scholarly journals Recommendation System for Human Resource Department

2022 ◽  
Vol 10 (1) ◽  
pp. 647-651
Author(s):  
Ulka Khobragade
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Collaborative Learning ◽  
Human Resource ◽  
Recommendation System ◽  
Human Resource Department ◽  
Schedule Time

Abstract: The objective is to find suitable skilled employees for the job among different departments within the organization. For finding the quality of an applicant or even the already employed employee, the HRs of companies goes through a lot of hectic schedule, time consuming processes, decision making, etc. In this case, Recommendation System, which is a part of Machine Learning, proves to be effective in making decisions on behalf of the HRs if an employee or an applicant is suitable enough for the job. The aim of the project is to predict whether the already employed employees, who belong to different department within the organization can perform well or not if assigned to a different department. Keywords: Recommendation system, Collaborative Learning, K-NN, Similarity, Similarity Correlation, Cosine etc.

scholarly journals Use of artificial intelligence technologies for building individual educational trajectories of students

2021 ◽  
Vol 18 (1) ◽  
pp. 27-35
Author(s):  
Roman B. Kupriyanov ◽  
Dmitry L. Agranat ◽  
Ruslan S. Suleymanov
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Recommendation System ◽  
Educational Process ◽  
Content Filtering ◽  
Machine Learning Methods ◽  
Educational Trajectories ◽  
Textual Data ◽  
Second Year

Problem and goal. Developed and tested solutions for building individual educational trajectories of students, focused on improving the educational process by forming a personalized set of recommendations from the optional disciplines. Methodology. Data mining and machine learning methods were used to process both numeric and textual data. The approaches based on collaborative and content filtering to generate recommendations for students were also used. Results. Testing of the developed system was carried out in the context of several periods of elective courses selection, in which 4,769 first- and second-year students took part. A set of recommendations was automatically generated for each student, and then the quality of the recommendations was evaluated based on the percentage of students who used these recommendations. According to the results of testing, the recommendations were used by 1,976 students, which was 41.43% of the total number of participants. Conclusion. In the study, a recommendation system was developed that performs automatic ranking of subjects of choice and forms a personalized set of recommendations for each student based on their interests for building individual educational trajectories.

Autonomous Interpretation Methods of Ultrasonic Data Through Machine Learning Facilitates Novel and Reliable Casing Annulus Characterization

2021 ◽  
Author(s):  
Ram Sunder Kalyanraman ◽  
Xiaoli Chen ◽  
Po-Yen Wu ◽  
Kevin Constable ◽  
Amit Govil ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Complex Task ◽  
Wireline Logs ◽  
Cement Sheath ◽  
Sonic Logs ◽  
Correct Processing

Abstract Ultrasonic and sonic logs are increasingly used to evaluate the quality of cement placement in the annulus behind the pipe and its potential to perform as a barrier. Wireline logs are carried out in widely varying conditions and attempt to evaluate a variety of cement formulations in the annulus. The annulus geometry is complex due to pipe standoff and often affects the behavior (properties) of the cement. The transformation of ultrasonic data to meaningful cement evaluation is also a complex task and requires expertise to ensure the processing is correctly carried out as well interpreted correctly. Cement formulations can vary from heavy weight cement to ultralight foamed cements. The ultrasonic log-based evaluation, using legacy practices, works well for cements that are well behaved and well bonded to casing. In such cases, a lightweight cement and heavyweight cement, when bonded, can be easily discriminated from gas or liquid (mud) through simple quantitative thresholds resulting in a Solid(S) - Liquid(L) - Gas(G) map. However, ultralight and foamed cements may overlap with mud in quantitative terms. Cements may debond from casing with a gap (that is either wet or dry), resulting in a very complex log response that may not be amenable to simple threshold-based discrimination of S-L-G. Cement sheath evaluation and the inference of the cement sheath to serve as a barrier is complex. It is therefore imperative that adequate processes mitigate errors in processing and interpretation and bring in reliability and consistency. Processing inconsistencies are caused when we are unable to correctly characterize the borehole properties either due to suboptimal measurements or assumptions of the borehole environment. Experts can and do recognize inconsistencies in processing and can advise appropriate resolution to ensure correct processing. The same decision-making criteria that experts follow can be implemented through autonomous workflows. The ability for software to autocorrect is not only possible but significantly enables the reliability of the product for wellsite decisions. In complex situations of debonded cements and ultralight cements, we may need to approach the interpretation from a data behavior-based approach, which can be explained by physics and modeling or through observations in the field by experts. This leads a novel seven-class annulus characterization [5S-L-G] which we expect will bring improved clarity on the annulus behavior. We explain the rationale for such an approach by providing a catalog of log response for the seven classes. In addition, we introduce the ability to carry out such analysis autonomously though machine learning. Such machine learning algorithms are best carried out after ensuring the data is correctly processed. We demonstrate the capability through a few field examples. The ability to emulate an "expert" through software can lead to an ability to autonomously correct processing inconsistencies prior to an autonomous interpretation, thereby significantly enhancing the reliability and consistency of cement evaluation, ruling out issues related to subjectivity, training, and competency.

