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Semi-Supervised Feature Selection With Multi-Scale Fuzzy Information Fusion: From Both Global and Local Perspectives

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IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1825-1839页

作者： Zhou, Nan Liao, Shujiao Chen, Hongmei Ding, Weiping Lu, Yaqian Minnan Normal University School of Mathematics and Statistics Fujian Key Laboratory of Granular Computing and Applications Zhangzhou363000 China Southwest Jiaotong University School of Computing and Artificial Intelligence National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science 999078 China

In reality, the laborious nature of label annotation leads to the widespread existence of limited labeled data. Moreover, multi-scale data have received widespread attention due to its rich knowledge representation. However, current research on multi-scale data primarily focuses on supervised learning environments, while semi-supervised feature selection for multi-scale data with limited labels remains inadequately explored. Meanwhile, existing studies on multi-scale data often emphasize selecting optimal scales and further conducting feature selection, but this strategy may result in losing potentially valuable information from other scales. To overcome these limitations, by adopting a multi-scale fuzzy information fusion mechanism, this article proposes two new semi-supervised feature selection approaches for multi-scale data with limited labels from both global and local perspectives. Initially, by fusing fuzzy information at various scales, a label learning method is proposed to convert the decision of missing labels into a fuzzy decision of label distribution. Subsequently, label-distributed multi-scale fuzzy global and local rough sets are constructed from global and local perspectives, respectively. Based on the two models, two semi-supervised feature selection algorithms are developed based on global and local multi-scale fuzzy information fusion. Experimental results demonstrate that, compared to other advanced feature selection algorithms, the two proposed algorithms can effectively handle multi-scale data with limited labels and exhibit superior classification performance, computational efficiency and robustness. © 1993-2012 IEEE.

关键词： Self-supervised learning

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An Al Powered Exam Proctoring: Comprehensive Monitoring for Integrity

An Al Powered Exam Proctoring: Comprehensive Monitoring for ...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Mewada, Dhruv Gaikwad, Shlok Gharat, Bhavika Kamble, Priyanshu Vidyavardhini's College of Engineering and Technology Dept. of Artificial Intelligence&Data Science Mumbai India

ISBN: (纸本)9798350359688

In recent times, remote learning has experienced significant growth, yet the challenge of conducting academic examinations in a secure manner persists. Various approaches have been adopted by universities, such as collecting assignments susceptible to plagiarism or implementing remote proctoring with manual supervision. As our current lifestyle appears to be transitioning towards a new normal, it becomes imperative to explore effective solutions. This paper introduces a proposed remedy: the development of an AI-based integrated system designed to prevent cheating during examinations. The system not only detects fraudulent activities but also records evidence, ensuring both security and cost-effectiveness. © 2024 IEEE.

关键词： online tests proctoring remote learning

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Characterization of ultraviolet communication in a mixed aerosol environment

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Optoelectronics Letters 2025年第2期21卷 77-84页

作者： ZHAO Taifei SUN Yuxin WANG Wuxin ZHANG Haijun PAN Feixiang WU Hao Artificial Intelligence Key Laboratory of Sichuan Province Sichuan University of Science&EngineeringZigong 643000China Faculty of Automation and Information Engineering Xi’an University of TechnologyXi’an 710048China

Wireless ultraviolet (UV) has strong scattering characteristics and can communicate through non-direct *** UV signals are transmitted in the atmosphere,they are affected by the absorption and scattering effects of atmospheric particles and atmospheric turbulence,resulting in attenuation of UV signal energy and reduced reliability of the communication *** paper focuses on the channel model of UV non-direct-view single scattering communication,and simulates and analyzes the communication characteristics of UV light in atmospheric turbulence and mixed aerosol environment under horizontal,vertical and oblique range communication *** results show that at equal relative humidity,the wireless UV non-directive scattering communication performance for vertical communication scenarios is more affected by the mixed aerosol environment and the communication performance is worse.

