scholarly journals Design and Development of Machine Learning based Resume Ranking System

2021 ◽  
Author(s):  
Tejaswini K ◽  
Umadevi V ◽  
Shashank M Kadiwal ◽  
Sanjay Revanna
Keyword(s):  
Machine Learning ◽  
Design And Development ◽  
Ranking System

scholarly journals Development of Continuum Robot Arm and Gripper for Harvesting Cherry Tomatoes

2019 ◽  
Author(s):  
Azamat Yeshmukhametov ◽  
Koichi Koganezawa ◽  
Zholdas Buribayev ◽  
Yedilkhan Amirgaliyev ◽  
Yoshio Yamamoto
Keyword(s):  
Machine Learning ◽  
Recognition System ◽  
Stem Cutting ◽  
Robot Arm ◽  
Design And Development ◽  
Challenging Issue ◽  
Continuum Robot ◽  
Unstructured Environment ◽  
Backbone Structure ◽  
Cherry Tomatoes

Designing and development of agricultural robot is always a challenging issue, because of robot intends to work an unstructured environment and at the same time, it should be safe for the surrounded plants. Therefore, traditional robots cannot meet the high demands of modern challenges, such as working in confined and unstructured workspaces. Based on current issues, we developed a new tomato harvesting wire-driven discrete continuum robot arm with a flexible backbone structure for working in confined and extremely constrained spaces. Moreover, we optimized a tomato detaching process by using newly designed gripper with passive stem cutting function. Moreover, by designing the robot we also developed ripe tomato recognition by using machine learning. This paper explains the proposed continuum robot structure, gripper design, and development of tomato recognition system.

AI-Based Well Ranking System for Hydraulic Fracturing Events

2021 ◽  
Author(s):  
Andrei Erofeev ◽  
Denis Orlov ◽  
Dmitry Perets ◽  
Dmitry Koroteev
Keyword(s):  
Machine Learning ◽  
Hydraulic Fracturing ◽  
Oil Field ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Ranking System ◽  
Stable Regime ◽  
Flow Stabilization ◽  
Cumulative Production ◽  
Mean Square Errors

Abstract We are presenting a new, highly intelligent AI-based ranking system for selecting the most appropriate candidates for well treatment. The system is trained to predict flow rates after hydraulic fracturing (HF) and rank wells by the expected effect of the event with machine learning techniques. We demonstrate a significant effort for preprocessing the available field data to create a dataset for training machine learning (ML) models. The dataset included information about geology, transport and storage properties, depths, oil/liquid rates before fracturing for target and neighboring wells. Each ML model has been trained to predict monthly production of oil and liquid right after fracturing and after flow stabilization. Also, confidence intervals of the prediction have been provided. To study the dynamics of future oil rate decline after HF on a stable regime, we have trained several regression models to make predictions at each future point (6 next months after fracturing). To estimate the effect due to HF, we defined expected production "without fracturing." Typically, wells behave with a stable decline trend of production that is approximated by Arps function. The function is defined before HF, then extrapolated to the period after the event where it shows expected production without fracturing. One may conclude about the effectiveness of HF by calculating areas difference under the extrapolated curve (cumulative production without HF), and ML predicted cumulative production for future six months. Reservoir engineers could calculate these differences for each well and create a ranking list from the highest effect to the lowest. The developed system does this automatically for the required oilfield or its part. Therefore, one may easily define the list of best candidates for HF. Gradient Boosting algorithm has been applied to obtain results. Feature selection and tuning of hyperparameters have been provided with the application of cross-validation technique. To test the developed approach, we have divided the dataset from 8 conventional oil fields at a ratio of four to one. The total dataset included 700+ well interventions. Then we have trained and validated models for flow rate prediction on the major part and tested on the holdout part. For different oil field determination coefficients (R2) and normalized root mean square errors (n-RMSE) for oil rate predictions were around R2=0.8 and n-RMSE=0.35 correspondently. The proposed technique is a new approach for fast, accurate, and objective selection of the candidates for hydraulic fracturing based on real-time state of a field. Such AI-based system could become very handy assistant for reservoir engineer in addition to hydraulic fracturing and hydrodynamic simulators. The presented solution computationally efficient and does not require detailed information about HF design.

The design and development of intelligent university program ranking system using JEE frameworks

2012 ◽  
Author(s):  
Muaamar Al-Kubati ◽  
Syed Ahmad Aljunid ◽  
Nor Eleena Yusoff ◽  
Mohamad Daud ◽  
Mohd Azrul Amir Muhamad Tajudin ◽  
...  
Keyword(s):  
Design And Development ◽  
Ranking System

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.

Design and Development Methodology for Healthcare Monitoring System based on IoT and Machine Learning Approach

2020 ◽  
Author(s):  
Sourav Kumar Dubey ◽  
Shubham Kumar Tiwari ◽  
Shweta Rani Pushpa ◽  
Sunil Kumar Gouda ◽  
Vijay Hansda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Learning Approach ◽  
Design And Development ◽  
Development Methodology ◽  
Machine Learning Approach ◽  
Healthcare Monitoring
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