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LesionAid: vision transformers-based skin lesion generation and classification – A practical review

Multimedia Tools and Applications 2025年 1-22页

作者： K, Mallikharjuna Rao Krishna, Ghanta Sai Supriya, Kundrapu Sorgile, Meetiksha Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur India Computer Science and Engineering International Institute of Information Technology Naya Raipur India

Skin cancer is one of the most prevalent forms of human cancer. It is recognized mainly visually, beginning with clinical screening and continuing with the dermoscopic examination, histological assessment, and specimen collection. Deep convolutional neural networks (CNNs) perform highly segregated and potentially universal tasks against a classified fine-grained object. This study suggests a novel multi-class prediction framework that uses ViT and ViTGAN to categories skin lesions. To address the class disparity, GANs (Generative Adversarial Networks) based on vision transformers are used. The framework comprises four main phases: ViTGANs, Image processing, and explainable AI. Phase 1 consists of generating synthetic images using ViTGAN to balance all the classes in the dataset. To enhance the amount of the data, the second phase involves using various morphological processes and data augmentation techniques. In phases three and four, a ViT model for edge computing systems that can recognize patterns and classify skin lesions from the user's skin that is visible in the picture is developed. In phase 3, after classifying the lesions into the desired class with ViT, we will use explainable AI (XAI) that leads to more explainable results (using activation maps, etc.) while ensuring high predictive accuracy. The results demonstrate that the model used for generating synthetic images has achieved an FID score of 13.32 and the ViT model has achieved 99.2% as its training accuracy and 97.4% as its validation accuracy. The whole framework is compared with the existing frameworks for skin lesion detection. And explainable AI (XAI) is used in the proposed framework in order to increase model transparency and boost trust among users by illustrating the main factors affecting predictions. This interpretability helps physicians in making more informed decisions by providing clear insights into the model's reasoning. © The Author(s), under exclusive licence to Springer science+Bu

关键词： Generative adversarial networks

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Blockchain-based trust management for data exchange in internet of vehicle network

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Multimedia Tools and Applications 2025年第8期84卷 4837-4855页

作者： Srivastava, Sandeep Agarwal, Deepshikha Chaurasia, Brijesh Kumar Adhikari, Mainak Computer Science Indian Institute of Information Technology UP Lucknow226002 India Information Technology Indian Institute of Information Technology UP Lucknow226002 India Computer Science Pranveer Singh Institute of Technology UP Kanpur208029 India School of Data Science Indian Institute of Science Education & Research Thiruvananthapuram India

Internet of Vehicles (IoV) integrates with various heterogeneous nodes, such as connected vehicles, roadside units, etc., which establishes a distributed network. Vehicles are managed nodes providing all the services required during inter-vehicular communication which makes it essential to use a trust management mechanism. Existing trust management systems are insufficient to address the emerging IoV needs of transparency, mobility, immutability, and scalability. Motivated by the need to address the outlined issues, Proposed additive increase and multiplicative decrease (AIMD) trust management model, to improve the quality of communication, as well as vehicle trustworthiness based on multiple inter-vehicular interactions. The proposed layered architecture helps to reduce overhead in terms of latency and average execution time compared to other trust management mechanism and avoid risk of single point of failure. The Proposed AIMD trust management is implemented on permissioned blockchain, where transactions in the IoV network are only managed by minor nodes which accumulate interactions and uses PBFT and AIMD mechanism to derive immutable reputation score securely and transparently. Simulation results show the AIMD trust mechanism using permissioned blockchain can manage the trust of vehicles efficiently by reducing the latency by 0.29 sec, increasing packet delivery ratio from 0.8 to 0.95, and significantly reduce transaction finality time. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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A Long Short-Term Memory Network-Based Approach for Predicting the Trends in the S&P 500 Index

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 19-26页

作者： Siddesh, G.M. Sekhar, S. R. Mani Srinivasa, K.G. Department of Artificial Intelligence & Data Science M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Data Science and Artificial Intelligence Programme International Institute of Information Technology Naya Raipur India

