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3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Kabir, Sheikh Ridwan Raihan Mondal, Hirak Nag, Anindya Jamil, S. M. Hasan Das, Piya Department of Computer Science and Engineering North Western University Khulna9000 Bangladesh Computer Science and Engineering Discipline Khulna University Khulna9208 Bangladesh

ISBN: (纸本)9789819739363

Lung cancer is a specific kind of malignancy that arises in the tissues of the lungs, specifically in the cells that comprise the lining of the respiratory passages. Smoking is well recognized as the primary etiological factor contributing to the development of lung cancer. The inhalation of secondhand smoke, as well as exposure to asbestos or radon inside one’s own residence, Being employed, and having a familial predisposition to the ailment are further factors. Factors contributing to the chance of developing lung cancer. In their lifetime, 1 in 16 men and 1 in 17 women were diagnosed with lung cancer. The variability in survival rates may be attributed to the extent of illness dissemination at the time of its identification. Early detection may have substantial benefits. The term "impact" refers to the effect or influence that something has on a particular situation. The dataset was collected from a website. The study used a global-based Particle swarm Optimization (PSO) algorithm for feature selection. With our approach, we want to improve the dataset’s statistical feature accuracy and predict lung cancer accurately based on the given data. Among the employed classifiers, the Support vector machine and Naive Bayes obtained maximum accuracy of 97% with 8 out of 16 features. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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MRI Image-Based Brain Tumor Classification Using Transfer Learning and XAI 3rd

MRI Image-Based Brain Tumor Classification Using Transfer Le...

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3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Rayhan, Masum Mondal, Saykat Pinki, Farhana Tazmim Computer Science and Engineering Discipline Khulna University Sher-E-Bangla Rd Khulna9208 Bangladesh

ISBN: (纸本)9789819739363

Brain tumors, particularly those of the highest grade, can substantially lower life expectancy. A precise diagnosis is required for individuals with brain tumors to receive the best possible care. The conventional way of diagnosing brain tumors is Magnetic Resonance Imaging (MRI) while recent tumor detection models leverage Convolution Neural Networks (CNN) and pre-trained models, many prioritize accuracy over providing explanation and interpretability. This paper addresses these shortcomings by proposing an innovative approach to the categorization of brain tumors by exploring transfer learning and Explainable Artificial Intelligence (XAI). The study uses the top five pre-trained models to classify three different types of tumors: VGG16, ResNet50, Xception, InceptionV3 and DenseNet201. Notably, a customized VGG16 model obtains a promising test accuracy of 99.04%, which is comparable to top literature scores. A significant contribution of this work is the integration of the top three explainable AI tools: Gradient-weighted Class Activation Mapping (Grad-CAM), Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). These tools enhance visualization and provide valuable explanations for model predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Magnetic resonance imaging

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Beta Function Based Compression Method for Crop Image Using Singular Vector Sparse Reconstruction

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SN computer science 2025年第4期6卷 1-13页

作者： Mishra, Deepak Kumar, Anil Rathore, Vijaypal Singh Discipline of Electronics and Communication Engineering PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur 482005 India Discipline of Computer Science & Engineering PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur 482005 India

In this research, a new hybrid compression method is introduced, which is based on Beta function and singular vector sparse reconstruction (SVSR). The proposed technique incorporates wavelet filters whose coefficients are obtained from the beta function and its derivatives. These filters are employed to decompose the original crop image, and SVSR is used to enhance the compression efficiency. The efficacy of the suggested compression method is examined by testing it on different grayscale and color crop images and compared the results to other existing approaches. The assessment is based on fidelity parameters, including compression ratio (CR), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). The Beta wavelet filters demonstrate superior compression ratios of 1.5% and 16.4% compared to Db4 and Bior4.4 wavelet filters, respectively. Additionally, they exhibit 47.8% and 80.13% higher compression ratios than SVSR and SVD, respectively, while maintaining approximately similar reconstruction quality. Experimental results show that the suggested compression technique achieves better performance than existing methods in terms of both image quality and compression efficiency. Therefore, it is concluded that this approach can be effectively utilized for grayscale and color crop image compression. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Beta wavelet Image compression Singular vector sparse reconstruction

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Dependency Dilemmas: A Comparative Study of Independent and Dependent Artifacts in Maven Central Ecosystem

arXiv

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arXiv 2025年

作者： Shanto, Mehedi Hasan Asaduzzaman, Muhammad Mondal, Manishankar Chowdhury, Shaiful Computer Science and Engineering Discipline Khulna University Bangladesh School of Computer Science University of Windsor Canada Department of Computer Science University of Manitoba Canada

