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EAMultiRes-DSPP: an efficient attention-based multi-residual network with dilated spatial pyramid pooling for identifying plant disease

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Neural Computing and Applications 2024年第26期36卷 16141-16161页

作者： Al-Gaashani, Mehdhar S. A. M. Muthanna, Ammar Chelloug, Samia Allaoua Kumar, Neeraj School of Resources and Environment University of Electronic Science and Technology of China 4 1st Ring Rd East 2 Section Sichuan Chengdu610056 China 6 Miklukho-Maklaya St Moscow117198 Russia Department of Telecommunication Networks and Data Transmission The Bonch-Bruevich Saint-Petersburg State University of Telecommunications 6 Miklukho-Maklaya St Saint Petersburg Saint Petersburg193232 Russia Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh610101 Saudi Arabia Department of Computer Science Thapar Institute of Engineering and Technology Patiala147004 India

Convolutional neural networks (CNNs) have made substantial contributions to the domain of plant disease diagnosis, attaining noteworthy levels of accuracy. The primary objective of this study is to enhance the capabilities of CNNs within this particular field. In this article, we suggest a unique strategy utilizing MultiRes blocks and attention mechanisms to enhance the classification performance of CNNs for plant diseases. Our solution involves stacking four MultiRes blocks, each of which gradually increases the number of filters to prevent excessive propagation of memory requirements to deeper network nodes. In order to collect more spatial information, we also implement a residual link and a 1 × 1 convolutional layer. After each MultiRes block, we employ a Convolutional Block Attention Module to emphasize vital information and decrease redundant noise by inferring attention mappings along the channel and spatial dimensions. Finally, our method includes the use of a Dilated Spatial Pyramid Pooling module which is designed to extract information from the input image at various scales. Our evaluation leveraged a comprehensive dataset compiled from multiple sources, including the New Plant Diseases Dataset, Corn Dataset, and Coffee Dataset, encompassing a wide range of plant leaves both healthy and diseased, across various classes. This diverse collection of images, augmented by several techniques to enhance data variance and model robustness, provided a solid foundation for assessing the performance of our proposed model. Experiments demonstrate that this method outperforms conventional machine learning techniques and delivers high classification accuracy for plant diseases, with a precision of 99.34%, recall of 99.25%, f1-score of 99.29%, and accuracy of 99.35%. Our proposed model has the potential to considerably enhance the effectiveness and efficiency of diagnosing plant diseases in the agricultural industry. © The Author(s), under exclusive licence to Springer-

关键词： Image classification

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Procrustean entanglement concentration in quantum-classical networking

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Physical Review Applied 2024年第4期21卷 044027-044027页

作者： Hsuan-Hao Lu Muneer Alshowkan Jude Alnas Joseph M. Lukens Nicholas A. Peters Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona 85287 USA

The success of a future quantum internet will rest in part on the ability of quantum and classical signals to coexist in the same optical fiber infrastructure, a challenging endeavor given the orders of magnitude differences in flux of single-photon-level quantum fields and bright classical traffic. We theoretically describe and experimentally implement Procrustean entanglement concentration for polarization-entangled states contaminated with classical light, showing significant mitigation of crosstalk noise in dense wavelength-division multiplexing. Our approach leverages a pair of polarization-dependent loss emulators to attenuate highly polarized crosstalk that results from imperfect isolation of conventional signals copropagating on shared fiber links. We demonstrate our technique both on the tabletop and over a deployed quantum local area network, finding a substantial improvement of two-qubit entangled state fidelity from approximately 75% to over 92%. This local filtering technique could be used as a preliminary step to reduce asymmetric errors, potentially improving the overall efficiency when combined with more complex error-mitigation techniques in future quantum repeater networks.

关键词： Entanglement manipulation Optical quantum information processing Quantum entanglement Quantum networks Quantum optics Quantum repeaters Internet Multilayer & multiplex networks

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Prognostication of zooplankton-driven cholera pathoepidemiological Dynamics: Novel Bayesian-regularized deep NARX neuroarchitecture

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computers in Biology and Medicine 2025年 192卷 110197-110197页

作者： Raja, Muhammad Junaid Ali Asif Sultan, Adil Chang, Chuan-Yu Shu, Chi-Min Kiani, Adiqa Kausar Shoaib, Muhammad Raja, Muhammad Asif Zahoor Department of Computer Science and Information Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan AI Center Yuan Ze University Taoyuan320 Taiwan

