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Exact and fractional solution of MHD generalized Couette hybrid nanofluid flow with Mittag–Leffler and power law kernel

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International Journal of Thermofluids 2024年 24卷

作者： Ali, Ali Hasan Raza, Ali Batiha, Belal Abed, Ahmed M. Abduljabbar, Zaid Ameen Department of Mathematics College of Education for Pure Sciences University of Basrah Basrah61001 Iraq Institute of the Mathematics University of Debrecen Pf. 400 DebrecenH-4002 Hungary Department of Business Management Al-imam University College Balad34011 Iraq Jadara University Research Center Jadara University Irbid21110 Jordan Department of Mathematics University of Engineering and Technology Lahore Pakistan Department of Mathematics Minhaj University Lahore Pakistan Mathematics Department Faculty of Science and Information Technology Jadara University Irbid21110 Jordan Department of the Industrial Engineering College of Engineering Prince Sattam Bin Abdulaziz University Al Kharj16273 Saudi Arabia Industrial Engineering Department Zagazig University Zagazig44519 Egypt Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61001 Iraq

This study investigates the complex behavior of Jeffrey nanofluid flow in a porous oscillating microchannel under the influence of magnetohydrodynamic (MHD) effects. The research explores how magnetic field distortions lead to diverse accumulation patterns of nanofluid particles, a phenomenon attributed to homogeneous magnetization in fluid dynamics. Nanoparticles ranging from 0.25 % to 0.5 % (low and inexpensive concentrations) are remarkably consistent for best results in most machining procedures. Different concentrations of various nanomaterial's is utilized (φ1 = φ2 = 0.01, 0.02, 0.03, 0.04) to make the simple nanfluid and hybrid nanofluid suspensions. By employing fractal-fractional derivatives governed by power law, a mathematical model developed to describe the time-varying, compressible MHD flow of Jeffrey nanofluid. The model incorporates the effects of heat transfer, pressure, and magnetic fields on the fluid dynamics. A novel fractional approach utilizing the Laplace transform is applied to solve the fractal MHD hybrid-fluid model integrated into a porous medium. The study reveals velocity flow decrease with increasing Reynolds numbers but increase with channel inclination. Additionally, both the Darcy number and magnetic field orientation enhance heat transfer rates. In addition, the velocity profile enhanced by the hybrid nanofluid suspension as compared to simple nanofluid flow. The research validates its findings by demonstrating the convergence of fractional and numerical solution methods. Furthermore, the study compares the performance of different hybrid nanofluids, concluding that water-based (H2O + GO + MoS2) hybrid fluids exhibit slightly superior characteristics compared to (CMC + GO + MoS2) hybrid nanofluids. © 2024 The Authors

关键词： Reynolds number

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network Traffic Prediction for Intelligent Transportation Systems: A Reinforcement Learning Approach

Network Traffic Prediction for Intelligent Transportation Sy...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Jian Song Hua Liu Laisen Nie Zhaolong Ning Mohammad S. Obaidat Balqies Sadoun School of Electronics and Information Northwestern Polytechnical University Xi'an P.R.China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science and Engineering Macau University of Science and Technology Macau China Computer Science Department Director of Cybersecurity Center University of Texas-Permian Basin Odessa TX USA King Abdullah II School of Information Technology The University of Jordan Amman Jordan and University of Science and Technology Beijing Beijing China College of Engineering Al Balqa’ Applied University Al Salt Jordan

ISBN: (纸本)9781665435413

Vehicular Ad-Hoc networks (VANETs), as the cru-cial support of Intelligent Transportation Systems (ITS), have received a great attention in recent years. network traffic prediction is useful for network management and security in VANETs, such as network planning and anomaly detection. Due to the movement of nodes, the traffic flow in VANETs consists of a great number of irregular fluctuations, which is the main challenge for network traffic prediction. This paper proposes a novel algorithm, which combines Deep Q-Learning (DQN) and Generative Adversarial networks (GAN) for network traffic prediction. We use DQN to carry out network traffic prediction, in which GAN is involved to represent Q-network. Meanwhile, the generative network can increase the number of samples to improve the prediction error. We evaluate the performance of our method by implementing it on two real network traffic data sets. Finally, we compare the two state-of-the-art competing methods with our method.

