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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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Enhancing Human Pose Estimation in the Internet of Things via Diffusion Generative Models

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IEEE Internet of Things Journal 2025年第10期12卷 13556-13567页

作者： Wu, Sifan Zhang, Hongzhe Liu, Zhenguang Chen, Haipeng Jiao, Yingying Jilin University College of Computer Science and Technology the Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China School of discipline inspection and supervision China University of Political Science and Law Beijing100088 China Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou310027 China Institute of Blockchain and Data Security Hangzhou310059 China Zhejiang University of Technology College of Computer Science and Technology Hangzhou310014 China

With the ongoing development of public video surveillance technology, accurate human pose estimation is becoming increasingly important in urban administration and law enforcement. However, existing methods rely on large-scale dense annotations, which are labor-intensive and time-consuming. To tackle this, we propose SparsePose which leverages training videos with sparse annotations (labeled every k frames) to learn to propagate temporal poses that help to estimate the poses in unlabeled frames. Technically, we engage in a novel dual-branch architecture that combines 1) pose forecasting of the consecutive neighboring frames with 2) visual clues of the current frame and the nearest labeled frames. We theoretically derive the intra-branch and inter-branch mutual information loss to supervise that maximized pose-relevant features are extracted from the current frame and different branches complement each other to approach precise pose estimation. Additionally, we propose a diffusion generative enhancement, which improves the robustness of the model to challenging scenes from the perspective of diversity. Empirical results show that our method significantly outperforms the state-of-the-art methods in sparsely labeled pose estimation on three benchmark datasets. © 2025 IEEE.

关键词： Generative adversarial networks

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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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Facial surgery preview based on the orthognathic treatment prediction

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computer Methods and Programs in Biomedicine 2025年 267卷 108781-108781页

作者： Han, Huijun Zhang, Congyi Zhu, Lifeng Singh, Pradeep Hsung, Richard Tai-Chiu Leung, Yiu Yan Komura, Taku Wang, Wenping Gu, Min Discipline of Orthodontics Faculty of Dentistry the University of Hong Kong Hong Kong Department of Computer Science Faculty of Engineering the University of Hong Kong Hong Kong School of Instrument Science and Engineering Southeast University Nanjing China Discipline of Oral and Maxillofacial Surgery the University of Hong Kong Hong Kong Department of Computer Science and Engineering Texas A&M University TX United States

Background and Objective: Orthognathic surgery consultations are essential for helping patients understand how their facial appearance may change after surgery. However, current visualization methods are often inefficient and inaccurate due to limited pre- and post-treatment data and the complexity of the treatment. This study aims to develop a fully automated pipeline for generating accurate and efficient 3D previews of postsurgical facial appearances without requiring additional medical images. Methods: The proposed method incorporates novel aesthetic criteria, such as mouth-convexity and asymmetry, to improve prediction accuracy. To address data limitations, a robust data augmentation scheme is implemented. Performance is evaluated against state-of-the-art methods using Chamfer distance and Hausdorff distance metrics. Additionally, a user study involving medical professionals and engineers was conducted to evaluate the effectiveness of the predicted models. Participants performed blinded comparisons of machine learning-generated faces and real surgical outcomes, with McNemar's test used to analyze the robustness of their differentiation. Results: Quantitative evaluations showed high prediction accuracy for our method, with a Hausdorff Distance of 9.00 millimeters and Chamfer Distance of 2.50 millimeters, outperforming the state of the art. Even without additional synthesized data, our method achieved competitive results (Hausdorff Distance: 9.43 millimeters, Chamfer Distance: 2.94 millimeters). Qualitative results demonstrated accurate facial predictions. The analysis revealed slightly higher sensitivity (54.20% compared to 53.30%) and precision (50.20% compared to 49.40%) for engineers compared to medical professionals, though both groups had low specificity, approximately 46%. Statistical tests showed no significant difference in distinguishing Machine Learning-Generated faces from Real Surgical Outcomes, with p-values of 0.567 and 0.256, respectively. Ablation

关键词： Transplantation (surgical)

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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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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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Resource-Aware Monkeypox Diagnosis: Leveraging High-Capacity and Lightweight Models with Knowledge Distillation

