scholarly journals Predicting Employee Attrition Using Machine Learning Techniques

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 86
Author(s):  
Francesca Fallucchi ◽  
Marco Coladangelo ◽  
Romeo Giuliano ◽  
Ernesto William De Luca
Keyword(s):  
Artificial Intelligence ◽  
False Negative ◽  
False Negative Rate ◽  
Recall Rate ◽  
Machine Learning Techniques ◽  
Business Strategies ◽  
Decision To Leave ◽  
Sales And Marketing ◽  
Employee Attrition ◽  
Worker Quality

There are several areas in which organisations can adopt technologies that will support decision-making: artificial intelligence is one of the most innovative technologies that is widely used to assist organisations in business strategies, organisational aspects and people management. In recent years, attention has increasingly been paid to human resources (HR), since worker quality and skills represent a growth factor and a real competitive advantage for companies. After having been introduced to sales and marketing departments, artificial intelligence is also starting to guide employee-related decisions within HR management. The purpose is to support decisions that are based not on subjective aspects but on objective data analysis. The goal of this work is to analyse how objective factors influence employee attrition, in order to identify the main causes that contribute to a worker’s decision to leave a company, and to be able to predict whether a particular employee will leave the company. After the training, the obtained model for the prediction of employees’ attrition is tested on a real dataset provided by IBM analytics, which includes 35 features and about 1500 samples. Results are expressed in terms of classical metrics and the algorithm that produced the best results for the available dataset is the Gaussian Naïve Bayes classifier. It reveals the best recall rate (0.54), since it measures the ability of a classifier to find all the positive instances and achieves an overall false negative rate equal to 4.5% of the total observations.

Research on Application of Artificial Intelligence in Computer Network Technology

2019 ◽  
Vol 33 (05) ◽  
pp. 1959015 ◽  
Author(s):  
Qingjun Wang ◽  
Peng Lu
Keyword(s):  
Artificial Intelligence ◽  
Intrusion Detection ◽  
Computer Network ◽  
False Negative ◽  
Back Propagation ◽  
False Negative Rate ◽  
Classification Performance ◽  
Network Size ◽  
Network Technology ◽  
Training Time

With the continuous expansion of the application scope of computer network technology, various malicious attacks that exist in the Internet range have caused serious harm to computer users and network resources. This paper attempts to apply artificial intelligence (AI) to computer network technology and research on the application of AI in computing network technology. Designing an intrusion detection model based on improved back propagation (BP) neural network. By studying the attack principle, analyzing the characteristics of the attack method, extracting feature data, establishing feature sets, and using the agent technology as the supporting technology, the simulation experiment is used to prove the improvement effect of the system in terms of false alarm rate, convergence speed, and false negative rate, the rate reached 86.7%. The results show that this fast algorithm reduces the training time of the network, reduces the network size, improves the classification performance, and improves the intrusion detection rate.

scholarly journals Artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks

2021 ◽  
Vol 22 (S5) ◽  
Author(s):  
Wen-Hsien Ho ◽  
Tian-Hsiang Huang ◽  
Po-Yuan Yang ◽  
Jyh-Horng Chou ◽  
Hong-Siang Huang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Macular Degeneration ◽  
Design Method ◽  
Signal To Noise Ratio ◽  
Uniform Design ◽  
False Negative ◽  
False Negative Rate ◽  
Classification Model ◽  
Parameter Design ◽  
Negative Rate

Abstract Background The prevalence of chronic disease is growing in aging societies, and artificial-intelligence–assisted interpretation of macular degeneration images is a topic that merits research. This study proposes a residual neural network (ResNet) model constructed using uniform design. The ResNet model is an artificial intelligence model that classifies macular degeneration images and can assist medical professionals in related tests and classification tasks, enhance confidence in making diagnoses, and reassure patients. However, the various hyperparameters in a ResNet lead to the problem of hyperparameter optimization in the model. This study employed uniform design—a systematic, scientific experimental design—to optimize the hyperparameters of the ResNet and establish a ResNet with optimal robustness. Results An open dataset of macular degeneration images (https://data.mendeley.com/datasets/rscbjbr9sj/3) was divided into training, validation, and test datasets. According to accuracy, false negative rate, and signal-to-noise ratio, this study used uniform design to determine the optimal combination of ResNet hyperparameters. The ResNet model was tested and the results compared with results obtained in a previous study using the same dataset. The ResNet model achieved higher optimal accuracy (0.9907), higher mean accuracy (0.9848), and a lower mean false negative rate (0.015) than did the model previously reported. The optimal ResNet hyperparameter combination identified using the uniform design method exhibited excellent performance. Conclusion The high stability of the ResNet model established using uniform design is attributable to the study’s strict focus on achieving both high accuracy and low standard deviation. This study optimized the hyperparameters of the ResNet model by using uniform design because the design features uniform distribution of experimental points and facilitates effective determination of the representative parameter combination, reducing the time required for parameter design and fulfilling the requirements of a systematic parameter design process.

