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Vision Transformers for Mpox Detection

Vision Transformers for Mpox Detection

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International conference on Artificial Intelligence in information and communication (ICAIIC)

作者： Gelan Ayana So-Yun Park Se-Woon Choe Medical IT Convergence Engineering Kumoh National Institute of Technology Gumi Republic of Korea IT Convergence Engineering Kumoh National Institute of Technology Gumi Republic of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Monkeypox (Mpox) has emerged as a significant public health concern, with increasing cases reported worldwide. Accurate and timely diagnosis is crucial for effective treatment and containment. Traditional diagnostic methods, such as polymerase chain reaction, though reliable, are time-consuming and require specialized equipment. Image-based detection methods have gained attention as a complementary diagnostic tool. Conventional image-based methods rely heavily on convolutional neural networks (CNNs); however, vision transformers (ViTs) have emerged as a novel deep learning approach with potential benefits in medical imaging. CNN shave been the cornerstone of medical image analysis, but they come with limitations, particularly in handling complex and diverse visual data, whereas ViTs, originally designed for natural language processing, have shown remarkable potential in visual tasks due to their ability to capture long-range dependencies in images. This paper explores the use of ViTs, a novel deep learning architecture, in detecting Mpox. The study compares ViTs with existing CNN-based methods, evaluates their performance in detecting Mpox, and discusses the implications of their use in clinical settings. Our findings suggest that ViTs offer superior accuracy, making them a valuable tool in disease detection.

关键词： Computer vision visualization Accuracy Materials reliability Transformers Natural language processing Convolutional neural networks Polymers Public healthcare Medical diagnostic imaging

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Medical Image Noise Reduction Methods Based on Cutting-Edge Hybrid Filtering Designs

Medical Image Noise Reduction Methods Based on Cutting-Edge ...

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International conference on Smart Systems and Wireless communication, SSWC 2024

作者： Saha, Tanusree Vishal, Kumar Magadh University Bodh Gaya India Department of Information Technology JIS College of Engineering Kalyani India Department of Mathematics Magadh University Bodh Gaya India

ISBN: (纸本)9789819613472

A high-resolution noise free medical image is an essential requirement for accurate disease detection. Medical image processing and analysis techniques have become utmost important in recent decades as a supplementary competence for medical professionals in the fields of diagnosis and prevention. Obtaining high-quality images without any interference is a crucial requirement for effectively managing therapeutic images. Medical images may contain many types of noises for involvement of simultaneous operation of multiple equipment involved in data capturing and transmission. Various filters have been suggested for eliminating different types of noises, each having its own merits and drawbacks depending on its capabilities and projected benefits. Our research depicts a technique for removing noises from medical images using a hybrid filtering approach based on ensemble adaptive mean filtering and Block-matching-3D filtering. The proposed methodologies specifically aim to reduce the speckle noise in brain MRI images obtained from ultrasonic therapy by employing hybrid filtering techniques. This proposed hybrid filtering algorithms integrate the most effective aspects of high-performance filters to achieve a de-noised image that retains fine details, edges, and textures. The statistical performance measurement metrics like Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR) are employed to evaluate the improved visual quality of the images. The results indicate that our proposed hybrid filtering technique functioning efficiently and provide better quality de-noised image compare with other existing de-noising techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image enhancement

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Toward Consistency in Writing Proficiency Assessment: Mitigating Classification Variability in Developmental Education 17

Toward Consistency in Writing Proficiency Assessment: Mitiga...

