This document summarizes a research paper that proposes using an artificial neural network to analyze the best route for automobile vehicles in traffic conditions. It presents a neural network architecture with 5 input parameters (distance, traffic volume, number of signals, road condition, travel time) and 5 output routes. The network is trained on 243 input combinations and tested on unseen data. Test results showed the network with backpropagation had 91.2% accuracy and lower error than without backpropagation. The research aims to help commuters optimize routes based on traffic parameters, though assumptions about static conditions limit realism. Future work will analyze more dynamic traffic flow conditions.
Auto mobile vehicle direction in road traffic using artificial neural networkscsandit
So far Most of the current work on this area deals with traffic volume prediction during peak
hours and the reasons behind accidents only. This work presents the analysis of automobile
vehicle directing in various traffic flow conditions using Artificial neural network architecture.
Now a days, due to unprecedented increase in automobile vehicular traffic especially in metro-
Politian cities, it has become highly imperative that we must choose an optimum road route in
accordance with our requirements. The requirements are : volume of the traffic, Distance
between source and destination, no of signals in between the source and destination, the nature
of the road condition , fuel consumption and Travel Timing. Artificial Neural networks, a soft
computing technique, modeled after brain biological neuron functioning, helps to obtain the
required road way or route as per the training given to it. Here we make use of Back
propagation network, which changes the weights value of the hidden layers, thereby activation
function value which fires the neuron to get the required output
AUTO-MOBILE VEHICLE DIRECTION IN ROAD TRAFFIC USING ARTIFICIAL NEURAL NETWORKScsandit
So far Most of the current work on this area deals with traffic volume prediction during peak hours and the reasons behind accidents only. This work presents the analysis of automobile vehicle directing in various traffic flow conditions using Artificial neural network architecture.
Now a days, due to unprecedented increase in automobile vehicular traffic especially in metro-Politian cities, it has become highly imperative that we must choose an optimum road route in accordance with our requirements. The requirements are : volume of the traffic, Distance
between source and destination, no of signals in between the source and destination, the nature of the road condition , fuel consumption and Travel Timing. Artificial Neural networks, a soft computing technique, modeled after brain biological neuron functioning, helps to obtain the
required road way or route as per the training given to it. Here we make use of Back propagation network, which changes the weights value of the hidden layers, thereby activation function value which fires the neuron to get the required output.
Multilayer extreme learning machine for hand movement prediction based on ele...journalBEEI
Brain computer interface (BCI) technology connects humans with machines via electroencephalography (EEG). The mechanism of BCI is pattern recognition, which proceeds by feature extraction and classification. Various feature extraction and classification methods can differentiate human motor movements, especially those of the hand. Combinations of these methods can greatly improve the accuracy of the results. This article explores the performances of nine feature-extraction types computed by a multilayer extreme learning machine (ML-ELM). The proposed method was tested on different numbers of EEG channels and different ML-ELM structures. Moreover, the performance of ML-ELM was compared with those of ELM, Support Vector Machine and Naive Bayes in classifying real and imaginary hand movements in offline mode. The ML-ELM with discrete wavelet transform (DWT) as feature extraction outperformed the other classification methods with highest accuracy 0.98. So, the authors also found that the structures influenced the accuracy of ML-ELM for different task, feature extraction used and channel used.
Optimized Robot Path Planning Using Parallel Genetic Algorithm Based on Visib...IJERA Editor
An analysis is made for optimized path planning for mobile robot by using parallel genetic algorithm. The
parallel genetic algorithm (PGA) is applied on the visible midpoint approach to find shortest path for mobile
robot. The hybrid ofthese two algorithms provides a better optimized solution for smooth and shortest path for
mobile robot. In this problem, the visible midpoint approach is used to make the effectiveness for avoiding
local minima. It gives the optimum paths which are always consisting on free trajectories. But the
proposedhybrid parallel genetic algorithm converges very fast to obtain the shortest route from source to
destination due to the sharing of population. The total population is partitioned into a number subgroups to
perform the parallel GA. The master thread is the center of information exchange and making selection with
fitness evaluation.The cell to cell crossover makes the algorithm significantly good. The problem converges
quickly with in a less number of iteration.
EFFECTIVE REDIRECTING OF THE MOBILE ROBOT IN A MESSED ENVIRONMENT BASED ON TH...Wireilla
The use of fuzzy logic in redirecting mobile robot is based on two sets of received information. First set is
the instantaneous distance of the robot from the obstacle and second set is the instantaneous information of
the robot's position. For this purpose, the fuzzy rules base consists of forty-two bases, which is extracted
based on the robot's distance from obstacles, and the target position relative to the instantaneous
orientation of the robot. In the structure of fuzzy systems, minimal inference engine are considered. Also,
Extended Kalman filter is used for localization in a noisy environment. Accordingly, the inputs of the fuzzy
systems are determined based on the estimation of the localization process, the information of the obstacles
center and the target position. Also, the linear acceleration and instantaneous orientation of the mobile
robot are determined by the desired fuzzy structures which are applied to its kinematic model.
Auto mobile vehicle direction in road traffic using artificial neural networkscsandit
So far Most of the current work on this area deals with traffic volume prediction during peak
hours and the reasons behind accidents only. This work presents the analysis of automobile
vehicle directing in various traffic flow conditions using Artificial neural network architecture.
Now a days, due to unprecedented increase in automobile vehicular traffic especially in metro-
Politian cities, it has become highly imperative that we must choose an optimum road route in
accordance with our requirements. The requirements are : volume of the traffic, Distance
between source and destination, no of signals in between the source and destination, the nature
of the road condition , fuel consumption and Travel Timing. Artificial Neural networks, a soft
computing technique, modeled after brain biological neuron functioning, helps to obtain the
required road way or route as per the training given to it. Here we make use of Back
propagation network, which changes the weights value of the hidden layers, thereby activation
function value which fires the neuron to get the required output
AUTO-MOBILE VEHICLE DIRECTION IN ROAD TRAFFIC USING ARTIFICIAL NEURAL NETWORKScsandit
So far Most of the current work on this area deals with traffic volume prediction during peak hours and the reasons behind accidents only. This work presents the analysis of automobile vehicle directing in various traffic flow conditions using Artificial neural network architecture.
Now a days, due to unprecedented increase in automobile vehicular traffic especially in metro-Politian cities, it has become highly imperative that we must choose an optimum road route in accordance with our requirements. The requirements are : volume of the traffic, Distance
between source and destination, no of signals in between the source and destination, the nature of the road condition , fuel consumption and Travel Timing. Artificial Neural networks, a soft computing technique, modeled after brain biological neuron functioning, helps to obtain the
required road way or route as per the training given to it. Here we make use of Back propagation network, which changes the weights value of the hidden layers, thereby activation function value which fires the neuron to get the required output.