关键词： Training Q-learning Telecommunication traffic Prediction methods Generative adversarial networks Prediction algorithms Multitasking

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Deep Attention-guided Graph Clustering with Dual Self-supervision

arXiv

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arXiv 2021年

作者： Peng, Zhihao Liu, Hui Jia, Yuheng Hou, Junhui The Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong The School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong The School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

Existing deep embedding clustering methods fail to sufficiently utilize the available off-the-shelf information from feature embeddings and cluster assignments, limiting their performance. To this end, we propose a novel method, namely deep attention-guided graph clustering with dual self-supervision (DAGC). Specifically, DAGC first utilizes a heterogeneity-wise fusion module to adaptively integrate the features of the autoencoder and the graph convolutional network in each layer and then uses a scale-wise fusion module to dynamically concatenate the multi-scale features in different layers. Such modules are capable of learning an informative feature embedding via an attention-based mechanism. In addition, we design a distribution-wise fusion module that leverages cluster assignments to acquire clustering results directly. To better explore the off-the-shelf information from the cluster assignments, we develop a dual self-supervision solution consisting of a soft self-supervision strategy with a Kullback-Leibler divergence loss and a hard self-supervision strategy with a pseudo supervision loss. Extensive experiments on nine benchmark datasets validate that our method consistently outperforms state-of-the-art methods. Especially, our method improves the ARI by more than 10.29% over the best baseline. The code will be publicly available at https://***/ZhihaoPENG-CityU/DAGC. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports

arXiv

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arXiv 2024年

作者： Li, Haopeng Deng, Andong Liu, Jun Rahmani, Hossein Guo, Yulan Schiele, Bernt Bennamoun, Mohammed Ke, Qiuhong School of Computing and Information Systems University of Melbourne Australia Center for Research in Computer Vision University of Central Florida United States Pillar Singapore University of Technology and Design Singapore School of Computing and Communications Lancaster University United Kingdom School of Electronics and Communication Engineering Sun Yat-sen University China Department of Computer Vision and Machine Learning Max Planck Institute for Informatics Saarland Informatics Campus Germany School of Physics Maths and Computing University of Western Australia Australia Department of Data Science & AI Monash University Australia

Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not been explored due to the lack of relevant datasets and the challenging nature it presents. Most datasets for video question answering (VideoQA) focus mainly on general and coarse-grained understanding of daily-life videos, which is not applicable to sports scenarios requiring professional action understanding and fine-grained motion analysis. In this paper, we introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task. The Sports-QA dataset includes various types of questions, such as descriptions, chronologies, causalities, and counterfactual conditions, covering multiple sports. Furthermore, to address the characteristics of the sports VideoQA task, we propose a new Auto-Focus Transformer (AFT) capable of automatically focusing on particular scales of temporal information for question answering. We conduct extensive experiments on Sports-QA, including baseline studies and the evaluation of different methods. The results demonstrate that our AFT achieves state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

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Using Cycling 3D Chaotic Map and DNA Sequences for Introducing a Novel Algorithm for Color Image Encryption *

Using Cycling 3D Chaotic Map and DNA Sequences for Introduci...

