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10th IEEE International Conference on Power Systems, ICPS 2023

作者： Hadee, M.A. Akter, Farzana Hossain, Md. Rakib Mahedy Hasan, S.M. Akter, Sheikh Shraboni Datto, Sham Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350318739

This paper proposes a new LQR-based feedforward current control scheme for the three-phase inverter of a grid-tied PV system to achieve better power quality. In order to mitigate the THD in the grid current and variation in the grid voltage, the proposed control mechanism combines an LQR-based feedforward current regulator with a disturbance decoupling approach. The feedback gains of the current regulator are obtained using the LQR method and the fluctuation in the grid voltage is modeled as a disturbance to get rid of its effect. In order to mitigate the computational complexity, the control strategy is developed in the synchronous reference frame (SRF) and to verify its effectiveness, the PV system along with the controller is simulated under various operating situations using the Matlab/Simulink platform. The simulation results show that the proposed LQR-based current control scheme has been more successful in improving the power quality of grid-tied PV systems than some of the conventional controllers, particularly in decreasing the Total Harmonic Distortion (THD) in the grid current. © 2023 IEEE.

关键词： Power quality

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Channel Attention-Guided Lightweight CNN Meets Extreme Learning Machine: Multi-Class Crop Leaf Disease Classification with Explainable AI 26

Channel Attention-Guided Lightweight CNN Meets Extreme Learn...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Islam, Md. Minhazul Hasan, S. M. Mahedy Hossain, Md. Rakib Srizon, Azmain Yakin Akter, Farzana Faruk, Md. Farukuzzaman Shraboni Akter, Sheikh Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

Crop leaf diseases pose a significant challenge to global agriculture, leading to substantial economic losses and reduced yields. These ailments, caused by various pathogens, manifest as symptoms such as leaf discoloration and impaired photosynthesis, severely impacting crop health and productivity. Existing classification methods face challenges related to accuracy, computational efficiency, and interpretability. This study introduces an innovative solution that combines a meticulously designed lightweight convolutional neural network (CNN) architecture with a channel attention (CA) mechanism for extracting crucial features and leverages the discriminative power of an extreme learning machine (ELM) for classification. The lightweight CNN developed demonstrates significantly reduced training times. Additionally, the CA mechanism within the model effectively prioritizes channel-wise critical features while eliminating irrelevant and redundant ones. An empirical evaluation is conducted using a publicly available dataset and the proposed model achieves a flawless 100% accuracy in distinguishing between 39 distinct crop leaf diseases. Furthermore, the study employs gradient class activation maps (Grad-CAM) visualization to delve into the model's decision-making process, shedding light on influential factors and regions. It holds the promise of transforming crop health and agricultural yields, instilling hope for farmers and researchers dedicated to achieving agricultural prosperity and sustainability. © 2023 IEEE.

关键词： Channel Attention Convolutional Neural Network Crop Leaf Disease Extreme Learning Machine Grad-CAM

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Unveiling Hidden Water Resources: Deep Learning and Remote Sensing for Subsurface Hydrology for Environmental Health

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 352-364页

作者： Babu, Jampani Satish G, Dinesh Suganya, R. Radhakrishnan, P. Sutar, Anand S, Alex David Rao, L. Malleswara Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur AP Vaddeswaram India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of Computer Science and Engineering (Data Science) New Horizon College of Engineering Karnataka Bengaluru India Department of ECE Tagore Engineering College Chennai India Department of Electronics and Telecommunication Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai India Department of Physics Sri Y N College (A) West Godavari District Andhra Pradesh Narsapur India

Water scarcity in arid regions necessitates innovative solutions to uncover and manage subsurface water resources for environmental health. This study explores the potential of GeoHydroNet, a specialized deep learning framework, in identifying hidden water resources within arid regions. Utilizing remote sensing data such as multispectral and hyperspectral imagery, geological maps, and soil moisture datasets, GeoHydroNet combines graph neural Networks (GNNs), AutoEncoders, and generative adversarial networks (GANs) to address challenges in subsurface hydrology. The model achieved an accuracy of 93.5%, surpassing traditional models like CNNs (85.4%), LSTMs (88.1%), and RNNs (82.6%). GeoHydroNet demonstrates significant precision (0.88), recall (0.87), and sensitivity to data imbalance (0.91). Its physics-informed learning approach incorporates hydrological principles, ensuring realistic predictions. By analyzing subsurface water patterns, recharge zones, and geological influences, the model not only enhances prediction accuracy but also aids in sustainable groundwater management. GeoHydroNet’s performance in identifying groundwater levels, with minimal errors ranging from 0.62 to 3.07%, indicates its robustness in tackling water scarcity challenges in data-constrained environments. These findings highlight its transformative role in hydrological modeling and sustainable water resource management in arid regions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aquifers Arid regions Deep learning Generative adversarial networks GeoHydroNet Physics-informed learning Remote sensing Subsurface hydrology Sustainable water management

