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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Kumar, Naween Murmu, Anita Dash, Yajnaseni Abraham, Ajith School of Computer Science Engineering and Technology Bennett University UP Greater Noida India School of Artificial Intelligence Bennett University UP Greater Noida India School of Artificial Intelligence Sai University Chennai India

ISBN: (纸本)9783031810794

Classification of skin abnormalities in dermatology plays an important role in the detection and diagnosis of various skin diseases such as melanoma. However, accurately classifying skin cancer remains difficult due to imbalanced classes, variances within classes, lack of training data, and failure to focus on semantically significant lesion parts. To address these issues, the proposed novel Deep-Convolutional Network (DCN) with a Machine-Learning (ML)-based SVM technique has been used to categorize skin cancer. To prevent overfitting, the DCN-SVM method is used as a classifier. Furthermore, the classification model applies the expert framework for learning based on domain functions. The effectiveness of test results using a UNet-MobileNet with 10,000 training images (HAM10000) dataset in seven distinct human-machine classes. The results demonstrate the effectiveness of this method in classifying dermatological diseases, demonstrating its potential to improve dermatological care and diagnostic accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Classification Dermoscopy Detection Skin disease Skin lesion

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A Comprehensive Review of Real-Time Sign Language Interpretation Using Various Machine Learning Models

A Comprehensive Review of Real-Time Sign Language Interpreta...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Balaraman Sundarambal Maneesh J J Chokiyan Karthikeyini Suresh Subramanian Senthil Kumar G Kirubakaran N Department of Computer Science and Business Systems Chennai Institute of Technology Chennai India Department of Electronics and Communication Engineering Excel Engineering College Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Real-time Sign Language interpretation is among the fastest-growing fields that can potentially minimize the communication gap between deaf people and the hearing population and prepare the ground for inclusivity and accessibility throughout social, educational, and professional contexts. The review discusses the development of technologies for sign languages interpretation in real time, having the main emphasis that have to be met such as high interpretation accuracy, low latency, and computational efficiency. This review methodology, which compared machine learning and deep learning algorithms and discussed different model architectures, analyzed the trends and emerging methodologies that have shaped the current state of the field and identified significant gaps in the literature. In particular, the tendency is such that the multimodality involving hand, face, and body movement participation is becoming increasingly more effective; the pre-trained models get adapted to perform sign language tasks. Most importantly, diversity in datasets promotes model robustness across languages and dialects. A bit further, practical problems are discussed with real-time interpretation deployment on mobile devices and other resource-limited platforms. The following review is designed to point out the likely best directions for future research and development that would generalize, through the advancement of real time sign language interpretation, to become an accessible and reliable technology for everyday use.

关键词： Deep learning Sign language Adaptation models Accuracy Recurrent neural networks Translation Reviews Real-time systems Convolutional neural networks Optimization

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Mental Healthcare Chatbots: Technological Advancements, Challenges, and Opportunities

Mental Healthcare Chatbots: Technological Advancements, Chal...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Balaraman Sundarambal Amirthavarshini H Suresh Subramanian Chokiyan Karthikeyini Senthil Kumar G Kirubakaran N Department of Computer Science and Business Systems Chennai Institute of Technology Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai India Department of Electronics and Communication Engineering Excel Engineering College Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Mental healthcare chatbots are artificially intelligent systems that have been built with the intent of simulating human conversation and provide emotional support to one’s user. They are also widely used as tools to help individuals cope with the stress, anxiety, and other mental health challenges they have been facing, including easily accessible and affordable solutions. These chatbots use NLP and emotion recognition technology to assist users in understanding their feelings and responding accordingly. This review unpacks the latest advancements of recently developed chatbots, such as GPT-4-based models, BERT and MBERT based models, sentiment analysis, and the study of multimodal systems combining text, voice, and facial expression recognition. Also addressed are important challenges, including privacy concerns, cultural inclusivity, and clinical validation. Finally, the review identifies opportunities to improve chatbots, including supporting multi-language and primary telehealth platforms and customizing user interaction with chatbots to create more inclusive and effective mental health support worldwide.

