An Empirical Study for Defect Prediction using Clusteringidescitation
Â
Reliably predicting defects in the software is one of
the holy grails of software engineering. Researchers have
devised and implemented a method of defect prediction
approaches varying in terms of accuracy, complexity, and the
input data they require. An accurate prediction of the number
of defects in a software product during system testing
contributes not only to the management of the system testing
process but also to the estimation of the productâs required
maintenance [1]. A prediction of the number of remaining
defects in an inspected artefact can be used for decision making.
Defective software modules cause software failures, increase
development and maintenance costs, and decrease customer
satisfaction. It strives to improve software quality and testing
efficiency by constructing predictive models from code
attributes to enable a timely identification of fault-prone
modules [2]. In this paper, we will discuss clustering techniques
are used for software defect prediction. This helps the
developers to detect software defects and correct them.
Unsupervised techniques may be used for defect prediction in
software modules, more so in those cases where defect labels
are not available [3].
AN EFFECTIVE SEMANTIC ENCRYPTED RELATIONAL DATA USING K-NN MODELijsptm
Â
Data exchange and data publishing are becoming an important part of business and academic practices.
Data owners need to maintain the rights over the datasets they share. A right-protection mechanism can be
provided for the ownership of shared data, without revealing its usage under a wide range of machine
learning and mining. In the approach provide two algorithms: the Nearest-Neighbors (NN) and determiner
preserves the Minimum Spanning Tree (MST). The K-NN protocol guarantees that relations between object
remain unaltered. The algorithms preserve the both right protection and utility preservation. The rightprotection
scheme is based on watermarking. Watermarking methodology preserves the distance
relationships.
Due to continuous growth of the Internet technology, it needs to establish security mechanism. Intrusion Detection System (IDS) is increasingly becoming a crucial component for computer and network security systems. Most of the existing intrusion detection techniques emphasize on building intrusion detection model based on all features provided. Some of these features are irrelevant or redundant. This paper is proposed to identify important input features in building IDS that is computationally efficient and effective. In this paper, we identify important attributes for each attack type by analyzing the detection rate. We input the specific attributes for each attack types to classify using Naive Bayes, and Random Forest. We perform our experiments on NSL-KDD intrusion detection data set, which consists of selected records of the complete KDD Cup 1999 intrusion detection dataset.
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Classical methods for classification of pixels in multispectral images include supervised classifiers such as
the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector
machines, and decision trees. Recently, there has been an increase of interest in ensemble learning â a
method that generates many classifiers and aggregates their results. Breiman proposed Random Forestin
2001 for classification and clustering. Random Forest grows many decision trees for classification. To
classify a new object, the input vector is run through each decision tree in the forest. Each tree gives a
classification. The forest chooses the classification having the most votes. Random Forest provides a robust
algorithm for classifying large datasets. The potential of Random Forest is not been explored in analyzing
multispectral satellite images. To evaluate the performance of Random Forest, we classified multispectral
images using various classifiers such as the maximum likelihood classifier, neural network, support vector
machine (SVM), and Random Forest and compare their results.
An Empirical Study for Defect Prediction using Clusteringidescitation
Â
Reliably predicting defects in the software is one of
the holy grails of software engineering. Researchers have
devised and implemented a method of defect prediction
approaches varying in terms of accuracy, complexity, and the
input data they require. An accurate prediction of the number
of defects in a software product during system testing
contributes not only to the management of the system testing
process but also to the estimation of the productâs required
maintenance [1]. A prediction of the number of remaining
defects in an inspected artefact can be used for decision making.
Defective software modules cause software failures, increase
development and maintenance costs, and decrease customer
satisfaction. It strives to improve software quality and testing
efficiency by constructing predictive models from code
attributes to enable a timely identification of fault-prone
modules [2]. In this paper, we will discuss clustering techniques
are used for software defect prediction. This helps the
developers to detect software defects and correct them.
Unsupervised techniques may be used for defect prediction in
software modules, more so in those cases where defect labels
are not available [3].
AN EFFECTIVE SEMANTIC ENCRYPTED RELATIONAL DATA USING K-NN MODELijsptm
Â
Data exchange and data publishing are becoming an important part of business and academic practices.
