Clustering is a process of partitioning data points into different clusters due to their similarity, as a powerful technique of data mining, clustering is widely used in many fields. Membrane computing is a computing model abstracting from the biological area, these computing systems are proved to be so powerful that they are equivalent with Turing machines. In this paper, a modified inversion ...

Diverse demands and dynamic environment of e-market makes market segmentation imperative for decision makers to devise strategies to woo varied groups. Market Segmentation is a process to segment the large and dynamic markets into separate sub markets where customers in the same group are similar with respect to a given set of characteristics. The most prevalent technique that is used for marke...

In pattern recognition, clustering is a powerful technique that can be used to find the identical group of objects from a given dataset. It has proven its importance in various domains such as bioinformatics, machine learning, pattern recognition, document clustering, and so on. But, in clustering, it is difficult to determine the optimal cluster centers in a given set of data. So, in this pape...

Analyzing and understanding human biosignals have been important research areas that have many practical applications in everyday life. For example, Brain Computer Interface is a research area that studies the connection between the human brain and external systems by processing and learning the brain signals called Electroenceplograhpy (EEG) signals. Similarly, various assistive robotics appli...

The Markov Weighted Fuzzy Time Series (MWFTS) is a method for making predictions based on developing fuzzy time series (FTS) algorithm. MWTS has overcome certain limitations of FTS, such as repetition logic relationships and weight considerations relationships. main challenge the MWFTS absence standardized rules determining partition intervals. This study compares model to methods Genetic Algor...

Microarray data are often extremely asymmetric in dimensionality, highly redundant and noisy. Most genes are believed to be uninformative with respect to studied classes. This paper proposed a novel feature selection approach for the classification of high dimensional cancer microarray data, which used filtering technique such as signal-tonoise ratio (SNR) score and optimization technique as Pa...

Modern machine learning and data analysis hinge on sophisticated search techniques. In general, exploration in high-dimensional and multi-modal spaces is needed. Some algorithms that imitate certain natural principles, the so-called evolutionary algorithms, have been used in different aspects of Environmental Science and have found numerous applications in Environmental related problems. In thi...

In this paper we present a comparison between six novel approaches to the fundamental problem of cyclic instability in Ambient Intelligence. These approaches are based on different optimization algorithms, Particle Swarm Optimization (PSO), Bee Swarm Optimization (BSO), micro Particle Swarm Optimization (μ-PSO), Artificial Immune System (AIS), Genetic Algorithm (GA) and Mutual Information Maxim...

In Dynamic Economic Load Dispatch (DELD), optimization and evolution computation become a major part with the strategy for solving the issues. From various algorithms Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms are used to encode in a vector form and in sharing information and both approaches are based on the master-apprentice mechanism for the Dual Evolution St...

Developing specialized software tools is essential to support studies of solar activity evolution. With new space missions such as Solar Dynamics Observatory (SDO), solar images are being produced in unprecedented volumes. To capitalize on that huge data availability, the scientific community needs a new generation of software tools for automatic and efficient data processing. In this paper a p...