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5th International Conference on Advanced Research in computing, ICARC 2025

作者： Wijesundera, Sarith Peiris, Suresh Michael University of Westminster Department of Computer Science and Engineering London United Kingdom Informatics Institute of Technology School of Computing Colombo Sri Lanka

ISBN: (纸本)9798331530983

Data Augmentation (DA) is an effective strategy to increase model generalisation. In Natural Language Processing (NLP), DA remains in its early stages, primarily due to the inherent sensitivity of textual data, which complicates the augmentation process. These complexities are further increased in multilingual contexts, as DA techniques effective in one language may not yield comparable results in others. As a result, manually identifying optimal DA techniques that preserve semantic integrity and are language agnostic remains a significant challenge. It emphasises the necessity for automating the text DA process with Explainable Artificial Intelligence (XAI), which offers a promising approach by calculating word importance scores to effectively guide the augmentation. In this paper, a comprehensive review of existing approaches in auto-text DA is presented, beginning with an exploration of the concept of auto-text DA, followed by a discussion on its cross-lingual applications, and concluding with the integration of XAI for semantic preservation. The paper highlights the critical need for further research to enhance the effectiveness and applicability of XAI in auto-text DA, enabling its use across diverse languages. © 2025 IEEE.

关键词： Transfer learning

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Attention-Based Recurrent Neural Networks for Medical Image Classification 5th

Attention-Based Recurrent Neural Networks for Medical Image ...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Manjunath, K.V. Alam, Intekhab Nidhya, M.S. Verma, Tushar K. School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Attention-primarily based Recurrent Neural Networks (RNNs) are a technique for medical image classification. RNNs can robotically extract temporal and spatial facts from scientific imaging statistics and make sensible decisions. RNNs are especially nicely-perfect for clinical photograph category obligations, cutting-edge their potential to perceive lengthy-term temporal styles, recognize the relationships between distinct medical picture datasets, and enable In this paper, we overview the existing tries to use interest-based RNNs for medical image classification, discuss the contemporary demanding situations and suggest some ability future guidelines for studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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Policy iteration-based adaptive optimal control for Markov jump systems: a transition-probability-free asynchronous approach

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Science China(Information Sciences) 2024年第11期67卷 363-364页

作者： Weidi CHENG Chengcheng REN Shuping HE Changyin SUN Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education) School of Electrical Engineering and Automation Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Engineering Research Center of Autonomous Unmanned System Technology (Ministry of Education) School of Artificial Intelligence Anhui University

The controller and filter design problems of Markov jump systems(MJSs) have gained significant attention over the past few decades. These studies include various aspects,including stochastic stabilization [1], optimal tracking control [2], and dissipative filter design [3]. Although numerous publications address the optimal controller design for MJSs, the issue of hidden MJSs, particularly those with mismatched jumping modes between the system and the controller, has rarely been explored.

关键词： Markov processes

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Advanced Video-Based Deep Learning Framework for Comprehensive Detection, Diagnosis, and Classification of Dermatological Conditions in Real-Time Datasets 6

Advanced Video-Based Deep Learning Framework for Comprehensi...

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6th International Conference on Futuristic Trends in Networks and computing Technologies, FTNCT 2024

作者： Ahmed, Syed Thouheed Guthur, Amogh S. Rai, Pratyush Kumar Pranava Swaroop, N. School of Computing and Information Technology REVA University Bengaluru India Department of Computer Science and Engineering Sri Venkateshwara College of Engineering VTU Bengaluru India School of Computer Science and Engineering Presidency University Bengaluru India

The advanced acne detection model showcased in this project utilizes deep learning methods to accurately classify skin conditions, including blackheads, dark areas, blemishes, and creases. It employs a YOLOv5 format annotation scheme to analyze spatial and temporal information from video sequences, resulting in exceptional performance in detecting seven distinct classes. The model's resilient performance indicates high accuracy, with a mean Average Precision (mAP) of about 0.85-0.9 at an IoU threshold of 0.5. It also demonstrates generalization and robustness with an mAP of 0.5-0.55 across IoU thresholds from 0.5 to 0.95, making it suitable for real-world dermatological assessments. The proposed method enables early detection and more effective treatment by monitoring skin conditions over time, significantly impacting dermatological image analysis. The goal is to improve patient outcomes and provide personalized skincare recommendations using deep learning techniques, benefiting clinicians and researchers in analyzing and categorizing skin conditions. Additionally, incorporating additional data sources like clinical images or medical histories can enhance the model's diagnostic capabilities and accuracy. Expanding the dataset will enhance the model's generalizability and robustness for new skin conditions. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Deep learning

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Evaluation of Different Network Coding Algorithm Strategies for Wireless Systems 5th

Evaluation of Different Network Coding Algorithm Strategies ...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Parameswaran, Murali Savita Kannagi, A. Panwar, Rahul School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

These research objectives are to evaluate and compare distinct community coding algorithms that can be applied in wireless networks to maximize network efficiency, throughput, and facts switch velocity. Special procedures are taken, such as coding in any respect of nodes within the community or sure nodes, which includes area nodes or routers. One-of-a-kind coding algorithm techniques might be tested in this study, including threshold coding and coded pooling. The chosen method will depend on the type of community, the scale, the traffic charge, and the favored performance goals, which include latency or bandwidth. Through leveraging network coding, packets can be transmitted and retransmitted without re-encoding. Studies have been carried out to evaluate the overall performance of diverse network coding algorithms for wireless networks inclusive of flooding, random linear network coding (RLNC), most distance separable codes (MDSC), directional network coding (DANC) and layered community coding (LNC). At the same time, MDSC and directional network coding have been proven more potent in factor-to-factor conversation eventualities. LNC is the most efficient average in many instances because of its ability to modify in real-time to changing link conditions. All of those algorithms provide a couple of advantages for wireless networks. This abstract affords an overview of the unique algorithm techniques, evaluates their effectiveness in numerous scenarios, and highlights their blessings for wireless networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network coding

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Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need 38

Unlearnable 3D Point Clouds: Class-wise Transformation Is Al...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC. © 2024 Neural information processing systems foundation. All rights reserved.

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AI-Powered Diagnostic Tool for Retinal Disease Detection Using Convolutional Neural Networks 5

AI-Powered Diagnostic Tool for Retinal Disease Detection Usi...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Moovendran, Manikandan Kumar, K. K. Dhanush Sashanth, J.M. Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai603203 India Chennai603203 India

ISBN: (纸本)9798350389609

Accurate and timely detection of retinal disease helps prevent severe vision impairment, although traditional diagnostics take plenty of time and are demanding to perform. This led to the design of the advanced diagnostic tool, in which AI and Convolutional Neural Networks (CNNs) will enhance the identification of retinal disorders. This system will analyze a retina image to detect among others, diabetic retinopathy and age-related macular degeneration, among others. Advanced deep learning algorithms enable the tool to rapidly identify the subtle patterns and anomalies in retinal scans, which even an astute human analyst might not identify, providing insightful and diagnostic support to healthcare experts. This AI-based approach leads to the acceleration of the diagnostic workflow and the minimization of errors, which elevates overall diagnostic accuracy and allows earlier intervention. This technology will contribute to making high-quality retinal care more accessible and efficient, finally arriving at the benefit of a patient at an earlier time of early detection and treatment. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on a New Speech Synthesis Fraud Method 9

Research on a New Speech Synthesis Fraud Method

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9th International Conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Zhang, Hang Li, Yuhui Kui, Shijian Qin, Zhaoyi Harbin Institute of Technology Faculty of Computing Harbin China School of Automotive Engineering Harbin Institute of Technology Harbin China Harbin Institute of Technology Faculty of New Energy Harbin China School of Information Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350376548

With the rapid development of deep learning technology, deep counterfeiting has also quietly ascended to the stage of history, and voice disguise fraud, as a new type of attack means, has laid a huge hidden danger for people's privacy security. Based on the research of speech synthesis, this paper designs and implements a speech disguise synthesis system based on the Tacotron2 model, and uses the WaveGlow model as a vocoder to replace the original WaveNet module, which is used to simulate experimental research from the direction of the attack to achieve a better synthesis effect. After this experiment, this paper verifies the feasibility of the program and provides technical references for subsequent security technology development. © 2024 IEEE.

关键词： Differential privacy

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Automated Radiology Report Generation from Chest X-ray Scans Using Deep Learning 6th

Automated Radiology Report Generation from Chest X-ray Scans...

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6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Veer, V. Druva Priya, S. Baghavathi Tamilselvi, M. Department of Computer Science and Engineering Amrita School of Computing AmritaVishwaVidyapeetham Chennai India Information Technology Department University of Technology and Applied Sciences Nizwa Oman

ISBN: (纸本)9789819778614

A chest X-ray is a common diagnostic tool for many thoracic illnesses. Interpreting these images and coming up with accurate diagnostic results is a difficult and time-consuming task for radiologists. Recent results using deep learning approaches to automate the analysis of chest X-rays are promising. This paper provides a comprehensive description of deep learning techniques used to chest X-ray processing for the diagnosis of several diseases, including TB, pneumonia, and COVID-19. This paper provide a summary of the primary deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention models, that are used for disease classification, localization, segmentation, and report generation from chest X-rays. This paper also discuss explainability techniques that are used to offer transparency and increase trust in model predictions. Along with existing issues, future directions are taken into consideration, such as multimodal learning, few-shot learning, and model evaluation. This paper explains the model which takes less time and give the optimal results. In order to facilitate future research into developing AI systems that are more accurate, understandable, and advantageous from a medical standpoint, this article provides an overview of the most current developments in deep learning techniques for automated chest X-ray diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Contrastive Language-Entity Pre-training for Richer Knowledge Graph Embedding 4th

Contrastive Language-Entity Pre-training for Richer Knowled...

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4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Papaluca, Andrea Krefl, Daniel Lensky, Artem Suominen, Hanna School of Computing The Australian National University Canberra Australia Canberra Australia School of Engineering and Technology The University of New South Wales Kensington Australia School of Biomedical Engineering The University of Sydney Sydney Australia School of Medicine and Psychology The Australian National University Canberra Australia Department of Computing University of Turku Turku Finland

ISBN: (纸本)9789819787012

In this work we propose a pretraining procedure that aligns a graph encoder and a text encoder to learn a common multi-modal graph-text embedding space. The alignment is obtained by training a model to predict the correct associations between Knowledge Graph nodes and their corresponding descriptions. We test the procedure with two popular Knowledge Bases: Wikidata (formerly Freebase) and YAGO. Our results indicate that such a pretraining method allows for link prediction without the need for additional fine-tuning. Furthermore, we demonstrate that a graph encoder pretrained on the description matching task allows for improved link prediction performance after fine-tuning, without the need for providing node descriptions as additional inputs. We make available the code used in the experiments on GitHub(https://***/BrunoLiegiBastonLiegi/CLEP) under the MIT license to encourage further work. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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