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17th International conference on Computer Supported Education, CSEDU 2025

作者： Da Corte, Miguel Baptista, Jorge University of Algarve Faro Portugal INESC-ID Lisboa Lisbon Portugal

ISBN: (纸本)9789897587467

This study investigates the adequacy of Machine Learning (ML)-based systems, specifically ACCUPLACER, compared to human rater classifications within U.S. Developmental Education. A corpus of 100 essays was assessed by human raters using 6 linguistic descriptors, with each essay receiving a skill-level classification. These classifications were compared to those automatically generated by ACCUPLACER. Disagreements among raters were analyzed and resolved, producing a gold standard used as a benchmark for modeling ACCUPLACER'S classification task. A comparison of skill levels assigned by ACCUPLACER and humans revealed a "weak" Pearson correlation (ρ = 0.22), indicating a significant misplacement rate and raising important pedagogical and institutional concerns. Several ML algorithms were tested to replicate ACCUPLACER'S classification approach. Using the Chi-square (χ2) method to rank the most predictive linguistic descriptors, Naïve Bayes achieved 81.1% accuracy with the top-four ranked features. These findings emphasize the importance of refining descriptors and incorporating human input into the training of automated ML systems. Additionally, the gold standard developed for the 6 linguistic descriptors and overall skill levels can be used to (i) assess and classify students' English (L1) writing proficiency more holistically and equitably;(ii) support future ML modeling tasks;and (iii) enhance both student outcomes and higher education efficiency. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Natural language processing systems

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Automated Classification of Snoring and Non-Snoring Sound Signals Based on Singular Superlet Transform

Automated Classification of Snoring and Non-Snoring Sound Si...

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International conference on Signal processing and communication (ICSC)

作者： Dhanhanjay Pachori Kamal Raj Tapan Kumar Jain Department of Electronics and Communication Engineering Indian Institute of Information Technology Nagpur India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

This paper proposes a new method for automated classification of snoring and non-snoring sound signals. The snoring and non-snoring sound signals have been represented using time-frequency representations (TFRs) obtained from singular superlet transform (SST). The obtained TFRs have been given as an input to various machine learning based classifiers, like convolutional neural network (CNN), visual geometry group 16-layer network (VGG-16), EfficientNet-B0, and hybrid VGG-16 models for the classification of snoring and non-snoring sound signals. From the experimental study, it has been observed that the hybrid VGG-16 with support vector machine (SVM) model has provided best performance among all the studied models with an accuracy, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 96.00%, 95.60%, 96.40%, 0.9602, 95.63%, and 0.9200 respectively. The good performance measures in automated classification of snoring and non-snoring sound signals show the effectiveness of the proposed method. The proposed method finds applications in the area of sleep disorder diagnosis and it is suitable for real-time implementation.

关键词： Support vector machines Time-frequency analysis visualization Accuracy Transforms Machine learning Signal processing Sleep apnea Convolutional neural networks Matrix converters

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A Novel Channel Selection and Classification in Motor Imagery for Brain Computer Interface Using Meta Heuristic Algorithms

A Novel Channel Selection and Classification in Motor Imager...

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International conference on Smart Systems and Wireless communication, SSWC 2024

作者： Devarakonda, Nagaraju Kamarajugadda, Raviteja Anandarao, Sarvani School of Computer Science and Engineering VIT-AP University Amaravathi India Department of Information Technology Lakireddy Bali Reddy College of Engineering Mylavaram India Department of Computer Science and Engineering SRM University AP Amaravati India

ISBN: (纸本)9789819613472

A vital input for a task using the brain in BCI (Brain Computer Interface) applications is the motor imagery (MI) signal from the brain. Users of BCI systems can operate external equipment by using their brain activity, using motor imagery as a control method. Innumerous Electroencephalography (EEG) channels are used to gather nerve impulses from the brain, which are the most prevalent input for Brain Computer Interface systems while they are minimally invasive, flexible, and low in price. The computational overhead is increased by multichannel BCI systems’ high-dimensional data, which causes processing to be slower and to cost more money. EEG details are regularly gathered from over 100 different brain regions;therefore, it is essential to use channel selection algorithms to choose the ideal channels for a given circumstance. However, the primary objective of choosing the channel in EEG data analysis is to lessen the computer intricacy, improve the precision of classification by eliminating over fitting, and save setup time. In this study, we suggested a remora optimization technique that was inspired by nature to lessen the computational load brought on by several channels. Using predetermined criteria, a number of channel selection evaluation techniques, whether classification-based methods used or not it extracted the proper channel subsets. In order to determine the greatest classification accuracy, the classification procedures were utilized in the end. Three publicly available EEG datasets are used to validate the experiment (BCI Competition IV-1,2a, Competition iii-3a), and it resulted in superior classification accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Random forests

