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Automated analysis of fundus images for the diagnosis of retinal diseases: a review

Research on Biomedical engineering 2024年第1期40卷 225-251页

作者： Berto, Alexa Scarpa, Fabio Tsiknakis, Nikos Manikis, Georgios Fotiadis, Dimitrios I. Marias, Kostas Scarpa, Alberto D-Eye s.r.l. Padua Italy Department of Information Engineering University of Padova Padua35131 Italy Heraklion70013 Greece Department of Biomedical Research Institute of Molecular Biology and Biotechnology FORTH Ioannina45115 Greece Department of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina45110 Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion71004 Greece

Purpose: Colour fundus images are widely used in diagnosis treatment decision of several retinal diseases such as diabetic retinopathy (DR), glaucoma and age-related macular degeneration (AMD). These very common conditions must be detected early and monitored to prevent progression and avoid permanent damage. Fundus images revealed to be useful also for the diagnosis of cataracts. In recent years, various automatic diagnostic support methods have been proposed in the literature, with the aim of facilitating widespread screening and obtaining quantitative, objective, and reproducible information. Methods: In this review paper, an overview of traditional, machine learning and modern deep learning techniques for ophthalmic disease diagnosis (i.e. glaucoma, DR, AMD, and cataract) using retinal fundus images is presented. In addition, various publicly available image datasets used for such purposes are described. Results: The current main challenges and findings are identified, as well as common aspects and discrepancies between the various methods developed for the various diseases. Conclusion: The overview of what has been done about all pathologies, rather than only on a specific one, could favour a migration of the best solutions and, hopefully, the development of a more precise and clinically useful automatic analysis of all pathologies. Important critical insights and research trends are also discussed. Graphical abstract: (Figure presented.) © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2023.

关键词： Pathology

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Privacy Concerns from Single-Word Search Query Leakage from Web Browsers Through DNS

IEEE Networking Letters

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IEEE Networking Letters 2022年第1期4卷 48-52页

作者： Isobe, Katsuki Kondo, Daishi Tode, Hideki Computer Science Course College of Engineering Osaka Prefecture University Sakai599-8531 Japan Department of Computer Science and Intelligent Systems Graduate School of Engineering Osaka Prefecture University Sakai599-8531 Japan

Some Web browsers working on certain operating systems (OSs) specify that a single-word search query entered into the search bar be sent to the recursive resolver of the domain name system (DNS) as a DNS request that includes the word. Such behavior is unexpected for users and induces privacy concerns through single-word search query leakage. We present our survey on the conditions during which this behavior occurs. By analyzing the DNS traffic captured at the recursive resolvers, we demonstrate that a considerable number of queries are leaked and the interests of individuals or organizations can be inferred through passive eavesdroppers. © 2019 IEEE.

关键词： Search engines

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Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets

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Neural Computing and Applications 2025年第5期37卷 3287-3301页

作者： Chen, Chen Xu, Enze Yang, Defu Yan, Chenggang Wei, Tao Chen, Hanning Wei, Yong Chen, Minghan Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou China Department of Computer Science Wake Forest University Winston-SalemNC United States Department of Chemical Engineering Howard University WashingtonDC United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Department of Computer Science & Information Systems University of North Georgia DahlonegaGA United States

This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications;however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development. © The Author(s) 2024.

关键词： Graphitic Carbon Nitride

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Quantum Calculus-Based Two-Dimensional Least Mean Square Algorithm 58

Quantum Calculus-Based Two-Dimensional Least Mean Square Alg...

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58th Asilomar Conference on Signals, systems and computers, ACSSC 2024

作者： Moinuddin, Muhammad Zerguine, Azzedine Al-Saggaf, Ubaid M. Arif, Muhammad King Abdulaziz University Electrical and Computer Engineering Department The Centre of Excellence in Intelligent Engineering Systems Jeddah Saudi Arabia Kfupm Electrical Engineering Department The Interdisciplinary Research Center for Communication Systems and Sensing Dhahran31261 Saudi Arabia Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria The Ubiquitous Sensing Systems Lab University of Klagenfurt-Silicon Austria Labs Klagenfurt Austria

ISBN: (纸本)9798350354058

Recent work of the one-dimensional q-LMS algorithm give insight that the Quantum Calculus based gradient called the q-gradient is capable of improving the convergence behavior in contrast to the standard LMS algorithm. The reason for this is the fact that the q-derivative takes larger steps in minimizing a cost function as it evaluates the secant in contrast to the tangent of the cost function. Motivated by this, we propose a two-dimensional version of the q-LMS algorithm. More precisely, we employ Quantum Calculus based q-gradient in steepest descent optimization of mean-square-error (MSE) cost function in 2D adaptive filtering scenario to obtain two-dimensional q-LMS (2D q-LMS) algorithm. A thorough analytical investigation of the proposed 2D q-LMS algorithm in both mean and mean-square sense is provided. Both the transient and steady-state convergence behaviours are examined. Consequently, excess MSE (EMSE) learning curve and its steady-state expression are evaluated in closed form. Simulation results are presented for the application of noise cancelation in images to show the superiority of the proposed 2D q-LMS algorithm. © 2024 IEEE.

