Volume 4 Issue 1 (2024): In Progress

Article

Multi UAV Cooperative Reconnaissance based on Dynamic Programming VDN Algorithm

This paper proposes a multi agent value decomposition network (VDN) based multi UAV collaborative reconnaissance and control method to address the issue of insufficient strategies for multi UAV collaborative reconnaissance and control. By designing corresponding algorithm networks and training processes, the goal of autonomy, collaboration, and intelligence among multiple unmanned aerial vehicle systems has been achieved, assisting unmanned aerial vehicle combat forces in achieving collaborative operations and decision-making. This article uses AirSim as the simulation verification environment to verify the effectiveness of the proposed algorithm. The experimental results show that the algorithm proposed in this paper can achieve multi UAV collaborative reconnaissance tasks in complex environments, providing an intelligent solution for UAV collaborative control.

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Review

An Intelligence-Based Cybersecurity Approach: A Review

Nowadays, cybersecurity stands out as a prominent topic frequently discussed by companies aiming to safeguard their data from hacking attempts. The rise of cyberspace has fuelled the expansion of electronic systems, creating a virtual digital realm that links computers and smartphones within the Internet of Things framework. Thus, the purpose of this study is to present the progress made thus far in applying AI-based intrusion detection systems (IDSs) to address cybercrimes, demonstrating their efficacy in detecting and preventing cyberattacks. The review utilized 4 scholarly databases; ScienceDirect, IEEE-Explore, Web of science, and Springer. 437 studies were extracted from the 4 chosen databases out of which only 54 studies were found to be relevant, thus included. The review found AI-based intrusion detection systems (IDSs) to be more robust and flexible compared to other conventional IDSs. Interestingly, the study results highlight how AI-based intrusion detection systems such as; ANN-based intrusion detection systems, agent-based intrusion detection systems, and Genetic-fuzzy intrusion detection systems, and other machine learning detection systems can be used to assist security experts in analyzing, designing, and developing security frameworks for combating cybercrimes. Lastly, the study proposed some areas for future studies.

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Review

Rethinking Plagiarism in the Era of Generative AI

The emergence of generative artificial intelligence (AI) technologies, such as large language models (LLMs) like ChatGPT, has precipitated a paradigm shift in the realms of academic writing, plagiarism, and intellectual property. This article explores the evolving landscape of English composition courses, traditionally designed to develop critical thinking through writing. As AI becomes increasingly integrated into the academic sphere, it necessitates a reevaluation of originality in writing, the purpose of learning research and writing, and the frameworks governing intellectual property (IP) and plagiarism. The paper commences with a statistical analysis contrasting the actual use of LLMs in academic dishonesty with educator perceptions. It then examines the repercussions of AI-enabled content proliferation, referencing the limitation of three books self-published per day in September 2023 by Amazon due to a suspected influx of AI-generated material. The discourse extends to the potential of AI in accelerating research akin to the contributions of digital humanities and computational linguistics, highlighting its accessibility to the general public. The article further delves into the implications of AI on pedagogical approaches to research and writing, contemplating its impact on communication and critical thinking skills, while also considering its role in bridging the digital divide and socio-economic disparities. Finally, it proposes revisions to writing curricula, adapting to the transformative influence of AI in academic contexts. 

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Article

The Impact of Marketing Mix on Indigenous Business Development in Uzbekistan: A Regression Analysis

This study proposes a marketing strategy framework tailored to address the challenges faced by small and medium-sized enterprises (SMEs) in Uzbekistan. Amidst a fiercely competitive business landscape, SMEs encounter obstacles in establishing brand awareness, attracting customers, and optimizing financial performance. To address these challenges and sustain growth, SMEs can leverage the extended marketing mix, encompassing product, price, place, promotion, people, process, and physical evidence. Employing a combination of descriptive and exploratory research methodologies, this study utilizes quantitative data-gathering techniques, including a survey of entrepreneurs’ perspectives on the implementation of marketing mix strategies by SMEs. Regression analysis, facilitated by STATA software, examines the correlation between marketing mix variables and SME development. Key findings underscore the importance of integrating marketing mix elements to enhance SME marketing efforts, cultivate brand loyalty, and gain a competitive advantage. These findings contribute to a deeper understanding of marketing strategies for SMEs in developing economies like Uzbekistan, offering actionable insights for policymakers and entrepreneurs alike to foster SME growth and economic development.

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