Multilayer extreme learning machine for hand movement prediction based on ele...journalBEEI
Brain computer interface (BCI) technology connects humans with machines via electroencephalography (EEG). The mechanism of BCI is pattern recognition, which proceeds by feature extraction and classification. Various feature extraction and classification methods can differentiate human motor movements, especially those of the hand. Combinations of these methods can greatly improve the accuracy of the results. This article explores the performances of nine feature-extraction types computed by a multilayer extreme learning machine (ML-ELM). The proposed method was tested on different numbers of EEG channels and different ML-ELM structures. Moreover, the performance of ML-ELM was compared with those of ELM, Support Vector Machine and Naive Bayes in classifying real and imaginary hand movements in offline mode. The ML-ELM with discrete wavelet transform (DWT) as feature extraction outperformed the other classification methods with highest accuracy 0.98. So, the authors also found that the structures influenced the accuracy of ML-ELM for different task, feature extraction used and channel used.
Optimized Robot Path Planning Using Parallel Genetic Algorithm Based on Visib...IJERA Editor
An analysis is made for optimized path planning for mobile robot by using parallel genetic algorithm. The
parallel genetic algorithm (PGA) is applied on the visible midpoint approach to find shortest path for mobile
robot. The hybrid ofthese two algorithms provides a better optimized solution for smooth and shortest path for
mobile robot. In this problem, the visible midpoint approach is used to make the effectiveness for avoiding
local minima. It gives the optimum paths which are always consisting on free trajectories. But the
proposedhybrid parallel genetic algorithm converges very fast to obtain the shortest route from source to
destination due to the sharing of population. The total population is partitioned into a number subgroups to
perform the parallel GA. The master thread is the center of information exchange and making selection with
fitness evaluation.The cell to cell crossover makes the algorithm significantly good. The problem converges
quickly with in a less number of iteration.
EFFECTIVE REDIRECTING OF THE MOBILE ROBOT IN A MESSED ENVIRONMENT BASED ON TH...Wireilla
The use of fuzzy logic in redirecting mobile robot is based on two sets of received information. First set is
the instantaneous distance of the robot from the obstacle and second set is the instantaneous information of
the robot's position. For this purpose, the fuzzy rules base consists of forty-two bases, which is extracted
based on the robot's distance from obstacles, and the target position relative to the instantaneous
orientation of the robot. In the structure of fuzzy systems, minimal inference engine are considered. Also,
Extended Kalman filter is used for localization in a noisy environment. Accordingly, the inputs of the fuzzy
systems are determined based on the estimation of the localization process, the information of the obstacles
center and the target position. Also, the linear acceleration and instantaneous orientation of the mobile
robot are determined by the desired fuzzy structures which are applied to its kinematic model.
EFFECTIVE REDIRECTING OF THE MOBILE ROBOT IN A MESSED ENVIRONMENT BASED ON TH...ijfls
The use of fuzzy logic in redirecting mobile robot is based on two sets of received information. First set is the instantaneous distance of the robot from the obstacle and second set is the instantaneous information of the robot's position. For this purpose, the fuzzy rules base consists of forty-two bases, which is extracted based on the robot's distance from obstacles, and the target position relative to the instantaneous orientation of the robot. In the structure of fuzzy systems, minimal inference engine are considered. Also, Extended Kalman filter is used for localization in a noisy environment. Accordingly, the inputs of the fuzzy systems are determined based on the estimation of the localization process, the information of the obstacles center and the target position. Also, the linear acceleration and instantaneous orientation of the mobile robot are determined by the desired fuzzy structures which are applied to its kinematic model.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A comprehensive review on hybrid network traffic prediction model IJECEIAES
Network traffic is a typical nonlinear time series. As such, traditional linear and nonlinear models are inadequate to describe the multi-scale characteristics of traffic, thus compromising the prediction accuracy. Therefore, the research to date has tended to focus on hybrid models rather than the traditional linear and non-linear ones. Generally, a hybrid model adopts two or more methods as combined modelling to analyze and then predict the network traffic. Against this backdrop, this paper will review past research conducted on hybrid network traffic prediction models. The review concludes with a summary of the strengths and limitations of existing hybrid network prediction models which use optimization and decomposition techniques, respectively. These two techniques have been identified as major contributing factors in constructing a more accurate and fast response hybrid network traffic prediction.
Handover Algorithm based VLP using Mobility Prediction Database for Vehicular...IJECEIAES
This paper proposes an improved handover algorithm method for vehicle location prediction (VLP-HA) using mobility prediction database. The main advantage of this method is the mobility prediction database is based on real traffic data traces. Furthermore, the proposed method has the ability to reduce handover decision time and solve resource allocation problem. The algorithm is simple and can be computed very rapidly; thus, its implementation for a high-speed vehicle is possible. To evaluate the effectiveness of the proposed method, QualNet simulation is carried out under different velocity scenarios. Its performance is compared with conventional handover method. The superiority of the proposed method over conventional handover method in deciding the best handover location and choosing candidate access points is highlighted by simulation. It was found that VLP-HA has clearly reduced handover delay by 45% compared to handover without VLP, give high accuracy, hence low complexity algorithm.
Autonomous system to control a mobile robotjournalBEEI
This paper presents an ongoing effort to control a mobile robot in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. Several algorithms have been proposed for obstacle avoidance, having drawbacks and benefits. In this paper, the fuzzy controller is used to tackle the problem of mobile robot autonomous navigation in unstructured environment. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the fuzzified, adaptive inference engine and defuzzification engine. Also number of linguistic labels is optimized for the input of the mobile robot in order to reduce computational time for real-time applications. The proposed fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Semi-Autonomous Control of a Multi-Agent Robotic System for Multi-Target Oper...Waqas Tariq
Since multi-targets often occur in most applications, it is required that multi-robots are grouped to work on multi-targets simultaneously. Therefore, this paper proposes a control method for a single-master multi-slave (SMMS) teleoperator to control cooperative mobile multi-robots for a multi-target mission. The major components of the proposed control method are the robot-target pairing method and modified potential field based leader-follower formation The robot-target paring method is derived from the proven auction algorithm for a single target and is extended for multi-robot multi-target cases, which optimizes effect-based robot-target pairing based on heuristic and sensory data. The multi-robot multi-target pairing method can produce a weighted attack guidance table (WAGT), which contains benefits of different robot-target pairs. The robot-target pairing converges rapidly - as is the case for auction algorithms with integer benefits. Besides, as long as optimal robot-target pairs are obtained, a team is split into subteams formed by paired robots regarding types and numbers of the robot-target pairs with the robot-target pairing method. The subteams approach and then capture their own paired targets in the modified potential field based leader-follower formation while avoiding sensed obstacles. Simulation studies illustrate system efficacy with the proposed control method for multi-target operations. Moreover, the paper is concluded with observations of enhanced system performance.
