Number of Volumes 1
Number of Issues 2
Number of Articles 10
Number of Contributors 24
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Number of Submissions 28
Time to Accept (Days) 55
Number of Indexing Databases 1
Number of Reviewers 32

Journal of Trends and Challenges in Artificial Intelligence (Abbreviation: J. Tr., Chal. Art. Intell.) is a quarterly, academic publication by the Association for Scientific Publishing and Research – ASPUR. Journal of Trends and Challenges in Artificial Intelligence is a medium for global academics to exchange and disseminate their knowledge as well as the latest discoveries and advances in the filed of artificial intelligence.  Journal of Trends and Challenges in Artificial Intelligence follows a open access policy under the Creative Commons Attribution 4.0 International public license (Open Access Statement). We do not charge authors for any submission or processing fees. Submitted manuscripts are peer-reviewed by the Editorial Board and 2 "blind peer" reviewers of Journal of Trends and Challenges in Artificial Intelligence before acceptance for publication.

To submit a new manuscript, please start by reading the journal’s instructions for authors.

*Publication process of manuscripts submitted to Journal of Trends and Challenges in Artificial Intelligence is free of charge.


 

 

Please quote this journal as: J. Tr., Chal. Art. Intell.

 


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Current Issue: Volume 1, Issue 3, September 2024 

Research Papers


AN INTROSPECTION INTO THE EFFICIENT DATA ANALYSIS WITH ARTIFICIAL INTELLIGENCE

Pages 81-84

DOI 10.61552/JAI.2024.03.001

ORCID Anurag Hazarika - Samiskhya Madhukullya, Anwesha Hazarika


SEGMENTING AND TARGETING-BASED STRATEGIES AT PT BANK SYARIAH INDONESIA JEMBER BRANCH

Pages 85-90

DOI 10.61552/JAI.2024.03.002

ORCID Asa Korina, ORCID Novi Puspitasari


ARTIFICIAL INTELLIGENCE IN THE INDIAN JUDICIARY: A SYSTEMATIC ANALYSIS OF POTENTIAL APPLICATIONS AND CHALLENGES IN ADDRESSING CASE BACKLOGS

Pages 91-96

DOI 10.61552/JAI.2024.03.003

ORCID Vivek Trivedi, ORCID Nilakshi Nilakshi


ASSESSMENT OF REGIONAL INNOVATION ACTIVITY

Pages 97-112

DOI 10.61552/JAI.2024.03.004

Arzu Huseynova, ORCID Ramil Guliyev, ORCID Arzu Suleymanov, ORCID Olga Ostrovskaia


AN EFFICIENT MALWARE DETECTION MODEL THROUGH REDUCED HYBRID FEATURES AND ENSEMBLE MACHINE LEARNING

Pages 113-112

DOI 10.61552/JAI.2024.03.005

ORCID Om Prakash Samantray, ORCID Siva Sai Geethika Penta, ORCID Satya Narayan Tripathy