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

作者： Menon, Aparna Yashwanthika, R. Shreya, J.V. Somasundaram, K. Thangavel, Senthil Kumar Asawa, Shreeya 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

ISBN: (纸本)9798331523923

Due to the increasing demand for public safety and the rapid development of intelligent surveillance networks, the re-identification of individuals (Re-ID) has become one of the most important research topics in computer vision. Person re-identification (Re-ID) has a wide range of applications, which increases its popularity in recent studies. The main goal of person re-identification (Re-ID) is to retrieve people with the same identity from different cameras. In the open world, there are still some challenges. First, existing re-ID research usually assumes that only one-factor variable (view, clothing, pedestrian pose, pedestrian occlusion, image resolution, RGB/IR modality) changes, and ignores the complexity of multi-factor variables in the open world. Second, existing ID methods are overly dependent on clothing color and other obvious pedestrian features that can be easily obscured or changed. In addition, the lack of benchmark datasets that include multifactor variables hinders the practical application of ID in the open world. The proposed model uses a Deep learning model -Siamese triplet network to extract the features, and a similarity measure to re-identify the individual. The evaluation metrics used are cumulative matching features (CMC) and mean average precision (mAP). Preliminary results show that the model performs well in comparison to existing methods, and future work will focus on improving robustness under varying environmental conditions. © 2025 IEEE.

关键词： Reamers

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An Interpretable Machine Learning Model for Meningioma Grade Prediction

An Interpretable Machine Learning Model for Meningioma Grade...

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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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Simple One-step Multi-view Clustering with Fast Similarity and Cluster Structure Learning

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IEEE Signal Processing Letters 2025年 32卷 1850-1854页

作者： Xia, Xianxian Huang, Dong Yang, Chen-Min He, Chaobo Wang, Chang-Dong South China Agricultural University College of Mathematics and Informatics Guangzhou China South China Normal University School of Computer Science Guangzhou China Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China

Multi-view clustering (MVC) is essential for integrating heterogeneous data from multiple sources. However, many existing approaches are hindered by high computational complexity and the separate optimization of similarity and cluster structures. In light of these challenges, this paper presents a novel anchor-based MVC method termed simple one-step multi-view clustering with fast similarity and cluster structure learning (SONIC), which models adaptive anchor learning, multi-view similarity structure learning, and discrete cluster structure learning in a joint framework. In particular, we employ the anchor-based multi-view similarity learning to capture the consensus manifold structure latent in multiple views, thereby constructing a unified bipartite graph with adaptive anchor learning and view weighting. Then we impose a low-rank constraint on the bipartite graph structure to directly reveal the desired number of clusters without additional post-processing. An efficient alternating minimization algorithm is developed to optimize the model, resulting in a computational complexity that scales linearly with the number of samples. Extensive experiments on eight benchmark datasets demonstrate the superior performance of SONIC in both clustering quality and computational efficiency. © 1994-2012 IEEE.

关键词： Data assimilation

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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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ACRL-10K: A Dataset for Air Conditioner Refrigerant Leak Smoke Detection

ACRL-10K: A Dataset for Air Conditioner Refrigerant Leak Smo...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jiang, Yuhan Wang, Zhongyuan Hua, Jie Liang, Jinbi Xu, Zengmin National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350368741

In this paper, we introduce a new dataset for air conditioner refrigerant leak smoke detection, called ACRL-10K. The dataset is designed to develop algorithms for detecting refrigerant leak smoke faults during air conditioner recycling. It contains a total of 10, 724 images covering three common scenarios of air conditioner refrigerant leak: loading port, refrigerant extraction, and disassembly. All images are sourced from 656 video segments of real air conditioner recycling scenarios, captured by surveillance cameras deployed on an environmental company's recycling production line. The annotations for the refrigerant leak smoke in the images are performed by industry experts to ensure high accuracy and consistency. To the best of our knowledge, ACRL-10K is the first dataset specifically designed for refrigerant leak smoke detection. Based on the ACRL-10K dataset, we present the performance of mainstream object detectors, including the YOLO series, as a baseline to conduct the following work: 1) preliminarily summarize the challenges of using the dataset for refrigerant leak smoke detection;2) show the detection results of benchmark methods;and 3) make a comparison to identify the strengths and weaknesses of the baseline algorithms. In practice, the ACRL-10K dataset would hopefully advance research and applications in refrigerant leak early warning during air conditioner dismantling and recycling processes. © 2025 IEEE.

关键词： Air Conditioner Dataset Object Detection Refrigerant Leak Smoke Detection

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