The price level of a considered security in the stock market is largely determined by perceived consumer demand. Variations in price level in the stock market are essentially a manifestation of public psychology aiming to either cut losses or maximize profits. The assumption that market movements are controlled by a guiding trend is proved by establishing that the time-series variations of daily S&P 500 indices are a non-stationary process using the Dickey–Fuller test. This paper presents a long short-term memory (LSTM) network adaptation of an autoregressive predictive model for S&P 500 indices. Later, an LSTM network-based model is devised to learn the underlying patterns that are responsible for driving the trends in market dynamics. The proposed model accepts as input at each time-step a vector of prior S&P indices over the past two weeks and estimates the current market index. The network weights assume the correct settings by training the model using daily S&P indices over the past ten years. The proposed model decisively captures subtleties in market trends, which are in turn verified using the Granger causality approach. The result demonstrates that the proposed model achieved an accuracy of 82.6%, whereas random forest results in 79.12%. The results obtained are truly the latest in this line of predictive models and reaffirm that LSTM networks are powerful learning models capable of deciphering intricate underlying patterns. © 2023, The Institution of Engineers (India).

关键词： Long short-term memory

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Partial Label Learning with Noisy Labels

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Annals of data science 2025年第1期12卷 199-212页

作者： Zhao, Pan Tang, Long Pan, Zhigeng School of Artificial Intelligence Nanjing University of Information Science & Technology Nanjing210044 China Research Institute of Talent Big Data Nanjing University of Information Science & Technology Nanjing210044 China

Partial label learning (PLL) is a particular problem setting within weakly supervised learning. In PLL, each sample corresponds to a candidate label set in which only one label is true. However, in some practical application scenarios, the emergence of label noise can make some candidate sets lose their true labels, leading to a decline in model performance. In this work, a robust training strategy for PLL, derived from the joint training with co-regularization (JoCoR), is proposed to address this issue in PLL. Specifically, the proposed approach constructs two separate PLL models and a joint loss. The joint loss consists of not only two PLL losses but also a co-regularization term measuring the disagreement of the two models. By automatically selecting samples with small joint loss and using them to update the two models, our proposed approach is able to filter more and more suspected samples with noise candidate label sets. Gradually, the robustness of the PLL models to label noise strengthens due to the reduced disagreement of the two models. Experiments are conducted on two state-of-the-art PLL models using benchmark datasets under various noise levels. The results show that the proposed method can effectively stabilize the training process and reduce the model's overfitting to noisy candidate label sets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Phase locked loops

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Novel Rifle Number Recognition Based on Improved YOLO in Military Environment

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Computers, Materials & Continua 2024年第1期78卷 249-263页

作者： Hyun Kwon Sanghyun Lee Department of Artificial Intelligence and Data Science Korea Military AcademySeoulKorea Graduate School of Information Security Korea Advanced Institute of Science and TechnologyDaejeonKorea

Deep neural networks perform well in image recognition,object recognition,pattern analysis,and speech *** military applications,deep neural networks can detect equipment and recognize *** military equipment,it is necessary to detect and recognize rifle management,which is an important piece of equipment,using deep neural *** have been no previous studies on the detection of real rifle numbers using real rifle image *** this study,we propose a method for detecting and recognizing rifle numbers when rifle image data are *** proposed method was designed to improve the recognition rate of a specific dataset using data fusion and transfer *** the proposed method,real rifle images and existing digit images are fusedas trainingdata,andthe final layer is transferredto theYolov5 *** detectionand recognition performance of rifle numbers was improved and analyzed using rifle image and numerical *** used actual rifle image data(K-2 rifle)and numeric image datasets,as an experimental *** was used as the machine learning *** results show that the proposed method maintains 84.42% accuracy,73.54% precision,81.81% recall,and 77.46% F1-score in detecting and recognizing rifle *** proposed method is effective in detecting rifle numbers.

关键词： Machine learning deep neural network rifle number recognition detection

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Spatio-Temporal Location Recommendation for Urban Facility Placement via Graph Convolutional and Recurrent Networks

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Journal of Computer science & technology 2024年第6期39卷 1419-1440页

作者： Pu Wang Jian-Jiang Lu Wei Chen Peng-Peng Zhao Lei Zhao Institute of Artificial Intelligence School of Computer Science and TechnologySoochow UniversitySuzhou 215006China Department of Data Resource and Information Development Soochow UniversitySuzhou 215006China

