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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： K P Pratiksha Cauvery R Thrisha T Harshitha Suman Panigrahi Senthil Kumar Thangavel K Somasundaram C.P. Boopathy Department of Mathematics Amrita School of Physical Sciences Coimbatore Tamil Nadu India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Tamil Nadu India Department of Elelctical and Electronics Engineering Amrita School of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The flowers of Jasmine hold immense economic importance in the agricultural sector and the perfume industry, as the value and satisfaction of the market, are heavily influenced by the quality of these flowers. The already very labor-intensive, subjective, and inefficient manual method of grading plants based on their physical and aromatic characteristics is unsuitable on a large scale. This paper handles these limitations by introducing automation in the grading process using Deep Learning and Quantum Machine Learning. The conceptual methodology mandates automatic flower placement and high-resolution imagery with a conveyor belt. The deep learning model, exactly a type of Convolutional Neural Network (CNN), would explain freshness in terms of visual features like color, texture, structural strength, and vice versa. Additionally, quantum-inspired algorithms have been employed to enhance the computation and performance of the model. From the analysis, a decision-making module classifies entrant flowers into fresh and otherwise, thereby sending them to their respective places, where all packing or disposal could be achieved. This DL-QML hybrid approach points out the exemplary high accuracy, scalability, and efficiency in jasmine grading and hence minimizes manual intervention in quality control assembly lines. The findings provide the card of the feasibility of joining QML in addressing computational challenges of agro-product quality assessment, opening avenues in application far broader than floriculture.

关键词： Deep learning Visualization Sentiment analysis Accuracy Computational modeling Scalability Quality control Manuals Flowering plants Convolutional neural networks

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Correction to: Optimal Error Estimates for Gegenbauer Approximations in Fractional Spaces

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Journal of Scientific computing 2025年第2期103卷 1-2页

作者： Xie, Ruiyi Liu, Wenjie Wang, Haiyong Wu, Boying School of Mathematics Harbin Institute of Technology Harbin People’s Republic of China Key Laboratory of Computing and Stochastic Mathematics Ministry of Education (LCSM) Changsha People’s Republic of China School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan People’s Republic of China China and Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan People’s Republic of China

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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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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yuhan Jiang Zhongyuan Wang Jie Hua Jinbi Liang Zengmin Xu 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: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： YOLO Image segmentation Accuracy Surveillance Loading Signal processing algorithms Production Detectors Cameras Recycling

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Person Re-Identification Using Siamese Triplet Network

Person Re-Identification Using Siamese Triplet Network

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Aparna Menon Yashwanthika R Shreya J V K Somasundaram Senthil Kumar Thangavel Shreeya Asawa Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Mathematics Amrita School of Physical Sciences Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

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.

关键词： Deep learning Measurement Sentiment analysis Pedestrians Surveillance Clothing Feature extraction Robustness Public security Identification of persons

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BDPFL: Backdoor Defense for Personalized Federated Learning via Explainable Distillation

arXiv

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arXiv 2025年

作者： Zhu, Chengcheng Zhang, Jiale Wu, Di Long, Guodong School of Information Engineering Yangzhou University China School of Mathematics Physics and Computing University of Southern Queensland Australia Australian Artificial Intelligence Institute FEIT University of Technology Sydney Australia

Federated learning is a distributed learning paradigm that facilitates the collaborative training of a global model across multiple clients while preserving the privacy of local datasets. To address inherent challenges related to data heterogeneity and satisfy personalized needs, a new direction within FL, known as personalized Federated Learning (pFL), has gradually evolved. Extensive attention has been directed toward developing novel frameworks and methods to enhance the performance of pFL. Regrettably, the aspect of security in pFL has been largely overlooked. Our objective is to fill this gap. Similar to FL, pFL is susceptible to backdoor attacks. However, existing backdoor defense strategies are primarily tailored to general FL frameworks, and pFL lacks robustness against backdoor attacks. We propose a novel, backdoor-robust pFL framework named BDPFL to address these challenges. First, BDPFL introduces layer-wise mutual distillation that enables clients to learn their personalized local models while mitigating potential backdoors. Then, BDPFL employs explanation heatmap to learn high-quality intermediate representations and enhance the effect of eliminating deeper and more entrenched backdoors. Moreover, we perform empirical evaluations of BDPFL’s performance on three datasets and compare BDPFL with four backdoor defense methods. The experiments demonstrate that BDPFL outperforms baseline methods and is effective under various settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Similia Suite: An AI-Driven Electronic Record Management System for Homeopathic Hospital Administration

