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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Buyukbicakci, Erdal Atli, Cahit Gedik, Istanbul Dumanli, Metin Bulat, Selcuk Nithiya, C. Gulati, Monika Sakarya University of Applied Sciences Information Technologies Vocational Highschool Department of Computer Technologies Sakarya Turkey Istanbul Gedik University Gedik Vocational School Cumhuriyet Mah. іlkbahar Sok Istanbul Turkey Istanbul Gelişim University Faculty of Engineering Department of Computer Engineering Cihangir Turkey Institute of Natural Sciences Sakarya University Turkish Standards Institution Department of Nanoscience and Nanoengineering Istanbul Turkey R.M.K. College of Engineering and Technology Department of Ece Puduvoyal India Mullana Haryana Ambala India

ISBN: (纸本)9798350367904

Electric vehicles (EVs) are generally considered to be more eco-friendly than traditional forms of mobility. Smart cities are environmentally conscious, and their operation is based on the conversion of electrical energy into mechanical energy using different kinds of motors. Motors in electric vehicles get electricity from renewable energy (RE) sources through power electronics-based interface connections in order to spin and provide mechanical power. The proposed approach incorporates preprocessing, feature extraction, and training of the model. Data cleansing, data noise reduction, data slicing, and integration are the primary components of data preparation. The principle of sparse principal component analysis (SPCA) is used for statistical analysis and analysis of multivariate datasets in order to identify features. The DESN model was utilized consistently throughout the training process. This state-of-the-art method outperforms SVM and LSTM with an average accuracy of 9 5. 6 5 %. © 2024 IEEE.

关键词： Solar power generation

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Forecasting of Monkeypox Cases Using Optimized SARIMAX Based Model

Forecasting of Monkeypox Cases Using Optimized SARIMAX Based...

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Electronic engineering (ICEEM), International Conference on

作者： Mahmoud Elshabrawy Marwa M. Eid Abdelaziz A. Abdelhamid El-Sayed M. El-Kenawy Abdelhameed Ibrahim Dept. of Communications & Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Dep. of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Engineering and Control Systems Dept. Faculty of Engineering University Mansoura Egypt

This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models.

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Entirety of Quantum Uncertainty and Its Experimental Verification

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Chinese Physics Letters 2021年第7期38卷 11-16页

作者： Jie Xie Li Zhou Aonan Zhang Huichao Xu Man-Hong Yung Ping Xu Nengkun Yu Lijian Zhang National Laboratory of Solid State Microstructures College of Engineering and Applied Sciences and School of PhysicsNanjing UniversityNanjing 210093China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjing 210093China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China Centre for Quantum Software and Information School of SoftwareFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNSWAustralia

As a foundation of quantum physics,uncertainty relations describe ultimate limit for the measurement uncertainty of incompatible ***,uncertainty relations are formulated by mathematical bounds for a specific *** we present a method for geometrically characterizing uncertainty relations as an entire area of variances of the observables,ranging over all possible input *** find that for the pair of position and momentum operators,Heisenberg's uncertainty principle points exactly to the attainable area of the variances of position and ***,for finite-dimensional systems,we prove that the corresponding area is necessarily semialgebraic;in other words,this set can be represented via finite polynomial equations and inequalities,or any finite union of such *** particular,we give the analytical characterization of the areas of variances of(a)a pair of one-qubit observables and(b)a pair of projective observables for arbitrary dimension,and give the first experimental observation of such areas in a photonic system.

关键词： observable union operators

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The Prediction of Health information Quality Perception Using Machine Learning and Deep Learning Techniques

The Prediction of Health Information Quality Perception Usin...

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International Conference of information and Communication technology (ICoICT)

作者： Yousef Baqraf Pantea Keikhosrokiani School of Computer Sciences Universiti Sains Malaysia Minden Penang Malaysia Faculty of Information Technology and Electrical Engineering Faculty of Medicine University of Oulu Oulun Yliopisto Finland

With the substantial rise in the number of people seeking health information online, manually evaluating their perceptions of the quality of health information has become increasingly difficult. These perceptions can impact the acceptance or rejection of the information. To address this issue, the study has employed deep learning and machine learning models to automatically identify consumer perceptions of health information. Furthermore, the study used a survey to collect data from 253 individuals to train the models, measuring the perception of 18 dimensions related to health information quality. This will help health information providers to provide personalized health information that aligns with individual preferences. The RandomForest and neural network model are found to have achieved the best performance among all the algorithms with an Accuracy of over 90% in all the quality dimensions. In sum, our findings show that automating the identification of consumer perception is feasible, which is an essential step toward providing online health information that matches consumer perception and increases the willingness to use it.

