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Employee Attrition Prediction using machine learning

Employee Attrition Prediction using Machine Learning

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Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Sanidhya Barara Umang Soni Modern School New Delhi India Netaji Subhash University of Technology New Delhi India

data mining plays an important role in the internal Human Resource management processes of any firm [1]. These processes include prevention and prediction of employee attrition. This paper presents a machine learning based approach to attrition prediction for individual employees, by training different machine learning models on attrition data. In this paper, machine learning models like the logistic regression model, the linear regression model, the decision tree model, the random forest model, K nearest neighbors model, radius nearest neighbors model, the naïve bayes classifier model and Bayesian ridge model are trained on data obtained from Kaggle, an online community platform for data scientists and machine learning enthusiasts. The resulting models then predict whether an employee with particular attributes will attrit, and if so, within how many years they can be expected to do so. Prior to training the models, the data is cleaned (outlier removal, feature removal), scaled, and categorical variables are converted to numerical ones. Then, predictions are carried out and feature importances are found using random forest and decision tree models. The utilities of these models are then compared against each other based on accuracy, precision, recall, f-beta score, kappa score and three self made metrics. The results show that, surprisingly, the nearest neighbor models outperformed all others by a large margin (possibly due to data scaling being a part of preprocessing), and the logistic regression model was unable to predict attrition very satisfactorily. The results showed that satisfaction_level, average_monthly_hours and last_evaluation_rating are the most important features when predicting attrition or years. The research also shows that it is viable to use traditional ML models to predict the time in which an employee will attrit, and using the methodology defined in this research on a real dataset will provide useful information to the corporation applying i

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Deep learning-based Functional Prediction Model for Genome Sequence

Deep Learning-based Functional Prediction Model for Genome S...

引用

data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Bo Lan United World College of South East Asia East Singapore Singapore

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

This study proposes a deep learning-based genome sequence function prediction model and systematically evaluates its performance. The study begins with an overview of the importance of genome sequence function prediction and its applications in biomedicine, followed by a detailed description of the main current research methods and advances. We use Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Transformer model for capturing local features, sequence dependencies and long-range dependencies, respectively. Through the combined application of multiple feature extraction methods, the model demonstrates excellent prediction performance on different genomic datasets. The experimental results show that the deep learning model can effectively improve the accuracy of genomic sequence function prediction, especially in complex feature extraction and pattern recognition. Convolutional neural networks have obvious advantages in local feature extraction, recurrent neural networks excel in dealing with the time dependence of sequence data, and the Transformer model demonstrates its superiority in dealing with long-distance dependencies and parallel computation.

关键词： Deep learning Recurrent neural networks Computational modeling Biological system modeling Genomics Predictive models Feature extraction Transformers Convolutional neural networks Bioinformatics

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Research on Sales Forecasting Model Based on GRU Neural Network and machine learning Model

Research on Sales Forecasting Model Based on GRU Neural Netw...

引用

data Science and Computer Application (ICDSCA), IEEE international conference on

作者： Chunchang Zhang Ruoxuan Shi Harbin Institute of Technology Weihai Weihai China Lanzhou University Lanzhou China

In the age of big data, the retail industry generates a vast amount of sales data daily. Analyzing and mining these data can facilitate management decision-making and provide great benefits to enterprises. With competition intensifying in the retail industry, precise sales forecasts are essential to assist companies in effectively managing their inventory of specific commodities, developing marketing plans, and formulating timely and effective strategic policies to reduce costs and increase efficiency. In this paper, the STL method is combined with SVR and GRU to build a daily sales forecasting model for retail enterprises. The trend, seasonal, and residual sub-series decomposed from past sales are used to directly predict future sales to achieve a better forecasting effect, and are compared with some traditional models and STL-ML combined models based on the decomposition integration idea. The results show that the method used in this paper has higher forecasting accuracy, and the forecasting ideas proposed in this paper have important reference significance for the sales forecasting practice of retail enterprises.

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A Brute-Force Attack Detection Method Based on the Combination of Artificial Neural Network and Auto Encoder

A Brute-Force Attack Detection Method Based on the Combinati...

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Cloud Computing, Big data Applications and Software Engineering (CBASE), international conference on

作者： Xiaojun Ma Qingyuan Li Xiaofei Wang Quan Wen Yingming Zeng Beijing Institute of Computer Technology and Application Beijing China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

The ongoing advancement of information technology has led to the emergence of a multitude of network attack methodologies and techniques. As a prevalent form of network attack, it is imperative to undertake targeted research on the deliberate disruption of network traffic. Two issues currently impede the efficacy of machine learning and deep learning models in detecting brute force attacks. First, the presence of noise in the traffic data reduces the accuracy and robustness of detection. Second, when an attacker modifies the frequency, intensity, time, and other parameters of their attack to evade detection, the characteristics of the attack traffic become more complex and diverse. This, in turn, results in a notable decline in detection accuracy. In order to address the aforementioned issues, this paper presents a novel detection model that integrates the capabilities of an artificial neural network (ANN) and an auto-encoder (AE). The proposed model leverages the ANN's proficiency in recognizing intricate data patterns and the AE's effectiveness in removing noise, thereby facilitating precise detection of brute-force attacks. The experimental results demonstrate that the proposed detection model exhibits an enhanced accuracy rate of 99.93% in CICIDS-2017, exhibiting superior overall detection performance compared to existing algorithmic models such as AE and DAE. Additionally, it demonstrates a reduced false alarm rate and effective mitigation of the impact of noise and interference on attack data.

