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Current segmentation systems almost invariably focus on linear segmentation and can only divide text into linear sequences of segments. This suits cohesive text such as news feed but not coherent texts such as documents of a digital library which have hierarchical structures. To overcome the focus on linear segmentation in document segmentation and to realize the purpose of hierarchical segmentation for a digital library’s structured resources, this paper aimed to propose a new multi-granularity hierarchical topic-based segmentation system (MHTSS) to decide section breaks.

MHTSS adopts up-down segmentation strategy to divide a structured, digital library document into a document segmentation tree. Specifically, it works in a three-stage process, such as document parsing, coarse segmentation based on document access structures and fine-grained segmentation based on lexical cohesion.

This paper analyzed limitations of document segmentation methods for the structured, digital library resources. Authors found that the combination of document access structures and lexical cohesion techniques should complement each other and allow for a better segmentation of structured, digital library resources. Based on this finding, this paper proposed the MHTSS for the structured, digital library resources. To evaluate it, MHTSS was compared to the TT and C99 algorithms on real-world digital library corpora. Through comparison, it was found that the MHTSS achieves top overall performance.

With MHTSS, digital library users can get their relevant information directly in segments instead of receiving the whole document. This will improve retrieval performance as well as dramatically reduce information overload.

This paper proposed MHTSS for the structured, digital library resources, which combines the document access structures and lexical cohesion techniques to decide section breaks. With this system, end-users can access a document by sections through a document structure tree.

This article demonstrates the application of geodemographic segmentation to the service industry by using commercial banking as a case example.

Data were collected through self‐administered questionnaires. Two sets of variables were used to profile market segments.

Study results indicate that there are substantial differences between customers of local and national US banks in their evaluation of the relative importance of bank service charges and overall confidence in the bank. Compared to national banks, local banks were evaluated more positively by customers in areas such as extra services offered by the bank, image of the bank, and convenience of the bank.

More focused and concentrated marketing strategies are suggested to achieve better local bank performance.

The digital marketing landscape is rapidly evolving, but the integration of visual content still heavily depends on human expertise. Driven by the quest for innovative marketing strategies that resonate with family-oriented consumers, this study seeks to bridge this gap by applying machine learning to analyze visual content in the maternity and baby care product sector.

This study incorporates a range of machine learning techniques – including open science framework feature detection, panoptic segmentation, customized instance segmentation, and face detection calculation methods – to analyze and predict the appeal of images, thereby enhancing user engagement and parent-child intimacy.

The exploration of various ML models, such as DT, LightGBM, RIPPER algorithm, and CNNs, has offered a comparative analysis that addresses a methodological gap in the existing literature, which frequently depends on isolated model evaluations. According to our quadrant analysis with respect to engagement rate and parent-child intimacy, the selection of a model for real-world applications depends on balancing performance and interpretability.

The proposed system offers a series of actionable recommendations designed to enhance customer engagement and foster brand loyalty. This study contributes to image design in maternity and baby care marketing and provides analytical insights for recommendation systems.

In view of the intense competition in the banking industry all over the world, managers of both commercial and public banks more and more are applying marketing strategy techniques in the operations. The use of marketing techniques in banking becomes even more important in developing country environments, as the supply of bank services far exceeds the demand in most cases. To be able to remain competitive, commercial banks in developing countries need to be more market‐oriented. In the light of this, the factors used in selecting a private/public commercial bank in an advanced developing country – Turkey – are examined. Study results indicate that there are differences among bank customers in their bank patronage behaviour when demographic and socioeconomic factors are considered.

The main purpose is to construct the mapping relationship between garment flat and pattern. Particle swarm optimization–least-squares support vector machine (PSO-LSSVM), the data-driven model, is proposed for predicting the pattern design dimensions based on small sample sizes by digitizing the experience of the patternmakers.

For this purpose, the sleeve components were automatically localized and segmented from the garment flat by the Mask R-CNN. The sleeve flat measurements were extracted by the Douglas–Peucker algorithm. Then, the PSO algorithm was used to optimize the LSSVM parameters. PSO-LSSVM was trained by utilizing the experience of patternmakers.

The experimental results demonstrated that the PSO-LSSVM model can effectively improve the generation ability and prediction accuracy in pattern design dimensions, even with small sample sizes. The mean square error could reach 1.057 ± 0.06. The fluctuation range of absolute error was smaller than the others such as pure LSSVM, backpropagation and radial basis function prediction models.

By constructing the mapping relationship between sleeve flat and pattern, the problems of the garment flat objective recognition and pattern design dimensions accurate prediction were solved. Meanwhile, the proposed method overcomes the problem that the parameters are determined by PSO rather than empirically. This framework could be extended to other garment components.

The paper faces artificial intelligence issues in the venture creation process, exploring how artificial intelligence solutions intervene and forge the venture creation process. Drawing on the most recent literature on artificial intelligence and entrepreneurship, the authors propose a set of theoretical propositions.

