Computer Science & Engineering

Ad-hoc network consists of a set of identical nodes that move freely and independently and communicate via wireless links. The most interesting feature of this network is that it does not require any predefined infrastructure or central administration and hence it is very suitable for establishing temporary communication links in emergency situations. This flexibility however is achieved at the price of communication link uncertainties due to frequent topology changes. In this article we describe the system dynamics using the proven concept of time series modeling. Specifically, we analyze variations of the number of neighbor nodes of a particular node over a geographical area and for given total number of nodes assuming different values of (i) the speeds of nodes, (ii) the transmission powers, (iii) sampling periods and (iv) different mobility patterns. We consider three different mobility models: (i) Gaussian mobility model, (ii) random walk mobility model and (iii) random way point mobility model. The number of neighbor nodes of a particular node behaves as a random variable for any mobility pattern. Through our analysis we find that the variation of the number of neighbor nodes can be well modeled by an autoregressive AR(p) model. The values of p evaluated for different scenarios are found to be in the range between 1 and 5. Moreover, we also investigate the relationship between the speed and the time of measurements, and the transmission range of a specific node under various mobility patterns.

Ad hoc network consists of a set of identical nodes that move freely and independently and communicate among themselves via wireless links. The most interesting feature of this network is that they do not require any existing infrastructure of central administration and hence is very suitable for temporary communication links in an emergency situation. This flexibility, however, is achieved at a price of communication uncertainty induced due to frequent topology changes. In this article, we have tried to identify the system dynamics using the proven concepts of time series modeling. Here, we have analyzed variation of path length between a particular source destination pair nodes over a fixed area for different mobility patterns under different routing algorithm. We have considered four different mobility models—(i) Gauss-Markov mobility model, (ii) Manhattan Grid mobility model and (iii) Random Way Point mobility model and (iv) Reference Point Group mobility model. The routing protocols under which, we carried out our experiments are (i) Ad hoc On demand Distance Vector routing (AODV), (ii) Destination Sequenced Distance Vector routing (DSDV) and (iii) Dynamic Source Routing (DSR). The path length between two particular nodes behaves as a random variable for all mobility models for all routing algorithms. The pattern of path length for every combination of mobility model and for every routing protocol can be well modeled as an autoregressive model of order p i.e. AR(p). The order p is estimated and it is found that most of them are of order unity only. We also calculate the average path length for all mobility models and for all routing algorithms.

Mobile Ad-hoc networks(MANET)s are self organizing networks which can form a communication network without any fixed infrastructure. The constant bit rate (CBR) traffic is very well known traffic model for mobile Ad-hoc network. CBR traffic generates data packets at a constant rate. This constant rate packet generational is good enough for text data packets. The multimedia applications generally generate packets which sometimes increase and might be sometimes followed by some idle period. CBR traffic model does not accommodate the specific features of multimedia data. The day by day increase of multimedia data prompts us to investigate some other traffic models which can accommodate the specific features of these data. One such traffic model is Exponential traffic model which is based on exponential distribution. The other model is Pareto traffic model based on Pareto distribution. In this article, we have tried to study the behavior of mobile Ad-hoc network routing protocols under Exponential and Pareto traffic sources for nodes moving with Reference Point Group Mobility model (RPGM). To the best of our knowledge, this is the first attempt to analyze the effect of traffic patterns with Reference Point Group Mobility (RPGM) model on mobile ad hoc network routing. We have chosen the Normalized routing load, Packet Delivery Fraction and throughput as our figures of merit to compare various protocols. We found high Normalized Routing Load for both Exponential and Pareto traffic compared to CBR traffic in both Dynamic Source Routing (DSR) and Ad hoc On-demand Distance Vector protocol. The Packet Delivery Fraction is comparable in all types of traffic patterns and routing protocols. Throughput is high for Ad hoc On-demand Distance Vector (AODV) routing protocol across all traffic models.

