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A Method for Fast Feature Selection Utilizing Cross-Similarity within the Context of Fuzzy Relations

Computers, Materials & Continua 2025年第4期83卷 1195-1218页

作者： Wenchang Yu Xiaoqin Ma Zheqing Zhang Qinli Zhang School of Big Data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education Big Data Intelligent Perception and Application Engineering Research Center Anhui Provincial Joint Construction Key Laboratory of Intelligent Education Equipment and TechnologyChizhou247000China

Feature selection methods rooted in rough sets confront two notable limitations:their high computa-tional complexity and sensitivity to noise,rendering them impractical for managing large-scale and noisy *** primary issue stems from these methods’undue reliance on all *** overcome these challenges,we introduce the concept of cross-similarity grounded in a robust fuzzy relation and design a rapid and robust feature selection ***,we construct a robust fuzzy relation by introducing a truncation ***,based on this fuzzy relation,we propose the concept of cross-similarity,which emphasizes the sample-to-sample similarity relations that uniquely determine feature importance,rather than considering all such relations *** studying the manifestations and properties of cross-similarity across different fuzzy granularities,we propose a forward greedy feature selection algorithm that leverages cross-similarity as the foundation for information *** algorithm significantly reduces the time complexity from O(m2n2)to O(mn2).Experimental findings reveal that the average runtime of five state-of-the-art comparison algorithms is roughly 3.7 times longer than our algorithm,while our algorithm achieves an average accuracy that surpasses those of the five comparison algorithms by approximately 3.52%.This underscores the effectiveness of our *** paper paves the way for applying feature selection algorithms grounded in fuzzy rough sets to large-scale gene datasets.

关键词： Fuzzy rough sets feature selection cross-similarity fuzzy relations

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A NeRF-Based Approach for Monocular Traffic Sign Reconstruction and Localization 27

A NeRF-Based Approach for Monocular Traffic Sign Reconstruct...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Eisemann, Leon Maucher, Johannes Porsche Engineering Group GmbH Department of Artificial Intelligence & Big Data Weissach71287 Germany Institute for Applied Artificial Intelligence Stuttgart Media University Stuttgart70569 Germany

ISBN: (纸本)9798331505929

High-resolution maps are a crucial factor for the functionality of highly automated driving functions. These contain precise information about lane geometry, road conditions and traffic signs. Although the importance of high-definition maps, creating these is still an ongoing research topic. While the extraction of road geometry and lane information has seen significant progress, the reconstruction of traffic signs is in its early stages. We propose a novel approach for the accurate 3D localization and reconstruction of directional traffic signs, based on monocular images. For a flexible and extensible process, we use a neural radiance field based reconstruction pipeline. We demonstrate that our approach can localize signs with high precision, outperforming current methods, while also generating 3D representations, for use in mapping or simulation applications. © 2024 IEEE.

关键词： Traffic signs

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Optimised EfficientNet for multi-class skin cancer diagnosis: integrating chaotic grey wolf algorithms

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Current Science 2025年第7期128卷 728-736页

作者： Rama, P. Angayarkanni, V. Theresa, W. Gracy Pandiaraj, A. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603 203 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai600 123 India

The detection of skin cancer holds paramount importance worldwide due to its impact on global health. While deep convolutional neural networks (DCNNs) have shown potential in this domain, current approaches often struggle with fine-grained variability in skin lesion features, imbalanced datasets and inadequate augmentation. The objectives of our study encompass building a model that not only enhances accuracy but also reduces training time and cost, improves dimensionality reduction during classification and segmentation, and is evaluated on larger datasets for robustness. We enhance the learning process using an improved nature-inspired optimisation algorithm tailored specifically for skin cancer classification tasks. This algorithm optimises model parameters to maximise classification accuracy while minimising computational overhead. Our proposed architecture leverages a gold standard dermatological image dataset meticulously curated and validated by experts. This dataset spans eight distinct classes, including actinic keratosis, basal cell carcinoma, melanoma, nevus, and others, ensuring a diverse representation of skin conditions. The balanced class distribution in the training and test sets enables fair evaluation and robustness assessment of our deep learning models. The developed algorithm showcases novelty in its approach, contributing to advancements in early skin cancer detection technologies. This study introduces an optimised deep learning model designed for the early detection of skin cancer, achieving a notable accuracy of 97.48%. Our study aims to contribute significantly to the medical field by providing a reliable and efficient tool for dermatologists and healthcare practitioners to assist in diagnosing skin conditions accurately and promptly, ultimately improving patient outcomes and healthcare efficiency. © (2025), (Indian Academy of Sciences). All rights reserved.