Similia Suite: An AI-Driven Electronic Record Management Sys...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Y Venkata Krishna Vivek Inian Kaviyanban A V T Senthilkumar Mukesh S Ambiga P. Subathra K. Somasundaram Sountharrajan S Department of computer science and engineering Amrita school of computing Amrita Vishwa Vidyapeetham Chennai Campus Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Meera Homeo clinic Madurai Department of Mathematics Amrita School of Physical Sciences Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Healthcare management systems have seen significant advancements with the integration of electronic record management solutions. While allopathic hospitals have widely adopted such systems, homeopathic healthcare has largely remained reliant on traditional record-keeping methods. Similia Suite is a pioneering electronic record management system designed specifically for homeopathy hospital management, marking a significant step toward digitization in this field. Developed for a client in Madurai, Tamil Nadu, India, Similia Suite streamlines patient record management, appointment scheduling, medicine tracking, and administrative tasks through a robust, AI-enhanced digital framework. The system offers specialized modules for doctors, patients, and administrative staff, enabling efficient data handling and improving patient care. An offline AI-driven chatbot, powered by a 7-billion-parameter Llama 3 model on the Ollama platform, enhances user interaction by providing medical guidance and appointment scheduling assistance. This paper explores the architecture, functionalities, and transformative potential of Similia Suite, emphasizing its role in improving homeopathic healthcare management through AI-driven insights, real-time patient tracking, and data-driven decision-making.

关键词： Hospitals Text recognition Scalability Decision making Medical services Chatbots Real-time systems Wearable devices Sustainable development Secure storage

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BAST: Blockchain-Assisted Secure and Traceable Data Sharing Scheme for Vehicular Networks

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4664-4678页

作者： Liu, Xinzhong Cui, Jie Zhang, Jing Yin, Rongwang Zhong, Hong Wei, Lu Bolodurina, Irina He, Debiao Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui Engineering Laboratory of IoT Security Technologies Hefei230039 China Hefei University Department of Basic Experiment and Training Hefei230601 China Orenburg State University Faculty of Mathematics and Information Technologies Orenburg460018 Russia Wuhan University School of Cyber Science and Engineering Wuhan430072 China Shanghai Key Laboratory of Privacy Preserving Computation MatrixElements Technologies Shanghai201204 China

In vehicular networks, caching service content on edge servers (ESs) is a widely accepted strategy for promptly responding to vehicle requests, reducing communication overhead, and improving service experience. However, implementing such an architecture requires addressing the challenges associated with ES response data reliability and communication security. In this study, to tackle the ES response data reliability issue, a blockchain-assisted threshold signature scheme for cache-based vehicular networks is proposed. The scheme utilizes a threshold mechanism to sign the data broadcast by the ES, incorporates blockchain to trace malicious signers, and avoids the shortcomings and limitations associated with idealized assumptions for the ES in existing data-sharing schemes. Moreover, considering the communication security and high-speed mobility of vehicles, using the non-interactive signatures of knowledge based on the Σ-protocol, a secure and efficient message authentication scheme for vehicles and ESs is provided. Through rigorous security proofs and comprehensive analyses, our scheme satisfies the communication security requirements of vehicular networks. By leveraging the JPBC library for performance analysis, the proposed scheme demonstrates advantages as concerns both computation and communication overheads compared to related schemes. Moreover, we implemented the proposed scheme on an Ethereum test network (i.e., Goerli) to validate its feasibility. © 2005-2012 IEEE.

关键词： Sensitive data

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Multi-directional multiscroll memristive chaotic systems with diverse multistability and application to high-speed rail image encryption

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Nonlinear Dynamics 2025年 1-17页

作者： Huang, Wang-Peng Lai, Qiang Zhao, Xiao-Wen Iu, Herbert Ho-Ching School of Tian You East China Jiaotong University Nanchang 330013 China School of Electrical and Automation Engineering East China Jiaotong University Nanchang 330013 China School of Mathematics Hefei University of Technology Hefei 230009 China School of Electrical Electronic and Computing Engineering University of Western Australia Crawley WA6009 Australia

Memristors can be applied to the generation of chaotic systems for enhancing the complexity of chaotic signals and assembling potential applicable dynamic behaviors, thereby increasing concerns have been focused on the research of memristive chaotic system (MCS). This paper constructs multi-directional multiscroll memristive chaotic systems (MMCSs) by introducing newly designed locally active in the same dimension. The number and expansion direction of scrolls can be controlled by the internal variables and memristors. For changing the parameters and initial values, the multiscroll attractors break into tunable ratios of periodic and chaotic attractors, yielding diverse multistability. The dynamic behaviors (including multiscroll attractors, multistability, amplitude control), circuit realization and NIST tests are studied for giving a comprehensive observation of the proposed MMCSs. A secure and fast encryption scheme which can be used to protect high-speed rail (HSR) images with important technical information is designed based on the proposed MMCSs for verifying their application potential. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Circuit realization HSR technology protection Image encryption Multiscroll attractors Multistability

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Pattern recognition in programming to support the evaluation of Computational Thinking

Pattern recognition in programming to support the evaluation...

