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The Internet of Things (IoT) is a network of various interconnected objects capable of collecting and exchanging data without human interaction. These objects have limited processing power, storage space, memory, bandwidth and energy. Therefore, due to these limitations, data transmission and routing are challenging issues where data collection and analysis methods are essential. The Routing Protocol for Low-power and Lossy Networks (RPL) is one of the best alternatives to ensure routing in LoWPAN6 networks. However, RPL lacks scalability and basically designed for non-dynamic devices. Another drawback of the RPL protocol is the lack of load balancing support, leading to unfair distribution of traffic in the network that may decrease network efficiency. This study proposes a novel RPL-based routing protocol, QFS-RPL, using Q-learning algorithm policy and ideation from the Fisheye State Routing (FSR) protocol. The proposed QFS-RPL is as lightweight and agile as the standardized RPL and partially outperforms the mRPL protocol on mobile networks. This method supports multi-path routing, and at any given time in the network lifetime, all possible paths for sending data from any node to the sink are available. Therefore, QFS-RPL provides high resilience against errors, failures, and sudden network changes. To evaluate the performance of the proposed method, the Contiki operating system and Cooja simulator have been used in scenarios with mobile and stationary nodes and random network topologies. The results have been compared with RPL and mRPL. We have developed an algorithm for ease of data transfer in the IoT, which provides better performance than existing protocols, especially when dealing with a mobile network. The performance evaluation criteria considered for simulation are load balancing, energy consumption, number of table entries, Packet Delivery Ratio (PDR), End-to-End (E2E) latency, network throughput, convergence speed, and control packet overhead.

The production of blood cells is affected by leukemia, a type of bone marrow cancer or blood cancer. Deoxyribonucleic acid (DNA) is related to immature cells, particularly white cells, and is damaged in various ways in this disease. When a radiologist is involved in diagnosing acute leukemia cells, the diagnosis is time consuming and needs to provide better accuracy. For this purpose, many types of research have been conducted for the automatic diagnosis of acute leukemia. However, these studies have low detection speed and accuracy. Machine learning and artificial intelligence techniques are now playing an essential role in medical sciences, particularly in detecting and classifying leukemic cells. These methods assist doctors in detecting diseases earlier, reducing their workload and the possibility of errors. This research aims to design a deep learning model with a customized architecture for detecting acute leukemia using images of lymphocytes and monocytes. This study presents a novel dataset containing images of Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML). The new dataset has been created with the assistance of various experts to help the scientific community in its efforts to incorporate machine learning techniques into medical research. Increasing the scale of the dataset is achieved with a Generative Adversarial Network (GAN). The proposed CNN model based on the Tversky loss function includes six convolution layers, four dense layers, and a Softmax activation function for the classification of acute leukemia images. The proposed model achieved a 99% accuracy rate in diagnosing acute leukemia types, including ALL and AML. Compared to previous research, the proposed network provides a promising performance in terms of speed and accuracy; and based on the results, the proposed model can be used to assist doctors and specialists in practical applications.

There is a wide range of association rules mining algorithms concerning the importance of weighted data to extract frequent rules. In some algorithms, each item has only one weight in the data set, while, in others, each item in each segment of the data set has only one specific weight. In this work, a new efficient algorithm named weighted frequent itemset mining using weighted subtrees (WST-WFIM) has been proposed to discover the average weight of frequent rules. This algorithm uses specific trees and some new data structures on the frequent pattern growth (FP-Growth) algorithm to calculate the average weight of discovered rules. It works on the data set that each item in each transaction has a specific weight and keeps them in the dedicated tree. Moreover, we have proposed the shared transactions’ concept and WST-WFIM by using it and calculate the average weight for discovered frequent rules. The algorithm capability was evaluated by standard sparse and dense weighted data sets. The results show that, while the algorithm works on weighted transactions, in sparse data sets, relative runtime increases by a decrease in minimum support (MinSup) parameter in comparison to the FP-Growth, and memory usage is approximately the same. The variations of runtime and memory usage with the MinSup parameter are opposite for dense data sets.

This paper employs machine learning algorithms to detect tax evasion and analyzes tax data. With the development of commercial businesses, traditional algorithms are not appropriate for solving the tax evasion detection problem. Hence, other algorithms with acceptable speed, precision, analysis, and data decisions must be used. In the case of assets and tax assessment, the integration of machine learning models with meta-heuristic algorithms increases accuracy due to optimal parameters. In this paper, intelligent machine learning algorithms are used to solve tax evasion detection. This research uses an improved particle swarm optimization (IPSO) algorithm to improve the multilayer perceptron neural network by finding the optimal weight and improving support vector machine (SVM) classifiers with optimal parameters. The IPSO-MLP and IPSO-SVM models using the IPSO algorithm are used as new models for tax evasion detection. Our proposed system applies the dataset collected from the general administration of tax affairs of West Azerbaijan province of Iran with 1500 samples for the tax evasion detection problem. The evaluations show that the IPSO-MLP model has a higher accuracy rate than the IPSO-SVM model and logistic regression. Moreover, the IPSO-MLP model has higher accuracy than SVM, Naive Bayes, k-nearest neighbor, C5.0 decision tree, and AdaBoost. The accuracy of IPSO-MLP and IPSO-SVM models is 93.68% and 92.24%, respectively.