Background: Cholera outbreaks pose significant health concerns, particularly through freshwater contamination through zooplankton serving as reservoirs for Vibrio Cholerae. Understanding these complex interactions within the aquatic ecosystem through mathematical modeling regimes may help us predict and prevent the spread of Cholera disease spread in affected regions. Method: In this study, an innovative Bayesian regularized deep nonlinear autoregressive exogenous (BRDNARX) neural networks are employed to model the intricate dynamics of Zooplankton-Driven Cholera Disease Transmission (ZDCDT) system. The cholera epidemic propagation through freshwater contamination is uncovered with analysis on densities of phytoplankton, vibrio cholerae carrying zooplankton, human population vector and microbial pathogen vector populous in the marine biosphere. Synthetic data for the ZDCDT is presented for diverse simulations using a modified Adams-Bashforth-Moulton predictor corrector numerical scheme. Subsequently, these temporal data sequences are preprocessed for the novel BRDNARX computing paradigm with an exhaustive assessment on mean square error iterative convergence plots, error histogram charts, regression index reports, input-error crosscorrelation charts, error autocorrelation charts, and time-series response dynamics. Results and conclusions: Comparative absolute error analysis with reference numerical solution adheres to diminutive disparities of range 10−3 to 10−9. Finally, BRDNARX neurostructures are reconfigured for predictive analysis of ZDCDT system in terms of single and multi-step ahead predictors with mean square error outcomes that range from 10−9 to 10−11. This establishes the efficacy of BRDNARX in correctly adhering to the intricacies of the zooplankton-driven cholera pathoepidemiological dynamics with precise forward prognostication. © 2025 Elsevier Ltd

关键词： Mean square error

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Adaptive Compression-Encryption Scheme for Medical Image Based on Improved Compressive Sensing and Deoxyribonucleic Acid Coding-Compression

Adaptive Compression-Encryption Scheme for Medical Image Bas...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xianglian Xue Haiyan Jin Changjun Zhou School of Computer Science and Engineering Xi’an University of Technology Xi’an China Section of Computer Teaching and Research Shaanxi University of Chinese Medicine Xianyang China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China School of Computer Science and Technology Zhejiang Normal University Jinhua China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Medical images often occupy large storage space and contain patient privacy or sensitive information, which makes them difficult and unsafe to be transmitted through the network. This paper proposed an adaptive compression-encryption scheme based on improved compressive sensing (CS) and deoxyribonucleic acid (DNA) coding-compression, which helps to solve the above problem. In the proposed scheme, first the original medical image was compressed to a floating point CS matrix using the discrete wavelet transform and the partial Hadamard matrix. The CS matrix was then normalized and rounded for matrix quantification. The result of quantification was fed to DNA fixed encoding and DNA run length coding for encryption and secondary compression. Finally the compressed-encrypted image was obtained after DNA dynamic encoding-decoding and regroups operation. The proposed scheme was tested against 9 images and proved to be effective in reducing the quantization errors and enhancing the compression performance. For instance, when the benchmark compression ratio (CR) is 0.5, the CR can be reduced by 5%(CR = 0.4453) ∼ 23%(CR = 0.2636), with the corresponding peak signal-to-noise ratio values consistently surpassing the benchmark. Furthermore, the execution of DNA compression and DNA dynamic encoding-decoding provided double guarantee for the algorithm’s security.

关键词： Image coding Heuristic algorithms DNA Transforms Benchmark testing Encoding Sensors Compressed sensing Secure storage Biomedical imaging

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Novel polynomial Abet data augmentation algorithm with GRU paradigm for nuclear power prediction

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Annals of Nuclear Energy 2024年 201卷

作者： Khan, Saifullah Awan, Saeed Ehsan Muhammad, Yasir Jadoon, Ihtesham Raja, Muhammad Asif Zahoor Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Campus Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 0.3 Yunlin Douliou64002 Taiwan

