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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Golbaf, Ali Veljanoski, Damjan Chawda, Prutha Gaudl, Swen Hanemann, C. Oliver Ifeachor, Emmanuel School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom Peninsula Schools of Medicine University of Plymouth University Plymouth United Kingdom

Accurate preoperative prediction of meningioma grade is crucial for enhancing the clinical management of these tumours. In this study, we developed a non-invasive machine learning (ML) model to predict meningioma grade using clinical features and radiomics features from preoperative MRI scans, focusing on interpretability to improve clinical adoption of such models. A dataset of 94 patients from The Cancer Imaging Archive (TCIA) was analysed. Clinical features and radiomics features from T1-weighted contrast-enhanced (T1C) and T2-weighted Fluid Attenuated Inversion Recovery (T2 FLAIR) scans were utilised. Two feature subsets were constructed: one using radiomics features alone and the other combining clinical and radiomics features. Feature selection was performed using a modified Least Absolute Shrinkage and Selection Operator (LASSO) technique. Four ML models: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB), were developed. SHapley Additive exPlanations (SHAP) was employed to address the blackbox nature of ML models by providing radiomics overall feature importance scores and model interpretation. Results using the clinical-radiomics subset showed that the SVM outperformed others (test AUC: 0.83), indicating its reliability for predicting meningioma grade. SHAP highlights discriminative radiomics features and their interaction with clinical features, thereby enhancing the clinical adoption of such models. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Logistic regression

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An Enhanced Optical-Flow Based Attendance Tracking System for Campus Environment 4

An Enhanced Optical-Flow Based Attendance Tracking System fo...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Ram, S Karthik Somasundaram, K. Tangavel, Senthil Kumar Venkatesa, M.G. Kolandapalayam, Selvanayaki Reddy, N Manjunadh Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India Amrita School of Physical Sciences Department of Mathematics Amrita Vishwa Vidyapeetham Coimbatore India Ashland University Shanmugam Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

Manual attendance marking consumes significant time during classes, as it involves calling out students' names individually and recording their presence either physically or digitally. Although efforts to automate this process have been made, such as using biometric data like fingerprints, these methods are still time-consuming due to the need for students to queue up for scanning. A computer vision-based attendance system offers a more efficient and seamless alternative by leveraging advanced face recognition algorithms to identify students or employees automatically. This approach uses convolutional neural networks (CNNs) to extract facial features from images and match them with pre-registered data. A pipeline combining the Multi-task Cascaded Convolutional Networks (MTCNN) for face detection and alignment with the FaceNet model for recognition and classification is employed to ensure high accuracy. The system marks attendance in the backend, eliminating the need for manual intervention. Additionally, the model's performance is compared with traditional recognition and classification algorithms to demonstrate its superior efficiency and reliability, making it an ideal solution for automating attendance systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Robotic Cognitive Behavioural Therapy: rCBT 16th

Robotic Cognitive Behavioural Therapy: rCBT

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16th International Conference on Social Robotics, ICSR + BioMed 2024

作者： Samani, Hooman Zaraki, Abolfazl Davies, Daisy-Rose Fernando, Owen Noel Newton Creative Computing Institute University of the Arts London London United Kingdom and Robotics Research Group University of Hertfordshire Hatfield United Kingdom School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789819789627

This paper introduces Robotic Cognitive Behavioural Therapy (rCBT) as a novel approach to overcoming the limitations of traditional Cognitive Behavioural Therapy (CBT) in treating emotional and psychological issues. After a brief overview of CBT and its inherent challenges, this paper explores the innovative application of physical robots in therapy, building on the success of technology-based CBT methods that have effectively treated various mental health conditions. The proposed rCBT solution is a compact, portable, and cost-effective 3D-printed robot designed to aid individuals with anxiety or depression. This robot includes interactive features such as a diary, health questionnaires, daily mood monitoring, to-do lists, mindfulness exercises, and a crisis helpline, enabling users to track their emotional states and activities. These functions support healthcare professionals and users in identifying emotional patterns and triggers. The robot can monitor physiological signals like heart rate. This research aims to enhance the accessibility and effectiveness of CBT, suggesting that rCBT could significantly broaden the therapy’s reach and improve mental health outcomes for a larger population. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Social robots

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Mixed finite element methods for biharmonic obstacle problems

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SeMA Journal 2025年 1-10页

作者： Piersanti, Paolo Sun, Tianyu School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) 2001 Longxiang Blvd. Longgang District Shenzhen China Department of Mathematics and Institute for Scientific Computing and Applied Mathematics Indiana University Bloomington 729 East Third Street Bloomington IN United States

This paper is devoted to the study of an alternative Finite Element Method to the one originally proposed by Ciarlet & Raviart, and then complemented by Ciarlet & Glowinski, for studying the numerical approximation of the solution of the biharmonic problem. In addition to providing an alternative way of approximating the solution of the biharmonic problem, the method here presented is valid also in the context where the solution to the biharmonic problem is subjected to a constraint. The theoretical results we derived are complemented by a series of numerical experiments. © The Author(s), under exclusive licence to Sociedad Española de Matemática Aplicada 2025.