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Cyber Resilience (ICCR), International Conference on

作者： Mahdi Sahlabadi Morteza Saberikamarposhti Ravie Chandren Muniyandi Zarina Shukur Research Centre for Cyber Security Faculty of Information Science and Technology University Kebangsaan Malaysia Selangor Malaysia Department of Computer Engineering South Tehran Branch Islamic Azad University Tehran Iran Center for Cyber Security (CYBER) Faculty of Information Science and Technology University Kebangsaan Malaysia Selangor Malaysia

ISBN: (数字)9781665461221

ISBN: (纸本)9781665461238

Today, social communication through the Internet has become more popular and has become a crucial part of our daily life. Naturally, sending and receiving various data through the Internet has also grown a lot. Keeping important data secure in transit has become a challenge for individuals and even organizations. Therefore, the trinity of confidentiality, integrity, and availability will be essential, and encryption will definitely be one of the best solutions to this problem. Of course, for image data, it will not be possible to use conventional encryption methods for various reasons, such as the redundancy of image data, the strong correlation of adj acent pixels, and the large volume of image data. Therefore, special methods were developed for image encryption. Among the prevalent methods for image encryption is the use of DNA sequences as well as chaos signals. In this paper, a cycling 3D chaotic map and DNA sequences are used to present a new method for color image encryption. Several experimental analyses were performed on the proposed method, and the results proved that the presented method is secure and efficient.

关键词： Three-dimensional displays Correlation Chaotic communication Redundancy DNA Color Organizations

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Recent Research on Artificial Intelligence, Machine Learning, and Deep Learning in Consumer Electronics Technology

Recent Research on Artificial Intelligence, Machine Learning...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Brij B. Gupta Akshat Gaurav Varsha Arya Kwok Tai Chui Department of Computer Science and Information Engineering International Center for AI and Cyber Security Research and Innovations (CCRI) Asia University Taichung Taiwan Lebanese American University Beirut Lebanon Ronin Institute Montclair NJ USA Asia University Taichung Taiwan Hong Kong Metropolitan University (HKMU) Hong Kong

In this article, we show how research on artificial intelligence (AI), machine learning (ML), and deep learning (DL) has been used to develop consumer electronics (CE) technology from 1985 to 2022. The most significant themes for CE are discovered by a combination of quantitative and qualitative methods. The examination of the subject was based on quantitative data groups and themes. In addition, this research employs thematic maps to illustrate the connections between the core principles of the CE community.

关键词：

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HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding

arXiv

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arXiv 2022年

作者： Wang, Shi Tang, Daniel Zhang, Luchen Li, Huilin Han, Ding Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China TruX Luxembourg National Computer Network Emergency Response Technical Team Coordination Center of China China Department of Civil Engineering Technical University of Denmark Lyngby2800 Denmark Huazhong University of Science and Technology China

International Classification of Diseases (ICD) is a set of classification codes for medical records. Automated ICD coding, which assigns unique International Classification of Diseases codes with each medical record, is widely used recently for its efficiency and error-prone avoidance. However, there are challenges that remain such as heterogeneity, label unbalance, and complex relationships between ICD codes. In this work, we proposed a novel Bidirectional Hierarchy Framework(HieNet) to address the challenges. Specifically, a personalized PageRank routine is developed to capture the co-relation of codes, a bidirectional hierarchy passage encoder to capture the codes’ hierarchical representations, and a progressive predicting method is then proposed to narrow down the semantic searching space of prediction. We validate our method on two widely used datasets. Experimental results on two authoritative public datasets demonstrate that our proposed method boosts the state-of-the-art performance by a large margin. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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BaseSAP: Modular Stealth Address Protocol for Programmable Blockchains

arXiv

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arXiv 2023年

作者： Wahrstätter, Anton Solomon, Matthew DiFrancesco, Ben Buterin, Vitalik Svetinovic, Davor The Research Institute for Cryptoeconomics Department of Information Systems and Operations Management Vienna University of Economics and Business Vienna Austria ScopeLift BroomallPA United States Ethereum Foundation Singapore Singapore The Center for Cyber-Physical Systems Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

