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Olive Leaf Disease Detection via Wavelet Transform and Feature Fusion of Pre-Trained Deep Learning Models

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Computers, Materials & Continua 2024年第3期78卷 3431-3448页

作者： Mahmood A.Mahmood Khalaf Alsalem Department of information systems College of Computer and Information SciencesJouf UniversitySakakahKingdom of Saudi Arabia Department of information systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityGizaEgypt

Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic *** detection of these diseases is essential for effective *** propose a novel transformed wavelet,feature-fused,pre-trained deep learning model for detecting olive leaf *** proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf *** model has four main phases:preprocessing using data augmentation,three-level wavelet transformation,learning using pre-trained deep learning models,and a fused deep learning *** the preprocessing phase,the image dataset is augmented using techniques such as resizing,rescaling,flipping,rotation,zooming,and *** wavelet transformation,the augmented images are decomposed into three frequency *** pre-trained deep learning models,EfficientNet-B7,DenseNet-201,and ResNet-152-V2,are used in the learning *** models were trained using the approximate images of the third-level sub-band of the wavelet *** the fused phase,the fused model consists of a merge layer,three dense layers,and two dropout *** proposed model was evaluated using a dataset of images of healthy and infected olive *** achieved an accuracy of 99.72%in the diagnosis of olive leaf diseases,which exceeds the accuracy of other methods reported in the *** finding suggests that our proposed method is a promising tool for the early detection of olive leaf diseases.

关键词： Olive leaf diseases wavelet transform deep learning feature fusion

research on a multi‑dimensional image information fusion algorithm based on NSCT transform

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Frontiers of Optoelectronics 2024年第1期17卷 29-44页

作者： Yuxiang Su Xi Liang Danhua Cao Zhenyu Yang Yuanlong Peng Ming Zhao School of Optical and Electronic information Huazhong University of Science and TechnologyWuhan 430074China State Grid information and Telecommunication Branch Beijing 100761China

Traditional inspection cameras determine targets and detect defects by capturing images of their light intensity,but in complex environments,the accuracy of inspection may *** based on polarization of light can characterize various features of a material,such as the roughness,texture,and refractive index,thus improving classification and recognition of *** paper uses a method based on noise template threshold matching to denoise and preprocess polarized *** also reports on design of an image fusion algorithm,based on NSCT transform,to fuse light intensity images and polarized *** results show that the fused image improves both subjective and objective evaluation indicators,relative to the source image,and can better preserve edge information and help to improve the accuracy of target *** study provides a reference for the comprehensive application of multi-dimensional optical information in power inspection.

关键词： Power inspection Object detection Polarization imaging Image fusion Image denoising

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A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid

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Building Simulation 2024年第3期17卷 441-457页

作者： Mohammad Kaosain Akbar Manar Amayri Nizar Bouguila Concordia Institute for information systems Engineering Concordia UniversityMontrealCanada

Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial *** plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level *** enables informed decision-making,energy optimization,and cost ***,NILM encounters substantial challenges like signal noise,data availability,and data privacy concerns,necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world *** learning techniques have recently shown some promising results in NILM research,but training these neural networks requires significant labeled *** initial sets of labeled data for the research by installing smart meters at the end of consumers’appliances is laborious and expensive and exposes users to severe privacy *** is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states(On/Off)from their respective energy consumption *** paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network(TCN)and long short-term memory(LSTM)for classifying appliance operation states from labeled and unlabeled *** two thresholding techniques,namely Middle-Point Thresholding and Variance-Sensitive Thresholding,which are needed to derive the threshold values for determining appliance operation states,are also compared *** superiority of the proposed model,along with finding the appliance states through the Middle-Point Thresholding method,is demonstrated through 15%improved overall improved F1micro score and almost 26%improved Hamming loss,F1 and Specificity score for the p

关键词： semi-supervised learning non-intrusive load monitoring middle-point thresholding deep learning TCN LSTM

