This document presents a study on using an artificial neural network to analyze automobile vehicle routing in traffic conditions. Specifically, it aims to select the optimal route given inputs like distance, traffic volume, number of signals, road conditions and travel time. A backpropagation neural network with 5 input nodes, 10 hidden nodes and 5 output nodes representing 5 potential routes is created. The network is trained on 180 combinations of the 5 input parameters to learn the best route. Testing shows the backpropagation network achieves 91.2% accuracy in route selection, outperforming a network without backpropagation. The study concludes the network provides an effective approach for dynamic vehicle routing analysis, though some assumptions could be improved.
Dynamic K-Means Algorithm for Optimized Routing in Mobile Ad Hoc Networks IJCSES Journal
In this paper, a dynamic K-means algorithm to improve the routing process in Mobile Ad-Hoc networks
(MANETs) is presented. Mobile ad-hoc networks are a collocation of mobile wireless nodes that can
operate without using focal access points, pre-existing infrastructures, or a centralized management point.
In MANETs, the quick motion of nodes modifies the topology of network. This feature of MANETS is lead
to various problems in the routing process such as increase of the overhead massages and inefficient
routing between nodes of network. A large variety of clustering methods have been developed for
establishing an efficient routing process in MANETs. Routing is one of the crucial topics which are having
significant impact on MANETs performance. The K-means algorithm is one of the effective clustering
methods aimed to reduce routing difficulties related to bandwidth, throughput and power consumption.
This paper proposed a new K-means clustering algorithm to find out optimal path from source node to
destinations node in MANETs. The main goal of proposed approach which is called the dynamic K-means
clustering methods is to solve the limitation of basic K-means method like permanent cluster head and fixed
cluster members. The experimental results demonstrate that using dynamic K-means scheme enhance the
performance of routing process in Mobile ad-hoc networks.
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.
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.
Dynamic K-Means Algorithm for Optimized Routing in Mobile Ad Hoc Networks IJCSES Journal
In this paper, a dynamic K-means algorithm to improve the routing process in Mobile Ad-Hoc networks
(MANETs) is presented. Mobile ad-hoc networks are a collocation of mobile wireless nodes that can
operate without using focal access points, pre-existing infrastructures, or a centralized management point.
In MANETs, the quick motion of nodes modifies the topology of network. This feature of MANETS is lead
to various problems in the routing process such as increase of the overhead massages and inefficient
routing between nodes of network. A large variety of clustering methods have been developed for
establishing an efficient routing process in MANETs. Routing is one of the crucial topics which are having
significant impact on MANETs performance. The K-means algorithm is one of the effective clustering
methods aimed to reduce routing difficulties related to bandwidth, throughput and power consumption.
This paper proposed a new K-means clustering algorithm to find out optimal path from source node to
destinations node in MANETs. The main goal of proposed approach which is called the dynamic K-means
clustering methods is to solve the limitation of basic K-means method like permanent cluster head and fixed
cluster members. The experimental results demonstrate that using dynamic K-means scheme enhance the
performance of routing process in Mobile ad-hoc networks.
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.
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.
ITA: The Improved Throttled Algorithm of Load Balancing on Cloud ComputingIJCNCJournal
Cloud computing makes the information technology industry boom. It is a great solution for businesses who want to save costs while ensuring the quality of service. One of the key issues that make cloud computing successful is the load balancing technique used in the load balancer to minimize time costs and optimize costs economically. This paper proposes an algorithm to enhance the processing time of tasks so that it can help improve the load balancing capacity on cloud computing. This algorithm, named as Improved Throttled Algorithm (ITA), is an improvement of Throttled Algorithm. The paper uses the Cloud Analyst tool to simulate. The selected algorithms are used to compare: Equally Load, Round Robin, Throttled and TMA. The simulation results show that the proposed algorithm ITA has improved the processing time of tasks, time spent processing requests and reduced the cost of Datacenters compared to the selected popular algorithms as above. The improvement of ITA is because of selecting virtual machines in an index table that is available but in order of priority. It helps response times and processing times remain stable, limits the idling resources, and cloud costs are minimized compared to selected algorithms.
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.
