AN OVERVIEW OF AUTO-CONFIGURATION PROTOCOLS IN MOBILE AD HOC WIRELESS MULTI-HOP NETWORK
Adel R. Alharbi, University of Tabuk, Saudi Arabia
An ad hoc wireless IP multi-hop network is a collection of wireless IP protocol capable nodes that start in an unknown physical formation in the vicinity of a wireless IP portal to a wired IP network. While some wireless nodes might be in radio (wireless) range of the portal, other nodes might only be in radio range of one or more other nodes that in turn may in range of the portal and/or other wireless nodes. IP data-grams would travel from one node to another until the data-gram is delivered to the portal or the destination node. All wireless nodes are assumed to be one or more hop away from the wireless IP portal. This paper reviews an auto configure method of a mobile ad hoc network and to route IP traffic using existing mobile ad hoc network routing protocols. This method have the best characteristics in protocol overhead, robustness, convergence time, and scalability. The optimal mobile ad hoc network routing protocol can be chosen which best meets these characteristics for the given topology and operational profile. Finally, this method will efficiently use the address space allotted to the DHCP server.
Wireless LAN, communication systems routing, mobile communications, auto-configuration protocols, MANET.
For More Details :
https://aircconline.com/csit/papers/vol9/csit91804.pdf
Volume Link :
http://airccse.org/csit/V9N18.html
IMPLEMENTATION OF VLAN VIA WIRELESS NETWORKS USING OPNET MODELER
Tareq Al-Khraishi and Muhannad Quwaider , Jordan University of Science and Technology, Jordan
A VLAN is a logical rather than physical connection that allows grouping hosts together in the same broadcast domain, so that packets are only delivered to ports that are combined to the same VLAN. By characteristic VLAN network, we can improve efficiency of wireless network and save bandwidth. Furthermore, implementing VLAN greatly improves wireless network security by decreasing the number of hosts that receive copies of frames broadcasted by switches, so hosts holding critical data are kept on a separate VLAN. This paper compares wireless network with wireless network having VLAN deployment. The proposed Network is evaluated in terms of average throughput and delay using file transfer in heavy traffic and web browsing applications. The simulation was carried out by employing OPNET 14.5 modeler simulation and the results show that the use of VLAN via wireless network had improved the performance by decreasing the traffic resulting in minimizing delay time. In addition, implementing VLAN reduces the network throughput because the traffic that is received and forwarded has a positive relationship with throughput. Furthermore, we investigated to improve the throughput in a wireless VLAN network by using ad hoc routing protocols. Evaluation, comparison of broad adhoc routing protocols like AODV, DSR, OLSR, TORA and GPR are conducted in order to show the effect of the proposed VLAN on the performance results, like throughput and delay.
WLAN, OPNET, AODV, Throughput, VLAN, Routing Protocols, Access Point.
For More Details :
https://aircconline.com/csit/papers/vol9/csit91805.pdf
Volume Link :
http://airccse.org/csit/V9N18.html
OPTIMIZING THE PERFORMANCE OF CONVOLUTIONAL NEURAL NETWORKS ON RASPBERRY PI FOR REAL-TIME OBJECT DETECTION
Hyun Woo Jung Hankuk Academy of Foreign Studies, Seoul, Republic of Korea
Deep learning has facilitated major advancements in various fields including image detection. This paper is an exploratory study on improving the performance of Convolutional Neural Network (CNN) models in environments with limited computing resources, such as the Raspberry Pi. A pretrained state-of-art algorithm for doing near-real time object detection in videos, YOLO (“You-Only-Look-Once”) CNN model, was selected for evaluating strategies for optimizing the runtime performance. Various performance analysis tools provided by the Linux kernel were used to measure CPU time and memory footprint. Our results show that loop parallelization, static compilation of weights, and flattening of convolution layers reduce the total runtime by 85% and reduce memory footprint by 53% on a Raspberry Pi 3 device. These findings suggest that the methodological improvements proposed in this work can reduce the computational overload of running CNN models on devices with limited computing resources.
