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Artificial intelligence for assessment of Stargardt macular atrophy

Neural Regeneration Research 2022年第12期17卷 2632-2636页

作者： Ziyuan Wang Zhihong Jewel Hu Doheny Image Analysis Laboratory Doheny Eye InstituteLos AngelesCAUSA Department of Electrical Engineering University of CaliforniaLos AngelesCAUSA

Stargardt disease(also known as juvenile macular degeneration or Stargardt macular degeneration)is an inherited disorder of the retina,which can occur in the eyes of children and young *** is the most prevalent form of juvenile-onset macular dystrophy,causing progressive(and often severe)vision *** with Stargardt disease are characterized by the appearance of flecks in early and intermediate stages,and the appearance of atrophy,due to cells wasting away and dying,in the advanced *** primary measure of late-stage Stargardt disease is the appearance of *** autofluorescence is a widely available two-dimensional imaging technique,which can aid in the diagnosis of the ***-domain optical coherence tomography,in contrast,provides three-dimensional visualization of the retinal microstructure,thereby allowing the status of the individual retinal *** disease may cause various levels of disruption to the photoreceptor segments as well as other outer retinal *** recent years,there has been an exponential growth in the number of applications utilizing artificial intelligence for help with processing such diseases,heavily fueled by the amazing successes in image recognition using deep *** review regarding artificial intelligence deep learning approaches for the Stargardt atrophy screening and segmentation on fundus autofluorescence images is first provided,followed by a review of the automated retinal layer segmentation with atrophic-appearing lesions and fleck features using artificial intelligence deep learning *** paper concludes with a perspective about using artificial intelligence to potentially find early risk factors or biomarkers that can aid in the prediction of Stargardt disease progression.

关键词： artificial intelligence assessment deep learning fundus autofluorescence screening segmentation spectral-domain optical coherence tomography Stargardt atrophy Stargardt disease Stargardt flecks

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An Efficient Motor imagery BCI Classification Architecture Using Non-linear Network Tuning suitable for Cloud-Edge Environments 11

An Efficient Motor Imagery BCI Classification Architecture U...

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11th International Symposium on Telecommunication, IST 2024

作者： Danayi, Abolfazl Soltanian-Zadeh, Hamid University of Tehran Electrical Engineering Department Tehran Iran School of Cognitive Sciences Tehran Iran Henry Ford Health System Image Analysis Laboratory Department of Radiology DetroitMI United States

ISBN: (纸本)9798350356250

This paper proposes a novel deep learning architecture for EEG-based Brain-Computer Interface (BCI) systems, optimized for real-world deployment. BCI is fundamentally a communication problem, yet many works focus on lab-based implementations. Our approach addresses practical implementation considerations crucial for use beyond controlled environments. By incorporating a non-linear Network Tuning Block (NTB) and applying a two-phase training process, our model enhances classification accuracy while minimizing computational demands. Tested on the BCI Competition IV-2b dataset, our architecture achieves a 93.2% accuracy, surpassing state-of-the-art methods with fewer parameters, underscoring its potential for practical, real-world applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Spatial Quality Assessment of Pansharpened images Based on Gray Level Co-Occurrence Matrix 12

Spatial Quality Assessment of Pansharpened Images Based on G...

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12th Iranian/2nd International Conference on Machine Vision and image Processing, MVIP 2022

作者： Aghapour Maleki, Shiva Ghassemian, Hassan Tarbiat Modares University Image Processing and Information Analysis Laboratory Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9781665412162

Assessing the quality of pansharpened images is a critical issue in order to obtain a quantitative score to represent the quality and compare the performance of different fusion methods. Most of the introduced metrics for pansharpened image quality assessment, evaluate the spectral content of the image, while in different applications of remote sensing like detection and identification of image objects, spatial quality has an important role. In the current study, a new index for spatial quality assessment is introduced that extracts gray level co-occurrence matrix (GLCM) from distorted and reference images and compares the similarities of these features. The tempere image database 2013 (TID2013) that provides reference and different types of distorted images with subjective scores of each image is used as the desired database. To solve the high computational complexity of obtaining GLCM features, the fast GLCM method is employed. In this way, 16 different features are extracted. To select the features that have the most consistency with the human visual system (HVS), the forward floating search method is used as a feature selection method and five features are obtained as the final features to form the desired index. Experimental results show the efficiency of the proposed method in determining the spatial quality of fused images compared with that of the available quality assessment metrics. © 2022 IEEE.