Hybrid multi objectives genetic algorithms and immigrants scheme for dynamic ...khalil IBRAHIM
the main concept of intelligent optimization techniques, artificial neural networks, and new genetic algorithms to solve the multi-objective multicast routing problems with shortest path (SP) problem that used in the addresses networks and improve all processes addressing in the wireless communications based on multi-objective optimization. The most important characteristics in mobile wireless networks is the topology dynamics and the network topology changes over time, the routing problem (SPRP) in mobile ad hoc networks (MANETs) turns out to be a dynamic optimization problem[13], the hybrid immigrants multiple-objective genetic algorithm (HIMOGAs) in the real- world are dynamic in nature, that has objective functions, constraints, and parameters, the dynamic optimization problems (DOPs) are a big challenges to evolutionary multi-objective, since any environmental change may affect the objective vector, constraints, and parameters, HIMOGA for the optimization goal is to track the moving of parameters and get a sequence of approximations solutions over time. The quantity of services (QoS) is supporting guarantee for all data traffic and getting the maximizing utilization for network, the QoS based on multicast routing offers significant challenges, and increases to use an efficient multicast routing protocol that will be able to check multicast routing and satisfying QoS constraints, The author propose to use HIMOGAs and SP algorithm to solve multicast problem that produces new generation wireless networks with immigrants schema to get high-quality solutions after each change and satisfying all objectives.
Neural Network Model Development with Soft Computing Techniques for Membrane ...IJECEIAES
Membrane bioreactor employs an efficient filtration technology for solid and liquid separation in wastewater treatment process. Development of membrane filtration model is significant as this model can be used to predict filtration dynamic which is later utilized in control development. Most of the available models only suitable for monitoring purpose, which are too complex, required many variables and not suitable for control system design. This work focusing on the simple time seris model for membrane filtration process using neural network technique. In this paper, submerged membrane filtration model developed using recurrent neural network (RNN) train using genetic algorithm (GA), inertia weight particle swarm optimization (IWPSO) and gravitational search algorithm (GSA). These optimization algorithms are compared in term of its accuracy and convergent speed in updating the weights and biases of the RNN for optimal filtration model. The evaluation of the models is measured using three performance evaluations, which are mean square error (MSE), mean absolute deviation (MAD) and coefficient of determination (R2). From the results obtained, all methods yield satisfactory result for the model, with the best results given by IW-PSO.
Mobile robot controller using novel hybrid system IJECEIAES
Hybrid neuro-fuzzy controller is one of the techniques that is used as a tool to control a mobile robot in unstructured environment. In this paper a novel neuro-fuzzy technique is proposed in order to tackle the problem of mobile robot autonomous navigation in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the artificial neural network instead of the fuzzified engine then the output from it is processed using adaptive inference engine and defuzzification engine. In this approach, the real processing time is reduced that is increase the mobile robot response. The proposed neuro-fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.
Soft Computing based Learning for Cognitive Radioidescitation
Over the last decade the world of wireless communications has been undergoing
some crucial changes, which have brought it at the forefront of international research and
development interest, eventually resulting in the advent of a multitude of innovative
technologies and associated products such as WiFi, WiMax, 802.20, 802.22, wireless mesh
networks and Software Defined Radio. Such a highly varying radio environment calls for
intelligent management, allocation and usage of a scarce resource, namely the radio
spectrum. One of the most prominent emerging technologies that promise to handle such
situations is Cognitive Radio. Cognitive Radio systems are based on Software Defined Radio
technology and utilize intelligent software packages that enrich their transceivers with the
highly attractive properties of self-awareness, adaptability and capability to learn. The
Cognitive Engine, the intelligent system behind the Cognitive Radio, combines sensing,
learning, and optimization algorithms to control and adapt the radio system from the
physical layer and up the communication stack. The integration of a learning engine can be
very important for improving the stability and reliability of the discovery and evaluation of
the configuration capabilities. To this effect, many different learning techniques are
available and can be used by a Cognitive Radio ranging from pure lookup tables to
arbitrary combinations of soft Computing techniques, which include among others:
Artificial Neural Networks, evolutionary/Genetic Algorithms, reinforcement learning, fuzzy
systems, Hidden Markov Models, etc. The proposed work contributes in this direction,
aiming to develop a learning scheme and work towards solving problems related to learning
phase of Cognitive Radio systems. Interesting scenarios are to be mobilized for the
performance assessment work, conducted in order to design and use an appropriate
structure, while indicative results need to be presented and discussed in order to showcase
the benefits of incorporating such learning schemes into Cognitive Radio systems.
Subsequently feasibility of such learning schemes could be tested with simulations. In the
near future, such learning schemes are expected to assist a Cognitive Radio system to
compare among the whole of available, candidate radio configurations and finally select the
best one to operate in.
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
An Artificial Neural Network Model for Neonatal Disease DiagnosisWaqas Tariq
The significance of disease diagnosis by artificial intelligence is not obscure now days. The increasing demand of Artificial Neural Network application for predicting the disease shows better performance in the field of medical decision making. This paper represents the use of artificial neural networks in predicting neonatal disease diagnosis. The proposed technique involves training a Multi Layer Perceptron with a BP learning algorithm to recognize a pattern for the diagnosing and prediction of neonatal diseases. A comparative study of using different training algorithm of MLP, Quick Propagation, Conjugate Gradient Descent, shows the higher prediction accuracy. The Backpropogation algorithm was used to train the ANN architecture and the same has been tested for the various categories of neonatal disease. About 94 cases of different sign and symptoms parameter have been tested in this model. This study exhibits ANN based prediction of neonatal disease and improves the diagnosis accuracy of 75% with higher stability. Key words: Artificial Intelligence, Multi Layer Perceptron, Neural Network, Neonate
EFFECTIVE REDIRECTING OF THE MOBILE ROBOT IN A MESSED ENVIRONMENT BASED ON TH...ijfls
The use of fuzzy logic in redirecting mobile robot is based on two sets of received information. First set is the instantaneous distance of the robot from the obstacle and second set is the instantaneous information of the robot's position. For this purpose, the fuzzy rules base consists of forty-two bases, which is extracted based on the robot's distance from obstacles, and the target position relative to the instantaneous orientation of the robot. In the structure of fuzzy systems, minimal inference engine are considered. Also, Extended Kalman filter is used for localization in a noisy environment. Accordingly, the inputs of the fuzzy systems are determined based on the estimation of the localization process, the information of the obstacles center and the target position. Also, the linear acceleration and instantaneous orientation of the mobile robot are determined by the desired fuzzy structures which are applied to its kinematic model.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A comprehensive review on hybrid network traffic prediction model IJECEIAES
Network traffic is a typical nonlinear time series. As such, traditional linear and nonlinear models are inadequate to describe the multi-scale characteristics of traffic, thus compromising the prediction accuracy. Therefore, the research to date has tended to focus on hybrid models rather than the traditional linear and non-linear ones. Generally, a hybrid model adopts two or more methods as combined modelling to analyze and then predict the network traffic. Against this backdrop, this paper will review past research conducted on hybrid network traffic prediction models. The review concludes with a summary of the strengths and limitations of existing hybrid network prediction models which use optimization and decomposition techniques, respectively. These two techniques have been identified as major contributing factors in constructing a more accurate and fast response hybrid network traffic prediction.
