iman zabbah

Heart diseases are one of the most common types of diseases, which cause the death of many people. Arrhythmias are an irregular heartbeat that causes the heart to beat abnormally fast (tachycardia) or slow (bradycardia). Therefore, the identification and classification of cardiac arrhythmias using ECG signals is of great importance. This research aimed to provide a data mining-based model to improve the diagnosis of previous arrhythmia.

In this descriptive-analytical study, the UCI reference dataset, which consists of 452 samples with 279 features, was used. The samples were categorized into five classes for the detection and identification of different types of cardiac arrhythmias. The algorithm employed in this research is a combination of hierarchical neural networks (expert system combination).

In all networks, 70% of the samples were used for training, while the remaining 30% were used for testing. After modeling and comparing the generated models and recording the results, the prediction accuracy for cardiac arrhythmia in the absence of combination learning reached 89.5%, and it increased to 93.5% after employing the hierarchical expert combination approach.

The results of this research show that the proposed method based on the combination of neural networks in a hierarchical form, which leads to the specialization of the task of each class, can have better performance compared to similar models in diagnosing cardiac arrhythmia.

Iran is the largest saffron producer in the world. Saffron is of great economic importance and due to increasing activities of export competitors, it requires support, expansion, and development of exports. The growing trend of saffron export share on one hand and income generation and entrepreneurship for rural residents on the other hand double the necessity of attention to this product. Competition in this supply chain, despite powerful competitors, will be possible through mechanizing processes from cultivation to harvesting and grading to packaging. Therefore, evaluating the physical characteristics of saffron for proper grading is considered essential. In this study, the physical characteristics for the purpose of saffron grading using various artificial intelligence methods including Multilayer Perceptron Neural Networks (MLP), Learning Vector Quantization (LVQ), Self-Organizing Maps (SOM), Fuzzy Neural Networks (FNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) have been investigated. The database under study relates to 113 saffron samples categorized into 4 classes of Saffron Negin (top quality), Grade 2 Saffron (good), Grade 3 Saffron (normal), and Grade 4 Saffron (poor) collected by the researchers. The analysis results show that saffron grading considering uncertainties in the ANFIS model is superior to other methods, with a classification accuracy of 97.5% and a test sample detection error rate of 0.3484.

Predicting students’ performance in a course is one of the major aims of educational data mining systems. In the present study, two and three-layer artificial neural networks (ANN) and neuro-fuzzy systems (NFS) were used to predict Iranian EFL learners’ final scores and compare them with scores given by their instructor. Sixty-six students’ scores in an English reading comprehension course comprising of five sub-scores of midterm (out of 40), quiz (out of 60), final (out of 50), class participation (out of 5) and bonus (out of 2) were used for training the systems. Two and three-layer ANNs and an NFS were trained to predict students’ final scores using training data. Researchers compared the students’ final scores given by their instructor and those achieved through the ANNs and NFS. The results showed that the NFS could predict and deliver scores that were closer to the linear sum of students’ scores. Moreover, three-layer ANN had a better performance than the two-layer ANN. According to these results, data mining techniques could deliver an accurate estimate of students' abilities in a particular course.

Cancer and particularly breast cancer is one of the most common diseases among women worldwide. Early detection of breast cancer is a major challenge for physicians and is key in successful treatment and patient survival. This study introduces some data mining methods for the prediction of breast cancer based on a dataset containing 683 independent records with 9 features from the UCI machine learning repository. The models were used to diagnose benign and malignant breast cancer. Results showed that the accuracy of Multi-Layer Perceptron Neural Network (MLP), Learning Vector Quantization (LVQ) Neural Network, Radial Basis Function (RBF), Fuzzy Clustering (KFC), Adaptive Neuro-Fuzzy Inference System Model (ANFIS) were 97.5%, 97.5%, 98.3%, 75% and 99.2%, respectively. Early diagnosis of breast cancer disease reduces the cost of treatment and increases the chance of successful treatment. This study demonstrated that neuro-fuzzy inference system performed better than other models for breast cancer diagnosis. In this study, while diagnosing breast cancer, it was illustrated that models based on fuzzy neural inference had a more acceptable performance than other methods in diagnosing breast cancer. The proposed model can assist the medical community, particularly mammography specialists.