关键词： communication scattering ultraviolet

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Few-shot image semantic segmentation based on meta-learning: A review

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期47卷 351-367页

作者： Liu, Yu Zhu, Ye Chong, Haoze Yu, Ming School of Electronic and Information Engineering Hebei University of Technology Tianjin China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China College of Computer and Information Engineering Tianjin Agricultural University Tianjin China

Deep learning-based image semantic segmentation approaches heavily rely on large-scale training datasets with dense annotations and often suffer from scarce semantic labels for unseen categories. This limitation has spurred a research trend in Few-shot image Semantic Segmentation (FSS), which makes it possible to segment objects of new categories using only a few labeled samples. Although more and more FSS methods are emerging and gradually integrated into practical applications, a deep understanding of its achievements and issues is still missing. In this survey, we focus on the recent developments of FSS, specifically on FSS methods based on meta-learning. According to different network architectures, we summarize the related research into three classes, that are Convolutional Neural Network-based (CNN-based) models, Graph Neural Network-based (GNN-based) models, and Transformer-based models. Then, we explore the specific implementations of these models, including parameter-based methods, metric-based methods, attention-based methods, and optimization-based methods. Furthermore, we illustrate datasets and analyze the experimental results of various kinds of methods. Toward the end of the paper, we discuss the limitations of FSS and present its applications and challenges to provide further research directions. © 2024 - IOS Press. All rights reserved.

关键词： Semantic Segmentation

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Interpreting Doctors' Notes Using Handwriting Recognition and Deep Learning Techniques 9

Interpreting Doctors' Notes Using Handwriting Recognition an...

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9th International Conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Maheswari, G. Ramya, R. Babitha, S. Sriram, R. Jennis, M. Ram Prasad, P. Hindustan Institute of Technology And Science Department of Information Technology Chennai India Psna College of Engineering And Technology Department of Artificial Intelligence And Data Science India

ISBN: (纸本)9798350365092

This research provides a deep learning approach and handwriting recognition to understand medical notes. The system processes and interprets textual medical notes using connectionist temporal classification (CTC) and optical character recognition (OCR) techniques. The written text is transcribed using the OCR component, and then the CTC algorithm is utilized to analyze the text and give significance to particular note portions. The suggested system's ability to correctly understand and arrange written medical information is demonstrated by training it on prescription notes from doctors taken from a dataset. According to the final forecast, the system can read medical notes with high accuracy, making medical professionals' usage and accessibility of these records easier. The suggested paradigm offers fast and precise access to patient information, which could increase the efficacy and efficiency of medical care. © 2024 IEEE.

关键词： Optical character recognition

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ViGT: proposal-free video grounding with a learnable token in the transformer

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science China(Information sciences) 2023年第10期66卷 196-212页

作者： Kun LI Dan GUO Meng WANG School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province Institute of Artificial Intelligence Hefei Comprehensive National Science Center

The video grounding(VG) task aims to locate the queried action or event in an untrimmed video based on rich linguistic descriptions. Existing proposal-free methods are trapped in the complex interaction between video and query, overemphasizing cross-modal feature fusion and feature correlation for VG. In this paper, we propose a novel boundary regression paradigm that performs regression token learning in a transformer. Particularly, we present a simple but effective proposal-free framework, namely video grounding transformer(ViGT), which predicts the temporal boundary using a learnable regression token rather than multi-modal or cross-modal features. In ViGT, the benefits of a learnable token are manifested as follows.(1) The token is unrelated to the video or the query and avoids data bias toward the original video and query.(2) The token simultaneously performs global context aggregation from video and query ***, we employed a sharing feature encoder to project both video and query into a joint feature space before performing cross-modal co-attention(i.e., video-to-query attention and query-to-video attention) to highlight discriminative features in each modality. Furthermore, we concatenated a learnable regression token [REG] with the video and query features as the input of a vision-language transformer. Finally, we utilized the token [REG] to predict the target moment and visual features to constrain the foreground and background probabilities at each timestamp. The proposed ViGT performed well on three public datasets:ANet-Captions, TACoS, and YouCookⅡ. Extensive ablation studies and qualitative analysis further validated the interpretability of ViGT.

关键词： video grounding temporal sentence grounding boundary regression token learning proposal-free

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Cardiomegaly Detection Using Deep Learning 4

Cardiomegaly Detection Using Deep Learning

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Suganthi, S.U. Sivabalan, A. Akash, M.V. Barath, B. Goushik, G. Anto Akash, P. Sri Sairam Institute of Technology Dept. of Artificial Intelligence And Data Science Tamil Nadu Chennai India Sri Sairam Engineering College Dept. of Artificial Intelligence And Data Science Tamil Nadu Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

This research introduces an innovative method for forecasting cardiomegaly, a common heart condition marked by an enlarged heart, by combining deep learning and machine learning methods. Using the ResNet50 architecture, we examine chest X-ray images to identify cardiomegaly indications. At the same time, a Random Forest algorithm analyses clinical data in a table format, such as age, gender, and different cardiac indicators, to improve prediction accuracy. Our approach combines these models to use unstructured images and structured health data, resulting in a comprehensive diagnostic tool. The data, collected from public health databases, was carefully labelled to guarantee accurate training and validation. Preliminary results showed the promising accuracy of the hybrid model, surpassing individual approaches, and demonstrating the potential of integrated models to improve diagnostic capabilities in cardiology. This study helps improve medical imaging and predictive analytics in healthcare, providing a productive solution for early detection and management of cardiomegaly. The results highlight the promise of combining AI models to change diagnostic methods and enhance patient results. © 2024 IEEE.