Handover Algorithm based VLP using Mobility Prediction Database for Vehicular...IJECEIAES
This paper proposes an improved handover algorithm method for vehicle location prediction (VLP-HA) using mobility prediction database. The main advantage of this method is the mobility prediction database is based on real traffic data traces. Furthermore, the proposed method has the ability to reduce handover decision time and solve resource allocation problem. The algorithm is simple and can be computed very rapidly; thus, its implementation for a high-speed vehicle is possible. To evaluate the effectiveness of the proposed method, QualNet simulation is carried out under different velocity scenarios. Its performance is compared with conventional handover method. The superiority of the proposed method over conventional handover method in deciding the best handover location and choosing candidate access points is highlighted by simulation. It was found that VLP-HA has clearly reduced handover delay by 45% compared to handover without VLP, give high accuracy, hence low complexity algorithm.
Autonomous system to control a mobile robotjournalBEEI
This paper presents an ongoing effort to control a mobile robot in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. Several algorithms have been proposed for obstacle avoidance, having drawbacks and benefits. In this paper, the fuzzy controller is used to tackle the problem of mobile robot autonomous navigation in unstructured environment. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the fuzzified, adaptive inference engine and defuzzification engine. Also number of linguistic labels is optimized for the input of the mobile robot in order to reduce computational time for real-time applications. The proposed fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Semi-Autonomous Control of a Multi-Agent Robotic System for Multi-Target Oper...Waqas Tariq
Since multi-targets often occur in most applications, it is required that multi-robots are grouped to work on multi-targets simultaneously. Therefore, this paper proposes a control method for a single-master multi-slave (SMMS) teleoperator to control cooperative mobile multi-robots for a multi-target mission. The major components of the proposed control method are the robot-target pairing method and modified potential field based leader-follower formation The robot-target paring method is derived from the proven auction algorithm for a single target and is extended for multi-robot multi-target cases, which optimizes effect-based robot-target pairing based on heuristic and sensory data. The multi-robot multi-target pairing method can produce a weighted attack guidance table (WAGT), which contains benefits of different robot-target pairs. The robot-target pairing converges rapidly - as is the case for auction algorithms with integer benefits. Besides, as long as optimal robot-target pairs are obtained, a team is split into subteams formed by paired robots regarding types and numbers of the robot-target pairs with the robot-target pairing method. The subteams approach and then capture their own paired targets in the modified potential field based leader-follower formation while avoiding sensed obstacles. Simulation studies illustrate system efficacy with the proposed control method for multi-target operations. Moreover, the paper is concluded with observations of enhanced system performance.
Hybrid multi objectives genetic algorithms and immigrants scheme for dynamic ...khalil IBRAHIM
the main concept of intelligent optimization techniques, artificial neural networks, and new genetic algorithms to solve the multi-objective multicast routing problems with shortest path (SP) problem that used in the addresses networks and improve all processes addressing in the wireless communications based on multi-objective optimization. The most important characteristics in mobile wireless networks is the topology dynamics and the network topology changes over time, the routing problem (SPRP) in mobile ad hoc networks (MANETs) turns out to be a dynamic optimization problem[13], the hybrid immigrants multiple-objective genetic algorithm (HIMOGAs) in the real- world are dynamic in nature, that has objective functions, constraints, and parameters, the dynamic optimization problems (DOPs) are a big challenges to evolutionary multi-objective, since any environmental change may affect the objective vector, constraints, and parameters, HIMOGA for the optimization goal is to track the moving of parameters and get a sequence of approximations solutions over time. The quantity of services (QoS) is supporting guarantee for all data traffic and getting the maximizing utilization for network, the QoS based on multicast routing offers significant challenges, and increases to use an efficient multicast routing protocol that will be able to check multicast routing and satisfying QoS constraints, The author propose to use HIMOGAs and SP algorithm to solve multicast problem that produces new generation wireless networks with immigrants schema to get high-quality solutions after each change and satisfying all objectives.
Neural Network Model Development with Soft Computing Techniques for Membrane ...IJECEIAES
Membrane bioreactor employs an efficient filtration technology for solid and liquid separation in wastewater treatment process. Development of membrane filtration model is significant as this model can be used to predict filtration dynamic which is later utilized in control development. Most of the available models only suitable for monitoring purpose, which are too complex, required many variables and not suitable for control system design. This work focusing on the simple time seris model for membrane filtration process using neural network technique. In this paper, submerged membrane filtration model developed using recurrent neural network (RNN) train using genetic algorithm (GA), inertia weight particle swarm optimization (IWPSO) and gravitational search algorithm (GSA). These optimization algorithms are compared in term of its accuracy and convergent speed in updating the weights and biases of the RNN for optimal filtration model. The evaluation of the models is measured using three performance evaluations, which are mean square error (MSE), mean absolute deviation (MAD) and coefficient of determination (R2). From the results obtained, all methods yield satisfactory result for the model, with the best results given by IW-PSO.
Mobile robot controller using novel hybrid system IJECEIAES
Hybrid neuro-fuzzy controller is one of the techniques that is used as a tool to control a mobile robot in unstructured environment. In this paper a novel neuro-fuzzy technique is proposed in order to tackle the problem of mobile robot autonomous navigation in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the artificial neural network instead of the fuzzified engine then the output from it is processed using adaptive inference engine and defuzzification engine. In this approach, the real processing time is reduced that is increase the mobile robot response. The proposed neuro-fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.