Optimizing the quality of lime, while using the energy system in the lime baking oven is a great service. Since lime baking is always allowed, you can easily control it. The purpose of this study was to predict the quality of lime during the manufacturing process in a lime baking oven and adjust the parameters provided before service delivery. The system variables presented in this study include: input tonnage and parameters of each round. Improper adjustment of these parameters will result in increased fuel consumption, resulting in poor quality lime production. Accordingly, in this paper, artificial neural networks as well as fuzzy neural networks have been used as predictive tools to predict the quality of lime produced during the baking process. These parameters are feeder, idle furnace, preheater, air conditioner, furnace, time and fuel consumption and output of the produced lime quality model. Modeling in matlab software (matlab2017) was performed using 472 samples with 8 properties. Eighty percent of the samples were used for training and 20% for testing. At the end of modeling, artificial neural networks error 0.066 and fuzzy neural network error 0.054 were obtained.

Knee osteoarthritis is one of the common diseases in humans and due to its increasing spread, early diagnosis of this disease is very important. Consideration of cartilage volume in knee osteoarthritis studies from radiological images is very necessary. The aim of this study is to help improve the diagnosis of knee osteoarthritis with the help of artificial intelligence and image processing techniques.

This is a diagnostic study that has been evaluated on 957 MRI images. Images were collected from Tehran Hospital database, such that 111 samples were related to healthy individuals and 48 samples to people with knee osteoarthritis. In this study, in order to diagnose osteoarthritis automatically, a new method called “image distinguishing and teaching it to artificial neural network”,using MATLAB software was used. MRI images were received and after pre-processing they were processed to diagnose osteoarthritis conditions with the help of artificial neural networks.

Experiments show acceptable performance of the proposed method, such that using this technique the diagnose of knee osteoarthritis was possible, with 93% accuracy.

the proposed model can be used in screening plans in order to identify people in danger of developing osteoarthritis and can serve as doctor assistants.

Groundwater resources are one of the most important water sources in each country. That proper knowledge and basic exploitation in this field can play a principal role in the sustainable development of the social and economic activities of a region, especially in semiarid and dry areas. The prediction of groundwater level fluctuations for supplying management and exploit Akon of watering is essential the purpose of this research is to predict Zawah-Torbat Heidarieh groundwater level32 fluctuations with a range of about 2054 square kilometers is located in the north of the desert pans on desert flats in, south of Mashhad. In order to training of the model, information from 18 piezometers extracted by the researchers of this study, which had a staggered surface alignment level with a time series of 20 years (1375-1395), was used. Each piezometer is registered on a monthly basis with a delay of t0-1 (last month), and in each piezometer, seven parameters form the system inputs. For process modeling, multi-layer perceptron neural networks with error propagation algorithm and LVQ network are used. The calculation error is calculated using the least squares method (MSE). The amount of groundwater level is also the only output of this neural network. The results of this study showed that the artificial neural network with the Gradient Descent, Gradient Descent With Momentum, Levenberg Marquardt algorithms was able to predict groundwater levels in the monthly interval is (RMSE=0/0012) in the training phase and is (RMSE=0/021) in the testing  phase in the study area.

Timely diagnosis and treatment of abnormal thyroid function can reduce the mortality associated with this disease. However, lack of timely diagnosis will have irreversible complications for the patient. Using data mining techniques, the aim of this study is to determine the status of the thyroid gland in terms of normality, hyperthyroidism or hypothyroidism.

Using supervised and unsupervised methods after data preprocessing, predictive modeling was performed to classify thyroid disease. This is an analytical study and its dataset contains 215 independent records based on 5 continuous features retrieved from the UCI machine learning data reference.

In supervised method, multilayer perception(MLP), learning vector quantization(LVQ), and fuzzy neural network(FNN) were used; and in unsupervised method, fuzzy clustering was employed. Besides, these precision figures(0.055, 0.274, 0.012 and 1.031) were obtained by root mean square error(RMSE) method, respectively.

Reducing the diagnosis error of thyroid disease was one of the goals of researchers. Using data mining techniques can help reduce this error. In this study, thyroid disease was diagnosed by different pattern recognition methods. The results show that the fuzzy neural network(FNN) has the least error rate and the highest accuracy.

Saffron is an important commercial good in Iran and it is important to pay attention to its mechanization from production to packaging. Upon arrival of the saffron to the laboratory's qualitative process, an initial assessment is carried out by an expert on the basis of the apparent features. However, human error in determining the quality of saffron based on its apparent features is inevitable; use of artificial intelligence techniques can be effective in reducing human errors while mechanizing the system. It was a diagnostic study and its database consisted of 113 samples of saffron with 7 features, which were collected by the researchers on October 2016 from the valid laboratory of Saffron and under the supervision of an expert. Sample qualitative analysis was performed with the help of features in 4 different classes including excellent, good, average and second grade average. Artificial neural networks have been used to classify saffron. After analyzing and comparing the generated models using multilayer perceptron neural networks and learning vector neural network, the highest accuracy of classification on the training and testing samples was obtained with 75.93 and 75.75%, respectively. The accuracy obtained indicated that the multi-layer perceptron neural network model can be used as a decision maker by an expert or independently in saffron lab centers.

