Diabetes is a long-term disease. Inappropriate blood sugar level control in diabetic patients can lead to serious issues like kidney and heart diseases. Obesity is widely regarded as a major risk factor for type 2 diabetes. In this research, a model proposed to predict diabetic obese patients based on Expectation Maximization, PCA, and SMOTE Algorithms in the preprocessing and feature extraction phases, and using Fuzzy KNN classifier in the prediction phase. The model applied on real dataset and the accuracy of prediction results reflects the positive effect of the preprocessing techniques. The accuracy of the proposed model is 95.97% and outperforms other model applied on the same dataset.
Competent employees are a rare commodity for great companies. The problem of maintaining good employees with experience threatens the owners of companies. The issue of employee attrition can cost employers a lot as it takes a lot to compensate for their expertise and efficiency. For this reason, in this research, we present an automated model that can predict employee attrition based on different predictive analytical techniques. These techniques have been applied with different pipeline architectures to select the best champion model. Also, an autotuning approach has been implemented to calculate the best combination of hyper parameters to build the champion model. Finally, we propose an ensemble model for selecting the most efficient model subject to different assessments measures. The results of the proposed model show that no model up until now could be considered ideal and perfect for each case of business context. Yet, our chosen model was pretty much optimal as per our requirements and adequately satisfied the intended goal.
The spread of data on the web has increased in the last twenty years. One of the reasons is the appearance of social media. The data on social sites describe many real-life events in our daily lives. In the period of the COVID-19 pandemic, a lot of people and media organizations were writing and documenting their health status and the latest news about the coronavirus on social media. Using these tweets (sentiments) about the coronavirus and analyzing them in a computational model can help decision makers in measuring public opinion and yielding remarkable findings. In this research article, we introduce a deep learning sentiment analysis model based on Universal Sentence Encoder. The dataset used in this research was collected from Twitter, and it was classified as positive, neutral, and negative. The sentence embedding model determines the meaning of word sequences instead of individual words. The model divides the dataset into training and testing and depends on the sentence similarity in detecting sentiment class. The obtained accuracy results reached 78.062%, and this result outperforms many traditional ML classifiers based on TF-IDF applied on the same dataset and another model based on the CNN classifier.
The graduates who have finished their study program will be given a merit award and their award certificates will be graded in accordance with the degree of their academic accomplishment. The awards are generally offered using two methods; one is by the cumulative grade point average (CGPA) and the other is by the average percentage of all marks for the students. The problem is when assigning a course final grade; each student's final percentage is translated to a letter, allowing the discrepancy within the same letter grade range in the final ranking. If two students have the same final score, that means equal results. However, this equality can be false if one student hits a percentage of the highest grade, while the second student earns a percentage of the lowest grade of the same letter grade. This paper introduced a new equation that transforms between the awarded cumulative grade point average and the awarded percentage ranking based on fuzzy system. The proposed approach was tested using three actual benchmarks collected from three different colleges in Beni-Suef university. The obtained results reflects the effect of the fuzzy logic in helping converting form CGPA measures to percentage measure in educational systems.
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