Deep Learning, Convolutional Neural Networks, Raspberry Pi, real-time object detection
For More Details :
https://aircconline.com/csit/papers/vol9/csit91711.pdf
Volume Link :
http://airccse.org/csit/V9N17.html
A SYSTEMATIC EVALUATION OF MANET ROUTING PROTOCOLS OVER UDP AND TCP IN MULTI-HOP WIRELESS NETWORK
Adebayo Seyi1 and Ogunseyi Taiwo2 , 1China University of Mining and Technology, China , 2Communication University of China, China
There are genuine concerns for the right transport connection to be deployed on a particular routing protocol in order to have a reliable, fast and robust communication in spite of the size and the dynamics of the mobile ad-hoc network (MANET) topology. This paper comparatively studies the individual implementation of reactive and proactive protocols on both UDP and TCP transport connection using packet delivery ratio (PDR), throughput, end to end delay and delay variation (jitter) as quality of service (QoS) metrics. We studied the combination of both the transport connection and routing protocol that will deliver the best QoS in simple and complex network scenarios with source and destination nodes fixed and the intermediate nodes randomly moving throughout the simulation time. More so, the intrinsic characteristics of the routing protocols regarding the QoS metrics and transport connection are studied. Forty simulations were run for simple and complex multi-hop network models and the results were analyzed and presented.
MANET, Proactive, Reactive, QoS, UDP, TCP
For More Details :
https://aircconline.com/csit/papers/vol9/csit91717.pdf
Volume Link :
http://airccse.org/csit/V9N17.html
CLICK VOLUME POTENTIAL MAXIMIZATION IN AFFILIATE NETWORK
Krishna Kumar Tiwari and Ritesh Ghodrao InMobi Technology Services Pvt Ltd, Bangalore, India
An affiliate network is all about running advertiser’s campaign (acquire new user, download campaign) on multiple/chain of ad-tech companies (aka affiliates), most of the affiliate in affiliate network deals with a huge volume of clicks (pretty much 500M to 1.5B roughly, with click QPS varying from 10K to 25K). Only a small fraction for clicks leads to conversions which leads to revenue to affiliate but hosting a huge volume of clicks costs a lot based on engineering setup. The real challenge here is that we need to maintain the profit after paying for the infra cost, hence it becomes mandatory to optimize on infra cost and revenue equation. In this paper, we have presented a unique way of modeling the Infra-to-Revenue equation based on click volume and provided a Knapsack way of solving the Infra-to-Revenue equation and maximising our revenue by keeping a constraint on infra cost, which we are calling as CVPM (click volume potential maximization). We have compared CVPM with greedy based optimizations and concluded that CVPM outperforms many of these approaches in most of the real scenarios.
Infra cost optimization, Click optimization, 0-1 Knapsack, Adtech optimizations
For More Details :
https://aircconline.com/csit/papers/vol9/csit91605.pdf
Volume Link :
http://airccse.org/csit/V9N16.html
PRECEDENT CASE RETRIEVAL USING WORDNET AND DEEP RECURRENT NEURAL NETWORKS
Sai Vishwas Padigi, Mohit Mayank and S. Natarajan Department of Computer Science and Engineering, PES University, Bengaluru, India
The slowness of legal proceedings in the common law legal system is a widely known fact. Any tool which could help reduce the time taken for the resolution of a case is invaluable. Common legal systems place a great importance on precedents and retrieving the correct set of precedents is considerably time consuming. Hence, for any case whose proceedings are in progress, if there are suitable prior cases, then the court has to follow the same interpretations that were passed in the prior cases. This is to ensure that similar situations receive similar treatment, thus maintaining uniformity amongst the legal proceedings across all courts at all times. Hence, precedent cases are treated as important as any other written law (a statute) in this legal system. In this paper, we propose two new approaches to solve this information retrieval problem wherein the system accepts the current case document as the query and returns the relevant precedent cases as the result. The first approach is to calculate the document similarity using Wordnet, which is a lexical database that could be leveraged to quantify the semantic relatedness between two documents, using a semantic network. The second approach is the use of a Siamese Manhattan Long Short Term Memory network, which is a supervised model trained to understand the underlying similarity between two documents.
