Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date. A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM scheme for categorizing employees. In 1st stage, analytic hierarchy process (AHP) has been utilized for assigning relative weights for employee accomplishment factors. In second stage, TOPSIS has been used for expressing significance of employees for performing employee categorization. A simple 20-30-50 rule in DE PARETO principle has been applied to categorize employees into three major groups namely enthusiastic, behavioral and distressed employees. Random forest algorithm is then applied as baseline algorithm to the proposed employee churn framework to predict class-wise employee churn which is tested on standard dataset of the (HRIS), the obtained results are evaluated with other ML methods. The Random Forest ML algorithm in SNEC scheme has similar or slightly better overall accuracy and MCC with significant less time complexity compared with that of ECPR scheme using CATBOOST algorithm.
Despite the great economic and commercial importance given to real estate by virtue of its view of the landscape or public roads, US courts have differed in their position on compensation for damages resulting from blocking that view or vision by public projects. Some courts compensated for such damages, other courts approved such compensation. Hence, this research came to shed light on the extent of the possibility of compensation for blocking the view or vision as a result of public projects, and the research has supported us with many judicial decisions.
When scheduling rules become incapable to tackle the presence of a variety of unexpected disruptions frequently occurred in manufacturing systems, it is necessary to develop a reactive schedule which can absorb the effects of such disruptions. Such responding requires efficient strategies, policies, and methods to controlling production & maintaining high shop performance. This can be achieved through rescheduling task which defined as an essential operating function to efficiently tackle and response to uncertainties and unexpected events. The framework proposed in this study consists of rescheduling approaches, strategies, policies, and techniques, which represents a guideline for most manufacturing companies operatin
... Show MoreLung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detectio
... Show MoreThe paper aims to propose Teaching Learning based Optimization (TLBO) algorithm to solve 3-D packing problem in containers. The objective which can be presented in a mathematical model is optimizing the space usage in a container. Besides the interaction effect between students and teacher, this algorithm also observes the learning process between students in the classroom which does not need any control parameters. Thus, TLBO provides the teachers phase and students phase as its main updating process to find the best solution. More precisely, to validate the algorithm effectiveness, it was implemented in three sample cases. There was small data which had 5 size-types of items with 12 units, medium data which had 10 size-types of items w
... Show MoreMachine learning is considered a powerful technique in many applications such as classification, clustering, recognition and prediction. Deep learning is a modern, vital and superior machine learning that gives stunning performance, especially with huge data. Stock market price prediction is the process of determining the future value of a prospect of a financial instrument traded in the market, to gain a great profit a successful prediction must be conducted, in order to achieve that machine learning is used, in this article, two approaches are proposed to predict the stock market prices and movement using two datasets, the first approach employs two machine learning models (J48 & logistic regression) while the second approach based on rec
... Show MoreDans le romantisme et ses textes, il est important de mentionner qu'il n'existe qu'un seul thème romantique qui est l'amour, mais assez pour avoir fait du XIXème siècle, une révolution du texte romantique. L'amour consiste à la réduction de l'univers, à un seul être, puis la dilatation de ce seul être, voilà l'amour. cet amour passionnel n'est qu'un arrangement froid et réfléchi excluant d'emblée l'exaltation des sentiments. Néanmoins, cet amour passionnel peut être brutal. quant à la mort, dans le drame romantique, l'amour et la mort sont liés. Beaucoup d'histoires d'amour finissent le plus souvent par un suicide passionnel : le romantique cherche à l'atteindre par l'amour un sentiment sanctifié, et divinisé ; mais c
... Show MoreToday, the science of artificial intelligence has become one of the most important sciences in creating intelligent computer programs that simulate the human mind. The goal of artificial intelligence in the medical field is to assist doctors and health care workers in diagnosing diseases and clinical treatment, reducing the rate of medical error, and saving lives of citizens. The main and widely used technologies are expert systems, machine learning and big data. In the article, a brief overview of the three mentioned techniques will be provided to make it easier for readers to understand these techniques and their importance.