Soft Computing based Learning for Cognitive Radioidescitation
Over the last decade the world of wireless communications has been undergoing
some crucial changes, which have brought it at the forefront of international research and
development interest, eventually resulting in the advent of a multitude of innovative
technologies and associated products such as WiFi, WiMax, 802.20, 802.22, wireless mesh
networks and Software Defined Radio. Such a highly varying radio environment calls for
intelligent management, allocation and usage of a scarce resource, namely the radio
spectrum. One of the most prominent emerging technologies that promise to handle such
situations is Cognitive Radio. Cognitive Radio systems are based on Software Defined Radio
technology and utilize intelligent software packages that enrich their transceivers with the
highly attractive properties of self-awareness, adaptability and capability to learn. The
Cognitive Engine, the intelligent system behind the Cognitive Radio, combines sensing,
learning, and optimization algorithms to control and adapt the radio system from the
physical layer and up the communication stack. The integration of a learning engine can be
very important for improving the stability and reliability of the discovery and evaluation of
the configuration capabilities. To this effect, many different learning techniques are
available and can be used by a Cognitive Radio ranging from pure lookup tables to
arbitrary combinations of soft Computing techniques, which include among others:
Artificial Neural Networks, evolutionary/Genetic Algorithms, reinforcement learning, fuzzy
systems, Hidden Markov Models, etc. The proposed work contributes in this direction,
aiming to develop a learning scheme and work towards solving problems related to learning
phase of Cognitive Radio systems. Interesting scenarios are to be mobilized for the
performance assessment work, conducted in order to design and use an appropriate
structure, while indicative results need to be presented and discussed in order to showcase
the benefits of incorporating such learning schemes into Cognitive Radio systems.
Subsequently feasibility of such learning schemes could be tested with simulations. In the
near future, such learning schemes are expected to assist a Cognitive Radio system to
compare among the whole of available, candidate radio configurations and finally select the
best one to operate in.
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
An Artificial Neural Network Model for Neonatal Disease DiagnosisWaqas Tariq
The significance of disease diagnosis by artificial intelligence is not obscure now days. The increasing demand of Artificial Neural Network application for predicting the disease shows better performance in the field of medical decision making. This paper represents the use of artificial neural networks in predicting neonatal disease diagnosis. The proposed technique involves training a Multi Layer Perceptron with a BP learning algorithm to recognize a pattern for the diagnosing and prediction of neonatal diseases. A comparative study of using different training algorithm of MLP, Quick Propagation, Conjugate Gradient Descent, shows the higher prediction accuracy. The Backpropogation algorithm was used to train the ANN architecture and the same has been tested for the various categories of neonatal disease. About 94 cases of different sign and symptoms parameter have been tested in this model. This study exhibits ANN based prediction of neonatal disease and improves the diagnosis accuracy of 75% with higher stability. Key words: Artificial Intelligence, Multi Layer Perceptron, Neural Network, Neonate
ARTIFICIAL INTELLIGENCE / AI (Kecerdasan Buatan)iimpunya3
ARTIFICIAL INTELLIGENCE / AI (Kecerdasan Buatan) Definisi : - Awalnya komputer difungsikan sebagai alat hitung. - Seiring dengan perkembangan jaman, komputer diharapkan dapat diberdayakan untuk mengerjakan segala sesuatu yang dikerjakan oleh manusia. - Manusia bisa pandai menyelesaikan masalah karena mempunyai pengetahuan, penalaran dan pengalaman. - Agar komputer bisa bertindak seperti dan sebaik manusia, maka komputer harus diberi bekal pengetahuan dan mempunyai kemampuan menalar. - AI merupakan salah satu bagian ilmu komputer yang membuat agar mesin (komputer) dapat melakukan pekerjaan seperti dan sebaik yang dilakukan oleh manusia. AI dilihat dari berbagai sudut pandang : 1. Sudut pandang Kecerdasan : mesin menjadi ‘cerdas’ (mampu berbuat apa yang dilakukan oleh manusia) 2. Sudut pandang Penelitian : studi bagaimana membuat agar komputer dapat melakukan sesuatu sebaik yang dilakukan oleh manusia. Domain penelitian : a. Mundande task Persepsi (vision & speech) Bahasa alami (understanding, generation & translation) Pemikiran yang bersifat commonsense Robot control b. Formal task Permainan/games Matematika (geometri, logika, kalkulus, integral, pembuktian) 1-Kecerdasan Buatan 1
Autonomous Driver Assistance System Using Swarm IntelligenceMadura Pradeep
This is a research regarding driver assistance system for avoid bad traffic on the roads, using Swarm Intelligence technologies. This project gives traffic information in different location in the road network by using color code. So unlike other existing solutions, in this one driver can take decision according to the traffic density of different roads. Swarm Intelligence describes the collective behavior of decentralized, self-organized systems, that can be either natural or artificial. We have validate this project by building a simulator.
Augmented Reality for Marketers: Mapping the Future of Consumer InteractionsLynne d Johnson
Talk from Web 2.0 Expo SF 2011 -- Augmented Reality (AR), is an emerging technology that allows for digital images and information to be overlaid on smartphone screens or computer monitors. While still an emerging technology, many major players in retail and technology are executing successful AR campaigns that move beyond catchy 3-D graphics to deliver ROI by connecting to people’s social networks and providing clear incentives to purchase.
For the duration of the summer, we completely instrumented a small engine given to us by a private company to convert it to run completely on natural gas. During the summer we installed all sorts of instrumentation including thermo couplesr, CO2 sensors, High preforming heaters, fabricated a simple intake system. The importance Of doing this is because we needed to know exactly what is going on with the engine and monitor even the slightest change. when trying to run an engine on a different fuel source all the temperatures and pressures in different chambers have to be at an specific point so in instrumenting the engine is a mandatory task. At this point the engine is running well but there seems to be some discrepancies between the meters on installed on the engine. After some further calculations then we may be able to get the engine up and running for a small amount of time.
AUTO-MOBILE VEHICLE DIRECTION IN ROAD TRAFFIC USING ARTIFICIAL NEURAL NETWORKS cscpconf
So far Most of the current work on this area deals with traffic volume prediction during peak hours and the reasons behind accidents only. This work presents the analysis of automobile
vehicle directing in various traffic flow conditions using Artificial neural network architecture. Now a days, due to unprecedented increase in automobile vehicular traffic especially in metroPolitian cities, it has become highly imperative that we must choose an optimum road route in accordance with our requirements. The requirements are : volume of the traffic, Distancebetween source and destination, no of signals in between the source and destination, the nature of the road condition , fuel consumption and Travel Timing. Artificial Neural networks, a soft computing technique, modeled after brain biological neuron functioning, helps to obtain the required road way or route as per the training given to it. Here we make use of Back propagation network, which changes the weights value of the hidden layers, thereby activation function value which fires the neuron to get the required output.
