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2024 IEEE Conference on Computational Imaging Using Synthetic Apertures, CISA 2024

作者： Papadimitropoulos, Symeon Tsogka, Chrysoula Hasan, Mohamed University of California Applied Mathematics Department MercedCA95343 United States University of California Computer Science Department MercedCA95343 United States

ISBN: (纸本)9798350308648

We propose a physically informed neural network (PINN) approach to address the problem of locating scatterers using synthetic aperture radar (SAR). The proposed approach uses the physical properties of wave propagation to aid the neural network in learning to detect and localize scatterers from SAR data. This methodology is especially efficient in superresolution cases, i.e. when the scatterers are located close to each other, at distances smaller than the nominal resolution of the imaging system. Using the proposed approach, we are able to detect an a priorly unknown number of scatterers and localize them with high accuracy. © 2024 IEEE.

关键词： Synthetic aperture radar

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A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing

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Archives of Computational Methods in Engineering 2025年第1期32卷 269-310页

作者： Hosseinzadeh, Mehdi Rahmani, Amir Masoud Husari, Fatimatelbatoul Mahmoud Alsalami, Omar Mutab Marzougui, Mehrez Nguyen, Gia Nhu Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia School of Computer Science Duy Tan University Da Nang Viet Nam Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam13120 Korea Republic of

In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheuristic algorithms that was invented in 2022 based on the foraging and migration behavior of the hummingbird for modeling and solving optimization problems. The algorithm initially starts with an initial random population of solutions. It then uses iterative processes and hummingbird position updates to balance exploration and exploitation toward the most optimal solutions. This paper has a detailed and extensive review of the AHA algorithm considering the aspects of hybrid, improved, binary, multi-objective, and optimization problems. In addition, a wide range of applications of AHA in various fields such as feature selection, image processing, scheduling, Internet of Things, classification, clustering, financial and economic issues, forecasting, wireless sensor networks, and many engineering challenges are explored. The statistical and numerical results showed that the AHA algorithm with deep learning methods, Levy flight, and opposition-based learning had the best performance. Also, the AHA algorithm is most widely used in solving multimodal optimization problems and continuous functions. © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2024.

关键词： Optimization

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Random Selection for Cloud-Based Load-Distributing in Varied P2P Networks using Enhanced Throttled Algorithm 5

Random Selection for Cloud-Based Load-Distributing in Varied...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Prakash, V.S. Mary, Margaret T. Shivakumar, B.N. Gnanadurai, Jasmine Divya, K.S. Vennila, J. Kristu Jayanti College Department of Computer Science Karnataka Bangalore India Cmr Institute of Technology Department of Mathematics Karnataka Bengaluru India George Fox University Dept. Electrical and Computer Science Engineering OR United States Manipal College of Health Professions Mahe Department of Statistics Manipal India

ISBN: (纸本)9798350379884

A peer-to-peer network connection is essential to most computer users' daily activities. On heterogeneous P2P networks, maintaining cloud-based load balance is a difficult goal to achieve. Several previous research have provided load rearrangement procedures for additional load diversions, and load division on the point where the original peer arrives. The goal of cloud-based load-sharing in P2P structures is to evenly distribute the burden among net nodes, then it is not effective in efficiently removing traffic that is already happening on the P2P network's routes. By removing the traffic flow and consuming the Random Selection Load Balancing (RSLB) method and the Incredible Store Model, the behaviors in this study are characterized. Using an improved throttled method, the Incredible Store Model is examined to broaden the effects of node variety in addition to agitation toward the capacity circulation in P2P systems. Now different from the current load distribution balancing strategy, the presentation of the Random Selection Load Balancing (RSLB) technique by the Incredible Store Model decreases the network traffic flow pathways with little cloud rate of P2P. By combining the burden in the P2P net system, an investigative and observed conclusion delivers a destruction-free system with information calculated. Peer failure probability in addition to traffic control effectiveness is used to estimate the presentation of the random selection load assessment. © 2024 IEEE.