Data owners need to maintain the rights over the datasets they share. A right-protection mechanism can be
provided for the ownership of shared data, without revealing its usage under a wide range of machine
learning and mining. In the approach provide two algorithms: the Nearest-Neighbors (NN) and determiner
preserves the Minimum Spanning Tree (MST). The K-NN protocol guarantees that relations between object
remain unaltered. The algorithms preserve the both right protection and utility preservation. The rightprotection
scheme is based on watermarking. Watermarking methodology preserves the distance
relationships.
Due to continuous growth of the Internet technology, it needs to establish security mechanism. Intrusion Detection System (IDS) is increasingly becoming a crucial component for computer and network security systems. Most of the existing intrusion detection techniques emphasize on building intrusion detection model based on all features provided. Some of these features are irrelevant or redundant. This paper is proposed to identify important input features in building IDS that is computationally efficient and effective. In this paper, we identify important attributes for each attack type by analyzing the detection rate. We input the specific attributes for each attack types to classify using Naive Bayes, and Random Forest. We perform our experiments on NSL-KDD intrusion detection data set, which consists of selected records of the complete KDD Cup 1999 intrusion detection dataset.
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Classical methods for classification of pixels in multispectral images include supervised classifiers such as
the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector
machines, and decision trees. Recently, there has been an increase of interest in ensemble learning â a
method that generates many classifiers and aggregates their results. Breiman proposed Random Forestin
2001 for classification and clustering. Random Forest grows many decision trees for classification. To
classify a new object, the input vector is run through each decision tree in the forest. Each tree gives a
classification. The forest chooses the classification having the most votes. Random Forest provides a robust
algorithm for classifying large datasets. The potential of Random Forest is not been explored in analyzing
multispectral satellite images. To evaluate the performance of Random Forest, we classified multispectral
images using various classifiers such as the maximum likelihood classifier, neural network, support vector
machine (SVM), and Random Forest and compare their results.
Design of a Reliable Wireless Sensor Network with Optimized Energy Efficiency...paperpublications3
Â
Abstract: Data gathering in wireless sensor network (WSN) is a crucial field of study and it can be optimized various algorithms like clustering, aggregation, and cryptographic technique in order to reliably transfer data between sensor and sink. But these techniques do not provide an optimized data gathering wireless sensor network because of the fact that they do not leverage the advantages of various techniques. Our problem definition is to create a reliable data gathering wireless sensor network which ensures good energy efficiency and lower delay as compared to existing techniques.
Keywords: Aggregation, Clustering, Data Gathering, Cryptography, Data Compression, Run Length Encoding.
Title: Design of a Reliable Wireless Sensor Network with Optimized Energy Efficiency and Delay
Author: Neelam Ashok Meshram
ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Paper Publications
Using Neighborâs State Cross-correlation to Accelerate Adaptation in Docitiv...paperpublications3
Â
Abstract: In WSN, sensor nodes have limited energy budget therefore this paper mainly focus on power saving by using the docition paradigm. Docition is a new teacher-student paradigm proposed to improve cognitive radio. Although it improves the infrastrucÂŹture based networks it has a weakness in case of ad-hoc mobile netÂŹworks. The energy constraints and the total mobility of the netÂŹwork complicate the selection of the appropriate teacher for a student. By selecting the wrong teacher, there is a high probabilÂŹity that the taught information may be faulty, and thus the student radio diverges from the best state. This causes a high amount of energy loss, though the most important concern in ad-hoc networks is energy limitation. In this paper, we propose a dynamic docition for teacher selection based on the auto-correlaÂŹtion degree of the teacherâs candidate environment and the cross-correlation degree between the teacher candidate and the student environments. We validate our approach in the context of coexistÂŹence between WSN and WiFi. The WSN detects, models and exploits the unused time slots in the electromagnetic spectrum, left by WiFi, using dynamic docition. The simulation results show that the use of dynamic docition outperforms the existing docition in mobile networks. The improvements are shown through the low link overhead percentage (20% less overhead) and the low packet loss ratio (30% improvement).
Keywords: Docitive; Online Prediction Problem; WSN; pareto model; IEEE802.11 b/g;cognitive radio.