Information retrieval, Text similarity, Deep learning, Legal documents, Wordnet, Siamese Manhattan LSTM
For More Details :
https://aircconline.com/csit/papers/vol9/csit91608.pdf
Volume Link :
http://airccse.org/csit/V9N12.html
A LOW COST METHANE ABSORPTION FUELING SYSTEM IN WIRELESS SENSOR NETWORKS USING SBC-MS
A.Rehash Rushmi Pavitra1 and Dr. E. Srie Vidhya Janani2 1,2 Anna University Regional Campus, Madurai
The isolated accessible Methane (CH4) Absorption Fueling System (MAFS) is matured based on the mechanics of Wireless Sensor Network (WSN) comprised of gas sensing capable motes complementing a MAFS-WSN. Discrete routing protocols have been designed earlier for data collection in both compatible and divergent networks. This research presents a novel Scheduling based Clustering (SBC) with Mobile Sink (MS) strategy (SBC-MS) which supplements data collection in MAFS-WSN. The SBC-MS strategy attempts to exploit the vital parameters of energy and distance in selecting the appropriate cluster head that well suits MAFS-WSN in reliable gas detection. The MSs are exploited to reduce the energy expenditure in data communication. Extensive experimentations have been carried out with the proposed SBC-MS to ensure the QOS of MAFS-WSN in terms of schedulability and reliability. The simulation results prove that SBC-MS outperforms the earlier clustering technique M-LEACH in terms of network lifetime, energy consumption, end-to-end delay and data rate.
Methane Absorption Fueling system, Gas Sensing Capable Motes, Mobile Sinks, Scheduling and Clustering
For More Details :
https://aircconline.com/csit/papers/vol9/csit91503.pdf
Volume Link :
http://airccse.org/csit/V9N15.html
TRIT: A ROBUST TRACKER BASED ON TRIPLET NETWORK
Peng Zou and Yunfei Cai Nanjing University of Science and Technology, China
In this paper, a target tracking algorithm, TriT(Triplet Network Based Tracker), based on Triplet network is proposed to solve the problem of visual target tracking in complex scenes. Compared with Siamese-fc algorithm, which adopts a two-way feature extraction network, TriT uses three parallel convolutional neural networks to extract the features of the target in the first frame, the target in the previous frame and the search regions of the current frame, and then obtains the high-level semantic information of the three areas. Then, the features of the target in the first frame and the target in the previous frame are respectively convolved with the features of the current search region to obtain the similarity between each position in the search area and the target in the first frame and the target in the previous frame, so as to generate two similarity score maps. Then, interpolate and enlarge the two low-resolution score maps, and use the APCE value of the score maps as the medium to fuse the two score maps, according to which the position of the tracking target in the current frame can be located. Experiments in this paper have confirmed that, compared with some other real-time target tracking algorithms such as Siamese-fc, TriT has great advantages in tracking robustness and can effectively execute tracking tasks in complex scenes, such as illumination change, occlusion and interference of similar targets. Experimental results also show that the proposed algorithm has good real-time performance.
Target Tracking, High Robustness, Triplet Network, Score Maps Fusion
For More Details :
https://aircconline.com/csit/papers/vol9/csit91311.pdf
Volume Link :
http://airccse.org/csit/V9N13.html
AN ARTIFICIAL NEURAL NETWORK APPROACH FOR THE CLASSIFICATION OF HUMAN LOWER BACK PAIN
Shubham Sharma and Rene V.Mayorga, University of Regina, Canada
In today’s world, the problem of lower back pain is one of the fastest growing crucial ailments to deal with. More than half of total population on the earth, suffers from it at least once in a lifetime. Human Lower Back Pain symptoms are commonly categorized as Normal or Abnormal. In order to remedy Human Lower Back Pain, with the growth of technology over the time, many medical methods have been developed to diagnose and cure this pain at its earliest stage possible. This study aims to develop two Machine Learning (M.L.) models which can classify Human Lower Back Pain symptoms in a human body using non-conventional techniques such as Feedforward/Backpropagation Artificial Neural Networks, and Fully Connected Deep Networks. An Automatic Feature Engineering technique is implemented to extract featured data used for the classification. The proposed models are compared with respect to a Support Vector Machine model; considering different performance parameters.
Machine Learning, Artificial Neural Networks, Fully Connected Deep Networks, Support Vector Machine, Lower Back Pain, Automatic Feature Engineering technique.