Web database is combination of database and web technology. Web database is placed on the Internet, there are many security problems. Web and distributed databases play the key role in most of these Web applications and thus it is critical to protect them from unauthorized access and malicious attacks. The secrecy and the integrity are two important demands of security system. When database access control and the network security are addressed separately, the security systems are not optimized sufficiently as a whole. We propose a method of integrating network security with criterion based access control to handle network security and the fine grained Web database access control simultaneously. This access control mechanism is applicable for any existing web databases and is capable to prevent many kinds of attacks, thus significantly decreases the web databases' attack surface.

n the information age we are drowning with information. To
secure the databases in this information is a wild task, because
the information can cost millions of dollars. Web and distributed
databases play the key role in most of these Web applications and
thus it is critical to protect them from unauthorized access and
malicious attacks. The secrecy and the integrity are two important
demands of security system. When database access control and
the network security are addressed separately, the security systems
are not optimized sufficiently as a whole. We propose a method of
integrating network security with criterion based access control to
handle network security and the fine grained Web database access
control simultaneously. In this paper we have presented a novel
approach to protect the databases in the web server through cells
which we can call the fine grains.

Information is the most valuable asset
for organizations. One of the goals of
organizations is to share their data and at the
same time to enforce their policies. Web database
is a combined production with database
technology and Web technology. Web database is
placed on the Internet, there are many security
problems. The secrecy and the integrity are two
important demands of security system. When
database access control and the network security
are addressed separately, the security systems
are not optimized sufficiently as a whole. Fine-
grained access control (FGAC) must be
supported by web relational databases to satisfy
the requirements of privacy preserving and
Internet-based applications. We propose a model
of integrating network security with criterion
based access control to handle network security
and the fine grained Web database access control
simultaneously. We have implemented our model
in college database and performance is evaluated.
Whenever any unauthorized user altered our
data a system called Web-Secure report to the
authorized user via E-mail or Short Message
Service (SMS). The implementation results show
that how our model is suitable for web database
security.

Diabetes is a chronic disease and a major public health challenge worldwide.
It is a disease in which the blood glucose level is higher than normal. Diabetes
happens when a body is not able to produce or respond properly to insulin
which is needed to maintain the rate of glucose. There are several approaches
of Soft Computing such as Support vector machines (SVMs), Ant Colony
Optimization (ACO), Rough Sets, Neural Network, Fuzzy Logic, Genetic
Algorithm, etc. which diagnosis of diabetes. The focus of this paper is on the
survey of several research paper which diagnosis of diabetes disease by soft
computing approaches. This paper would be concluded with the suggestions
on the future research directions.

Diabetes is a condition in which the amount of sugar in the blood is higher than normal. Classification systems have been widely used in medical domain to explore patient’s data and extract a predictive model or set of rules. The prime objective of this research work is to facilitate a better diagnosis (classification) of diabetes disease. There are already several methodology which have been implemented on classification for the diabetes disease. The proposed methodology implemented work in 2 stages: (a) In the first stage Genetic Algorithm (GA) has been used as a feature selection on Pima Indian Diabetes Dataset. (b) In the second stage, Multilayer Perceptron Neural Network (MLP NN) has been used for the classification on the selected feature. GA is noted to reduce not only the cost and computation time of the diagnostic process, but the proposed approach also improved the accuracy of classification. The experimental results obtained classification accuracy (79.1304%) and ROC (0.842) show that GA and MLP NN can be successfully used for the diagnosing of diabetes disease.

Classification is an efficient and most widely used technique in various applications, such as medical diagnosis of diabetes patients. There are various techniques implemented for the classification of diabetes patients, such as using supervised learning approach of Support Vector Machine (SVM). This technique provides not only the high accuracy of classification but also the high true positive rate when applied on some popular diabetes datasets, such as Pima Indian Diabetes Dataset. This paper summarises and compares various techniques that are implemented for the classification of medical diabetes diagnosis on various datasets. The techniques are analysed and compared on the basis of their advantages, issues, classification accuracy. So that on the basis of their issues a new and efficient technique for the classification of diabetes patients can be implemented.. She has approximately 9 years of teaching and 11 years of research experiences. She has approximately 40 international publications. Her research areas include cloud computing, big data, NLP, soft computing, bioinformatics, AI and machine learning, data mining, etc.