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An Electromagnetically Consistent and Mutual Coupling-Aware Near-Field Communication Model for Holographic MIMO

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IEEE Transactions on Wireless Communications 2025年

作者： Ye, Lv Song, Yunchao Li, Bo Hu, Shengbo Zhu, Lei Liu, Chen Nanjing University of Posts and Telecommunications College of Electronic and Optical Engineering Nanjing210023 China Guizhou Normal University Institute of Intelligent Information Processing Guiyang550001 China University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology 550001 China

As a potential technology to support the extreme requirements of next-generation communications, holographic multiple-input multiple-output (H-MIMO) refers to arrays with a large number of individually controlled and densely deployed antennas. An important challenge to realize this innovative concept is the need to integrate information theory, electromagnetic theory and circuit theory in modeling, which is also required for electromagnetic information theory (EIT). This paper aims to connect information theory and electromagnetic theory using circuit theory to provide electromagnetically consistent and mutual coupling-aware near-field communication models for H-MIMO systems. Specifically, first, the considered H-MIMO system model is given and represented as a linear multiport network in circuit theory. Unlike the mainstream models that only consider the wireless propagation environment, the circuit-based model is physically consistent and considers the radio frequency components, mutual coupling between different antenna elements, noise, etc. Second, the mutual coupling and propagation effects are represented in the circuit model by the impedance, which requires relating the quantities of the circuit to the fields generated by the antenna. The relationship between the quantities of the circuit and the fields generated by the antenna is given according to the reciprocity and reaction theorems. Furthermore, the method of moments (a computational electromagnetic method) is introduced to calculate the impedance in the line-of-sight case. Finally, the integration of the physical model with the digital model provides a framework for the design of H-MIMO. In the numerical simulations, the H-MIMO performance is provided using the proposed model. © 2002-2012 IEEE.

关键词： Linear network analysis

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Framework for IOT Based Real-Time Monitoring System of Rainfall Water Level for Flood Prediction Using LSTM Network 3

Framework for IOT Based Real-Time Monitoring System of Rainf...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： William, P. Oyebode, Oluwadare Joshua Ramu, Gandikota Lakhanpal, Sorabh Gupta, Keerat Kumar Al-Jawahry, Hassan M. Sanjivani College of Engineering Savitribai Phule Pune University Department of Information Technology Pune India Afe Babalola University Civil and Environmental Engineering Ekiti State Ado-Ekiti Nigeria Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad500043 India Lovely Professional University Phagwara India Graphic Era Deemed to Be University Department of Civil Engineering Uttarakhand Dehradun248002 India The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9798350322842

Recent floods have consistently resulted in human casualties in addition to harm to the environment and the economy. People are less likely to be aware of oncoming floods since there is not a reliable early warning system. A deep learning algorithm is suggested in this study to forecast groundwater levels. Even with inadequate data, the model can nevertheless successfully fulfil the prediction objective. In order to determine the degree of temporal dependence that exists between groundwater level and meteorological indicators, a hybrid model called CNN-LSTM-ML is developed. This model makes use of a network structure that is a mix of CNN and LSTM networks. Long Short-Term Memory (LSTM) networks are used by the rainfall forecasting model in order to provide predictions about future rainfall and water level values, which may lead to floods. In order to conduct experiments on the principal finding, historical data from two sites with rainfall and one location with streamflow were used. Additional information was acquired from two water level stations and one rainfall station in order to confirm the basic findings. With assessment errors for historical data MAE, RMSE, and MSE of 0.93, 1.7, and 3.025 correspondingly, the forecasting technique that used LSTM demonstrated great 'accuracy' of the outcome reaching more than 92% using IoT. According to these results, the system has the potential to be used as a cure that does not involve the construction of new structures in order to minimize the damage caused by urban floods. © 2023 IEEE.

关键词： Internet of things

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Model-Driven Sensing-Node Selection and Power Allocation for Tracking Maneuvering Targets in Perceptive Mobile Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Xie, Lei He, Hengtao Song, Shenghui Eldar, Yonina C. Southeast University School of Cyber Science and Engineering Nanjing China Southeast University National Mobile Communications Research Laboratory School of Information Science and Engineering Nanjing China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot7610001 Israel

Maneuvering target tracking is an important service of future wireless networks to assist innovative applications such as intelligent transportation. However, tracking maneuvering targets by cellular networks faces many challenges. In particular, the dense network and high-speed targets make the selection of the sensing nodes (SNs) and the associated power allocation very challenging. Existing methods demonstrated engaging performance, but with high computational complexity. In this paper, we propose a model-driven deep learning (DL)-based approach for SN selection. To this end, we first propose an iterative SN selection method by jointly exploiting the majorization-minimization (MM) framework and the alternating direction method of multipliers (ADMM). Then, we unfold the iterative algorithm as a deep neural network and prove its convergence. The proposed method achieves lower computational complexity, as the number of layers is less than the number of iterations required by the original algorithm, and each layer only involves simple matrix-vector additions/multiplications. Finally, we propose an efficient power allocation method based on fixed point (FP) water filling and solve the joint SN selection and power allocation problem under the alternative optimization framework. Simulation results show that the proposed method achieves better performance than conventional optimization-based algorithms with much lower computational complexity. © 2002-2012 IEEE.

