IoT devices to be cluster head nodes. The design of this function is based on trust level, residual energy, hop count, communication radius, and centrality. Also, FSRF involves an on-demand routing framework to decide on reliable and energy-efficient paths that can send the data to the destination faster. Finally, FSRF is compared to the energy-efficient multi-level secure routing protocol (EEMSR) and the enhanced balanced energy-efficient network-integrated super heterogeneous (E-BEENISH) routing method based on network lifetime, energy stored in IoT devices, and packet delivery rate (PDR). These results prove that FSRF improves network longevity by 10.34% and 56.35% and the energy stored in the nodes by 10.79% and 28.51% compared to EEMSR and E-BEENISH, respectively. However, FSRF is weaker than EEMSR in terms of security. Furthermore, PDR in this method has dropped slightly (almost 1.4%) compared to that in EE

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Foreground Object Structure Transfer for Unsupervised Domain Adaptation

arXiv

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arXiv 2021年

作者： Cheng, Jieren Liu, Le Tang, Xiangyan Tu, Wenxuan Liu, Boyi Zhou, Ke Da, Qiaobo Yang, Yue School of Computer Science and Technology Hainan University Haikou570228 China Hainan Blockchain Technology Engineering Research Center Haikou570228 China School of Cyberspace Security Hainan University Haikou570228 China College of Intelligence and Computing Tianjin University Tianjin300350 China College of Computing National University of Defense Technology Changsha410073 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Univerisity of Macau 999078 China

Unsupervised domain adaptation aims to train a classification model from the labeled source domain for the unlabeled target domain. Since the data distributions of the two domains are different, the model often performs poorly on the target domain. Existing methods align the feature distributions of the source and target domains and learn domain-invariant features to improve the performance of the model. However, the features are usually aligned as a whole, and the domain adaptation task fails to serve the classification, which will ignore the class information and lead to *** this paper, we investigate those features that should be used for domain alignment, introduce prior knowledge to extract foreground features to guide the domain adaptation task for classification tasks, and perform alignment in the local structure of objects. We propose a method called Foreground Object Structure Transfer(FOST). The key to FOST is the new clustering based condition, which combines the relative position relationship of foreground objects. Based on this conditions, FOST makes the data distribution of the same class more compact in geometry. In practice, since the label of the target domain is not available, we use the clustering information of the source domain to assign pseudo labels to the target domain samples, and then according to the source domain data prior knowledge guides those positive features to maximum the inter-class distance between different classes and mimimum the intra-class distance. Extensive experimental results on various benchmarks (i:e: ImageCLEF-DA, Office-31, Office-Home, Visda-2017) under different domain adaptation settings prove that our FOST compares favorably against the existing state-of-the-art domain adaptation methods. © 2021, CC BY.

关键词： Classification (of information)

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