scholarly journals GLAUDIA: A predicative system for glaucoma diagnosis in mass scanning

2021 ◽  
Vol 27 (2) ◽  
pp. 146045822110092
Author(s):  
Yasser Omar ◽  
Mohamed Abd-ElSalam ElSheikh ◽  
Rania Hodhod
Keyword(s):  
Mass Screening ◽  
Ganglion Cells ◽  
False Negative ◽  
False Negative Rate ◽  
Majority Voting ◽  
Machine Learning Techniques ◽  
Final Decision ◽  
Fiber Layer ◽  
Glaucoma Diagnosis ◽  
Negative Rate

Glaucoma is a serious eye disease characterized by dysfunction and loss of retinal ganglion cells (RGCs) which can eventually lead to loss of vision. Robust mass screening may help to extend the symptom-free life for the affected patients. The retinal optic nerve fiber layer can be assessed using optical coherence tomography, scanning laser polarimetry (SLP), and Heidelberg Retina Tomography (HRT) scanning methods which, unfortunately, are expensive methods and hence, a novel automated glaucoma diagnosis system is needed. This paper proposes a new model for mass screening that aims to decrease the false negative rate (FNR). The model is based on applying nine different machine learning techniques in a majority voting model. The top five techniques that provide the highest accuracy will be used to build a consensus ensemble to make the final decision. The results from applying both models on a dataset with 499 records show a decrease in the accuracy rate from 90% to 83% and a decrease in false negative rate (FNR) from 8% to 0% for majority voting and consensus model, respectively. These results indicate that the proposed model can reduce FNR dramatically while maintaining a reasonable overall accuracy which makes it suitable for mass screening.

scholarly journals Hybrid Machine Learning: A Tool to Detect Phishing Attacks in Communication Networks

2021 ◽  
Vol 15 (3) ◽  
pp. 374-389
Author(s):  
Ademola Philip Abidoye ◽  
Boniface Kabaso
Keyword(s):  
Machine Learning ◽  
Communication Networks ◽  
Credit Card ◽  
Personal Information ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Machine Learning Techniques ◽  
Sensitive Information ◽  
Cyber Attack

Phishing is a cyber-attack that uses disguised email as a weapon and has been on the rise in recent times.  Innocent Internet user if peradventure clicking on a fraudulent link may cause him to fall victim of divulging his personal information such as credit card pin, login credentials, banking information and other sensitive information. There are many ways in which the attackers can trick victims to reveal their personal information. In this article, we select important phishing URLs features that can be used by attacker to trick Internet users into taking the attacker’s desired action. We use two machine learning techniques to accurately classify our data sets. We compare the performance of other related techniques with our scheme. The results of the experiments show that the approach is highly effective in detecting phishing URLs and attained an accuracy of 97.8% with 1.06% false positive rate, 0.5% false negative rate, and an error rate of 0.3%. The proposed scheme performs better compared to other selected related work. This shows that our approach can be used for real-time application in detecting phishing URLs.

Clinically Meaningful Change

Methodology ◽  
2019 ◽  
Vol 15 (3) ◽  
pp. 97-105
Author(s):  
Rodrigo Ferrer ◽  
Antonio Pardo
Keyword(s):  
Effect Size ◽  
False Negative ◽  
False Negative Rate ◽  
Point Of View ◽  
Skewed Distribution ◽  
Effect Sizes ◽  
False Negatives ◽  
Large Size ◽  
Before And After ◽  
Post Test

