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Background and objectives Using self-care apps can control CL and its complications, might reduce costs, and improve treatment quality. Considering the role of self-care in patient education and treatment compliance, this study aimed to identify factors affecting self-care skills improvement in CL patients and designing a self-care mobile-based app for CL patients. Materials and methods This was a developmental-applied study. First, databases such as PubMed, Scopus, Google Scholar, and Web of Science were searched to obtain data elements related to CL, self-care, and surveillance. Next, a researcher-made questionnaire was prepared for information needs assessment and to determine factors affecting self-care. The questionnaire was completed by 15 dermatologists at the Center for Research & Training in Skin Diseases & Leprosy of Tehran University of Medical Sciences. Collected data were then analyzed, and factors affecting self-care skills improvement were determined. Findings The participating dermatologists considered most of the data elements listed in the needs assessment questionnaire necessary. The factors determined were User Information, Disease Information, Education, Contact with Physicians, and Useful Links. Conclusion Using self-care for CL patients’ surveillance can provide grounds for training, prevention, and treatment follow-up, thereby significantly reduce disease complications.

In this paper, a novel distributed approach, named GDSOM-P2P, for clustering distributed data resources is proposed by combining, an improved version of Silhouette algorithm, the dynamic Self-Organizing Map (SOM) neural network, and VICINITY protocol as a generic overlay management framework based on self-organization. The proposed GDSOM-P2P is adapted to the dynamic conditions of these networks. In the proposed GDSOM-P2P algorithm, at first, each node extracts a number of important data through the SOM and Silhouette algorithms. Then each of the nodes chooses one of its neighbors with the help of the VICINITY algorithm, and exchanges their important data with their neighbors. By doing this, over a period, the nodes’ data will be distributed over the entire network and the nodes in the network access the summary data model of the whole data. Finally, each node aggregates its internal data with a summary model and then performs the final clustering to cluster its internal data correctly. Evaluation results over a real P2P environment verify the efficiency of proposed GDSOM-P2P. Furthermore, the proposed GDSOM-P2P is also compared with the existing well-established distributed data clustering techniques. The results show a significant accuracy improvement of the proposed method.

The necessity of the existence of Question Answering (QA) systems becomes evident by considering the fact that the enormous amount of unstructured data created by humans nowadays, results in ineffectiveness of search engines to provide the exact solution for a given question. However, an outstanding question answering system requires an outstanding Question Classification (QC) system. Question classifier is a system that assigns a label to each question. There exist different ways of solving this problem such as rule-based, machine learning, and hybrid approaches. This paper provides a better solution for QC using machine-learning approaches. Three methods of feature extraction are proposed in this paper. The First method uses clustering algorithms to partition vocabulary into clusters and acquires feature vector corresponding to each question using clustering information. The second one suggests a method of extracting features from questions to dispose of using recurrent neural networks and to use feedforward neural networks, which have the advantage of learning faster and less need for data, instead. Each question is converted to a feature vector, which is obtained by the Word2vec method and weighted by tf-idf coefficients. The results of question classification using Support Vector Machine and Neural Network classifiers indicate the effectiveness of this type of feature vector and based on that, high performance of the proposed QC system. Finally, the third approach keeps the innovation behind first approach, but it also keeps the fact that we are dealing with a sequence based type of data into consideration. Eventually, it would be concluded that even with a limited amount of data it is reasonable to take Recurrent Neural Networks into consideration.

This paper aims to employ Machine Learning (ML) classifying algorithms to predict whether the patient has Parkinson's Disease (PD) or not. Motor disorders mainly characterize PD, and consequently, a variety of data sets are recorded from the motor system. These data sets consist of either physical behaviors of patients or neuroimaging data captured from their brains. However, the disease mostly begins years before the motor symptoms. Consequently, non-motor symptoms have been studied more in the last decade. Since about 90% of patients experience vocal disorders in the early stages, these symptoms can be more useful for diagnosing the disease. We will review data sets developed for PD diagnosis and some machine learning classification models applied to these data sets. We will offer some models to accurately predict PD according to vocal symptoms characteristics provided in the UCI Machine Learning database, which suffers a low number of samples compared to features and being imbalanced. The results of comparative studies demonstrate that the proposed classic classification models can outperform various Deep learning methods that have been previously used in the literature. The accuracy of 97.22% was obtained by using Logistic Regression and Voting algorithms.