For More Details :
https://aircconline.com/csit/papers/vol9/csit91313.pdf
Volume Link :
http://airccse.org/csit/V9N13.html
AUTOMATED MUSIC MAKING WITH RECURRENT NEURAL NETWORK
You Peng1, Ariel Jiang2 and Qi Lu3 1Department of Computer Science California State Polytechnic University, CA 2Department of Computer Science University of California, Irvine 3Department of Social Science University of California, CA
Today, the growing market of entertainment has placed a higher demand for music. Quality music is essential for video making, video game making, or even in any public places. However, sometimes finding a suitable list of music can be hard and expensive. This may be solved by automatic, deep-learning based music making. Using Recurrent Neural Network, computers are able to learn the patterns from existing music pieces and convert them to a possibility map. Companies like Google, Sony, and Amper are creating their applications for music generation. We plan to set up a platform where generating music can be done and retrieved directly online. With different options for genre and length, the users can conveniently generate music that fits their needs.
Music Generation, Machine Learning, RNN, Web Service
For More Details :
https://aircconline.com/csit/papers/vol9/csit91315.pdf
Volume Link :
http://airccse.org/csit/V9N13.html
FINDING MAXIMAL LOCALIZABLE REGION IN WIRELESS SENSOR NETWORKS BY MERGING RIGID CLUSTERS
Saroja Kanchi , Kettering University, Flint, MI, USA
Localization of Wireless Sensor Network (WSN) is the problem of finding the geo-locations of sensors in a sensor network deployed in various applications. Given the prolification of sensors in various applications, the localization and tracking of sensors have received considerable attention. Properties of rigidity and flexibility of the underlying graph of the WSN have been studied as a means of determining the localizability of the nodes in the WSN. In this paper, we present a new 3-merge technique for merging three rigid clusters of a network graph, into larger rigid cluster and we use this algorithm for finding maximal localizable regions within the WSN. We provide simulation results on random deployments of WSN to prove that this technique outperforms previously known algorithms for finding maximal localizable subregions. Moreover, simulation results show that the number of anchors needed to localize the entire WSN decreases due to finding large localizable regions.
Wireless Sensor Network, localization, rigidity, cluster, merging
For More Details :
https://aircconline.com/csit/papers/vol9/csit91326.pdf
Volume Link :
http://airccse.org/csit/V9N13.html
COMPARATIVE STUDY BETWEENDECISION TREES AND NEURAL NETWORKS TO PREDICTFATAL ROAD ACCIDENTSIN LEBANON
Zeinab FARHAT1, Ali KAROUNI2, Bassam DAYA3 and Pierre CHAUVET4 1EDST, Lebanese University, Lebanon, Beirut 2,3 University Institute of Technology, Lebanese University, Lebanon, Sidon 4LARIS EA, Angers University France, France, Angers
Nowadays, road traffic accidents are one of the leading causes of deaths in this world. It is a complex phenomenon leaving a significant negative impact on human’s life and properties. Classification techniques of data mining are found efficient to deal with such phenomena. After collecting data from Lebanese Internal Security Forces, data are split into training and testing sets using 10-fold cross validation. This paper aims to apply two different algorithms of Decision Trees C4.5 and CART, and various Artificial Neural Networks (MLP) in order to predict the fatality of road accidents in Lebanon. Afterwards, a comparative study is made to find the best performing algorithm. The results have shown that MLP with 2 hidden layers and 42 neurons in each layer is the best algorithm with accuracy rate of prediction (94.6%) and area under curve (AUC 95.71%).
Data mining, Fatal Road Accident Prediction, Neural Networks, Decision trees
For More Details :
https://aircconline.com/csit/papers/vol9/csit91101.pdf
Volume Link :
http://airccse.org/csit/V9N11.html
MULTI-VARIABLE LINEAR REGRESSIONBASED PREDICTION OF A COMPUTATIONALLY HEAVY LINK STABILITY METRIC FOR MOBILE SENSOR NETWORKS
Natarajan Meghanathan Jackson State University, USA
Until now, we were determining stable data gathering (DG) trees for mobile sensor networks (MSNs) using a link stability metric (computationally-light or computationally-heavy) that is directly computed on the egocentric edge network. Among such DG trees, the BPI' (complement of bipartivity index)-based DG trees were observed to be the most stable, but the BPI' metric is also computationally-heavy. Hence, we seek to build a multi-variable linear regression model to predict the BPI' values for the egocentric networks of edges using three computationally-light metrics (neighborhood overlap: NOVER, one-hop two-hop neighborhood: OTH, and normalized neighbor degree: NND) that are also computed on the egocentric edge networks. The training and testing are conducted as part of a single simulation run (i.e., in-situ). The training dataset comprises of the BPI', NOVER, OTH and NND values of randomly sampled egocentric edge networks during the first phase of the simulation (1/5th of the total simulation time). We observe the R-square values for the prediction to be above 0.85 for both low density and high density networks. We also observe the lifetimes of the predicted BPI'-based DG trees to be 87-92% and 55-75% of the actual BPI'-based DG trees for low-moderate and moderate-high density networks respectively.