Back-Bone Assisted HOP Greedy Routing for VANETijsrd.com
Using advanced wireless local area network technologies, vehicular ad hoc networks (VANETs) have become viable and valuable for their wide variety of novel applications, such as road safety, multimedia content sharing, commerce on wheels, etc., currently, geographic routing protocols are widely adopted for VANETs as they do not require route construction and route maintenance phases. Again, with connectivity awareness, they perform well in terms of reliable delivery. Further, in the case of sparse and void regions, frequent use of the recovery strategy elevates hop count. Some geographic routing protocols adopt the minimum weighted algorithm based on distance or connectivity to select intermediate intersections. However, the shortest path or the path with higher connectivity may include numerous intermediate intersections. As a result, these protocols yield routing paths with higher hop count. In this paper, we propose a hop greedy routing scheme that yields a routing path with the minimum number of intermediate intersection nodes while taking connectivity into consideration. Moreover, we introduce back-bone nodes that play a key role in providing connectivity status around an intersection. Apart from this, by tracking the movement of source as well as destination, the back-bone nodes enable a packet to be forwarded in the changed direction. Simulation results signify the benefits of the proposed routing strategy in terms of high packet delivery ratio and shorter end-to-end delay.
Ieee projects 2012 2013 - Mobile ComputingK Sundaresh Ka
ieee projects download, base paper for ieee projects, ieee projects list, ieee projects titles, ieee projects for cse, ieee projects on networking,ieee projects 2012, ieee projects 2013, final year project, computer science final year projects, final year projects for information technology, ieee final year projects, final year students projects, students projects in java, students projects download, students projects in java with source code, students projects architecture, free ieee papers
Applications of Artificial Neural Networks in Civil EngineeringPramey Zode
An artificial brain-like network based on certain mathematical algorithms developed using a numerical computing environment is called as an ‘Artificial Neural Network (ANN)’. Many civil engineering problems which need understanding of physical processes are found to be time consuming and inaccurate to evaluate using conventional approaches. In this regard, many ANNs have been seen as a reliable and practical alternative to solve such problems. Literature review reveals that ANNs have already being used in solving numerous civil engineering problems. This study explains some cases where ANNs have been used and its future scope is also discussed.
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
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.
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.
Analysis of a chaotic spiking neural model the nds neuroncsandit
Further analysis and experimentation is carried out in this paper for a chaotic dynamic model,
viz. the Nonlinear Dynamic State neuron (NDS). The analysis and experimentations are
performed to further understand the underlying dynamics of the model and enhance it as well.
Chaos provides many interesting properties that can be exploited to achieve computational
tasks. Such properties are sensitivity to initial conditions, space filling, control and
synchronization. Chaos might play an important role in information processing tasks in human
brain as suggested by biologists. If artificial neural networks (ANNs) is equipped with chaos
then it will enrich the dynamic behaviours of such networks. The NDS model has some
limitations and can be overcome in different ways. In this paper different approaches are
followed to push the boundaries of the NDS model in order to enhance it. One way is to study
the effects of scaling the parameters of the chaotic equations of the NDS model and study the
resulted dynamics. Another way is to study the method that is used in discretization of the
original R¨ossler that the NDS model is based on. These approaches have revealed some facts
about the NDS attractor and suggest why such a model can be stabilized to large number of
unstable periodic orbits (UPOs) which might correspond to memories in phase space.
Combined feature extraction techniques and naive bayes classifier for speech ...csandit
Speech processing and consequent recognition are important areas of Digital Signal Processing
since speech allows people to communicate more natu-rally and efficiently. In this work, a
speech recognition system is developed for re-cognizing digits in Malayalam. For recognizing
speech, features are to be ex-tracted from speech and hence feature extraction method plays an
important role in speech recognition. Here, front end processing for extracting the features is
per-formed using two wavelet based methods namely Discrete Wavelet Transforms (DWT) and
Wavelet Packet Decomposition (WPD). Naive Bayes classifier is used for classification purpose.
After classification using Naive Bayes classifier, DWT produced a recognition accuracy of
83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new
feature extraction method which produces improvements in the recognition accuracy. So, a new
method called Dis-crete Wavelet Packet Decomposition (DWPD) is introduced which utilizes
the hy-brid features of both DWT and WPD. The performance of this new approach is evaluated
and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.
