International Journal of Deep Learning

ACSIE (International Journal of Application Computer Science and Informatic Engineering)

The ACSIE (International Journal of Application Computer Science and Informatic Engineering) is an open access, peer-reviewed journal dedicated to the publication of high-quality research in applied computer science and informatics engineering, with particular emphasis on the advancement and application of deep learning methodologies in digital heritage domains. The journal prioritizes innovative studies that explore theoretical developments, practical implementations, and interdisciplinary applications of deep learning and artificial intelligence for the preservation, digitization, analysis, and interpretation of cultural heritage in digital form.

Established in September 2018, ACSIE is managed and published by INFOTEKS (Information Technology, Computer, and Sciences), with e-ISSN 2685-4600 and p-ISSN: xxxx-xxxx. The journal is published two times a year (May and November) and accepts original research articles featuring well-designed studies with clearly analyzed and logically interpreted results. Preference is given to contributions that demonstrate significant impact on both academic research and real-world technological solutions, particularly those grounded in deep learning-based approaches for digital heritage applications.

We invite authors to submit articles that explore innovations in Deep Learning within the Cultural Heritage ecosystem. The primary focus includes the development of neural network–based methodologies, natural language processing for ancient manuscripts, and computer vision for digital restoration. In addition, we strongly encourage research that applies advanced data analytics and immersive technologies (augmented reality and virtual reality) to enhance preservation efforts and support business incubation in the cultural heritage sector.

ACSIE is published by INFOTEKS and has implemented a fully digital editorial and publication workflow to ensure efficiency, transparency, and accessibility in the dissemination of scientific knowledge.

Submitting to the Journal

ACSIE utilizes an online submission and peer review system based on the Open Journal Systems (OJS) platform, enabling authors to submit manuscripts electronically and track the progress of their submissions in real time. All manuscripts are processed exclusively through this system.

Before submission, authors must ensure that their manuscripts are prepared using the official ACSIE paper template, have been thoroughly proofread and polished, and comply with the journal’s author guidelines.

Authors are required to register in the system prior to submission. Manuscripts submitted through other means will not be considered. Through OJS, authors, reviewers, editors, and editorial board members can access up-to-date information regarding manuscript status throughout the review process.

For further information regarding submission procedures, templates, and guidelines, authors are encouraged to consult the official journal resources available through the OJS platform.