Introduction: Liver disease is one of the most common and dangerous diseases the early detection of which can be very effective in preventing complications as well as controlling and treating the disease. The purpose of this study was to improve Adaboost Algorithm using Firefly Algorithm for diagnosing liver disease. Method: This is a descriptive-analytic study. The dataset consists of 583 independent records including 10 features of machine learning dataset in the University of California, Irvine. In this study, Adaboost and Firefly Algorithm were combined to increase the effectiveness of liver disease diagnosis. 80% of the data were used for training and 20% for testing. Results: The results highlighted the superiority of the hybrid model of feature selection over the models without feature selection. Of course, the selection of important features affect the performance of the model. The accuracy of the hybrid model considering 5 and all features was 98. 61% and 94. 15%, respectively. Overall, the hybrid model proved more accurate compared with most of the other data mining models. Conclusion: Hybrid model can be used to help physicians identify and classify healthy and unhealthy individuals; it can also be used in medical centers to enhance accuracy and speed, and reduce costs. It cannot be claimed that the hybrid model is the best model; however, it proved more accurarate.

With the increasing spread of attacks on computer networks, the use of intrusion detection systems is inevitable. The purpose of an intrusion detection system is to monitor abnormal activities and to distinguish between normal and abnormal behaviors (intrusion) in a host system or in a network. One of the main problems of intrusion detection systems is the high volume of alarms, which practically eliminates the possibility of dealing with them. An intrusion detection system is effective that can detect a wide range of attacks while reducing the amount of false alarms. In this paper, a new feature-based intrusion detection approach is proposed in which the Adaboost Algorithm combines with the Harris Hawks optimization Algorithm and optimized parameters. Studies show that the proposed method detects malicious samples in computer networks with an average accuracy in the CICIDS2017 dataset is 99.86% and for the NSL_KDD dataset is 99.88%; comparing the findings with similar works also indicates that the proposed method is more accurate than them in distinguishing invasive samples from normal.

Background: Temperament (Mizaj) determination is an important stage of diagnosis in Persian Medicine. This study aimed to evaluate thermal imaging as a reliable tool that can be used instead of subjective assessments. Methods: The temperament of 34 participants was assessed by a PM specialist using standardized Mojahedi Mizaj Questionnaire (MMQ) and thermal images of the wrist in the supine position, the back of the hand, and their whole face under supervision of the physician were recorded. Thirteen thermal features were extracted and a classifying Algorithm was designed based on the genetic Algorithm and Adaboost classifier in reference to the temperament questionnaire. Results: The results showed that the mean temperature and temperature variations in the thermal images were relatively consistent with the results of MMQ. Among the three body regions, the results related to the image from Malmas were most consistent with MMQ. By selecting six of the 13 features that had the most impact on the classification, the accuracy of 94. 7 ±,13. 0, sensitivity of 95. 7 ±,11. 3, and specificity of 98. 2 ±,4. 2 were obtained. Conclusions: The thermal imaging was relatively consistent with standardized MMQ and can be used as a reliable tool for evaluating warm/cold temperament. However, the results reveal that thermal imaging features may not be only main features for temperament classification and for more reliable classification, it needs to add some different features such as wrist pulse features and some subjective characteristics.

In recent years, production of text documents has seen an exponential growth, which is the reason why their proper classification seems necessary for better access. One of the main problems of classifying text documents is working in high-dimensional feature space.Feature Selection (FS) is one of the ways to reduce the number of text attributes. So, working with a great bulk of the feature space without FS increases the computational cost which is a function of the length of the vector, and also, it helps to remove irrelevant attributes. The general approach in this paper combines the hybrid of Flower Pollination Algorithm (FPA) with Ada-Boost Algorithm. The FPA is used for FS and the Ada-Boost is used for classification of text documents. Tests were conducted on Reuters-21578, WEBKB and CADE 12 datasets. The results show that the hybrid model has higher detection accuracy in FS compared with Ada-Boost Algorithm with model. And comparisons are indicative of higher detection accuracy of the proposed model compared with KNN-K-Means, NB-K-Means and learning models.

