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IEEE Transactions on artificial intelligence 2024年第3期5卷 1411-1424页

作者： Zhang, Yuhong Shi, Congmei Li, Xinzheng Zhang, Zan Hu, Xuegang School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei230009 China

Cross-network node classification aims to train a classifier for an unlabeled target network using a source network with rich labels. In applications, the degree of nodes mostly conforms to the long-tail distribution, i.e., most nodes in the network are tail nodes with sparse neighborhoods. The established methods focus on either the discrepancy cross network or the long tail in a single network. As for the cross-network node classification under long tail, the coexistence of sparsity of tail nodes and the discrepancy cross-network challenges existing methods for long tail or methods for the cross-network node classification. To this end, a multicomponent similarity graphs for cross-network node classification (MS-CNC) is proposed in this article. Specifically, in order to address the sparsity of the tail nodes, multiple component similarity graphs, including attribute and structure similarity graphs, are constructed for each network to enrich the neighborhoods of the tail nodes and alleviate the long-tail phenomenon. Then, multiple representations are learned from the multiple similarity graphs separately. Based on the multicomponent representations, a two-level adversarial model is designed to address the distribution difference across networks. One level is used to learn the invariant representations cross network in view of structure and attribute components separately, and the other level is used to learn the invariant representations in view of the fused structure and attribute graphs. Extensive experimental results show that the MS-CNC outperforms the state-of-the-art methods. Impact Statement-Node classification is an important task in graph mining. With the unavailability of labels, some researchers propose cross-network node classification, using one labeled network to assist the node classification of another unlabeled network. However, the long-tail of nodes leads to unsatisfactory performance and challenges the recent cross-network node classification m

关键词： Graphic methods

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An empirical review of Machine Learning models for Energy Optimizations in IoT Networks 14

An empirical review of Machine Learning models for Energy Op...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Charde, Pallavi Lonkar, Bhupesh B. Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Wardha Sawangi India Datta Meghe Institute of Engineering Technology & Research Department of Information Technology Wardha Sawangi India

ISBN: (纸本)9798350335095

The Internet of Things (IoT) has transformed how users interact with their devices and enabled the accumulation of enormous quantities of data from various sources. There has been an increase in energy consumption due to the expanding use of IoT devices, resulting in environmental concerns and higher operational costs. The use of Machine Learning (ML) algorithms to optimize energy consumption in IoT networks is widespread for different scenarios. This paper empirically analyzes the most widely used machine learning models for energy optimization in IoT networks. The researchers evaluate the efficacy of various ML models regarding energy efficiency, precision, and scalability. The review includes a summary of the most recent research in this field and a comparison of different algorithms, including Decision Trees, Random Forests, Support Vector Machines, Neural Networks, and Deep Learning methods. In addition, this paper discusses the difficulties and limitations of ML-based energy optimization in IoT networks, including data acquisition and processing, model selection, and scalability levels. Finally, this paper presents recommendations for future research and prospective machine-learning applications in energy optimization for IoT networks. This review emphasizes the potential of machine learning (ML) models to reduce energy consumption in IoT networks. It provides researchers and practitioners with insight into this field under real-time scenarios. © 2023 IEEE.

关键词： Internet of things

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HCMHS: High-Order SNP Interactions Detection Based on Hierarchical Clustering and Multi-Task Harmony Search Algorithm

HCMHS: High-Order SNP Interactions Detection Based on Hierar...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tan, Kaiwen Tian, Jin Zhang, Yongbing Shu, Zhenqiu Yu, Zhengtao Kunming University of Science and Technology Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming China

ISBN: (纸本)9798350386226

The interaction between SNPs plays a key role in revealing the genetic mechanisms of complex diseases. However, as the order of SNP interactions increases, the number of SNP combinations increases exponentially, presenting a serious combinatorial explosion problem. Although swarm intelligence algorithms can alleviate the combinatorial explosion problem by optimizing search paths and have been widely used in SNP interaction detection, the performance of swarm intelligence algorithms is greatly affected by initialization and search direction. To mitigate these issues, we propose a high-order SNP interaction detection algorithm based on hierarchical clustering and multitask harmony search (abbreviated as HCMHS). In harmony memory initialization, considering that similar SNPs are more likely to form pathogenic combinations, hierarchical clustering is first used to cluster SNPs into different clusters, and then based on these clusters, harmony memory is initialized. In harmony search, considering the correlation between different orders of SNP interactions, the search directions for SNPs of various orders are optimized through a multi-task framework. To verify the performance of HCMHS, we carried out experiments on 58 simulated datasets and one real dataset, and HCMHS achieved the best results compared to nine advanced algorithms. The code of HCMHS is available at https://***/huoluan17-tian/HCMHS. © 2024 IEEE.

关键词： Hierarchical clustering

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Customer Segmentation Based on Recency, Frequency, Monetary, Variety and Duration (RFMVD) 2

Customer Segmentation Based on Recency, Frequency, Monetary,...

