e-ISSN 
Archives
-
June
Vol. 5 No. 1 (2025) -
June
Vol. 1 No. 1 (2021)DISTANCES: The Journal of Data Science, Technology, and Computer Science is a peer-reviewed biannual journal (published in June and December) by CV. ADMITECH SOLUTIONS. It is dedicated to the exchange of high-quality research articles in Data Science, Technology, and Computer Science. The journal publishes state-of-the-art papers in fundamental theory, experiments, and simulations, as well as applications, with a systematic proposed method, sufficient review of previous works, expanded discussion, and concise conclusion. As part of our commitment to the advancement of science and technology, DISTANCES: The Journal of Data Science, Technology, and Computer Science follows an open-access policy, allowing published articles to be freely available online without any subscription.
-
December
Vol. 4 No. 2 (2024)DISTANCES: The Journal of Data Science, Technology, and Computer Science is a peer-reviewed biannual journal (published in June and December) by CV. ADMITECH SOLUTIONS. It is dedicated to the exchange of high-quality research articles in Data Science, Technology, and Computer Science. The journal publishes state-of-the-art papers in fundamental theory, experiments, and simulations, as well as applications, with a systematic proposed method, sufficient review of previous works, expanded discussion, and concise conclusion. As part of our commitment to the advancement of science and technology, DISTANCES: The Journal of Data Science, Technology, and Computer Science follows an open-access policy, allowing published articles to be freely available online without any subscription.
-
June
Vol. 4 No. 1 (2024)DISTANCES: The Journal of Data Science, Technology, and Computer Science is a peer-reviewed biannual journal (published in June and December) by CV. ADMITECH SOLUTIONS. It is dedicated to the exchange of high-quality research articles in Data Science, Technology, and Computer Science. The journal publishes state-of-the-art papers in fundamental theory, experiments, and simulations, as well as applications, with a systematic proposed method, sufficient review of previous works, expanded discussion, and concise conclusion. As part of our commitment to the advancement of science and technology, DISTANCES: The Journal of Data Science, Technology, and Computer Science follows an open-access policy, allowing published articles to be freely available online without any subscription.
-
December
Vol. 3 No. 2 (2023)DISTANCES: The Journal of Data Science, Technology, and Computer Science is a peer-reviewed biannual journal (published in June and December) by CV. ADMITECH SOLUTIONS. It is dedicated to the exchange of high-quality research articles in Data Science, Technology, and Computer Science. The journal publishes state-of-the-art papers in fundamental theory, experiments, and simulations, as well as applications, with a systematic proposed method, sufficient review of previous works, expanded discussion, and concise conclusion. As part of our commitment to the advancement of science and technology, DISTANCES: The Journal of Data Science, Technology, and Computer Science follows an open-access policy, allowing published articles to be freely available online without any subscription.
-
June
Vol. 3 No. 1 (2023)DISTANCES: The Journal of Data Science, Technology, and Computer Science is a peer-reviewed biannual journal (published in June and December) by CV. ADMITECH SOLUTIONS. It is dedicated to the exchange of high-quality research articles in Data Science, Technology, and Computer Science. The journal publishes state-of-the-art papers in fundamental theory, experiments, and simulations, as well as applications, with a systematic proposed method, sufficient review of previous works, expanded discussion, and concise conclusion. As part of our commitment to the advancement of science and technology, DISTANCES: The Journal of Data Science, Technology, and Computer Science follows an open-access policy, allowing published articles to be freely available online without any subscription.