Abstract. In a recent paper, Ferrer and Pardo (2014) tested several distribution-based methods designed to assess when test scores obtained before and after an intervention reflect a statistically reliable change. However, we still do not know how these methods perform from the point of view of false negatives. For this purpose, we have simulated change scenarios (different effect sizes in a pre-post-test design) with distributions of different shapes and with different sample sizes. For each simulated scenario, we generated 1,000 samples. In each sample, we recorded the false-negative rate of the five distribution-based methods with the best performance from the point of view of the false positives. Our results have revealed unacceptable rates of false negatives even with effects of very large size, starting from 31.8% in an optimistic scenario (effect size of 2.0 and a normal distribution) to 99.9% in the worst scenario (effect size of 0.2 and a highly skewed distribution). Therefore, our results suggest that the widely used distribution-based methods must be applied with caution in a clinical context, because they need huge effect sizes to detect a true change. However, we made some considerations regarding the effect size and the cut-off points commonly used which allow us to be more precise in our estimates.

Diagnostic Value of a Carpal Tunnel Corticosteroid Injection in Patients with Negative Electrodiagnostic Studies

2020 ◽  
Author(s):  
Brian M. Katt ◽  
Casey Imbergamo ◽  
Fortunato Padua ◽  
Joseph Leider ◽  
Daniel Fletcher ◽  
...  
Keyword(s):  
Carpal Tunnel ◽  
Corticosteroid Injection ◽  
Carpal Tunnel Release ◽  
False Negative ◽  
False Negative Rate ◽  
Signs And Symptoms ◽  
Diagnostic Value ◽  
Symptom Improvement ◽  
Study Cohort ◽  
Chi Square Analysis

Abstract Introduction There is a known false negative rate when using electrodiagnostic studies (EDS) to diagnose carpal tunnel syndrome (CTS). This can pose a management dilemma for patients with signs and symptoms that correlate with CTS but normal EDS. While corticosteroid injection into the carpal tunnel has been used in this setting for diagnostic purposes, there is little data in the literature supporting this practice. The purpose of this study is to evaluate the prognostic value of a carpal tunnel corticosteroid injection in patients with a normal electrodiagnostic study but exhibiting signs and symptoms suggestive of carpal tunnel, who proceed with a carpal tunnel release. Materials and Methods The group included 34 patients presenting to an academic orthopedic practice over the years 2010 to 2019 who had negative EDS, a carpal tunnel corticosteroid injection, and a carpal tunnel release. One patient (2.9%), where the response to the corticosteroid injection was not documented, was excluded from the study, yielding a study cohort of 33 patients. Three patients had bilateral disease, yielding 36 hands for evaluation. Statistical analysis was performed using Chi-square analysis for nonparametric data. Results Thirty-two hands (88.9%) demonstrated complete or partial relief of neuropathic symptoms after the corticosteroid injection, while four (11.1%) did not experience any improvement. Thirty-one hands (86.1%) had symptom improvement following surgery, compared with five (13.9%) which did not. Of the 32 hands that demonstrated relief following the injection, 29 hands (90.6%) improved after surgery. Of the four hands that did not demonstrate relief after the injection, two (50%) improved after surgery. This difference was statistically significant (p = 0.03). Conclusion Patients diagnosed with a high index of suspicion for CTS do well with operative intervention despite a normal electrodiagnostic test if they have had a positive response to a preoperative injection. The injection can provide reassurance to both the patient and surgeon before proceeding to surgery. Although patients with a normal electrodiagnostic test and no response to cortisone can still do well with surgical intervention, the surgeon should carefully review both the history and physical examination as surgical success may decrease when both diagnostic tests are negative. Performing a corticosteroid injection is an additional diagnostic tool to consider in the management of patients with CTS and normal electrodiagnostic testing.

Comparative study of ultrasonographic findings with the operative findings of biliary surgery

2020 ◽  
Vol 22 (1) ◽  
pp. 25-29
Author(s):  
Zubayer Ahmad ◽  
Mohammad Ali ◽  
Kazi lsrat Jahan ◽  
ABM Khurshid Alam ◽  
G M Morshed
Keyword(s):  
Bile Duct ◽  
Common Bile Duct ◽  
Imaging Modality ◽  
False Positive Rate ◽  
Gallstone Disease ◽  
False Negative ◽  
False Negative Rate ◽  
Biliary Disease ◽  
Biliary Surgery ◽  
Operative Findings

