Online ISSN: 2515-8260

Keywords : AI

Investigation and development of machine Learning Challenges in Video Interviews


European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 425-433

This paper audits and talks about examination propels on "logical AI" in PC vision. We centre on a specific zone of the "Seeing People" (LAP) topical space: primary imitations and character investigation. Our point is to variety the computational knowledge and PC vision networks mindful of the significance of creating logical systems for PC helped dynamic applications, for example, robotizing enlistment. Decisions dependent on character attributes are being made routinely by human asset offices to assess the up-and-comers' ability of social inclusion and their capability of profession development. Be that as it may, deducing character attributes and, as a rule, the procedure by which we people structure a first impression of individuals, is profoundly emotional and might be one-sided. Past investigations have shown that knowledge machineries can figure out how to imitate human choices. In this paper, we go above and beyond and figure the issue of clarifying the choices of the models as methods for distinguishing what visual perspectives are significant, seeing how they identify with choices recommended, and potentially picking up knowledge into unfortunate negative inclinations. We structure another test on reasonableness of knowledge machineries for first impressions examination. We portray the setting, situation, assessment measurements and starter results of the opposition. Supposedly this is the first exertion regarding difficulties for logic in PC vision. Moreover, our test configuration involves a few other measurable and subjective components of oddity, including a "competition" setting, which joins rivalry and coordinated effort.

Selection Based Recursive Feature Elimination (SBRFE) Algorithm Used for EEG based Alzheimer’s Disease

N. Deepa; SP. Chokkalingam

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2287-2296

A enlightening of a framework utilizing cerebrum shape data for depiction of sound subjects and Alzheimer's distress patients there is no answer for Alzheimer's Disease (AD), a definite early assessment is fundamental for both the patient and social idea, and it will wind up being altogether constantly tremendous once infirmity changing managers are accessible to ruin, fix, or even log jam the improvement of the ailment. Disclosure of Alzheimer's sullying is mentioning a consequence of the likeness in earlier dementia with its information and standard sound MRI information of logically settled individuals. Social occasion of AD through noteworthy learning frameworks has been one of the most interesting examination territories in the clinical field. Regardless, the majority of the current strategies can't use the whole spatial data so we generalize at magnificent up an irrelevant effort EEG based Alzheimer's sickness territory structure different specialists have focused on presenting a high precision electronic estimation for get-together the AD and the Normal Control (NC) cases. In this paper, we propose a Selection Based Recursive Feature Elimination (SBRFE) check to confine patients having AD, smooth mental obstruction (MCI), and intellectually standard (CN) utilizing a social event of mix huge learning models to perform OASIS dataset show that better execution of the proposed noteworthy learning technique to be better than that of the some customary frameworks.

Deep Learning in Tuberculosis Diagnosis: A Survey

B. Sandhiya; Dr.R. Punniyamoorthy; Saravanan. B; Vijay Prabhu. R; Subhash. V

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2736-2740

Tuberculosis is a contagious syndrome that leads to death Worldwide. In majority of the developing countries, the access to the diagnostic tool and the test usage is relatively poor. Now the recent advancement in the field of Artificial Intelligence may help them to fill this technology gap. Computer Aided Detection and Diagnosis helps in diagnosing the diseases through some clinical symptoms as well as X-ray images of the patients. Nowadays many strategies are formulated to increase the classification accuracy of tuberculosis diagnosis using AI and Deep Learning approaches. Our survey paper, focus to describe the wide AI and deep learning approaches employed in the diagnosis of tuberculosis.

The Advent of Artificial Intelligence in Cardiology: The Current Applications and Future Prospects

K Prashanth; M Manjappa; C Srikar

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 1, Pages 14-20

The technology of artificial intelligence is emerging as a promising entity in cardiovascular medicine, with the potential to improve diagnosis and patient care. In this article we review the literature on artificial intelligence and its utility in cardiology. We provide a detailed description of concepts of artificial intelligence tools like machine learning, deep learning and cognitive computing. This review discusses the current evidence, applications, future prospects and limitations of artificial intelligence in cardiology.