关键词： Emotion recognition Analytical models Sentiment analysis Reviews Text recognition Telemedicine Medical services Mental health Speech recognition Chatbots

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Latent 3D Scene Graph with Aligned Visual-Language Perception for Object-Goal Navigation

Latent 3D Scene Graph with Aligned Visual-Language Perceptio...

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Mechatronics, Control and Robotics (ICMCR), International Conference on

作者： Jianwei Zhang Kang Zhou Junjie Yang Xiaojian Li College of Engineering Southern University of Science and Technology Shen Zhen China School of Computer Science City University of Hong Kong Hong Kong China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shen Zhen China

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

Object-goal navigation requires agents to accurately locate and navigate to specified target objects in complex indoor environments. Existing methods primarily rely on single visual observations or simple semantic matching, making it difficult to effectively understand 3D scene relationships between target objects, which leads to inefficient path planning during navigation. Furthermore, the feature discrepancy between visual and language modalities poses challenges for accurate target identification. To address these issues, we propose Latent 3D Scene Graph with Aligned Visual-Language Perception (LSGAP), a novel approach that explicitly models 3D scene relationships between objects through depth perception and latent 3D scene graph construction. Meanwhile, we leverage multimodal pretraining strategies to achieve effective visual-language feature alignment, enhancing the model's target recognition capabilities. This end-to-end architectural design improves spatial cognition ability and enables more efficient navigation decision-making. We conduct extensive experimental evaluations in the AI2THOR interactive environment to validate the effectiveness of our navigator.

关键词： Visualization Solid modeling Three-dimensional displays Systematics Navigation Target recognition Semantics Indoor environment Standards Robots

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Structure-Aware Conversational Legal Case Retrieval

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ACM Transactions on Information Systems 2025年第3期43卷 1-28页

作者： Bulou Liu Yiran Hu Qingyao Ai Yueyue Wu Yiqun Liu Chenliang Li Fan Zhang Weixing Shen Chong Chen Qi Tian Quan Cheng Laboratory Department of Computer Science and Technology Institute for Internet Judiciary Tsinghua University and School of Law Tsinghua University Beijing China School of Cyber Science and Engineering Wuhan University Wuhan China School of Information Management Wuhan University Wuhan China School of Law Tsinghua University Beijing China Huawei Cloud BU Beijing China

Legal case retrieval is an important task in information retrieval that aims to retrieve relevant cases for given query cases. Conversational search paradigms have been shown to improve the search experience in legal case retrieval. However, there are two challenges in applying conversational search to legal scenarios. Firstly, legal search conversations often focus on different parts of legal case documents, but existing models struggle to capture the complex structural information and extract accurate relevance signals. Secondly, collecting large-scale conversational search datasets is costly, making it difficult to build reliable conversational legal case retrieval models. To address these challenges, we propose a Structure-Aware Matching Model (SAMM) for conversational legal case retrieval. SAMM extracts matching signals between conversational utterances and segments of the legal cases to incorporate structural information. We decouple the conversational search task into three subtasks and design pre-training tasks to overcome the lack of training data. Additionally, we create ConvLegal, the largest conversational legal case retrieval dataset to the best of our knowledge, for better evaluation of different methods. We train and evaluate SAMM and baselines on both a public dataset (CLCR) and ConvLegal. Experimental results demonstrate that SAMM outperforms existing models in legal case retrieval and conversational search.

关键词： conversational search legal case retrieval datasets pre-train models

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Multilingual Sentiment Analysis: A Comprehensive Review for Indian Languages

Multilingual Sentiment Analysis: A Comprehensive Review for ...