Title: Using Neighborâs State Cross-correlation to Accelerate Adaptation in Docitive WSN
Author: Dr. Charbel Nicolas
ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Paper Publications
A Novel Classification via Clustering Method for Anomaly Based Network Intrus...IDES Editor
Â
Intrusion detection in the internet is an active
area of research. Intruders can be classified into two
types, namely; external intruders who are unauthorized
users of the computers they attack, and internal
intruders, who have permission to access the system but
with some restrictions. The aim of this paper is to present
a methodology to recognize attacks during the normal
activities in a system. A novel classification via sequential
information bottleneck (sIB) clustering algorithm has
been proposed to build an efficient anomaly based
network intrusion detection model. We have compared
our proposed method with other clustering algorithms
like X-Means, Farthest First, Filtered clusters, DBSCAN,
K-Means, and EM (Expectation-Maximization)
clustering in order to find the suitability of our proposed
algorithm. A subset of KDDCup 1999 intrusion detection
benchmark dataset has been used for the experiment.
Results show that the proposed method is efficient in
terms of detection accuracy, low false positive rate in
comparison to the other existing methods.
A NOVEL ROUTING PROTOCOL FOR TARGET TRACKING IN WIRELESS SENSOR NETWORKSIJCNCJournal
Â
Wireless sensor networks (WSNs) are large scale integration consists of hundreds or thousands or more
number of sensor nodes. They are tiny, low cost, low weight, and limited battery, primary storage,
processing power. They have wireless capabilities to monitor physical or environmental conditions. This
paper compared the performance analysis of some existing routing protocols for target tracking
application with proposed hierarchical binary tree structure to store the routing information. The sensed
information is stored in controlled way at multiple sensor nodes (e.g. node, parent node and grandparent
node) which deployed using complete binary tree data structure. This reduces traffic implosion and
geographical overlapping. Simulation result showed improved network lifetime by 20%, target detection
probability by 25%, and reduces error rate by 20%, energy efficiency, fault tolerance, and routing
efficiency. We have evaluated our proposed algorithm using NS2.
Improved Performance of Unsupervised Method by Renovated K-MeansIJASCSE
Â
Clustering is a separation of data into groups of similar objects. Every group called cluster consists of objects that are similar to one another and dissimilar to objects of other groups. In this paper, the K-Means algorithm is implemented by three distance functions and to identify the optimal distance function for clustering methods. The proposed K-Means algorithm is compared with K-Means, Static Weighted K-Means (SWK-Means) and Dynamic Weighted K-Means (DWK-Means) algorithm by using Davis Bouldin index, Execution Time and Iteration count methods. Experimental results show that the proposed K-Means algorithm performed better on Iris and Wine dataset when compared with other three clustering methods.
This session talks about how to define a problem as a machine learning one. What are the steps toward reaching a satisfying solution from data preparation, feature engineering, evaluating suitable algorithms until releasing the model and putting it in practice. It presents a case study and go through some algorithms mostly implemented in Python.
By Hussein Natsheh - Data Mining entrepreneur, scholar, and founder of CiApple
YouTube video: https://youtu.be/NGbyeX4kpU4
Abstractâ Cloud storage is usually distributed infrastructure, where data is not stored in a single device but is spread to several storage nodes which are located in different areas. To ensure data availability some amount of redundancy has to be maintained. But introduction of data redundancy leads to additional costs such as extra storage space and communication bandwidth which required for restoring data blocks. In the existing system, the storage infrastructure is considered as homogeneous where all nodes in the system have same online availability which leads to efficiency losses. The proposed system considers that distributed storage system is heterogeneous where each node exhibit different online availability. Monte Carlo Sampling is used to measure the online availability of storage nodes. The parallel version of Particle Swarm Optimization is used to assign redundant data blocks according to their online availability. The optimal data assignment policy reduces the redundancy and their associated cost.
X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...csandit
Â
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees
from neural networks. We empirically evaluated the performance of the algorithm on a set of
databases from real world events. This benchmark enhancement was achieved by adapting
Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The
models are then compared with X-TREPAN for comprehensibility and classification accuracy.
Furthermore, we validate the experimentations by applying statistical methods. Finally, the
modified algorithm is extended to work with multi-class regression problems and the ability to
comprehend generalized feed forward networks is achieved.
