INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
This paper highlights significant problems in AI research, Artificial intelligence (AI) has gained recent public prominence with the release of deep-learning models that can generate anything from art to term papers with minimal human intervention. This development has reinvigorated discussion of the existing and potential roles of AI in all aspects of life.
In today's world where all tasks can be done with the speed of a single click, autonomous vehicles have come into picture which not only ensures safe self- driving but also an effortless driving experience. The demand of such vehicle tends to grow much more in the near future due to which technologies supporting these vehicles are being improvised and researched. Computer Vision enables the computer to see, understand and make observations just as Artificial Intelligence helps the computer to think. Computer Vision is a branch of AI which helps the computer systems to visualize and obtain information through various inputs just as the human eye works in the human body.
The recent advances in e-commerce and payment systems have sparked an increase in fiscal fraud cases similar to credit card fraud. It's thus pivotal to apply mechanisms that can decry credit card fraud. Features of credit card fraud play an important part when machine literacy is used for credit card fraud discovery, and they must be chosen duly. This paper proposes a machine literacy (ML) grounded credit card fraud discovery machine using the inheritable algorithm (GA) for point selection
This paper presents a comprehensive review of product recommendation systems, focusing on the integration of survey-based insights to enhance recommendation effectiveness. The rapid growth of e-commerce platforms has emphasized the importance of personalized product recommendations. However, the challenge lies in accurately understanding users' preferences and needs. In this study, we analyse existing methodologies that utilize survey data to improve recommendation algorithms, allowing for a more personalized and effective recommendation approach.
The use of chatbot is accelerated after the pandemic and it will continue to grow. It a are software agent that interacts with the end- user for conversation. A Chat-bot is computer program which conduct a conversation via auditory or textual method. This interface uses the AI algorithm to give appropriate answer to the end-user. If the answer is not correct then system declares answer is unable to find the right answer. They also recommend us places , food ,etc….Customer care chatbots are supported by several technology platforms, including IBM Watson, Microsoft Bot Framework, and Google-owned Dialog Flow, which are all available for purchase
"Igenomic collaborative using AI ", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 11, page no.c669-c676, November-2023, Available :http://www.ijnrd.org/papers/IJNRD2311282.pdf
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2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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