ITA: The Improved Throttled Algorithm of Load Balancing on Cloud ComputingIJCNCJournal
Cloud computing makes the information technology industry boom. It is a great solution for businesses who want to save costs while ensuring the quality of service. One of the key issues that make cloud computing successful is the load balancing technique used in the load balancer to minimize time costs and optimize costs economically. This paper proposes an algorithm to enhance the processing time of tasks so that it can help improve the load balancing capacity on cloud computing. This algorithm, named as Improved Throttled Algorithm (ITA), is an improvement of Throttled Algorithm. The paper uses the Cloud Analyst tool to simulate. The selected algorithms are used to compare: Equally Load, Round Robin, Throttled and TMA. The simulation results show that the proposed algorithm ITA has improved the processing time of tasks, time spent processing requests and reduced the cost of Datacenters compared to the selected popular algorithms as above. The improvement of ITA is because of selecting virtual machines in an index table that is available but in order of priority. It helps response times and processing times remain stable, limits the idling resources, and cloud costs are minimized compared to selected algorithms.
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.
Back-Bone Assisted HOP Greedy Routing for VANETijsrd.com
Using advanced wireless local area network technologies, vehicular ad hoc networks (VANETs) have become viable and valuable for their wide variety of novel applications, such as road safety, multimedia content sharing, commerce on wheels, etc., currently, geographic routing protocols are widely adopted for VANETs as they do not require route construction and route maintenance phases. Again, with connectivity awareness, they perform well in terms of reliable delivery. Further, in the case of sparse and void regions, frequent use of the recovery strategy elevates hop count. Some geographic routing protocols adopt the minimum weighted algorithm based on distance or connectivity to select intermediate intersections. However, the shortest path or the path with higher connectivity may include numerous intermediate intersections. As a result, these protocols yield routing paths with higher hop count. In this paper, we propose a hop greedy routing scheme that yields a routing path with the minimum number of intermediate intersection nodes while taking connectivity into consideration. Moreover, we introduce back-bone nodes that play a key role in providing connectivity status around an intersection. Apart from this, by tracking the movement of source as well as destination, the back-bone nodes enable a packet to be forwarded in the changed direction. Simulation results signify the benefits of the proposed routing strategy in terms of high packet delivery ratio and shorter end-to-end delay.
Ieee projects 2012 2013 - Mobile ComputingK Sundaresh Ka
ieee projects download, base paper for ieee projects, ieee projects list, ieee projects titles, ieee projects for cse, ieee projects on networking,ieee projects 2012, ieee projects 2013, final year project, computer science final year projects, final year projects for information technology, ieee final year projects, final year students projects, students projects in java, students projects download, students projects in java with source code, students projects architecture, free ieee papers
Applications of Artificial Neural Networks in Civil EngineeringPramey Zode
An artificial brain-like network based on certain mathematical algorithms developed using a numerical computing environment is called as an ‘Artificial Neural Network (ANN)’. Many civil engineering problems which need understanding of physical processes are found to be time consuming and inaccurate to evaluate using conventional approaches. In this regard, many ANNs have been seen as a reliable and practical alternative to solve such problems. Literature review reveals that ANNs have already being used in solving numerous civil engineering problems. This study explains some cases where ANNs have been used and its future scope is also discussed.
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
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.
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.
Analysis of a chaotic spiking neural model the nds neuroncsandit
Further analysis and experimentation is carried out in this paper for a chaotic dynamic model,
viz. the Nonlinear Dynamic State neuron (NDS). The analysis and experimentations are
performed to further understand the underlying dynamics of the model and enhance it as well.
Chaos provides many interesting properties that can be exploited to achieve computational
tasks. Such properties are sensitivity to initial conditions, space filling, control and
synchronization. Chaos might play an important role in information processing tasks in human
brain as suggested by biologists. If artificial neural networks (ANNs) is equipped with chaos
then it will enrich the dynamic behaviours of such networks. The NDS model has some
limitations and can be overcome in different ways. In this paper different approaches are
followed to push the boundaries of the NDS model in order to enhance it. One way is to study
the effects of scaling the parameters of the chaotic equations of the NDS model and study the
resulted dynamics. Another way is to study the method that is used in discretization of the
original R¨ossler that the NDS model is based on. These approaches have revealed some facts
about the NDS attractor and suggest why such a model can be stabilized to large number of
unstable periodic orbits (UPOs) which might correspond to memories in phase space.