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2nd International Conference on technology Innovation and Its Applications, ICTIIA 2024

作者： Sundari, Agus Lydia, Maya Silvi Muchtar, Muhammad Anggia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Data Science and Artificial Intelligence Medan Indonesia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Computer Science Medan Indonesia

ISBN: (纸本)9798350351613

In banking, maintaining customer retention and customer satisfaction are important. Effective customer segmentation can be a strategic tool to improve customer loyalty and business performance. This research can assist banks in developing marketing strategies to retain customers and improve services based on bill payment transactions. The data that will be used in this study is bill payment transaction data at regional banks in 2023, as for the types of bill payments made by customers, namely electricity, water, telephone payments, and internet bills. The amount of data used is 702,174 rows. This research uses K-Means clustering to group customers based on recency (the last time the customer made a transaction), frequency (the number of customers making transactions), monetary (the nominal amount of money spent by the customer), variety (the number of types of bills paid) and duration (the average duration of time the customer makes the previous transaction until the next transaction) (RFMVD). The results of customer segmentation based on recency, frequency, monetary, variety and duration using K-Means clustering produce 3 customer groups, namely passive customers, loyal customers, and VIP customers. The silhouette score value on customer segmentation using K-Means clustering is 0.7303, which indicates that the grouping is quite good and the number of clusters is correct. © 2024 IEEE.

关键词： bill payments Customer segmentation K-Means clustering RFMVD Model silhouette score

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Question Embedding on Weighted Heterogeneous Information Network for Knowledge Tracing

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ACM Transactions on Knowledge Discovery from data 2024年第1期19卷 1-28页

作者： Sun, Jianwen Du, Shangheng Zhou, Jianpeng Yuan, Xin Shen, Xiaoxuan Liang, Ruxia National Engineering Research Center of Educational Big Data Central China Normal University Wuhan China Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China Shanghai Institute of Artificial Intelligence for Education East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

Knowledge Tracing (KT) aims to predict students' future performance on answering questions based on their historical exercise sequences. To alleviate the problem of data sparsity in KT, recent works have introduced auxiliary information to mine question similarity, resulting in the enhancement of question embeddings. Nonetheless, there remains a gap in developing an approach that effectively incorporates various forms of auxiliary information, including relational information (e.g., question-student, question-skill relation), relationship attributes (e.g., correctness indicating a student's performance on a question), and node attributes (e.g., student ability). To tackle this challenge, the Similarity-enhanced Question Embedding (SimQE) method for KT is proposed, with its central feature being the utilization of weighted and attributed meta-paths for extracting question similarity. To capture multi-dimensional question similarity semantics by integrating multiple relations, various meta-paths are constructed for learning question embeddings separately. These embeddings, each encoding different similarity semantics, are then fused to serve the task of KT. To capture finer-grained similarity by leveraging the relationship attributes and node attributes on the meta-paths, the biased random walk algorithm is designed. In addition, the auxiliary node generation method is proposed to capture high-order question similarity. Finally, extensive experiments conducted on six datasets demonstrate that SimQE performs the best among 10 representative question embedding methods. Furthermore, SimQE proves to be more effective in alleviating the problem of data sparsity. © 2024 Copyright held by the owner/author(s).

关键词： Question answering

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Deep E-Cue:Systematic Deep Network for Multimodal Emotional Cues Recognition in Email Communication 4

Deep E-Cue:Systematic Deep Network for Multimodal Emotional ...

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

作者： Revathi, K. Pabitha, C. Theresa, W. Gracy Chawengsaksopark, Pornpimol Sathiyanarayanan, Mithileysh Srm Valliammai Engineering College Department of Information Technology Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India Shinawatra University Faculty of Engineering Pathum Thani Thailand

ISBN: (纸本)9798331529635

Organizations require virtual communication because efficient communication is essential to successful company operations. Among the features of virtual communication typically email communication, which is textual and asynchronous, meaning that the sender and the recipient do not encounter it simultaneously. Email can be troublesome because it eliminates in-person interactions, which can distort our capacity to develop interpersonal cues that are essential for communicating, exchanging information, and establishing trust. The proposed research is aimed to investigate the adaptability of emotional cues in email communication. A deep learning model is built to extract the emotional cues in email considering multiple parameters like text, audio, video and context of communication. The proposed models achieve accuracy of 96.86%, precision of 97.44%, and sensitivity of 97.69% which is highest score when compared to other existing models. © 2024 IEEE.

关键词： Contrastive Learning

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3D E-commerce Using AI Depth Algorithm Augmented Reality 8th

3D E-commerce Using AI Depth Algorithm Augmented Reality

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Silviya, S. H. Annie Devi, S. Renuka Sriman, B. Aswinrani, M. Raja, S. Sunitha, T. Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9789819784219

In today's dynamic world of online shopping, augmented reality (AR) integration has emerged as a game-changing innovation. It transcends the limitations of traditional online shopping by harnessing AR technologies, such as Google's ARCore, to create immersive 3D product models. These models empower customers to virtually interact with and visualize products in their own environment, effectively bridging the experiential gap with in-store shopping. Employing AR software development kits and game engines, these 3D models seamlessly come to life on customers’ devices, offering a more engaging and informative shopping experience. This not only bolsters customer satisfaction but also helps online retailers manage operational costs more efficiently. With AR’s ability to let customers virtually try on clothes, visualize furniture placements, and inspect products from the comfort of their homes, it's poised to reshape the e-commerce landscape. The ongoing technological revolution is characterized by increasingly immersive, personalized online shopping, setting new benchmarks for customer expectations and retail success. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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AI in Education: A Neutrosophic Graph Framework for Evaluating Challenges and Opportunities 2

AI in Education: A Neutrosophic Graph Framework for Evaluati...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Sasipriya, A.S. Reddy, K.T.V. Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Basic Science and Humanities 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

ISBN: (纸本)9798331528713

The framework proposed in this paper evaluates key strengths, such as AI's ability to enhance personalized learning and automation of administrative tasks, while addressing significant weaknesses, including ethical concerns, privacy issues, and inequitable access to AI technologies. Opportunities created by AI, such as the potential for global accessibility to education, the development of innovative pedagogical models, and the creation of intelligent tutoring systems, are balanced against threats like the displacement of educational jobs, biases in algorithmic outcomes, and the risk of widening socioeconomic disparities. The use of SVNGs allows the study to establish causal relationships between various SWOT components, with the indeterminacy of connections between these factors represented graphically through neutrosophic graphs. Energy analysis is employed to assess the impact of AI in each quadrant - Strength-Opportunities (SO), Strength-Threats (ST), Weakness-Opportunities (WO), and Weakness-Threats (WT). By calculating the energy of each quadrant, the study quantifies the potential benefits and risks of AI integration into educational frameworks. The findings suggest that while the opportunities presented by AI outweigh some of its associated threats, a careful and strategic approach is required to ensure that its implementation is ethical, equitable, and effective. © 2024 IEEE.

关键词： Adversarial machine learning

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Extracting Valuable Insights from The Handwritten Feedback 7

Extracting Valuable Insights from The Handwritten Feedback

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Chawale, Yugandhar Patwardhan, Prasanna Dewani, Rahul More, Ankit Jadhav, Varsha Pandey, Mrityunjoy Sharma, Neha Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Maharashtra Pune India Army Institute of Technology Department of Computer Engineering Maharashtra Pune India Society for Data Science Pune India

ISBN: (纸本)9798331527822

This paper describes the collection and analysis of feedback data which is crucial for evaluating the success of any event and identifying areas of improvement. However, when feedback forms are distributed in paper format, extracting the text responses into a machine-readable format for analysis can be a tedious and error-prone manual process. Optical Character Recognition (OCR) technology offers an automated solution for digitizing text from scanned documents, but its accuracy can be impacted by various factors like image quality, font styles, and language complexities. This study investigates the use of Gemini 1.5 Pro, a multimodal AI, to extract text data from PDF scans of collected handwritten feedback forms. After evaluation of Gemini's performance across a range of PDF qualities and comparing its output to manually transcribed data, the results demonstrate Gemini's high accuracy rates, even on lower quality scans, enabling efficient extraction of feedback responses while minimizing manual effort. This solution offers a scalable and reliable approach for educational institutions, professional organizations and other bodies to digitize survey responses for deeper qualitative and quantitative computational techniques. © 2024 IEEE.

关键词： data accuracy

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Monkeypox Disease Classification from Skin Lesion Images using Deep Convolution Neural Network 3

Monkeypox Disease Classification from Skin Lesion Images usi...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Thangaraj, Rajasekaran Vadivel, Balasubramaniam Kumar, S. Manoj Sadesh, S. Karunakaran, P. Prakash, P. Nandha Engineering College Department of Computer Science and Engineering Erode India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Velalar College of Engineering and Technology Department of Artificial Intelligence and Data Science Erode India Nandha Engineering College Department of Artificial Intelligence and Data Science Erode India Velalar College of Engineering and Technology Department of Information Technology Erode India

ISBN: (纸本)9798331523893

Recently monkeypox outbreak has raised concerns due to its increasing number of cases and diverse dermatological symptoms in 2024, which can complicate early diagnosis due to similarities with other viral infections such as measles and chickenpox. To enhance diagnostic accuracy, transfer learning models and artificial intelligence (AI) have been explored as effective tools. Pre-trained models such as VGG16, VGG19, ResNet50V2, and MobileNetV2 have been employed for monkeypox detection, each with distinct trade-offs in terms of computational efficiency and diagnostic precision. MobileNetV2 offers superior efficiency, but this comes at the cost of reduced accuracy. VGG models provide higher accuracy but require much more computational *** these models, DenseNet121 stands out by achieving 99% accuracy while requiring 37% less computational power, making it the model optimized choice for efficient and accurate monkeypox classification. © 2025 IEEE.

关键词： Transfer learning

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