ARTIFICIAL INTELLIGENCE IN RESEARCH METHODOLOGY

Front Cover
Sonzal publishers - Computers

Artificial Intelligence (AI) integration has become a revolutionary

force in the constantly changing field of research technique,

transforming inquiry and analytic paradigms. Artificial intelligence

(AI) has a significant influence on research procedures in a wide

range of disciplines, including the social sciences and natural

sciences. This is because AI breaks through conventional limits and

adds new dimensions to the investigative process. This introduction

aims to guide the reader through the rapidly developing field of

artificial intelligence (AI) and research, explaining the many ways

that AI technologies have developed into vital resources for

modern academics and researchers. The origins of artificial

intelligence (AI) in research methods may be found in the

extraordinary rise in data collection and accumulation, which was

fueled by information digitalization and technical breakthroughs.

Artificial Intelligence (AI), which offers unmatched skills in data

processing, pattern recognition, and predictive modelling, emerged

as a potent ally when conventional research tools and approaches

failed to handle the sheer amount and complexity of data. The

mutually beneficial association between artificial intelligence (AI)

and research methodology stems from machine learning

algorithms' capacity to identify significant patterns in large

datasets, which empowers researchers to derive useful insights and

arrive at well-informed conclusions. Furthermore, AI has an

impact on every step of the research process,

not just data analysis.Artificial Intelligence (AI) has become a crucial tool for

researchers, helping with anything from creating research

questions and planning experiments to automating literature

reviews and producing hypotheses. Since the development of

natural language processing (NLP), information can now be

extracted from a wide range of textual sources, allowing academics

to synthesise knowledge on a never-before-seen scale. This

paradigm change not only speeds up the research process but also

creates opportunities to investigate interdisciplinary relationships

and promote a more comprehensive comprehension of complicated

phenomena.


 

Selected pages

Common terms and phrases

able achieve action addition amount applications approach arc consistency artificial intelligence ecosystem assignment best-first best-first search binary constraints brain branching factor breadth-first search Bucharest challenges complete component concept conflict set constraint graph cost depth-first search depth-limited search despite the fact discussed domain environment essential ethical event example forward checking function goal GOFAI graph search heuristic human flourishing individuals instance issue knowledge limits machine learning method necessary neural networks node number of different objective operations perception perceptual sequence performance possible potential problem programming queue rational reasoning referred reflex agent representation result robot Romania route scenario search algorithms search strategy search tree sensors shown in Figure Sibiu significant situations socio-technical systems solution solve space specific subproblems Sudoku take into consideration technique theory Turing Test uniform-cost search utilise variables variety virtue ethics

About the author

Dr. Ajay Prakash is an Indian researcher, academic, and clinician widely distinguished for his significant contributions to the fields of oral and maxillofacial pathology, Head and Neck cancer , Oral cancer epidemiology, genetic epidemiology, molecular biology, DNA profiling, and immunology etc

Bibliographic information

TitleARTIFICIAL INTELLIGENCE IN RESEARCH METHODOLOGY
AuthorAjay Prakash Pasupulla
PublisherSonzal publishers
Subjects ›  › 

Computers / Artificial Intelligence / Computer Vision & Pattern Recognition
  
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