Multi-variable Regression, Bipartivity Index, Computationally-Light, Computationally-Heavy, Mobile Sensor Networks, Data Gathering Tree
For More Details :
https://aircconline.com/csit/papers/vol9/csit91103.pdf
Volume Link :
http://airccse.org/csit/V9N11.html
TGRID LOCATION SERVICE IN AD-HOC NETWORKS
Baktash Motlagh Farrokhlegha Department of Computer, Technical& vocational University, IRAN
Geographic addresses are essential in position-based routing algorithms in mobile ad hoc networks, i.e. a node that intends to send a packet to some target node, has to know the target's current position. A distributed location service is required to provide each node's position to the other network nodes. Hierarchical Location Service (HGRID) has been known as a promising location service approach. In this paper we present a new approach called TGRID and describe the performance of a novel multi-level Tree-walk grid location management protocol for large scale ad hoc networks. The Tree-walk grid location service mechanism is evaluated by GLOMOSIM against well known location service protocol HGRID when increasing node density and node speed. It is observed that TGRID outperforms HGRID in terms of packet delivery fraction and storage cost and also maintains low control overhead in a uniformly randomly distributed network.
Location based routing, location service, location management, Mobile Ad Hoc Networks, HGRID, and TGRID
For More Details :
https://aircconline.com/csit/papers/vol9/csit91001.pdf
Volume Link :
http://airccse.org/csit/V9N10.html
AIRPORT CYBER SECURITY & CYBER RESILIENCE CONTROLS
Alex Mathew Department of Computer Science & Cyber Security, Bethany College, WV, USA.
Cyber Security scares are the main areas of demerits associated with the advent and widespread of internet technology. While the internet has improved life and business processes, the levels of security threats have been increasing proportionally. As such, the web and the related cyber systems have exposed the world to the state of continuous vigilance because of the existential threats of attacks. Criminals are in the constant state of attempting cybersecurity defense of various infrastructures and businesses. Airports are some of the areas where cybersecurity means a lot of things. The reason for the criticality of cybersecurity in airports concerns the high integration of internet and computer systems in the operations of airports. This paper is about airport cybersecurity and resilience controls. At the start of the article is a comprehensive introduction that provides a preview of the entire content. In the paper, there are discussions of airport intelligence classification, cybersecurity malicious threats analysis, and research methodology. A concise conclusion marks the end of the article.
Cyber Security, IOT, Resilience Controls.
For More Details :
https://aircconline.com/csit/papers/vol9/csit91007.pdf
Volume Link :
http://airccse.org/csit/V9N10.html
INTRANET SECURITY USING A LAN PACKET SNIFFER TO MONITOR TRAFFIC
Ogbu N. Henry1 and Moses Adah Agana2, 1Ebonyi State University, Nigeria ,2University of Calabar, Nigeria
This paper was designed to provide Intranet traffic monitoring by sniffing the packets at the local Area Network (LAN) server end to provide security and control. It was implemented using five computer systems configured with static Internet Protocol (IP) addresses used in monitoring the IP traffic on the network by capturing and analyzing live packets from various sources and destinations in the network. The LAN was deployed on windows 8 with a D-link 16- port switch, category 6 Ethernet cable and other LAN devices. The IP traffics were captured and analyzed using Wireshark Version 2.0.3. Four network instructions were used in the analysis of the IP traffic and the results displayed the IP and Media Access Control (MAC) address sources and destinations of the frames, Ethernet, IP addresses, User Datagram Protocol (UDP) and Hypertext Transfer Protocol (HTTP). The outcome can aid network administrators to control Intranet access and provide security.
