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A novel hybrid machine learning approaches for prediction of greenhouse energy demand and production

e-Prime - Advances in Electrical engineering, Electronics and Energy 2025年 11卷

作者： Ouazzani Chahidi, Laila Bounoua, Zineb Mechaqrane, Abdellah Al Idrissi Laboratory for Science and Engineering Research Faculty of Sciences and Techniques Sidi Mohamed Ben Abdellah University PO. Box 2202 Fez Morocco LESSI Electronics Signals Systems and Computer Science Laboratory Faculty of Sciences Dhar Mehraz Sidi Mohamed Ben Abdellah University PO. Box 1796 Atlas Fez 30003 Morocco SIEDD Intelligent Systems Energy and Sustainable Development Laboratory Private University of Fez Fez Morocco

In response to the growing environmental complexities, the agricultural sector is actively integrating advanced technologies to fortify its adaptability and operational efficiency. A pivotal avenue of exploration centers on exploiting machine learning models for the prediction of greenhouse parameters. This study explores the prediction of greenhouse energy demand and production. For that, the study employs a new hybrid approaches (series and parallel), combining artificial neural networks and boosting trees, to predict air-conditioning electrical consumption and photovoltaic modules' electrical production. Model performance is evaluated based on statistical indicators, including the coefficient of correlation (R) and the normalized root mean square error (nRMSE). Results reveal that series and parallel hybrid models demonstrate acceptable to good performance (10%

关键词： Artificial neural network Boosting trees Greenhouse energy prediction Hybrid models Parallel hybrid model Series hybrid model

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Models of Exchanged datasets and Interactions of Buyers in the data Market: Toward Multi-Agent Simulators for System Design

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Procedia Computer science 2022年 207卷 1695-1704页

作者： Teruaki Hayashi Hiroyasu Matsushima Hiroki Sakaji Yoshiaki Fukami Takumi Shimizu Department of Systems Innovation School of Engineering The University of Tokyo Tokyo Japan Center for Data Science Education and Research Shiga University Shiga Japan Department of International Digital and Design Management Tokyo University of Science Tokyo Japan Faculty of Policy Management Keio University Kanagawa Japan

Value creation by reusing data as exchangeable resources has been widely studied as a new source of innovation, resulting in the establishment of a data market and its ecosystem. However, owing to the nascent nature of the market, observable information on exchanged datasets and interactions among stakeholders, such as buyers and providers, in the market are limited. Therefore, a lack of observable information that contributes to designing the market system and formulating regulations to promote the sound growth of data markets exists. This study modeled exchanged datasets and data buyers as agents, the smallest units of the data market components, and prepared seven scenarios for four market sizes. We simulated the effects of datasets and agent models with different market sizes on the agents’ data purchases and emergence of popular datasets and discussed the factors that affect the purchase frequency distribution of the datasets. We present the experimental results and new implications for developing a data market simulator. The development of this simulator is expected to significantly advance research on the market understanding and system design in data markets.

关键词： data market data exchange data ecosystem system design simulation

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Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning

arXiv

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arXiv 2023年

作者： Ismail, Leila Materwala, Huned Hennebelle, Alain 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 Independent Data Scientist and Engineer Al Ain Abu Dhabi United Arab Emirates

COVID-19 has infected more than 68 million people worldwide since it was first detected about a year ago. Machine learning time series models have been implemented to forecast COVID-19 infections. In this paper, we develop time series models for the Gulf Cooperation Council (GCC) countries using the public COVID-19 dataset from Johns Hopkins. The dataset set includes the one-year cumulative COVID-19 cases between 22/01/2020 to 22/01/2021. We developed different models for the countries under study based on the spatial distribution of the infection data. Our experimental results show that the developed models can forecast COVID-19 infections with high *** Codes 68-04, 68T05, 68T07, 68T20 © 2023, CC BY-NC-ND.

关键词： Coronavirus

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Reversible data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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TAO-Based data Governance in intelligent Transportation systems

TAO-Based Data Governance in Intelligent Transportation Syst...

