Discover Artificial Intelligence最新文献_第2页

Clustering graph data: the roadmap to spectral techniques 图形数据聚类：光谱技术路线图

Discover Artificial Intelligence Pub Date : 2024-01-22 DOI: 10.1007/s44163-024-00102-x

Rahul Mondal, Evelina Ignatova, Daniel Walke, David Broneske, Gunter Saake, Robert Heyer

引用次数: 0

Wanet: weight and attention network for video summarization Wanet：用于视频摘要的权重和注意力网络

Discover Artificial Intelligence Pub Date : 2024-01-11 DOI: 10.1007/s44163-024-00101-y

Arpan Basu, Rishav Pramanik, Ram Sarkar

引用次数: 0

Between artificial intelligence and customer experience: a literature review on the intersection 人工智能与客户体验之间：关于交叉点的文献综述

Discover Artificial Intelligence Pub Date : 2024-01-09 DOI: 10.1007/s44163-024-00105-8

Melise Peruchini, Gustavo Modena da Silva, Julio Monteiro Teixeira

引用次数: 0

Context-flexible cartography with Siamese topological neural networks 利用连体拓扑神经网络进行上下文灵活制图

Discover Artificial Intelligence Pub Date : 2024-01-09 DOI: 10.1007/s44163-023-00098-w

Pitoyo Hartono

引用次数: 0

Modeling online customer purchase intention behavior applying different feature engineering and classification techniques 运用不同的特征工程和分类技术对在线顾客购买意愿行为进行建模

Discover Artificial Intelligence Pub Date : 2023-11-14 DOI: 10.1007/s44163-023-00086-0

Md. Shahriare Satu, Syed Faridul Islam

{"title":"Modeling online customer purchase intention behavior applying different feature engineering and classification techniques","authors":"Md. Shahriare Satu, Syed Faridul Islam","doi":"10.1007/s44163-023-00086-0","DOIUrl":"https://doi.org/10.1007/s44163-023-00086-0","url":null,"abstract":"Abstract In the evolution of digital technology, e-commerce sectors are gradually changing to realize customers’ demands and supply required things with low cost and due time. Recently, various machine learning techniques have been used to investigate different activities of customers and estimate different characteristics and requirements of customers. The goal of this work is to propose a machine-learning model that employs multiple data analytics and machine learning techniques to manipulate customer records and predict their buying intention more precisely. In this study, we collected an online shoppers’ purchasing intention dataset from a public data repository. Different feature transformation methods were employed in the primary dataset and generated its transformed datasets. Besides, we balanced the transformed datasets and detected outliers from them. Then, we applied different feature selection methods into primary and transformed-balanced datasets and again generated several feature subsets. Finally, various state-of-the-art classifiers were employed in primary, transformed, and all of their generated subsets. Then, different outcomes of the proposed model were analyzed and Random Forest was found as the stable classifier that produces more feasible results for any online shoppers’ buying instances. In this work, this classifier provided the best accuracy of 92.39% and f-score of 0.924 for the Z-Score and Gain Ratio transformed subset. In addition, it gave the highest AUROC of 0.975 for the Square Root and Information Gain subset. We also found Z-Score transformation and Information Gain more reliable methods to convert online shoppers’ customer intention dataset and get more feasible results from different classifiers.","PeriodicalId":470440,"journal":{"name":"Discover Artificial Intelligence","volume":"48 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134902355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A dual-path instance segmentation network based on nuclei contour in histology image 一种基于组织图像核轮廓的双路径实例分割网络

Discover Artificial Intelligence Pub Date : 2023-10-26 DOI: 10.1007/s44163-023-00083-3