关键词： Cost functions

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Counterfactual discriminative micro-expression recognition

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Visual Intelligence 2024年第1期2卷 1-10页

作者： Li, Yong Liu, Menglin Lao, Lingjie Wang, Yuanzhi Cui, Zhen Department of Computer Science City University of Hong Kong 999077 Hong Kong School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China

Micro-expressions are spontaneous, rapid and subtle facial movements that can hardly be suppressed or fabricated. Micro-expression recognition (MER) is one of the most challenging topics in affective computing. It aims to recognize subtle facial movements which are quite difficult for humans to perceive in a fleeting period. Recently, many deep learning-based MER methods have been developed. However, how to effectively capture subtle temporal variations for robust MER still perplexes us. We propose a counterfactual discriminative micro-expression recognition (CoDER) method to effectively learn the slight temporal variations for video-based MER. To explicitly capture the causality from temporal dynamics hidden in the micro-expression (ME) sequence, we propose ME counterfactual reasoning by comparing the effects of the facts w.r.t. original ME sequences and the counterfactuals w.r.t. counterfactually-revised ME sequences, and then perform causality-aware prediction to encourage the model to learn those latent ME temporal cues. Extensive experiments on four widely-used ME databases demonstrate the effectiveness of CoDER, which results in comparable and superior MER performance compared with that of the state-of-the-art methods. The visualization results show that CoDER successfully perceives the meaningful temporal variations in sequential faces. © The Author(s) 2024.

关键词： Affective computing Causal graph Counterfactual reasoning Micro-expression recognition Temporal variation

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Influence of donor age on liver transplantation outcomes: A multivariate analysis and comparative study

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World Journal of Gastrointestinal Surgery 2024年第2期16卷 331-344页

作者： Miran Bezjak Ivan Stresec Branislav Kocman Stipislav Jadrijević Tajana Filipec Kanizaj Miro Antonijević Bojana Dalbelo Bašić Danko Mikulić Department of Surgery University Hospital MerkurZagreb 10000Croatia Department of Electronics MicroelectronicsComputer and Intelligent SystemsFaculty of Electrical Engineering and ComputingZagreb 10000Croatia Department of Gastroenterology University Hospital MerkurZagreb 10000Croatia Development of Software Nanobit Ltd.Zagreb 10000Croatia

BACKGROUND The growing disparity between the rising demand for liver transplantation(LT)and the limited availability of donor organs has prompted a greater reliance on older liver ***,utilizing livers from elderly donors has been associated with outcomes inferior to those achieved with grafts from younger *** accounting for additional risk factors,we hypothesize that the utili-zation of older liver grafts has a relatively minor impact on both patient survival and graft *** To evaluate the impact of donor age on LT outcomes using multivariate analysis and comparing young and elderly donor *** In the period from April 2013 to December 2018,656 adult liver transplants were performed at the University Hospital *** multivariate Cox propor-tional hazards models were developed to independently assess the significance of donor *** age was treated as a continuous *** approach involved univariate and multivariate analysis,including variable selection and assessment of interactions and ***,to exemplify the similarity of using young and old donor liver grafts,the group of 87 recipients of elderly donor liver grafts(≥75 years)was compared to a group of 124 recipients of young liver grafts(≤45 years)from the *** rates of the two groups were estimated using the Kaplan-Meier method and the log-rank test was used to test the differences between *** Using multivariate Cox analysis,we found no statistical significance in the role of donor age within the constructed *** when retained during the entire model development,the donor age's impact on survival remained insignificant and transformations and interactions yielded no substantial effects on *** insigni-ficance and low coefficient values suggest that donor age does not impact patient survival in our ***,there was no statistical evidence that the five developed models did not adhe

关键词： Liver transplantation Elderly donors Survival analysis Postoperative complications Cox proportional hazard models

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OBSO Based Fractional PID for MPPT-Pitch Control of Wind Turbine systems

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computers, Materials & Continua 2022年第5期71卷 4001-4017页

作者： Ibrahim M.Mehedi Ubaid M.Al-Saggaf Mahendiran T.Vellingiri Ahmad H.Milyani Nordin Bin Saad Nor Zaihar Bin Yahaya Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical&Electronics Engineering Universiti Teknologi PETRONASMalaysia

