Due to the importance of automatic identification of brain conditions, many researchers concentrate on Epilepsy disorder to aim to the detecting of eye states and classification systems. Eye state recognition has a vital role in biomedical informatics such as controlling smart home devices, driving detection, etc. This issue is known as electroencephalogram signals. There are many works in this context in which traditional techniques and manually extracted features are used. The extraction of effective features and the selection of proper classifiers are challenging issues. In this study, a classification system named PEML-E was proposed in which a different pre-processing stage is used. The ensemble methods in the classification stage are compared to the base classifiers and the most important works in this context. To evaluate, a freely available public EEG eye state dataset from UCI is applied. The highest accuracy, precision, recall, F1, specificity, and sensitivity are obtained 95.88, 95.39, 96.25, 96.18, 96.25, and 95.44%, respectively. Manuscript profile

Diabetes is one of the most common metabolic diseases, and diagnosis of it is a classification problem. The most challenge is this area is missing value problem. Artificial Intelligence techniques have been successfully implemented over medical disease diagnoses. Classification systems aim clinicians to predict the risk factors that cause diabetes. To address this challenge, we introduce a novel model to investigate the role of pre-processing and data reduction for classification problems in the diagnosis of diabetes. The model has four stages consists of Pre-processing, Feature sub-selection, Classification, and Performance. In the classification technique, ensemble techniques such as bagging, boosting, stacking, and voting were used. We considered both states with/without for pre-processing stage to reveal the high performance of our model. Two experiments were conducted to reveal the performance of the model for the diagnosis of diabetics Mellitus. The results confirmed the superiority of the proposed method over the state-of-the-art systems, and the best accuracy and F1 achieved 97.12% and 97.40%, respectively. Manuscript profile

The main problem related to heart disease is the lack of timely diagnosis or the general weakness in the diagnosis of this disease, which is also due to the lack of selection of the appropriate model by the doctor or the lack of proper use of standard models. One of the essential applications of data mining techniques is related to medicine and disease diagnosis. One of the data mining techniques is information clustering. This paper will try to provide a model for the diagnosis of heart disease and its improvement in terms of accuracy on the standard UCI heart database. In this research, with a comprehensive and complete review of the C-Meaning fuzzy clustering method and neural networks in the field of heart disease prediction, an attempt is made to improve these solutions and provide new solutions in this field. The main goal is to combine these two data mining algorithms, both of which alone showed the highest accuracy and the fastest speed in past research. The current authors are trying to find a model that has higher accuracy and speed than the previous methods and makes fewer mistakes and has significantly higher efficiency than other models. The numerical tests implemented on the proposed model show the superiority of the new model compared to the conventional methods in the literature. Manuscript profile

Educational data mining utilizes information from academic fields to develop renewed techniques and spot unusual patterns to gauge students' academic achievement. Evaluating student learning is a complicated issue. Data mining in this field enables to predict students' performance to recommend performance in universities. Therefore, the current authors have recently seen the rapid growth of data mining and knowledge extraction as tools used by academic institutions to optimize student learning processes. Here, a method based on a certain kind of artificial neural network called Long Short Term Memory recurrent neural network for prediction will operate. The proposed approach tries to use the educational characteristics of different people to predict the best educational process future educational. It career for students and thereby take steps to improve the effectiveness of the educational system. For comparison, one of the newest algorithms presented in this field was implemented using the proposed technique. The evaluations' findings were performed in the form of two scenarios with different data sizes and different amounts of test and training data. For the evaluation, the dataset taken from an online educational system was used. The evaluation results are presented in the form of four well-known criteria precision, recall, accuracy, and F1, which demonstrate the superiority of the proposed method. Manuscript profile

Chronic Kidney Disease is one of the most common metabolic diseases. The challenge in this area is a pre-processing problem. Artificial Intelligence techniques have been implemented over medical disease diagnoses successfully. Classification systems aim clinicians to predict the risk factors that cause Chronic Kidney Disease. To address this challenge, we introduce an effective model to investigate the role of pre-processing and machine learning techniques for classification problems in the diagnosis of Chronic Kidney Disease. The model has four stages including, Pre-processing, Feature Selection, Classification, and Performance. Missing values and outliers are two problems that are addressed in the pre-processing stage. Many classifiers are used for classification. Two tools are conducted to reveal model performance for the diagnosis of Chronic Kidney Disease. The results confirmed the superiority of the proposed model over its counterparts. Manuscript profile

Diagnosis of diabetes is a classification problem that attracts more in recent years. Diabetes mellitus happens when the whole body cannot provide an adequate quantity of insulin to adjust glucose levels. In the low insulin level, food products in glucose are turned into glucose, improving the sugar to a more than average level. All existing works show that many techniques are successful for this disease, Artificial Intelligence. There exist many classification models to aim the prediction of diabetes. We introduce a novel model to investigate the role of pre-processing and data reduction for classification problems in the diagnosis of diabetes. The model has four steps consisting of Pre-processing, Feature sub-selection, Classification, and Performance. In the classification technique, we apply the voting technique with three classifiers. Many experiments were conducted to reveal the performance of the proposed work for the diagnosis of diabetics. The results confirmed the superiority of our model over its counterparts, and the best accuracy, precision, recall, and F1 were achieved at 96.67, 100, 100, and 94.01%, respectively. Manuscript profile

With the extensive Internet applications, review sentiment classification has attracted increasing interest among text mining experts. Traditional bag of words approaches did not indicate multiple relationships connecting words while emphasizing the pre-processing phase and data reduction techniques, making a huge performance difference in classification. This study suggests a model as a different efficient model for multi-class sentiment classification using sampling techniques, feature selection methods, and ensemble supervised classification to increase the performance of text classification. The feature selection phase of our model has applied n-grams, a computational method that optimizes feature selection procedure by extracting features based on the relationships of the words to improve a candidate selection of features. The proposed model classifies the sentiment of tweets and online reviews through ensemble methods, including boosting, bagging, stacking, and voting in conjunction with supervised methods. Besides, two sampling techniques were applied in the pre-processing phase. In the experimental study, a comprehensive range of comparative experiments was conducted to assess the effectiveness of our model using the best existing works in the literature on well-known movie reviews and Twitter datasets. The highest accuracy and f-measure for our model obtained 92.95 and 92.65% on the movie dataset, 90.61 and 87.73% on the Twitter dataset, respectively. Manuscript profile