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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Aishwarya, S. Selvamurugan, C. Parthiban, K.G. Nathan, B. Manoj Prabhakar, J. Mohamed Noordeen, A. Sri Eshwar college of Engineering Department of Computer Science and Engineering Coimbatore India Dhaanish Ahmed Institute of Technology Department of Science and Humanities Coimbatore India Dhaanish Ahmed Institute of Technology Department of Biomedical Engineering Coimbatore India Dhaanish Ahmed Institute of Technology Department of Computer Science and Engineering Coimbatore India Dhaanish Ahmed Institute of Technology Department of Artificial Intelligence & Data Science Coimbatore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

GenAI has revolutionized the generation of realistic and imaginative data in ways that were previously beyond the capabilities of other machine learning algorithms. This area is rapidly gaining traction, with extensive research currently underway to advance it further. The most recent EY Reimagining Industry Futures Study highlights the generative AI (GenAI) to be a pivotal technology. The surge in GenAI's prominence can largely be attributed to advancements in deep neural networks, hardware technology such as GPUs and increased availability of data. This research study reviews the most popular GenAI models, spanning text, image, and audio modalities. This study provides a comparative analysis of various models, serving as a comprehensive resource for understanding the functionality and efficiency of the top GenAI models available today. © 2024 IEEE.

关键词： Generative adversarial networks

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Optimizing Purchase Intention Prediction in E-Commerce 1

Optimizing Purchase Intention Prediction in E-Commerce

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Jayanthi, S. Rajeshwari, D. Goud, Naresh M. Geetha, R. Franklin, S. Benjamin Rajyalakshmi, Pannangi Department of Artificial Intelligence &data Science Telangana India Sri Indu Institute of Engineering and Technology Department of CSE Telangana Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Telangana Hyderabad India Bengaluru India Faculty of Behavioural and Social Sciences Department of Social Work Chennai India TKR College of Engineering and Technology Department of CSE Hyderabad India

ISBN: (纸本)9798331518943

This study investigates the effectiveness of deep learning models - Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM - in predicting purchase intention on e-commerce platforms using session-level data. The dataset, sourced from the UCI Machine Learning Repository, comprises 12,330 sessions with features such as 'PageValues,' 'BounceRates,' 'ExitRates,' and user behavior attributes like 'Month' and 'VisitorType.' After preprocessing categorical variables using Label Encoding and standardizing numerical features, the data was split into an 80-20% training and testing set. Each of the five models was then trained and assessed using metrics such as accuracy, ROC-AUC, and other classification measures. Among the models, the CNN achieved the highest performance with an accuracy of 90.34% and a ROC-AUC score of 0.9156, followed by the LSTM model with an accuracy of 89.72% and a ROC-AUC score of 0.9102. Although the RNN model performed well, it showed relatively lower metrics with an accuracy of 88.40% and a ROC-AUC of 0.8934. 'PageValues' exhibited the strongest positive correlation with purchase intention (Pearson correlation: 0.4926, Spearman correlation: 0.6264), while 'ExitRates' and 'BounceRates' had significant negative correlations of -0.2071 and -0.1507, respectively. This study identifies CNN and LSTM as robust models for predicting purchase intentions in e-commerce, with 'PageValues' being the most influential predictor. The results provide valuable insights for optimizing e-commerce platforms to improve conversion rates. Future research will focus on external validation and expanding the dataset to ensure broader applicability across various e-commerce contexts. © 2024 IEEE.

关键词： Convolutional neural networks

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Fault Tolerant Property of Interdependent Networks 12

Fault Tolerant Property of Interdependent Networks

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12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2022

作者： Li, Xifeng Bi, Dongjie Peng, Libiao Tang, Yu Xie, Yongle Xu, Juan School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Artificial Intelligence and Big Data Yibin University Yibin China