关键词： Convolutional neural networks

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Transverse Velocity Field Measurement of Solar High-resolution Images Based on Unsupervised Deep Learning

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Research in Astronomy and Astrophysics 2025年第3期25卷 236-248页

作者： Zhen-Hong Shang Long Chen Zhen-Ping Qiang Yi Bi Run-Xin Li Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology College of Big Data and Intelligent Engineering Southwest Forestry University Yunnan Observatories Chinese Academy of Sciences

Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar *** paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field,addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar *** proposed method converts the transverse velocity field computation problem into an optical flow computation problem,using two forward propagations of features to get rid of the reliance on optical flow ***,it reduces the impact of the“Brightness Consistency”constraint on optical flow accuracy by identifying and handling optical flow *** apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the Hαand TiO bands,observed by the New Vacuum Solar *** experiments with several wellestablished optical flow methods,including those based on supervised deep learning models,show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean,Residual Map Variance,Cross Correlation,and Structural Similarity Index ***,since optical flow captures the fundamental motion information in image sequences,the proposed method can be applied to a variety of research areas,including solar image registration,sequence alignment,image super-resolution,magnetic field calibration,and solar activity *** code is available at https://***/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images.

关键词： methods: data analysis techniques: image processing Sun: fundamental parameters

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An Efficient artificial intelligence based Human-Machine Interaction System 9

An Efficient Artificial Intelligence based Human-Machine Int...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Suriya, M. Anitha, E. Sudarssan, N. Venu Venkat, Abishek Deepak Saran, V.S. Kunguma Sakthivel, K. Sri Eshwar College of Engineering Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350397376

In today's computer systems, the mouse is an essential input device. Touch interfaces are high-contact planes that we use on a regular basis and frequently throughout the period. As a result, the input device gets infested with bacteria and pathogens. Despite the fact that wireless mouse have eliminated the bunch of tangled wires, there is still a desire to tap the gadget. In light of the epidemic, this proposed method employs a outlying webcam or an in-built image sensor to capture arm gestures and identify fingertip detection, allowing users to execute standard mouse activities such as left click, scrolling and other mouse activities. The algorithm is trained using machine learning with the use of image sensor and the fingers are identified efficiently. As a result, this reliance on corporeal devices to manage the computational system cancels out the requirement of man-machine interface. Thus the suggested approach will prevent the proliferation of Covid-19. © 2023 IEEE.

关键词： Machine learning

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Secure container Orchestration: A framework for detecting and mitigating Orchestrator - level vulnerabilities

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Multimedia Tools and Applications 2025年第17期84卷 18351-18371页

作者： Mahavaishnavi, V. Saminathan, R. Prithviraj, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Annamalai University Chidambaram India Department of Computer Science and Engineering Annamalai University Chidambaram India

This paper presents Secure Orchestration, a novel framework meticulously planned to uphold rigorous security measures over the profound security concerns that lie within the container orchestration platforms, especially in Kubernetes. The widespread adoption of containerized environments has raised concerns about the vulnerabilities at the level of the orchestrator. Secure Orchestration delivers a comprehensive approach by leveraging cutting-edge methods to detect and prevent the exposure of vulnerabilities from orchestrator misconfigurations, privilege escalation exploits that can thwart the orchestration security layer, and unauthorized access attempts targeting the orchestration system itself. In addition, it is further strengthened by deploying an Intrusion Detection and Prevention System (IDPS), which is responsible for actively monitoring the orchestration infrastructure. The IDPS utilizes sophisticated algorithms and mechanisms for detecting anomalies to continuously assess the orchestration environment for indications of intrusion, vulnerabilities, and anomalous activities. By using this proactive approach, the framework is able to rapidly identify potential threats and respond through countermeasures in a timely manner, thus greatly reducing the risks associated with orchestrator level vulnerabilities. Conducting a set of real-world experiments that closely simulate a variety of threat scenarios is imperative for empirically validating the efficiency of this framework. The obtained empirical results yield insightful revelations on the efficacy of the framework in accurately identifying and mitigating vulnerabilities, thereby ensuring the privacy and integrity of container orchestration systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Containers

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AI-Driven Approaches to Enhancing Mental Wellbeing and Stress Relief

AI-Driven Approaches to Enhancing Mental Wellbeing and Stres...

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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Revathi, K. Priyanka, S. Priyadharshini, S. Niranjan, N.N. Visalachi, N. Sumathi, P. Sns College of Engineering Department of Information Technology Tamil Nadu Coimbatore India Sns College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Sns College of Engineering Department of Information Technology & Artificial Intelligence and Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798331509828

Mental health issues affected millions of peoples in worldwide, who often struggle with stigma, high costs, and low access to real life care. Conventional therapy methods faced lot of challenges to identify the issues. These challenges need to highlight for more accessible, affordable, and effective mental health solutions. Traditional models of care often fall short of providing adequate support to all those in need, leading to gaps in care and disparities in mental health outcomes. So, need for Innovation in Mental Health Support for effective mental health solutions to address these challenges. Introduction to 'InnerPeaceAI' Aims to provide personalized, on-demand therapy using advanced AI technology, offering a confidential and user-friendly platform to improve mental health care accessibility. InnerPeaceAI aims to bridge the gap between the need for mental health care and its accessibility by leveraging advanced AI technology to deliver therapy sessions tailored to individual needs. The platform uses natural language processing and machine learning algorithms to create personalized treatment plans, identify mental health issues, and provide real-time support to users. This innovative approach allows for immediate access to therapy, without the constraints of time, location, or cost. © 2025 IEEE.