关键词： Time-frequency analysis Accuracy Prevention and mitigation Noise Telecommunication traffic Feature extraction Brute force attacks Robustness pattern recognition Software engineering

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Effective heart disease prediction using hybridmachine learning 3

Effective heart disease prediction using hybridmachine learn...

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3rd international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Pandiaraj, A. Prakash, S. Lakshmana Kanna, P. Rajesh Bannari Amman Institute of Technology Department of ISE Sathyamangalam Erode India Bannari Amman Institute of Technology Department of IT Sathyamangalam Erode India

ISBN: (纸本)9780738111834

machine learning based heart disease prediction is presented in this research work. Heart related diseases increases everyday throughout the world due to stress, high blood pressure, family backgrounds, etc., Early prediction helps to prevent serious issues due to heart diseases. Classification approaches are widely used to analyse medical data to prevent major issues. Real time large medical data are evaluated through data mining applications to control, contradict and manage large data and make use of it to predict the risk factors. This research work is also aims to diagnose heart disease through support vector machine (SVM) and genetic algorithm (GA) as a data analytic approach. Experimental results demonstrate the superior performance of proposed approach in predicting heart disease. © 2021 IEEE.

关键词： Genetic algorithms

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Skin Cancer Detection Using Image Classification in Deep learning

Skin Cancer Detection Using Image Classification in Deep Lea...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： D. Prasanna R. Manishvarma Department of Computer science and Engineering Mahendra Engineering College Namakkal India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The understanding of deep learning in skin cancer results in a dependable and faster method to diagnose malignant skin lesions at an initial stage. To improve the accuracy of detection, this research uses a number of steps in its methodology. First, we apply the Weiner filter to smooth the input images, minimizing the noise effects and improving the overall image quality. The next step is to use Adaptive Histogram Equalization (AHE) because it enhances the contrast of the image and thus helps in feature extraction. The feature selection process is accomplished through an Autoencoder that decreases dimensionality while preserving essential data characteristics. Finally, classification is executed using a Convolutional Neural Network (CNN), which performs inadequately for pattern recognition in medical images. The model shows a high accuracy of 96.3%, reflecting its effectiveness in diagnosing skin cancer. Such high accuracy proves the effective applicability of the proposed method as a diagnostic aid to dermatologists for timely and efficient reactions. Future works will emphasize external validity and thereby set the system into real practice settings.

关键词： Deep learning Training Accuracy Wiener filters Transfer learning Feature extraction Skin Convolutional neural networks Medical diagnostic imaging Skin cancer

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Packaging recognition Fusion Algorithm Based on Visual Imaging Technology

Packaging Recognition Fusion Algorithm Based on Visual Imagi...

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data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Hanfei Zhao Xi’an Fanyi University Xi’an China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

In the process of online terminal inspection of packaging bags, the traditional packaging inspection method is mainly carried out manually. However, due to factors such as work fatigue and emotional fluctuations, the efficiency of online terminal packaging inspection cannot be guaranteed. Manual testing also affects the surface cleanliness of packaging bags and consumes a lot of labor. At present, the packaging industry generally adopts intelligent packaging technology to solve the problems existing in the production process of traditional packaging products, among which machine vision technology is its core. However, nowadays packaging companies mostly use machine vision to check for labels, color defects, and other issues. This is beneficial at the end of the production line to achieve the integrity and aesthetics of the appearance information of packaging bags, thereby ensuring the production quality of products. However, currently, there are still problems such as skewness and folding in the inhalable plastic bags transported at the end of the assembly line, which leads to uneven packaging and affects the appearance of the product. In severe cases, it can also result in missing quantities. This article explored the research on packaging recognition fusion algorithms based on visual imaging technology, and verified through experiments that the accuracy of the recognition model based on CNN (Convolutional Neural Networks) algorithm was higher than 81% under different experimental times. Compared to CNN algorithm, the recognition accuracy of BP (Back Propagation) algorithm was much lower. The highest accuracy of the packaging recognition model based on BP algorithm was only about 79%.

关键词： Industries Visualization Image recognition Accuracy machine learning algorithms Imaging Manuals Packaging Inspection Convolutional neural networks

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Predicting Employee Selection using machine learning Techniques 3

Predicting Employee Selection using Machine Learning Techniq...