A COST EFFECTIVE COMPRESSIVE DATA AGGREGATION TECHNIQUE FOR WIRELESS SENSOR N...ijasuc
Â
In wireless sensor network (WSN) there are two main problems in employing conventional compression
techniques. The compression performance depends on the organization of the routes for a larger extent.
The efficiency of an in-network data compression scheme is not solely determined by the compression
ratio, but also depends on the computational and communication overheads. In Compressive Data
Aggregation technique, data is gathered at some intermediate node where its size is reduced by applying
compression technique without losing any information of complete data. In our previous work, we have
developed an adaptive traffic aware aggregation technique in which the aggregation technique can be
changed into structured and structure-free adaptively, depending on the load status of the traffic. In this
paper, as an extension to our previous work, we provide a cost effective compressive data gathering
technique to enhance the traffic load, by using structured data aggregation scheme. We also design a
technique that effectively reduces the computation and communication costs involved in the compressive
data gathering process. The use of compressive data gathering process provides a compressed sensor
reading to reduce global data traffic and distributes energy consumption evenly to prolong the network
lifetime. By simulation results, we show that our proposed technique improves the delivery ratio while
reducing the energy and delay
HCIFR: Hierarchical Clustering and Iterative Filtering Routing Algorithm for ...IJAEMSJORNAL
Â
The hierarchical clustering and iterative filtering algorithms are combined to form an energy efficient routing algorithm which supports in improved performance, efficient routing at the time of link failure, collusion robust and secure data aggregation. The idea of combining these two algorithms which may lead to improved performance. Initially clusters are formed by neighborhood. The cluster is a combination of one clusterhead, two deputy clusterheads and cluster members. This system uses a Hierarchical clustering algorithm for efficient data transmission to their clusterhead by cluster members. The clusterhead aggregate the collected data and check for trustworthiness. The data is aggregated by clusterhead using the iterative filtering algorithm and resistant to collusion attacks. Simulation results depict the average energy consumption, throughput, packet drops and packet delivery under the influence of proposed algorithm.
Energy Efficient Multipath Data Fusion Technique for Wireless Sensor NetworksIDES Editor
Â
In wireless sensor networks (WSN), data fusion
should be energy efficient. But, determining the optimal
number of aggregators in an energy efficient manner is a
challenging task. Moreover, the existing data fusion
techniques mostly use the same path for transmitting
aggregated data to the sink which reduces the nodes lifetime.
In this paper, we propose a technique which combines energy
efficiency and multiple path selection for data fusion in WSN.
The network is partitioned into various clusters and the node
with highest residual energy is selected as the cluster head.
The sink computes multiple paths to each cluster head for
data transmission. The distributed source coding and the
lifting scheme wavelet transform are used for compressing
the data at the CH. During each round of transmission, the
path is changed in a round robin manner, to conserve the
energy. This process is repeated for each cluster. From our
simulation results we show that this data fusion technique
has less energy consumption with increased packet delivery
ratio, when compared with the existing schemes.
A Novel Dencos Model For High Dimensional Data Using Genetic Algorithms ijcseit
Â
Subspace clustering is an emerging task that aims at detecting clusters in entrenched in
subspaces. Recent approaches fail to reduce results to relevant subspace clusters. Their results are
typically highly redundant and lack the fact of considering the critical problem, âthe density divergence
problem,â in discovering the clusters, where they utilize an absolute density value as the density threshold
to identify the dense regions in all subspaces. Considering the varying region densities in different
subspace cardinalities, we note that a more appropriate way to determine whether a region in a subspace
should be identified as dense is by comparing its density with the region densities in that subspace. Based
on this idea and due to the infeasibility of applying previous techniques in this novel clustering model, we
devise an innovative algorithm, referred to as DENCOS(DENsity Conscious Subspace clustering), to adopt
a divide-and-conquer scheme to efficiently discover clusters satisfying different density thresholds in
different subspace cardinalities. DENCOS can discover the clusters in all subspaces with high quality, and
the efficiency significantly outperforms previous works, thus demonstrating its practicability for subspace
clustering. As validated by our extensive experiments on retail dataset, it outperforms previous works. We
extend our work with a clustering technique based on genetic algorithms which is capable of optimizing the
number of clusters for tasks with well formed and separated clusters.