In recent times,wind energy receives maximum attention and has become a significant green energy source *** wind turbine(WT)entered into several domains such as power electronics that are employed to assist the connection process of a wind energy system and *** turbulent characteristics of wind profile along with uncertainty in the design of WT make it highly challenging for prolific power *** pitch control angle is employed to effectively operate the WT at the above nominal wind ***,the pitch controller needs to be intelligent for the extraction of sustainable secure energy and keep WTs in a safe operating *** achieve this,proportional–integral–derivative(PID)controllers are widely used and the choice of optimal parameters in the PID controllers needs to be properly *** this motivation,this paper designs an oppositional brain storm optimization(OBSO)based fractional order PID(FOPID)design for sustainable and secure energy in WT *** proposed model aims to effectually extract the maximum power point(MPPT)in the low range of weather conditions and save the WT in high wind regions by the use of pitch *** OBSO algorithm is derived from the integration of oppositional based learning(OBL)concept with the traditional BSO algorithm in order to improve the convergence rate,which is then applied to effectively choose the parameters involved in the FOPID *** performance of the presented model is validated on the pitch control of a 5 MW WT and the results are examined under different *** simulation outcomes ensured the promising characteristics of the proposed model over the other methods.

关键词： Wind turbine wind energy pitch control brain storm optimization PID controller maximum power point

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Experimental evaluation of MU-MIMO in TDD environment for 5G NR using exploiting channel reciprocity

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4651-4666页

作者： Kumar, Rajesh Sinwar, Deepak Singh, Vijander Department of Computer and Communication Engineering Manipal University Jaipur Rajasthan Jaipur 303007 India Department of IoT and Intelligent Systems Manipal University Jaipur Rajasthan Jaipur 303007 India Department of ICT and Natural Sciences Faculty of Information Technology and Electrical Engineering NTNU - Norwegian University of Science and Technology Ålesund Norway

5G New Radio (NR) operates in two frequency ranges viz. Frequency Range 1 (sub-6 GHz band) and Frequency Range 2 (millimeter wave communication). It utilizes Time Division Duplex (TDD) communication, allowing the same frequency band for uplink and downlink transmission using sounding reference signals. These signals help estimate channel conditions for scheduling downlink traffic. Multi-User Multiple Input Multiple Output (MU-MIMO) technology enables simultaneous data transmission to multiple users using multiple antennas. This study examines MU-MIMO performance in TDD by analyzing variables like radio conditions, modulation schemes, code rates, precoding algorithms, and sub-carrier spacing. Simulation results show that Zero Forcing (ZF) precoding outperforms Block Diagonalization (BD) and Regularized Zero Forcing (RZF) in good radio conditions. However, in bad radio conditions, RZF performs better than ZF. Choosing higher code rates in 16 and 64-QAM and lower code rates in 256-QAM from the MCS table improves throughput. This indicates that MCS and user selection are crucial for higher throughput in TDD-based MU-MIMO for 5G NR. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： 5G new radio Beamforming MU-MIMO Precoding Sounding reference signal Time division duplex

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An Efficient Hybrid Model Based on Modified Whale Optimization Algorithm and Multilayer Perceptron Neural Network for Medical Classification Problems

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Journal of Bionic engineering 2022年第5期19卷 1504-1521页

作者： Saeid Raziani Sajad Ahmadian Seyed Mohammad Jafar Jalali Abdolah Chalechale Department of Computer Engineering Razi UniversityKermanshah ***Iran Faculty of Information Technology Kermanshah University of TechnologyKermanshah ***Iran Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsMelbourne 3217Australia

Feedforward Neural Network(FNN)is one of the most popular neural network models that is utilized to solve a wide range of nonlinear and complex *** models such as stochastic gradient descent have been developed to train ***,they mainly suffer from falling into local optima leading to reduce the accuracy of ***,the convergence speed of training process depends on the initial values of weights and biases in ***,these values are randomly determined by most of the training *** deal with these issues,in this paper,we develop a novel evolutionary algorithm by modifying the original version of Whale Optimization Algorithm(WOA).To this end,a nonlinear function is introduced to improve the exploration and exploitation phases in the search process of ***,the modified WOA is applied to automatically obtain the initial values of weights and biases in FNN leading to reduce the probability of falling into local *** addition,the FNN model trained by the modified WOA is used to develop a classification approach for medical diagnosis *** medical diagnosis datasets are utilized to evaluate the efficiency of the proposed ***,four evaluation metrics including accuracy,AUC,specificity,and sensitivity are used in the experiments to compare the performance of classification *** experimental results demonstrate that the proposed method is better than other competing classification models due to achieving higher values of accuracy,AUC,specificity,and sensitivity metrics for the used datasets.

关键词： Feed forward neural network Meta-heuristic algorithm Whale optimization algorithm Optimization Classification Bionic algorithm

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case 63

Optimal Linear Deception Attacks on Remote State Estimation ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Shang, Jun Zhang, Hanwen Zhou, Jing Chen, Tongwen Tongji University Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai200092 China University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing100083 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

ISBN: (纸本)9798350316339

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack's stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback-Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples. © 2024 IEEE.

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