关键词： Elasticity theory Finite element method Obstacle problems Penalty method Variational inequalities

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Domination Number in Sierpiński Networks

Domination Number in Sierpiński Networks

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International Conference on Computational engineering, ICCE 2023

作者： Anitha, J. Rajasingh, Indra Sharon, Jane Olive Department of Mathematics Rajalakshmi Institute of Technology Chennai India Department of Mathematics for Excellence Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computing Technologies SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9783031777639

If there is at least one neighbour in K for each vertex in V that is not in K, then a set K⊆V(G) is a dominating set (DS) in G(V,E). The minimal cardinality of a DS of G is the domination number(DN) of G, represented by γ(G). In this paper, we determine the γ(G) for Sierpiński networks, Str and the divide-and-swap cubic networks, DSCr. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 68R10 81Q30 82B20 82C20 Divide-and-swap cubic networks Dominating set Sierpiński Networks

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Adaptive Genghis Khan Shark based convolutional denoising autoencoder for quality enhancement of satellite images

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International Journal of Information Technology (Singapore) 2025年 1-14页

作者： Rajalakshmi, M. Subramanian, R. S. Sankara Tamilselvan, S. Bhuvanesh, A. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India

Satellite image processing often enhances feature perception and visibility for exact feature identification. Additionally, the change improves remote sensing data quality and clarity. Due to their long distances, these photos often have weather-related noise and distortion that must be fixed. This paper addresses these challenges with a Deep Learning (DL) satellite image denoising technique. An Adaptive Genghis Khan Shark Algorithm (AGKSA) and a Convolutional Denoised Autoencoder are used to improve satellite images in this study. The CDAE settings are optimized using the AGKSA while keeping important image attributes. AGKSA is used in this hybrid model to optimize CDAE performance under various noise conditions. Experimental results showed that the proposed model outperforms traditional image denoising approaches in satellite imagery feature extraction and visual quality. In very precise image processing applications like environmental monitoring and land use classification, the CDAE-AGKSA works well. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Adaptive Genghis Khan algorithm Convolutional denoised autoencoder Satellite image enhancement Satellite image processing

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Enhancing Diagnostic Accuracy of Drug-Resistant Tuberculosis on Chest X-Rays Using Data-Efficient Image Transformers

Enhancing Diagnostic Accuracy of Drug-Resistant Tuberculosis...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Mnyambo, Joan Jonathan Aly, Amir Zhou, Shang-Ming Wei, Yinghui Mullin, Stephen Ifeachor, Emmanuel School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom School of Nursing and Midwifery University of Plymouth Plymouth United Kingdom Peninsula Medical School University of Plymouth Plymouth United Kingdom

Tuberculosis is an infectious disease with increasing fatalities around the world. The diagnosis of the disease is a major challenge to its control and management due to the lack of adequate diagnostic tools, contributing significantly to the prevalence of drug-resistant tuberculosis. Convolutional Neural Network (CNN) models have recently been developed to detect drug-resistant tuberculosis by analyzing chest radiograph images from the TB portal, but the classification results are low. This is because CNNs struggle to capture complex global and overlapping features in medical imaging, such as chest radiographs of drug-resistant tuberculosis. In contrast, transformers excel in these areas by utilizing self-attention mechanisms that detect inherent subtle and long-range dependencies across images. In this study, we used a pretrained data-efficient image transformer (DEiT) model to enhance the diagnosis of drug-resistant tuberculosis and differentiate it from drug-sensitive tuberculosis. The new model achieved an AUC of 80% in the detection of drug-resistant tuberculosis, an improvement of 13% in the AUC compared to current CNN models using data from the same source. The bootstrap significance test shows that the difference in AUCs is statistically significant. The results of the study can help healthcare providers improve drug-resistant tuberculosis diagnostic accuracy and treatment outcomes. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Disease control

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Grape Guard:A YOLO-based mobile application for detecting grape leaf diseases

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Journal of Electronic Science and Technology 2025年第1期23卷 60-75页

作者： Sajib Bin Mamun Israt Jahan Payel Md.Taimur Ahad Anthony S.Atkins Bo Song Yan Li Department of Computer Science and Engineering Daffodil International UniversityDhaka1207Bangladesh Department of Computer Science University of Southern QueenslandToowoomba4350Australia Faculty of Digital TechnologyInnovationand BusinessStaffordshire UniversityStoke-on-TrentST42DEUnited Kingdom School of Engineering University of Southern QueenslandToowoomba4350Australia School of Mathematics Physics and ComputingUniversity of Southern QueenslandToowoomba4350Australia