Packet, Sniffer, Protocol, Address, Network, Frame
For More Details :
https://aircconline.com/csit/papers/vol9/csit90806.pdf
Volume Link :
http://airccse.org/csit/V9N08.html
WEAKLY-SUPERVISED NETWORK ALIGNMENT WITH ADVERSARIAL LEARNING
Nguyen Thanh Toan1, Phan Thanh Cong2 and Quan Thanh Tho1 , 1Ho Chi Minh City University of Technology, Vietnam , 2Griffith University, Queensland, Australia
Network alignment, the task of seeking the hidden underlying correspondence between nodes across networks, has become increasingly studied as an important task to multiple network analysis. A few of the many recent applications of network alignment include protein network alignment, social network reconciliation, and computer vision. However, traditional methods which are based on matrix factorization directly work on networks themselves rather than exploit their intrinsic structural consistency, and thus their performance is sensitive to structural variations of networks. Recently, many supervised approaches which leverage latent representation have been proposed. Although they can handle large-scale datasets, most of them rely on a large number of parallel anchor links which are unavailable or expensive to obtain for many domains. Therefore, in this paper, we propose the WENA Framework, a representation learning based network alignment, in which we study how to design weakly-supervised methods to align large-scale networks with a limit of ground truth available. Empirical results show that, with only two anchor links, WENA significantly outperforms existing unsupervised aligners and even outperforms state-of-the-art supervised methods that use richer resources in terms of both noise robustness and accuracy.
Network embedding, Graph mining, Network alignment, Graph matching, Knowledge representation
For More Details :
https://aircconline.com/csit/papers/vol9/csit90809.pdf
Volume Link :
http://airccse.org/csit/V9N08.html
AIRPORT CYBER SECURITY & CYBER RESILIENCE CONTROLS
Alex Mathew Department of Computer Science & Cyber Security, Bethany College, WV, USA.
Cyber Security scares are the main areas of demerits associated with the advent and widespread of internet technology. While the internet has improved life and business processes, the levels of security threats have been increasing proportionally. As such, the web and the related cyber systems have exposed the world to the state of continuous vigilance because of the existential threats of attacks. Criminals are in the constant state of attempting cybersecurity defense of various infrastructures and businesses. Airports are some of the areas where cybersecurity means a lot of things. The reason for the criticality of cybersecurity in airports concerns the high integration of internet and computer systems in the operations of airports. This paper is about airport cybersecurity and resilience controls. At the start of the article is a comprehensive introduction that provides a preview of the entire content. In the paper, there are discussions of airport intelligence classification, cybersecurity malicious threats analysis, and research methodology. A concise conclusion marks the end of the article.
Cyber Security, IOT, Resilience Controls.
For More Details :
https://aircconline.com/csit/papers/vol9/csit91007.pdf
Volume Link :
http://airccse.org/csit/V9N10.html
INTRANET SECURITY USING A LAN PACKET SNIFFER TO MONITOR TRAFFIC
Ogbu N. Henry1 and Moses Adah Agana2 , 1Ebonyi State University, Abakaliki, Nigeria , 2University of Calabar, Nigeria
This paper was designed to provide Intranet traffic monitoring by sniffing the packets at the local Area Network (LAN) server end to provide security and control. It was implemented using five computer systems configured with static Internet Protocol (IP) addresses used in monitoring the IP traffic on the network by capturing and analyzing live packets from various sources and destinations in the network. The LAN was deployed on windows 8 with a D-link 16- port switch, category 6 Ethernet cable and other LAN devices. The IP traffics were captured and analyzed using Wireshark Version 2.0.3. Four network instructions were used in the analysis of the IP traffic and the results displayed the IP and Media Access Control (MAC) address sources and destinations of the frames, Ethernet, IP addresses, User Datagram Protocol (UDP) and Hypertext Transfer Protocol (HTTP). The outcome can aid network administrators to control Intranet access and provide security.
Packet, Sniffer, Protocol, Address, Network, Frame
For More Details :
https://aircconline.com/csit/papers/vol9/csit90806.pdf
Volume Link :
http://airccse.org/csit/V9N08.html
EVOLVING RANDOM TOPOLOGIES OF SPIKING NEURAL NETWORKS FOR PATTERN RECOGNITION
Gustavo López-Vázquez1, Manuel Ornelas-Rodríguez1, Andrés Espinal2, Jorge A. Soria-Alcaraz2, Alfonso Rojas-Domínguez1, Héctor J. PugaSoberanes1, J. Martín Carpio1 and Horacio Rostro-González3 1León Institute of Technology. León, México , 1University of Guanajuato. México 3University of Guanajuato. Salamanca, México
Artificial Neural Networks (ANNs) have been successfully used in Pattern Recognition tasks. Evolutionary Spiking Neural Networks (ESNNs) constitute an approach to design third generation ANNs (also known as Spiking Neural Networks, SNNs) involving Evolutionary Algorithms (EAs) to govern some intrinsic aspects of the networks, such as topology, connections and/or parameters. Concerning the practicality of the networks, a rather simple standard is commonly used; restricted feed-forward fully-connected network topologies deprived from more complex connections are usually considered. Notwithstanding, a wider prospect of configurations in contrast to standard network topologies is available for research. In this paper, ESNNs are evolved to solve pattern classification tasks, using an EA-based algorithm known as Grammatical Evolution (GE). Experiments demonstrate competitive results and a distinctive variety of network designs when compared to a more traditional approach to design ESNNs.