Symmetric silicon microring resonator optical crossbar array for accelerated inference and training in deep learning

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Photonics research 2024年第08期12卷 1681-1688页

作者： RUI TANG SHUHEI OHNO KEN TANIZAWA KAZUHIRO IKEDA MAKOTO OKANO KASIDIT TOPRASERTPONG SHINICHI TAKAGI MITSURU TAKENAKA Department of Electrical Engineering and information systems The University of Tokyo Quantum ICT research Institute Tamagawa University National Institute of Advanced Industrial Science and Technology

Photonic integrated circuits are emerging as a promising platform for accelerating matrix multiplications in deep learning, leveraging the inherent parallel nature of light. Although various schemes have been proposed and demonstrated to realize such photonic matrix accelerators, the in situ training of artificial neural networks using photonic accelerators remains challenging due to the difficulty of direct on-chip backpropagation on a photonic chip. In this work, we propose a silicon microring resonator(MRR) optical crossbar array with a symmetric structure that allows for simple on-chip backpropagation, potentially enabling the acceleration of both the inference and training phases of deep learning. We demonstrate a 4 × 4 circuit on a Si-on-insulator platform and use it to perform inference tasks of a simple neural network for classifying iris flowers, achieving a classification accuracy of 93.3%. Subsequently, we train the neural network using simulated on-chip backpropagation and achieve an accuracy of 91.1% in the same inference task after training. Furthermore, we simulate a convolutional neural network for handwritten digit recognition, using a 9 × 9 MRR crossbar array to perform the convolution operations. This work contributes to the realization of compact and energy-efficient photonic accelerators for deep learning.

关键词： Deep learning Neural networks Optical signals Photonic integrated circuits Silicon Stochastic gradient descent

Enhancing Ulcerative Colitis Diagnosis:A Multi-Level Classification Approach with Deep Learning

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 1129-1142页

作者： Hasan J.Alyamani Department of information systems Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia

The evaluation of disease severity through endoscopy is pivotal in managing patients with ulcerative colitis,a condition with significant clinical ***,endoscopic assessment is susceptible to inherent variations,both within and between observers,compromising the reliability of individual *** study addresses this challenge by harnessing deep learning to develop a robust model capable of discerning discrete levels of endoscopic disease *** initiate this endeavor,a multi-faceted approach is embarked *** dataset is meticulously preprocessed,enhancing the quality and discriminative features of the images through contrast limited adaptive histogram equalization(CLAHE).A diverse array of data augmentation techniques,encompassing various geometric transformations,is leveraged to fortify the dataset’s diversity and facilitate effective feature extraction.A fundamental aspect of the approach involves the strategic incorporation of transfer learning principles,harnessing a modified ResNet-50 *** augmentation,informed by domain expertise,contributed significantly to enhancing the model’s classification *** outcome of this research endeavor yielded a highly promising model,demonstrating an accuracy rate of 86.85%,coupled with a recall rate of 82.11%and a precision rate of 89.23%.

关键词： Ulcerative colitis deep learning CLAHE transfer learning geometric transformations

A framework of force of information influence and application for C4KISR system

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Journal of systems Engineering and Electronics 2024年第2期35卷 430-443页

作者： MAO Shaojie DIAO Lianwang SUN Yu WANG Heng YI Kan XU Xin MAO Xiaobin ZHANG Kecheng SHENG Long Science and Technology on information systems Engineering Laboratory The 28th Research Institute of China Electronics Technology Group CorporationNanjing 210007China Unit 94860 of the PLA Nanjing 210008China

The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and effect of information flow through command, control, communications, computer, kill, intelligence,surveillance, reconnaissance (C4KISR) system. In this work, we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing, intelligence processing,decision making and fire attack. Specifically, the basic concept of force of information influence between nodes in C4KISR system is formally proposed and its mathematical definition is provided. Then, based on the information entropy theory, the model of force of information influence between C4KISR system nodes is constructed. Finally, the simulation experiments have been performed under an air defense and attack scenario. The experimental results show that, with the proposed force of information influence framework, we can effectively evaluate the contribution of information circulation through different C4KISR system nodes to the corresponding tasks. Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architecture.