Maven Central ecosystem forms the backbone of Java dependency management, hosting artifacts that vary significantly in their adoption, security, and ecosystem roles. Artifact reuse is fundamental in software development, and ecosystems like Maven facilitate this process. However, prior studies predominantly analyzed popular artifacts with numerous dependencies, leaving those without incoming dependencies (i.e., independent artifacts) unexplored. In this study, we analyzed 658,078 artifacts, of which 635,003 had at least one release. Among these, 93,101 artifacts (15.4%) were identified as independent (in-degree = 0), while the rest were classified as dependent. We looked at the impact of individual artifacts using PageRank and out-degree centrality and discovered that independent artifacts were very important to the ecosystem. Further analysis using 18 different metrics revealed several advantages and comparability of independent artifacts with dependent artifacts: comparable popularity (25.58 vs. 7.30), fewer vulnerabilities (60 CVEs vs. 179 CVEs), and zero propagated vulnerabilities. These findings suggest that independent artifacts might be a beneficial choice for dependencies but have some maintenance issues. Therefore, developers should carefully incorporate independent artifacts into their projects, and artifact maintainers should prioritize this group of artifacts to mitigate the risk of transitive vulnerability propagation and improve software sustainability. Copyright © 2025, The Authors. All rights reserved.

关键词： computer software reusability

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Ferroelectrically gated two-dimensional bismuth oxyselenides for strain-invariant flexible synaptic thin-film transistors

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science China(Information sciences) 2025年第5期68卷 396-403页

作者： Jie WEN Fei XIAO Zheng-Dong LUO Dongxin TAN Xuetao GAN Dawei ZHANG Zhufei CHU Yinshui XIA Yan LIU Genquan HAN Hangzhou Institute of Technology Xidian University State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology School of MicroelectronicsXidian University Key Laboratory of Light Field Manipulation and Information Acquisition Ministry of Industry and Information Technology Shaanxi Key Laboratory of Optical Information Technology School of Physical Science and Technology Northwestern Polytechnical University School of Materials Science and Engineering The University of New South Wales ARC Centre of Excellence in Future Low-Energy Electronics Technologies (FLEET) The University of New South Wales Faculty of Electrical Engineering Computer Science Ningbo University

Two-dimensional van der Waals（2D vdW） semiconductors have proven to be of great importance for flexible thinfilm transistors（TFTs） owing to their intrinsic mechanical flexibility and superior electronic *** particular,bismuth oxyselenide（Bi2O2Se）,featuring ultrahigh electron mobility along with facile scalable thin-film growth methods,could offer a new option to deliver massively enhanced potential for flexible ***,it has remained a challenge to achieve nonvolatile flexible memory devices based on Bi2O2Se TFTs,thereby hindering the extension of Bi2O2Se TFTs to storage and emerging neuromorphic computing ***,a flexible synaptic TFT is demonstrated through the creation of a Bi2O2Se-based ferroelectric field-effect transistor（FeFET） structure on the flexible mica *** proposed device exhibits excellent nonvolatile memory characteristics,including a large memory window,excellent current modulation ratio,great retention,and strong ***,the Bi2O2Se-based FeFET can be operated as a synaptic device with analog conductance-modulating *** to the superior mechanical flexibility of the component materials and the mica substrate,the Bi2O2Se-based FeFETs can retain their performance against various bending states,showing a straininvariant electrical *** study marks the advancement of Bi2O2Se-based TFTs toward flexible nonvolatile memories and synaptic devices.

关键词： Bi$_{2}$O$_{2}$Se FeFET nonvolatile memory flexible device synaptic transistor

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Deep Learning for Fish Freshness Detection: An XAI Oriented Knowledge Validation Based Approach