Artificial Neural Networks, Spiking Neural Networks, Evolutionary Spiking Neural Networks, Evolutionary Algorithms, Grammatical Evolution
For More Details :
https://aircconline.com/csit/papers/vol9/csit90704.pdf
Volume Link :
http://airccse.org/csit/V9N07.html
INTRANET SECURITY USING A LAN PACKET SNIFFER TO MONITOR TRAFFIC
Palash Pal1, Rituparna Datta2, Aviv Segev2, and Alec Yasinsac2 , 1Burdwan University, West Bengal, India , 2University of South Alabama, , USA
System and sub-system maintenance is a significant task for every dynamic system. A plethora of approaches, both quantitative and qualitative, have been proposed to ensure the system safety and to minimize the system downtime. The rapid progress of computing technologies and different machine learning approaches makes it possible to integrate complex machine learning techniques with maintenance strategies to predict system maintenance in advance. The present work analyzes different methods of integrating an Artificial Neural Network (ANN) and ANN with Principle Component Analysis (PCA) to model and predict compressor decay state coefficient and turbine decay state coefficient of a Gas Turbine (GT) mounted on a frigate characterized by a Combined Diesel-Electric and Gas (CODLAG) propulsion plant used in naval vessels. The input parameters are GT parameters and the outputs are GT compressor and turbine decay state coefficients. Due to the presence of a large number of inputs, more hidden layers are required, and as a result a deep neural network is found appropriate. The simulation results confirm that most of the proposed models accomplish the prediction of the decay state coefficients of the gas turbine of the naval propulsion. The results show that a consistently declining hidden layers size which is proportional to the input and to the output outperforms the other neural network architectures. In addition, the results of ANN outperforms hybrid PCAANN in most cases. The ANN architecture design might be relevant to other predictive maintenance systems.
Condition based maintenance, Neural Network, Deep neural network, Principle Component Analysis (PCA), Naval propulsion
For More Details :
https://aircconline.com/csit/papers/vol9/csit90601.pdf
Volume Link :
http://airccse.org/csit/V9N06.html
IMAGE SEGMENTATION BASED ON MULTIPLEX NETWORKS AND SUPER PIXELS
Ivo S. M. de Oliveira1,2, Oscar A. C. Linares1, Ary H. M. de Oliveira3, Glenda M. Botelho3 and João Batista Neto1 1Universidade de São Paulo, Brazil 2Campus de Paraíso do Tocantins, Brazil. 3Universidade Federal do Tocantins, Brazil
Despite the large number of techniques and applications in the field of image segmentation, it is still an open research field. A recent trend in image segmentation is the usage of graph theory. This work proposes an approach which combines community detection in multiplex networks, in which a layer represents a certain image feature, with super pixels. There are approaches for the segmentation of images of good quality that use a single feature or the combination of several features of the image forming a single graph for the detection of communities and the segmentation. However, with the use of multiplex networks it is possible to use more than one image feature without the need for mathematical operations that can lead to the loss of information of the image features during the generation of the graphs. Through the related experiments, presented in this work, it is possible to identify that such method can offer quality and robust segmentations.
community detection; complex networks; image segmentation; multiplex networks; super pixels
For More Details :
https://airccj.org/CSCP/vol9/csit90304.pdf
Volume Link :
http://airccse.org/csit/V9N03.html
NETWORK SECURITY ARCHITECTURE AND APPLICATIONS BASED ON CONTEXT-AWARE SECURITY
Hoon Ko1, Chang Choi2, Pankoo Kim3 and Junho Choi4 , 1,2,3,4Chosun University, Gwangju, South Korea
The number of services and smart devices which require context is increasing, and there is a clear need for new security policies which provide security that is convenient and flexible for the user. In particular, there is an urgent need for new security policies regarding IT vulnerability layers for children, the elderly, and the disabled who experience many difficulties using current security technology. For a convenient and flexible security policy, it is necessary to collect and analyze data such as user service use patterns, locations, etc., which can be used to distinguish attack contexts and define a security service provision technology which is suitable to the user. This study has designed a user context-aware network security architecture which reflects the aforementioned requirements, collected user context-aware data, studied a user context analysis platform, and studied and analyzed context-aware security applications.