In current study innovative approach is presented to improve the accuracy and efficiency of machine learning models on the dataset for each of the 31 US states, solely comprising a single power output column in Terra watt-hours (TWh) spanning from January 2013 to November 2020 by exploiting the knacks of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), hybrid GRU-LSTM, Polynomial Feature Transform (PFT) and Polynomial Abet (PA) data augmentation with GRU for efficacious prediction. Achieving sufficient accuracy, a small univariate dataset of 95 rows was augmented through the PFT method, resulting in a significant reduction in prediction errors. Encouraged by this improvement, a novel algorithm PA data augmentation with GRU is introduced for further enhancing the accuracy, correlation, and computational efficiency of the prediction model. This research not only demonstrates the superior performance of the proposed PA data augmentation method but also highlights the benefit of using 31 individual univariate datasets for each state, allowing comprehensive testing and validation of the novel algorithm, providing compelling evidence of its efficacy in nuclear power prediction. The results from the proposed algorithms are evaluated using Root Mean Square Error (RMSE) and Relative Root Mean Square Error (RRMSE). A quantitative overview of PA and PFT data augmentation techniques’ comparison show the result in terms of accuracy (PA's 0.464% RRMSE versus PFT's 1.088 % RRMSE), correlation (PA's −0.5877 versus PFT's 0.4892), and computational efficiency (PA's 14.02 % less computational time then PFT) for the nuclear power prediction model. The research framework introduced in this study, with its focus on utilizing state-specific data for nuclear energy prediction, holds significant implications for governmental decision-making policies. It provides a comprehensive overview of the trends in power generation and can be utilized for both renewable and thermal sources ac

关键词： Long short-term memory

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A comprehensive study on Frequent Pattern Mining and Clustering categories for topic detection in Persian text stream

arXiv

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arXiv 2024年

作者： Zafarani-Moattar, Elnaz Kangavari, Mohammad Reza Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Computer Engineering Iran University of Science and Technology Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

Topic detection in social networks is extracting and summarizing frequent posts about an event or subject. Topic detection is a complex process and depends on language because it somehow needs to analyze text. There have been few studies on topic detection in Persian, and the existing algorithms are not remarkable. Therefore, we aimed to study topic detection in Persian. The objectives of this study are: 1) to conduct an extensive study on the best algorithms for topic detection, 2) to identify necessary adaptations to make these algorithms suitable for the Persian language, and 3) to evaluate their performance on Persian social network texts. To achieve these objectives, we have formulated two research questions: First, considering the lack of research in Persian, what modifications should be made to existing frameworks, especially those developed in English, to make them compatible with Persian? Second, how do these algorithms perform, and which one is superior? There are various topic detection methods that can be categorized into different categories. Frequent pattern and clustering are selected for this research, and a hybrid of both is proposed as a new category. Then, ten methods from these three categories are selected. All of them are re-implemented from scratch, changed, and adapted with Persian. These ten methods encompass different types of topic detection methods and have shown good performance in English. The text of Persian social network posts is used as the dataset. Additionally, a new multiclass evaluation criterion, called FS, is used in this paper for the first time in the field of topic detection. Approximately 1.4 billion tokens are processed during experiments. The results indicate that if we are searching for keyword-topics that are easily understandable by humans, the hybrid category is better. However, if the aim is to cluster posts for further analysis, the frequent pattern category is more suitable. © 2024, CC BY-NC-ND.

关键词： Data mining

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Intelligent Cancer Detection System

Intelligent Cancer Detection System

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2021 International Conference on Intelligent Technologies, CONIT 2021

作者： Das, Utpol Kanti Sikder, Juel Salma, Umme Anwar, A M Shahed Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati Bangladesh Bangladesh University Dept. of Computer Science and Engineering Dhaka Bangladesh Rangamati Science and Technology University Dept. of IT Section Rangamati Bangladesh

ISBN: (纸本)9781728185835

Histopathology and MRI produce different types of images and a single procedure to detect cancerous cells from both of them is considered tedious and time-consuming. Therefore, this study proposes a strategy that can perform segmentation, classification and detection over numerous categories of cancer cells. This experimentation demands the preprocessing to intensify the cells' pictorial characteristic and to segment the intended cancerous cell k-means algorithm applied. The Convolutional Neural Network (CNN) classifier being considered as the best method classifying the test image using the feature extraction method on the filtered cell. Considering the system detects cancer, different morphological operations are applied on the extracted cancer region, and finally, we calculated the percentage of the cancerous area. The MATLAB environment has been used on Histopathology and MRI images of the infected cell from various authentic datasets of various cancers mainly Brain, Breast, Leukemia and Lung cancer. The proposed arrangements of methods give improved accuracy as compared to the other existing methods. © 2021 IEEE.

