Difficulties Found by Students in the Disciplines of Post-graduation in Electrical Engineering

In this study, we explore the difficulties of students in the disciplines of post-graduation in electrical engineering. To the extent that the student is able to elucidate his difficulties during the disciplines of the postgraduate course, your research can flow with greater satisfaction and success. Our findings are based on interviews of students with different backgrounds and educational experiences, allowing to capture different difficulties and motivations found in the classroom, which influence the researches of masters and doctoral students. We found that most of the students in the postgraduate course in electrical engineering had background training in distinct areas (73.3%), and that they are generally related area students, such as math, computing, and other areas of engineering. Another aspect is that most interviewees reported that their difficulties were related to the disciplines that addressed the development of algorithms and mathematical calculations (66%), suggesting that this problem was a consequence of insufficient knowledge base for the disciplines. The findings suggest that even with the difficulties encountered in the classroom, the students of the course had no disapproval, because most of the time they sought to discuss their difficulties in groups of studies created by classmates, and thus, elucidating the difficulties faced with colleagues who had different skills

Computer models for disease prediction

With the increased computational power and ease of gathering medical information, Artificial Intelligence has helped all areas of health in developing algorithms and techniques for disease diagnosis and staging. The technology has been applied in several areas, due to its wide range of features, some activities become simpler with your help. Thus, this study aimed to identify the main computational models for disease prediction. Data collection was performed in the virtual databases present in the Health Library Research Portal (VHL): LILACS: Latin American and Caribbean Health Sciences Literature, Scielo - ScientificElectronic Library Online and Literature Analysis and Retrieval System Medical Online (MEDLINE). We found 52 articles and 10 of these in the review. From the reading and evaluation of the included articles, which can be aided by computer vision techniques, machine learning through neural networks and pattern recognition can be developed algorithms capable of identifying diseases. Thus, from this diagnosis provided by the algorithm, the health professional will have conditions for early prevention, diagnosis and treatment of diseases

Difficulties Found by Students in the Disciplines of Post-graduation in Electrical Engineering

In this study, we explore the difficulties of students in the disciplines of post-graduation in electrical engineering. To the extent that the student is able to elucidate his difficulties during the disciplines of the postgraduate course, your research can flow with greater satisfaction and success. Our findings are based on interviews of students with different backgrounds and educational experiences, allowing to capture different difficulties and motivations found in the classroom, which influence the researches of masters and doctoral students. We found that most of the students in the postgraduate course in electrical engineering had background training in distinct areas (73.3%), and that they are generally related area students, such as math, computing, and other areas of engineering. Another aspect is that most interviewees reported that their difficulties were related to the disciplines that addressed the development of algorithms and mathematical calculations (66%), suggesting that this problem was a consequence of insufficient knowledge base for the disciplines. The findings suggest that even with the difficulties encountered in the classroom, the students of the course had no disapproval, because most of the time they sought to discuss their difficulties in groups of studies created by classmates, and thus, elucidating the difficulties faced with colleagues who had different skills.</jats:p

COMPUTER MODELS FOR DISEASE PREDICTION

With the increased computational power and ease of gathering medical information, Artificial Intelligence has helped all areas of health in developing algorithms and techniques for disease diagnosis and staging. The technology has been applied in several areas, due to its wide range of features, some activities become simpler with your help. Thus, this study aimed to identify the main computational models for disease prediction. Data collection was performed in the virtual databases present in the Health Library Research Portal (VHL): LILACS: Latin American and Caribbean Health Sciences Literature, Scielo - ScientificElectronic Library Online and Literature Analysis and Retrieval System Medical Online (MEDLINE). We found 52 articles and 10 of these in the review. From the reading and evaluation of the included articles, which can be aided by computer vision techniques, machine learning through neural networks and pattern recognition can be developed algorithms capable of identifying diseases. Thus, from this diagnosis provided by the algorithm, the health professional will have conditions for early prevention, diagnosis and treatment of diseases.</jats:p