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Cognitive Computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Shelendra Yadav Sudha Shanker Prasad Sudhanshu Maurya Rachit Garg Arya Kashikar Nilesh Shelke School of Computer Science and Engineering Lovely Professional University Jalandhar Punjab India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Unleashing the power of deep learning, text classification for Indian languages is experiencing a renaissance (2019–2024). Pre-trained language models like BERT and XLM-RoBERTa, along with transfer learning, are pushing accu- racy boundaries. Researchers are customizing these methods to conquer language-specific challenges like unique writing systems, complex grammar, and the ever-present code-mixing phenomenon. However, hurdles remain. Building massive, annotated datasets, especially for resource-constrained languages, and ensuring interpretability in deep learning models are ongoing battles. The future promises exciting advancements: improved handling of low-resource languages, conquering code-mixing, and integrating explainable AI to shed light on deep models' decision-making. By overcoming these challenges, text classification has the potential to transform how we interact with and analyze the rich tapestry of information within Indian languages.

关键词： Deep learning Sentiment analysis Accuracy Computational modeling Biological system modeling Text categorization Transfer learning Finance Transforms Data models

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RCA-SI: A Rapid Consensus Algorithm for Swarm Intelligence in unstable network environments

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Journal of Network and computer Applications 2025年 241卷

作者： Zeng, Guangquan Hu, Wan Zhou, Yongchao Zheng, Desheng Li, Xiaoyu Shi, Chuang School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 610054 China Kash Institute of Electronics and Information Industry Kashk 844000 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054 China School of Computer Science and Software Engineering Southwest Petroleum University Chengdu 610500 China

Swarm intelligence systems are a class of distributed systems in which device nodes utilize distributed algorithms to achieve data consensus and execute complex collective tasks. These systems operate in highly dynamic environments, where unstable network conditions, often induced by environmental complexities, can significantly affect the progress and efficiency of data consensus. To tackle this challenge, we propose RCA-SI (Raft-based Consensus Algorithm for Swarm Intelligence), a novel method specifically designed for the task scenarios of swarm intelligence. RCA-SI is designed with a node management protocol and a cluster operation protocol to address the challenge of rapid data consensus in unstable network environments. The correctness of RCA-SI was formally verified using TLA＋. Furthermore, we evaluated the algorithm's functionality and performance through simulations of swarm intelligence systems under unstable network environments caused by increased latency, network jitter, and data packet loss. Experimental results demonstrate that RCA-SI outperforms Raft, Paxos, and Multi-Paxos in terms of throughput under unstable network conditions, particularly in high-latency and high-packet-loss scenarios. The algorithm's correctness is formally verified using TLA＋, and its efficiency is confirmed through simulations, highlighting its suitability for swarm intelligence systems. © 2025 Elsevier Ltd

关键词： Data consistency Distributed system Raft Swarm intelligence

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Novel intelligent exogenous neuro-architecture–driven machine learning approach for nonlinear fractional breast cancer risk system

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Communications in Nonlinear science and Numerical Simulation 2025年 149卷

作者： Fida, Afshan Raja, Muhammad Asif Zahoor Chang, Chuan-Yu Raja, Muhammad Junaid Ali Asif Khan, Zeshan Aslam Shoaib, Muhammad of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin 64002 Taiwan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan AI center Yuan Ze University Taoyuan 320 Taiwan