关键词： Peer to peer networks

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Morpho-contour exponential estimation algorithm for predicting breast tumor growth from MRI imagery

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International Journal of Information Technology (Singapore) 2024年 1-16页

作者： Kaladevi, P. Kanimozhi, N. Nirmala, B. Sivasankari, R. Department of Computer Science and Engineering K. S. Rangasamy College of Technology (Autonomous) Tamil Nadu Tiruchengode 637215 India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai 603203 India Computer Science and Engineering Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Porur Chennai 600116 India Department of Computer Science Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamil Nadu Chennai 600 048 India

Breast tumor grows rapidly have an elevated risk of progressing to a metastatic state, leading to the spread of tumor tissues to other regions of the body. Early detection of such metastatic sites can be facilitated by predicting these tumors' growth rate. In this context, we introduce a novel Morpho-Contour Exponential Estimation (MoCEE) method to estimate the potential growth rate of identified breast tumors using magnetic resonance imaging (MRI) datasets. This methodology not only effectively captures morphological traces but also pinpoints regions indicative of infectious spread. This precision is achieved by enhancing the Mask R-CNN with an active contour function, ensuring the accurate segmentation of the breast tumor. Once segmented, the tumor's growth rate is computed using the exponential growth model, which accurately determines the rate by analyzing the distance between two distinct random points on the tumor's segmented region. Subsequently, this derived information, processed through a gradient boosting approach, predicts the likelihood of tumor growth across two different breast tumor categories. In our study, we assessed the proposed MoCEE method from two crucial angles. Firstly, we rigorously evaluated its ability to accurately segment both benign and malignant breast tumors. Our analysis involved comparing MoCEE's performance to that of various deep learning networks, yielding impressive segmentation accuracy percentages, such as 93.12% and 92.22% for benign and malignant tumors in BUSnet, and even higher values for other networks. Secondly, we explored MoCEE's capability to estimate tumor size, considering different probability growth rates (k^=0.2, 0.3, 0.4, and 0.5). Notably, proposed MoCEE proved highly accurate in predicting tumor growth rates across this range of probabilities. These findings highlight MoCEE's versatility and robustness, making it a valuable tool for precise breast tumor analysis and prediction. © The Author(s), under exclusiv

关键词： And gradient boosting approach Breast tumors Exponential growth model Mask R-CNN Morpho-contour exponential estimation (MoCEE)

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3D-CNNHSR: A 3-Dimensional Convolutional Neural Network for Hyperspectral Super-Resolution

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computer Systems science & Engineering 2023年第11期47卷 2689-2705页

作者： Mohd Anul Haq Siwar Ben Hadj Hassine Sharaf J.Malebary Hakeem A.Othman Elsayed M.Tag-Eldin Department of Computer Science College of Computer and Information Sciences Majmaah UniversityAlmajmaah11952Saudi Arabia Department of Computer Science College of Science and Arts at MuhayelKing Khalid UniversitySaudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityP.O.Box 344Rabigh21911Saudi Arabia Department of Mathematics AL-Qunfudhah University CollegeUmm Al-Qura UniversityKSASaudi Arabia Faculty of Engineering and Technology Future University in EgyptNew Cairo11835Egypt

Hyperspectral images can easily discriminate different materials due to their fine spectral ***,obtaining a hyperspectral image(HSI)with a high spatial resolution is still a challenge as we are limited by the high computing *** spatial resolution of HSI can be enhanced by utilizing Deep Learning(DL)based Super-resolution(SR).A 3D-CNNHSR model is developed in the present investigation for 3D spatial super-resolution for HSI,without losing the spectral *** 3DCNNHSR model was tested for the Hyperion *** pre-processing of the HSI was done before applying the SR model so that the full advantage of hyperspectral data can be utilized with minimizing the *** key innovation of the present investigation is that it used 3D convolution as it simultaneously applies convolution in both the spatial and spectral dimensions and captures spatial-spectral *** clustering contiguous spectral content together,a cube is formed and by convolving the cube with the 3D kernel a 3D convolution is *** 3D-CNNHSR model was compared with a 2D-CNN model,additionally,the assessment was based on higherresolution data from the Sentinel-2 *** on the evaluation metrics it was observed that the 3D-CNNHSR model yields better results for the SR of HSI with efficient computational speed,which is significantly less than previous studies.