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A Proposed Model to Transliterate Digitalized Braille Text into Gujarati: An Aid for visually Challenged People 3rd

A Proposed Model to Transliterate Digitalized Braille Text i...

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3rd International conference on Advancements in Smart Computing and information Security, ASCIS 2024

作者： Jariwala, Nikisha Popat, Kalpesh Smt. Tanuben & Dr. Manubhai Trivedi College of Information Science Affiliated to Veer Narmad South Gujarat University Gujarat Surat India Faculty of Computer Applications Marwadi University Gujarat Rajkot India

ISBN: (纸本)9783031863011

information and communication Technology (ICT), in the modern digital era has become so cutting-edge and touched to each and every field but there is still a disconnect between visually impaired persons and communication technologies because of ignorance and inattention. Due to the limited availability of resources and tools;people with visual impairments are unable to communicate in writing. In this paper, researcher has tried to draw the attention towards the challenges that are faced by visually impaired individuals in written communication and also discussed the benefits of having a tool that eradicate the communication gap. To overcome the challenges faced by blind people;author has proposed a model based on Natural Language processing (NLP) and character mapping technique that act as an aid to transliterate the Braille text written by blind people to our regional language Gujarati. The technique used in the model uses standard Braille syntax. Rules are formed, lookup table is created, Braille characters are identified and they are transliterated to Gujarati language text. The model shows satisfactory result in the transliteration of Braille characters to Gujarati. It will also help visually impaired students in their education. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Natural language processing systems

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mmHIU: a human-to-human interaction understanding system based on mmWave sensing

mmHIU: a human-to-human interaction understanding system bas...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Fenglin Zhang Chenglin Wu Anfu Zhou Huadong Ma School of Computer Science Beijing University of Posts and Telecommunications

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Human-to-human interaction understanding(HIU) plays a significant role in both physical and mental health of individuals in their daily lives. Nowadays, many of HIU tasks are based on visual information, which can compromise individuals’ privacy in daily life. In this paper, we propose mmHIU, a privacy-preserving HIU system based on mmWave sensing. To achieve mmHIU, we need to address two challenges: Extraction of interaction features and processing of raw data in a manner that preserves interaction features. We design multi-level feature extraction network mmHIU-STNet, and clustering-based frame normalization strategy to address these two challenges. We evaluate mmHIU on 10,811 point cloud sequences from 9 pairs of inter-actors, and results show that its accuracy reaches 90.56%, outperforming baseline methods, with a significant improvement in recognizing complex interaction behaviors.

关键词： Point cloud compression Privacy visualization Accuracy Neural networks Signal processing Feature extraction Sensors Millimeter wave communication Speech processing

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UAV-VLA: Vision-Language-Action System for Large Scale Aerial Mission Generation 25

UAV-VLA: Vision-Language-Action System for Large Scale Aeria...