Traffic Congestion Prediction using Deep Reinforcement Learning in Vehicular ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
TRAFFIC CONGESTION PREDICTION USING DEEP REINFORCEMENT LEARNING IN VEHICULAR ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Simulation Based Analysis of Bee Swarm Inspired Hybrid Routing Protocol Param...Editor IJCATR
Vehicular Ad-hoc Networks (VANET's) are basically emanated from Mobile Ad hoc networks (MANET's) in which
vehicles act as the mobile nodes, the nodes are vehicles on the road and mobility of these vehicles are very high. The main objective of
VANET is to enhance the safety and amenity of road users. It provides intelligent transportation services in vehicles with the
automobile equipment to communicate and co-ordinates with other vehicles in the same network that informs the driver’s about the
road status, unseen obstacles, internet access and other necessary travel service information’s. The evaluation of vehicular ad hoc
networks applications in based on the simulations. A Realistic Mobility model is a basic component for VANET simulation that
ensures that conclusion drawn from simulation experiments will carry through to real deployments. This paper attempts to evaluate the
performance of a Bee swarm inspired Hybrid routing protocol for vehicular ad hoc network, that protocol should be tested under a
realistic condition including, representative data traffic models, and the realistic movement of the mobile nodes which are the vehicles.
In VANET the simulation of Realistic mobility model has been generated using SUMO and MOVE software and network simulation
has been performed using NS2 simulator, we conducted performance evaluation based on certain metric parameters such as packet
delivery ratio, end-to-end delay and normalized overhead ratio.
Neural network training for serial multisensor of autonomous vehicle systemIJECEIAES
This study aims to find the best artificial neural network weight values to be applied to the autonomous vehicle system with ultrasonic multisensor. The implementation of neural network in the system required long time process due to its training process. Therefore, this research is using offline training before implementing to online training by embedding the best network weight values to obtain the outputs faster according to desired targets. Simulink were used to train the system offline. Eight ultrasonic sensors are used on all sides of the vehicle and arranged in a serial multisensory configuration as inputs of neural network. With eight inputs, one sixteen-depth hidden layer, and five outputs, it was trained using the back-propagation algorithm of artificial neural network. By 100000 iterations, the output values and the target values are almost the same, indicating its convergency with minimum of errors. The result of this training is the best weights of the networks. These weight values can be implemented as fixed-weight in online training.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Route optimization in manets with aco and ga eSAT Journals
Abstract A mobile ad-hoc network consists of a collection of mobile nodes which can communicate with each other with the help of wireless links without the help of any pre-existing communication infrastructure. Due to the lack of infrastructure in these type of networks, nodes itself can act as a routers and relay the packets from source to destination. There are so many routing protocols used in MANET which also maintains connectivity from source to destination when links on these paths are break due to some causes like node movements, radio propagation, drainage of battery, and wireless interference. Routing is one of the important issues which are having a significant impact on network performance. Different measures which are concerned with the Quality of Service are like end to end delay, packet delivery ratio, control overhead, pause time, routing overhead, and distance in between source and destination pair. Different optimization techniques can be used to find out an available optimal path from source to destination. In this paper, we are using Ant Colony Optimization for finding out best possible paths, along with Genetic Algorithm which helps in giving the globally optimal solution from all the best possible paths which were produced by Ant colony optimization. This proposed algorithm called as GA-API overcomes the delay in packet delivery by producing the shortest path and also overcomes the problem of communication interruption due to node or link failure by finding multiple paths between pair of source and destination nodes. Index Terms: MANET, Genetic Algorithm, Ant colony Optimization, API.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Abstract—This paper provides a brief overview of the Intelligent Traffic Management System based on Artificial
Neural Networks (ANN). It is being utilized to enhance the present traffic management system and human resource
reliance. The most basic problem with the current traffic lights is their dependency on humans for their working.
The technologies used in the making of this automated traffic lights are Internet of Things, Machine Learning and
Artificial Intelligence. The basic steps used in Internet of Things are reported along with different ANN trainings.
This ANN model can be used for the minimization of traffic on roads and less waiting time at traffic lights. As a
result, we can make traffic lights more automated which in turn eventually deceases our dependency on human
resources
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
How world-class product teams are winning in the AI era by CEO and Founder, P...
AUTO-MOBILE VEHICLE DIRECTION IN ROAD TRAFFIC USING ARTIFICIAL NEURAL NETWORKS.
1. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 4, No. 4, July 2013
DOI : 10.5121/ijaia.2013.4414 157
AUTO-MOBILE VEHICLE DIRECTION IN
ROAD TRAFFIC USING
ARTIFICIAL NEURAL NETWORKS.
1
M.Rathinakumar ,2
B.SankaraSubramanian, 3
R.Vasanth Kumar Mehta and
4
N. Kumaran
1
Professor and Head, EEE Department , SCSVMV University , kanchipuram,
Tamil Nadu,India
rathinamari@rediffmail.com , 9942085320
2
Department of CSE, SCSVMV University, kanchipuram, Tamil Nadu,India.
coolsankara@gmail.com, 919486118617
3
Department of CSE, SCSVMV University, kanchipuram, Tamil Nadu,India.
vasanthmehta@gmail.com, 919095984004
4
Department of IT, SCSVMV University, kanchipuram, Tamil Nadu,India.
natarajankumn@rediffmaill.com, 919894744597
ABSTRACT
So far Most of the current work on this area deals with traffic volume prediction during peak hours and the
reasons behind accidents only. This work presents the analysis of automobile vehicle directing in various
traffic flow conditions using Artificial neural network architecture. Now a days, due to unprecedented
increase in automobile vehicular traffic especially in metro-Politian cities, it has become highly imperative
that we must choose an optimum road route in accordance with our requirements. The requirements are :
volume of the traffic, Distance between source and destination, no of signals in between the source and
destination, the nature of the road condition , fuel consumption and Travel Timing. Artificial Neural
networks, a soft computing technique, modeled after brain biological neuron functioning, helps to obtain
the required road way or route as per the training given to it. Here we make use of Back propagation
network, which changes the weights value of the hidden layers, thereby activation function value which
fires the neuron to get the required output.
KEYWORDS:
Vehicle route selection and direction, Artificial neural network, Road Traffic Analysis, Back Propagation
network.