Resource-Aware Monkeypox Diagnosis: Leveraging High-Capacity...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Avi Deb Raha Mrityunjoy Gain Sudip Kumar Saha Md Shohanur Rahman Apurba Adhikary Rameswar Debnath Anupam Kumar Bairagi Sujit Biswas Computer Science and Engineering Discipline Khulna University Khulna Bangladesh AI Innovation Lab Khulna Bangladesh United International University Dhaka Bangladesh Dept. of Mechatronics Engineering KUET Khulna Bangladesh Dept. of ICE Noakhali Science and Technology University Noakhali Bangladesh Computer Science Department City University of London UK

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Monkeypox, a novel zoonotic disease akin to smallpox, necessitates prompt and accurate diagnosis for effective treatment. Conventional diagnostic techniques, such as Polymerase Chain Reaction (PCR), offer high precision but require specialized equipment and trained personnel, rendering them impractical in resource-limited settings. Existing deep learning approaches for monkeypox diagnosis have predominantly relied on single, resource-intensive models, prioritizing accuracy over deployment feasibility across diverse computing platforms. In this study, we present a resource-conscious model deployment strategy that balances diagnostic accuracy with computational efficiency, enabling precise diagnosis in both resource-rich environments, such as hospitals, and resource-constrained contexts. We utilize a pretrained ConvNeXt-B model, trained on the extensive ImageNet-22K dataset, for deployment in resource-abundant scenarios, and a SqueezeNet model, optimized for resourcelimited devices using the ImageNet-1K dataset. To enhance the performance of the lightweight SqueezeNet model without increasing computational complexity, we apply Knowledge Distillation. Experimental results demonstrate that the ConvNeXt-B model achieves an accuracy of 95.75%, which is 3.47% higher than the previous studies. Similarly, the Knowledge Distillation-enhanced SqueezeNet model attains an accuracy of 91.89%, representing a 2.32% improvement over the baseline. This dualmodel approach ensures that accurate monkeypox diagnostics are accessible across a wide range of computational environments, thereby supporting more effective outbreak management and contributing to improved public health outcomes.

关键词： Accuracy Hospitals Computational modeling Telemedicine Rendering (computer graphics) Skin Security Polymers Public healthcare Diseases

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High-accuracy prediction of vessels’ estimated time of arrival in seaports: A hybrid machine learning approach

Maritime Transport Research

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Maritime Transport Research 2025年 8卷

作者： Saber, Sunny Md. Thowai, Kya Zaw Rahman, Muhammad Asifur Hassan, Md. Mehedi Bari, A.B.M. Mainul Raihan, Asif Department of Industrial and Production Engineering Bangladesh University of Engineering and Technology Dhaka 1000 Bangladesh Computer Science and Engineering Discipline Khulna University Khulna 9208 Bangladesh Institute of Forestry and Environmental Sciences University of Chittagong Chittagong 4331 Bangladesh

Optimizing the Estimated Time of Arrival (ETA) for seaport-bound vessels is crucial to maritime operations since inaccurate ETA predictions can have a ripple effect, causing vessel schedule disruptions, congestion, and decreased port operational effectiveness. To address these challenges and fill substantial deficiencies in existing prediction models, we have introduced a novel hybrid tree-based stacking machine learning framework integrating Extra Trees, AutoGluon Tabular, and LightGBM, with Random Forest Regressor (RFR) as the meta-learner. Utilizing Automatic Identification System (AIS) data from vessels in the Baltic Sea, our model significantly improves ETA predictions, achieving a mean absolute percentage error (MAPE) of 0.25 %. Compared to existing machine learning algorithms, our stacking model exhibits superior prediction performance. Our study's feature importance analysis highlights the crucial role of variables like speed, distance, course, and vessel type in ETA forecasts. Cross-validation further confirms the robustness of our ensemble model. In conclusion, this study improves predictive analytics in marine logistics by giving useful information about real-time ETA estimates. This helps port authorities make the best use of their resources, reduces vessel idle time and congestion, and increases overall efficiency and sustainability. This way, this study can significantly contribute towards attaining operational excellence and provide a strong foundation for future predictive models, advancing smart port management and maritime logistics. © 2025

关键词： Ensemble method Estimated time of arrivals (ETA) prediction Machine learning Marine vessels Port management

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