Keywords : Pattern recognition

Investigation into the Implementation of Medical Imaging

Bharath Bhaskar; Arjun Kundapur

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue 9S, Pages 43-51
DOI: 10.47392/irjash.2021.248

In this article, we evaluate and discuss the implications of data analytics and Hadoop on clinical image analysis based on predictive algorithms. Each day, the healthcare industry analyses massive amounts of data. A large number of images were produced by various instruments on the patient in various medical situations. Numerous image processing methods and techniques are being developed in an attempt to obtain the most accurate information from images in order to provide an accurate diagnosis. To achieve maximum performance, both current and constantly evolving big data and hadoop ideas may provide more from image processing. The article examines the importance of big data analytics and hadoop in medical image processing utilizing HIPI and Map reduction inside the future implementation of Big data analytics (BDA) and Map reduction.

Enhancement in the World of Artificial Intelligence

Suneetha V; Salini Suresh; Niharika Sinha; Sabyasachi Prusty; Syed Jamal J

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue Special Issue ICARD 2020, Pages 276-280
DOI: 10.47392/irjash.2020.132

Artificial Intelligence is a developing zone in the field of innovation and furthermore attempts to show that the eventual fate of AI gains ground so that machines would function according to a human and would likewise convey the action of the person. It is difficult to create a machine like individuals who can appear feelings or think like individuals in different conditions. Directly we have recognized that AI is the examination of how to form things which can accurately fill in as people do. A working framework that utilizes AI reasoning procedures has a computerized reasoning motor, and experience scientific and Statistical module, an adjustment module and a UI. The computerized reasoning motor processes an accomplished expository boundary from a front code and a back code. The experience of scientific and Statistical module records and changes the experience's systematic boundary. The alteration module changes the front code and the back code as per the consequence of the experience logical and Statistical module computation of the experience systematic boundary. The UI inputs information or showcases the consequence of the computation. In the man-made consciousness motor, the experience diagnostic boundary is then again added to either the front code or the back code to register another experience investigative boundary. Such a game plan, the working framework can consequently change the consequence of the computation as per the decision or past decisions of the client.

Machine Learning: An Intuitive Approach In Healthcare

Salini Suresh; Suneetha V; Niharika Sinha; Sabyasachi Prusty; Sriranga H.A

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue 7, Pages 67-74
DOI: 10.47392/irjash.2020.67

Health is a crucial resource for a person's being to measure in our society from any disease. The fast development of the population appears to be trying to record and dissect the massive measure of knowledge about patients. Healthcare may be a need, and clinical specialists are constantly attempting to get approaches to actualize innovations and give effective outcomes. The main problem faced by the healthcare industry is the rising costs which include diagnosis and prediction of diseases, drug discovery, medical imaging diagnosis, personalized medicine, behavior therapy, and smart health records. Machine learning gives us an advantage of processing these information naturally which helps in making the human services framework progressively powerful. Getting the correct determination may be a key part of Healthcare. It clarifies a patient's medical issue and suggests health care treatment. The disease diagnostic technique is a complex, community-oriented action that has clinical, intelligent and data social events to make a decision about a patient's medical issue. Google has built up a ML model to assist recognize dangerous tumours on mammograms. Stanford’s profound learning calculation to differentiate skin malignancy. This paper is focused on the importance of Machine Learning in Healthcare just like the different application areas, latest research works in healthcare, wise machine learning contribution in Healthcare, and so on. Machine Learning is an application of Artificial Intelligence that helps in automatically learning and improving itself from experience. It is used in many other sectors like Law, Marketing & Advertising, Finance, Retail& Customer Services and Healthcare which also includes Covid-19. This paper presents various research in the Medicine and Healthcare sector