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ACM/IEEE International conference on Human-Robot Interaction (HRI)

作者： Oleg Sautenkov Yasheerah Yaqoot Artem Lykov Muhammad Ahsan Mustafa Grik Tadevosyan Aibek Akhmetkazy Miguel Altamirano Cabrera Mikhail Martynov Sausar Karaf Dzmitry Tsetserukou Skoltech Moscow Russia

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

The UAV-VLA (visual-Language-Action) system is a tool designed to facilitate communication with aerial robots. By integrating satellite imagery processing with the visual Language Model (VLM) and the powerful capabilities of GPT, UAV-VLA enables users to generate general flight paths-and-action plans through simple text requests. This system leverages the rich contextual information provided by satellite images, allowing for enhanced decision-making and mission planning. The combination of visual analysis by VLM and natural language processing by GPT can provide the user with the path-and-action set, making aerial operations more efficient and accessible. The newly developed method showed the difference in the length of the created trajectory in 22% and the mean error in finding the objects of interest on a map in 34.22 m by Euclidean distance in the K-Nearest Neighbors (KNN) approach. Additionally, the UAV-VLA system generates all flight plans in just 5 minutes and 24 seconds, making it 6.5 times faster than an experienced human operator. The code is available here: https://***/sautenich/uav-vla

关键词： visualization Navigation Human-robot interaction Nearest neighbor methods Autonomous aerial vehicles Natural language processing Satellite images Planning Trajectory Robots

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Intelligent Computing System for Breast Cancer Identification Utilizing U-Net for Digital Image Segmentation

Intelligent Computing System for Breast Cancer Identificatio...

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Intelligent Data communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Ranadheer Reddy Goli Vijaya Chandra Jadala Bhavana Jamalpur Department of Computer Science and Artificial Intelligene School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

As per WHO calculation of deaths, globally due to Breast Cancer is around one million. Breast cancer can be cured by early detection, in which abnormal breast cells proliferate uncontrollably, leading to tumor formation. Early detection of breast cancer may reduce deaths, as per World Health Organization records there are about one million deaths are caused by 2024 among them only 0.5 percent are of men. The different methodologies adopted in medical image processing related to breast cancer detection methods include mammography. We concentrated on medical image segmentation which helps to get visual information about breast tissue. This paper presents an intelligent computing system designed to support medical image processing through utilizing U-Net methodology for segmentation as part of deep learning methodologies. As part of Experimental Analysis, a BUSI dataset is used for the implementation of Deep Learning methodologies. A graphic analysis based on medical images, followed by an analysis of the results. This Intelligent Computing System based on artificial intelligence helps radiologists, medical professionals, and doctors with early and simplified breast cancer diagnosis.

关键词： Deep learning Image segmentation visualization Organizations Medical services Breast cancer Biomedical image processing Internet of Things Medical diagnostic imaging Tumors

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GRU Based Multiview Summarisation to obtain fast effective Key-frame

GRU Based Multiview Summarisation to obtain fast effective K...

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International conference on Computer, Electrical & communication Engineering (ICCECE)

作者： Vijay Anand Kandaswamy S Joshua Kumaresan Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences. Saveetha University Chennai India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

This paper proposes a novel approach to video summarisation by introducing a feature variance model that enhances video frame extraction. In typical video record aggregation systems, a variety of image processing tasks, such as acquisition, preprocessing, segmentation, and feature extraction, should be carried out with high precision to guarantee seamless system performance. Among these, feature extraction is essential since it breaks down large amounts of visual data into more manageable, informative vectors, and the success of the system is directly impacted by how effectively this process functions. Widely Recurrent Neural Networks (RNNs) are used in supervised video summarisation processes to obtain a model for the temporal dependencies between video frames. RNN’s ability to learn long-range memory in the video is limited, and CNN often focusses on local information while losing long-term knowledge when the input sequence is lengthy. Therefore, a Gated Recurrent Unit based Neural Network (GRU-NN) for video summarisation is proposed, to classify them into keyframes and non-keyframes. Keyframes are selected for summarisation, while non keyframes are discarded or reduced. We explore various keyframe extraction techniques, analyse the performance of the proposed summarisation model, and discuss the results. Finally, we offer insights into the model’s potential improvements and its broader applications in video summarisation tasks.

关键词： Deep learning Image segmentation Computer vision Analytical models Computational modeling visual analytics System performance Feature extraction Vectors Artificial intelligence

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