Introduction to Multi-Objective Clustering EnsembleIJSRD
Â
Association rule mining is a popular and well researched method for discovering interesting relations between variables in large databases. In this paper we introduce the concept of Data mining, Association rule and Multilevel association rule with different algorithm, its advantage and concept of Fuzzy logic and Genetic Algorithm. Multilevel association rules can be mined efficiently using concept hierarchies under a support-confidence framework.
Design of a Reliable Wireless Sensor Network with Optimized Energy Efficiency...paperpublications3
Â
Abstract: Data gathering in wireless sensor network (WSN) is a crucial field of study and it can be optimized various algorithms like clustering, aggregation, and cryptographic technique in order to reliably transfer data between sensor and sink. But these techniques do not provide an optimized data gathering wireless sensor network because of the fact that they do not leverage the advantages of various techniques. Our problem definition is to create a reliable data gathering wireless sensor network which ensures good energy efficiency and lower delay as compared to existing techniques.
Keywords: Aggregation, Clustering, Data Gathering, Cryptography, Data Compression, Run Length Encoding.
Title: Design of a Reliable Wireless Sensor Network with Optimized Energy Efficiency and Delay
Author: Neelam Ashok Meshram
ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Paper Publications
Using Neighborâs State Cross-correlation to Accelerate Adaptation in Docitiv...paperpublications3
Â
Abstract: In WSN, sensor nodes have limited energy budget therefore this paper mainly focus on power saving by using the docition paradigm. Docition is a new teacher-student paradigm proposed to improve cognitive radio. Although it improves the infrastrucÂŹture based networks it has a weakness in case of ad-hoc mobile netÂŹworks. The energy constraints and the total mobility of the netÂŹwork complicate the selection of the appropriate teacher for a student. By selecting the wrong teacher, there is a high probabilÂŹity that the taught information may be faulty, and thus the student radio diverges from the best state. This causes a high amount of energy loss, though the most important concern in ad-hoc networks is energy limitation. In this paper, we propose a dynamic docition for teacher selection based on the auto-correlaÂŹtion degree of the teacherâs candidate environment and the cross-correlation degree between the teacher candidate and the student environments. We validate our approach in the context of coexistÂŹence between WSN and WiFi. The WSN detects, models and exploits the unused time slots in the electromagnetic spectrum, left by WiFi, using dynamic docition. The simulation results show that the use of dynamic docition outperforms the existing docition in mobile networks. The improvements are shown through the low link overhead percentage (20% less overhead) and the low packet loss ratio (30% improvement).
Keywords: Docitive; Online Prediction Problem; WSN; pareto model; IEEE802.11 b/g;cognitive radio.
Title: Using Neighborâs State Cross-correlation to Accelerate Adaptation in Docitive WSN
Author: Dr. Charbel Nicolas
ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Paper Publications
A Novel Classification via Clustering Method for Anomaly Based Network Intrus...IDES Editor
Â
Intrusion detection in the internet is an active
area of research. Intruders can be classified into two
types, namely; external intruders who are unauthorized
users of the computers they attack, and internal
intruders, who have permission to access the system but
with some restrictions. The aim of this paper is to present
a methodology to recognize attacks during the normal
activities in a system. A novel classification via sequential
information bottleneck (sIB) clustering algorithm has
been proposed to build an efficient anomaly based
network intrusion detection model. We have compared
our proposed method with other clustering algorithms
like X-Means, Farthest First, Filtered clusters, DBSCAN,
K-Means, and EM (Expectation-Maximization)
clustering in order to find the suitability of our proposed
algorithm. A subset of KDDCup 1999 intrusion detection
benchmark dataset has been used for the experiment.
Results show that the proposed method is efficient in
terms of detection accuracy, low false positive rate in
comparison to the other existing methods.