A new initialization technique is proposed to improve the performance of K-means.A data transformation approach is proposed to solve empty cluster problem.An efficient method is proposed to estimate the optimal number of clusters.Proposed clustering method provides more accurate clustering results. Clustering provides a knowledge acquisition method for intelligent systems. This paper proposes a novel data-clustering algorithm, by combining a new initialization technique, K-means algorithm and a new gradual data transformation approach to provide more accurate clustering results than the K-means algorithm and its variants by increasing the clusters coherence. The proposed data transformation approach solves the problem of generating empty clusters, which frequently occurs for other clustering algorithms. An efficient method based on the principal component transformation and a modified silhouette algorithm is also proposed in this paper to determine the number of clusters. Several different data sets are used to evaluate the efficacy of the proposed method to deal with the empty cluster generation problem and its accuracy and computational performance in comparison with other K-means based initialization techniques and clustering methods. The developed estimation method for determining the number of clusters is also evaluated and compared with other estimation algorithms. Significances of the proposed method include addressing the limitations of the K-means based clustering and improving the accuracy of clustering as an important method in the field of data mining and expert systems. Application of the proposed method for the knowledge acquisition in time series data such as wind, solar, electric load and stock market provides a pre-processing tool to select the most appropriate data to feed in neural networks or other estimators in use for forecasting such time series. In addition, utilization of the knowledge discovered by the proposed K-means clustering to develop rule based expert systems is one of the main impacts of the proposed method.

Steganalysis is the art and skill of discriminating stego images from cover images. Image steganalysis algorithms can be divided into two broad categories, specific and universal. In this paper, a novel universal image steganalysis algorithm is proposed which is called RISAB, Region based Image Steganalysis using Artificial Bee colony. The goal of the proposed method is to realize a sub-image from stego and cover images through ABC with respect to density according to the cover, stego and difference images. In our method, we look for the best sub-image, which contains the highest density with respect to the changed embedding pixels. Furthermore, after selecting the best sub-image, we extract the features, which have been selected by IFAB, Image steganalysis based on Feature selection using Artificial Bee colony. At the end, both selected features by IFAB and extracted features by RISAB are combined. As a result, a feature vector is generated which improves accuracy of steganalysis. Experimental results show that our proposed method outperforms other approaches.

Clustering is one of the important data mining issues, especially for large and distributed data analysis. Distributed computing environments such as Peer-to-Peer (P2P) networks involve separated/scattered data sources, distributed among the peers. According to unpredictable growth and dynamic nature of P2P networks, data of peers are constantly changing. Due to the high volume of computing and communications and privacy concerns, processing of these types of data should be applied in a distributed way and without central management. Today, most applications of P2P systems focus on unstructured P2P systems. In unstructured P2P networks, spreading gossip is a simple and efficient method of communication, which can adapt to dynamic conditions in these networks. Recently, some algorithms with different pros and cons have been proposed for data clustering in P2P networks. In this paper, by combining a novel method for extracting the representative data, a gossip-based protocol and a new centralized clustering method, a Gossip Based Distributed Clustering algorithm for P2P networks called GBDC-P2P is proposed. The GBDC-P2P algorithm is suitable for data clustering in unstructured P2P networks and it adapts to the dynamic conditions of these networks. In the GBDC-P2P algorithm, peers perform data clustering operation with a distributed approach only through communications with their neighbours. The GBDC-P2P does not need to rely on a central server and it performs asynchronously. Evaluation results demonstrate the superior performance of the GBDC-P2P algorithm. Also, a comparative analysis with other well-established methods illustrates the efficiency of the proposed method.

The goal of steganalysis algorithms is detection of stego images from clean images. Each steganography method based on its embedding mechanism puts a special pattern on the stego images. Finding this pattern in the images leads us to employ a classifier to be constructed specially for detecting stego images which are the results of a special steganography algorithm. In this paper, to have high detection accuracy, we propose an approach for Steganography Pattern Discovery (SPD). Our proposed approach employs an evolutionary method to extract the signature of stego images against clean images via fuzzy if-then rules. Based on the discovered knowledge, suitable trained models for steganalysis can be employed and stego images will be detected with high accuracy. Using SPD, we can predict the type of steganography method from a stego image. Employing SPD can enhance the approaches, which assume that a special steganography method is used. The effect of SPD before applying steganalysis methods has been investigated by some steganography and steganalysis techniques and it has been validated using some image databases. The results indicate that the pattern of a steganography method is extracted well and the type of steganography method used to make a stego image can be predicted with high accuracy.

