Estimating Development Time and Effort of Software Projects by using a Neuro_Fuzzy Approach

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Mapping Fuzzy Petri Net to Fuzzy Extended Markup Language

Use of model gives the knowledge and information about the phenomenon also eradicates the cost, the effort and the hazard of using the real phenomenon. Characteristics and concepts of Petri nets are in a way that makes it simple and strong to describe and study the information processing system; especially it is shown in those which are dealing with discrete, concurrent, distributed, parallel and indecisive events. Yet, due to Petri nets inability to face with systems working on obscure data and continues events, the interest to develop fundamental concept of Petri nets has been raised which is led to new style of presented model named "fuzzy Petri nets". The difference in Petri nets is in the elements that have been fuzzed. Transitions, places, signs and arcs can be fuzzed. PMNL, on the other hand as a markup language has been engaged in uttering Petri nets in previous researches. Fuzzy markup nets can model the uncertainty of concurrent scenarios different from a dynamic system by a board of parameters and use of fuzzy membership dependencies. Therefore, in order to define these uncertain data, it is vital to use a formal language to describe fuzzy Petri nets. To support this version in this thesis, a markup language will be presented stating the structure and grammar of markup language and covering fuzzy concepts in Petri nets as well. Presenting the suggested grammar accommodates the support of fuzzy develope.DOI:http://dx.doi.org/10.11591/ijece.v3i5.403

EMCSO: An Elitist Multi-Objective Cat Swarm Optimization

This paper introduces a novel multi-objective evolutionary algorithm based on cat swarm optimizationalgorithm (EMCSO) and its application to solve a multi-objective knapsack problem. The multi-objective optimizers try to find the closest solutions to true Pareto front (POF) where it will be achieved by finding the less-crowded non-dominated solutions. The proposed method applies cat swarm optimization (CSO), a swarm-based algorithm with ability of exploration and exploitation, to produce offspring solutions and uses thenon-dominated sorting method to findthe solutionsas close as to POFand crowding distance technique toobtain a uniform distribution among thenon-dominated solutions. Also, the algorithm is allowedto keep the elites of population in reproduction processand use an opposition-based learning method for population initialization to enhance the convergence speed.The proposed algorithm is tested on standard test functions (zitzler’ functions: ZDT) and its performance is compared with traditional algorithms and is analyzed based onperformance measures of generational distance (GD), inverted GD, spread,and spacing. The simulation results indicate that the proposed method gets the quite satisfactory results in comparison with other optimization algorithms for functions of ZDT1 and ZDT2. Moreover, the proposed algorithm is applied to solve multi-objective knapsack problem

Using layer-wise training for Road Semantic Segmentation in Autonomous Cars

A recently developed application of computer vision is pathfinding in self-driving cars. Semantic scene understanding and semantic segmentation, as subfields of computer vision, are widely used in autonomous driving. Semantic segmentation for pathfinding uses deep learning methods and various large sample datasets to train a proper model. Due to the importance of this task, accurate and robust models should be trained to perform properly in different lighting and weather conditions and in the presence of noisy input data. In this paper, we propose a novel learning method for semantic segmentation called layer-wise training and evaluate it on a light efficient structure called an efficient neural network (ENet). The results of the proposed learning method are compared with the classic learning approaches, including mIoU performance, network robustness to noise, and the possibility of reducing the size of the structure on two RGB image datasets on the road (CamVid) and off-road (Freiburg Forest) paths. Using this method partially eliminates the need for Transfer Learning. It also improves network performance when input is noisy

Recurrent Interval Type-2 Fuzzy Wavelet Neural Network with Stable Learning Algorithm: Application to Model-Based Predictive Control

Fuzzy neural networks, with suitable learning strategy, have been demonstrated as an effective tool for online data modeling. However, it is a challenging task to construct a model to ensure its quality and stability for non-stationary dynamic systems with some uncertainties. To solve this problem, this paper presents a novel identification model based on recurrent interval type-2 fuzzy wavelet neural network (RIT2FWNN) with new learning algorithm. The model benefits from both advantages of recurrent and wavelet neural networks such as use of temporal data and fast convergence properties. The proposed antecedent and consequent parameters update rules are derived using sliding-mode-control-theory. To evaluate the proposed fuzzy model, it is utilized to design a nonlinear model-based predictive controller and is applied for the synchronization of fractional-order time-delay chaotic systems. Using Lyapunov stability analysis, it is shown that all update rules of the parameters are uniformly ultimately bounded. The adaptation laws obtained in this method are very simple and have closed forms. Some stability conditions are derived to prove learning dynamics and asymptotic stability of the network by using an appropriate Lyapunov function. The efficacy and performance of the proposed method is verified by simulation examples

An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed the temporal and anterior lobes of hippocampus regions of brain get affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. The magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to obtain accurate diagnosis of SZ. This paper presents a comprehensive overview of studies conducted on automated diagnosis of SZ using MRI modalities. Main findings, various challenges, and future works in developing the automated SZ detection are described in this paper

Comparison of existing LFC approaches in a deregulated environment

With introduction of the deregulation policy to the power system operation, a lot of interests have been recently refocused on the LFC. Several notable LFC scenarios based on classical, robust/adaptive and intelligent control theorems have been proposed. In this paper, using the obtained results, the approaches are compared and a discussion is given on their advantages and disadvantages