Welcome to The IJMRS Journal
DOI: 10.70558/IJMRS | Impact Factor: 5.429 (SJIF) | ISSN: 3108-1681
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Indian Journal of Multidisciplinary Research and Studies (IJMRS) is a peer reviewed, open access, multidisciplinary quarterly journal that publishes research papers for academic and scholarly circles around the globe. IJMRS receives contributions from authors all over the world, covering various fields of study for postgraduate students, researchers, and academics from different faculties, such as natural and applied science, social science, humanities, arts, education, health sciences, engineering technology, and business administration. The objective of our journal is to promote academic discussion, cross-field cooperation, and accessibility of high-quality, methodologically sound, and theoretically significant articles.
The journal releases four issues annually, ensuring the latest advancements in the field and offering a platform for scholars. Scholars from their respective fields review all published articles in IJMRS, adhering to the research methodologies of esteemed academies. According to the editorial team of the IJMRS, the journal’s purpose is to link academia to society to enhance the practical usage of theories in addressing everyday challenges. We promote great discoveries that have a bearing on global issues, the development of which policies, the improvement of education, technology, and the economy. This is possible since our approach is multidisciplinary; it helps to discover interconnections between different academic disciplines to gain deeper insights that are not limited to the subject areas of the disciplines.
Author(s): Yalmaz Najmaldeen Taher Country: Iraq Author(s): Pabitra Hembram, Dr. Dhanu Murmu Country: India Author(s): Salge Kisku Country: India Author(s): Dr. Anjan Karmakar Country: India Author(s): Nasiru Bashir Umar, B. L. Pal, Ahmad Aminu, Ajay Singh Dhabariya Country: IndiaLatest Published Research Papers
Workload-Aware Evaluation of Thread Parallelism and Memory Configuration for Analytical Query Processing in DuckDB
ᱵᱟᱫᱚᱞ ᱦᱮᱢᱵᱽᱨᱚᱢᱟᱜ ᱨᱮᱰᱤᱭᱳ-ᱜᱟᱭᱟᱱ ‘ᱜᱷᱟᱹᱨᱫᱤ ᱡᱟᱶᱟᱭ’ ᱨᱮᱭᱟᱜ ᱢᱤᱫ ᱵᱤᱥᱲᱤᱭᱟᱱ ᱯᱚᱲᱦᱚᱱ
ᱯᱚᱸᱰᱮᱛ ᱨᱚᱜᱷᱩᱱᱟᱛᱷ ᱢᱩᱨᱢᱩᱣᱟᱜ ᱡᱤᱭᱚᱱ ᱪᱤᱛᱟᱹᱨ ᱺ ᱢᱤᱫ ᱯᱚᱯᱚᱲᱦᱚᱱ
ᱱᱟᱦᱟᱜ ᱥᱟᱱᱛᱟᱲᱤ ᱚᱱᱚᱲᱦᱮᱸ ᱨᱮᱭᱟᱜ ᱫᱷᱟᱨᱟᱫᱷᱚᱨᱚᱱ ᱺ ᱢᱤᱫ ᱛᱚᱡᱽᱵᱤᱡᱽ-ᱟᱱᱟᱜ ᱯᱚᱲᱦᱚᱲ
A Controlled Comparative Study of EfficientNetB0, ResNet50, and DenseNet121 for Multi-Class Skin Disease Classification