Context-aware Security, Network Security Policy, Malicious Code Detection
For More Details :
https://airccj.org/CSCP/vol9/csit90308.pdf
Volume Link :
http://airccse.org/csit/V9N03.html
IN-VEHICLE CAMERA IMAGES PREDICTION BY GENERATIVE ADVERSARIAL NETWORK
Junta Watanabe and Tad Gonsalves Faculty of Science & Technology Sophia University, Japan
Moving object detection is one of the fundamental technologies necessary to realize autonomous driving. In this study, we propose the prediction of an in-vehicle camera image by Generative Adversarial Network (GAN). From the past images input to the system, it predicts the future images at the output. By predicting the motion of a moving object, it can predict the destination of the moving object. The proposed model can predict the motion of moving objects such as cars, bicycles, and pedestrians.
Deep Learning, Image Processing, Convolutional Neural Network, GAN, DGAN
For More Details :
https://airccj.org/CSCP/vol9/csit90205.pdf
Volume Link :
http://airccse.org/csit/V9N02.html
DETECTION OF HATE SPEECH IN SOCIAL NETWORKS: A SURVEY ON MULTILINGUAL CORPUS
Areej Al-Hassan1 and Hmood Al-Dossari2 , 1,2King Saud University, Riyadh, Saudi Arabia
In social media platforms, hate speech can be a reason of “cyber conflict” which can affect social life in both of individual-level and country-level. Hateful and antagonistic content propagated via social networks has the potential to cause harm and suffering on an individual basis and lead to social tension and disorder beyond cyber space. However, social networks cannot control all the content that users post. For this reason, there is a demand for automatic detection of hate speech. This demand particularly raises when the content is written in complex languages (e.g. Arabic). Arabic text is known with its challenges, complexity and scarcity of its resources. This paper will present a background on hate speech and its related detection approaches. In addition, the recent contributions on hate speech and its related anti-social behaviour topics will be reviewed. Finally, challenges and recommendations for the Arabic hate speech detection problem will be presented.
Text Mining, Social Networks, Hate Speech, Natural Language Processing, Arabic NLP
For More Details :
https://airccj.org/CSCP/vol9/csit90208.pdf
Volume Link :
http://airccse.org/csit/V9N02.html
MAGNETIC ANOMALIES DUE TO 2-D CYLINDRICAL STRUCTURES - AN ARTIFICIAL NEURAL NETWORK BASED INVERSION
Bhagwan Das Mamidala1 and Sundararajan Narasimman2 , 1Osmania University, India, 2Sultan Qaboos University, Oman
Application of Artificial Neural Network Committee Machine (ANNCM) for the inversion of magnetic anomalies caused by a long-2D horizontal circular cylinder is presented. Although, the subsurface targets are of arbitrary shape, they are assumed to be regular geometrical shape for convenience of mathematical analysis. ANNCM inversion extract the parameters of the causative subsurface targets include depth to the centre of the cylinder (Z), the inclination of magnetic vector(Ɵ) and the constant term (A) comprising the radius(R) and the intensity of the magnetic field (I). The method of inversion is demonstrated over a theoretical model with and without random noise in order to study the effect of noise on the technique and then extended to real field data. It is noted that the method under discussion ensures fairly accurate results even in the presence of noise. ANNCM analysis of vertical magnetic anomaly near Karimnagar, Telangana, India, has shown satisfactory results in comparison with other inversion techniques that are in vogue.
Magnetic anomaly, Artificial Neural Network, Committee machine, Levenberg – Marquardt algorithm, Hilbert transform, modified Hilbert transform.
For More Details :
https://airccj.org/CSCP/vol9/csit90105.pdf
Volume Link :
http://airccse.org/csit/V9N01.html