ISBN: (纸本)9781839538360

Recent years have witnessed a surge of extensive study upon the robustness of a system consisting of interdependent networks. Both the mechanism of cascading failures and the corresponding recovery strategies, which means to heal malfunction of a system before it runs completely into collapse, are two key issues in the field of robustness analysis of interdependent networks. An essential question that has always been ignored is how to design a fault tolerance scheme to buffer against cascading failures. In order to deeply understand the relationship between the fault-tolerance assignment and the robustness of interdependent networks, we propose a cascading failure model with the consideration of the fault tolerance property of the nodes in interdependent networks, where the node are specified different fault tolerant capacity according to their degree values. By theoretical and numerical studies, it is found that the robustness of a system can be improved significantly if the high-degree nodes are assigned high fault tolerant capacity. However, robustness of a system can be hampered if the high degree nodes are assigned low fault tolerant capacity. Our results prove that the fault tolerance assignment scheme of the nodes has great impacts on the robustness of interdependent networks and provide important thoughts to reinforce reliability of the interdependent systems by the proper allocation of fault tolerant capacity. © 2022 IEEE.

关键词： Fault tolerance

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Clause-level Relationship-aware Math Word Problems Solver

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Machine intelligence Research 2022年第5期19卷 425-438页

作者： Chang-Yang Wu Xin Lin Zhen-Ya Huang Yu Yin Jia-Yu Liu Qi Liu Gang Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Data ScienceUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou 450001China

Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence *** math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level *** this end,inspired by the theory of two levels of process in comprehension,we propose a novel clause-level relationship-aware math solver(CLRSolver)to mimic the process of human comprehension from lower level to higher ***,in the lower-level processes,we split problems into clauses according to their natural division and learn their *** the higher-level processes,following human′s multi-view understanding of clause-level relationships,we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local ***,we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global ***,we enhance the representation of clauses based on the learned clause-level dependency *** expression generation,we develop a tree-based decoder to generate the mathematical *** conduct extensive experiments on two datasets,where the results demonstrate the superiority of our framework.

关键词： artificial intelligence(AI) artificial neural network(ANN) computational mathematics machine intelligence machine learning

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Real-time Monitoring and Early Detection of Diabetes with Bioactive and Biological Impedance Sensors using Hybrid Machine Learning Algorithm 4

Real-time Monitoring and Early Detection of Diabetes with Bi...

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4th International Conference for Emerging technology, INCET 2023

作者： Hariharan, S. Dhanush Sridharan, Deveshwaran Kalaiyarasi, R. Mohanaprakash, T.A. Tamilselvi, C. Sam, Dahlia Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India Rajalakshmi Institue of Technology Department of Artificial Intelligence and Data Science Chennai India Panimalar Engineeering College Department of CSE Chennai India Dr. M.G.R Educational and Research Institute Department of IT Chennai India

ISBN: (纸本)9798350335750

Diabetes is an ongoing infection that influences a large number of individuals overall and can prompt serious unexpected issues whenever left untreated. Early identification of diabetes can altogether diminish the risk of intricacies and work on significant results. Lately, the utilization of wearable technology has arisen as a promising device for illness identification and checking. Smartwatches furnished with bioactive sensors can give ceaseless, painless observing of body vitals, making them ideal for diabetes screening. This study proposes a framework that uses patient information for preparing a hybrid AI model to distinguish the presence of diabetes. The framework consolidates body vitals estimated utilizing a smartwatch with a bioactive sensor to get exact and nonstop information on the wearer's wellbeing status. The mixture model coordinates both profound learning and conventional AI calculations to accomplish predominant precision in identifying diabetes. The framework gathers information on different body vitals, for example, pulse, circulatory strain, and skin conductance, which are known to be firmly connected with diabetes. The gathered information is pre-handled and afterward used to prepare the hybrid model. The profound learning calculation is utilized to remove significant level highlights from the crude information, while the conventional AI calculation is utilized to arrange the information into diabetic or non-diabetic classifications. The cross breed model is intended to work on the accuracy of diabetes location by integrating the qualities of both profound learning and conventional AI. © 2023 IEEE.