Deep Learning for Fish Freshness Detection: An XAI Oriented ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Bristy Biswas Dhrubalika Mandal Chandan Paul Rameswar Debnath Avi Deb Raha Mrityunjoy Gain AI Innovation Lab Khulna Bangladesh Environmental Science Discipline Khulna University Khulna Bangladesh Computer Science and Engineering Discipline Khulna University Khulna Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Fish is a vital source of protein and essential nutrients both in Bangladesh and globally. Consuming fresh fish preserves its nutritional benefits and supports good health, whereas non-fresh fish can lose key nutrients and may become harmful depending on the level of spoilage. Typically, fish are sourced from rivers and seas or are farmed in ponds and lakes. However, many people live far from these fish-producing regions, necessitating extended transportation and storage, which increases the risk of quality degradation. In urban areas, consumers often struggle to accurately assess fish freshness, sometimes being misled into purchasing spoiled fish. This situation highlights the need for an affordable and automated method to determine fish freshness reliably. To address this issue, we propose a lightweight deep-learning framework designed for deployment on low-configuration smartphone devices, ensuring accessibility and practicality. Developing a robust deep-learning model typically requires a high-quality dataset; however, no standard dataset currently exists for fish freshness detection. To overcome this limitation, we introduce an explainable AI-driven framework that leverages Grad-CAM-guided feature activation during model training. This approach enhances model reliability and interpretability, ensuring trustworthy performance even when using limited or non-standard datasets. By ensuring the model focuses on relevant regions of interest, our approach enhances both reliability and interpretability, even when trained on a small dataset. Experimental results validate the effectiveness of our framework, achieving over 99% accuracy. These results underscore the model’s robustness and practical applicability, offering a promising solution for ensuring fish quality in the supply chain.

关键词： Proteins Deep learning Training Visualization Accuracy Urban areas Transportation Fish Nutrients Robustness

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Design and Analysis of Compact UWB Circular Ring Planar Antenna for mmWave FR-II NR Bands n259, n262, n263 (V-Band), and n260 (Ka-Band) for 5G Applications 6th

Design and Analysis of Compact UWB Circular Ring Planar Ante...

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6th International Conference on Intelligent Communication, Control and Devices, ICICCD 2024

作者： Kumar, Pradeep Raj, Tej Kapoor, Ankush Krishna, Nippani Satya Discipline of Electrical Electronic and Computer Engineering University of KwaZulu-Natal Durban4041 South Africa Electrical Cluster SoAE UPES Uttarakhand Dehradun India Department of ECE J. N. Government Engineering College Sundernagar India Applied Science Cluster SoAE UPES Uttarakhand Dehradun India

ISBN: (纸本)9789819783281

This manuscript proposes an ultra-wideband circular ring-type planar microstrip patch antenna with a partial ground plane. The antenna achieves an impedance bandwidth of approximately 38.1 GHz, encompassing a frequency range of 36.517–74.624 GHz. The presented radiator attains a fractional bandwidth of 68.57%, with a peak gain of 5.64 dBi, cover FR-II NR frequency bands n259 (39.50–43.50 GHz), n262 (47.20–48.20 GHz), n263 (57–71 GHz), and n260 (37–40 GHz). The proposed antenna is modeled on dielectric material Taconic (TLY-5) with a dielectric constant (Εr) 2.2, a substrate width of 0.51 mm, and a loss tangent of 0.0009. The suggested element features a compact size with substrate dimensions of 10 × 6.30 mm2, exhibiting a quasi-omnidirectional radiation pattern with a maximum radiation efficiency of 96%. The simulation results, including return loss and VSWR, have been validated using CST Microwave Studio and Ansys HFSS, showing close agreement. This indicates that the proposed antenna is a strong candidate for 5G mmWave FR-II NR V-band and Ka-band wireless applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Slot antennas

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Spacers and Mismatches-Based Algorithm (SMA): G Domain Prediction and Application in Human Biology and Human Health

Spacers and Mismatches-Based Algorithm (SMA): G Domain Predi...

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International Health Informatics Conference, IHIC 2023

作者： Sanghavi, Hiral M. Rashmi, Richa Dasgupta, Anirban Majumdar, Sharmistha School of Forensic Sciences National Forensic Sciences University Gujarat Gandhinagar382007 India Department of Cell Biology and Genetics Texas A&M University School of Medicine College StationTX77807-3260 United States Discipline of Computer Science and Engineering Indian Institute of Technology Gujarat Gandhinagar382355 India Discipline of Biological Engineering Indian Institute of Technology Gujarat Gandhinagar382355 India