关键词： Convolutional neural networks

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Centroidal-Polygon: Solving First Order Ordinary Differential Equation using Centroidal mean to improve Euler Method 1

Centroidal-Polygon: Solving First Order Ordinary Differentia...

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1st International Recent Trends in engineering, Advanced Computing and Technology Conference, RETREAT 2020

作者： Nur Shahirah, Z. Nooraida, S. Wan Farah Hanan, W.A. Suzanna, R.A. Iliana, M.A. Section of Instrumentation Technology Faculty of Computer Media and Technology Management Faculty of Engineering Tati University College Teluk Kalong Kemaman Terengganu Malaysia

Euler method is one of the oldest and simplest numerical techniques to solve mathematical problems. However, the Euler method lacks accuracy compared to other methods such as the Runge-Kutta and the Adam Bashforth. This research proposed a new scheme by enhancing the Euler method using centroidal mean, i.e. combining the Euler method and the mean concept. In this research, the proposed scheme would be used to improve the accuracy of the first order Ordinary Differential Equation (ODE). The enhanced Euler method was used based on the improved Euler method, which is a midpoint method. To analyse the accuracy of the proposed scheme and the existing schemes, SCILAB 6.0 software was used to compare the results of different step sizes. This research compared the proposed scheme with the Polygon scheme by Zul Zamri and the Harmonic-Polygon scheme by Nurhafizah. By using Centroidal-Polygon as the proposed scheme, more accurate results were yielded. However, the scheme was limited to some of the linear and non-linear equations only. © Published under licence by IOP Publishing Ltd.

关键词： Ordinary differential equations

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Elevating Ocular Diagnosis: Harnessing the Power of EfficientNet for Eye Disease Classification

Elevating Ocular Diagnosis: Harnessing the Power of Efficien...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Vankadari Mohith Kalpana Raja I. R. Oviya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven USA

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

In response to the evolving landscape of healthcare technology this study addresses the need for automated diagnostic tools in the field of ophthalmology. By utilizing deep learning methods specifically leveraging the EfficientNetV2S framework we achieve an overall accuracy rate of 94.6% in classifying images depicting common eye conditions like cataract, diabetic retinopathy, glaucoma, and normal cases. Our model exhibits capabilities across a diverse spectrum of medical conditions due to meticulous preprocessing and fine-tuning procedures. The results highlight the potential for automated technology integration in healthcare settings, which might improve patient care and treatment plans. This paper highlights the use of advanced neural network designs in the diagnosis of ocular illnesses, offering insights at the nexus of learning and medical image processing.

关键词： Glaucoma Deep learning Diabetic retinopathy Accuracy Computational modeling Neural networks Medical treatment

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Analysis of Convective Transport of Temperature-Dependent Viscosity forNon-Newtonian Erying Powell Fluid: A Numerical Approach

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computers, Materials & Continua 2021年第1期66卷 675-689页

作者： Ahlam Aljabali Abdul Rahman Mohd Kasim Nur Syamilah Arifin Sharena Mohamad Isa Noor Amalina Nisa Ariffin Centre for Mathematical Sciences UMPGambang26300Malaysia Faculty of Computer and Math Sciences UiTM JohorPasir Gudang CampusMasai81750Malaysia Manufacturing Engineering Technology Section UniKLItaly Design InstituteKuala Lumpur56100Malaysia

Non-Newtonian is a type of fluid that does not comply with the viscosity under the Law of Newton and is being widely used in industrial *** include those related to chemical industries,cosmetics manufacturing,pharmaceutical field,food processing,as well as oil and gas *** inability of the conventional equations of Navier–Stokes to accurately depict rheological behavior for certain fluids led to an emergence study for non-Newtonian fluids’*** line with this,a mathematical model of forced convective flow on non-Newtonian Eyring Powell fluid under temperature-dependent viscosity(TDV)circumstance is *** fluid model is embedded with the Newtonian heating(NH)boundary condition as a heating circumstance and is assumed to move over a stretching sheet acting *** appropriate similarity variables,the respective model was converted into ordinary differential equations(ODE),which was later solved utilizing the Keller box *** present model is validated by comparing the existing output in literature at certain special limiting cases,where the validation results display a firm *** current outputs for the proposed model are shown in tabular and graphical form for variation of skin friction plus Nusselt number,velocity and temperature distribution,respectively.

关键词： Temperature-dependent viscosity erying powell fluid vertical stretching sheet numerical solution boundary layer

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