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

IJOSpontaneous Group Decision Making in Distributed Collaborative Learning

2013 ◽  
Vol 3 (2) ◽  
pp. 40-58
Author(s):  
Geoffrey Z. Liu
Keyword(s):  
Decision Making ◽  
Collaborative Learning ◽  
Group Decision Making ◽  
Information Science ◽  
Group Performance ◽  
Group Decision ◽  
Decision Quality ◽  
Library And Information Science ◽  
Poor Group

The paper reports on an exploratory study of student spontaneous group decision making (GDM) in distributed collaborative learning environments. Recordings of group meetings were collected from graduate students working on a database design project (in a library and information science program in California), from which group decision instances were extracted and formally coded for quantitative analysis. A follow-up survey was conducted to gather more information. The study finds that students are generally in favor of an unfacilitated and semi-structured GDM process, with group decisions typically made by consensus. A rigidly structured GDM process tends to be associated with poor group performance. GDM efficiency is an important predictor of the quality of final group products, and too much brainstorming may lead to difficulties. Students relying exclusively on text chatting tend to be unsure if their opinion was given equal attention, and those in underperforming groups are more doubtful about decision quality.

scholarly journals Proposal of an automatic tool for evaluating the quality of decision-making on Checkers player agents

2018 ◽  
Author(s):  
Matheus Prado Prandini Faria ◽  
Rita Maria Silva Julia ◽  
Lídia Bononi Paiva Tomaz
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Unsupervised Methods ◽  
Automatic Tool

Checkers player agents represent an appropriate case study for the best unsupervised methods of Machine Learning. This work presents a tool to measure the performance of these methods based on the quality of the decision making of these agents. The proposed tool, based on the data of movements performed in real games by the agents under evaluation, provides a statistical way of automatically comparing the coincidence rates between the decision making of the evaluated agents with those that the remarkable player agent Cake would do in the same situations. The tool was validated through tournaments between agents comparing their respective coincidence rates and their performance.

Heart Disease Diagnosis

2019 ◽  
pp. 161-181
Author(s):  
Siddhartha Kumar Arjaria ◽  
Abhishek Singh Rathore
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Heart Disease ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Correct Decision ◽  
Heart Disease Diagnosis ◽  
Modern Era

In the modern era of information technology, machine learning algorithms are used in different domains for boosting the quality of decision making. The correct decision making about the disease diagnosis is one of the applications where these approaches are applied successfully for assisting the doctors. Correct and timely diagnosis of disease is the primary requirement of effective treatment. Today, one of the most leading causes of death is heart disease. This chapter deals with the application of different machine learning algorithms for effective heart disease diagnosis. Diagnosis through the machine learning algorithms involves the three major steps, data preprocessing, feature selection, and classification. The chapter covers the experimental study of performance of SVM, ANN, logistic regression, random forest, KNN, AdaBoost, Naive Bayes, decision tree, SGD, CN2 rule inducer approaches.

scholarly journals Workforce Management Decisions in the Construction Industry in the UAE

2020 ◽  
Vol 23 (1) ◽  
pp. 55-69
Author(s):  
Mohammed Abdul Nayeem
Keyword(s):  
Decision Making ◽  
Human Resource Management ◽  
Construction Industry ◽  
Human Resource ◽  
Productivity Growth ◽  
Management Decisions ◽  
Workforce Management ◽  
Construction Companies ◽  
Contextual Framework

This study is guided by the need to develop a framework for workforce management decisions in the context of the construction industry in the UAE. A full contextual framework involving all the factors that can possibly play a role in the success of workforce management decisions has been delineated. The factors considered are related to hiring, selection, and quality of workers. The literature has focused not only on the overarching strategies and tactics but also on how day-to-day decision-making occurs while using human resource management for productivity growth of an organization. The data was collected from three construction companies in the UAE. The findings of the study are in line with the findings of previous works.

scholarly journals Word Embedding and Feature Reduction for Sentiment Analysis Using GA

2020 ◽  
pp. 111-117
Author(s):  
Prajakta P. Shelke ◽  
Ankita N. Korde
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Sentiment Analysis ◽  
Recommendation System ◽  
Opinion Mining ◽  
Hybrid Approach ◽  
Feature Reduction ◽  
Word Embedding ◽  
Support Vector ◽  
Svm Classifier

Sentiment analysis (SA), also called as opinion mining is the technique for the removal of opinions of a specific entity or feature from reviews dataset. The opinions of other users help in decision making process of people. This paper studies different methods that are aimed at SA. These approaches vary from semantic based methods, machine learning, neural networks, syntactical methods with each having its own strength. Although hybrid approach also exists where the idea is to combine strengths of two or more methods to increase the accuracy. A framework in which sentiment analysis is done by using word embedding and feature reduction techniques is also proposed. Word embedding is a technique in which low-dimensional vector representation of words is provided. Feature reduction method is used with Support Vector Machine (SVM) classifier. The framework will perform sentiment analysis of user opinions by using a machine learning approach and provides a recommendation system for the ease of decision making for users. The proposed system in this paper has solved the scalability problem and improved the accuracy.

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