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As the number of credit card users has increased, detecting fraud in this domain has become a vital issue. Previous literature has applied various supervised and unsupervised machine learning methods to find an effective fraud detection system. However, some of these methods require an enormous amount of time to achieve reasonable accuracy. In this paper, an Asexual Reproduction Optimization (ARO) approach was employed, which is a supervised method to detect credit card fraud. ARO refers to a kind of production in which one parent produces some offspring. By applying this method and sampling just from the majority class, the effectiveness of the classification is increased. A comparison to Artificial Immune Systems (AIS), which is one of the best methods implemented on current datasets, has shown that the proposed method is able to remarkably reduce the required training time and at the same time increase the recall that is important in fraud detection problems. The obtained results show that ARO achieves the best cost in a short time, and consequently, it can be considered a real-time fraud detection system.

The Internet of Things (IoT) is a paradigm that connects everyday items to the Internet. In the recent decade, the IoT's spreading popularity is a promising opportunity for people and industries. IoT utilizes in a wide range of respects such as agriculture, healthcare, smart cities, and manufacturing sectors. IoT data quality is crucial in IoT real-life applications. IoT data quality dimensions and issues should be considered because we require data to make accurate and timely decisions, produce commodities, and gain insights about events, people, and the environment. It is essential to point out that we cannot reach valuable results by using poor quality data. This paper aims to develop a new category for IoT data quality. Hence, we examine existing IoT data quality dimensions and IoT data quality issues in general and specific domains and IoT data quality dimensions' categories. It is worth considering that categories in the context of IoT are not many. We developed a new category in which IoT data quality dimensions and issues are separated. Concerning this category, we can get familiar with related dimensions and issues in each category. To enhance data quality dimensions and minimize data quality issues, we suggest potential solutions using Blockchain to overcome IoT's security issues.

This research focuses on the algorithms and approaches for learning Hidden Markov Models (HMMs) and compares HMM learning methods and algorithms. HMM is a statistical Markov model in which the system being modeled is assumed to be a Markov process. One of the essential characteristics of HMMs is their learning capabilities. Learning algorithms are introduced to overcome this inconvenience. One of the main problems of the newly proposed algorithms is their validation. This research aims by using the theoretical and experimental analysis to 1) compare HMMs learning algorithms proposed in the literature, 2) provide a validation tool for new HMM learning algorithms, and 3) present a new algorithm called Asexual Reproduction Optimization (ARO) with one of its extensions - Modified ARO (MARO) - as a novel HMM learning algorithm to use the validation tool proposed. According to the literature findings, it seems that populationbased algorithms perform better among HMMs learning approaches than other algorithms. Also, the testing was done in nine benchmark datasets. The results show that MARO outperforms different algorithms in objective functions in terms of accuracy and robustness., Comment: arXiv admin note: text overlap with arXiv:0811.4413 by other authors