Diabetes means blood sugar is above desired level on a sustained basis. The prime objective of this research work is to provide a better classification of diabetes. There are already several existing method, which have been implemented for the classification of diabetes dataset. In medical sector, the classifications systems have been widely used to exploit the patient's data and make the predictive models or build set of rules. In this manuscript firstly NBs used for the classification on all the attributes and then GA used as an attribute selection and NBs used on that selected attribute for classification. The experimental results show the performance of this work on PIDD and provide better classification for diagnosis. 2.1 Used diabetes disease dataset The Pima Indian Diabetes Dataset (PIDD) has been taken from the UCI Machine Learning repository. The same dataset used in the reference (Polat

The modern society is prone to many life-threatening diseases, which if diagnosed early, can be easily controlled. The implementation of a disease diagnostic system has gained popularity over the years. The main aim of this research is to provide a better diagnosis of diabetes disease. There are already several existing methods, which have been implemented for the diagnosis of diabetes dataset. Here, the proposed approach consists of two stages: in first stage Genetic algorithm (GA) used as an attribute (feature) selection which reduces 4 attributes among 8 attributes, and in the second stage Radial Basis Function Neural Network (RBF NN) has been used for classification on selected attributes among all the attributes. The experimental results show the performance of the proposed methodology on Pima Indian Diabetes Dataset (PIDD) and provide better classification for diagnosis of diabetes patients on PIDD. GA is removing insignificant features, reducing the cost and computation time and improving the accuracy, ROC of classification. The proposed method can also be used for other kinds of medical diseases.

Thunderstorms are fascinating and elegant event, which occurred frequently all over the world. When sudden rumbling of sound associated with a bolt of lightning flashed across the sky, then thunderstorm is said to be occurring. Lightning is associated with every thunderstorm, which kills more people than hurricanes and tornadoes. Heavy rain from thunderstorm causes extensive loss of property and leads to flooding. Accurate prediction of thunderstorm is a difficult task in weather forecasting due to its temporal extension and spatial either physically or dynamically. Different technological and scientific researchers are carried on forecasting of thunderstorm and lightning in advance to reduce damages. In this regard, there are many different methodologies have been proposed such as statistical, machine learning, numerical weather prediction, weather research forecasting, image processing etc. This paper focuses on the survey of several research papers on the thunderstorm and lightning using soft computing and data mining techniques such as neural network, rough set, support vector machine, fuzzy logic, genetic algorithm, k-means clustering, k-nearest neighbor etc. This paper has been also enclosed the suggestions on the future research direction.

Diabetes is a condition in which the amount of sugar in the blood is higher than normal. Classification systems have been widely used in the medical domain to explore patient's data and extract a predictive model or set of rules. The prime objective of this research work is to facilitate a better diagnosis (classification) of diabetes disease. There are already several methodologies which have been implemented on classification for the diabetes disease. The proposed methodology implemented work in 2 stages: (a) In the first stage Genetic Algorithm (GA) has been used as a feature selection method on Pima Indian Diabetes Dataset (PIDD) (b) In the second stage, J48graft Decision Tree (J48graft DT) has been used for the classification and prediction on the selected feature. Early diagnosis of any disease with less cost is preferable. Diabetes is also one of such diseases. GA is noted to reduce not only the storage capacity, cost and computation time of the diagnostic process, but the proposed approach also improved the ROC of classification. The experimental results obtained classification accuracy (74.7826%) and ROC (0.786) show that GA and J48graft DT can be successfully used for the diagnosing of diabetes disease.

The modern society is prone to many life-threatening diseases which if diagnosis early can be easily controlled. The implementation of a disease diagnostic system has gained popularity over the years. The main aim of this research is to provide a better diagnosis of diabetes. There are already several existing methods, which have been implemented for the diagnosis of diabetes. In this manuscript, firstly, Polynomial Kernel, RBF Kernel, Sigmoid Function Kernel, Linear Kernel SVM used for the classification of PIDD. Secondly GA used as an Attribute selection method and then used Polynomial Kernel, RBF Kernel, Sigmoid Function Kernel, Linear Kernel SVM on that selected attributes of PIDD for classification. So, here compared the results with and without GA in PIDD, and Linear Kernel proved better among all of the noted above classification methods. It directly seems in the paper that GA is removing insignificant features, reducing the cost and computation time and improving the accuracy, ROC of classification. The proposed method can be also used for other kinds of medical diseases.

Environmental disasters affect the economy, biodiversity, human life, and living organisms. Thunderstorms are one of such environmental disaster. By using proper methodology of forecasting thunderstorms, the adverse effects can be reduced. The prediction of thunderstorms is the most difficult task in weather forecasting due to their temporal and spatial extension either physically or dynamically. Lightning is associated with thunderstorms, which causes wildfires, kills people and other living organisms. Heavy rain from thunderstorms causes flash flooding. In this regard, several researchers have proposed different methodology such as statistical, numerical mode, data mining, soft computing, and machine learning for forecasting of severe weather to reduce the damages. This chapter focuses existing classification methods on thunderstorms and lightning prediction. This chapter includes suggestions on the future research directions.