关键词： Remote sensing

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Supervised machine learning-based salp swarm algorithm for fault diagnosis of photovoltaic systems

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Journal of engineering and Applied Science 2024年第1期71卷 12页

作者： Hichri, Amal Hajji, Mansour Mansouri, Majdi Nounou, Hazem Bouzrara, Kais Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kairouan Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir Monastir5019 Tunisia

The diagnosis of faults in grid-connected photovoltaic (GCPV) systems is a challenging task due to their complex nature and the high similarity between faults. To address this issue, we propose a wrapper approach called the salp swarm algorithm (SSA) for feature selection. The main objective of SSA is to extract only the most important features from the raw data and eliminate unnecessary ones to improve the classification accuracy of supervised machine learning (SML) classifiers. Subsequently, the selected features are used to train supervised machine learning (SML) techniques in distinguishing between various operating modes. To evaluate the efficiency of the technique, we used healthy and faulty data from GCPV systems that have been injected with frequent faults, 20 different types of faults were introduced, including line-to-line, line-to-ground, connectivity faults, and those affecting the operation of bay-pass diodes. These faults present diverse conditions, such as simple and multiple faults in the PV arrays and mixed faults in both arrays. The performances of the developed SSA-SML are compared with those using principal component analysis (PCA) and kernel PCA (KPCA) based SML techniques through different criteria (i.e., accuracy, recall, precision, F1 score, and computation time). The experimental findings demonstrated that the proposed diagnosis paradigm outperformed the other techniques and achieved a high diagnostic accuracy (an average accuracy greater than 99%) while significantly reducing computation time. © 2023, The Author(s).

关键词： Failure analysis

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Effective uncertain fault diagnosis technique for wind conversion systems using improved ensemble learning algorithm

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Energy Reports 2023年 10卷 3113-3124页

作者： Attouri, Khadija Dhibi, Khaled Mansouri, Majdi Hajji, Mansour Bouzrara, Kais Nounou, Mohamed Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine1200 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Qatar Chemical Engineering Program Texas A&M University at Qatar Qatar Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir 5019 Tunisia

This paper introduces a pioneering fault diagnosis technique termed Interval Ensemble Learning based on Sine Cosine Optimization Algorithm (IEL- SCOA), tailored to tackle uncertainties prevalent in wind energy conversion (WEC) systems. The approach unfolds in three integral phases. Firstly, the establishment of interval centers and ranges, employing upper and lower bounds, effectively manages the inherent uncertainties arising from noise and measurement errors intrinsic to the wind system. Subsequently, the dataset undergoes processing via the Sine-Cosine Optimization Algorithm (SCOA), enabling the extraction of the most pertinent attributes. The culmination of predictive precision and classification performance is achieved through the integration of the refined dataset into an ensemble learning paradigm, harmonizing bagging, boosting techniques, and an artificial neural network classifier. The principal aim of the IEL-SCOA approach is to discern the spectrum of operational conditions within WEC systems, encompassing a healthy mode alongside six distinct faulty modes. These anomalies, encompassing short circuits, open circuits, and wear-out incidents, are deliberately induced at diverse locations and facets of the system, notably the generator and grid sides. Empirical results underscore the robustness and efficiency of the proposed methodology, showcasing an exceptional accuracy rate of 99.76 %. These outcomes definitively establish the IEL-SCOA approach as a potent and efficacious tool for precise fault diagnosis in uncertain WEC systems. © 2023

