Classification of skin cancer images by applying simple evolving connectionist system

<span id="docs-internal-guid-eea5616b-7fff-5d26-eeb4-1d8c084ec93d"><span>Simple evolving connectionist system (SECoS) is one of data mining classification techniques that recognizing data based on the tested and the training data binding. Data recognition is achieved by aligning testing data to trained data pattern. SECoS uses a feedforward neural network but its hidden layer evolves so that each input layer does not perform epoch. SECoS distance has been modified with the normalized Euclidean distance formula to reduce error in training. This paper recognizes skin cancer by classifying benign malignant skin moles images using SECoS based on parameter combinations. The skin cancer classification has learning rate 1 of 0.3, learning rate 2 of 0.3, sensitivity threshold of 0.5, error threshold of 0.1 and MAPE is 0.5184845 with developing hidden node of 23. Skin cancer recognition by applying modified SECoS algorithm is proven more acceptable. Compared to other methods, SECoS is more robust to error variations.</span></span>

Improved Hybrid Swarm Intelligence Algorithm for Voice Command Recognition

Swarm intelligence involves the aggregation of the boids, which interact with each other in their own environment. The boids are agents that follow simple rules in the absence of centralized structure. The Artificial Neural Network is also known as a connectionist system that originated from the biological neural network. The aim of the present study is to improve the hybrid swarm intelligence algorithm for voice command recognition. The proposed algorithm hybrid combines the lion optimization algorithm with the fish swarm algorithm, and was improved upon using voice features. Approximately ten voice commands were recorded, including “open”, “close”, “open door”, “close door”, “open window”, “close window”, “on”, “off”, “play”, and “stop”.

Use of Multiple Astrocytic Configurations within an Artificial Neuro-Astrocytic Network

The artificial neural networks used in a multitude of fields are achieving good results. However, these systems are inspired in the vision of classical neuroscience where neurons are the only elements that process information in the brain. Advances in neuroscience have shown that there is a type of glial cell called astrocytes that collaborate with neurons to process information. In this work, a connectionist system formed by neurons and artificial astrocytes is presented. The astrocytes can have different configurations to achieve a biologically more realistic behaviour. This work indicates that the use of different artificial astrocytes behaviours is beneficial.

Phonological Perspective in Recognizing Kurdish Language Word

This research is confers Speech Perception and a Phonological perspective in a frame of TRACE model in a Connectionist point of view and that’s all to Analysis Kurdish and Persian language relatives. According to this view the research entitled (Speech perception and phonological Analysis in Word Recognition). In this research we assumes that Kurdish and Persian language have many similarity especially in phonological level , for reasoning this assumption and analyzing our data we use TRACE model which, developed by McClelland and Elman (1986), is speech recognition model similar to the interactive model of word recognition , and it is based on a connectionist approach in psycholinguistics. The data that this research investigate drive us to serial Conclusion which are close to our first assumption (more relative between two language ) and every difference between two related languages should be explicable to a high degree of plausibility, and systematic changes by this the study declares historical relationships between the two languages and the synchronically difference. The research content a preface and a body which is divide to three sections and a conclusion: First section: it contents a review of Trace Model (concept and the principle) and the reason to work with this model and selecting it among all another models, because TRACE is based on a connectionist system. There are connections among units at three levels: features, phonemes and words and The TRACE model are thus consistent with the idea of competition among units in the lexicon. In other words, at given time. Second section: in this section we achieve the TRACE model in Kurdish language, particularly in word cognition and phonological level because the models base on three basic levels: phonological Features, phonemes and word. Third section : we compare in this section both of the two languages(Kurdish and Persian ) through what we got from the results in the second section and this comparative lead us to three main results, like that the both languages have similar phonemic system with a rare different in phonemes feature.