scholarly journals Student Future Prediction Using Machine Learning

2019 ◽  
pp. 1104-1108
Author(s):  
Dileep Chaudhary ◽  
Harsh Prajapati ◽  
Rajan Rathod ◽  
Parth Patel ◽  
Rajiv Kumar Gurjwar
Keyword(s):  
Machine Learning ◽  
High Schools ◽  
Human Resource ◽  
Personality Trait ◽  
Career Paths ◽  
Web Application ◽  
Career Selection ◽  
Career Option ◽  
Future Prediction ◽  
Career Options

Selecting an appropriate career is one of the most important decisions and with the increase in the number of career paths and opportunities, making this decision have become quite difficult for the students. According to the survey conducted by the Council of Scientific and Industrial Research's (CSIR), about 40% of students are confused about their career options. This may lead to wrong career selection and then working in a field which was not meant for them, thus reducing the productivity of human resource. Therefore, it is quite important to take a right decision regarding the career at an appropriate age to prevent the consequences that results due to wrong career selection. This system is a web application that would help students studying in high schools to select a course for their career. The system would recommend the student, a career option based on their personality trait, interest and their capacity to take up the course.

scholarly journals Machine Learning base Student Future Orientation and Recommendation System

2021 ◽  
pp. 482-487
Author(s):  
Poonam Parate ◽  
Mukesh Barapatre ◽  
Harish Kalode ◽  
Shruti Harkut ◽  
Shivam Kamdi ◽  
...  
Keyword(s):  
Career Paths ◽  
Recommendation System ◽  
Future Orientation ◽  
Educational Policies ◽  
Career Selection ◽  
Academic Credit ◽  
Credit Score ◽  
Career Option ◽  
Career Options ◽  
Selection Of

As students are rummaging their academics and pursuing their interested courses, they need to assess their capabilities and identify their interests in understanding during which career area their interests and abilities will put them in. With the rise in the number of career paths and opportunities, making this decision has become quite tricky for scholars. In keeping with the survey conducted by the Council of Scientific and Industrial Research (CSIR), about 40% of scholars are confused about their career options. This could result in wrong career selection, so working in a field that wasn't meant for them reduces human resource productivity. This project proposes feasible predictions for students' field selection supported by their marks and selection of interest. The system would recommend the scholar a career option that helped their personality trait, interest, and capacity to require up the course. This technique provides students with many career options per their part, self-talent, memorizing power, and most vital per their academic credit score. This technique generates a credit score supported by new government educational policies, helping students settle on correct streams for the longer-term career path.

A Database for Counterfeit Electronics and Automatic Defect Detection Based on Image Processing and Machine Learning

2016 ◽  
Author(s):  
Navid Asadizanjani ◽  
Sachin Gattigowda ◽  
Mark Tehranipoor ◽  
Domenic Forte ◽  
Nathan Dunn
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Integrated Circuits ◽  
Web Application ◽  
The United States ◽  
Global Market ◽  
Online Database ◽  
Ongoing Effort ◽  
Time Period ◽  
Physical Defects

Abstract Counterfeiting is an increasing concern for businesses and governments as greater numbers of counterfeit integrated circuits (IC) infiltrate the global market. There is an ongoing effort in experimental and national labs inside the United States to detect and prevent such counterfeits in the most efficient time period. However, there is still a missing piece to automatically detect and properly keep record of detected counterfeit ICs. Here, we introduce a web application database that allows users to share previous examples of counterfeits through an online database and to obtain statistics regarding the prevalence of known defects. We also investigate automated techniques based on image processing and machine learning to detect different physical defects and to determine whether or not an IC is counterfeit.

Prediction of aircraft estimated time of arrival using machine learning methods

2021 ◽  
pp. 1-15
Author(s):  
O. Basturk ◽  
C. Cetek
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Time Of Arrival ◽  
Learning Models ◽  
Trajectory Data ◽  
Different Sources ◽  
Machine Learning Models

ABSTRACT In this study, prediction of aircraft Estimated Time of Arrival (ETA) is proposed using machine learning algorithms. Accurate prediction of ETA is important for management of delay and air traffic flow, runway assignment, gate assignment, collaborative decision making (CDM), coordination of ground personnel and equipment, and optimisation of arrival sequence etc. Machine learning is able to learn from experience and make predictions with weak assumptions or no assumptions at all. In the proposed approach, general flight information, trajectory data and weather data were obtained from different sources in various formats. Raw data were converted to tidy data and inserted into a relational database. To obtain the features for training the machine learning models, the data were explored, cleaned and transformed into convenient features. New features were also derived from the available data. Random forests and deep neural networks were used to train the machine learning models. Both models can predict the ETA with a mean absolute error (MAE) less than 6min after departure, and less than 3min after terminal manoeuvring area (TMA) entrance. Additionally, a web application was developed to dynamically predict the ETA using proposed models.

scholarly journals PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features