Rainfall is one of the most important elements of water cycle used in evaluating climate conditions of each region. Long-term forecast of rainfall for arid and semi-arid regions is very important for managing and planning of water resources. To forecast appropriately, accurate data regarding humidity, temperature, pressure, wind speed etc. is required.This article is analytical and its database includes 7336 records situated in 11 features from daily brainstorm data within a twenty year period. The samples were selected based on a case study in Torbat-e Heydariyeh. 70% were chosen for learning and 30% were chosen for taking tests. From 7181 available data, 75% and 25% were used for training and evaluating, respectively. This research studied the performance of different neural networks in order to predict precipitation and then presented an algorithm for combining neural networks with linear and nonlinear methods. After modeling and comparing their results using neural networks, the root mean square error was recorded for each method. In the first modeling, the artificial neural network error was 0.05, in the second modeling, linear combination of neural networks error was 0.07, and in the third model, nonlinear combination neural networks error was 0.001. Reducing the error of forecasting precipitation has always been one of the goals of the researchers. This study, with the forecast of precipitation by neural networks, suggested that the use of a more robust method called a nonlinear combination neural network can lead to improve men is in for cast diagnostic accuracy.

Coronary artery disease is one of the most common diseases in different societies. Coronary angiography is established as one of the best methods for diagnosis of this disease. Angiography is an invasive and costly method. Furthermore, it is associated with risks such as death, heart attack, and stroke. Thus, this study introduces a neuro-fuzzy-based method which can help the physicians in prediction of patient’s coronary artery condition. This is an analytical study carried on 200 patients of Cardiovascular Center in Torbat Heydarieh. Patient records include 13 risk factors and are non-attributable. In this work, models are presented based on data mining methods for the diagnosis of coronary artery disease Furthermore, artificial neural network and neuro-fuzzy method were used for modeling the diagnosis of coronary artery disease. The mean square error (MSE) of prediction for artificial neural network and neuro-fuzzy method were p=0.2574 and p=0.0007, respectively. Since angiography is invasive and associated with various risks, we suggest the use of non-invasive methods with low error and high reliability. New data mining strategies can be effective in reducing the mentioned complications.

A main problem in diabetes is its timely and accurate diagnosis. This study aimed at diagnosing diabetes using data mining methods. The present study is an analytical investigation including 768 individuals with 8 attributes. Artificial neural networks and fuzzy neural networks were used to diagnose the diabetes. To achieve a realaccuracy, the Kfold method was used to divide samples into training and test groups. The mean square errors in multilayer perceptron network (MLP), learning vector quantization and Nero fuzzy networks were 98.6%, 98.2% and 99.6%, respectively. According to the results of this study, , data mining method can be effective in diagnosing diabetes. In this regard, both used methods are useful; however, higher precision was obtained following the use of Neuro-Fuzzy approach.

The intelligence of a mobile robot is highly dependent on its vision. The main objective of an intelligent mobile robot is in its ability to the online image processing, object detection, and especially visual tracking which is a complex task in stochastic environments. Tracking algorithms suffer from sequence challenges such as illumination variation, occlusion, and background clutter, so an accurate tracker should employ the appropriate visual features to identify target. In this paper, we propose using the histogram of oriented gradient (HOG), as an important descriptor. The descriptor simulates the performance of the complex cells in the primary visual cortex (V1) and it has low sensitivity to the illumination changes. In the proposed method, firstly, an object model is generated by training the HOG of multi first frames via an SVM classifier. Then, in order to track a new frame, the HOG descriptors are extracted from the surrounding areas of the target in the previous frame and convolved with the object model. Finally, the location with the highest score is defined as the target. The experimental results demonstrate the proposed method has significant performance compare to the state-of-the-art methods. Furthermore, we apply our algorithm to the mobile robot built by the robotics team to ensure its performance in a real environment.

Coronary Artery Disease (CAD) is one of the most common heart diseases and the main cause of mortality in men and women. This study aimed to predict the disease status using Neural Network compound (mixture of experts).

The present study was a diagnostic study conducted on 200 patients referred to a heart specialty center in Torbat-e-Heydarieh. Patients' files contained their demographic information including13 risk factors. A model for predicting CAD based on multilayer perceptron neural network and mixture of experts was produced.