Background: Biliary disease is one of the most common surgical problems encountered all over the world. Ultrasound is widely accepted for the diagnosis of biliary system disease. However, it is a highly operator dependent imaging modality and its diagnostic success is also influenced by the situation, such as non-fasting, obesity, intestinal gas. Objective: To compare the ultrasonographic findings with the peroperative findings in biliary surgery. Methods: This prospective study was conducted in General Hospital, comilla between the periods of July 2006 to June 2008 among 300 patients with biliary diseases for which operative treatment is planned. Comparison between sonographic findings with operative findings was performed. Results: Right hypochondriac pain and jaundice were two significant symptoms (93% and 15%). Right hypochondriac tenderness, jaundice and palpable gallbladder were most valuable physical findings (respectively, 40%, 15% and 5%). Out of 252 ultrasonically positive gallbladder, stone were confirmed in 249 cases preoperatively. Sensitivity of USG in diagnosis of gallstone disease was 100%. There was, however, 25% false positive rate detection. Specificity was, however, 75% in this case. USG could demonstrate stone in common bile duct in only 12 out of 30 cases. Sensitivity of the test in diagnosing common bile duct stone was 40%, false negative rate 60%. In the series, ultrasonography sensitivity was 100% in diagnosing stone in cystic duct. USG could detect with relatively good but less sensitivity the presence of chronic cholecystitis (92.3%) and worm inside gallbladder (50%). Conclusion: Ultrasonography is the most important investigation in the diagnosis of biliary disease and a useful test for patients undergoing operative management for planning and anticipating technical difficulties. Journal of Surgical Sciences (2018) Vol. 22 (1): 25-29

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

scholarly journals TO010A NEW IMMUN-TOXICOLOGICAL TEST TO DETECT POLYSULFONE HYPERSENSITIVITY IN HEMODIALYSIS PATIENTS

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Joachim Beige ◽  
Ralph Wendt ◽  
Despina Rüssmann ◽  
Karl-Peter Ringel
Keyword(s):  
False Negative ◽  
False Negative Rate ◽  
Lymphocyte Transformation ◽  
Clinical Problem ◽  
Lymphocyte Transformation Test ◽  
Laboratory Research ◽  
Humoral Immune ◽  
Before And After ◽  
Positive Results

Abstract Background and Aims Incompatibility of dialysis procedure due to hypersensitivity against dialyzer material which currently is mainly based on polysulfone and derivatives can not be assessed by routine laboratory tests. Although the frequency of such symptoms is suspected to be low (below 2%) such resembles an important clinical problem because dialysis procedures are frequently accompanied by symptoms of non-tolerability with reasons not being entirely clear while circulatory reasons are suspected to play a major role. Method To enlighten the role of polysulfone hypersensitivity, we adapted known standardized material immune-toxicological tests (lymphocyte transformation test, basophil degranulation test) to the specific conditions of dialysis and polysulfone material sensitivity. We developed a method of polysulfone micronisation and measured humoral immune response of isolated patient´s lymphocytes when incubated with polysulfone dispersion. Results 39 samples from 103 patients with suspected polysulfone hypersensitivity showed positive results for type 1 (n=19), type 4 (n=18) or both type (n=2) reactions. There were no significant differences in the level of stimulation measured for DI, SI and lymphogenesis before and after dialysis (average delta -0.4; -0.28; - 1.74, p = 0.71; 0.34; 0.37) and with different dialyzer materials (Tab. 1). Patients with pos. type 4 results (LTT and lymphogenesis) showed highly correlated results in either LTT or lymphogenesis test (Fig. 1, R=0.87, p<0.0001). 8 out of 8 samples from patients with repeated test on different PS showed positive results on either PS. One patient tested positive on PS showed no hypersensitivity with another non-PS (PMMA) material. Conclusion This is the first methodological report showing plausible in-vitro results of patients samples concerning polysulfone intolerance. On the first superficial view, a “false-negative” rate of 60% looks rather disappointing, because all samples derived from patients with suspicion of PS hypersensitivity. However, due to the clinical variability of intolerance symptoms and the high prevalence of any problems after HD initiation, mainly of circulatory origin after initiating extracorporeal circuit, this rate may obviously express the true frequency of isolated PS material hypersensitivity in suspected patients. Alternative pathophysiological pathways of material sensitivity like complement activation, remain to be elucidated and incorporated into a comprehensive future testing panel. Further clinical and laboratory research is needed to define true polysulfone hypersensitivity and to enlighten the field of hypothetic subclinical material incompatibility in patients with impaired dialysis tolerability.

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