In the current business environment, where the customer is the primary focus, effective communication between marketing and senior management is vital for success. Effective customer profiling is a cornerstone of strategic decision-making for digital start-ups seeking sustainable growth and customer satisfaction. This research investigates the clustering of customers based on recency, frequency, and monetary (RFM) analysis and employs validation metrics to derive optimal clusters. The K-means clustering algorithm, coupled with the Elbow method, Silhouette coefficient, and Gap Statistics method, facilitates the identification of distinct customer segments. The study unveils three primary clusters with unique characteristics: new customers (Cluster A), best customers (Cluster B), and intermittent customers (Cluster C). For platform-based Edutech start-ups, Cluster A underscores the importance of tailored learning content and support, Cluster B emphasizes personalized incentives, and Cluster C suggests re-engagement strategies. By understanding and addressing the diverse needs of these clusters, digital start-ups can forge enduring connections, optimize customer engagement, and fuel sustainable business growth.

This article considers the task of handwritten text recognition using attention-based encoder–decoder networks trained in the Kazakh and Russian languages. We have developed a novel deep neural network model based on a fully gated CNN, supported by multiple bidirectional gated recurrent unit (BGRU) and attention mechanisms to manipulate sophisticated features that achieve 0.045 Character Error Rate (CER), 0.192 Word Error Rate (WER), and 0.253 Sequence Error Rate (SER) for the first test dataset and 0.064 CER, 0.24 WER and 0.361 SER for the second test dataset. Our proposed model is the first work to handle handwriting recognition models in Kazakh and Russian languages. Our results confirm the importance of our proposed Attention-Gated-CNN-BGRU approach for training handwriting text recognition and indicate that it can lead to statistically significant improvements (p-value < 0.05) in the sensitivity (recall) over the tests dataset. The proposed method’s performance was evaluated using handwritten text databases of three languages: English, Russian, and Kazakh. It demonstrates better results on the Handwritten Kazakh and Russian (HKR) dataset than the other well-known models.

Tables are everywhere, from scientific journals, articles, websites, and newspapers all the way to items we buy at the supermarket. Detecting them is thus of utmost importance to automatically understanding the content of a document. The performance of table detection has substantially increased thanks to the rapid development of deep learning networks. The goals of this survey are to provide a profound comprehension of the major developments in the field of Table Detection, offer insight into the different methodologies, and provide a systematic taxonomy of the different approaches. Furthermore, we provide an analysis of both classic and new applications in the field. Lastly, the datasets and source code of the existing models are organized to provide the reader with a compass on this vast literature. Finally, we go over the architecture of utilizing various object detection and table structure recognition methods to create an effective and efficient system, as well as a set of development trends to keep up with state-of-the-art algorithms and future research. We have also set up a public GitHub repository where we will be updating the most recent publications, open data, and source code. The GitHub repository is available at https://github.com/abdoelsayed2016/table-detection-structure-recognition.

The Big Data-driven economy has emerged, and data is known as the new petroleum of the current century. Big Data (BD) analytics can play an essential role in building a self-sustainable competitive advantage for the pharmaceutical industry by feeding pharmaceutical drug corporations with different directions, patterns, and behaviours of the health’s ecosystem. Unfortunately, many Egyptian local pharmaceutical companies use obsolete strategies to create a standalone competitive advantage. Most of these firms face numerous challenges in adopting a smart strategy based on BD analytics. This research provides an overview of the barriers that hinder the full utilization of BD analytics in local Egyptian pharmaceutical firms. These obstacles are categorized into four groups: culture, strategies, technology and processes. A descriptive analysis methodology was applied to create an in-depth understanding of BD challenges. The qualitative study consists of two approaches: case-analysis study and semi-structured interviews. Furthermore, we suggest a comprehensive strategy that includes five phases (Analysis, Benchmark, Design, Implement, and Evaluate). This strategy is an advanced adaptation of the digital transformation maturity model and can be used as a guideline to effectively adopt and utilize the BD analytics approach.

Construction projects are usually regarded as massive and mostly have significant complexity and risky projects, they take longer times to be accomplished, also have a lot of phases, for that fact this research will be dedicated to finding how can project management software help in the success and deliverable of construction projects in high efficiency. Hence, project management software is used in different types of projects not only construction projects but also are used in IT projects, oil and gas projects, industrial projects …etc., this article is focused on mainly “construction projects” as mentioned earlier they are the vast and hard to delivered and overdue time can happen in most cases. The research seeks to discover the different types of project management software popularly used in construction projects and how they help or assist in such projects. The research is carried out within the commercial cities of Kazakhstan country (Almaty, Nur sultan, and Atyrau) with a qualitative mode; all data are sourced by individual interviews, recommendations, questionnaires, and observations. At the end of the research, a comprehensive result was gathered on the relative usage of project management software, with the analysis of how they are used and how they improve efficiency and support their roles in such projects.