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An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer with XAI Techniques

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IEEE Access 2025年 13卷 96317-96336页

作者： Haque, Fariha Hasan, Mohammad Asif Siddique, Md Abu Ismail Roy, Tonmoy Shaha, Tonmoy Kanti Islam, Yamina Paul, Avijit Chowdhury, Muhammad E. H. Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi Bangladesh 797-1000 United States Chittagong University of Engineering Technology Electrical Electronic Engineering Bangladesh International Islamic University Chittagong Department of Computer Science & Engineering Chittagong4318 Bangladesh Qatar University Department of Electrical Engineering Doha2713 Qatar

In the field of medical imaging, deep learning (DL) techniques have made significant contributions to the detection and classification of various cancers. Identifying the precise regions in medical images containing cancerous cells plays a crucial role in the diagnostic process. Early and accurate cancer detection is essential for effective treatment and improved patient outcomes. However, manual diagnosis is labor-intensive, requiring the specialized expertise of radiologists, and the increasing number of cancer cases presents challenges in processing large volumes of image data efficiently. To address these challenges, an end-to-end concatenated Convolutional Neural Network (CNN) attention model has been proposed for automatic lung cancer classification. This approach integrates two distinct CNNs, followed by a multi-layer perceptron (MLP) and a multi-head attention (MHA) mechanism, to enhance performance. The model leverages explainable AI techniques, such as gradient-weighted class activation mapping (grad-CAM) and Shapley additive explanations (SHAP), to highlight critical regions within the images that influence the decision-making process. This model achieves impressive performance, with an accuracy of 99.54%, precision of 99.31%, recall of 99.95%, F1-score of 99.66%, and an AUC of 99.97%. These results demonstrate that Thisapproach not only surpasses existing methods but also provides a highly accurate and interpretable solution. By reducing the need for extensive manual intervention, this model enables faster and more reliable lung cancer diagnosis, paving the way for timely and effective treatments. © 2013 IEEE.

关键词： Convolutional neural networks

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A Novel CNN-BiLSTM Fusion Model for Robust Age Estimation from Facial Images 26

A Novel CNN-BiLSTM Fusion Model for Robust Age Estimation fr...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Basak, Anindya Akter, Farzana Hossain, Md. Rakib Srizon, Azmain Yakin Hasan, S. M. Mahedy Faruk, Md. Farukuzzaman Sakib, Shadman Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

This study unveils a novel approach to age estimation from facial images, achieved by amalgamating Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory networks (BiLSTMs) into a cohesive fusion model. A dataset, comprised of facial photographs of 80 individuals aged between 16 and 25 years, was meticulously assembled to facilitate the development of a robust and reliable age estimation model. This specific age bracket is characterized by significant facial transformations attributable to both growth and ageing, thereby posing a considerable challenge for accurate age estimation. The introduced model employs metric regression techniques within a structure that integrates CNN and BiLSTM, capitalizing on their synergistic capabilities to estimate age with precision. CNNs are renowned for their prowess in feature extraction, while BiLSTMs excel in discerning temporal relationships. This amalgamation ensures the comprehensive capture of both static and dynamic facial signals instrumental for precise age estimation. Evaluative metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) are employed to gauge the model's robustness and efficacy within the designated age bracket, offering insights into its competitive standing in the realm of age estimation from facial imagery. © 2023 IEEE.

关键词： Age estimation BiLSTM CNN LSTM

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Metasurfaces for next-generation wireless communication systems

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National science Review 2023年第8期10卷 17-19页

作者： Younes Ra'di Nikita Nefedkin Petar Popovski Andrea Alù Department of Electrical Engineering and Computer Science Syracuse University Photonics Initiative Advanced Science Research Center City University of New York Department of Electronic Systems Aalborg University Physics Program Graduate Center City University of New York

The pace of development in the world of 5G communication systems has proven to be much more demanding than previous generations, with 5G-Advanced seemingly around the corner [1]. Extensive research is already underway to structure the next generation of wireless systems(i.e. 6G), which may potentially enable an unprecedented level of human–machine interaction [2].