Combined feature extraction techniques and naive bayes classifier for speech ...csandit
Speech processing and consequent recognition are important areas of Digital Signal Processing
since speech allows people to communicate more natu-rally and efficiently. In this work, a
speech recognition system is developed for re-cognizing digits in Malayalam. For recognizing
speech, features are to be ex-tracted from speech and hence feature extraction method plays an
important role in speech recognition. Here, front end processing for extracting the features is
per-formed using two wavelet based methods namely Discrete Wavelet Transforms (DWT) and
Wavelet Packet Decomposition (WPD). Naive Bayes classifier is used for classification purpose.
After classification using Naive Bayes classifier, DWT produced a recognition accuracy of
83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new
feature extraction method which produces improvements in the recognition accuracy. So, a new
method called Dis-crete Wavelet Packet Decomposition (DWPD) is introduced which utilizes
the hy-brid features of both DWT and WPD. The performance of this new approach is evaluated
and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.
En la Siguiente presentación podran observar ¿qué es? y ¿para qué sirve?, la Nube -Cloud computing, sus principales ventajas para nosotros los usuarios y algunos servidores que utilizan la NUBE.
An approach for software effort estimation using fuzzy numbers and genetic al...csandit
One of the most critical tasks during the software development life cycle is that of estimating the
effort and time involved in the development of the software product. Estimation may be
performed by many ways such as: Expert judgments, Algorithmic effort estimation, Machine
learning and Analogy-based estimation. In which Analogy-based software effort estimation is
the process of identifying one or more historical projects that are similar to the project being
developed and then using the estimates from them. Analogy-based estimation is integrated with
Fuzzy numbers in order to improve the performance of software project effort estimation during
the early stages of a software development lifecycle. Because of uncertainty associated with
attribute measurement and data availability, fuzzy logic is introduced in the proposed model.
But hardly a historical project is exactly same as the project being estimated due to some
distance associated in similarity distance. This means that the most similar project still has a
similarity distance with the project being estimated in most of the cases. Therefore, the effort
needs to be adjusted when the most similar project has a similarity distance with the project
being estimated. To adjust the reused effort, we build an adjustment mechanism whose
algorithm can derive the optimal adjustment on the reused effort using Genetic Algorithm. The
proposed model Combine the fuzzy logic to estimate software effort in early stages with Genetic
algorithm based adjustment mechanism may result to near the correct effort estimation.
Analytical study of feature extraction techniques in opinion miningcsandit
Although opinion mining is in a nascent stage of development but still the ground is set for
dense growth of researches in the field. One of the important activities of opinion mining is to
extract opinions of people based on characteristics of the object under study. Feature extraction
in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first
part discusses various techniques and second part makes a detailed appraisal of the major
techniques used for feature extraction
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.
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
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.
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
Residual balanced attention network for real-time traffic scene semantic segm...IJECEIAES
Intelligent transportation systems (ITS) are among the most focused research in this century. Actually, autonomous driving provides very advanced tasks in terms of road safety monitoring which include identifying dangers on the road and protecting pedestrians. In the last few years, deep learning (DL) approaches and especially convolutional neural networks (CNNs) have been extensively used to solve ITS problems such as traffic scene semantic segmentation and traffic signs classification. Semantic segmentation is an important task that has been addressed in computer vision (CV). Indeed, traffic scene semantic segmentation using CNNs requires high precision with few computational resources to perceive and segment the scene in real-time. However, we often find related work focusing only on one aspect, the precision, or the number of computational parameters. In this regard, we propose RBANet, a robust and lightweight CNN which uses a new proposed balanced attention module, and a new proposed residual module. Afterward, we have simulated our proposed RBANet using three loss functions to get the best combination using only 0.74M parameters. The RBANet has been evaluated on CamVid, the most used dataset in semantic segmentation, and it has performed well in terms of parameters’ requirements and precision compared to related work.
Machine learning in Dynamic Adaptive Streaming over HTTP (DASH)Eswar Publications
Recently machine learning has been introduced into the area of adaptive video streaming. This paper explores a novel taxonomy that includes six state of the art techniques of machine learning that have been applied to Dynamic Adaptive Streaming over HTTP (DASH): (1) Q-learning, (2) Reinforcement learning, (3) Regression, (4) Classification, (5) Decision Tree learning, and (6) Neural networks.