Stealth addresses represent an approach to enhancing privacy within public and distributed blockchains, such as Ethereum and Bitcoin. Stealth address protocols generate a distinct, randomly generated address for the recipient, thereby concealing interactions between entities. In this study, we introduce BaseSAP, an autonomous base-layer protocol for embedding stealth addresses within the application layer of programmable blockchains. BaseSAP expands upon previous research to develop a modular protocol for executing unlikable transactions on public blockchains. BaseSAP allows for developing additional stealth address layers using different cryptographic algorithms on top of the primary implementation, capitalizing on its modularity. To demonstrate the effectiveness of our proposed protocol, we present simulations of an advanced Secp256k1-based dual-key stealth address protocol. This protocol is designed on top of BaseSAP and is deployed on the Goerli and Sepolia test networks as the first prototype implementation. Furthermore, we provide cost analyses and underscore potential security ramifications and attack vectors that could affect the privacy of stealth addresses. Our study reveals the flexibility of the BaseSAP protocol and offers insight into the broader implications of stealth address technology. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Multi-Hop Routing for IoT-Based Digital Twin: Novel Metaheuristic Approaches

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IEEE Internet of Things Journal 2025年

作者： Dao, Tran Cong Luong, Nguyen Cong Nguyen, Ngoc Hung Li, Xingwang Niyato, Dusit Kim, Dong In Phenikaa University Faculty of Computer Science Hanoi12116 Viet Nam Center for Environmental Intelligence College of Engineering and Computer Science Hanoi Viet Nam Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China Nanyang Technological University School of Computer Science and Engineering Singapore Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of

This paper addresses the challenge of optimizing multi-hop routing in IoT-based digital twin systems, referred to as the MOUNTAIN problem. Multi-hop routing is inherently complex due to the need to balance energy consumption and communication reliability across multiple nodes, especially in dynamic and large-scale IoT networks. In MOUNTAIN, multiple IoT devices in the physical network (PN) frequently transmit data to the digital network twin (DNT), managed by a central server. Given the limited energy resources of IoT devices, our approach considers both energy efficiency and communication reliability. We formulate the MOUNTAIN problem as an optimization task aimed at reducing overall energy consumption while maintaining robust data transmission. Moreover, we address the problem with both single-task optimization and multi-task optimization and propose two corresponding evolution-based metaheuristics that utilize well-designed solution representations and genetic operators to obtain near-optimal solutions to the problem. Among them, the proposed Single-task Evolutionary Algorithm (STEA) solves each problem instance independently, while the proposed Multi-task Evolutionary Algorithm (MTEA) solves multiple instances at the same time to take advantage of exchanging useful solution information during parallel solution searches. Extensive experiments on synthetic datasets demonstrate that our proposed algorithms significantly outperform existing methods, reducing energy consumption and improving network stability. This research contributes to the development of sustainable and efficient IoT infrastructures, which are essential for the operational demands of digital twin applications. © 2014 IEEE.

关键词： Energy efficiency

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Collaborative filtering-based recommendation system for big data

Collaborative filtering-based recommendation system for big ...

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作者： Shen, Jian Zhou, Tianqi Chen, Lina Jiangsu Engineering Center of Network Monitoring Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology School of Computer and Software Nanjing University of Information Science and Technology Nanjing China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Guangxi Key Laboratory of Cryptography and Information Security Guilin China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Guizhousheng College of Electronics and Information Guizhou China

Collaborative filtering algorithm is widely used in the recommendation system of e-commerce website, which is based on the analysis of a large number of users' historical behaviour data, so as to explore the users' interest and recommend the appropriate products to users. In this paper, we focus on how to design a reliable and highly accurate algorithm for movie recommendation. It is worth noting that the algorithm is not limited to film recommendation, but can be applied in many other areas of e-commerce. In this paper, we use Java language to implement a movie recommendation system in Ubuntu system. Benefiting from the MapReduce framework and the recommendation algorithm based on items, the system can handle large datasets. The experimental results show that the system can achieve high efficiency and reliability in large datasets. © 2020 Inderscience Enterprises Ltd.

关键词： Collaborative filtering

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