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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Galih, Brilliant Syifaa Nang Novamizanti, Ledya Akhyar, Fityanul School of Electrical Engineering Telkom University Bandung Indonesia School of Electrical Engineering Telkom University Center of Excellence Artificial Intelligence for Learning and Optimization Bandung Indonesia Universiti Tunku Abdul Rahman Department of Computer and Communication Technology Perak Kampar Malaysia

ISBN: (纸本)9798331506490

Lung disease is a widespread health concern affecting millions of people worldwide. Accurate diagnosis is essential for effective treatment, yet it often requires expert interpretation of chest X-rays to identify specific disease types. Unfortunately, many healthcare facilities, particularly in remote or resource-limited areas, lack access to skilled radiologists, leading to delayed diagnoses and potentially poor patient outcomes. To address this gap, this research introduces an automated lung disease classification system that leverages knowledge distillation for accurate and efficient diagnosis. The proposed system employs the Vision Transformer (ViT) architecture as the teacher model and MobileNet as the student model, aiming to classify chest X-ray images into five categories: normal, COVID-19, viral pneumonia, bacterial pneumonia, and tuberculosis. Knowledge distillation enables the student model to learn critical diagnostic features from the teacher model, resulting in a compact, high-performing model suitable for real-world deployment in settings with limited computational resources. Experimental evaluation on an original dataset of X-ray images demonstrated a classification accuracy of 93.07%, with high precision, recall, and F1-score, indicating robust performance across multiple disease types. This system holds promise as a valuable tool for assisting healthcare providers in making quick, accurate diagnoses, potentially improving treatment timelines and outcomes for patients in underserved regions. © 2024 IEEE.

关键词： Lung cancer

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Linear Active Disturbance Rejection Control with a Fractional-Order Integral Action

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computers, Materials & Continua 2022年第11期73卷 3057-3079页

作者： Maâmar Bettayeb Rachid Mansouri Ubaid M.Al-Saggaf Abdulrahman U.Alsaggaf Mohammed Moinuddin Electrical Engineering Department University of SharjahUnited Arab Emirates L2CSP Laboratory University Mouloud Mammeri of Tizi-OuzouAlgeria Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddahSaudi Arabia Electricaland Computer Engineering Department King Abdulaziz UniversityJeddahSaudi Arabia

Linear active disturbance rejection control(LADRC)is a powerful control structure thanks to its performance in uncertainties,internal and external disturbances estimation and *** extended state observer(ESO)based controller is the key to the LADRC *** this article,the LADRC scheme combined with a fractional-order integral action(FOILADRC)is proposed to improve the robustness of the standard *** the robust closed-loop Bode’s ideal transfer function(BITF),an appropriate pole placement method is proposed to design the set-point tracking controller of the FOI-LADRC *** simulations and experimental results on a cart-pendulum system will illustrate the effectiveness of the proposed FOI-LADRC scheme for the disturbance rejection,the set-point tracking and the improved *** illustrate the LADRC control schemes and to verify the performance of the proposed FOI-LADRC,compared to the standard LADRC and IOI-LADRC structures,two tests will be carried ***,simulation tests on an academic example will be presented to show the effect of the different parameters of the control law on the performance of the closed-loop ***,these three control structures are implemented on an experimental test bench,the cart-pendulum system,to show their efficiency and to show the superiority of the proposed method compared to the two other structures.