关键词： NP-hard

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Partially Hidden Policy Attribute-Based Multi-Keyword Searchable Encryption with Verification and Revocation

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IEEE Transactions on Mobile Computing 2025年

作者： Chen, Liqing Xu, Shiguo Jin, Chunhua Zhang, Hao Weng, Jian Huaiyin Institute of Technology Faculty of Computer and Software Engineering Jiangsu Huai'an223003 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China Jinan University College of Information Science and Technology Guangdong Guangzhou510632 China

Data security is becoming increasingly critical as outsourced data services flourish. An effective solution for ensuring data confidentiality in the cloud is attribute-based searchable encryption (ABSE). However, most extant ABSE solutions have not been able to simultaneously achieve rich expressive queries, result validation, attribute revocation, and policy hiding, and there are problems such as inflexibility in query methods and access control capabilities, insufficient scalability and practicality, and privacy leakage. In particular, it should be noted that most ABSE schemes fail to promise the privacy security of access policies and are susceptible to offline keyword guessing attack. Hidden policy-ABSE (HP-ABSE) has been proposed to tackle the issue of explicit attribute values in access policies potentially compromising privacy. Nevertheless, in most existing HP-ABSE schemes, an adversary may initiate attribute values guessing attack to speculate on the values of attribute within access strategy. To this end, this paper proposes partially hidden policy attribute-based multi-keyword searchable encryption with verification and revocation (PHP-ABMKSE-VR). PHP-ABMKSE-VR scheme is practical, secure, as well as effective, making it appropriate for real-world scenarios like smart healthcare, as demonstrated by the experimental outcomes and safety analysis. © 2002-2012 IEEE.

关键词： Differential privacy

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Encryption with Image Steganography Based Data Hiding Technique in IIoT Environment

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computers, Materials & Continua 2022年第7期72卷 1323-1338页

作者： Mahmoud Ragab Samah Alshehri Hani A.Alhadrami Faris Kateb Ehab Bahaudien Ashary SAbdel-khalek Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesKing Abdulaziz UniversityJeddah21589Saudi Arabia Molecular Diagnostic Lab King Abdulaziz University HospitalKing Abdulaziz UniversityJeddah21589Saudi Arabia Special Infectious Agent Unit King Fahd Medical Research CenterKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohag82524Egypt

Rapid advancements of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)pose serious security issues by revealing secret ***,security data becomes a crucial issue in IIoT communication where secrecy needs to be guaranteed in real ***,AI techniques can be utilized to design image steganographic techniques in *** addition,encryption techniques act as an important role to save the actual information generated from the IIoT devices to avoid unauthorized *** order to accomplish secure data transmission in IIoT environment,this study presents novel encryption with image steganography based data hiding technique(EISDHT)for IIoT *** proposed EIS-DHT technique involves a new quantum black widow optimization(QBWO)to competently choose the pixel values for hiding secrete data in the cover *** addition,the multi-level discrete wavelet transform(DWT)based transformation process takes ***,the secret image is divided into three R,G,and B bands which are then individually encrypted using Blowfish,Twofish,and Lorenz Hyperchaotic *** last,the stego image gets generated by placing the encrypted images into the optimum pixel locations of the cover *** order to validate the enhanced data hiding performance of the EIS-DHT technique,a set of simulation analyses take place and the results are inspected interms of different *** experimental outcomes stated the supremacy of the EIS-DHT technique over the other existing techniques and ensure maximum security.

关键词： IIoT security data hiding technique image steganography encryption secure communication

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RVAFM: Re-parameterizing Vertical Attention Fusion Module for Handwritten Paragraph Text Recognition

arXiv

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arXiv 2025年

作者： Zheng, Jinhui Liu, Zhiquan Si, Yain-Whar Li, Jianqing Zhang, Xinyuan Li, Xiaofan Huang, Haozhi Gong, Xueyuan School of Intelligent Systems Science and Engineering Jinan University Guangdong Province China College of Cyber Security Jinan University Guangdong Province China Department of Computer and Information Science Faculty of Science and Technology University of Macau China School of Computer Science and Engineering Macau University of Science and Technology China

Handwritten Paragraph Text Recognition (HPTR) is a challenging task in computer Vision, requiring the transformation of a paragraph text image, rich in handwritten text, into text encoding sequences. One of the most advanced models for this task is Vertical Attention Network (VAN), which utilizes a Vertical Attention Module (VAM) to implicitly segment paragraph text images into text lines, thereby reducing the difficulty of the recognition task. However, from a network structure perspective, VAM is a single-branch module, which is less effective in learning compared to multi-branch modules. In this paper, we propose a new module, named Re-parameterizing Vertical Attention Fusion Module (RVAFM), which incorporates structural re-parameterization techniques. RVAFM decouples the structure of the module during training and inference stages. During training, it uses a multi-branch structure for more effective learning, and during inference, it uses a single-branch structure for faster processing. The features learned by the multi-branch structure are fused into the single-branch structure through a special fusion method named Re-parameterization Fusion (RF) without any loss of information. As a result, we achieve a Character Error Rate (CER) of 4.44% and a Word Error Rate (WER) of 14.37% on the IAM paragraph-level test set. Additionally, the inference speed is slightly faster than VAN. © 2025, CC BY.

关键词： Parameterization

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