Grape crops are a great source of income for *** yield and quality of grapes can be improved by preventing and treating *** farmer’s yield will be dramatically impacted if diseases are found on grape *** detection can reduce the chances of leaf diseases affecting other healthy *** studies have been conducted to detect grape leaf diseases,but most fail to engage with end users and integrate the model with real-time mobile *** study developed a mobile-based grape leaf disease detection(GLDD)application to identify infected leaves,Grape Guard,based on a TensorFlow Lite(TFLite)model generated from the You Only Look Once(YOLO)v8 model.A public grape leaf disease dataset containing four classes was used to train the *** results of this study were relied on the YOLO architecture,specifically YOLOv5 and *** extensive experiments with different image sizes,YOLOv8 performed better than ***8 achieved 99.9%precision,100%recall,99.5%mean average precision(mAP),and 88%mAP50-95 for all classes to detect grape leaf *** Grape Guard android mobile application can accurately detect the grape leaf disease by capturing images from grape vines.

关键词： Bacterial diseases Grape Guard Mobile-based application YOLOv5 YOLOv8

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Deciphering of solvent fraction, thermal relaxation and cubic autocatalysis in unsteady nonlinear radiative ternary nanoliquid flow over a slow rotating disk

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Multiscale and Multidisciplinary Modeling, Experiments and Design 2025年第6期8卷 1-15页

作者： Kumar, Thanesh Deevi Reddy, Prabhakara Reddy Reddy, Anitha Deevi Remidi, Srinivas Chohan, Jasgurpreet Singh Kedia, Ankit Mathematics and Computing Skills Unit University of Technology and Applied Sciences - Nizwa Nizwa Oman School of Mechanical Engineering Rayat Bahra University Mohali India Faculty of Engineering Sohar University Sohar Oman Department of Mechanical and Aerospace Engineering NIMS Institute of Engineering & Technology NIMS University Rajasthan Jaipur India

The present study explores the consequence of homogeneous–heterogeneous chemical reactions on the unsteady flow of Boger ternary nanoliquid via a slow rotating disk with the porous medium, suction, and non-linear thermal radiation. Additionally, the temperature equation is modelled using the Cattaneo–Christov heat flux theory. Fluid flow past revolving disk concepts are utilized in various technical applications, including gas turbine engines, flywheels, gears, and brakes. The analysis considers titanium dioxide, cobalt ferrite, and magnesium oxide nanoparticles that are suspended in water. Using similarity variables, the governing partial differential equations (PDEs) are converted to dimensionless ordinary differential equations (ODEs). Further, the resultant ODEs are numerically solved using the finite difference method (FDM). The impact of several parameters on the velocity, thermal, and concentration profiles are depicted with the graphical illustrations. The velocity profiles decline as the ratio of relaxation time and porous parameter values rise. An increase in the solvent fraction parameter enhances the velocity profiles. The velocity profiles diminish as the suction parameter increases. As the radiation parameter values increase, the temperature profile rises. The concentration profile diminishes as the intensity of the homogeneous and heterogeneous reaction parameters escalates. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Stream flow

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ICNN-LSTM+: A batch-based incremental ransomware detection system using Sysmon

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IEEE Access 2025年 13卷 87978-87998页

作者： Ispahany, Jamil Islam, Md Rafiqul Arif Khan, M. Islam, Md Zahidul Cyber Security Cooperative Research Centre (CSCRC) Kingston 2600 ACT Australia Charles Sturt University School of Computing Mathematics and Engineering Bathurst 2795 NSW Australia Charles Sturt University School of Computing Mathematics and Engineering Albury 2640 NSW Australia

Ransomware remains a persistent and evolving cyber threat, requiring adaptive and efficient detection mechanisms. This study presents a novel CNN-LSTM-based detection system that leverages Sysmon logs for real-time analysis on Windows endpoints. Through the integration of batch-based incremental learning, the model achieves continuous adaptation to previously unseen ransomware variants, eliminating the necessity for full retraining and effectively addressing the constraints associated with conventional static detection methodologies. The proposed framework achieves an average F2-score of 99.65%, with false positive and false negative rates of 0.16% and 3.96%, respectively, even in a highly imbalanced dataset. To comprehensively evaluate its performance, we introduce the Weighted Efficiency Score (WES), a novel metric that balances detection accuracy and throughput. The iCNN-LSTM+ model attains the highest WES among CNN-LSTM architectures, demonstrating its efficiency in real-world applications. Furthermore, parallel LSTM processing with attention mechanisms enhances throughput, making the system scalable for large-scale deployments. These findings establish the iCNN-LSTM+ framework as a resilient and adaptable solution for real-time ransomware detection, effectively mitigating the risks posed by emerging threats. © 2013 IEEE.

关键词： attention mechanism CNN-LSTM deep learning incremental learning ransomware detection sysmon

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