Problem The lately emerged SARS-CoV-2 infection, which has put the whole world in an aberrant demanding situation, has generated an urgent need for developing effective responses through artificial intelligence (AI). Aim This paper aims to overview the recent applications of machine learning techniques contributing to prevention, diagnosis, monitoring, and treatment of coronavirus disease (SARS-CoV-2). Methods A progressive investigation of the recent publications up to November 2020, related to AI approaches towards managing the challenges of COVID-19 infection was made. Results For patient diagnosis and screening, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are broadly applied for classification purposes. Moreover, Deep Neural Network (DNN) and homology modeling are the most used SARS-CoV-2 drug repurposing models. Conclusion While the fields of diagnosis of the SARS-CoV-2 infection by medical image processing and its dissemination pattern through machine learning have been sufficiently studied, some areas such as treatment outcome in patients and drug development need to be further investigated using AI approaches.

As the volume of medicinal information stored electronically grows, the necessity to boost the security increases too. Health care systems are now very common in the world. A healthcare system must maintain privacy while preventing patient information from being transmitted. Therefore, securing such systems is crucial for gaining public confidence in the use of network-based healthcare systems. Information hiding is particularly used for security applications to protect the secret message from an unauthorized person. Employing information hiding in healthcare systems, deals with storing, sharing, and managing medical data in a reliable and secure manner. This paper reviews the algorithms used to provide security in healthcare systems. Furthermore, these algorithms are categorized in this paper based on the application and the way of providing security. Finally, the challenges in implementing the security of healthcare systems are described.

Introduction of a new database (PHOND) for handwriting Recognition of Digits, Signs etc. in Persian.Presentation of a modified framing feature for handwriting Recognition.Recognition of digits, signs, and numerical strings in Persian using PHOND and other databases. This paper presents an important step towards the standardization of research works on Optical Character Recognition in Persian language. It describes the formations of a standard handwritten database, including isolated digits, isolated signs, multi-digit numbers, numerical strings, courtesy amounts, and postal codes. In this regard, binary images of 72,180 samples were extracted from the designed forms. These forms were filled by 180 writers selected from different ages, genders, and jobs. Then these forms were scanned at 300 dpi with a high-speed scanner. Finally, forms are segmented into samples and are stored in bitmap format. This database is named PHOND, Persian Handwritten Optical Numbers & Digits, and it is available to the research community. Comparisons with the previous related databases illustrate the advantages of PHOND against other databases. Different experiments are done using PHOND database and the results are compared with other research works in handwritten recognition. Display Omitted

Data mining techniques have numerous applications in credit scoring of customers in the banking field. One of the most popular data mining techniques is the classification method. Previous researches have demonstrated that using the feature selection (FS) algorithms and ensemble classifiers can improve the banks' performance in credit scoring problems. In this domain, the main issue is the simultaneous and the hybrid utilization of several FS and ensemble learning classification algorithms with respect to their parameters setting, in order to achieve a higher performance in the proposed model. As a result, the present paper has developed a hybrid data mining model of feature selection and ensemble learning classification algorithms on the basis of three stages. The first stage, as expected, deals with the data gathering and pre-processing. In the second stage, four FS algorithms are employed, including principal component analysis (PCA), genetic algorithm (GA), information gain ratio, and relief attribute evaluation function. In here, parameters setting of FS methods is based on the classification accuracy resulted from the implementation of the support vector machine (SVM) classification algorithm. After choosing the appropriate model for each selected feature, they are applied to the base and ensemble classification algorithms. In this stage, the best FS algorithm with its parameters setting is indicated for the modeling stage of the proposed model. In the third stage, the classification algorithms are employed for the dataset prepared from each FS algorithm. The results exhibited that in the second stage, PCA algorithm is the best FS algorithm. In the third stage, the classification results showed that the artificial neural network (ANN) adaptive boosting (AdaBoost) method has higher classification accuracy. Ultimately, the paper verified and proposed the hybrid model as an operative and strong model for performing credit scoring.

The existing powerful steganalyzers can find out the presence of secret information in images with high accuracy. Increasing the embedding capacity of cover images reduces the detection risk of stego images. In this respect, we introduce boosted steganography scheme (BSS) that has a preprocessing stage before applying steganography methods. The goal of BSS is increasing the undetectability of stego images. Due to the dependence of embedding capacity of images to their content, we proposed an ensemble steganalyzer to estimate the embedding capacity of each cover image. Since the content of cover images has less significance in steganography, therefore to have more security, the steganographer can select a cover image from a database to achieve higher security and satisfactory embedding capacity. We present several experiments that show the effectiveness of boosted steganography scheme in improving the security of stego images. The experimental results demonstrate that considering a preprocessing stage can significantly improve the steganography security.