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| Title | Systematic Review-Innovative Approaches In Artificial Intelligence Development |
|---|---|
| ID_Doc | 54333 |
| Authors | Blahodelskyi O. |
| Year | 2024 |
| Published | Nigerian Journal of Technology, 43, 4 |
| DOI | http://dx.doi.org/10.4314/njt.v43i4.24 |
| Abstract | This study analyses current advances and challenges of artificial intelligence (AI) development through a systematic literature review. The literature search was conducted across major databases including IEEE, ScienceDirect and Scopus. Peer-reviewed articles published between 2016-2023 were included based on relevance to the research topic. Out of 152 initial search results, 48 articles were selected for in-depth review using PRISMA guidelines. The results were analyzed to identify key focus areas, techniques and applications of AI over the selected period. The study found a significant increase in AI-related publications in 2022-2023 (62% of selected articles), indicating growing research interest. Key application areas identified are smart cities, education, image processing and medicine. Machine learning methods like neural networks and deep learning were frequently applied for tasks like classification, prediction and pattern recognition. Along with opportunities, ethical concerns like privacy, security, transparency and bias were major AI challenges discussed. Developing standards, regulations and testing mechanisms to ensure reliability and fairness of AI systems was highlighted. In summary, the systematic review demonstrates the rising significance of AI across industries, while underscoring the need to proactively address risks for its safe and ethical development. Overall, the study confirms that AI has great potential in various industries, but its implementation requires the development of ethical standards and data security to maximise benefits and minimise risks. The practical significance of the study is to deepen knowledge about AI and its impact on modern society. © 2024 by the author(s). |
| Author Keywords | Applications; Data security; Ethical issues; Machine learning; Neural Networks; Social aspects |
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