Journals

  • International Transactions on Data Science (ITDS)

    International Transactions on Data Science (ITDS) is a peer-reviewed journal dedicated to advancing the field of data science through high-quality research contributions from academia, industry, and government institutions worldwide. ITDS serves as a platform for disseminating cutting-edge research findings, innovative methodologies, and practical applications in all areas of data science.

    Scope:

    ITDS covers a broad spectrum of topics related to data science, including but not limited to:

    1. Data Mining and Knowledge Discovery: Original research in data mining techniques, algorithms, and methodologies for extracting valuable insights and knowledge from large datasets.

    2. Machine Learning and Artificial Intelligence: Advances in machine learning algorithms, deep learning models, reinforcement learning techniques, and their applications in various domains.

    3. Big Data Analytics: Innovative approaches for processing, analyzing, and visualizing massive volumes of structured and unstructured data to derive actionable insights.

    4. Data-driven Decision Making: Studies on using data-driven approaches to inform decision making in business, healthcare, finance, marketing, and other domains.

    5. Data Science Applications: Practical applications of data science techniques in real-world scenarios, including predictive analytics, fraud detection, recommendation systems, personalized medicine, and smart cities.

    6. Data Privacy and Ethics: Research addressing privacy-preserving data mining, fairness and transparency in machine learning models, ethical considerations in data collection and analysis, and regulatory compliance.

    Publication Criteria:

    ITDS welcomes original research articles, review papers, survey articles, and case studies that contribute significant insights to the field of data science. Submissions undergo a rigorous peer-review process to ensure the quality, originality, and relevance of published content.

    Audience:

    The primary audience of ITDS includes researchers, academics, data scientists, practitioners, policymakers, and professionals interested in advancing the theory, methodologies, and applications of data science. The journal serves as a valuable resource for staying updated on the latest developments and trends in the rapidly evolving field of data science.

    Editorial Board:

    ITDS is supported by an esteemed editorial board comprising leading experts and scholars in the field of data science. The editorial team ensures the integrity and quality of published content while fostering collaboration and knowledge exchange among researchers worldwide.

    Submission Guidelines:

    Authors interested in submitting their work to ITDS are encouraged to review the journal's submission guidelines available on the official website. Submissions should adhere to the journal's formatting requirements and ethical standards.

    Mission:

    The mission of ITDS is to promote interdisciplinary research, innovation, and collaboration in data science, thereby advancing knowledge discovery, decision making, and societal impact through the effective use of data-driven approaches. By providing a platform for scholarly exchange and dissemination of research findings, ITDS aims to contribute to the continued growth and advancement of the field of data science on a global scale.

  • International Transactions on Machine Learning (ITML)

    International Transactions on Machine Learning (ITML) is a peer-reviewed scholarly journal dedicated to advancing the theory, algorithms, and applications of machine learning (ML) on an international scale. ITML provides a platform for researchers, academics, and practitioners from diverse backgrounds to contribute high-quality research articles, reviews, and survey papers in the field of ML.

    Scope: ITML covers a broad spectrum of topics related to machine learning, including but not limited to:

    1. Foundations of Machine Learning: Fundamental theories, principles, and methodologies underlying machine learning algorithms and models.
    2. Supervised Learning: Techniques for learning from labeled data, including classification, regression, and structured prediction.
    3. Unsupervised Learning: Methods for discovering patterns, structures, and relationships in unlabeled data, such as clustering, dimensionality reduction, and density estimation.
    4. Reinforcement Learning: Approaches for sequential decision making and learning from interactions with an environment.
    5. Deep Learning: Architectures, algorithms, and applications of deep neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning models.
    6. Transfer Learning: Techniques for transferring knowledge from one domain or task to another, including domain adaptation, multitask learning, and transfer reinforcement learning.
    7. Interpretable Machine Learning: Methods for understanding, explaining, and interpreting the decisions and predictions made by machine learning models.
    8. Applications of Machine Learning: Real-world applications and case studies across various domains, including computer vision, natural language processing, healthcare, finance, cybersecurity, robotics, and more.

    Submission Guidelines: ITML welcomes original research articles, survey papers, and review articles that contribute significant insights to the field of machine learning. Submissions undergo rigorous peer review to ensure high quality and relevance to the journal's scope. Authors are encouraged to adhere to the journal's formatting and submission guidelines for efficient processing of their manuscripts.

    Audience: ITML caters to a diverse audience, including researchers, academics, students, industry professionals, and policymakers interested in the latest advancements and trends in machine learning. The journal fosters collaboration and knowledge exchange among experts from academia, industry, and other sectors worldwide.