关键词： Fault detection

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Enhanced fault diagnosis of wind energy conversion systems using ensemble learning based on sine cosine algorithm

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Journal of engineering and Applied Science 2023年第1期70卷 56页

作者： Attouri, Khadija Dhibi, Khaled Mansouri, Majdi Hajji, Mansour Bouzrara, Kais Nounou, Hazem Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine1200 Tunisia Chemical Engineering Program Texas A&M University at Qatar 23874 Doha Qatar Electrical and Computer Engineering Program Texas A&M University at Qatar 23874 Doha Qatar Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir Monastir5019 Tunisia

This paper investigates the problem of incipient fault detection and diagnosis (FDD) in wind energy conversion systems (WECS) using an innovative and effective approach called the ensemble learning-sine cosine optimization algorithm (EL-SCOA). The evolved strategy involves two primary steps: first, a sine-cosine algorithm is used to extract and optimize features in order to only select the most descriptive ones. Second, to further improve the capability, thereby providing the highest accuracy performance, the newly gathered dataset is introduced as input to an ensemble learning paradigm, which merges the benefits of boosting and bagging techniques with an artificial neural network classifier. The essential goal of the developed proposal is to discriminate between the diverse operating conditions (one healthy and six faulty conditions). Three potential and frequent types of faults that can affect the system behaviors including short-circuit, open-circuit, and wear-out are considered and thereby injected at diverse locations and sides (grid and generator sides) in order to evaluate the availability and performance of the proposed technique when compared to the conventional FDD methods. The diagnosis performance is analyzed in terms of accuracy, recall, precision, and computation time. The acquired outcomes demonstrate the efficiency of the suggested diagnostic paradigm compared to conventional FDD techniques (accuracy rate has been successfully achieved 98.35%). © 2023, This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply.

关键词： Failure analysis

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Pyramidal deep neural network for classification of retinal OCT images

Pyramidal deep neural network for classification of retinal ...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Mohammad Almasganj Emad Fatemizadeh Department of Electrical Engineering Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology Tehran Iran

Retinal optical coherence tomography (OCT) images are widely used to diagnose and grade macular diseases, such as age-related macular degeneration (AMD). However, manual interpretation of OCT images is time-consuming and subjective. Therefore, automated and accurate classification of OCT images is essential for assisting ophthalmologists in clinical decision-making. This paper proposes a pyramidal deep neural network that can diagnose normal and two types of AMD (dry and wet) in OCT images. Our network leverages features from different scales of a pre-trained convolutional neural network (CNN) and integrates them with two advanced versions of feature pyramid networks: bidirectional feature pyramid network (BiFPN) and path aggregation network (PANet). We evaluate our network on the NEH dataset and compare it with its predecessor. Our results show that our BiFPN-VGG16 and PAN-VGG16 models achieve accuracies of 94.S% and 95.0%, respectively, which are 2.8 to 3% higher than the previous models. Our approach demonstrates the potential of multi-scale feature networks for OCT image classification and can serve as an auxiliary diagnostic tool for ophthalmologists.

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Dermoscopic image Classification Using Attention Mechanism and Ensemble Learning Approaches

Dermoscopic Image Classification Using Attention Mechanism a...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Huang, Shanchuan Lei, Hongwei Jin, Liuhan Yang, Jinzhu Jiang, Tao Yao, Yudong Grzegorzek, Marcin Li, Chen Northeastern University Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang China Chengdu University of Traditional Chinese Medicine School of Intelligent Medicine Chengdu China Stevens Institute of Technology Department of Electrical and Computer Engineering United States Institute of Medical Informatics University of Luebeck Luebeck Germany