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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Diversity-Optimized Group Extraction in Social Networks

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IEEE Transactions on Computational Social Systems 2024年第1期11卷 756-769页

作者： Hsu, Bay-Yuan Chen, Yi-Ling Ho, Ya-Chi Chang, Po-Yuan Chang, Chih-Chieh Shia, Ben-Chang Shen, Chih-Ya National Tsing Hua University Department of Industrial Engineering and Engineering Management Hsinchu30013 Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei10607 Taiwan Hsinchu31040 Taiwan Fu Jen Catholic University Department of Business Administration New Taipei City24205 Taiwan National Taiwan University of Science and Technology Management Undergraduate Program Taipei10607 Taiwan National Tsing Hua University Department of Computer Science Hsinchu30013 Taiwan

In this article, we propose to study a novel research problem to boost group performance, that is, social-aware diversity-optimized group extraction (SDGE), which takes into consideration the two important factors: 1) group diversity and 2) social tightness. We prove the NP-hardness of SDGE and propose an effective algorithm, named group shrinking for diversity maximization (GSDM) with a performance guarantee, that is, GSDM is a three-approximation algorithm to the SDGE problem studied in this article. We further propose three effective pruning strategies that are able to boost the efficiency of GSDM but do not deteriorate its performance. We conduct extensive experiments on multiple large-scale real datasets to evaluate the performance of GSDM. The experimental results show that our proposed GSDM outperforms the other baseline approaches significantly, in terms of solution quality and efficiency. Moreover, the experimental results also confirm that our proposed pruning strategies indeed boost the efficiency of the algorithm. © 2014 IEEE.

关键词： Costs

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Enhanced Brain Tumor Classification using Squeeze and Excitation Attention-Guided Modified EfficientNetB2 Architecture 26

Enhanced Brain Tumor Classification using Squeeze and Excita...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Islam, Rifatul Hossain, Md. Rakib Hasan, S. M. Mahedy Srizon, Azmain Yakin Akter, Farzana Islam, Md. Minhazul Shakin, Md. Musab Us Saber Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

Brain tumors, spanning from benign to malignant forms, present a substantial medical challenge due to their capacity to disrupt crucial brain functions. Conventional diagnostic techniques, reliant on the manual interpretation of MRI images, often exhibit limited accuracy and efficiency. Recent research endeavors show promise but encounter difficulties in extracting essential features and providing model interpretability. In this study, an innovative approach to classify brain tumors is introduced, leveraging a modified EfficientNetB2 architecture guided by a Squeeze and Excitation Attention (SEA) block. The original EfficientNetB2 architecture undergoes fine-tuning, with its top layers being tailored for this specific investigation. Subsequently, after feature extraction from the modified EfficientNetB2, the SEA block is integrated to enhance the emphasis on relevant features while suppressing irrelevant ones. An empirical assessment is conducted on a merged dataset, resulting in an impressive accuracy rate of 97.98%, surpassing other state-of-the-art methods. Furthermore, Grad-CAM visualization is employed to reveal the specific image regions exerting influence on the model's predictions. © 2023 IEEE.

关键词： Brain MRI Images Brain Tumor Classification EfficientNetB2 Squeeze and Excitation Attention

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Prediction of Medical Insurance Costs: A SHAP-Enhanced Predictive Analysis for Transparency and Interpretability 26

Prediction of Medical Insurance Costs: A SHAP-Enhanced Predi...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Emon, Shahriar Hossain, Md. Rakib Hasan, S. M. Mahedy Srizon, Azmain Yakin Akter, Farzana Faruk, Md. Farukuzzaman Hossain, Md. Shakib Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

The escalation of healthcare expenses has been markedly pronounced in the aftermath of the COVID-19 pandemic's onset. Consequently, health insurance has ascended to prominence as a critical domain of investigation, with a series of ongoing endeavors underway to navigate this complex issue. The task of forecasting medical insurance expenditures is laden with challenges, attributed to the dynamic nature of healthcare, the intricacies and occasional disarray of data, unpredictable human behaviors, economic oscillations, unforeseen events, and prevailing privacy apprehensions. In the quest to surmount these obstacles, a predictive model has been unveiled, engineered to impart insights into the nuances of healthcare pricing structures. The examination of SHAP values facilitates the discernment of pivotal elements that steer cost prognostications, a process instrumental in augmenting transparency and enabling judicious choices for both insurance entities and the insured. This inquiry underscores the cardinal role of amalgamating predictive precision with an element of transparency within the realm of healthcare insurance. © 2023 IEEE.

关键词： Gradient Boosting Interpretability Machine Learning Multiple Linear Regression Random Forest Regression Ridge Regression Support Vector Regression

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