    Publication Frequency: ITML is published on a regular basis, with issues released periodically throughout the year. Each issue features a collection of original research articles, reviews, and other contributions that advance the state-of-the-art in machine learning.

    Editorial Board: ITML's editorial board comprises renowned experts and scholars in the field of machine learning, ensuring the quality and integrity of the journal's content. The board members oversee the peer review process, provide guidance on journal policies, and contribute to the strategic direction of ITML.

    Mission: The mission of ITML is to facilitate the dissemination of innovative research and knowledge in machine learning on a global scale. By providing a platform for scholarly exchange and collaboration, ITML aims to accelerate progress in the theory, algorithms, and applications of machine learning, ultimately contributing to advancements in science, technology, and society.

  • International Transactions on Supply Chain Management (ITSCM)

    International Transactions on Supply Chain Management (ITSCM) is a premier peer-reviewed journal dedicated to advancing the theory, practice, and application of supply chain management worldwide. ITSCM serves as a platform for researchers, academics, industry practitioners, and policymakers to exchange insights, innovations, and best practices in supply chain management.

    Scope:

    ITSCM covers a wide range of topics related to supply chain management, including but not limited to:

    1. Logistics and Transportation: Innovative approaches and best practices in logistics management, transportation planning, distribution network design, and last-mile delivery solutions.

    2. Procurement and Sourcing: Strategies for effective procurement, strategic sourcing, supplier relationship management, and supplier diversity initiatives.

    3. Inventory Management: Inventory optimization techniques, demand forecasting models, inventory control systems, and inventory risk management strategies.

    4. Sustainability and Green Supply Chains: Sustainable supply chain practices, environmental impact assessments, carbon footprint reduction, circular economy initiatives, and sustainable sourcing strategies.

    5. Supply Chain Analytics: Data-driven decision making in supply chain management, predictive analytics, supply chain visibility, real-time tracking, and performance measurement.

    6. Emerging Technologies: Adoption and integration of emerging technologies such as blockchain, Internet of Things (IoT), artificial intelligence (AI), and robotics in supply chain operations.

    7. Global Supply Chain Management: Challenges and opportunities in managing global supply chains, cross-border logistics, international trade regulations, and geopolitical risks.

    8. Resilient Supply Chains: Strategies for building resilience in supply chains, risk mitigation, business continuity planning, supply chain disruptions, and disaster recovery.

    Publication Criteria:

    ITSCM welcomes original research articles, review papers, case studies, and practical insights that contribute significant knowledge and advancements to the field of supply chain management. Submissions undergo a rigorous peer-review process to ensure the quality, relevance, and originality of published content.

    Audience:

    ITSCM caters to a diverse audience including researchers, academics, supply chain professionals, logistics managers, procurement specialists, policymakers, and students interested in supply chain management. The journal serves as a valuable resource for staying updated on the latest trends, developments, and challenges in the dynamic field of supply chain management.

    Editorial Board:

    ITSCM is supported by an esteemed editorial board comprising leading experts and scholars from academia, industry, and research institutions worldwide. The editorial team ensures the integrity and quality of published content while providing strategic guidance to advance the journal's mission and objectives.

    Mission:

    The mission of ITSCM is to promote research excellence, innovation, and collaboration in the field of supply chain management. By providing a platform for scholarly exchange and dissemination of research findings, ITSCM aims to facilitate knowledge creation, enhance supply chain performance, and contribute to the sustainable and resilient management of global supply chains.

  • International Transactions on Artificial Intelligence (ITAI)

    International Transactions on Artificial Intelligence (ITAI)

    Description: International Transactions on Artificial Intelligence (ITAI) is a premier interdisciplinary journal dedicated to advancing the field of artificial intelligence (AI) through high-quality research contributions. ITAI provides a platform for researchers, practitioners, and policymakers worldwide to publish and access cutting-edge research findings, innovative methodologies, and impactful applications in AI.

    Scope: ITAI covers a broad spectrum of topics within the field of artificial intelligence, including but not limited to:

    1. Machine Learning: Novel algorithms, methodologies, and techniques in supervised learning, unsupervised learning, reinforcement learning, deep learning, and ensemble methods.
    2. Natural Language Processing (NLP): Advances in NLP models, sentiment analysis, text summarization, language translation, dialogue systems, and language generation.
    3. Computer Vision: State-of-the-art techniques in image recognition, object detection, image segmentation, facial recognition, and video understanding.
    4. Robotics: Research on robot perception, motion planning, robot control, human-robot interaction, autonomous navigation, and multi-robot systems.
    5. AI Ethics and Fairness: Discussions on ethical considerations, fairness, transparency, accountability, and responsible AI development and deployment.
    6. AI Applications: Innovative applications of AI in various domains such as healthcare, finance, education, cybersecurity, transportation, agriculture, and entertainment.
    7. Societal Impacts: Studies on the societal impacts of AI technologies, including economic implications, job displacement, privacy concerns, bias and discrimination, and AI governance and regulation.