关键词： Fractional calculus active disturbance rejection control pole placement cart-pendulum system robust control

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Integrated Attitude-Position Formation Control of Multiple Vehicles on SE(3) with Individual Objectives

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Meng, Qingkai Kasis, Andreas Polycarpou, Marios M. University of Cyprus KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering Nicosia1678 Cyprus

This paper explores the integrated attitude-position formation control problem of multiple vehicles, where individual entities within the formation have heterogeneous objectives. The six-degree-of-freedom multi-vehicle model is established by using the Lie group SE(3), where the attitude of each vehicle is represented by the rotation matrix. Initially, leveraging left-invariant metrics on SE(3), cost functions with respect to individual objectives and the group goal are established, formulating the formation control problem as a non-cooperative differential game. The existence of Nash equilibrium (NE) points is proven by using the monotonic game function analysis technique. To account for vehicle individual objectives, we apply a regularization technique for a strongly monotone operator to reformulate the game, providing conditions such that the NE points of the two games coincide. This transforms the original coordinated optimization problem into a NE-seeking problem for the augmented game. The aforementioned game control problem is solved using a Laplacian-flow-consensus-based NE-seeking algorithm, which yields a distributed geometric controller, for which we offer analytic convergence guarantees. Finally, simulation results demonstrate the practical validity of the proposed method. © 1965-2011 IEEE.

关键词： Cost functions

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XVAE-mViT: Explinable Hybrid Artificial Intelligence Framework for Predicting COVID-19 from Chest X-Ray and CT Scans 7

XVAE-mViT: Explinable Hybrid Artificial Intelligence Framewo...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Addo, Daniel Al-Antari, Mugahed A. Zhou, Shijie Sarpong, Kwabena Butun, Ertan Talo, Muhammed Abualkebash, Humam Ukwuoma, Chiagoziem C. School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Sejong University DaeyangAI Center Department of Artificial Intelligence Seoul Korea Republic of Firat University Department of Computer Engineering Elaziǧ Turkey Firat University Department of Software Engineering Elazig Turkey Kocaeli University Department of Mechatronics Engineering Kocaeli Turkey The College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan China

ISBN: (纸本)9798350342154

The COVID-19 virus has rapidly spread as a global pandemic, causing significant impacts on public health, economies, and daily life worldwide. Accurately and quickly predicting COVID-19 is crucial to maintaining stronger healthcare systems. This paper introduces a novel hybrid model of artificial intelligence that combines the benefits of the Variational Auto-Encoder (VAE) with the attention mechanism based on the Vision Transformer (ViT). The novel encoder network is structured with four sequential blocks, each involving residual connections of two multiscale kernel depth-wise separable convolution (MKnDSC) modules. The mobile ViT is coupled with the V AE to serve as the classification head for predicting COVID- 19 using chest X-ray (CXR) and computed tomography (CT) scan modalities. We achieved promising classification results with overall accuracies of 96.16% and 95.42% using CXR and CT images, respectively. The proposed hybrid AI framework appears to be a practical solution, especially considering its lightweight structure of 2.15 million parameters and 0.68 FLOPs. © 2023 IEEE.

关键词： COVID-19

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Sliding Mode Control for Robustness in Networked Switched Systems under Denial-of-Service Attacks

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IEEE Transactions on Automatic Control 2025年

Denial-of-service (DoS) attacks impose an important threat for networked switched systems, as they impede the effective transmission of the control signals and measurement data and disrupt the synchronization of the switching modes between the controllers and the plant. To address this challenge, this paper proposes a sliding-mode controller design for safety against DoS attacks in networked switched systems. Firstly, we analyze the impact of the time delays induced by signal quantization and DoS attacks on the switching signals, control laws, and measurement data. We then design an integral sliding surface associated with the switching modes such that, when system uncertainties, disturbances, and the frequency and duration of DoS attacks are suitably bounded, input-to-state practical stability is guaranteed. Moreover, a reaching law is designed such that the trajectories of the networked switched system reach the sliding surface in finite time. The practicality and applicability of the proposed approach is demonstrated with numerical simulations on a networked continuous stirred tank reactor system. © 1963-2012 IEEE.

关键词： Quantization (signal)

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CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

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computers, Materials & Continua 2023年第9期76卷 2779-2793页

作者： Mehwish Zafar JaveriaAmin Muhammad Sharif Muhammad Almas Anjum Seifedine Kadry Jungeun Kim Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science University ofWahWah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Worldwide cotton is the most profitable cash *** year the production of this crop suffers because of several *** an early stage,computerized methods are used for disease detection that may reduce the loss in the production of *** several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex *** to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease *** in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×*** that,the extracted features are serially concatenated having a feature vector lengthN×*** prominent features are selected usingEmperor PenguinOptimizer(EPO)*** method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle *** EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,*** classification is performed using 5,7,and 10 folds *** Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

关键词： Deep learning cotton disease detection features selection classification efficientnet-b0 inception-v3 quadratic discriminant analysis subspace KNN

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Recycled silicon solar cell-derived nanostructured p-black silicon device for high performance NO₂ gas sensor applications

RSC Applied Interfaces

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RSC Applied Interfaces 2024年第1期2卷 220-229页

作者： Aleem, Mahaboobbatcha Vishnuraj, Ramakrishnan Pullithadathil, Biji Nanosensors & Clean Energy Laboratory Department of Chemistry & Nanoscience and Technology PSG Institute of Advanced Studies Coimbatore 641 India Advanced Self-Powered Systems of Integrated Sensors and Technology Center Department of Electrical and Computer Engineering North Carolina State University Raleigh 27695 NC United States Center of Excellence in Advanced Materials and Green Technologies Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Chemical Engineering and Materials Science Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

Nitrogen dioxide (NO2) is a toxic gas that can cause respiratory problems, and sensing its presence is crucial for environmental monitoring and industrial safety. This investigation presents a novel approach towards s...

Nitrogen dioxide (NO₂) is a toxic gas that can cause respiratory problems, and sensing its presence is crucial for environmental monitoring and industrial safety. This investigation presents a novel approach towards sensing NO₂ gas by utilizing partially completed/recycled silicon solar cells employing a metal-assisted etching process to fabricate a high-performance p-black-silicon based sensor. Structural and morphological analyses using X-ray diffraction patterns, Raman spectroscopy and cross sectional FESEM characterization confirm the integrity of the p-B-silicon sensor. By combining recycling techniques with advanced fabrication methods, the resulting sensor exhibits exceptional sensitivity, a low detection limit of 1 ppm, and rapid response times (12-14 s) when exposed to NO₂ gas concentrations ranging from 1 to 5 ppm. The enhanced sensitivity is attributed to the unique nanostructured comb-like morphology of the sensor material, which facilitates fast charge transport mechanisms, and a plausible sensing mechanism has been proposed and explained using a depletion model diagram and energy model diagram. This eco-friendly and cost-effective solution not only addresses electronic waste concerns but also highlights the potential of sustainable practices in scientific research. The findings emphasize on the importance of environmental consciousness and innovation, showcasing a promising future for gas sensing technology. By utilizing recycled materials and advanced fabrication techniques, this study contributes to the development of efficient, eco-friendly sensors for environmental monitoring applications, paving the way for a more sustainable and technologically advanced future in the field of gas sensors. © 2025 RSC.

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Self-Guided Autoencoders for Unsupervised Change Detection in Heterogeneous Remote Sensing Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2458-2471页

作者： Shi, Jiao Wu, Tiancheng Kai Qin, Alex Lei, Yu Jeon, Gwanggil Northwestern Polytechnical University School of Electronics and Information Xi'an710129 China Northwestern Polytechnical University in Shenzhen Research and Development Institute Shenzhen518057 China Chongqing Innovation Center of Northwestern Polytechnical University Chongqing401135 China Swinburne University of Technology Department of Computer Science and Software Engineering HawthornVIC3122 Australia Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

