With the advent of the Internet and social media, while hundreds of people have benefitted from the vast sources of information available, there has been an enormous increase in the rise of cyber-crimes, particularly targeted towards women. According to a 2019 report in the [4] Economics Times, India has witnessed a 457% rise in cybercrime in the five year span between 2011 and 2016. Most speculate that this is due to impact of social media such as Facebook, Instagram and Twitter on our daily lives. While these definitely help in creating a sound social network, creation of user accounts in these sites usually needs just an email-id. A real life person can create multiple fake IDs and hence impostors can easily be made. Unlike the real world scenario where multiple rules and regulations are imposed to identify oneself in a unique manner (for example while issuing one's passport or driver's license), in the virtual world of social media, admission does not require any such checks. In this paper, we study the different accounts of Instagram, in particular and try to assess an account as fake or real using Machine Learning techniques namely Logistic Regression and Random Forest Algorithm.
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.
Nowadays, a huge amount of data is available everywhere. Therefore, we need to prioritize analysing this dataset which would help us in gaining some meaningful information for the development of an algorithm based on the analysis. These feet can be obtained by using Machine Learning, Data Mining, and Data Analysis. Machine Learning which is a part of Artificial Intelligence is used for designing algorithms based on trends of data, patterns and the relation found between them. ML has been used in various fields such as Marketing, gameplay, intrusion detection, bioinformatics, information retrieval, healthcare, entertainment and also on COVID-19 applications and so on. This paper presents an overview of the contribution of ML in Entertainment industry.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.