Current Issue

Volume 21 Number 4 December 2023
Editorial
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This issue
 
Editorial
 
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Research
101
Recruitment Management System Design and Deployment-Based on PHP Analysis-
 
Youyu Hu
  DOIhttps://10.6025/jdim/2023/21/4/101-109
 
ABSTRACT: This article aims to create a three-level architecture based on PHP to implement a company’s recruitment management system. This architecture will be combined with the new features in the existing recruitment work, considering their understanding of the recruitment process and the recruitment system they have designed and implemented. This article will analyze the benefits and drawbacks of PHP language, discuss the system design and implementation method based on analysing the advantages and disadvantages of the threelevel architecture of PHP and various driver model research. Based on this analysis, choose the most appropriate design drive method for the design of a recruitment management system. Based on the features of the existing recruitment company, the system will be designed to implement company recruitment management. The actual recruitment work, divided into four parts, will be discussed in this article. The realization and function of these four modules, and their realization and function, will help standardize and systematize the recruitment process. In this article, we will discuss how the four modules of the Access control system are realized and how they work, and how the following modules are realized and function to standardize and standardize the recruitment process and significantly reduce staff recruitment workload, improve the efficiency of the transfer of recruitment, shorten the hiring process, and reduce recruitment management work costs.
 
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110 ERP Project Lifecycle costs: A Review-
 
L’YARFI Hanane, MOTAKI Noureddine, DERRHI Mostafa, LAHLOU Imane
  DOIhttps://doi.org/10.6025/jdim/2023/21/4/110-116
 
ABSTRACT: Today, many organizations acquire and implement enterprise resource planning (ERP) solutions to improve their operations performance and create value. However, they fail to achieve these objectives due to a lack of knowledge and a better estimating of all the costs related to an ERP system project. The purchasing costs of ERP systems are the most visible expense for many organizations that have adopted ERP systems, but the entire ERP lifecycle phase of implementation contains many hidden costs. As a result, institutions have long struggled with information system implementation cost overruns. This document presents the research on the various costs associated with the different stages of the life cycle of the REP project according to the literature on ERP lifecycle costs.
 
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117
Convolutional Neural Networks for Handwritten Text Recognition of Medical Prescription-
       
Makarand Shahade, Mayuri Kulkarni, Vivek Pawar, Jatin Chaudhari, Yash Lakade, Darshan Kotkar
  DOIhttps://doi.org/10.6025/jdim/2023/21/4/117-124
 
ABSTRACT: Converting handwritten prescriptions into electronic format offers several advantages and is crucial for modern healthcare systems. It is essential nowadays because of some factors such as – Legibility and Accuracy: Handwritten prescriptions can be challenging to read and interpret; accessibility and Portability: Electronic prescriptions can be easily stored; Decision Support Systems: By digitising prescriptions, healthcare systems can integrate them with electronic health records (EHRs) and utilise decision support systems. Convolutional neural networks (CNNs) are a class of deep learning algorithms that have proven effective in extracting handwritten text from various documents, including medical prescriptions. By leveraging CNNs for handwritten text extraction, healthcare systems can automate the process of digitising prescriptions, reducing manual effort and potential human errors. This enables seamless integration with electronic systems, facilitating better patient care and overall healthcare management. In this paper, we have trained the CNN model for different parameters and observed the accuracy and loss for various parameters. We got a maximum training accuracy of 89% and a maximum testing accuracy of 70%.
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Book Review

Multi-Agent Reinforcement Learning: Foundations and Modern Approaches by Stefano V. Albrecht, Filippos Christianos, Lukas Schäfer, published by MIT Press, print version scheduled for late 2024. This work is subject to a Creative Commons CC-BY-NC-ND license.

Conference Notification
126

Fourth International Conference on Science & Technology Metrics (STMet 2023)

Author Index
130 Author Index