关键词： COVID-19

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Quantum Computing's Double-Edged Sword Unravelling the Vulnerabilities in Quantum Key Distribution for Enhanced Network Security

Quantum Computing's Double-Edged Sword Unravelling the Vulne...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Anantraj, I. Umarani, B. Karpagavalli, C. Usharani, C. Lakshmi, S. Jothi Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Namakkal India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Rajapalayam India

ISBN: (纸本)9798350319088

Quantum computing has emerged as both a boon and a bane in the realm of network security. This paper delves into the intriguing interplay between quantum computing and the vulnerabilities of Quantum Key Distribution (QKD) protocols, a cornerstone of secure quantum communication. While QKD offers unmatched security through the principles of quantum mechanics, it is not impervious to attacks in the age of rapidly advancing quantum computing. We examine these vulnerabilities, highlighting the challenges of ensuring the long-term security of quantum networks in a post-quantum world. Through a comprehensive exploration of threat models and the practicality of QKD, we uncover potential weaknesses in quantum network security. In particular, we investigate QKD's susceptibility to evolving quantum algorithms, hardware vulnerabilities, and post-processing security concerns. This work aims to provide a roadmap for understanding and addressing these vulnerabilities, ensuring the continued robustness of quantum communication networks, and fostering the development of post-quantum cryptographic solutions. As quantum computing advances, its implications for quantum network security are far-reaching. This study underlines the critical need for research and innovation in securing quantum communication. By unravelling the vulnerabilities within Quantum Key Distribution and exploring post-quantum cryptographic alternatives, this research contributes to the development of more resilient and secure quantum networks, safeguarding sensitive information and communications in an increasingly quantum-enabled world. © 2023 IEEE.

关键词： Quantum cryptography

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Early Detection of Black Fungus Using Deep Learning Models for Efficient Medical Diagnosis 4

Early Detection of Black Fungus Using Deep Learning Models f...

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4th IEEE International Conference on Emerging Techniques in Computational intelligence, ICETCI 2024

作者： Hassan, Esraa Saber, Abeer El-Kenawy, El-Sayed M. Bhatnagar, Roheet Shams, Mahmoud Y. Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning Kafrelsheikh33516 Egypt Damietta University Faculty of Computers and Artificial intelligence Information technology Dept. Damietta34517 Egypt Delta Higher Institute of Engineering and Technology Dept. of Communications and Electronics Mansoura35111 Egypt Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350387063

Mucormycosis, also known as black fungus, is a rare infection caused by mould that can affect the lungs, brain, skin, and sinuses. People with weakened immune systems due to underlying health conditions (e.g., organ transplant, uncontrolled diabetes, cancer, neutropenia, AIDS, and COVID-19) or drug use are more likely to contract the infection. Recent reports have indicated that a small number of COVID-19 survivors with co-morbidities are particularly vulnerable to black fungus. Therefore, recovered COVID-19 patients should seek medical attention immediately if they experience any mucormycosis symptoms. Patients with COVID-19 have numerous concerns regarding this life-threatening infection, particularly those with immunocompromised states such as solid organ transplant (SOT) and hematopoietic stem cell transplant (HSCT) or tumors, who are more likely to be infected. Early diagnosis and treatment are essential to prevent patient mortality. In this paper, we present the Multi-Class Black Fungus (MCBF) dataset, which consists of three compressed folders containing images. The first folder includes 66 images of eye black fungus after data preprocessing, while the second folder contains 63 images of mouth black fungus after data preprocessing. The third contains 62 images of the mouth after applying data preprocessing, and the last one contains 79 images of black skin fungus after applying data preprocessing. The MCBF dataset contains images that aid in training and validation when using deep learning algorithms to recognise and classify black fungus diseases, then we apply a resnet(50) pretrained model by using MCBF accuracy reaches 96.12%. © 2024 IEEE.

关键词： COVID-19

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Vision-Based PM2.5 Concentration Estimation with Natural Scene Statistical Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第6期5卷 2805-2815页

作者： Wang, Guangcheng Shi, Quan Wang, Han Gu, Ke Wei, Mengting Wong, Lai Kuan Wang, Mingxing Nantong University School of Transportation and Civil Engineering Nantong226019 China Beijing University of Technology Faculty of Information Technology Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Artificial Intelligence Institute Beijing100124 China Chinese Academy of Sciences Institute of Software Beijing100045 China Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia Environmental Science and Technology Company Ltd. Beijing102100 China

