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A deep learning LSTM-based approach for AMD classification using OCT images

Neural Computing and Applications 2024年第31期36卷 19531-19547页

作者： Hamid, Laila Elnokrashy, Amgad Abdelhay, Ehab H. Abdelsalam, Mohamed M. Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Mansoura Ophthalmic Center Faculty of Medicine Mansoura University Mansoura Egypt Electronics and Communications Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Faculty of Engineering Mansoura National University Mansoura Egypt

Age-related macular degeneration (AMD) is an age-related, persistent, painless eye disease that impairs central vision. The central area (macula) of the retina, located at the back of the eye, sustains damage that is the cause of loss of vision. The early detection of AMD can increase the probability of treatment and prevent vision loss. The AMD can be classified into dry and wet AMD based on the absence of neovascularization. This study introduces a new methodology for the classification of AMD using optical coherence tomography (OCT) retinal images. The proposed methodology is based on three stages. The first stage is the data preparation stage for resizing and normalizing the used images. The second stage is the image processing stage for enhancing the image quality as contrast and resolution these enhancements have been checked by the weighted peak signal-to-noise ratio (WPSNR) methodology. The third stage is the deep feature extraction and classification stage, which consists of two sub-models. The first model is MobileNet V1 which has been used as a deep feature extractor. The second model is LSTM (long short-term memory), fed with deep features to classify the AMD stages. A multi-classification with six separate trials has been employed with the proposed methodology, and compared with other models like DenseNet201 and InceptionV3. The proposed model has been tested on a sample of benchmark data with 4005 grayscale images labeled into three classes. The proposed methodology has achieved an accuracy of 98.85%, a sensitivity of 99.09%, and a specificity of 99.1%. To ensure the effectiveness of the proposed methodology, a comparative analysis has been established with previous approaches in the related field, and the results demonstrated the superiority of the proposed system in AMD multi-classification. © The Author(s) 2024.

关键词： Optical tomography

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Parallel Seeds:From Foundation Models to Foundation Intelligence for Agricultural Sustainability

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 481-484页

作者： Laiyi Fu Shunkang Ling Danyang Wu Mengzhen Kang Fei-Yue Wang Hequan Sun the Shool of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi'an Jiaotong University the College of Information Engineering Northwest A&F University IEEE the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences

THE development of agriculture faces significant challenges due to population growth, climate change, land depletion, and environmental pollution, threatening global food security [1]. This necessitates the development of sustainable agriculture, where a fundamental step is crop breeding to improve agronomic or economic traits, e.g., increasing yields of crops while decreasing resource usage and minimizing pollution to the environment [2].

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Fleet data based traffic modeling

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Communications in Transportation Research 2024年第1期4卷 270-278页

作者： Tamás Tettamanti Levente Tőkés Balázs Varga Department of Control for Transportation and Vehicle Systems Faculty of Transportation Engineering and Vehicle EngineeringBudapest University of Technology and EconomicsMűegyetem rkp.3.BudapestH-1111Hungary

Although the available traffic data from navigation systems have increased steadily in recent years,it only reflects average travel time and possibly Origin-Destination information as samples,***,the number of vehicles participating in the traffic-in other words,the traffic flows being the basic traffic engineering information for strategic planning or even for real-time management-is still missing or only available sporadically due to the limited number of traditional traffic sensors on the network *** tackle this gap,an efficient calibration process is introduced to exploit the Floating Car Data combined with the classical macroscopic traffic assignment *** optimally scaling the Origin-Destination matrices of the sample fleet,an appropriate model can be approximated to provide traffic flow data beside average *** iterative tuning method is developed using a genetic algorithm to realize a complete macroscopic traffic *** method has been tested through two different real-world traffic networks,justifying the viability of the proposed ***,the contribution of the study is a practical solution based on commonly available fleet traffic data,suggested for practitioners in traffic planning and management.