The ability to recommend candidate locations for service facility placement is crucial for the success of urban planning. Whether a location is suitable for establishing new facilities is largely determined by its potential popularity. However, it is a non-trivial task to predict popularity of candidate locations due to three significant challenges: 1) the spatio-temporal behavior correlations of urban dwellers, 2) the spatial correlations between candidate locations and existing facilities, and 3) the temporal auto-correlations of locations themselves. To this end, we propose a novel semi-supervised learning model, Spatio-Temporal Graph Convolutional and Recurrent Networks (STGCRN), aiming for popularity prediction and location recommendation. Specifically, we first partition the urban space into spatial neighborhood regions centered by locations, extract the corresponding features, and develop the location correlation graph. Next, a contextual graph convolution module based on the attention mechanism is introduced to incorporate local and global spatial correlations among locations. A recurrent neural network is proposed to capture temporal dependencies between locations. Furthermore, we adopt a location popularity approximation block to estimate the missing popularity from both the spatial and temporal domains. Finally, the overall implicit characteristics are concatenated and then fed into the recurrent neural network to obtain the ultimate popularity. The extensive experiments on two real-world datasets demonstrate the superiority of the proposed model compared with state-of-the-art baselines.

关键词： location recommendation popularity prediction spatio-temporal correlation deep learning

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A data representation method using distance correlation

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Frontiers of Computer science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO Keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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SmartDine: A Comprehensive System for Enhancing Customer Satisfaction and Optimizing Business Operations in the Restaurant Context 6

SmartDine: A Comprehensive System for Enhancing Customer Sat...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Kumarasiri, Kanishka Premarathne, Janitha Mendis, Navinga Bandhuratne, Monali Karunasena, Anuradha Weerasinghe, Lokesha Specializes in Data Science Sri Lanka Institute of Information Technology Malabe Department of Information Technology Sri Lanka

ISBN: (纸本)9798331517878

This research explores the integration of real-time video analysis, deep learning techniques, and advanced data processing tools to enhance customer satisfaction and operational efficiency in restaurant management. Leveraging CCTV footage, along with tools such as Python,PyTorch, OpenCV, and advanced models like YOLOv10 for object detection and YOLO-pose for key point extraction, the study focuses on optimizing key aspects such as food satisfaction analysis, customer engagement and wait time reduction, optimal use and planning of resources. Custom models, including an LSTM network, analyze detected key points to identify customer actions and estimate time spent on various activities. data augmentation techniques enhance model performance, with metrics like precision and recall used for evaluation. The study further aims to provide restaurant managers with a dynamic dashboard that tracks real-time insights, including table turnover rates, preferred menu items, and optimal resource allocation. This research contributes to data-driven restaurant management by offering a robust framework for real-time operational analysis, ultimately improving decision-making and enhancing the dining experience. © 2024 IEEE.

关键词： Sales

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Integrated Sensing-Communication-Computation for Edge Artificial Intelligence

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IEEE Internet of Things Magazine 2024年第4期7卷 14-20页

作者： Wen, Dingzhu Li, Xiaoyang Zhou, Yong Shi, Yuanming Wu, Sheng Jiang, Chunxiao School of Information Science and Technology ShanghaiTech University China Shenzhen Research Institute of Big Data China Beijing University of Posts and Telecommunications China Tsinghua University Beijing National Research Center for Information Science and Technology China

Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twins, holographic projection, semantic communications, and auto-driving, for achieving intelligence of everything. The performance of edge AI tasks, including edge learning and edge AI inference, depends on the quality of three highly coupled processes, i.e., sensing for data acquisition, computation for information extraction, and communication for information transmission. However, these three modules need to compete for network resources for enhancing their own quality-of-services. To this end, integrated sensing-communication-computation (ISCC) is of paramount significance for improving resource utilization as well as achieving the customized goals of edge AI tasks. By investigating the interplay among the three modules, this article presents various kinds of ISCC schemes for federated edge learning tasks and edge AI inference tasks in both application and physical layers. © 2018 IEEE.

关键词： Artificial intelligence

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Attention Span and it's Correlation with Mental Health 1

Attention Span and it's Correlation with Mental Health

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1st International Conference on AIML-Applications for Engineering and technology, ICAET 2025

作者： Shah, Richa Bewoor, Laxmi Rak, Sarthak Patne, Ayush Vishwakarma Institute of Information Technology Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Information Technology Computer Engineering Pune India

ISBN: (纸本)9798350355611

This paper delves into the correlation between attention span and mental health. Attention span is the ability to focus on a task before being distracted by certain factors. It ranges from 2 seconds to more than 20 minutes. Meanwhile, mental health constitutes a person's psychological, sociological, and emotional well-being. Using a clustering algorithm, this research seeks to provide a concrete relation between the two across an age group of 18-25. © 2025 IEEE.

关键词： Electronic health record

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