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Abundant data of the patients is recorded within the health care system. During data mining process, we can achieve useful knowledge and hidden patterns within the data and consequently we will discover the meaningful knowledge. The discovered knowledge can be used by physicians and managers of health care to improve the quality of their services and to reduce the number of their medical errors. Since by the usage of a single data mining algorithm, it is difficult to diagnose or predict diseases, therefore in this research, we take a combination of the advantages of some algorithms in order to achieve better results in terms of efficiency. Most of standard learning algorithms have been designed for balanced data (the data with the same frequency of samples in each class), where the cost of wrong classification is the same within all classes. These algorithms cannot properly represent data distribution characteristics when datasets are imbalanced. In some cases, the cost of wrong classification can be very high in a sample of a special class, such as wrongly misclassifying cancerous individuals or patients as healthy ones. In this article, it is tried to present a fast and efficient way to learn from imbalanced data. This method is more suitable for learning from the imbalanced data having very little data in class of minority. Experiments show that the proposed method has more efficiency compared to traditional simple algorithms of machine learning, as well as several special-to-imbalanced-data learning algorithms. In addition, this method has lower computational complexity and faster implementation time. [ABSTRACT FROM AUTHOR]

The increasing pollution caused by conventional cars and the problems caused by the use of fossil fuels have drawn the attention of researchers and manufacturers to the design of cars that use clean fuels. Electric vehicles connected to the network have a significant impact on reducing environmental pollution and transportation costs, especially in big cities. The cost of supplying loads to subscribers in the distribution network also includes generation and transmission costs. These costs are directly related to the intelligence of the distribution network and the total amount of energy of electric vehicles. The contribution of each generation unit and each transmission line must be calculated to determine the generation and transmission costs. In this research, in order to maximize the profit of the parking lot owner, improve voltage drop and load factor, a comprehensive framework for optimal energy management in a parking lot is presented, which can provide a method to control the charging of electric vehicles, in addition to meeting the needs of their owners, only as a series of controllable loads that they need to receive electrical energy to charge their batteries. In the next step, considering the inherent characteristic of electric cars, i.e. having a battery, and looking at them as a series of storage resources that can return the electric energy in their battery to the grid if necessary, a method to simultaneously control their charging and discharging is provided. In the final step of the paper, it is assumed that hydrogen storage systems will also enter the circuit, and thus, a comprehensive method for energy management is proposed. Finally, the linearized model of demand response and the proposed scheme along with the modeling of hydrogen storage and electric vehicles are considered to be part of contribution to improve the operation and economic situation of the network. • In practice, the high cost that battery depreciation imposes on car owners when using the V2G feature makes the use of this feature not economically justified. • A comprehensive framework for optimal energy management in a parking lot with the aim of maximizing the profit of the parking lot owner, improving the voltage drop and network power factor is provided. • The proposed method for energy management in the presence of hydrogenous storage systems was applied in a sample parking lot and how it works. • Considering the hydrogenous storage systems, while significantly increasing the profit of the parking lot owner, it also significantly reduces the power received from the network during the peak hours of the network. [ABSTRACT FROM AUTHOR]

• Optimal management (technical and economic) of generation expansion planning. • Improve the performance of generation expansion planning based on demand response programs. • Reduce operating costs of generation expansion planning based on models of probability distribution functions. • Increase the reliability and security of distribution networks based on the adaptive particle swarm optimization algorithm. Generation expansion planning in the power system is of particular importance. In traditional systems, investment in the generation expansion was made by the electricity company, but with the restructuring in the electricity industry, the owners of different parts of the system submit their proposals to the independent system operator and the independent system operator chooses the optimal design. Slowly increasing energy production from renewable sources can pose challenges for the grid. Increasing the penetration of renewable resources due to uncertainty in their production can reduce network reliability and thus increase system costs. The investigation on generation expansion planning is a multifaceted issue (technical and economic) that has been analyzed in various aspects in recent years. In this study, a multidimensional structure of generation expansion planning based on increasing the penetration level of distributed generation resources (renewable and non-renewable) as well as the application of load management and demand response algorithms is proposed. The proposed model is scheduled based on two levels of primary and secondary development. In the primary, the development of generation and transmission based on large-scale power plants as well as solar and wind farms are presented. In the secondary, in order to reduce the power fluctuations caused by the distributed generation's units, non-stochastic power generation units such as micro turbines, gas turbines and combined heat and power have been utilized. To overcome the difficulties in solving the problem of hybrid and non-convergent mixed-integer problem, the adaptive particle swarm optimization has been hired. The simulation results indicate that in the second scenario, where the development of the generation expansion planning is based on the integration of distributed generation resources and power plants, it is more cost-effective. In addition to, these simulation results represent the accuracy of the proposed probabilistic method in planning of dynamic generation systems in order to estimate the probability density function and the optimal output variables in multi-objective techno-economic planning. [ABSTRACT FROM AUTHOR]