Due to unstructured and large amounts of data, relational databases are no longer suitable for data management. As a result, new databases known as NOSQL have been introduced. The issue is that such a database is difficult to analyze. Online analytical processing (OLAP) is the foundational technology for data analysis in business intelligence. Because these technologies were designed primarily for relational database systems, performing OLAP in NOSQL is difficult. We present a model for extracting OLAP cubes from a document-oriented NOSQL database in this article. A scalable Naïve Bayes classifier method was used for this purpose. The proposed solution is divided into three stages of preparation, Naïve Bayes, and NBMR. Our proposed algorithm, NBMR, is based on the Naïve Bayes classifier (NBC) and the MapReduce (MR) programming model. Each NOSQL database document with nearly the same attribute will belong to the same class, and as a result, OLAP cubes can be used to perform data analysis. Because the proposed model allows for distributed and parallel Naïve Bayes Classifier computing, it is appropriate and suitable for large-scale data sets. Our proposed model is a proper and efficient approach when considering the speed and reduced the number of required comparisons.

A cancer diagnosis is one of the most difficult medical challenges. Leukemia is a type of cancer that affects the bone marrow and/or blood and accounts for approximately 8% of all cancers. Understanding the epidemiology and trends of leukemia is critical for planning. Specialists diagnose leukemia using morphological analysis, but there is a possibility of error in diagnosis. Since leukemia is so difficult to diagnose, intelligent methods of diagnosis are required. The primary goal of this study is to develop a novel method for extracting features hierarchically and accurately, in order to diagnose various types of acute leukemia. This method distinguishes between acute leukemia types, namely Acute Lymphocytic Leukemia (ALL) and Acute Myeloid Leukemia (AML), by distinguishing lymphocytes from monocytes. The images used in this study are obtained from the Shahid Ghazi Tabatabai Oncology Center in Tabriz. A type-II fuzzy deep network is designed for this purpose. The proposed model has an accuracy of 98.8% and an F1-score of 98.9%, respectively. The results show that the proposed method has a high diagnostic performance. Furthermore, the proposed method has the ability to generalize more satisfactorily and has a stronger learning performance than other methods.

Today, the relational database is not suitable for data management due to the large variety and volume of data which are mostly untrusted. Therefore, NoSQL has attracted the attention of companies. Despite it being a proper choice for managing a variety of large volume data, there is a big challenge and difficulty in performing online analytical processing (OLAP) on NoSQL since it is schema-less. This article aims to introduce a model to overcome null value in converting document-oriented NoSQL databases into relational databases using parallel similarity techniques. The proposed model includes four phases, shingling, chunck, minhashing, and locality-sensitive hashing MapReduce (LSHMR). Each phase performs a proper process on input NoSQL databases. The main idea of LSHMR is based on the nature of both locality-sensitive hashing (LSH) and MapReduce (MR). In this article, the LSH similarity search technique is used on the MR framework to extract OLAP cubes. LSH is used to decrease the number of comparisons. Furthermore, MR enables efficient distributed and parallel computing. The proposed model is an efficient and suitable approach for extracting OLAP cubes from an NoSQL database.

Introduction: Telemedicine in the pandemic of coronavirus disease 2019 (COVID-19) has responded to societal distancing in medical treatments by protecting health workers while also managing available resources. To attain best practices in telemedicine, a platform must be functional, and both patients and clinicians must be satisfied with the technology. To ensure the benefits of telehealth systems, usability refers to how easy the user interfaces of telehealth systems are to use. In this study, the usability of telemedicine systems has been investigated.Material and Methods: The authors of this study review the study from 2015 to 2021 using a combination of the keywords "health", "telemedicine", "telemedicine", "mobile health", "usability" "Software", "System" and "Program", which led to the extraction of 119 articles in this field.Results: Articles in the field of remote health software and evaluation of the usability of remote health applications in the form of applications based on mobile health technologies, web-based applications or a combination of both types with sample devices Primary are wearable electronics, sensors or robots.Conclusion: In this study, most of the remote health software are mobile based and their usability has been evaluated by a questionnaire. Satisfaction is the most important usability attributes to consider when designing Health mobile apps.

Today, Smart phones with different operating systems play an important role in people's lives. In the age of communication, the use of these devices and using their useful applications is inevitable that becomes part of the main requirements in order to enhance the quality and comfort of life in society. Smart phone soft wares are applications, each of them can enable the user to various activities and personalized depending on his needs do. One of the most important applications is banking applications that have managed make a big change in modern electronic banking. These applications or new capabilities makes saving resources, time and cost of users. Unfortunately, due to lack of proper usability, the use of these applications is very low. Usability is one of the factors affecting in construction and development of software that leads to improved efficiency and quality of them. This paper reports on the implementation and development of a banking application, based on the proposed new usability obtained from the study and analysis of usability previous models has focused.

Anticipating the political behavior of people will be considerable help for election candidates to assess the possibility of their success and to be acknowledged about the public motivations to select them. In this paper, we provide a general schematic of the architecture of participation anticipating system in presidential election by using KNN, Classification Tree and Na��ve Bayes and tools orange based on crisp which had hopeful output. To test and assess the proposed model, we begin to use the case study by selecting 100 qualified persons who attend in 11th presidential election of Islamic republic of Iran and anticipate their participation in Kohkiloye & Boyerahmad. We indicate that KNN can perform anticipation and classification processes with high accuracy in compared with two other algorithms to anticipate participation.

Although doctors are increasingly interested in electronic systems of registering medical record, but in practice such systems are used less. Mobile devices provide a new way for accessing users to data of health cares and services in a secure environment and user-selection. Mobile health cares' systems (M-health) are considered as a solution to reduce health care costs without reducing the quality of patient care. In this paper we are going to develop a common architecture for mobile health cares' services using NFC in order to facilitate providing health cares to people anywhere and anytime using the mobile devices that are connected to wireless communication technology, to be able to provide required services by a secure and available structure for patient' information in hospitals and health centers and treatment, especially intensive care units, emergency or patients needed home care. Also it can be avoided from forgery and misuse of physicians' stamp in current versions with this system by preparing electronic version using NFC technology.