Comparison of the Performance of Machine Learning Techniques in the Prediction of Employee

Adeniyi, Jide Kehinde; Adeniyi, Abidemi Emmanuel; Oguns, Yetunde J.; Egbedokun, Gabriel Olumide; Ajagbe, Kehinde Douglas; Obuzor, Princewill Chima; Ajagbe, Sunday Adeola

doi:10.55969/paradigmplus.v3n3a1

Cited by 23 publications

(5 citation statements)

References 32 publications

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“…If an employee is not recognized by the application i.e., he/she is not present, he/she is marked as absent for the day. The proposed system requires less hardware support also the processing time is also less in comparison to other conventional systems of signing papers, Radio Frequency Identification [RFID], and biometrics which proves this system to be more efficient for organizations to use in real-time application [8].…”

Section: Related Workmentioning

confidence: 96%

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Development of an Improved Convolutional Neural Network for an Automated Face Based University Attendance System

et al. 2023

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Because of the flaws of the present university attendance system, which has always been time intensive, not accurate, and a hard process to follow. It, therefore, becomes imperative to eradicate or minimize the deficiencies identified in the archaic method. The identification of human face systems has evolved into a significant element in autonomous attendance-taking systems due to their ease of adoption and dependable and polite engagement. Face recognition technology has drastically altered the field of Convolution Neural Networks (CNN) however it has challenges of high computing costs for analyzing information and determining the best specifications (design) for each problem. Thus, this study aims to enhance CNN’s performance using Genetic Algorithm (GA) for an automated face-based University attendance system. The improved face recognition accuracy with CNN-GA got 96.49% while the face recognition accuracy with CNN got 92.54%.

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Section: Related Workmentioning

confidence: 96%

“…In the realm of computer vision, face recognition is one of the most extensively used biometric identification technologies [8]. Face recognition-based attendance systems typically comprise picture collecting, dataset creation, face detection, and face recognition.…”

Section: Introductionmentioning

confidence: 99%

Development of an Improved Convolutional Neural Network for an Automated Face Based University Attendance System

et al. 2023

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“…Further contributions to the field were made by Adeniyi et al (2022) [7] , who showcased the superiority of ANN in predicting employee performance. [8] demonstrated the effectiveness of the KNN algorithm in early turnover prediction, adding to the diversity of approaches explored in the literature.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Study of Employee Turnover Analysis Methods

Singh,

Jain,

Ranjan

2024

Int. J. Res. Publ. Rev.

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In the rapidly evolving landscape of human resource management, understanding and mitigating employee turnover has emerged as a paramount challenge. This research delves into a comparative analysis of various machine learning algorithms applied to the same dataset over several research papers, with the objective of identifying the most effective method for predicting employee turnover. Through a meticulous evaluation based on accuracy, F1 score, and ROC value, this study aspires to identify the best methods for predicting employee turnover.This study highlights the Artificial Neural Network (ANN) as the top algorithm for predicting employee turnover due to its outstanding performance across multiple metrics, while XGBoost and LGBM Regression are also identified to be highly effective, providing a good mix of efficiency, interpretability, and reliability.• Performance Metrics: The evaluation of algorithms is based on accuracy, F1 score, and ROC AUC score. While these metrics provide a robust framework for comparison, they might not capture all aspects of model performance relevant to turnover prediction, such as calibration and the cost-sensitive nature of false positives versus false negatives in real-world HR decision-making.

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“…The proposed methodology provides a robust system for automated DR detection accomplished through a series of steps including data collection, preprocessing, augmentation, and modeling. The study employs preprocessing techniques such as data augmentation to address the class imbalance in the dataset, thereby outperforming a number of machine learning classifiers such as Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) [16].…”

Section: Related Workmentioning

confidence: 99%

Continuous Eye Disease Severity Evaluation System using Siamese Neural Networks

AbdulRaheem¹,

Oladipo²,

Ajagbe³

et al. 2023

paradigmplus

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Evaluating the severity of eye diseases using medical images is a very essential and routine task performed in medical diagnosis and treatment. Current grading systems which are largely based on discrete classification are unreliable and do reflect not the entire spectrum of eye disease severity. The unreliability of discrete classification systems for eye diseases is clear, as classification is subjective and done based on the personal opinion of various medical experts, which may vary. In a bid to solve these issues, this study proposes a system for determining the severity of eye diseases on a continuous range using a twin-convoluted neural network approach known as Siamese Neural Networks. This system is demonstrated in the domain of diabetic retinopathy. Samples of retinal fundus images from an eye clinic in India are taken as test cases to evaluate the performance of a Siamese Triplet network which attempts to find the distance between their image embedding. The outputs of the Siamese network when a reference image is juxtaposed with a collection of images with distant severity categories (negative images), as well as when two reference images are compared to each other, are found to have a positive correlation (95\%) with originally assigned severity classes. Hence, these outputs indicate a continuous range of the severity and change in eye diseases.

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Comparison of the Performance of Machine Learning Techniques in the Prediction of Employee

Cited by 23 publications

References 32 publications

Development of an Improved Convolutional Neural Network for an Automated Face Based University Attendance System

Development of an Improved Convolutional Neural Network for an Automated Face Based University Attendance System

Comparative Study of Employee Turnover Analysis Methods

Continuous Eye Disease Severity Evaluation System using Siamese Neural Networks

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