A NOVEL ROUTING PROTOCOL FOR TARGET TRACKING IN WIRELESS SENSOR NETWORKSIJCNCJournal
Â
Wireless sensor networks (WSNs) are large scale integration consists of hundreds or thousands or more
number of sensor nodes. They are tiny, low cost, low weight, and limited battery, primary storage,
processing power. They have wireless capabilities to monitor physical or environmental conditions. This
paper compared the performance analysis of some existing routing protocols for target tracking
application with proposed hierarchical binary tree structure to store the routing information. The sensed
information is stored in controlled way at multiple sensor nodes (e.g. node, parent node and grandparent
node) which deployed using complete binary tree data structure. This reduces traffic implosion and
geographical overlapping. Simulation result showed improved network lifetime by 20%, target detection
probability by 25%, and reduces error rate by 20%, energy efficiency, fault tolerance, and routing
efficiency. We have evaluated our proposed algorithm using NS2.
Improved Performance of Unsupervised Method by Renovated K-MeansIJASCSE
Â
Clustering is a separation of data into groups of similar objects. Every group called cluster consists of objects that are similar to one another and dissimilar to objects of other groups. In this paper, the K-Means algorithm is implemented by three distance functions and to identify the optimal distance function for clustering methods. The proposed K-Means algorithm is compared with K-Means, Static Weighted K-Means (SWK-Means) and Dynamic Weighted K-Means (DWK-Means) algorithm by using Davis Bouldin index, Execution Time and Iteration count methods. Experimental results show that the proposed K-Means algorithm performed better on Iris and Wine dataset when compared with other three clustering methods.
This session talks about how to define a problem as a machine learning one. What are the steps toward reaching a satisfying solution from data preparation, feature engineering, evaluating suitable algorithms until releasing the model and putting it in practice. It presents a case study and go through some algorithms mostly implemented in Python.
By Hussein Natsheh - Data Mining entrepreneur, scholar, and founder of CiApple
YouTube video: https://youtu.be/NGbyeX4kpU4
Abstractâ Cloud storage is usually distributed infrastructure, where data is not stored in a single device but is spread to several storage nodes which are located in different areas. To ensure data availability some amount of redundancy has to be maintained. But introduction of data redundancy leads to additional costs such as extra storage space and communication bandwidth which required for restoring data blocks. In the existing system, the storage infrastructure is considered as homogeneous where all nodes in the system have same online availability which leads to efficiency losses. The proposed system considers that distributed storage system is heterogeneous where each node exhibit different online availability. Monte Carlo Sampling is used to measure the online availability of storage nodes. The parallel version of Particle Swarm Optimization is used to assign redundant data blocks according to their online availability. The optimal data assignment policy reduces the redundancy and their associated cost.
X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...csandit
Â
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees
from neural networks. We empirically evaluated the performance of the algorithm on a set of
databases from real world events. This benchmark enhancement was achieved by adapting
Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The
models are then compared with X-TREPAN for comprehensibility and classification accuracy.
Furthermore, we validate the experimentations by applying statistical methods. Finally, the
modified algorithm is extended to work with multi-class regression problems and the ability to
comprehend generalized feed forward networks is achieved.
A COST EFFECTIVE COMPRESSIVE DATA AGGREGATION TECHNIQUE FOR WIRELESS SENSOR N...ijasuc
Â
In wireless sensor network (WSN) there are two main problems in employing conventional compression
techniques. The compression performance depends on the organization of the routes for a larger extent.
The efficiency of an in-network data compression scheme is not solely determined by the compression
ratio, but also depends on the computational and communication overheads. In Compressive Data
Aggregation technique, data is gathered at some intermediate node where its size is reduced by applying
compression technique without losing any information of complete data. In our previous work, we have
developed an adaptive traffic aware aggregation technique in which the aggregation technique can be
changed into structured and structure-free adaptively, depending on the load status of the traffic. In this
paper, as an extension to our previous work, we provide a cost effective compressive data gathering
technique to enhance the traffic load, by using structured data aggregation scheme. We also design a
technique that effectively reduces the computation and communication costs involved in the compressive
data gathering process. The use of compressive data gathering process provides a compressed sensor
reading to reduce global data traffic and distributes energy consumption evenly to prolong the network
lifetime. By simulation results, we show that our proposed technique improves the delivery ratio while
reducing the energy and delay
HCIFR: Hierarchical Clustering and Iterative Filtering Routing Algorithm for ...IJAEMSJORNAL
Â
The hierarchical clustering and iterative filtering algorithms are combined to form an energy efficient routing algorithm which supports in improved performance, efficient routing at the time of link failure, collusion robust and secure data aggregation. The idea of combining these two algorithms which may lead to improved performance. Initially clusters are formed by neighborhood. The cluster is a combination of one clusterhead, two deputy clusterheads and cluster members. This system uses a Hierarchical clustering algorithm for efficient data transmission to their clusterhead by cluster members. The clusterhead aggregate the collected data and check for trustworthiness. The data is aggregated by clusterhead using the iterative filtering algorithm and resistant to collusion attacks. Simulation results depict the average energy consumption, throughput, packet drops and packet delivery under the influence of proposed algorithm.