A novel k-means powered algorithm for an efficient clustering in vehicular ad...IJECEIAES
Considerable attention has recently been given to the routing issue in vehicular ad-hoc networks (VANET). Indeed, the repetitive communication failures and high velocity of vehicles reduce the efficacy of routing protocols in VANET. The clustering technique is considered an important solution to overcome these difficulties. In this paper, an efficient clustering approach using an adapted k-means algorithm for VANET has been introduced to enhance network stability in a highway environment. Our approach relies on a clustering scheme that accounts for the network characteristics and the number of connected vehicles. The simulation indicates that the proposed approach is more efficient than similar schemes. The results obtained appear an overall increase in constancy, proven by an increase in cluster head lifetime by 66%, and an improvement in robustness clear in the overall reduction of the end-to-end delay by 46% as well as an increase in throughput by 74%.
Artificial Neural Network (ANN) is a fast-growing method which has been used in different
industries during recent years. The main idea for creating ANN which is a subset of artificial
intelligence is to provide a simple model of human brain in order to solve complex scientific and
industrial problems. ANNs are high-value and low-cost tools in modelling, simulation, control,
condition monitoring, sensor validation and fault diagnosis of different systems. It have high
flexibility and robustness in modeling, simulating and diagnosing the behavior of rotating machines
even in the presence of inaccurate input data. They can provide high computational speed for
complicated tasks that require rapid response such as real-time processing of several simultaneous
signals. ANNs can also be used to improve efficiency and productivity of energy in rotating
equipment
Optical network is an emerging technology for data communication
inworldwide. The information is transmitted from the source to destination
through the fiber optics. All optical network (AON) provides good
transmission transparency, good expandability, large bandwidth, lower bit
error rate (BER), and high processing speed. Link failure and node failure
haveconsistently occurred in the traditional methods. In order to overcome
the above mentioned issues, this paper proposes a robust software defined
switching enabled fault localization framework (SDSFLF) to monitor the
node and link failure in an AON. In this work, a novel faulty node
localization (FNL) algorithm is exploited to locate the faulty node. Then, the
software defined faulty link detection (SDFLD) algorithm that addresses the
problem of link failure. The failures are localized in multi traffic stream
(MTS) and multi agent system (MAS). Thus, the throughput is improved in
SDSFLF compared than other existing methods like traditional routing and
wavelength assignment (RWA), simulated annealing (SA) algorithm, attackaware RWA (A-RWA) convex, longest path first (LPF) ordering, and
biggest source-destination node degree (BND) ordering. The performance of
the proposed algorithm is evaluated in terms of network load, wavelength
utilization, packet loss rate, and burst loss rate. Hence, proposed SDSFLF
assures that high performance is achieved than other traditional techniques.
An Analysis of Various Deep Learning Algorithms for Image Processingvivatechijri
Various applications of image processing has given it a wider scope when it comes to data analysis.
Various Machine Learning Algorithms provide a powerful environment for training modules effectively to
identify various entities of images and segment the same accordingly. Rather one can observe that though the
image classifiers like the Support Vector Machines (SVM) or Random Forest Algorithms do justice to the task,
deep learning algorithms like the Artificial Neural Networks (ANN) and its subordinates, the very well-known
and extremely powerful Algorithm Convolution Neural Networks (CNN) can provide a new dimension to the
image processing domain. It has way higher accuracy and computational power for classifying images further
and segregating their various entities as individual components of the image working region. Major focus will
be on the Region Convolution Neural Networks (R-CNN) algorithm and how well it provides the pixel-level
segmentation further using its better successors like the Fast-Faster and Mask R-CNN versions.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
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GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
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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/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
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Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...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
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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.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
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However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
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- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
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Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
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End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
2. 50 Computer Science & Information Technology (CS & IT)
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. Computer Science & Information Technology (CS & IT) 51
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. 52 Computer Science & Information Technology (CS & IT)
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 uses 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)
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
5. Computer Science & Information Technology (CS & IT) 53
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
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
6. 54 Computer Science & Information Technology (CS & IT)
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 (33
) from
243(35
)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
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.
7. Computer Science & Information Technology (CS & IT) 55
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
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.