1.INTRODUCTION
The daily mess that a commuter faces during his travel to office is felt a lot. People get stranded
and become weird while passing through a heavy traffic condition. Though the Government plans
and ideas like diverting the traffic during peak hours, change in timing between school-college
goers and company workers, laying by-pass road and etc., to reduce the complexity in road
traffic, it has not yielded a much expected result. As this is the case, the total burden lies on the
shoulder of the commuters. They need some intelligent method by which they can avoid this
2. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 4, No. 4, July 2013
158
difficult condition and make their journey a pleasant one The main constraints felt by them are :
traffic volume, no of signals they have to wait for clearance, road condition and fuel
consumption, distance they have to travel if they divert their journey in various alternative routes
to reach the destination. So, naturally, our aim is to select the best route that satisfies the user
requirements during normal working days. But in some special days, like holidays or strike call,
we can intuitively and easily judge the viable route to take up.
The paper is organized as follows. Section 1 is the Introduction. Section 2 deals with the basics of
Artificial neural networks and back propagation network. Section 3 describes about the related
work in this area. Section 4 describes the basic idea lying behind this work. and the architecture.
Section 5 discusses about the performance evaluation and result details about performance and
results. Section 6 finally concludes.
2. ARTIFICIAL NEURAL NETWORK.
Artificial Neural Network [1] is a mimic of biological brain neuron for processing information
given to it.The large number of neurons interconnected among themselves work altogether to
solve a specific problem. The inputs given to it are classified into two types: Training data, which
makes the network to learn to give the required output for a given set of inputs, Testing data,
which tests the prediction accuracy of the network for randomly incoming data. It can be used for
applications such as character and digit recognization or classification of data. It has got input
layers , hidden layers and output layers. The function of the hidden layers is to fire an activation
function like sigmoidal function to bring out the required output. The weight value, assigned
between input and hidden layers changes themselves ,helps to minimize the error while getting
the desired output.
2.1 Back Propagation Network.
A single layer network can accomplish very limited number of tasks. Minsky and Papert(1969)
[2] showed that a two-layer feed forward network can have many advantages but they question
remained unanswered was “ how the weights from the input to hidden layers can be adjusted?”
The answer was given by Rumelhart, Hinton and Williams in 1986. They reasoned that by
propagating errors of the output layers back into the units of the hidden layers, we can adjust the
weight values thereby making the network more adaptive and accurate . This method is also
called “Back Propagation Learning Rule”. It is a generalization of delta rules for non-linear
activation functions and multi-layer networks and a systematic method of training the networks.
A Back propagation network[2] consists of at least three layers of units:
1. An input layer
2. At least one intermediate layer called hidden layer.
3. An output layer.
The units are connected in feed-forward fashion with input units are fully connected to the hidden
layers and hidden layers are fully connected to the output layers. With back propagation
networks, learning occurs during learning phase. The patterns are applied to the input units and
propagated forward. The output layer pattern of activation is compared with the expected Pattern
to calculate the error signal. The error signal at the output pattern is then propagated back to the
input units in order to appropriately adjust the weights in each layer of the network. Once the
3. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 4, No. 4, July 2013
159
back propagation network has learnt to classify correctly for a set of inputs, a second set of inputs
can be given to see how well it classify the untrained inputs. The important point is to see how
well the network generalizes.
3. THE RELATED WORK
a) Konjicija.S, Avdagic . Z and Meier.G, Wurmthaler.C in their paper [4] have shown a simple
ability of using neural networks in longitudinal vehicle guidance. Here neural networks learn
from acquired real driver data, and also the driver behaviour styles for both ends ie., extremely
comfort to extremely sportive ones. This scenario is shown as a simulated model based on
longitudinal trajectory generation. In this an adjustable comfort parameter is used for different
sorts of driver behaviour.
b) In their paper Lioing Fua and L.R.Rilettba [5] presents an artificial neural network (ANN)
based method for estimating route travel times between individual locations in an urban traffic
network.. The methodology developed in this paper assumes that route travel times are time-
dependent and stochastic and their means and standard deviations need to be estimated. Three
feed-forward neural networks are developed to model the travel time behaviour during different
time periods of the day the AM peak, the PM peak, and the off-peak. These models are
subsequently trained and tested using data simulated on the road network for the City of
Edmonton, Alberta. The ANN model is compared with a distance-based model and a shortest
path algorithm. The practical implication of the ANN method is subsequently demonstrated
within a dial-a- ride para-transit vehicle routing and scheduling problem. The computational
results show that the ANN-based route travel time estimation model is appropriate, with respect
to accuracy and speed, for use in real applications.
c) Baluja, S presents [6] creation of an artificial neural network based autonomous land
vehicle controller. The performance of evolved controllers is better in unseen situations than
those trained with an error back propagation learning algorithm . An overview of the previous
connectionist based approaches is described and the exploration of methods for reducing the high
computational costs of training ANN with evolutionary algorithms is done. The evolutionary
algorithms guided by error metrics shows improved performance over those guided by the
standard sum-squared error metric. Both g evolutionary search and error back propagation are
integrated in this work.
d) In his paper, Dean A,Pomelleau [7] in The ALVINN (Autonomous Land Vehicle In a
Neural Network) project describes about real-time training of ANN to perform difficult
perception tasks. It is a back propagation network modeled to drive the CMU Navlab, a modified
Chevy van. He describes the training techniques for ALVINN to learn in under 5 minutes to
autonomously control the Navlab by observing the human driver reactions r. This enables the
ALVINN to drive in a variety of conditions including single-lane paved and unpaved roads, and
multilane lined and unlined roads, at speeds of up to 20 miles per hour.
4. THE PROPOSED WORK
We take source to destination distance (categorized as Lengthy, Medium and Short),Volume of
Traffic(categorized as Peak or Heavy, Medium and Mild), No of Signals( categorized as High,
Medium and Low), Road Condition(Categorized as Smooth, Good and Bad), Travel
4. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 4, No. 4, July 2013
160
Timing(Categorized as High, Normal and Low) as input parameters as shown in fig 2(Neural
Network Back Propagation Architecture) We eliminate fuel consumption just because it is
indicated by the source and destination distance and Travel Timing selection categories. So there
are five input parameters and each have three categories, thus giving a total of 243(35)
combinations. Here we consider 5 alternative routes as the output nodes. They are :
S-X1-Y1-Z1-D,
S-W2-X2-Y2-Z2,
S-X3-Y3-Z3-D,
S-V4-W4-X4-Y4-Z4-D,
S-X5-Y5-Z5-D
where V, W, X, Y and Z intermediate nodes along the routes.