Jiangqi Li, Xiang Li, Minglei Li, Pengfei Yan

{"title":"A dual-path instance segmentation network based on nuclei contour in histology image","authors":"Jiangqi Li, Xiang Li, Minglei Li, Pengfei Yan","doi":"10.1007/s44163-023-00083-3","DOIUrl":"https://doi.org/10.1007/s44163-023-00083-3","url":null,"abstract":"Abstract Accurate segmentation of nuclei in histology images is essential for digital pathology. However, previous work should have considered integrating nuclei contour information into network learning. The main problems are (1) nuclei contour information needs to be fully considered, resulting in inaccurate spatial location of nuclei. (2) Small nuclei in dense regions or irregularly shaped nuclei cannot be segmented. (3) Mainstream networks consider more long-distance semantic information and larger receptive fields and do not consider the fusion of feature maps with different semantics. To solve the above problems, we have proposed a contour-based dual-path instance segmentation network. Firstly we reconstructed the nuclei contour information using algorithms and morphological manipulations. Provide input for subsequent contouring networks. Then we designed a dual-path network. It can extract nuclei features and contour features independently in the encoding stage and fuse the feature maps at different scales in the decoding stage. In the decoding stage, we use the attention module with a newly designed fusion mechanism, which allows us to fuse different semantics of feature maps for simple and efficient fusion. Finally, using the watershed algorithm, we use the network segmentation results to get the instance segmentation results of the nuclei. We used four publicly available datasets, including Kumar, CPM-15, CPM-17, and TNBC, which contain the nuclei of many organs and different forms of nuclei in the human body. Compared with the mainstream methods, we obtained the best AJI metrics, which are 0.656,0.717, and 0.602. In conclocess can effectively improve the performance of the nuclei instance segmentation network. It can accurately locate the scattered nuclei in space with small segmented nuclei in dense regions.","PeriodicalId":470440,"journal":{"name":"Discover Artificial Intelligence","volume":"30 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136381884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence in Africa: a bibliometric analysis from 2013 to 2022 非洲的人工智能:2013年至2022年的文献计量分析

Discover Artificial Intelligence Pub Date : 2023-10-23 DOI: 10.1007/s44163-023-00084-2

Tabu S. Kondo, Salim A. Diwani

{"title":"Artificial intelligence in Africa: a bibliometric analysis from 2013 to 2022","authors":"Tabu S. Kondo, Salim A. Diwani","doi":"10.1007/s44163-023-00084-2","DOIUrl":"https://doi.org/10.1007/s44163-023-00084-2","url":null,"abstract":"Abstract This study employs bibliometric analysis to investigate the evolving research landscape of Artificial intelligence (AI) within Africa, focusing on the years 2013 to 2022. The central objective is to discern and analyze AI studies conducted in Africa, using a dataset compiled from research papers within the Scopus database. By conducting a comprehensive analysis, this research uncovers crucial insights, including primary authors, influential journals and publishers, nations with the highest research productivity, noteworthy funding sources, influential organizations, and prevalent research domains. Additionally, the study examines year-by-year growth trends and authorship patterns. Employing the VOSviewer software, it creates visual representations that illustrate the dynamic evolution of AI research within the African context. Notably, the analysis of 1646 publications reveals a significant increase in publications over the last decade, with South Africa emerging as a global leader in AI development, and the IEEE, Elsevier, and Springer as prominent publishers. The study also highlights the leading institutions, with the University of the Witwatersrand, University of Johannesburg, University of KwaZulu-Natal, University of Cape Town, and University of Pretoria at the forefront of AI research in Africa. The National Research Foundation is identified as the primary funder supporting AI research across the continent. In conclusion, this research aims to provide a comprehensive understanding of AI’s role in addressing African challenges, fostering innovation, and contributing to the continent’s technological advancement, shedding light on prevalent research areas and significant funding sources in the process.","PeriodicalId":470440,"journal":{"name":"Discover Artificial Intelligence","volume":"18 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135366705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Early detection of dyslexia based on EEG with novel predictor extraction and selection 基于脑电图的阅读障碍早期诊断与预测因子提取与选择

Discover Artificial Intelligence Pub Date : 2023-10-19 DOI: 10.1007/s44163-023-00082-4

Shankar Parmar, Chirag Paunwala

引用次数: 0

Mapping AI avant-gardes in time: posthumanism, transhumanism, genhumanism 描绘人工智能的先行者:后人类主义、超人类主义、genhumanism

Discover Artificial Intelligence Pub Date : 2023-10-03 DOI: 10.1007/s44163-023-00080-6

James Brusseau

引用次数: 0