检索结果-内蒙古大学图书馆

Past, present, and future of social commerce: a bibliometric analysis

Quality and Quantity 2025年 1-33页

作者： Herzallah, Doaa Liébana-Cabanillas, Francisco Muñoz-Leiva, Francisco Zarco, Carmen Department of Marketing and Market Research University of Granada Granada Spain Andalusian Institute of Research and Innovation in Tourism (IATUR) Granada Spain Andalusian Research Institute in Data Science and Computational Intelligence (DASCI) Granada Spain

Given the significance of social commerce, extensive research has been conducted from various perspectives, resulting in numerous reviews and meta-analytical papers. However, these previous studies have highlighted information gaps and variations in approaches. Therefore, there is a need for a comprehensive examination of social commerce research, encompassing past, present, and future themes, as well as an investigation into additional features of social commerce. To address this need, the present study undertakes a comprehensive thematic review of social commerce research for two distinct periods: 2008–2018 and 2019–2022. Bibliometric analyses were conducted using data extracted from the Web of science database. The research employed science mapping techniques through the utilization of SciMAT and VOSviewer software. The findings of this study identified several themes related to social commerce during the first period, including product, perceived sociability, impulse buying, information technology, motivation, mobile commerce, and others, which were extensively explored. In the second period, themes such as UTAUT, SMEs, perceived risk, live stream, SOR (stimulus-organism-response), and the concept swift guanxi received significant attention. These findings, among others, contribute to the evolving understanding of the social commerce concept. By conducting a comprehensive thematic review and employing bibliometric analyses, this research fills existing knowledge gaps and provides a broader understanding of social commerce research across different periods. The findings shed light on the evolution of social commerce and pave the way for future investigations in this field. © The Author(s) 2025.

关键词： Bibliometric study Co-word analysis Social commerce

来源：评论

学校读者我要写书评

暂无评论

An Analysis of Different Information Granularity Distribution Protocols to Improve Consistency in Intuitionistic Reciprocal Preference Relations 8th

An Analysis of Different Information Granularity Distributi...

引用

8th International Conference on Applied Cognitive Computing, ACC 2024, and 26th International Conference on Artificial intelligence, ICAI 2024, held as part of the World Congress in Computer science, Computer Engineering and Applied Computing, CSCE 2024

作者： González-Quesada, Juan Carlos Herrera-Viedma, Enrique Alonso, Sergio Morente-Molinera, Juan Antonio Pérez, Ignacio Javier Cabrerizo, Francisco Javier Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

ISBN: (纸本)9783031856273

Recently, an optimization of information granularity distribution has been proposed as a way in which the consistency of an intuitionistic reciprocal preference relation can be increased. Specifically, a symmetric and uniform distribution of information granularity has been taken into account for intuitionistic reciprocal preference relations. However, there are other information granularity distribution protocols that could also be used. The objective of this paper is to analyze all of them and determine their effectiveness in improving the consistency of an intuitionistic reciprocal preference relation. The performance of the protocols is discussed by conducting some numerical studies that help provide insights into the effectiveness of the protocols to increase the consistency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information granules

来源：评论

学校读者我要写书评

暂无评论

GPBus: Genetic Programming Based Automated Machine Learning for Bus Delay Prediction 28th

GPBus: Genetic Programming Based Automated Machine Learning ...

引用

28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Fuentes-Almoguera, Ángel García-Martínez, Carlos Luque, Gabriel Idener Seville41300 Spain University of Córdoba Córdoba14071 Spain Andalusian Research Institute in Data Science and Computational Intelligence University of Córdoba Córdoba14071 Spain ITIS Software University of Málaga Málaga29071 Spain

ISBN: (纸本)9783031900648

Urban bus services are a vital component of modern cities’ mobility. However, challenges such as traffic congestion, unpredictable weather, and operational incidents often cause delays, negatively impacting service quality and passenger experience. This study introduces GPBus, a genetic programming-based automated machine learning technique designed to predict bus delays. GPBus automates the selection and composition of predictive models while optimizing their hyperparameters. Additionally, the approach integrates transfer learning to reduce computational costs by reusing pretrained models. The proposed model was evaluated using real-world data from the public bus system in Málaga, Spain, spanning several months of operation. Experimental results demonstrate that GPBus significantly outperforms traditional machine learning methods, including Random Forest, Multi-Layer Perceptron, and Support Vector Machines, in terms of prediction accuracy. These results underscore the potential of AutoML approaches to enhance public transport systems by providing accurate and reliable delay predictions, ultimately improving user satisfaction and promoting sustainable urban mobility. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic programming

