scholarly journals CV Ranking System and Portfolio Detection

2021 ◽  
Vol 9 (VI) ◽  
pp. 447-450
Author(s):  
Juned Ahmad
Keyword(s):  
Machine Learning ◽  
Human Resource ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Ranking System ◽  
Web Technologies ◽  
Human Resource Department ◽  
Academic Records ◽  
Human Effort

In recent times there is an increase in population and since of that plenty of individuals apply for one position of job. For this every single applicant provides his/her CV which contains its biodata, academic records and skill set. Within the present times the human resource department has to manually undergo all the CVs then they call the eligible candidates for an interview. Even after this much of human effort this method isn’t efficient and involves manual reading of documents (CVs). Because of this the human resource requires many HR officers and plenty of their time. In this project we'll atomate this process with the assistance of web technologies and machine learning algorithms so we should not depend on humans for ranking the CVs and try this process in more efficient and faster way.

scholarly journals Digital Anthropology in Work Life Rhythm

2019 ◽  
Vol 8 (11S) ◽  
pp. 757-759
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Life Cycle ◽  
Human Resource ◽  
Learning Algorithms ◽  
Vital Role ◽  
Work Life ◽  
Machine Learning Algorithms ◽  
Intelligent Technology

Intelligent technology has touched and improved upon almost every aspect of employee life cycle, Human resource is one of the areas, which has greatly benefited. Transformation of work mainly question the way we work, where we work, how we work and mainly care about the environment and surroundings in which we work. The main goal is to support the organizations to break out their traditional way of work and further move towards to an environment, which brings more pleasing atmosphere, flexible, empowering and communicative. Machine learning, algorithms and artificial intelligence are the latest technology buzzing around the HR professional minds. Artificial intelligence designed to take decisions based on data fed into the programs. The key difference between rhythm and balance is of choice vs adjustment. The choice is made easier, only with the help of priority, quick decision-making, time and communication. To maintain the above scenario digitalisation plays a vital role. In this paper, we suggest the artificial assistants focus on improving the rhythm of individual

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

1290-P: Gut Microbiota in New-Onset Pediatric Patients with Type 1 Diabetes: Machine Learning Algorithms to Classify Microorganisms Disease-Linked

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1290-P
Author(s):  
GIUSEPPE D’ANNUNZIO ◽  
ROBERTO BIASSONI ◽  
MARGHERITA SQUILLARIO ◽  
ELISABETTA UGOLOTTI ◽  
ANNALISA BARLA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 1 Diabetes ◽  
Gut Microbiota ◽  
Pediatric Patients ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
New Onset
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