scholarly journals Automatic Student Analysis and Placement Prediction using Advanced Machine Learning Algorithms

2019 ◽  
Vol 8 (12) ◽  
pp. 4178-4183
Keyword(s):  
Machine Learning ◽  
Personality Traits ◽  
Machine Learning Algorithms ◽  
Main Process ◽  
Technical Knowledge ◽  
Specific Training ◽  
Faculty Mentor ◽  
Large Pool ◽  
Proper Training ◽  
Talent Acquisition

The amenable statement with respective to Company Organization, Institution and students is that the company organization are taking more time to recruit which is a big challenge to them and there is no specific platform to recruit candidates on preferred qualifications. The Institutions are unable to get 100% placements among eligible students.The institutions doesn’t provide proper training on minimum and preferred qualifications to the students. The candidates are unable to get specific training from college organization. The college organization should provide the training to candidates at what they are lagging behind and make the students to get stronger in preferred qualifications and all other aspects. “52% of Talent Acquisition leaders say the hardest part of recruitment is screening candidates from a large applicant pool”. The time spent on screening students from a large pool often takes up the largest portion of the time. Despite College Organization are also not training students effectively based on company requirements. The analysis of student is to be done to know where the student is failing to get the placement. The company doesn’t know the personalityof the student while recruiting the students. To solve this bottleneck in recruiting we created this automation tool. The main process of determining whether a candidate is qualified based on minimum qualifications like CGPA, Certifications, Projects done, Internships and respectively. There are two main goals of this project are: 1. To decide whether to move the student forward to an interview or to reject them. 2. The college organization can give more training to the students those who got rejected by small issues like communication, programming, aptitude… This process is based on minimum qualifications and preferred qualifications. Both types of qualifications are more useful to the recruiters. These qualifications can include experience on projects, education, skills and knowledge, personality traits, competencies. The minimum qualifications are the mandatory qualifications that the company organizations required and preferred qualifications are not mandatory but to make the student stronger from other students. The personality and also technical knowledge can be given accurately by the faculty, mentor, H.O.D.

scholarly journals Developing and evaluating language-based machine learning algorithms for inferring applicant personality in video interviews

2021 ◽  
Author(s):  
Louis Hickman ◽  
Rachel Saef ◽  
Vincent Ng ◽  
Sang Eun Woo ◽  
Louis Tay ◽  
...  
Keyword(s):  
Machine Learning ◽  
Personality Traits ◽  
Large Scale ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Future Research ◽  
Big Five Personality ◽  
Test Machine ◽  
Self Reports ◽  
The Cross

Organizations are increasingly relying on people analytics to aid human resources decision-making. One application involves using machine learning to automatically infer applicant characteristics from employment interview responses. However, management research has provided scant validity evidence to guide organizations’ decisions about whether and how best to implement these algorithmic approaches. To address this gap, we use closed vocabulary text mining on mock video interviews to train and test machine learning algorithms for predicting interviewee’s self-reported (automatic personality recognition) and interviewer-rated personality traits (automatic personality perception). We use 10-fold cross-validation to test the algorithms’ accuracy for predicting Big Five personality traits across both rating sources. The cross-validated accuracy for predicting self-reports was lower than large-scale investigations using language in social media posts as predictors. The cross-validated accuracy for predicting interviewer ratings of personality was more than double that found for predicting self-reports. We discuss implications for future research and practice.

scholarly journals Metadynamics sampling in atomic environment space for collecting training data for machine learning potentials

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Dongsun Yoo ◽  
Jisu Jung ◽  
Wonseok Jeong ◽  
Seungwu Han
Keyword(s):  
Machine Learning ◽  
Sampling Method ◽  
Local Environment ◽  
Training Data ◽  
Interatomic Potentials ◽  
Mathematical Form ◽  
Training Set ◽  
Large Pool ◽  
Automatic Sampling ◽  
Proper Training

AbstractThe universal mathematical form of machine-learning potentials (MLPs) shifts the core of development of interatomic potentials to collecting proper training data. Ideally, the training set should encompass diverse local atomic environments but conventional approaches are prone to sampling similar configurations repeatedly, mainly due to the Boltzmann statistics. As such, practitioners handpick a large pool of distinct configurations manually, stretching the development period significantly. To overcome this hurdle, methods are being proposed that automatically generate training data. Herein, we suggest a sampling method optimized for gathering diverse yet relevant configurations semi-automatically. This is achieved by applying the metadynamics with the descriptor for the local atomic environment as a collective variable. As a result, the simulation is automatically steered toward unvisited local environment space such that each atom experiences diverse chemical environments without redundancy. We apply the proposed metadynamics sampling to H:Pt(111), GeTe, and Si systems. Throughout these examples, a small number of metadynamics trajectories can provide reference structures necessary for training high-fidelity MLPs. By proposing a semi-automatic sampling method tuned for MLPs, the present work paves the way to wider applications of MLPs to many challenging applications.

scholarly journals Research on Image Classification Algorithm Based on Convolutional Neural Network

2021 ◽  
Vol 2083 (3) ◽  
pp. 032054
Author(s):  
Lihua Luo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Classification Algorithm ◽  
Machine Learning Algorithms ◽  
Image Feature ◽  
Main Process ◽  
Classification Image

Abstract Nowadays, we are in the information age. Pictures carry a lot of information and play an indispensable role. For a large number of images, it is very important to find useful image information within the effective time. Therefore, the excellent performance of the image classification algorithm has certain influence factors on the result of image classification. Image classification is to input an image, and then use a certain classification algorithm to determine the category of the image. The main process of image classification: image preprocessing, image feature extraction and classifier design. Compared with the manual feature extraction of traditional machine learning, the convolutional neural network under the deep learning model can automatically extract local features and share weights. Compared with traditional machine learning algorithms, the image classification effect is better. This paper focuses on the study of image classification algorithms based on convolutional neural networks, and at the same time compares and analyzes deep belief network algorithms, and summarizes the application characteristics of different algorithms.

scholarly journals Employability Prediction of Engineering Graduates using Machine Learning Algorithms

2020 ◽  
Vol 8 (5) ◽  
pp. 4521-4524
Keyword(s):  
Machine Learning ◽  
Skill Acquisition ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Digital Revolution ◽  
Talent Acquisition

Number of graduates produced in each year by higher education institutions is increasing. Thus prediction of employability of graduate’s plays a vital role for any industry for proper talent acquisition and Utilization and also it helps students in identifying the qualification and skills that they need to improve, before completion of degree to get desired jobs. In this Digital Revolution, informal learning and skill enhancements is happening in unconditional method, relating and converging all this learning’s to the employability rate is one of a biggest issue. The main objective is to address this issue by predicting and forecasting the skill acquisition continuously and mapping to industry needs using machine learning Algorithms. The proposed work used different machine learning algorithms like Logistic Regression, Decision tree, k-nearest neighbor, Support Vector Machine and Naïve Bayes for building model where ANN classifier resulted with the highest accuracy of 87.42%. This research would be helpful for all the organization including government, Private and corporations, including students and teachers for employability prediction.

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.

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