引用

3rd international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Singh, Krishna Kumar Pandey, Rina Symbiosis Centre for Information Technology Pune India Amity University Amity Global Business School Noida India

ISBN: (纸本)9781665438117

Employee selection is the most vital function of HRM as it has a significant impact on company's revenue growth and profit margins. Developing a strong and rigorous selection process ensures that the organization is able to achieve the objectives and at the same time saves money, time and energy on replacing and training new people. The aim of this paper is to construct a decision support model for employee selection using the concept of data mining classification. There are six data mining classification algorithms compared in this case, namely Linear regression, KNN, Random Forest, neural network, AdaBoost and stochastic gradient descent. The algorithm with the highest accuracy is utilized to produce the rule for DSM. The results are Random Forest has the highest accuracy of 92%;hence this algorithm is selected to process the value of twelve predictor parameter to identify the most appropriate employee during the selection process. © 2021 IEEE.

关键词： data mining

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3rd international conference on Computer Science, Engineering and Education Applications, ICCSEEA 2020

3rd International Conference on Computer Science, Engineerin...

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3rd international conference on Computer Science, Engineering and Education Applications, ICCSEEA 2020

ISBN: (纸本)9783030555054

The proceedings contain 59 papers. The special focus in this conference is on Computer Science, Engineering and Education Applications. The topics include: Construction of Individual learning Scenarios;technology-Enhanced Financial Education and Sustainability Goals;health Status recognition System for Communication Equipment Based on data mining;use of Sustainable Innovation in Information System: Integrate Individual’s Spirit and Team Climate;relations of the Genetic Code with Algebraic Codes and Hypercomplex Double Numbers;research on Logistics Center Location-Allocation Problem Based on Two-Stage K-Means Algorithms;operational Intelligence Software Concepts for Continuous Healthcare Monitoring and Consolidated data Storage Ecosystem;method of recognition and Indexing of People’s Faces in Videos Using Model of machine learning;deduplication Method for Ukrainian Last Names, Medicinal Names, and Toponyms Based on Metaphone Phonetic Algorithm;two-Layer Perceptron for Voice recognition of Speaker’s Identity;significant Parameters of the Keystroke for the Formation of the Input Field of a Convolutional Neural Network;detection of Ventricular Late Potentials in Electrocardiograms Using machine learning;smart Prothesis: Sensorization, Humane Vibration and Processor Control;optimization of Stock Basing on Improved Grey Prediction Model: A Case Study on Garment Supply Chain;improved Post-quantum Merkle Algorithm Based on Threads;improvement of Merkle Signature Scheme by Means of Optical Quantum Random Number Generators;integrated Water Management and Environmental Rehabilitation of River Basins Using a System of Non-linear Criteria;distribution of Resources Between Composite Applications in a Hyperconverged System;the Possibilistic Gustafson-Kessel Fuzzy Clustering Procedure for Online data Processing;information System Penetration Testing Using Web Attack Automated Simulation.

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Prediction Model for the Tenant's Potential Failure from Business Incubation Process during COVID-19 Period Using Supervised learning 3

Prediction Model for the Tenant's Potential Failure from Bus...

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3rd international conference on Cybernetics and Intelligent System (ICORIS)

作者： Budaya, I. Gede Bintang Arya Martha, Gede Indra Raditya Agustino, Dedy Panji Wijaya, I. Made Pasek Pradnyana Harsemadi, I. Gede STIKOM Bali Dept Informat Syst Inst Technol & Business Denpasar Indonesia

ISBN: (纸本)9781665425803

The COVID-19 has brought the world to a new economic crisis. Hence, it creates a disruption that causes the shutdown of many businesses. Business Incubator is also responsible for their tenant business sustainability, especially during this pandemic period. Analyzing the incubation parameter process can be the initial study to collect the relevant information to determine how each tenant is successful. Using the information, building the prediction model in the classification problem for the potential failure of the tenant can be initiated. The works involve the data mining analysis for 207 tenant's track record data from 6 university business incubators in Bali Province, Indonesia. The selected data as the features are the intensity of tenants joining the mentoring program, the level of financial literacy, and the amount of capital or saving. The prediction applies cost-sensitive classifiers combined with machine learning algorithms in WEKA. Based on the model analysis from the precision, recall, F-Measure, and AUC-ROC, the Random Forest model has the highest accuracy, followed by KNN, SVM, MLP, and Naive Bayes as the lowest. The AUC-ROC from the algorithms models sequentially: (1) 0.934, (2) 0.930, (3) 0.890, (4) 0.859, and (5) 0.846. The features are too few to generalize if they can be used to build a robust supervised learning model to predict the potential failure of the tenant incubation process, but at least based on the observation, the three features have a direct implication. University Business Incubator can consider the three features as the primary points to give their main attention.

关键词： prediction supervised learning covid-19 business incubation tenants

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