Adaptive neuro-fuzzy technique for autonomous ground vehicle navigation

This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD); right distance (RD) and left distance (LD) for the low-level motion control. Two heading controllers deploy the angle difference (AD) between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles

Automatic Classification of Oral Pathologies Using Orthopantomogram Radiography Images Based on Convolutional Neural Network

An attempt has been made to device a robust method to classify different oral pathologies using Orthopantomogram (OPG) images based on Convolutional Neural Network (CNN). This system will provide a novel approach for the classification of types of teeth (viz., incisors and molar teeth) and also some underlying oral anomalies such as fixed partial denture (cap) and impacted teeth. To this end, various image preprocessing techniques are performed. The input OPG images are resized, pixels are scaled and erroneous data are excluded. The proposed algorithm is implemented using CNN with Dropout and the fully connected layer has been trained using hybrid GA-BP learning. Using the Dropout regularization technique, over fitting has been avoided and thereby making the network to correctly classify the objects. The CNN has been implemented with different convolutional layers and the highest accuracy of 97.92% has been obtained with two convolutional layers

Inventory of the Aquatic Macrophytes in Lake Kharungpat, India

The present study has been undertaken in Lake Kharungpat situated in Manipur state, India. The focal objective of the study is to evaluate the quantitative characters of the aquatic macrophytes viz., frequency, density, abundance, abundance by frequency (A/F) ratios and importance value index (IVI).  During the whole study period, a total of 54 macrophytic plant species belonging to 28 families were found distributed in the lake. The aquatic plant species recorded were grouped into different categories viz., submerged (7 species), rooted with floating leaves (6 species), free floating (8 species) and emergent (33 species) respectively. Alternanthera philoxeroides, Azolla pinnata, Brachiaria mutica, Ceratophyllum demersum, Echinochloa stagnina, Eichhornia crassipes, Enhydra fluctuans, Hygroryza aristata, Ludwigia adscendens, Pistia stratiotes, Salvinia cucullata and Zizania latifolia were the dominant species found to occur in all the study sites during the entire study period. The maximum frequency was exhibited by Echinochloa stagnina (85%), whereas the maximum density value was shown by Ceratophyllum demersum (213.60 plants m–2). The highest abundance value was exhibited by Echinochloa stagnina (506.67 plants m–2).  The higher ranges of A/F ratios were reported in some species viz., Alternanthera philoxeroides (0.44), Echinochloa stagnina (0.42), Azolla pinnata (0.38) etc. Alternanthera philoxeroides (42.41) recorded peak value of importance value index (IVI). The analysis of variance (ANOVA) for all the aquatic macrophytes reported from the lake indicates that there is no significant variation within the four study sites in terms of distribution.  However, F-test result indicates significant variation in the quantitative characters between the different macrophytic plant species of the lake. Keywords: inventory, aquatic macrophytes, quantitative, Lake Kharungpat, India

Ranking of Sites for Installation of Hydropower Plant Using MLP Neural Network Trained with GA: A MADM Approach

Every energy system which we consider is an entity by itself, defined by parameters which are interrelated according to some physical laws. In recent year tremendous importance is given in research on site selection in an imprecise environment. In this context, decision making for the suitable location of power plant installation site is an issue of relevance. Environmental impact assessment is often used as a legislative requirement in site selection for decades. The purpose of this current work is to develop a model for decision makers to rank or classify various power plant projects according to multiple criteria attributes such as air quality, water quality, cost of energy delivery, ecological impact, natural hazard, and project duration. The case study in the paper relates to the application of multilayer perceptron trained by genetic algorithm for ranking various power plant locations in India

A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter

Entropy-Based Application Layer DDoS Attack Detection Using Artificial Neural Networks

Distributed denial-of-service (DDoS) attack is one of the major threats to the web server. The rapid increase of DDoS attacks on the Internet has clearly pointed out the limitations in current intrusion detection systems or intrusion prevention systems (IDS/IPS), mostly caused by application-layer DDoS attacks. Within this context, the objective of the paper is to detect a DDoS attack using a multilayer perceptron (MLP) classification algorithm with genetic algorithm (GA) as learning algorithm. In this work, we analyzed the standard EPA-HTTP (environmental protection agency-hypertext transfer protocol) dataset and selected the parameters that will be used as input to the classifier model for differentiating the attack from normal profile. The parameters selected are the HTTP GET request count, entropy, and variance for every connection. The proposed model can provide a better accuracy of 98.31%, sensitivity of 0.9962, and specificity of 0.0561 when compared to other traditional classification models

A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter