Research Article
The Application of New Technologies to Promote the Dissemination of Training Content in the Railway Field in the Context of Informational Economy
Pablo Papaleo,
Matias Zitello
Issue:
Volume 13, Issue 1, February 2024
Pages:
1-5
Received:
18 August 2023
Accepted:
7 September 2023
Published:
8 January 2024
Abstract: The work is a descriptive analysis of a training and technical training area of an Argentine railway company, taking the period 2019/2022 considering the conditions of pre-pandemic, pandemic and its flexibility. These events will serve to understand and contextualize the changes that have occurred in the area, focusing on the incorporation of new technologies in the delivery of technical content. It forms a different approach from previous works, focusing on this occasion on the effects of the implementation of incorporating new technologies in a railway field and seeking to generate value within the framework of informational economies. The application of new technologies implemented to enhance the dissemination of training content focused on the use of synchronous and asynchronous platforms, allowed to increase and deepen the training activity for railway workers, evidencing its impact on key indicators such as "the number of people trained" and "the number of total training hours developed" in the aforementioned period. This approach will allow analyzing the relevance of recognizing new external information, assimilating and applying it, identifying that useful knowledge and generating new knowledge as the potential articulation of laying the foundations. For the first guidelines of open innovation, seeking to answer the following questions: ¿does the application of new technologies improve training performance? Was it possible to transmit more content in terms of training and education? Through these, we will describe and analyze the scope with the implementation of platforms, allowing to achieve the basic guidelines of an informational economy; this will be through the statistical support prepared and confirmed by the area. We will finish it with the conclusions reached at the end of this period, outlining actions to be developed in the short and medium term.
Abstract: The work is a descriptive analysis of a training and technical training area of an Argentine railway company, taking the period 2019/2022 considering the conditions of pre-pandemic, pandemic and its flexibility. These events will serve to understand and contextualize the changes that have occurred in the area, focusing on the incorporation of new t...
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Research Article
Enhancing Crime Investigation and Emergency Response Through a Combined Machine Learning Approach
Obi Chukwuemeka Nwokonkwo,
Oladele Robert Buki,
Adetokunbo MacGregor John-Otumu*,
Victor Onyekachi Aniugo
Issue:
Volume 13, Issue 1, February 2024
Pages:
6-19
Received:
2 January 2024
Accepted:
17 January 2024
Published:
1 February 2024
Abstract: The contemporary world necessitates effective solutions for crime investigation and emergency response to ensure public safety. This research pioneers an innovative approach by creating a predictive model for an integrated Crime Investigation and Emergency Response. Utilizing data-driven analysis, advanced machine learning, and modern information technology, it aims to enhance the efficiency of law enforcement and emergency procedures. Recognizing the pivotal role of Information Systems/Information Technology (IS/IT) in disaster management and crime investigation, the study emphasizes the urgency for efficient IT solutions to manage critical incidents. This focus seeks to minimize their impact on human lives, societal norms, economic stability, and political arenas. Exploring the integration of data-centric tools and information systems highlights their potential for expediting coordinated responses across various organizational levels, from local to global scopes. Delving into the challenges facing law enforcement in analyzing crime patterns, especially in cases involving violent offenses with extensive statistical data, this research introduces a machine-learning strategy combining regression and classification techniques. The primary goal is to reveal crucial patterns, particularly in predicting perpetrator characteristics such as age, gender, and their relationship with the victim. The envisioned Crime Investigation System (CIS) aims to streamline investigative processes, championing data-centric approaches facilitated by data mining technologies. Moreover, the research underscores technology's transformative impact on reshaping emergency management and notification systems. It underscores the importance of reducing response times, involving the public more actively, and deriving practical insights from reported data. Through comprehensive data analysis spanning several years, the study sheds light on unsolved crimes, notably those involving handguns, showcasing the model's potential to enhance law enforcement capabilities. These findings highlight the significant promise of the developed predictive model in bolstering law enforcement and emergency response procedures, potentially revolutionizing public safety operations. Ultimately, this research aspires to contribute to a safer and more responsive society by leveraging predictive models and technology-driven systems within law enforcement and public safety domains.
Abstract: The contemporary world necessitates effective solutions for crime investigation and emergency response to ensure public safety. This research pioneers an innovative approach by creating a predictive model for an integrated Crime Investigation and Emergency Response. Utilizing data-driven analysis, advanced machine learning, and modern information t...
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