关键词： CNN super-resolution deep learning hyperspectral data computer vision

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Optimized digital watermarking: Harnessing the synergies of Schur matrix factorization, DCT, and DWT for superior image ownership proofing

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Multimedia Tools and Applications 2024年第18期84卷 1-36页

作者： Al-Aiash, Issa Alquran, Rabee AlJamal, Mahmoud Alsarhan, Ayoub Aljaidi, Mohammad Al-Fraihat, Dimah Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa13133 Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa13110 Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman11622 Jordan

The contemporary context of abundant digital dissemination inherently gives rise to the need for media protection and the clear identification of ownership rights. This paper responded to this nagging issue of unauthorized media capture by developing a new digital watermarking approach uniting the Discrete Wavelet Transform DWT, Discrete Cosine Transform DCT, and Schur matrix factorization. The proposed watermark, which was a positive vector uniformly distributed, presented a high degree of robustness to several adversarial operating conditions, including histogram equalization, salt and pepper noise, ripple impact, smoothing attack, and cropping. The obtained results reveal the effectiveness of our approach. The correlation coefficient between watermarked and original images remains exceptionally high at 0.999998, ensuring minimal visual distortion. Mean Squared Error (MSE) graphs for both high-frequency (HH) and low-frequency (LL) domains demonstrate negligible changes after attacks, confirming the watermark's robustness. Notably, the watermark extraction process remains successful even after histogram equalization, as indicated by significantly lower MSE compared to random vectors. Moreover, the average processing times for different attacks are promising;Histogram Equalization and Salt and Pepper attacks each took 0.3 s, Ripple Attack required 0.28 s, Smoothing Attack completed in 0.29 s, and the Cropping Attack took 0.6 s. The robustness of our approach appears against the salt and pepper attack, which historically presented a vulnerability when employing Schur matrix factorization in image watermarking, indicating the novelty of and this contribution to improving media ownership verification. By combining disparate transformation methodologies, this technique offers a promising avenue for reinforcing the integrity and authenticity of digital media, thereby strengthening the foundations of intellectual property rights in the digital domain. © The Author(s), und

关键词： Discrete wavelet transforms

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Emergency Care Patient Prediction using Electronic Health Records (EHR) Data: An End-to-End Machine Learning Pipeline 10

Emergency Care Patient Prediction using Electronic Health Re...

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10th International Conference on Future Internet of Things and Cloud, FiCloud 2023

作者： Sumon, Maruful Abedin Kwemebe, Tor A. Melapu, Venkata K. Hasan, Md Mohiuddin Jackson State University Computational Data-Enabled Science and Engineering Jackson United States Jackson State University Department of Mathematics and Computer Science Jackson United States Jackson State University Computer Science and Engineering Jackson United States

ISBN: (纸本)9798350316353

Using electronic health records (EHR) data for predicting the condition of patients who are in need of emergency care is a promising application of machine learning. With the help of machine learning, complex problems of identification of patterns can be solved with ease and accuracy when proper machine learning model is implemented. This paper uses EHR data to build an end-to-end machine learning pipeline for predicting the outcome of emergency care patients. As a machine learning process, the pipeline includes data preprocessing, feature engineering, model selection, data set training and testing, evaluation, and deployment. By using EHR data in this pipeline, the model has been trained and tested on a large dataset of many patients information with different types of machine learning algorithms to improve and get the best prediction accuracy. This pipeline can aid in managing primary care patients efficiently and effectively with the implementation of best suitable machine learning algorithm at the time of urgency. By implementing the model on the EHR dataset, an accuracy of more than 75\% is obtained which indicates the efficiency of the machine learning process in the process of identifying patients who need the emergency care more than the general patients when taken to the primary care or emergency. © 2023 IEEE.