2021 ◽  
Vol 22 (5) ◽  
pp. 2704
Author(s):  
Andi Nur Nilamyani ◽  
Firda Nurul Auliah ◽  
Mohammad Ali Moni ◽  
Watshara Shoombuatong ◽  
Md Mehedi Hasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Web Application ◽  
Computational Prediction ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Post Translational Modification ◽  
Multiple Sequence ◽  
Sequence Features

Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine is a prerequisite to understanding the molecular function of nitrated proteins. Thanks to the progress of machine learning, computational prediction can play a vital role before the biological experimentation. Herein, we developed a computational predictor PredNTS by integrating multiple sequence features including K-mer, composition of k-spaced amino acid pairs (CKSAAP), AAindex, and binary encoding schemes. The important features were selected by the recursive feature elimination approach using a random forest classifier. Finally, we linearly combined the successive random forest (RF) probability scores generated by the different, single encoding-employing RF models. The resultant PredNTS predictor achieved an area under a curve (AUC) of 0.910 using five-fold cross validation. It outperformed the existing predictors on a comprehensive and independent dataset. Furthermore, we investigated several machine learning algorithms to demonstrate the superiority of the employed RF algorithm. The PredNTS is a useful computational resource for the prediction of nitrotyrosine sites. The web-application with the curated datasets of the PredNTS is publicly available.

scholarly journals Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

2021 ◽  
Vol 11 (9) ◽  
pp. 4266
Author(s):  
Md. Shahriare Satu ◽  
Koushik Chandra Howlader ◽  
Mufti Mahmud ◽  
M. Shamim Kaiser ◽  
Sheikh Mohammad Shariful Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Learning Models ◽  
Short Term ◽  
Infected People ◽  
Infected Case ◽  
First Case ◽  
Novel Coronavirus ◽  
Short Term Prediction ◽  
Machine Learning Models

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

scholarly journals Signature-based and Machine-Learning-based Web Application Firewalls: A Short Survey

2021 ◽  
Vol 189 ◽  
pp. 359-367
Author(s):  
Simon Applebaum ◽  
Tarek Gaber ◽  
Ali Ahmed
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Short Survey

Music Therapy Knowledge and Interest: A Survey of Music Education Majors

2020 ◽  
Vol 57 (4) ◽  
pp. 455-474
Author(s):  
Lori F Gooding ◽  
D Gregory Springer
Keyword(s):  
Music Education ◽  
Music Therapy ◽  
Music Teachers ◽  
Degree Programs ◽  
Education Majors ◽  
Education Students ◽  
Career Option ◽  
Key Factor ◽  
Career Options ◽  
Music Education Majors

Abstract Music teachers play an important role in exposing students to career options in the field of music. As a result, there is a need to explore music education students’ interest in and knowledge of music therapy. The purpose of this study was to investigate music education students’ exposure to, knowledge of, and willingness to promote music therapy as a career option for prospective collegiate students. A survey was given to 254 music education majors from four research institutions, two with and two without music therapy degree programs. Participants answered demographic, yes/no, Likert-type scale, and open-ended questions about their exposure to, knowledge of, and willingness to promote careers in music therapy. Results indicate that exposure to music therapy occurred in both pre-collegiate and college settings, and that music teachers appear to be influential in exposing students to music therapy. Students often sought out information on music therapy independently, which played an important role in how individuals learned about music therapy, though it has the potential of providing misinformation. Significant differences were found in participants’ knowledge and willingness to promote music therapy as a career option based on the presence of music therapy degree programs. Exposure seemed to be a key factor in music therapy knowledge and promotion; thus, music therapists need to ensure accurate dissemination of music therapy-related information in both pre-collegiate and college settings. Increasing the visibility of the field has the potential to expand interest and potentially attract young musicians well suited for a career in music therapy.

scholarly journals Analysis of Factors that Influence Accounting Students Choose Career As A Public Accountant

2020 ◽  
Vol 8 (1) ◽  
pp. 19-26
Author(s):  
Fu'at Hasim ◽  
Novi Darmayanti ◽  
Alfian Manaf Dientri
Keyword(s):  
Social Values ◽  
Accounting Students ◽  
Primary Data ◽  
Public Accounting ◽  
Career Selection ◽  
Initial Stage ◽  
College Career ◽  
Career Options ◽  
Selection Of ◽  
Public Accountant

The selection of a career for students of accounting was the initial stage of the establishment of a career. After completion of the period of study College, career options for graduates in accounting not addressed only on the accounting profession but there were also other options for a career. There were several factors that affect students' career selection accounting for a career in public accounting. A sample of 115 students accounting for UNISDA and UNISLA. Methods of analysis used was logistic regression with SPSS version 22. The type of data being used was the primary data obtained from the questionnaire respondents. From the results of testing hypotheses obtained that the factors of financial rewards and social values significantly influential partially against the interest of the students in the choosing a career as a public accountant.

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