Energy Efficient Multipath Data Fusion Technique for Wireless Sensor NetworksIDES Editor
Â
In wireless sensor networks (WSN), data fusion
should be energy efficient. But, determining the optimal
number of aggregators in an energy efficient manner is a
challenging task. Moreover, the existing data fusion
techniques mostly use the same path for transmitting
aggregated data to the sink which reduces the nodes lifetime.
In this paper, we propose a technique which combines energy
efficiency and multiple path selection for data fusion in WSN.
The network is partitioned into various clusters and the node
with highest residual energy is selected as the cluster head.
The sink computes multiple paths to each cluster head for
data transmission. The distributed source coding and the
lifting scheme wavelet transform are used for compressing
the data at the CH. During each round of transmission, the
path is changed in a round robin manner, to conserve the
energy. This process is repeated for each cluster. From our
simulation results we show that this data fusion technique
has less energy consumption with increased packet delivery
ratio, when compared with the existing schemes.
A Novel Dencos Model For High Dimensional Data Using Genetic Algorithms ijcseit
Â
Subspace clustering is an emerging task that aims at detecting clusters in entrenched in
subspaces. Recent approaches fail to reduce results to relevant subspace clusters. Their results are
typically highly redundant and lack the fact of considering the critical problem, âthe density divergence
problem,â in discovering the clusters, where they utilize an absolute density value as the density threshold
to identify the dense regions in all subspaces. Considering the varying region densities in different
subspace cardinalities, we note that a more appropriate way to determine whether a region in a subspace
should be identified as dense is by comparing its density with the region densities in that subspace. Based
on this idea and due to the infeasibility of applying previous techniques in this novel clustering model, we
devise an innovative algorithm, referred to as DENCOS(DENsity Conscious Subspace clustering), to adopt
a divide-and-conquer scheme to efficiently discover clusters satisfying different density thresholds in
different subspace cardinalities. DENCOS can discover the clusters in all subspaces with high quality, and
the efficiency significantly outperforms previous works, thus demonstrating its practicability for subspace
clustering. As validated by our extensive experiments on retail dataset, it outperforms previous works. We
extend our work with a clustering technique based on genetic algorithms which is capable of optimizing the
number of clusters for tasks with well formed and separated clusters.
Introduction to Multi-Objective Clustering EnsembleIJSRD
Â
Association rule mining is a popular and well researched method for discovering interesting relations between variables in large databases. In this paper we introduce the concept of Data mining, Association rule and Multilevel association rule with different algorithm, its advantage and concept of Fuzzy logic and Genetic Algorithm. Multilevel association rules can be mined efficiently using concept hierarchies under a support-confidence framework.
Data mining is a process to extract information from a huge amount of data and transform it into an
understandable structure. Data mining provides the number of tasks to extract data from large databases such
as Classification, Clustering, Regression, Association rule mining. This paper provides the concept of
Classification. Classification is an important data mining technique based on machine learning which is used to
classify the each item on the bases of features of the item with respect to the predefined set of classes or groups.
This paper summarises various techniques that are implemented for the classification such as k-NN, Decision
Tree, NaĂŻve Bayes, SVM, ANN and RF. The techniques are analyzed and compared on the basis of their
advantages and disadvantages
A survey on Efficient Enhanced K-Means Clustering Algorithmijsrd.com
Â
Data mining is the process of using technology to identify patterns and prospects from large amount of information. In Data Mining, Clustering is an important research topic and wide range of unverified classification application. Clustering is technique which divides a data into meaningful groups. K-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. In this paper, we present the comparison of different K-means clustering algorithms.