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IEEE World engineering Education Conference (EDUNINE)

作者： Steven Pacheco-Portuguez Antonio Gonzalez-Torres Lilliana Sancho-Chavarria Ignacio Trejos-Zelaya Jorge Monge-Fallas School of Computing Costa Rica Institute of Technology Cartago Costa Rica School of Computer Engineering Costa Rica Institute of Technology Cartago Costa Rica School of Software Engineering & School of Computing Universidad CENFOTEC & Costa Rica Institute of Technology San José & Cartago Costa Rica School of Mathematics Costa Rica Institute of Technology Cartago Costa Rica

ISBN: (数字)9798331542788

ISBN: (纸本)9798331542795

Computational thinking is a high-level skill that involves both critical and creative thinking. This research proposes an advanced method for analyzing source code to assess computational thinking skills in elementary school students. We present a large-scale approach that examines source code from various programming exercises using a generalized abstract syntax tree, enabling language-independent analysis. Clustering techniques are applied to identify different levels of computational thinking development in areas such as parallelism, data representation, abstraction and decomposition, control flow, and programming structure. The results of the automated assessment provide insights into students' computational thinking abilities across diverse populations, highlighting opportunities for improving the educational framework.

关键词： Codes Source coding Data visualization Syntactics Parallel processing Pattern recognition Software measurement engineering education Programming profession

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Automatic melanoma and non-melanoma skin cancer diagnosis using advanced adaptive fine-tuned convolution neural networks

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Discover Oncology 2025年第1期16卷 1-27页

作者： Khan, Muhammad Amir Mazhar, Tehseen Ali, Muhammad Danish Khattak, Umar Farooq Shahzad, Tariq Saeed, Mamoon M. Hamam, Habib School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam 40450 Malaysia School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science COMSATS University Islamabad Abbottabad Campus Abbottabad 22060 Pakistan School of Information Technology UNITAR International University Kelana Jaya Petaling Jaya 47301 Malaysia Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Yemen Sana’a 00967 Yemen Faculty of Engineering University de Moncton Moncton E1A3E9 NB Canada School of Electrical Engineering Department of Electrical and Electronic Engineering Science University of Johannesburg Johannesburg South Africa Hodmas University College Taleh Area Mogadishu Banadir 521376 Somalia Bridges for Academic Excellence—Spectrum Tunis Centre-Ville 1002 Tunisia Department of Computer Science School Education Department Government of Punjab Layyah 31200 Pakistan

Skin Cancer is an extensive and possibly dangerous disorder that requires early detection for effective treatment. Add specific global statistics on skin cancer prevalence and mortality to emphasize the importance of early detection. Example: “Skin cancer accounts for 1 in 5 diagnosed cancers globally, with melanoma causing over 60,000 deaths annually. Manual skin cancer screening is both time-intensive and expensive. Deep learning (DL) techniques have shown exceptional performance in various applications and have been applied to systematize skin cancer diagnosis. However, training DL models for skin cancer diagnosis is challenging due to limited available data and the risk of overfitting. Traditionally approaches have High computational costs, a lack of interpretability, deal with numerous hyperparameters and spatial variation have always been problems with machine learning (ML) and DL. An innovative method called adaptive learning has been developed to overcome these problems. In this research, we advise an intelligent computer-aided system for automatic skin cancer diagnosis using a two-stage transfer learning approach and Pre-trained Convolutional Neural Networks (CNNs). CNNs are well-suited for learning hierarchical features from images. Annotated skin cancer photographs are utilized to detect ROIs and reset the initial layer of the pre-trained CNN. The lower-level layers learn about the characteristics and patterns of lesions and unaffected areas by fine-tuning the model. To capture high-level, global features specific to skin cancer, we replace the fully connected (FC) layers, responsible for encoding such features, with a new FC layer based on principal component analysis (PCA). This unsupervised technique enables the mining of discriminative features from the skin cancer images, effectively mitigating overfitting concerns and letting the model adjust structural features of skin cancer images, facilitating effective detection of skin cancer features. The sys

关键词： Advanced adaptive fine-tuned Convolution neural networks Melanoma and non-melanoma skin cancer

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