Accurate Prediction of Fake Job Offers Using Machine Learning

2021 ◽  
pp. 101-112
Author(s):  
Bodduru Keerthana ◽  
Anumala Reethika Reddy ◽  
Avantika Tiwari
Keyword(s):  
Machine Learning ◽  
Accurate Prediction ◽  
Job Offers

scholarly journals Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

2021 ◽  
Author(s):  
Kjell Jorner ◽  
Tore Brinck ◽  
Per-Ola Norrby ◽  
David Buttar
Keyword(s):  
Machine Learning ◽  
Activation Energies ◽  
Accurate Prediction ◽  
Nucleophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Mechanistic Modelling

Hybrid reactivity models, combining mechanistic calculations and machine learning with descriptors, are used to predict barriers for nucleophilic aromatic substitution.

A machine learning approach to the accurate prediction of monitor units for a compact proton machine

2018 ◽  
Vol 45 (5) ◽  
pp. 2243-2251 ◽  
Author(s):  
Baozhou Sun ◽  
Dao Lam ◽  
Deshan Yang ◽  
Kevin Grantham ◽  
Tiezhi Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Accurate Prediction ◽  
Learning Approach ◽  
Machine Learning Approach

DFT-Machine Learning Approach for Accurate Prediction of pKa

2021 ◽  
Author(s):  
Robin Lawler ◽  
Yao-Hao Liu ◽  
Nessa Majaya ◽  
Omar Allam ◽  
Hyunchul Ju ◽  
...  
Keyword(s):  
Machine Learning ◽  
Accurate Prediction ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Predicting the Tool Wear of a Drilling Process Using Novel Machine Learning XGBoost-SDA

Materials ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 4952
Author(s):  
Mahdi S. Alajmi ◽  
Abdullah M. Almeshal
Keyword(s):  
Machine Learning ◽  
Cast Iron ◽  
Tool Wear ◽  
Flank Wear ◽  
Accurate Prediction ◽  
Superior Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Drilling Process ◽  
Extreme Gradient Boosting

Tool wear negatively impacts the quality of workpieces produced by the drilling process. Accurate prediction of tool wear enables the operator to maintain the machine at the required level of performance. This research presents a novel hybrid machine learning approach for predicting the tool wear in a drilling process. The proposed approach is based on optimizing the extreme gradient boosting algorithm’s hyperparameters by a spiral dynamic optimization algorithm (XGBoost-SDA). Simulations were carried out on copper and cast-iron datasets with a high degree of accuracy. Further comparative analyses were performed with support vector machines (SVM) and multilayer perceptron artificial neural networks (MLP-ANN), where XGBoost-SDA showed superior performance with regard to the method. Simulations revealed that XGBoost-SDA results in the accurate prediction of flank wear in the drilling process with mean absolute error (MAE) = 4.67%, MAE = 5.32%, and coefficient of determination R2 = 0.9973 for the copper workpiece. Similarly, for the cast iron workpiece, XGBoost-SDA resulted in surface roughness predictions with MAE = 5.25%, root mean square error (RMSE) = 6.49%, and R2 = 0.975, which closely agree with the measured values. Performance comparisons between SVM, MLP-ANN, and XGBoost-SDA show that XGBoost-SDA is an effective method that can ensure high predictive accuracy about flank wear values in a drilling process.

Machine Learning Models for Accurate Prediction of Kinase Inhibitors with Different Binding Modes

2019 ◽  
Vol 63 (16) ◽  
pp. 8738-8748 ◽  
Author(s):  
Filip Miljković ◽  
Raquel Rodríguez-Pérez ◽  
Jürgen Bajorath
Keyword(s):  
Machine Learning ◽  
Kinase Inhibitors ◽  
Accurate Prediction ◽  
Binding Modes ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Machine Learning Enables Accurate Prediction of Asparagine Deamidation Probability and Rate

2019 ◽  
Vol 15 ◽  
pp. 264-274 ◽  
Author(s):  
Jared A. Delmar ◽  
Jihong Wang ◽  
Seo Woo Choi ◽  
Jason A. Martins ◽  
John P. Mikhail
Keyword(s):  
Machine Learning ◽  
Accurate Prediction ◽  
Asparagine Deamidation

scholarly journals Cricket Prediction using Machine Learning Algorithms

2020 ◽  
pp. 1128-1131
Author(s):  
Sudhanshu Akarshe ◽  
Rohit Khade ◽  
Nikhil Bankar ◽  
Prashant Khedkar ◽  
Prashant Ahire
Keyword(s):  
Machine Learning ◽  
Scoring System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Accurate Prediction ◽  
Prediction System ◽  
Winning Team ◽  
Quantitative Results ◽  
The Masses ◽  
Player Performance

Cricket is most popular sport played in India. It has huge spectator support and the masses show great interest in predicting the outcome of games in their Test, One-day international as well as in T-20 matches. The game is having number of rules and scoring system. Numerous parameters are present such as, cricketing skills and performances, match venues which has significant effect on the outcome of a game. Such parameters, along with their interdependence create a challenge to create an accurate prediction of a game. In this project, we are going to build a rigid prediction system that takes in historical match data, player performance and predicts future match events such as final results in a victory or loss. Our system will perform this prediction using various machine learning algorithms. We describe our system and algorithms and finally present quantitative results displayed by best suited algorithm having highest accuracy. Also, representing the winning team even before the match starts and provide best suited squad of both teams.

scholarly journals BonDNet: A Graph Neural Network for the Prediction of Bond Dissociation Energies for Charged Molecules

2020 ◽  
Author(s):  
Mingjian Wen ◽  
Samuel Blau ◽  
Evan Spotte-Smith ◽  
Shyam Dwaraknath ◽  
Kristin Persson
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chemical Reactivity ◽  
Absolute Error ◽  
Learning Model ◽  
Chemical Processes ◽  
Accurate Prediction ◽  
Global Features ◽  
Bond Dissociation ◽  
Machine Learning Model

<div><div><div><p>A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could con- sider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model’s predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.</p></div></div></div>

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