关键词： Deep learning

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A Review of Machine Learning Algorithms in Diabetes Management 4

A Review of Machine Learning Algorithms in Diabetes Manageme...

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

作者： Gayaki, Unnati Daronde, Subodh Tale, Abhay Barhate, Aditya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798331523923

Diabetes is a prevalent and chronic disease affecting millions worldwide, posing significant challenges in its management and treatment. This review article aims to explore the current and potential future roles of machine learning (ML) in diabetes management, focusing on progress, applications, and challenges. It discusses various ML techniques, including support vector machines (SVM), logistic regression, decision trees, ensemble methods, and advanced deep learning approaches such as long short-term memory (LSTM) networks and convolutional neural networks (CNN). These methods have demonstrated benefits in analytics, patient monitoring, decision support systems, personalised treatment, and patient engagement. However, significant challenges persist, such as computational complexity, data quality, limited interoperability of systems, and ethical concerns regarding AI use in healthcare. The article addresses these challenges by fostering collaboration between AI implementers and health care professionals. This collaboration is essential to develop long-term, legally compliant, and solution-oriented frameworks that enhance patient care and diabetes management and pave the way for more efficient and ethical AI applications in healthcare. © 2025 IEEE.

关键词： Support vector machines

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Smart Irrigation System using the Internet of Things 8

Smart Irrigation System using the Internet of Things

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Anpan, Sakshi Lanjewar, Rutuja Kedar, Rani Qureshi, Rukaiyya Zade, Nikita Gourshettiwar, Palash Faculty of Engineering and Technology Dept of Artificial Intelligence and DataScience Wardha India Faculty of Engineering and Technology Dept of Computer Science and MedicalEngineering Wardha India

ISBN: (纸本)9798350376425

Fast technical advancements have been implemented in multiple domains of life, including agriculture. technology can help the agriculture industry cut down on the energy and time lost on using traditional methods. The Internet of Things (IoT) is one of the technological developments that are currently in vogue. Real-time internet connectivity for physical items is made possible by the Internet of Things (IoT) technology. Through the use of remote water pump control and garden soil moisture monitoring, this research integrates the Internet of Things into the garden irrigation system. Garden owners may monitor and identify soil moisture levels in their plantations using the Internet of Things. After that, effectively, may control the usage of water utilized immediately. The water pump attached to the garden's water supply is designed to automatically change the moisture parameter to its ideal value when the soil moisture sensor detects low moisture levels. Additionally, the garden's owner can use a smartphone app to keep an eye on the moisture level of the soil. © 2024 IEEE.

关键词： Soil moisture

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AI-Driven Indoor Farming: An Algorithmic Approach to Sustainable Agriculture 1st

AI-Driven Indoor Farming: An Algorithmic Approach to Sustain...

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1st International Conference on Intelligent Computing, Smart Communication, and Network Technologies, ICICSCNT 2023

作者： Veeramalai, S. Saranya, K. Computer Science and Engineering Chennai İnstitute of Technology Chennai India Artificial Intelligence and Data Science Panimalar Engineering College Poonamallee Chennai India

ISBN: (纸本)9783031759567

Indoor farming has emerged as a promising alternative to traditional farming methods, offering a controlled and optimized environment for crop growth that can improve yield and reduce the environmental impact of agriculture. However, indoor farming can be resource-intensive and labor-intensive, and optimizing the growing conditions for each crop can be challenging. In this project, we propose an AI-based autonomous indoor farming system that uses advanced algorithms to manage the environmental conditions, monitor crop growth and health, and optimize crop yield. The system will use machine learning to analyze various data points such as temperature, humidity, light intensity, and nutrient levels to create an optimal growing environment for each crop. Additionally, computer vision will be used to monitor plant health and growth patterns, and adjust the environmental conditions accordingly. Our system will also provide a user interface for farmers to input their crop preferences, monitor crop growth and health, and receive real-time feedback and coaching from the algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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