Bu bildiride akıllı telefonlar üzerindeki ivmeölçerler kullanılarak insan hareketlerinin çevrimiçi tanınması konusunda sınıflandırma yöntemlerinin başarım değerlendirmesi araştırılmıştır. Öncelikle Naif Bayes ve en yakın mesafe sınıflandırma algoritmalarının başarım değerlendirmeleri gerçeklenmiş ve en yakın mesafe ve K-en yakın komşu (KNN) sınıflandırma yöntemlerinin gelişmişi olan, kümelenmiş KNN algoritması önerilmiştir. Kümelenmiş KNN algoritması, her aktivite için azaltılmış eğitim kümeleri oluşturarak orijinal KNN yönteminin karmaşıklığını azaltmakta ve sınıflandırma bu azaltılmış eğitim kümeleri üzerinden yapılmaktadır. Sınıflandırma yöntemlerinin başarımı beş denek ile yapılan deneylerle incelenmiş ve koşma, yürüme, oturma ve ayakta durma aktivitelerinin sınıflandırmasında Naif Bayes yönteminin doğruluk başarımı tatmin edici sonuçlar vermezken, kümelenmiş KNN algoritmasının ümit verici sonuçlar verdiği görülmüştür.

Information is the most valuable asset for organizations. One of the goals of organizations is to share their data and at the same time to enforce their policies. Web database is a combined production with database technology and Web technology. Web database is placed on the Internet, there are many security problems. The secrecy and the integrity are two important demands of security system. When database access control and the network security are addressed separately, the security systems are not optimized sufficiently as a whole. Finegrained access control (FGAC) must be supported by web relational databases to satisfy the requirements of privacy preserving and Internet-based applications. We propose a model of integrating network security with criterion based access control to handle network security and the fine grained Web database access control simultaneously. We have implemented our model in college database and performance is evaluated. Whenever any unauthorized user altered our data a system called Web-Secure report to the authorized user via E-mail or Short Message Service (SMS). The implementation results show that how our model is suitable for web database security.

Diabetes is a condition in which the amount of sugar in the blood is higher than normal. Classification systems have been widely used in the medical domain to explore patient's data and extract a predictive model or set of rules. The prime objective of this research work is to facilitate a better diagnosis (classification) of diabetes disease. There are already several methodologies which have been implemented on classification for the diabetes disease. The proposed methodology implemented work in 2 stages: (a) In the first stage Genetic Algorithm (GA) has been used as a feature selection method on Pima Indian Diabetes Dataset (PIDD) (b) In the second stage, J48graft Decision Tree (J48graft DT) has been used for the classification and prediction on the selected feature. Early diagnosis of any disease with less cost is preferable. Diabetes is also one of such diseases. GA is noted to reduce not only the storage capacity, cost and computation time of the diagnostic process, but the proposed approach also improved the ROC of classification. The experimental results obtained classification accuracy (74.7826%) and ROC (0.786) show that GA and J48graft DT can be successfully used for the diagnosing of diabetes disease.

Diabetes is a condition in which the amount of sugar in the blood is higher than normal. Classification systems have been widely used in the medical domain to explore patient's data and extract a predictive model or set of rules. The prime objective of this research work is to facilitate a better diagnosis (classification) of diabetes disease. There are already several methodologies which have been implemented on classification for the diabetes disease. The proposed methodology implemented work in 2 stages: (a) In the first stage Genetic Algorithm (GA) has been used as a feature selection method on Pima Indian Diabetes Dataset (PIDD) (b) In the second stage, J48graft Decision Tree (J48graft DT) has been used for the classification and prediction on the selected feature. Early diagnosis of any disease with less cost is preferable. Diabetes is also one of such diseases. GA is noted to reduce not only the storage capacity, cost and computation time of the diagnostic process, but the proposed approach also improved the ROC of classification. The experimental results obtained classification accuracy (74.7826%) and ROC (0.786) show that GA and J48graft DT can be successfully used for the diagnosing of diabetes disease.