来源：评论

学校读者我要写书评

暂无评论

Optimizing Version Innovation Age for Monitoring Markovian Source in Energy-Harvesting Systems

Optimizing Version Innovation Age for Monitoring Markovian S...

引用

IEEE Conference on Wireless Communications and Networking

作者： Mehrdad Salimnejad Anthony Ephremides Marios Kountouris Nikolaos Pappas Department of Computer and Information Science Linköping University Sweden Electrical and Computer Engineering University of Maryland College Park MD USA Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

We study the real-time remote tracking of a two-state Markov process powered by an energy harvesting source. The source dynamically decides whether to transmit over an unreliable channel based on the state of the system. This problem is formulated as a Markov decision process (MDP) to determine the optimal transmission policy that minimizes the average Version Innovation Age (VIA) as a key performance metric. We demonstrate that the optimal transmission policy is threshold-based, determined by the battery level, source state, and VIA value. We numerically validate the analytical structure of the optimal policy and compare its performance against two baseline policies under various system parameters, establishing the superior performance of our approach.

关键词： Wireless communication Technological innovation Wireless sensor networks Energy consumption Markov decision processes Receivers Real-time systems Batteries Energy harvesting Monitoring

来源：评论

学校读者我要写书评

暂无评论

On forecast stability

引用

International Journal of Forecasting 2025年

作者： Godahewa, Rakshitha Bergmeir, Christoph Erkin Baz, Zeynep Zhu, Chengjun Song, Zhangdi García, Salvador Benavides, Dario Department of Data Science and Artificial Intelligence Monash University Australia Department of Computer Science and Artificial Intelligence University of Granada Spain Data Science and Computational Intelligence Andalusian Institute DaSCI Andalucía Spain Meta Inc. CA United States

Forecasts are typically produced in a business context on a regular basis to make downstream decisions. Here, forecasts should not only be as accurate as possible, but also should not change arbitrarily, and be stable in some sense. In this paper, we explore two types of forecast stability that we call vertical stability (for forecasts from different origins for the same target) and horizontal stability (for forecasts from the same origin for different targets). Existing works in the literature are only applicable to certain base models and can only stabilise forecasts vertically. We propose a simple linear-interpolation-based approach to stabilise the forecasts provided by any base model, both vertically and horizontally. Our method makes the trade-off between stability and accuracy explicit, producing forecasts at any point in the spectrum of this trade-off. We used N-BEATS, pooled regression, LightGBM, ETS, and ARIMA as base models in our evaluation across different error and stability measures on four publicly available datasets. On some datasets, the proposed framework achieved forecasts that were both more accurate and stable than the base forecasts. On the others, we achieved forecasts that were slightly less accurate but much more stable. © 2025 International institute of Forecasters

关键词： Forecast combination Forecast congruence Forecast volatility Horizontal stability Instability Inter-plan stability Intra-plan stability Linear interpolation Nervousness Rolling origin stability Vertical stability

来源：评论

学校读者我要写书评

暂无评论

LSTM new gate for computing the efficiency on inputdata

引用

Knowledge-Based Systems 2025年 322卷

作者： García Cabello, Julia Carbó-García, Santiago Department of Applied Mathematics Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain

The recognized learning ability of neural networks (NNs) is determined by their training process. The NN data-dependent nature makes that their success depends to a large extent on the quality of the training data sets. This paper addresses the selection of the best training sets for Long Short Time Models (LSTMs) in real-life contexts. If such selection from a theoretical standpoint is already complex, it becomes challenging when addressed for real scenarios, where effectiveness should be replaced by efficiency (i.e., effectiveness in the shortest possible time and without increasing the computational cost). Our proposal revolves around the introduction, definition and structuring of a new LSTM gate: the efficiency gate, that applies to those inputs that have passed the forget gate. The efficiency gate is responsible for ensuring that training advances only with the best inputs in the sense that the corresponding outputs match as closely as possible with a target value (best estimators). For this purpose, new mathematical results will be demonstrated here in order to prove that our efficiency gate is mathematically feasible. We also fully describe how the new gate joins the LSTM operation, blending in with it. The consequence of such natural integration is that the computational effort is practically unchanged. Our methodology also allows the user to set the admissible error thresholds (which are different in each real scenario) as part of the setting options. © 2025 The Authors

