Universal Journal of Public Health  

Universal Journal of Public Health is an international peer-reviewed journal that publishes original and high-quality research papers in all areas of public health. As an important academic exchange platform, scientists and researchers can know the most up-to-date academic trends and seek valuable primary sources for reference.

ISSN: 2331-8880 (Print)

ISSN: 2331-8945 (Online)

Contact Us: ujph.editor@hrpub.org or editor@hrpub.org

Website: https://www.hrpub.org/journals/jour_info.php?id=76

Call for Special Issues

Diagnosis of any disease is perplexing, since numerous cyphers and indications are nonspecific, and can only be undertaken by registered and licensed health professionals. Accurate diagnosis of any disease needs an expert doctor. Diagnosis is the first and very crucial step. Earlier the diagnosis is established more affective the treatment can be given and better the chances of a successful treatment can be. Diagnosis can be time consuming and costly, apart from being one of the basic steps towards treatment. Artificial intelligence has played major roles in reinvention in every field. It has been used in diagnosis and is filling gaps for many others. Machine learning contributes to provide remunerations over conventional strategies for examination and settling on clinical choices. Various software has been built that permits Pathologists to make exact analyses. With expanded precision in the analysis of malignancy patients, exact Cancer Diagnosis, early diagnosis of fatal blood Diseases, it contributes as it acts as support system and provides required assistance with regards to diagnosing conceivably at a beginning phase. For some disease, diagnostic is a challenge; while for others, learning about their spread and prediction and forecasting the number of cases in future for a disease still remains problem to be conquered. Vector-borne diseases are the most protuberant intimidations to human health. They are transferred to the human population by infected insects or by unswerving transmission amid humans. Machine learning and deep learning models have given valuable input in designing representative epidemiological models incorporating environmental features that show a close relationship with the epidemic process observed in the human population. These machine learning models have given a better understanding of consequences a disease may leave. Among other applications, the contribution of artificial intelligence in time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease such as the novel coronavirus disease, COVID-19, which is spreading rapidly all over the world. The countries with dense populations, await impending risk in attempting the epidemic. Different forecasting models are being used to predict future cases of disease such as COVID-19. Under this issue, we will access all the contributions made by artificial intelligence in health care diagnosis and prediction.

Subjects Coverage
Artificial Intelligence in Healthcare Diagnosis.
Deep Learning for Diagnosis and Prediction.
Predicting Trends in Epidemiological Disease.
Deep learning in Medical Imaging.
Time series model for Viral Disease.
Devices for Healthcare/ Wearable Devices.
Vector Borne Diseases.
Epidemiological Models.

List of Guest Editors
Dr. Manju Bala, PhD.
Director, Professor
Computer Science and Engineering, Khalsa College of Engineering and Technology Amritsar, Punjab, India
Dr. Sandeep Kumar Sood, PhD
Associate Professor
Computer Applications, National Institute of Technology, Kurukshetra, Haryana, India
Dr. Arshpreet Kaur, PhD
Assistant Professor
School of Computing-CSE, DIT University, Dehradun, India
Dr. Kumar Shashvat, PhD
Assistant Professor
School of Computing-CSE, DIT University, Dehradun, India
Dr. Vartika Kulshrestha, PhD
Assistant Professor
School of Computing-CSE, DIT University, Dehradun, India

Submission Deadline: January 1, 2022
Review Results: February 20, 2022
Deadline for Revision: March 10th, 2022
Notification of Final Decision: March 25th, 2022
Approximate Publication Date: April 25, 2022

Please contact the editor at editor@hrpub.org for further information and discussion.