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International Conference on intelligent Transportation

作者： Xiaolong Liang Juanjuan Li Rui Qin Wenwen Ding Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems and State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences and Collaborative Laboratory for Intelligent Science and Systems Beijing China

intelligent Transportation systems (ITS) are heavily dependent on private user data. However, most ITS fails to harness the potential of this invaluable data due to the absence of effective data governance mechanisms promoting users' contributions. Moreover, data contributors face security risks such as privacy preservation, data leakage, etc., as well as high costs in data sharing, while benefits are disproportionately reaped by system operators and interaction. This systemic imbalance could indirectly incentivize a surge in inactions and even malicious actions. In response to these challenges, we propose the design of a True Autonomous Organization (TAO) for ITS, namely ITS TAO. Utilizing the newly designed decision models with decentralized organization structures and the three-power structure, ITS TAO aims to realize the fair distribution of rights and benefits for ITS data contributors. Furthermore, we design a real-time evaluation system based on parallel intelligence capable of identifying potential hazards.

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Infinite families of optimal and minimal codes over rings using simplicial complexes

arXiv

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arXiv 2024年

作者： Wu, Yanan Pang, Tingting Li, Nian Pan, Yanbin Zeng, Xiangyong The Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The School of Information Science and Engineering Shandong Normal University Jinan250358 China The Hubei Provincial Engineering Research Center of Intelligent Connected Vehicle Network Security School of Cyber Science and Technology Hubei University Wuhan430062 China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan430062 China

In this paper, several infinite families of codes over the extension of non-unital non-commutative rings are constructed utilizing general simplicial complexes. Thanks to the special structure of the defining sets, the principal parameters of these codes are characterized. Specially, when the employed simplicial complexes are generated by a single maximal element, we determine their Lee weight distributions completely. Furthermore, by considering the Gray image codes and the corresponding subfield-like codes, numerous of linear codes over Fq are also obtained, where q is a prime power. Certain conditions are given to ensure the above linear codes are (Hermitian) self-orthogonal in the case of q = 2, 3, 4. It is noteworthy that most of the derived codes over Fq satisfy the Ashikhmin-Barg's condition for minimality. Besides, we obtain two infinite families of distance-optimal codes over Fq with respect to the Griesmer bound. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimal systems

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A Preliminary Study of Frequency Shift Mechanism in Magnetic Resonance Coupled Human Body Communication

A Preliminary Study of Frequency Shift Mechanism in Magnetic...

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Cross Strait Quad-Regional Radio science and Wireless Technology Conference (CSRWTC)

作者： Lina Chen Jiamei Wang Ziliang Wei Sio Hang Pun Mang I. Vai Yueming Gao College of Physics and Information Engineering Fuzhou University China The International Joint Laboratory on Health Intelligent Monitoring Systems Fuzhou University China State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau China Faculty of Science and Technology University of Macau China

The Magnetic Resonance-Coupled Human Body Communication (MR-HBC) method presents advantages such as high path gain and enhanced information security, rendering it particularly suitable for establishing wireless body area networks. The optimal operation of MR-HBC relies on functioning at resonance frequency. However, owing to the intricate dielectric properties of human tissues and individual differences, the resonance frequency of communication systems frequently experiences varying degrees of deviation. To delve into the intrinsic mechanism behind frequency shifts, this paper establishes a finite element model based on the genuine dielectric properties and geometric structure of the human body. It investigates the augmentation effects of human tissue on MR-HBC, examines the influence of distinct tissue electrical properties on the electromagnetic field distribution of MR-HBC, and analyzes the inherent connection between frequency shifts and human tissue electrical characteristics. The results reveal that the electrical properties of human tissues stand as the primary factor affecting frequency shifts, with the extent of frequency deviation contingent upon the proportional composition of different tissues.