First, we used a neural network of multilayer perceptron with Propagation algorithm by different architectures. The best architecture could predict closed coronary artery with the accuracy of 71.7%. Then, by increasing the number of neural networks and training process, results were combined. Mixture of experts by liner method (majority voting) and nonlinear method (gating network) was applied and the accuracy rates of 75.8 percent and 78.3 percent were respectively obtained.

Angiography is an invasive diagnostic procedure with risk factors such as stroke and heart attack. Therefore, non-invasive methods should be used for the diagnosis of CAD. In this study, with increasing the number of learners and their nonlinear mixture, the accuracy of diagnosis was increased

Classic statistical evaluation models are generally absolute and therefore make it difficult to achieve reliable results. One reason for this is that the sources used, inherently, contain inaccurate information and make the conditions difficult for a valid evaluation. In this study, using fuzzy inference, educational evaluation of professors was conducted. Due to the uncertain nature of the fuzzy theory, it is possible to analyze and evaluate information more precisely. The standard questionnaire of Islamic Azad University was distributed among 105 students to evaluate teachers. Then, the priority of each survey question was determined by interviewing some professors. The weighting effect of each professor's experience in response to each question priority and, also, the number of assessors' parameter in their evaluation system were considered. Mamdani type fuzzy inference system was chosen which receives two input fuzzy and provides the desired output based on fuzzy rule base. Finally, using three methods for evaluation including classic evaluation, evaluation with the expected value and fuzzy evaluation, have shown that the rating of teachers using fuzzy logic could be closer to reality.

Breast cancer is the most common form of cancer in women. Breast cancer detection is considered as one of the most important issues in medical science. Diagnosis of benign or malignant type of cancer reduces costs and also is important in deciding about the treatment strategy. The aim of this study was to provide data mining based models that have the predictability of breast cancer detection.
This study was descriptive-analytic. Its database included 683 independent records containing nine clinical variables in the UCI machine learning. Multilayer Perceptron artificial neural network, Bayesian Neural Network and LVQ neural network were used for classification of breast cancer to benign and malignant types. In this study, 80% of data were used for network training and 20% were used for testing.
After pre-processing the data, different neural networks with different architectures were used to detect breast cancer. In the best condition, we could predict benign or malignant cancer in the MLP neural networks, LVQ and Bayesian Neural Networks with an average of ten tests with an accuracy of 97.5% and 97.6% and 98.3% respectively. Our investigations showed that Bayesian neural network had a better performance.
Breast cancer is one of the most common cancers among women. Early diagnosis of disease reduces healthcare costs and increases patient survival chance. In this study, using data mining techniques in diagnosis, the researchers were able to use Bayesian neural network to achieve high accuracy in diagnosis.

Problems in thyroid gland are more common than in other glands of human body, and if they are not diagnosed early, thyroid storm or myxedema coma is likely to happen that might lead to death; therefore, on-time diagnosis of thyroid disorders (Hypothyroidism or hyperthyroidism) based on Laboratory and clinical tests is necessary. The main object of this research was to present a model based on data mining techniques that is capable of predicting thyroid diseases.
This study was a descriptive-analytic study and its database included 7200 independent records based on 21 risk factors derived from UCI data reference. From all records, 70% were used for training and 30% for testing. First, neural networks performance was reviewed in order to diagnose thyroid diseases, and then an algorithm for combination of neural networks through hierarchical method was presented.
After modeling and comparing the generated models and recording the results, accuracies of predicting thyroid disorders using neural network and hierarchical method were found to be 96.6% and 100% respectively.
Reducing misdiagnosis of thyroid diseases has always been one of the most important aims of researchers. Using methods based on data mining can decrease these errors. This study showed that using combination of neural networks through hierarchical method improves diagnosis accuracy.

Coronary artery disease is among the common diseases in societies. The best method of assessing coronary artery diseases is through angiography. This study aimed at investigating the effect of disease parameters on the diagnosis of coronary artery disease using artificial neural networks.
This analytic study included a database of 200 non-attributable records. In this research, different neural networks such as MLP, LVQ and BR were used to predict whether the coronary arteries were blocked or not. In addition, the importance of the continuous risk factors of coronary artery disease was studied.
The most important criteria of the diagnosis systems are the specificity and sensitivity indicators. In this study, these two indicators were calculated in the test. The best accuracy was observed in MLP, with a back-error propagation of 88%. It was also observed that the removal of discrete parameters positively affects neural network convergence speed so that the prediction accuracy could reach 85%.
Angiography is a high-cost invasive procedure with risk factors such as death, stroke and heart attack. Therefore, noninvasive methods should be applied in order to minimize error and maximize reliability to predict the disease. Using data mining methods can decrease the complications of the disease.