Breast cancer remains one of the most prevalent and life-threatening diseases worldwide, necessitating mathematical modelling frameworks to capture the complexity of its progression and risk factors. This research endeavor uncovers the novel machine learning expedition using an Adaptive Nonlinear AutoRegressive eXogenous (ANARX) neural network on a Fractional Order Breast Cancer Risk (FO-BCR) model. A novel Caputo fractional operator-based breast cancer risk model is presented using a five compartmental system reflected by healthy, tumor, immune, estrogen, and fatty cells. A modified fractional Adams PECE method is opted to generate solutions of the five fractional order variants on the four diverse BCR scenarios. These temporal sequences are parsed as ground truth for the adept ANARX network, which is iteratively refined using the Levenberg-Marquardt (LM) algorithm. The performance evaluation of the temporal feature learning of the ANARX-LM algorithm is comprehensively evaluated against reference numerical outcomes using mean square error (MSE) performance graphics, input-error cross correlation, error autocorrelation, error histogram analysis, sequential response and comparative error analysis charts. Low disparity between reference solutions is observed for all FO-BCR systems, with MSE errors in the range of 10−8 to 10−11. Finally, the ANARX-LM's predictive prowess is evaluated using the single and multistep configurations. Minute errors in the range of 10−9 to 10−11, 10−8 to 10−10 suggest accurate anticipation of the FO-BCR system enabling preventive and prognostic measures for breast cancer models. These empirical findings underscore the potential of advanced machine-learning-driven neuro-architecture for next-generation predictive-oncology solutions that may facilitate treatment strategies. © 2025 Elsevier B.V.

关键词： Risk assessment

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Classifying the DNA sequence using Optimized Naive Bayes Approach: A Comparative Analysis of Machine Learning Algorithms

Classifying the DNA sequence using Optimized Naive Bayes App...

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Computational Intelligence, Communication technology and Networking (CICTN), International Conference on

作者： Aditya Pai H Chandrappa S Guru Prasad M S Ganesh Pathak Atul Thakare Pratik Kumar MIT School of Computing MIT Art Design and Technology University Pune India Dept. of Computer Science and Engineering JAIN (Deemed-to-be University) Bengaluru India Dept. of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India Department of Computer Application Graphic Era (Deemed-to-be University) Dehradun India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

In bioinformatics, the exact classification of DNA sequences is essential to increasing comprehension of genetic structures and functionalities. This research introduces an enhanced Naive Bayes approach (ENB) specifically designed to handle more complexities related to DNA sequence data. This research integrates advanced feature weighting and optimized probability estimation techniques to enhance classification accuracy substantially. The proposed work demonstrated superior efficacy to traditional ML algorithms like Random Forest, SVM, Decision-Trees, K-Nearest Neighbors, Gradient Boosting etc. The proposed method attained a level of accuracy of 98%, and proposed approach performs better than the other models, with 94% accuracy in Logistic Regression coming in second.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Nearest neighbor methods Boosting Bayes methods Complexity theory Medical diagnosis Random forests

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Adaptive AUV Hunting Policy with Covert Communication via Diffusion Model

arXiv

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arXiv 2025年

作者： Guo, Xu Hou, Xiangwang Xu, Minrui Chen, Jianrui Wang, Jingjing Du, Jun Ren, Yong Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Electronic Engineering Tsinghua University Beijing10084 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Cyber Science and Technology Beihang University Beijing100191 China

Collaborative underwater target hunting, facilitated by multiple autonomous underwater vehicles (AUVs), plays a significant role in various domains, especially military missions. Existing research predominantly focuses on designing efficient and high-success-rate hunting policy, particularly addressing the target’s evasion capabilities. However, in real-world scenarios, the target can not only adjust its evasion policy based on its observations and predictions but also possess eavesdropping capabilities. If communication among hunter AUVs, such as hunting policy exchanges, is intercepted by the target, it can adapt its escape policy accordingly, significantly reducing the success rate of the hunting mission. To address this challenge, we propose a covert communication-guaranteed collaborative target hunting framework, which ensures efficient hunting in complex underwater environments while defending against the target’s eavesdropping. To the best of our knowledge, this is the first study to incorporate the confidentiality of inter-agent communication into the design of target hunting policy. Furthermore, given the complexity of coordinating multiple AUVs in dynamic and unpredictable environments, we propose an adaptive multi-agent diffusion policy (AMADP), which incorporates the strong generative ability of diffusion models into the multi-agent reinforcement learning (MARL) algorithm. Experimental results demonstrate that AMADP achieves faster convergence and higher hunting success rates while maintaining covertness constraints. Copyright © 2025, The Authors. All rights reserved.

关键词： Autonomous underwater vehicles

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