Background: In this paper we compare a highly accurate supervised to an unsupervised technique that uses breast thermal images with the aim of assisting physicians in early detection of breast cancer.Methods: First, we segmented the images and determined the region of interest. Then, 23 features that included statistical, morphological, frequency domain, histogram and gray-level co-occurrence matrix based features were extracted from the segmented right and left breasts. To achieve the best features, feature selection methods such as minimum redundancy and maximum relevance, sequential forward selection, sequential backward selection, sequential floating forward selection, sequential floating backward selection, and genetic Algorithm were used. Contrast, energy, Euler number, and kurtosis were marked as effective features.Results: The selected features were evaluated by fuzzy C-means clustering as the unsupervised method and compared with the Adaboost supervised classifier which has been previously studied. As reported, fuzzy C-means clustering with a mean accuracy of 75% can be suitable for unsupervised techniques.Conclusion: Fuzzy C-means clustering can be a suitable unsupervised technique to determine suspicious areas in thermal images compared to Adaboost as the supervised technique with a mean accuracy of 88%.

Summary: In this paper, by combination of seismic data and well log data, the effective porosity in the space between wells is estimated. One of the important petroleum reservoir features is effective porosity that engineers are always looking to find an appropriate model for distribution of this parameter in the reservoir. The petrophysical properties of petroleum reservoirs are very complex. In the last few decades, the effective porosity estimation procedures have become one of the hot topics in the industry to evaluate these procedures or methods. In the current research, by integration of petrophysical, seismic data and seismic attributes classification using Adaboost Algorithm, it is tried to estimate the effective porosity in a twodimensional seismic cross section of the block F3 Dutch sector of the North Sea. ...

Multiple microbes can alter a plant's development and agricultural productivity, which has significant implications for the ecosystem and human life. As a result, timely identification, prevention, and prompt treatment are required. Fundamental methods have some drawbacks to plant disease identification like more time-consuming, accuracy, doesn't support multiple plant detection. This paper introduces a hybrid model that uses a random forest classifier combined with the Adaboost Classifier to classify plant diseases to overcome the above-said drawbacks. So as to individualize normal and abnormal leaves from data sets, the suggested methodology employs the Random Forest with Adaboost Algorithm. The operational processes in our suggested study are preprocessing, segmentation, feature extraction, training the classifier, and classification. The produced datasets of infected and uninfected leaves are combined and processed using the Random Forest classifier to categorize the infected and uninfected photos. Color Histogram is used to gather features from imagery. KNN, Naive Bayes, and SVM are all used to evaluate our suggested technique.

Analysis financial distress is an important phenomenon for investors, creditors and other users of financial information. Determining the probability of a company’ s distress before occurrence of distress and bankruptcy is considered a very interesting and attractive subject and can be useful for both managers, and investors and creditors. In this study, using the information of 6 financial years during the period 2011 to 2016 in industry agriculture and food materials industry, the factors affecting financial distress and predicting it through methods based on machine learning (NBC and Adaboost) have been studied. The results of the study indicate direct impack and inflationon, indirect impact of the ratio of non-executive directors, Stock returns, the ratio of operating cash flow financial distress. The results also show that Adaboost method, using financial and economic data, has higher capability in predicting financial distress compared to NBC method.

Background and Purpose: Nitrates have long been considered indicative of drinking water quality and a critical concern for human health. The evolution of advanced models for water quality management has spurred decision-makers to incorporate artificial intelligence technologies into water quality planning. This study aims to employ the Adaboost model, one of the cutting-edge models in water quality management, to predict nitrate concentrations in groundwater using pH and EC (Electrical Conductivity) as input variables. Materials and Methods: Initially, the study analyzed the Pearson correlation matrix and subsequently determined the input variables for multiple Adaboost models with varying hyperparameters. A sensitivity and dependence analysis of the model's input variables was conducted to assess their impact on nitrate prediction. Results: The results obtained from the Adaboost model reveal R-squared (R2) values of 0. 915 for the training dataset and 0. 924 for the test dataset. Additionally, the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) scores for the training dataset were recorded as 1. 02, 1. 01, 0. 823, and 7. 3%, respectively. For the test dataset, these metrics were observed in the order of 0. 228, 0. 477, 0. 375, and 3. 2%. The model's sensitivity analysis identified the pH variable as the most influential factor in nitrate prediction. Conclusion: The model analysis demonstrates that the proposed method performs well in predicting nitrate concentrations. This approach holds significant potential for implementation as an intelligent system for forecasting water quality parameters.