An Iterative Improved k-means ClusteringIDES Editor
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Clustering is a data mining (machine learning),
unsupervised learning technique used to place data elements
into related groups without advance knowledge of the group
definitions. One of the most popular and widely studied
clustering methods that minimize the clustering error for
points in Euclidean space is called K-means clustering.
However, the k-means method converges to one of many local
minima, and it is known that the final results depend on the
initial starting points (means). In this research paper, we have
introduced and tested an improved algorithm to start the kmeans
with good starting points (means). The good initial
starting points allow k-means to converge to a better local
minimum; also the numbers of iteration over the full dataset
are being decreased. Experimental results show that initial
starting points lead to good solution reducing the number of
iterations to form a cluster.
A HYBRID MODEL FOR MINING MULTI DIMENSIONAL DATA SETSEditor IJCATR
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This paper presents a hybrid data mining approach based on supervised learning and unsupervised learning to identify the closest data patterns in the data base. This technique enables to achieve the maximum accuracy rate with minimal complexity. The proposed algorithm is compared with traditional clustering and classification algorithm and it is also implemented with multidimensional datasets. The implementation results show better prediction accuracy and reliability.
Certain Investigation on Dynamic Clustering in Dynamic Dataminingijdmtaiir
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Clustering is the process of grouping a set of objects
into classes of similar objects. Dynamic clustering comes in a
new research area that is concerned about dataset with dynamic
aspects. It requires updates of the clusters whenever new data
records are added to the dataset and may result in a change of
clustering over time. When there is a continuous update and
huge amount of dynamic data, rescan the database is not
possible in static data mining. But this is possible in Dynamic
data mining process. This dynamic data mining occurs when
the derived information is present for the purpose of analysis
and the environment is dynamic, i.e. many updates occur.
Since this has now been established by most researchers and
they will move into solving some of the problems and the
research is to concentrate on solving the problem of using data
mining dynamic databases. This paper gives some
investigation of existing work done in some papers related with
dynamic clustering and incremental data clustering
Intrusion Detection System Based on K-Star Classifier and Feature Set ReductionIOSR Journals
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Abstract: Network security and Intrusion Detection Systems (IDSâs) is an important security related research
area. This paper applies K-star algorithm with filtering analysis in order to build a network intrusion detection
system. For our experimental analysis and as a case study, we have used the new NSL-KDD dataset, which is a
modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 66.0% for the
training set and the remainder for the testing set a 2 class classifications has been implemented. WEKA which is
a java based open source software consists of a collection of machine learning algorithms for Data mining tasks
has been used in the testing process. The experimental results show that the proposed approach is very accurate
with low false positive rate and high true positive rate and it takes less learning time in comparison with other
existing approaches used for efficient network intrusion detection.
Keywords: Information Gain, Intrusion Detection System, Instance-based classifier, K-Star, Weka.
Data mining is utilized to manage huge measure of information which are put in the data ware houses and databases, to discover required information and data. Numerous data mining systems have been proposed, for example, association rules, decision trees, neural systems, clustering, and so on. It has turned into the purpose of consideration from numerous years. A re-known amongst the available data mining strategies is clustering of the dataset. It is the most effective data mining method. It groups the dataset in number of clusters based on certain guidelines that are predefined. It is dependable to discover the connection between the distinctive characteristics of data.
In k-mean clustering algorithm, the function is being selected on the basis of the relevancy of the function for predicting the data and also the Euclidian distance between the centroid of any cluster and the data objects outside the cluster is being computed for the clustering the data points. In this work, author enhanced the Euclidian distance formula to increase the cluster quality.
The problem of accuracy and redundancy of the dissimilar points in the clusters remains in the improved k-means for which new enhanced approach is been proposed which uses the similarity function for checking the similarity level of the point before including it to the cluster.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
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Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
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Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
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Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
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Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
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Are you looking to streamline your workflows and boost your projectsâ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, youâre in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part âEssentials of Automationâ series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Hereâs what youâll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
Weâll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Donât miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
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In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
âą The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
âą Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
âą Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
âą Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
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After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more âmechanicalâ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!