As the primary pollutant in China's urban atmosphere, particulate matter (PM)2.5 poses a great threat to the health of residents and ecological stability. An efficient and effective PM2.5 concentration monitoring is essential. Nonetheless, the popular devices for PM2.5 monitoring are developed based on two standards: the microoscillation balance method and the β-ray method, which have high purchase and maintenance costs and slow calculation rates. To this end, we put forward a real-time and reliable vision-based estimation algorithm of PM2.5 concentration. To be specific, the proposed method first develops two natural scene statistical analysis-based visual priors to measure saturation and structural information losses caused by the 'haze' formed by PM2.5. Moreover, we develop a lightweight deep belief network-deep neural network-based PM2.5 concentration estimation model, which learns the mapping from the designed visual priors to PM2.5 concentrations. Experiments confirm the superiority of our vision-based PM2.5 concentration estimation method by comparison with state-of-the-art photo-based PM2.5 monitoring methods. © 2020 IEEE.

关键词： Entropy

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Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation 25

Hierarchical Time-Aware Mixture of Experts for Multi-Modal S...

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34th ACM Web Conference, WWW 2025

作者： Zhang, Shengzhe Chen, Liyi Shen, Dazhong Wang, Chao Xiong, Hui University of Science and Technology of China Hefei China Nanjing University of Aeronautics and Astronautics Nanjing China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei China Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798400712746

Multi-modal sequential recommendation (SR) leverages multi-modal data to learn more comprehensive item features and user preferences than traditional SR methods, which has become a critical topic in both academia and industry. Existing methods typically focus on enhancing multi-modal information utility through adaptive modality fusion to capture the evolving of user preference from user-item interaction sequences. However, most of them overlook the interference caused by redundant interest-irrelevant information contained in rich multi-modal data. Additionally, they primarily rely on implicit temporal information based solely on chronological ordering, neglecting explicit temporal signals that could more effectively represent dynamic user interest over time. To address these limitations, we propose a Hierarchical time-aware Mixture of experts for multi-modal Sequential Recommendation (HM4SR) with a two-level Mixture of Experts (MoE) and a multi-task learning strategy. Specifically, the first MoE, named Interactive MoE, extracts essential user interest-related information from the multi-modal data of each item. Then, the second MoE, termed Temporal MoE, captures user dynamic interests by introducing explicit temporal embeddings from timestamps in modality encoding. To further address data sparsity, we propose three auxiliary supervision tasks: sequence-level category prediction (CP) for item feature understanding, contrastive learning on ID (IDCL) to align sequence context with user interests, and placeholder contrastive learning (PCL) to integrate temporal information with modalities for dynamic interest modeling. Extensive experiments on four public datasets verify the effectiveness of HM4SR compared to several state-of-the-art approaches. © 2025 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Total losses and dispersion effects management and upgrading fiber reach in ultra-high optical transmission system based on hybrid amplification system

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Journal of Optical Communications 2025年第2期46卷 289-298页

作者： Ramkumar, Govindaraj Shahila, Ferlin Deva Lingaraj, Vanitha Chandran, Prabhu Chidambaram, Vivek Arumugam, Parimala Ramadan, Hegazy Mahmoud Saveetha University Tamil Nadu Chennai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of ECE S.A. Engineering College Tamilnadu Chennai India Department of ECE Prathyusha Engineering College Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamilnadu Chennai India Department of ECE Sree Sastha Institute of Engineering and Technology Tamilnadu Chennai India Sina Institute of Technology Sina Egypt

This work demonstrated the total losses and dispersion effects management and upgrading the fiber reach in ultra-high optical transmission system based on hybrid amplification system. Based on the requirements of the system, a number of trade-offs must be made between the numerous interconnected performance characteristics of each component in the design of a high quality transmission link. The transmission distance is the greatest distance that an optical signal has to travel. It affects the choice of optical component, fiber type, and signal amplification techniques. Fiber amplifiers or signal regeneration may be required to compensate for signal loss over greater distances. Reviewing these crucial aspects helps when selecting the best optical components and system layout. This ensures the optical link performs as expected. Hybrid amplification system unit (EDFA/Raman/EDFA), (EDFA/Raman), (BFA/PDFA) and (Raman/EDFA) is employed in the transmission system to strength the signal and upgrade the system performance operation efficiency. Space division multiplexing scheme is also employed with the propagation scheme in order to upgrade and control of the total losses and dispersion effects. The optimum total losses/dispersion/repeater spacing are studied with the variations of the total number of links per fiber core channel. © 2024 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Amplification

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