3
2
DESTINA1
SOURCE TION
4
5
Fig.1 Main Route with
Alternatives
1 Main route
2,3,4,5 Alternative routes
Signal point
So there are 15 input nodes and 5 output nodes . The hidden layers nodes are fixed as 10 , the
mean value of input and output nodes as per thumb rule. The Sigmoid function is selected as the
activation function in the hidden layer. Each input node is assigned a weight and fully connected
to the hidden layer and in turn hidden layer nodes are again fully connected to output nodes. Out
of total 243 combinations, we take any 180 as the training data sets. These inputs are used to train
the network to produce the actual result.
For example , if we choose
Source to Destination distance as Medium (0,1,0),
Volume of Traffic as Mild (0,0,1)
No of Signals as Medium(0,1,0)
Road Condition as Smooth(1,0,0)
Travel Timing as Low(0,0,1)
5. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 4, No. 4, July 2013
161
Then network is trained, say, to choose Route 5 (the route with higher output layer
value) Similarly for other input conditions the network is trained and we obtain Route
information as follows(only few samples are shown)
Now the testing data is fed into the network from unforeseen inputs. The error value , if any , in
output layer is fed back by the back propagation network and weight values of hidden layers are
changed accordingly to get the desired output. This is the usual practice for any working
day/Normal day. But for holidays or strike call we can choose always the shortest route(Default
route) to reach the destination.
Figure.2 Neural Network Back Propagation Architecture
Input Hidden Output
Layer Layer Layer
6. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 4, No. 4, July 2013
162
VT – Volume of Traffic
DIS – Distance between source and destination
SIG -- No of Signals
RC -- Road Condition
TT – Travel Timing
H1, H2, …..H10 – Hidden layer Units
R3 -- Main (Default) Route
R1, R2, R4, R5 – Alternative routes
5. PERFORMANCE EVALUATION AND RESULT
We considered the Koyambedu-Broadway route in Chennai,TamilNadu, India as the source and
destination pair and we put one route as the normal and selected other 4 routes(left-side 2 and
right-side 2 to the normal route,see fig 1) through various road links. Initially we tested
manually about the outcome of the best route among the five alternative route chosen for
various input parameters and these outcomes are used to train the network for various
combinations of input conditions. Then the testing data are given and the results obtained are
summarized as follows
Table 1, Percentage Accuracy Of Selection And Rmse
Parameters Network
tested
manually
Network tested
without Back-
Propagation
Network tested
with Back-
Propagation
%
Accuracy 95.40% 84.90% 91.20%
of Selection
Root-Mean
Square 0.28 0.42 0.12
Error
Equation : RMS for a Neural Network [3]
(1)
So we can conclude that the network with back propagation performs better than without it. The
reason behind the higher error value in case of without back propagation network is the curse of
dimensionality. Here there are 15 inputs and the categorical division(each 3) of it results in 243
combinations. The no of inputs can be reduced if we consider ANY CONDITION , say, Road
condition ANY or No of Signals ANY Numbers. Then the input reduces to 27 (3 to power 3)
from 243(3 to power 5) and the network performs more efficiently than with higher inputs.
6. CONCLUSION.
This paper presents the novel approach of neural network based analysis of best route selection
for Automobile vehicle travel and is the first of its kind. There are certain assumptions like : The
road condition remain same independent of time, the no of alternate routes remain fixed ,no of
signals are fixed, Volume of traffic remain same throughout the route during the time period
7. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 4, No. 4, July 2013
163
considered and the regulation over the movement of different vehicle types such as Heavy,
Medium and Light. But these assumptions are changeable and dynamic in nature. Hence the
results obtained may not hold good and give only approximate measures. In Future work, we are
proposing to analyze these dynamic nature of traffic flow conditions.
ACKNOWLEDGEMENT
We sincerely thank, University management, for giving constant support and Mr.L.Ganesh for
his valuable help in publishing this manuscript.
REFERENCES
[1] www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/ report.html :
[2] http://www.myreaders.info/03_Back_Propagation_ Network.pdf
[3] www.heatonresearch.com › ... › Chapter 4: How a Mac hine Learns
[4] Konjicija, S.; Avdagic,Z.; Meier,G.;Wurmthaler, “Longitudinal vehicle guidance using neural
networks Computational Intelligence in Robotics and Automation”,. CIRA 2005.
Proceedings.2005 IEEE International symposium.Page(s): 685 – 688
[5] “Estimation of Time-Dependent, Stochastic Route Travel Times Using Artificial Neural Networks”.
LIPING FUa'*and L.R.RILETTba 1Department of Civil Engineering,University of Waterloo,
Canada,; 2Department of Civil Engineering and Texas Transportation Institute, Texas A&M
University, Texas, , USA
[6] Baluja, S.Sch. “Evolution of an artificial neural network based autonomous land vehicle controller
Systems, Man, and Cybernetics”, Part B: Cybernetics, IEEE Transactions on Date of Publication:
June 1996 Author(s): of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA Volume: 26 , Issue:
3Page(s): 450 – 463
[7] Dean A. Pomerleau, “Efficient training of artificial neural networks for autonomous navigation”
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213 USA , Neural
Computation journal Volume 3 Issue 1, Spring 1991 Pages 88-97,MIT Press Cambridge, MA, US
Authors
Short Biography.
B.Sankara Subramanian. B.E(EEE), M.E(CSE), (Ph.D) He was born and brought-up
in Madurai, a small town in southern Peninsular India famous for temples. He graduated
B.E(EEE) in First Class from Madurai Kamaraj University in 1997 and his Post
Graduate M.E(CSE) in 2007 from Anna University, Chennai. He has around 10 years of
teaching experience in Electrical and Computer Science Departments. His area of
interest includes Information and Database Systems, Data warehousing and Data mining, Algorithms,
Fuzzy Logic, Rough set Theory and Knowledge Retrieval. He is currently doing his Ph.d in Soft
Computing area in SCSVMV University , Kanchipuram where he is presently employed.
R.Vasanth Kumar Mehta. M.S., (Ph.d), He completed his graduate studies from BITS,
Pilani and is presently pursuing Ph.D. from Sri Chandrasekharendra Saraswathi Viswa
Mahavidyalaya(SCSVMV University), Kanchipuram, where he is working as Assistant
Professor in the Department of Computer Science and Engineering. Areas of interest
include Data Mining and Highperformance Computing
N.Kumaran did B.E(CSE), M.Tech(IT), (Ph.D)
He did his B.E(Computer science and Engineering) in CIT, Coimbatore, Tamil Nadu, India
in the year 1998. And M.Tech(IT) Sathyabama University, Chennai in 2007. Currently he
is pursing Ph.D(Computer networks) in NIT, Trichy. His areas of interest include
Computer Networks, Neural networks, Network Security and cryptography. He has got
around12 years of teaching experience various engineering colleges.