关键词： Efficiency gate New LSTM gate Real-life contexts Selection of training data sets

来源：评论

学校读者我要写书评

暂无评论

Pattern Mining with Evolutionary Algorithms 1st ed. 2016

引用

2016年

作者： Sebastián Ventura José María Luna

ISBN: (数字)9783319338583

ISBN: (纸本)9783319338576

This book provides a comprehensive overview of the field of pattern mining with evolutionary algorithms. To do so, it covers formal definitions about patterns, patterns mining, type of patterns and the usefulness of patterns in the knowledge discovery process. As it is described within the book, the discovery process suffers from both high runtime and memory requirements, especially when high dimensional datasets are analyzed. To solve this issue, many pruning strategies have been developed. Nevertheless, with the growing interest in the storage of information, more and more datasets comprise such a dimensionality that the discovery of interesting patterns becomes a challenging process. In this regard, the use of evolutionary algorithms for mining pattern enables the computation capacity to be reduced, providing sufficiently good solutions. This book offers a survey on evolutionary computation with particular emphasis on genetic algorithms and genetic programming. Also included is an analysis of the set of quality measures most widely used in the field of pattern mining with evolutionary algorithms. This book serves as a review of the most important evolutionary algorithms for pattern mining. It considers the analysis of different algorithms for mining different type of patterns and relationships between patterns, such as frequent patterns, infrequent patterns, patterns defined in a continuous domain, or even positive and negative patterns. A completely new problem in the pattern mining field, mining of exceptional relationships between patterns, is discussed. In this problem the goal is to identify patterns which distribution is exceptionally different from the distribution in the complete set of data records. Finally, the book deals with the subgroup discovery task, a method to identify a subgroup of interesting patterns that is related to a dependent variable or target attribute. This subgroup of patterns satisfies two essential conditions: interpretability and i

关键词： data mining. Pattern recognition systems.

来源：评论

学校读者我要写书评

暂无评论

Flow-TSVAD: Target-Speaker Voice Activity Detection via Latent Flow Matching for Speaker Diarization

Flow-TSVAD: Target-Speaker Voice Activity Detection via Late...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhengyang Chen Bing Han Shuai Wang Yidi Jiang Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China National University of Singapore Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speaker diarization is typically considered as a discriminative task, using discriminative approaches to produce fixed diarization results. In this paper, we explore for the first time the use of neural network-based generative methods for speaker diarization. We implement a Flow-Matching (FM) based generative algorithm within the sequenceto-sequence target speaker voice activity detection (Seq2Seq-TSVAD) diarization system. Our experiments reveal that applying the generative method directly to the original binary label sequence space of the TS-VAD output is ineffective. To address this issue, we propose mapping the binary label sequence into a dense latent space before applying the generative algorithm, and our proposed Flow-TSVAD method can significantly outperform the traditional Seq2Seq-TSVAD system. Additionally, we observe that the FM algorithm converges rapidly during the inference stage, only requiring two inference steps to achieve promising results. Moreover, as a generative model, Flow-TSVAD allows for sampling different diarization results by running the model multiple times, so the ensemble system combining the results from various sampling instances can further boost the diarization performance.

关键词： Voice activity detection Frequency modulation computational modeling Signal processing algorithms Signal processing Inference algorithms Acoustics computational efficiency

来源：评论

学校读者我要写书评

暂无评论

Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

引用

IEEE Transactions on Multimedia 2025年

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

来源：评论

学校读者我要写书评

暂无评论

Pattern Mining with Evolutionary Algorithms

引用

2018年

作者： Sebastin Ventura José María Luna

ISBN: (纸本)9783319816180

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：