关键词： Forecasting

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Polarization Insensitive Broadband Zero Indexed Nano-Meta Absorber for Optical Region Applications

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computers, Materials & Continua 2022年第4期71卷 993-1009页

作者： Ismail Hossain Md Samsuzzaman Ahasanul Hoque Mohd Hafiz Baharuddin Norsuzlin Binti Mohd Sahar Mohammad Tariqul Islam Space Science Center(ANGKASA) Universiti Kebangsaan Malaysia43600UKM BangiSelangorMalaysia Department of Computer and Communication Engineering Faculty of Computer Science and EngineeringPatuakhali Science and Technology UniversityDumki8602PatuakhaliBangladesh Department of Electrical Electronic and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan Malaysia43600UKM BangiSelangorMalaysia

Broadband response metamaterial absorber(MMA)remains a challenge among researchers.A nanostructured new zero-indexed metamaterial(ZIM)absorber is presented in this study,constructed with a hexagonal shape resonator for optical region *** design consists of a resonator and dielectric layers made with tungsten and quartz(Fused).The proposed absorbent exhibits average absorption of more than 0.8972(89.72%)within the visible wavelength of 450–600 nm and nearly perfect absorption of 0.99(99%)at 461.61 *** on computational analysis,the proposed absorber can be characterized as *** developments of ZIM absorbers have demonstrated plasmonic resonance characteristics and a perfect impedance *** incidence obliquity in typically the range of 0◦–90◦both in TE and TM mode with maximum absorbance is more than 0.8972(∼89.72%),and up to 45◦angular stability is suitable for solar cell applications,like exploiting solar *** proposed structure prototype is designed and simulated by studying microwave technology numerical computer simulation(CST)*** finite integration technique(FIT)based simulator CST and finite element method(FEM)based simulator HFSS also helps validate the numerical data of the proposed ZIM *** proposed MMA design is appropriate for substantial absorption,wide-angle stability,absolute invisible layers,magnetic resonance imaging(MRI),color images,and thermal imaging applications.

关键词： Hexagonal resonator loaded star shaped polarization insensitive compact broadband metamaterial absorber optical region applications

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Leveraging Smote and Random Forest for Improved Credit Card Fraud Detection 5

Leveraging Smote and Random Forest for Improved Credit Card ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Ruchita, Maddala Bhargavi, Maridu Rakshita, Maddala Nandini, Bellamkonda Chaitanya Aziz, Irfan Gopi, Jujjuri Vignan's Foundation for Science Technology and Research Department of Computer Science and Engineering Vadlamudi Andhra Pradesh Guntur India Acharya Nagarjuna University Department of Mathematics Nagarjuna Nagar Andhra Pradesh Guntur India

ISBN: (纸本)9798331530013

The accelerating speed of digital transactions has made credit card theft a serious concern to customers and banks alike. Traditional means of fraud detection are no longer as strong as today's more sophisticated methods. This paper presents a summary of various machine learning techniques applied in credit card fraud detection. Several algorithms are highlighted, including logistic regression, random forests, and boosting methods such as gradient boosting and LightGBM. These algorithms were selected due to their capacity to manage big datasets and identify patterns unique to fraud. According to the results, the best performance was obtained by random forests, which achieved 99.30% accuracy, outperforming all other methods. Gradient boosting and logistic regression were also competitive, reaching 98.5% accuracy. This study offers recommendations for improving fraud detection techniques while showcasing the efficacy of machine learning in this regard. © 2024 IEEE.

关键词： Adversarial machine learning

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An Iterative Dual Reciprocity Method for a Class of Infiltration Problems in Two-Layered Soils with Different Types of Root-Water Uptake

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IAENG International Journal of Applied mathematics 2024年第7期54卷 1390-1399页

作者： Solekhudin, Imam Sari, Anny Kartika Makhrus, Faizal Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta55281 Indonesia

This study delves into the investigation of steady infiltration problems within a two-layered soil, featuring periodic channels, and taking into account the presence of rootwater uptake, with four different types of root-water uptake considered. The problems are governed by a set of Richards’ equations accompanied by boundary interface conditions. To tackle these problems, we transform the system of Richards’ equations, along with the corresponding boundary conditions, into a set of steady diffusion-convection equations with transformed boundary conditions. This mathematical model is then addressed using a numerical approach that leverages the Iterative Dual Reciprocity Method (IDRM). Through this numerical method, we obtain solutions that characterize the distribution of hydraulic conductivity within the soil and root-water uptake in the root zone. Furthermore, we conduct comparisons and analyze the root-water uptake resulting from the four different types of root-water uptake. The findings of this study provide insights into how the type of root influences the amount of water absorbed by the roots. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Infiltration

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