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 approach is novel for vehicle routing analysis but has limitations like static road conditions that could be improved in future work.
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.
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.
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.
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.
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
Using social media in education provides learners with an informal way for communication. Informal communication tends to remove barriers and hence promotes student engagement. This paper presents our experience in using three different social media technologies in teaching software project management course. We conducted different surveys at the end of every semester to evaluate students’ satisfaction and engagement. Results show that using social media enhances students’ engagement and satisfaction. However, familiarity with the tool is an important factor for student satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The amount of piracy in the streaming digital content in general and the music industry in specific is posing a real challenge to digital content owners. This paper presents a DRM solution to monetizing, tracking and controlling online streaming content cross platforms for IP enabled devices. The paper benefits from the current advances in Blockchain and cryptocurrencies. Specifically, the paper presents a Global Music Asset Assurance (GoMAA) digital currency and presents the iMediaStreams Blockchain to enable the secure dissemination and tracking of the streamed content. The proposed solution provides the data owner the ability to control the flow of information even after it has been released by creating a secure, selfinstalled, cross platform reader located on the digital content file header. The proposed system provides the content owners’ options to manage their digital information (audio, video, speech, etc.), including the tracking of the most consumed segments, once it is release. The system benefits from token distribution between the content owner (Music Bands), the content distributer (Online Radio Stations) and the content consumer(Fans) on the system blockchain.
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This paper discusses the importance of verb suffix mapping in Discourse translation system. In
discourse translation, the crucial step is Anaphora resolution and generation. In Anaphora
resolution, cohesion links like pronouns are identified between portions of text. These binders
make the text cohesive by referring to nouns appearing in the previous sentences or nouns
appearing in sentences after them. In Machine Translation systems, to convert the source
language sentences into meaningful target language sentences the verb suffixes should be
changed as per the cohesion links identified. This step of translation process is emphasized in
the present paper. Specifically, the discussion is on how the verbs change according to the
subjects and anaphors. To explain the concept, English is used as the source language (SL) and
an Indian language Telugu is used as Target language (TL)
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The using of information technology resources is rapidly increasing in organizations,
businesses, and even governments, that led to arise various attacks, and vulnerabilities in the
field. All resources make it a must to do frequently a penetration test (PT) for the environment
and see what can the attacker gain and what is the current environment's vulnerabilities. This
paper reviews some of the automated penetration testing techniques and presents its
enhancement over the traditional manual approaches. To the best of our knowledge, it is the
first research that takes into consideration the concept of penetration testing and the standards
in the area.This research tackles the comparison between the manual and automated
penetration testing, the main tools used in penetration testing. Additionally, compares between
some methodologies used to build an automated penetration testing platform.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
In order to treat and analyze real datasets, fuzzy association rules have been proposed. Several
algorithms have been introduced to extract these rules. However, these algorithms suffer from
the problems of utility, redundancy and large number of extracted fuzzy association rules. The
expert will then be confronted with this huge amount of fuzzy association rules. The task of
validation becomes fastidious. In order to solve these problems, we propose a new validation
method. Our method is based on three steps. (i) We extract a generic base of non redundant
fuzzy association rules by applying EFAR-PN algorithm based on fuzzy formal concept analysis.
(ii) we categorize extracted rules into groups and (iii) we evaluate the relevance of these rules
using structural equation model.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHcscpconf
Data collection is an essential, but manpower intensive procedure in ecological research. An
algorithm was developed by the author which incorporated two important computer vision
techniques to automate data cataloging for butterfly measurements. Optical Character
Recognition is used for character recognition and Contour Detection is used for imageprocessing.
Proper pre-processing is first done on the images to improve accuracy. Although
there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify
words of basic fonts. Contour detection is an advanced technique that can be utilized to
measure an image. Shapes and mathematical calculations are crucial in determining the precise
location of the points on which to draw the body and forewing lines of the butterfly. Overall,
92% accuracy were achieved by the program for the set of butterflies measured.
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXTcscpconf
This article presents Part of Speech tagging for Nepali text using General Regression Neural
Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is
trained and validated on both training and testing data. It is observed that 96.13% words are
correctly being tagged on training set whereas 74.38% words are tagged correctly on testing
data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
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
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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.