ISBN: (纸本)9789819771899

Guanine nucleotide-binding proteins (G proteins) are seen to play an important role in cellular processes such as sensual and visual perception, protein synthesis, vesicular transport, cell growth and differentiation to name a few. The ability of G proteins to function as molecular switches that cycle between a GTP bound (Active) and GDP bound (Inactive) states mediates their cellular functions. A structurally and mechanistically conserved GTP-binding domain (G domain) allows GTP binding to G proteins. The G domain comprises five adjacent consensus motifs called G boxes, which are separated by amino acid spacers of different lengths. Mutations in the G domain coding region of G proteins lead to heritable disorders. Changes in expression of G proteins lead to physiological or neurodegenerative disorders and their treatment. Accurate determination of the G domain sequence in G proteins is of utmost importance for their use in the identification of diseasecausing mutations, identification of mechanism of disorders and exploring their use as therapeutic proteins. In the current study, we introduce a Spacers and Mismatch Algorithm (SMA) which can predict G domains in a given protein sequence, based on user-specified constraints. SMA can be implemented via a web-based server at https://***/datascience/gboxes/. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dermatology

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Enhancing acute leukemia classification through hybrid fuzzy C means and random forest methods

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Measurement: Sensors 2025年 39卷

作者： Lakshmi Narayanan, K. Santhana Krishnan, R. Harold Robinson, Y. Vimal, S. Rashid, Tarik A. Kausha, Chetna Mehedi Hassan, Md. Department of Electronics and Communication Engineering Francis Xavier Engineering College Tirunelveli India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology India Department of Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering University of Kurdistan Hewler Erbil Iraq Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Discipline Khulna University Khulna9208 Bangladesh

Leukemia is a category of cancer that is normally found in blood and bone marrow, and which causes rapid abnormal development in the making of white blood cells than the required amount. The produced white blood cells could be ineffective to fight against harmful infections and can even prejudice or restrict the capability of the bone marrow to generate red blood cells and blood platelets. If this is not diagnosed in the earlier stage, it may start to affect the function of the internal organs and cause death. Normally, entire blood counts image analysis and diagnosis are done manually which is an inaccurate and time-intensive process. In this proposed method the classification is tested with two Machine Learning algorithms which are Hybrid Fuzzy C Means (FCM) and Random Forest algorithm (RF) and Support Vector Machine for the detection and classification of Acute Leukemia disease and their performance was evaluated. The dataset comprised of 8637 images which included infected images, normal images and augmented images from different dataset providers and RGB to CMYK conversion with histogram equalization is applied for pre-processing, K means for Image Segmentation. Experimental results convey that Hybrid FCM and RF Algorithm attained an accuracy of 99.06 %, a sensitivity of 99.4 %, and a specificity of 97.8 % respectively, and the ROC (Receiver Operating Characteristic) curve shows that the result produced by the Hybrid FCM & RF based Classifier is best suitable in diagnosing the classification of the Acute Leukemia disease. The tool used for developing the proposed method was Matlab R2018 software. © 2025 The Authors

关键词： Lung cancer

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Comparative Analysis of Deep Learning Models for Alzheimer’s Disease Classification Using MRI Scans

Comparative Analysis of Deep Learning Models for Alzheimer’...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md Shohanur Rahman Fabiha Fairuz Zahin Mrityunjoy Gain Avi Deb Raha Dept. of Mechatronics Engineering KUET Khulna Bangladesh Computer Science and Engineering Discipline Khulna University Khulna Bangladesh AI Innovation Lab Khulna Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Alzheimer’s disease (AD) is a progressive neurological disorder characterized by memory loss, cognitive decline, and behavioral changes, representing a major source of disability in the aging population. Accurate and early diagnosis is essential for enabling timely interventions that may help slow the progression of the disease. Traditional diagnostic procedures typically involve clinical assessments and MRI imaging to detect early structural brain alterations. However, manual interpretation of MRI data is often subjective, labor-intensive, and susceptible to inter-observer variability. Recent developments in deep learning (DL), particularly through Convolutional Neural Networks (CNNs), offer automated and efficient methods for analyzing medical images. This study presents a comparative evaluation of five advanced CNN architectures—ResNet-50, MobileNet, VGG16, InceptionV3, and Xception—applied to AD classification using transfer learning strategies. To address dataset imbalance, the Synthetic Minority Over-Sampling Technique (SMOTE) is employed, which generates new synthetic samples by interpolating between existing instances and their nearest neighbors. Experimental analysis shows that InceptionV3 achieves the highest performance with 98% accuracy and an F1-score of 98%, followed closely by MobileNet and VGG16, each with 97% accuracy. This approach can support healthcare practitioners by minimizing dependence on subjective analysis, leading to more consistent, scalable, and precise diagnostic practices for Alzheimer’s disease.

关键词： Deep learning Accuracy Magnetic resonance imaging Transfer learning Medical services Manuals Brain modeling Convolutional neural networks Alzheimer's disease Diseases

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