关键词： information warfare command control communications computer kill intelligence surveillance reconnaissance(C4KISR)system information circulation force of information influence information entropy

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Designing a Secure and Scalable Data Sharing Mechanism Using Decentralized Identifiers(DID)

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 809-822页

作者： Iuon-Chang Lin I-Ling Yeh Ching-Chun Chang Jui-Chuan Liu Chin-Chen Chang Department of Management information systems National Chung Hsing UniversityTaichung402Taiwan information and Communication Security research Center Feng Chia UniversityTaichung407Taiwan Department of information Engineering and Computer Science Feng Chia UniversityTaichung407Taiwan

Centralized storage and identity identification methods pose many risks,including hacker attacks,data misuse,and single points of ***,existing centralized identity management methods face interoperability issues and rely on a single identity provider,leaving users without control over their ***,this paper proposes a mechanism for identity identification and data sharing based on decentralized *** scheme utilizes blockchain technology to store the identifiers and data hashed on the chain to ensure permanent identity recognition and data *** is stored on InterPlanetary File System(IPFS)to avoid the risk of single points of failure and to enhance data persistence and *** the same time,compliance with World Wide Web Consortium(W3C)standards for decentralized identifiers and verifiable credentials increases the mechanism’s scalability and interoperability.

关键词： Self-sovereign identity decentralized identifier blockchain

Social Media-Based Surveillance systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1167-1202页

作者： Samina Amin Muhammad Ali Zeb Hani Alshahrani Mohammed Hamdi Mohammad Alsulami Asadullah Shaikh Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of information System College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their ***,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by *** paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic *** has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation *** recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM *** paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM ***,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

关键词： Social media epidemic machine learning deep learning health informatics pandemic

Towards an ultrafast 3D imaging scanning LiDAR system: a review

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Photonics research 2024年第8期12卷 1709-1729页

作者： ZHI LI YAQI HAN LICAN WU ZIHAN ZANG MAOLIN DAI SZE YUN SET SHINJI YAMASHITA QIAN LI H.Y.FU Tsinghua Shenzhen International Graduate School Tsinghua University Department of Electrical Engineering and information systems The University of Tokyo research Center for Advanced Science and Technology The University of Tokyo School of Electronic and Computer Engineering Peking University

Light detection and ranging (LiDAR), as a hot imaging technology in both industry and academia, has undergone rapid innovation and evolution. The current mainstream direction is towards system miniaturization and integration. There are many metrics that can be used to evaluate the performance of a LiDAR system, such as lateral resolution, ranging accuracy, stability, size, and price. Until recently, with the continuous enrichment of LiDAR application scenarios, the pursuit of imaging speed has attracted tremendous research interest. Particularly, for autonomous vehicles running on motorways or industrial automation applications, the imaging speed of LiDAR systems is a critical bottleneck. In this review, we will focus on discussing the upper speed limit of the LiDAR system. Based on the working mechanism, the limitation of optical parts on the maximum imaging speed is analyzed. The beam scanner has the greatest impact on imaging speed. We provide the working principle of current popular beam scanners used in LiDAR systems and summarize the main constraints on the scanning speed. Especially, we highlight the spectral scanning LiDAR as a new paradigm of ultrafast ***, to further improve the imaging speed, we then review the parallel detection methods, which include multiple-detector schemes and multiplexing technologies. Furthermore, we summarize the LiDAR systems with the fastest point acquisition rate reported nowadays. In the outlook, we address the current technical challenges for ultrafast LiDAR systems from different aspects and give a brief analysis of the feasibility of different approaches.

关键词： Frequency combs High speed imaging Optical access networks Remote sensing Single pixel imaging Spatial light modulators