关键词： Electronic health record

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FISH CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK

FISH CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, artificial intelligence and Telecommunication engineering, RMKMATE 2023

作者： Deepika, V. Dayana, M. Haritha, M. Jaya, R. Nagendiran, S. Sandra Johnson, M.E. Artificial Intelligence and Data Science RMK Engineering College Tamilnadu Kavaraipettai India

ISBN: (纸本)9798350305708

In the current generation, people's knowledge about the fish is reducing. So, they couldn't classify the fishes based on their image because they look similar. That's why we developed the model using deep learning technique to classify the fishes based on the images and also nowadays the demand of fishes is increasing and classifying the fishes are *** this paper discusses the classification of fishes using the convolutional neural network which has image processing, feature extraction and image classification. The unwanted noise and distortion have been removed by image processing after that dimensionality is reduced with the help of feature extraction finally the image will classify based on the label. By using this method, we achieved 94%accuracy for the classification of the fishes using CNN. © 2023 IEEE.

关键词： Convolutional neural network feature extraction image classification image processing

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An Efficient Driver Drowsiness Detection Using Deep Learning 9

An Efficient Driver Drowsiness Detection Using Deep Learning

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Suriya, M. Preethie, K. Amitha, M. Sumithra, M. Sumaiya Fathima, I. Vishnu, M. Sri Eshwar College of Engineering Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350397376

Most of the accidents that happens that happens nationwide is due to the drowsiness. Drowsy driving causes 21% of all fatal collisions. About 168 million adult drivers, or 60% of all drivers, admitted to driving while fatigued in the previous year. Drowsiness is an inattentive state. Drowsiness makes the individual to loss of their control during their driving state, thus leading to devastating accidents. To avoid such catastrophic accidents, a sleep detection system is proposed by which the model embeds an EEG sensor, to detect sleep onset while driving and a CNN-based hybrid model is developed which is used to track the progress of the drivers' mental and physiological conditions. The activities of brain is evaluated using EEG signal. The EEG signals identifies the fatigue which remains to be the primary reasons for accidents. The model aims to attain the terminology of detecting the driver drowsiness by EEG signals with the high accuracy and efficiency. All the signals are transmitted to the mobile of the driver thereby the CNN algorithm monitors the state of driver. Whenever the driver is in sleepy or fatigue state, then these results are sent to the Car system and the car gives an alert to the driver in the form of an alarm. The combination of CNN based model and EEG sensor will give accurate and optimized results with respect to the driver's condition. This was the scenario that were often used in the previous works which seems to be a tedious task to wear a hand band and drive for the drivers. So, we are proposing a model that only focuses on the eyes of the driver to detect the onset of the fatigue and drowsiness. © 2023 IEEE.

关键词： Accidents

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Unraveling the intricacies of EEG seizure detection: A comprehensive exploration of machine learning model performance, interpretability, and clinical insights

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Multimedia Tools and Applications 2024年第41期83卷 89217-89247页

作者： Mridha, Krishna Priyok, Masrur Ahsan Shukla, Madhu Department of Computer Engineering Marwadi University Rajkot India Department of Software Engineering Tianjin University Tianjin China Department of Computer Engineering Artificial Intelligence and Big Data Analytics Marwadi University Gujarat Rajkot India

In neurology, it is critical to promptly and precisely identify epileptic episodes using EEG data. Interpretability and thorough model evaluation are still crucial to guarantee reliability, even though machine learning provides sophisticated tools for automated EEG analysis. We undertook a thorough investigation of EEG seizure classification in this work, with equal emphasis on statistical validation, interpretability, and model performance. Following thorough preprocessing, we used statistical significance tests in conjunction with Chernoff and Bhattacharyya Bounds to select features. Top performers were found to be the Random Forest which had accuracies of 96%. A paired t-test and bootstrapped 95% confidence intervals were used to statistically validate the performance robustness, demonstrating the large variation in model performances. Going beyond simple performance measures, we set out to decipher the nuances of model choices by utilizing methods devoted to model interpretability and shedding light on the importance of distinct aspects. This helped neurologists grasp the primary EEG indicators predictive of seizures and opened the door for future clinical insights. It also provided transparency in diagnostic predictions. Our efforts highlight the mutually reinforcing importance of predictability and model interpretability in the context of medical machine-learning applications, hence intensifying the demand for the creation and use of trustworthy and transparent neurology diagnostic instruments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Neurology

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