To address the problem of enormous differences in two heterogeneous images, the traditional unsupervised frameworks are most normally realized by converting two images into a common domain with various auxiliary strategies, such as transformation and alignment, which requires extensive calculation and has difficulty balancing the training tasks. To achieve a concise framework, this article proposes self-guided autoencoders (SGAE) for unsupervised change detection (CD) in heterogeneous remote sensing images (RSIs). Unlike traditional methods that aim to narrow the differences of heterogeneous images to highlight the changed information, SGAE forces the flow of identification in formation generated in unlabeled data through self-guided iterations. First, initial unsupervised networks output an elementary change map that will be screened to obtain reliable pseudolabels. The selected pseudolabeled samples will be used as the input of a supervised network to obtain another change map. Then, multiple change maps will be fused to refine the confidence of pseudolabels again, obtaining new fused pseudolabeled samples for the self-guided network, which will be trained with pseudolabeled samples and unlabeled samples. Finally, the above operations will be repeated to continuously optimize the net, which helps itself to extract the discriminative features for classification in self-guided iterations. Experiments compared with several algorithms on four datasets demonstrate the effectiveness and robustness of our method, which can help unsupervised models improve discriminative feature extraction and classification performance with a more flexible learning method. © 2020 IEEE.

关键词： Feature extraction

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Optimized Reformed Histogram Equalization and SE-CNN with Chan Vese Segmentation for an Automatic Prediction of Lung Cancer

Optimized Reformed Histogram Equalization and SE-CNN with Ch...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Parvathi, C.S. Jayan, J.P. Noorul Islam Center for Higher Education Computer Application Tamil Nadu Kumaracoil India Noorul Islam Center for Higher Education Department of Software Engineering Tamil Nadu Kumaracoil India

ISBN: (纸本)9798350333244

Lung cancer is one of the deadliest illnesses and the leading cause of illness and death, when detected in its early stages. While many classical machines and deep learning automatic systems can identify lung cancer with effectiveness, they still have issues with recognition accuracy and take longer to analyze. The Squeeze and Excitation Convolutional Neural Network (SENET), a deep learning technique, is proposed in this research. To increase the clarity of an image, lung CT input pictures are pre-processed employing the Adaptive Bilateral Filter (ABF) and Reformed Histogram Equalization (RHE). Tuna-swam optimization is utilized to determine the tunable parameters of RHE approach. Chan Vese segmentation model is used for segment the object and background of pre-processed image. This segmented image is given to SENET classifier for lung cancer prediction. The suggested strategy achieves 98. 7% accuracy, 2.3% error, and 97.4% precision, based on experimental outcomes. As a result, the proposed framework successfully predicts lung cancer and raises the standard of living for patients. © 2023 IEEE.

关键词： Diseases

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Cleo: Smart Glasses to Monitor Consumption of Alcohol and Cigarettes

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SN computer Science 2023年第1期4卷 53页

作者： Sethuraman, Sibi Chakkaravarthy Kompally, Pranav Mohanty, Saraju P. Raju, Bksp Kumar Cruz, Meenalosini Vimal School of Computer Science and Engineering and Center of Excellence in Artificial Intelligence and Robotics (AIR) VIT-AP University Amaravati India Department of Computer Science and Engineering University of North Texas Denton 76207 TX United States Department of Information Technology Allen E. Paulson College of Engineering and Computing Georgia Southern University Statesboro GA United States

It is estimated that over 60% of people around the globe consume alcohol and cigars daily. Many people use them beyond the permitted limit, which causes lung cancer, liver and kidney failure. If there is a system that could monitor their intake level, it will alert them in case of excess consumption, which could help them control the intake. To help the users monitor their consumption, we introduce Cleo Eyeglasses in this paper. Cleo is a wearable spectacle device with a mounted camera and single onboard computer that performs custom trained object recognition to identify alcoholic beverages and cigarettes. Upon recognition, a log is automatically maintained in the corresponding mobile application. If the intake exceeds the user threshold, then our system generates an alert to both the user and concerned medical personnel. Cigarette/alcohol consumption can immediately affect the body without noticeable symptoms. Cleo addresses this with keen monitoring of vital body parameters and generates an alert when abnormalities are detected. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Alcohol monitoring CNN computer vision Deep learning Smart glasses Smoke monitoring

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