    Publication Types: ITAI welcomes the following types of contributions:

    1. Research Articles: Original research papers presenting novel algorithms, methodologies, experimental results, and theoretical insights.
    2. Review Papers: Comprehensive reviews and surveys summarizing the state-of-the-art in specific areas of AI research.
    3. Short Communications: Brief reports on preliminary research findings, innovative ideas, and emerging trends.
    4. Perspectives and Opinions: Thought-provoking perspectives, opinion pieces, and discussions on current issues and future directions in AI.

    Audience: The target audience of ITAI includes researchers, academics, practitioners, policymakers, industry professionals, and students interested in artificial intelligence and its applications across diverse domains.

    Editorial Board: ITAI's editorial board comprises renowned experts and scholars from academia, industry, and government institutions, ensuring the high quality and integrity of the journal's content.

    Publication Frequency: ITAI is published on a regular basis, with issues released quarterly or biannually, depending on the volume of submissions and editorial workflow.

    Open Access Policy: ITAI follows an open access policy, making all published articles freely accessible to readers worldwide, thus promoting the dissemination of knowledge and fostering collaboration in the field of artificial intelligence.

    Submission Guidelines: Authors are encouraged to adhere to the journal's submission guidelines, which can be found on the ITAI website. Submissions undergo rigorous peer review by experts in the field to ensure scientific rigor, clarity, and significance.

    Contact Information: For inquiries, manuscript submissions, and other correspondence, please contact the editorial office of ITAI via email or visit the journal's website for more information.

    Join the Conversation: Follow ITAI on social media platforms and participate in discussions, debates, and knowledge sharing activities to stay updated on the latest advancements and trends in artificial intelligence research and applications.

  • International Transactions on Healthcare (ITHC)

    International Transactions on Healthcare (ITHC) is a peer-reviewed scholarly journal dedicated to advancing the understanding and practice of healthcare through interdisciplinary research and innovation. ITHC provides a platform for researchers, practitioners, policymakers, and industry professionals worldwide to share their latest findings, insights, and perspectives on various aspects of healthcare.

    Scope: ITHC covers a wide range of topics at the intersection of healthcare and technology, including but not limited to:

    1. Healthcare Informatics: Novel approaches for collecting, storing, analyzing, and interpreting healthcare data to improve patient outcomes, clinical decision-making, and healthcare delivery efficiency.
    2. Medical Technologies: Advances in medical devices, diagnostic tools, imaging techniques, wearable sensors, and remote monitoring systems that enhance healthcare diagnosis, treatment, and patient care.
    3. Telemedicine and Telehealth: Innovations in telecommunication technologies and digital health platforms enabling remote consultations, virtual care delivery, telemedicine interventions, and telemonitoring services.
    4. Health Data Analytics: Techniques for leveraging big data analytics, artificial intelligence, machine learning, and predictive modeling to derive actionable insights from healthcare data, support evidence-based practice, and drive healthcare innovation.
    5. Healthcare Policy and Management: Research on healthcare policy development, healthcare economics, healthcare quality improvement, healthcare ethics, healthcare disparities, and healthcare system optimization.
    6. Healthcare Delivery Models: Evaluations of different healthcare delivery models, such as value-based care, patient-centered care, integrated care, and community-based care, with a focus on improving healthcare access, equity, and affordability.
    7. Health Information Privacy and Security: Strategies for safeguarding patient privacy, protecting healthcare data integrity, ensuring regulatory compliance, and mitigating cybersecurity risks in healthcare environments.
    8. Global Health Challenges: Studies addressing global health issues, infectious diseases, public health emergencies, healthcare infrastructure development, healthcare capacity building, and healthcare interventions in resource-constrained settings.

    ITHC publishes original research articles, reviews, case studies, perspectives, and commentaries that contribute to the advancement of knowledge and practice in healthcare. The journal encourages interdisciplinary collaboration and welcomes submissions from researchers, clinicians, educators, policymakers, healthcare administrators, industry professionals, and other stakeholders involved in healthcare innovation and transformation.

    Audience: ITHC is intended for a diverse audience, including researchers, academics, healthcare professionals, policymakers, industry leaders, healthcare administrators, technology developers, and students interested in healthcare research, technology-enabled healthcare solutions, and healthcare policy and management.

    Mission: The mission of ITHC is to facilitate the dissemination of high-quality research and promote collaboration among multidisciplinary stakeholders to address complex healthcare challenges, improve patient care, enhance healthcare outcomes, and promote health equity on a global scale.