关键词： Floating car data Origin-destination matrix Macroscopic traffic modeling Genetic algorithm Calibration

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ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

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Neural Computing and Applications 2024年第24期36卷 15149-15164页

作者： Helaly, Hadeer A. Badawy, Mahmoud El-Gendy, Eman M. Haikal, Amira Y. Electrical Engineering Department Faculty of Engineering Damietta University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Computer Science and Informatics Taibah University Medina Saudi Arabia

The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows spe

关键词： Image classification

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A NOTE ON THE QUALITATIVE BEHAVIOUR OF AN ODE MODEL RELATED TO ENDOTOXIN TOLERANCE

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第4期86卷 127-130页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

关键词： Differential equations

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Optimal MBU Positioning under Fault Scenario for Distribution Networks with high PV Penetration 14

Optimal MBU Positioning under Fault Scenario for Distributio...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Caruana, Cedric Bhattacharya, Somesh Raute, Reiko Micallef, Alexander Scerri, Kenneth Department of Electrical Engineering Faculty of Engineering University of Malta Msida Malta Department of Systems and Control Faculty of Engineering University of Malta Malta

ISBN: (纸本)9781837242689

Faults on distribution networks due to abnormal weather events can lead to disruption and can cause high socio-economic losses. In line with the rising frequency of such events, the paper proposes an algorithm for the optimal deployment of mobile battery units to restore sections of the network. The considered battery units are rated at 1MVA to keep them realistic for the mobile application. The placement algorithm is formulated as a mixed integer mathematical optimisation problem. Sectionalising/tie switches are assumed on each branch of the network in order to increase the restoration configuration flexibility. Photovoltaic systems, distributed along the network, are allowed to contribute to the restored network sections. A number of critical loads are defined and these are prioritised in the restoration process. The objective function also considers the reduction of the overall renewable energy curtailment. The performance of the algorithm is tested on the IEEE 33 bus network with four tie lines. Scenarios with increasing levels of PV power are considered and the effect of incorporating the PV power is studied. © The Institution of engineering & Technology 2024.

关键词： Mixed-integer linear programming

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AutYOLO-ATT: an attention-based YOLOv8 algorithm for early autism diagnosis through facial expression recognition

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Neural Computing and Applications 2024年第27期36卷 17199-17219页

作者： Hosney, Reham Talaat, Fatma M. El-Gendy, Eman M. Saafan, Mahmoud M. Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Nile Higher Institute for Engineering and Technology Mansoura Egypt

Autism Spectrum Disorder (ASD) is a developmental condition resulting from abnormalities in brain structure and function, which can manifest as communication and social interaction difficulties. Conventional methods for diagnosing ASD may not be effective in the early stages of the disorder. Hence, early diagnosis is crucial to improving the patient's overall health and well-being. One alternative and effective method for early autism diagnosis is facial expression recognition since autistic children typically exhibit distinct facial expressions that can aid in distinguishing them from other children. This paper provides a deep convolutional neural network (DCNN)-based real-time emotion recognition system for autistic kids. The proposed system is designed to identify six facial emotions, including surprise, delight, sadness, fear, joy, and natural, and to assist medical professionals and families in recognizing facial expressions in autistic children for early diagnosis and intervention. In this study, an attention-based YOLOv8 (AutYOLO-ATT) algorithm for facial expression recognition is proposed, which enhances the YOLOv8 model's performance by integrating an attention mechanism. The proposed method (AutYOLO-ATT) outperforms all other classifiers in all metrics, achieving a precision of 93.97%, recall of 97.5%, F1-score of 92.99%, and accuracy of 97.2%. These results highlight the potential of the proposed method for real-world applications, particularly in fields where high accuracy is essential. © The Author(s) 2024.

关键词： Diagnosis

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Augmented Reality Used in Technical Diagnostics and Maintenance 15

Augmented Reality Used in Technical Diagnostics and Maintena...

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15th International Conference Elektro, ELEKTRO 2024

作者： Janota, Ales Holecko, Peter Faculty of Electrical Engineering and IT UNIZA Dept. of Control and Information Systems Žilina Slovakia

ISBN: (纸本)9798350372359

Integrating digital data and human sensory input in real-world settings offers new possibilities in device diagnostics and maintenance. In this study, we utilized the EcoStruxure Augmented Operator Advisor to develop an augmented reality application for a family house heating system. Our application was designed without access to the real heating system;instead, we simulated its operation with a programmable logic controller. Our final application includes three primary scenes: the heat pump, its indoor unit, and the engine room. Drawing on our practical experience, we discuss the benefits of our approach and the software tools used and summarize our subjective opinions. © 2024 IEEE.

关键词： Maintenance

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Energy and Exergy Analysis of Pyramid-Type Solar Still Coupled with Magnetic and Electrical Effects by Using Matlab Simulation

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Frontiers in Heat and Mass Transfer 2024年第1期22卷 217-262页

作者： Karrar A.Hammoodi Hayder A.Dhahad Wissam H.Alawee Z.M.Omara Air Conditioning Engineering Department Faculty of EngineeringWarith Al-Anbiyaa UniversityBaghdadIraq Mechanical Engineering Department University of TechnologyBaghdadIraq Control and Systems Engineering Department University of TechnologyBaghdadIraq Mechanical Engineering Department Faculty of EngineeringKafrelsheikh UniversityKafrelsheikhEgypt

In the face of an escalating global water crisis,countries worldwide grapple with the crippling effects of scarcity,jeopardizing economic progress and hindering societal *** energy emerges as a beacon of hope,offering a sustainable and environmentally friendly solution to *** distillation technology,harnessing the power of the sun,transforms seawater into freshwater,expanding the availability of this precious *** solar still performance under specific climatic conditions and evaluating different configurations is crucial for practical implementation and widespread adoption of solar *** this study,we conducted theoretical investigations on three distinct solar still configurations to evaluate their performance under Baghdad’s climatic *** solar stills analyzed include the passive solar still,themodified solar still coupled with a magnetic field,and themodified solar still coupled with bothmagnetic and electrical *** results proved that the evaporation heat transfer coefficient peaked at 14:00,reaching 25.05 W/m^(2).℃for the convention pyramid solar still(CPSS),32.33 W/m^(2).℃for the magnetic pyramid solar still(MPSS),and 40.98 W/m^(2).℃for elecro-magnetic pyramid solar still(EMPSS),highlighting their efficiency in converting solar energy to ***,exergy efficiency remained notably lower,at 1.6%,5.31%,and 7.93%for the three still types,even as energy efficiency reached its maximum of 18.6%at 14:00 with a corresponding peak evaporative heat of 162.4 W/m^(2).

关键词： Pyramid solar still solar desalination energy and exergy analysis innovative solar still technique theoretical study Matlab

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TOWARDS A MULTI-USER EXPERIENCE DATA-DRIVEN DESIGN FRAMEWORK FOR SUSTAINABLE SMART TAKE-AWAY FOOD PACKAGING

TOWARDS A MULTI-USER EXPERIENCE DATA-DRIVEN DESIGN FRAMEWORK...

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ASME 2024 International Design engineering Technical Conferences and Computers and Information in engineering Conference, IDETC-CIE 2024

作者： Camilleri, Tamasine Farrugia, Philip Refalo, Paul Bugeja, Marvin Department of Industrial & Manufacturing Engineering Faculty of Engineering University of Malta Msida Malta Department of Systems & Control Engineering Faculty of Engineering University of Malta Msida Malta

ISBN: (纸本)9780791888353

Smart packaging transcends traditional packaging’s basic functions by meeting emerging consumer expectations for quality and sustainability. COVID-19 has notably increased take-away orders, spotlighting the need for sustainable food packaging. By incorporating smart features, packaging usability is enhanced, and the likelihood of disposal due to obsolescence is reduced. Moreover, smart packaging acceptance relies on consumer values. To address these evolving needs, designers are encouraged to embrace a user-centered design (UCD) approach to develop sustainable smart take-away packaging. However, a review of literature reveals a gap in design support systems that could assist designers in this endeavor. Interviews with packaging designers outlined framework requirements, leading to the creation of SUSTAIN-MULTI-UCD. This framework advocates for a multi-UCD approach tailored for sustainable smart take-away packaging development. The innovative use of big data analytics in this context enables gathering diverse user data to help enhance user experience. The SUSTAIN-MULTI-UCD contribution lies in applying big data analytics to the data-driven UCD of sustainable smart take-away packaging, offering insights that could be beneficial across various food packaging applications. Furthermore, innovations in the framework’s data-driven approaches, such as the application of topic modelling and text classification, identifies user requirements, and furthermore guides the designer on how to meet these needs. Copyright © 2024 by ASME.

关键词： UX Data-driven design Design Food products Packaging design support framework Sustainability smart food packaging sustainable design big data analysis

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