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2D-DOA Estimation for Polarized Massive Antenna Array: Geometric and Algorithmic Guidelines

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IEEE Transactions on Vehicular Technology 2025年

作者： Wen, Fangqing Wang, Han Gui, Guan Yuen, Chau Sari, Hikmet Adachi, Fumiyuki China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang443002 China Hubei University of Automotive Technology Institute of Vehicle Information Control and Network Technology Shiyan442002 China Yichun University College of Physical Sciences and Engineering Technology Yichun336000 China City University of Macau Faculty of Data Science China Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore Sendai980-8572 Japan

The utilization of an Electromagnetic Vector Sensor (EMVS) array presents valuable insights in wireless communications and radars by incorporating the advantages of spatial, temporal, and polarization diversities. This study focuses on addressing the problem of estimating the Two-Dimensional (2D) Direction-of-Arrival (DOA) for an arbitrarily spaced EMVS array. Firstly, the Cramer-Rao Bounds (CRB) for estimating the 2D-DOA and polarized parameters are derived, which provides design guideline for array geometry. As suggested by the CRB, a sparse planar EMVS array is a more appealing choice compared to the popular coprime EMVS array. Thereafter, a Parallel Factor (PARAFAC)-based weighted phase fitting algorithm is presented, designed to fully explore the aperture of the conformal array as well as capitalize on the tensorial structure inherent in array data. The proposed PARAFAC algorithm not only provides a closed-form solution for conformal arrays but also exhibits superior accuracy in 2D-DOA estimation compared to existing approaches. Numerical results corroborate the theoretical advantages of the proposed array geometry and PARAFAC algorithm. © 1967-2012 IEEE.

关键词： Cramer-Rao bounds

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Provably secure fog-based authentication protocol for VANETs

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Computer Networks 2024年 246卷

作者： Awais, Syed Muhammad Yucheng, Wu Mahmood, Khalid Badar, Hafiz Muhammad Sanaullah Kharel, Rupak Das, Ashok Kumar School of Microelectronics and Communication Engineering Chongqing University Chongqing China Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliu64002 Taiwan Department of Emerging Computing Technologies Emerson University Multan Pakistan School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom The Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad500 032 India

The integration of fog computing and vehicular ad-hoc networks (VANETs) has become increasingly important due to advancements in cloud computing, Internet of Things (IoT) technologies, and intelligent transportation systems. However, ensuring secure communication in fog-based VANETs poses a significant challenge. To address this challenge, we propose a novel authenticated key agreement protocol that achieves mutual authentication, generates a secure session key for confidential communication, and ensures privacy protection without relying on bilinear pairing. We provide rigorous security proofs for our protocol, specifically tailored for fog-based VANETs, demonstrating its ability to meet their stringent security requirements. Simulations using NS3 allow for a comprehensive performance evaluation, providing insights into the protocol's efficiency and scalability. Additionally, our protocol is highly efficient, featuring low computation and communication costs, thereby offering a practical and scalable solution for secure communication in fog-based VANETs. © 2024

关键词： Fog

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SGNet: Salient Geometric Network for Point Cloud Registration

SGNet: Salient Geometric Network for Point Cloud Registratio...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Qianliang Wu Yaqing Ding Lei Luo Haobo Jiang Shuo Gu Chuanwei Zhou Jin Xie Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Point Cloud Registration (PCR) is a critical and challenging task in computer vision and robotics. One of the primary difficulties in PCR is identifying salient and meaningful points that exhibit consistent semantic and geometric properties across different scans. Previous methods have encountered challenges with ambiguous matching due to the similarity among patch blocks throughout the entire point cloud and the lack of consideration for efficient global geometric consistency. To address these issues, we propose a new framework that includes several novel techniques. Firstly, we introduce a semantic-aware geometric encoder that combines object-level and patch-level semantic information. This encoder significantly improves registration recall by reducing ambiguity in patch-level superpoint matching. Additionally, we incorporate a prior knowledge approach that utilizes an intrinsic shape signature to identify salient points. This enables us to extract the most salient super points and meaningful dense points in the scene. Secondly, we introduce an innovative transformer that encodes High-Order (HO) geometric features. These features are crucial for identifying salient points within initial overlap regions while considering global high-order geometric consistency. We introduce an anchor node selection strategy to optimize this high-order transformer further. By encoding inter-frame triangle or polyhedron consistency features based on these anchor nodes, we can effectively learn high-order geometric features of salient super points. These high-order features are then propagated to dense points and utilized by a Sinkhorn matching module to identify critical correspondences for successful registration. The experiments conducted on the 3DMatch/3DLoMatch and KITTI datasets demonstrate the effectiveness of our method.

关键词： Point cloud compression Computer vision Accuracy Shape Semantics Transformers Feature extraction Encoding intelligent robots

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