ISBN: (纸本)9798350324457

Background and purpose: Skin tumours have become one of the most common diseases worldwide. While benign ones are not usually a threat to human health, malignant ones can develop into skin cancer and become life-threatening if left untreated. Early detection of the disease is important for the treatment of patients with skin tumours and dermoscopy is the most effective means of diagnosing skin tumours. However, the complexity of skin tumour cells makes the diagnosis somewhat erroneous for doctors. Therefore, a dermoscopic classification network based on deep learning and computer-aided diagnostic techniques is needed to obtain a high diagnostic accuracy rate for skin tumours. Methods: In this paper, Deep-skin, a model for dermoscopic image classification is proposed, which is based on both attention mechanism and ensemble learning. Considering the characteristics of dermoscopic images, embedding different attention mechanisms on top of Inception-V3 has been suggested to obtain more potential features. We then improve the classification performance by late fusion of the different models. To demonstrate the effectiveness of Deep-skin, experiments and evaluations are performed on the publicly available dataset Skin Cancer: Malignant vs. Benign and compare the performance of Deep-skin with other classification models. Results: The experimental results indicate that Deep-skin performs well on the dataset in comparison to other models, achieving a maximum accuracy of 87.8%.Conclusion: In this paper, the Deep-skin model is proposed for the classification of dermoscopic images and has shown better performance. In the future, we intend to investigate better classification models for automatic diagnosis of skin tumours. Such models can potentially assist physicians and patients in clinical settings. © 2023 IEEE.

关键词： Attention mechanism Ensemble learning image classification Late fusion Skin Cancer

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Energy and Spectral Efficiency for RIS-Aided Transmission

Energy and Spectral Efficiency for RIS-Aided Transmission

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electrical engineering and Automatic Control (ICEEAC), International Conference on

作者： El-Hadi Meftah Slimane Benmahmoud Department of Electronics LISIC Laboratory Faculty of Electrical Engineering USTHB Algiers Algeria Department of Electronics Engineering Signal and systems analysis' laboratory (LASS) M'sila University M'sila Algeria

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

Reconfigurable intelligent surface (RIS) appears as an interesting and promising technique for future wireless communication. Given its ability to adapt channel conditions through energy concentration, and interference cancellation through the use of a considerable amount of quasi-passive reflecting elements. In this paper, we discuss the potential applications of RIS in wireless networks operating within the energy efficiency (EE) and spectral efficiency (SE) framework. In this context, we develop energy-efficient solutions; both in terms of transmitting power allocation and number of reflective elements, while ensuring achievable spectral efficiency for the mobile station. The simulation results reveal that the number of RIS reflecting elements plays a crucial role in the overall performance. In addition, comparisons with relay-aided system are also performed to demonstrate the effectiveness of the proposed system in terms of EE and SE.

关键词： electrical engineering Power demand Interference cancellation Spectral efficiency Wireless networks Simulation Reconfigurable intelligent surfaces

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Acoustic User Authentication with Smartphones

Acoustic User Authentication with Smartphones

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IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems, COMCAS

作者： Neta Gevirtzer Ofir Ben Yosef Alon Eilam Signal and Image Processing Laboratory (SIPL) Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion - Israel Institute of Technology Technion City Haifa Israel

ISBN: (数字)9798350348187

ISBN: (纸本)9798350348194

This paper describes a work on an acoustic user authentication system using smartphones. The system implements two-factor authentication for Windows workstations, where the authentication procedure, including locking and unlocking the workstation is transparent to the user. Since workstations and smartphones have built-in microphones and speakers, the system does not require additional hardware. The uniqueness of the solution is being based on acoustic signals. These signals are transmitted by the user's smartphone and received by the workstation microphone. The system is “pure play acoustic” since no wiring or radio transmission is used. The system configuration supports multiple users in the same area. Eavesdropping prevention is provided by sequentially generated random one-time keys. Acoustic communication can be applied either in the audible range or beyond the human hearing range depending on the sampling rate of the smartphone and the workstation.

关键词： Microwave antennas Wiring Prevention and mitigation Authentication Auditory system Microwave communication Acoustics

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