The authors adopt a multiple case approach to assess propositions and analyse 4 case studies from which the authors provide (1) more detailed observation about entrepreneurial process phases influenced by artificial intelligence solutions and (2) more details about mechanics enabled by artificial intelligence.

The analysis demonstrates artificial intelligence contributes alongside the entrepreneurial process, enabling mechanisms that reduce costs or resources, generate new organizational processes but simultaneously expand the network needed for venture creation.

The paper adopts a deductive approach analyzing the contribution of AI-based startup offerings in changing the entrepreneurial process. Thus, the paper provides a practical view of the potentiality of artificial intelligence in enabling entrepreneurial processes through the analysis of compelling propositions and the technological ability of artificial intelligence solutions.

This paper aims to design a deep neural network for object instance segmentation and six-dimensional (6D) pose estimation in cluttered scenes and apply the proposed method in real-world robotic autonomous grasping of household objects.

A novel deep learning method is proposed for instance segmentation and 6D pose estimation in cluttered scenes. An iterative pose refinement network is integrated with the main network to obtain more robust final pose estimation results for robotic applications. To train the network, a technique is presented to generate abundant annotated synthetic data consisting of RGB-D images and object masks in a fast manner without any hand-labeling. For robotic grasping, the offline grasp planning based on eigengrasp planner is performed and combined with the online object pose estimation.

The experiments on the standard pose benchmarking data sets showed that the method achieves better pose estimation and time efficiency performance than state-of-art methods with depth-based ICP refinement. The proposed method is also evaluated on a seven DOFs Kinova Jaco robot with an Intel Realsense RGB-D camera, the grasping results illustrated that the method is accurate and robust enough for real-world robotic applications.

A novel 6D pose estimation network based on the instance segmentation framework is proposed and a neural work-based iterative pose refinement module is integrated into the method. The proposed method exhibits satisfactory pose estimation and time efficiency for the robotic grasping.

As the population and purchasing power of ethnic minority consumers in the USA continue to grow, more marketers are using subcultural segmentation and targeted marketing to reach these consumers. Meanwhile, some marketers have grown increasingly concerned with the cost‐effectiveness of ethnic segmentation and differentiated marketing. This research reviews various methods for segmenting the ethnic markets and suggests the nested approach and cost‐benefit optimization for analyzing the cost‐effectiveness of ethnic segmentation and marketing. Furthermore, this research proposes four alternative strategies for marketing in a multicultural environment. Directions for future research and managerial implications are explored.

The ethicality of using audience segmentation in social marketing contexts has typically been framed within either a consequentialist or non-consequentialist perspective, leading to a hitherto intractable debate. This paper seeks to shed new light on this debate using two alternative ethical frameworks: the theory of just health care (TJHC) and integrative social contracts theory (ISCT).

The paper uses cross-sectional survey data from a Kenyan social marketing campaign that aimed to increase awareness and support for the use of anti-retroviral therapy (ART), a class of drugs that inhibit the development of HIV.

Application of the TJHC and ISCT to the Kenyan social marketing campaign revealed the use of audience segmentation to be ethically justified. Moreover, the TJHC provided a useful framework for guiding decisions about the selection of target audience(s) in health-related contexts.

In situations where there are known asymmetries in exposure to mass media channels, adopting a non-segmented mass-media approach may unintentionally entrench pre-existing disparities in health knowledge.

The application of the TJHC and ISCT to health-related social marketing contexts offers a means of resolving the longstanding debate about the ethicality of audience segmentation. The ethical principles underpinning the TJHC also provide a decision-making framework to guide discussions about whether audience segmentation should be based on cost-effectiveness (consequentialism) or need (non-consequentialism). This is particularly relevant in social marketing settings, where the resources available for conducting campaigns are often limited and segmentation decisions about the groups that are targeted or excluded can have important health-related implications.

Container shipping is a standardized business-to-business service market where carriers need to stay customer focused to survive. Market segmentation is an ideal solution to develop customized marketing programs for each segment, but container lines need personalized marketing programs for each customer. Hence, the purpose of this study is to develop a segmentation framework that can help container lines to profile each customer more efficiently considering their needs, strategic importance and demographics.

This study has adopted an exploratory approach. Semi-structured interviews were conducted with managers of container lines.

Segmentation bases are the type of customer, container volume, loyalty, seasonality, decision maker, the industry of shipper, cargo characteristics, container type, destination region and export/import. Market segmentation in container shipping can be helpful in developing effective customized marketing offering, including effective price discrimination and customized marketing communications.

A port-specific segmentation approach was adopted and a flexible segmentation framework was proposed for container lines to adapt in different hinterlands.

Unlike the literature, this study suggests market segmentation can be very helpful in customized marketing in business-to-business services like container shipping industry. This study also suggests port-specific market segmentation for container lines instead of route-specific.