American Data Science Journal for Advanced Computations (ADSJAC)

About the Journal

Journal Summary — American Data Science Journal for Advanced Computations (ADSJAC)

Journal Overview

The American Data Science Journal for Advanced Computations (ADSJAC) is an international, peer-reviewed, gold open-access journal dedicated to publishing original, high-quality, and impactful scholarly research across data science, computational intelligence, and advanced computational systems. The journal serves as a premier global platform for researchers, engineers, practitioners, and academicians to contribute theoretical advancements, algorithmic innovations, empirical studies, and applied solutions that shape the future of data-driven science and intelligent computation.

ADSJAC is indexed in Scopus (Elsevier), Web of Science (Clarivate), DOAJ, CrossRef, and other leading international abstracting and indexing databases — ensuring that all published research achieves maximum global visibility, citation discoverability, and sustained long-term academic impact across the international scholarly community.

ADSJAC is committed to fostering global scientific collaboration and upholding the highest standards of research integrity, open science, and editorial transparency — ensuring that all published knowledge is freely, immediately, and permanently accessible to the worldwide research community.

Indexing & Abstracting

ADSJAC is formally recognized, indexed, and abstracted in the following leading international scholarly databases, bibliographic repositories, and research discovery platforms:

All published articles are assigned a unique CrossRef Digital Object Identifier (DOI), ensuring permanent citation traceability, persistent online accessibility, and seamless integration with global research discovery and bibliometric infrastructure.

Journal Performance Metrics

CiteScore, SJR, and SNIP values will be updated upon completion of the Scopus evaluation cycle. Authors may monitor current metrics directly via the Scopus Source List and the SCImago Journal Rankings portal.

Open Access & Publication Model

ADSJAC operates under a full Gold Open Access publishing model in full compliance with international open science frameworks, including the Budapest Open Access Initiative (BOAI), Plan S, NIH Open Access Policy, and Horizon Europe open-access requirements. All accepted articles are made immediately, permanently, and freely available upon publication — without subscription fees, paywalls, embargo periods, or access restrictions of any kind — published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

The CC BY 4.0 license permits unrestricted use, distribution, adaptation, translation, and reproduction in any medium or format, provided the original work is appropriately cited and any modifications are clearly indicated.

Article Processing Charges (APCs) are levied solely upon formal manuscript acceptance, following the completion of rigorous peer review. No fees are charged at the point of submission or at any stage during the review process. Full details of current APC rates, institutional membership discounts, and fee waiver eligibility for researchers from low- and middle-income countries (LMICs) are published transparently on the journal website.

Benefits of the ADSJAC Gold Open Access model:

• Immediate, worldwide visibility and unrestricted accessibility upon publication
• Enhanced readership, citation potential, and full discoverability in Scopus and Web of Science
• Rapid and broad dissemination of data science and computational scholarship across all disciplines
• Full compliance with all major international funder open-access mandates including Plan S, NIH, and Horizon Europe
• Equitable and barrier-free knowledge sharing across academia, industry, government, and society globally
• Accelerated real-world knowledge transfer and application of published research findings

Aims & Scope

ADSJAC publishes original research articles, systematic and scoping reviews, technical communications, short reports, and applied case studies spanning the full breadth of data science, computational intelligence, and advanced computational disciplines. The journal particularly encourages interdisciplinary submissions that integrate data science with engineering, mathematics, computational biology, social sciences, and application domains reliant on large-scale intelligent computation and data-driven methodologies.

Data Science & Analytics

Statistical and quantitative modeling; data mining and knowledge discovery; predictive, descriptive, and prescriptive analytics; big data algorithms and distributed data architectures; data visualization, storytelling, and result interpretability; data quality, integration, and governance frameworks; real-time and streaming analytics systems.

Machine Learning & Artificial Intelligence

Deep learning and advanced neural network architectures (CNNs, RNNs, Transformers, Graph Neural Networks); reinforcement and meta-learning; natural language processing (NLP) and large language models (LLMs); computer vision and multimodal AI; explainable AI (XAI), responsible AI, and fairness-aware machine learning; federated and transfer learning frameworks; generative AI and foundation models; AI ethics and algorithmic accountability.

Advanced Computation & Algorithms

High-performance and parallel computing; distributed, cloud-native, and serverless computation; combinatorial and metaheuristic optimization; scientific, numerical, and symbolic computing; computational mathematics and simulation-based modeling; algorithm design for real-time and resource-constrained environments.

Emerging Computational Technologies

Quantum computing algorithms and hybrid quantum-classical methods; edge, fog, and neuromorphic computing architectures; Internet of Things (IoT) data intelligence and sensing systems; autonomous, cyber-physical, and multi-agent systems; green and energy-efficient computing; digital twin technologies and smart infrastructure.

Applications of Data Science & Computation

Clinical informatics and healthcare data analytics; financial modeling, FinTech innovation, and business intelligence; environmental monitoring and climate informatics; cybersecurity analytics, anomaly detection, and threat intelligence; smart cities, industrial automation, and Industry 4.0; computational social science and behavioral analytics; educational technology and adaptive learning analytics.

Peer Review Policy & Editorial Process

ADSJAC operates a fully double-blind peer review system in which the identities of both authors and reviewers are mutually concealed throughout the entire evaluation process. All submitted manuscripts are assessed exclusively on the basis of scientific merit, methodological rigor, novelty, and relevance to the journal's defined scope.

Stage 1 — Initial Editorial Screening

All submissions undergo a thorough preliminary assessment by the editorial office for scope compliance, structural completeness, language quality, ethical adherence, and absence of plagiarism — prior to assignment for external peer review. Manuscripts failing to meet baseline standards are desk-rejected within ≤ 15 business days with constructive editorial feedback.

Stage 2 — Double-Blind Peer Review

Manuscripts passing initial screening are evaluated by a minimum of two independent expert reviewers with established subject-matter expertise. Reviewers assess scientific validity, methodological soundness, originality, clarity of presentation, and overall contribution to the discipline. Specialist reviewers may be additionally consulted for interdisciplinary or technically complex submissions.

Stage 3 — Revised Manuscript Evaluation

Where revisions are requested, authors submit a revised manuscript accompanied by a detailed point-by-point response letter addressing each reviewer comment individually. Revised manuscripts undergo one or more further rounds of evaluation at editorial discretion.

Stage 4 — Final Editorial Decision

Based on consolidated reviewer recommendations, the Editor-in-Chief issues one of the following formal decisions: