The document proposes a new method for indexing and searching high-dimensional data that improves on existing clustering-based approaches. It develops an adaptive distance bound for clusters based on separating hyperplane boundaries, rather than bounding spheres or rectangles. This tighter bound enables more efficient filtering of irrelevant clusters during nearest neighbor queries. Experiments show the new method outperforms other indexing techniques, reducing random I/O accesses by factors of up to 100 compared to vector approximation files.
Clustering of high dimensionality data which can be seen in almost all fields these days is becoming
very tedious process. The key disadvantage of high dimensional data which we can pen down is curse
of dimensionality. As the magnitude of datasets grows the data points become sparse and density of
area becomes less making it difficult to cluster that data which further reduces the performance of
traditional algorithms used for clustering. Semi-supervised clustering algorithms aim to improve
clustering results using limited supervision. The supervision is generally given as pair wise
constraints; such constraints are natural for graphs, yet most semi-supervised clustering algorithms are
designed for data represented as vectors [2]. In this paper, we unify vector-based and graph-based
approaches. We first show that a recently-proposed objective function for semi-supervised clustering
based on Hidden Markov Random Fields, with squared Euclidean distance and a certain class of
constraint penalty functions, can be expressed as a special case of the global kernel k-means objective
[3]. A recent theoretical connection between global kernel k-means and several graph clustering
objectives enables us to perform semi-supervised clustering of data. In particular, some methods have
been proposed for semi supervised clustering based on pair wise similarity or dissimilarity
information. In this paper, we propose a kernel approach for semi supervised clustering and present in
detail two special cases of this kernel approach.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A MIXTURE MODEL OF HUBNESS AND PCA FOR DETECTION OF PROJECTED OUTLIERSZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm
A HYBRID FUZZY SYSTEM BASED COOPERATIVE SCALABLE AND SECURED LOCALIZATION SCH...ijwmn
Localization entails position estimation of sensor nodes by employing different techniques and mathematical computations. Localizable sensors also form an inherent part in the functioning of IoT devices and robotics. In this article, the author extends1 a novel scheme for node localization implemented using a hybrid fuzzy logic system to trace the node locations inside the deployment region, presented by the
Abhishek Kumar et. al. The results obtained were then optimized using Gauss Newton Optimization to improve the localization accuracy by 50% to 90% vis-à-vis weighted centroid and other fuzzy based localization algorithms. This article attempts to scale the proposed scheme for large number of sensor nodes to emulate somewhat real world scenario by introducing cooperative localization in previous presented work. The study also analyses the effectiveness of such scaling by comparing the localization accuracy. In next section, the article incorporates security in the proposed cooperative localization approach to detect malicious nodes/anchors by mutual authentication using El Gamel digital Signature scheme. A detailed study of the impact of incorporating security and scaling on average processing time and localization coverage has also been performed. The processing time increased by a factor of 2.5s for 500 nodes (can be attributed to more number of iterations and computations and large deployment area with small radio range of nodes) and coverage remained almost equal, albeit slightly low by a factor of 1% to 2%. Apart from these, the article also discusses the impact of adding extra functionalities in the proposed hybrid fuzzy system based localization scheme on processing time and localization accuracy.Lastly, this study also briefs about how the proposed scalable, cooperative and secure localization scheme tackles the type of attacks that pose threat to localization.
Clustering of high dimensionality data which can be seen in almost all fields these days is becoming
very tedious process. The key disadvantage of high dimensional data which we can pen down is curse
of dimensionality. As the magnitude of datasets grows the data points become sparse and density of
area becomes less making it difficult to cluster that data which further reduces the performance of
traditional algorithms used for clustering. Semi-supervised clustering algorithms aim to improve
clustering results using limited supervision. The supervision is generally given as pair wise
constraints; such constraints are natural for graphs, yet most semi-supervised clustering algorithms are
designed for data represented as vectors [2]. In this paper, we unify vector-based and graph-based
approaches. We first show that a recently-proposed objective function for semi-supervised clustering
based on Hidden Markov Random Fields, with squared Euclidean distance and a certain class of
constraint penalty functions, can be expressed as a special case of the global kernel k-means objective
[3]. A recent theoretical connection between global kernel k-means and several graph clustering
objectives enables us to perform semi-supervised clustering of data. In particular, some methods have
been proposed for semi supervised clustering based on pair wise similarity or dissimilarity
information. In this paper, we propose a kernel approach for semi supervised clustering and present in
detail two special cases of this kernel approach.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A MIXTURE MODEL OF HUBNESS AND PCA FOR DETECTION OF PROJECTED OUTLIERSZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm
A HYBRID FUZZY SYSTEM BASED COOPERATIVE SCALABLE AND SECURED LOCALIZATION SCH...ijwmn
Localization entails position estimation of sensor nodes by employing different techniques and mathematical computations. Localizable sensors also form an inherent part in the functioning of IoT devices and robotics. In this article, the author extends1 a novel scheme for node localization implemented using a hybrid fuzzy logic system to trace the node locations inside the deployment region, presented by the
Abhishek Kumar et. al. The results obtained were then optimized using Gauss Newton Optimization to improve the localization accuracy by 50% to 90% vis-à-vis weighted centroid and other fuzzy based localization algorithms. This article attempts to scale the proposed scheme for large number of sensor nodes to emulate somewhat real world scenario by introducing cooperative localization in previous presented work. The study also analyses the effectiveness of such scaling by comparing the localization accuracy. In next section, the article incorporates security in the proposed cooperative localization approach to detect malicious nodes/anchors by mutual authentication using El Gamel digital Signature scheme. A detailed study of the impact of incorporating security and scaling on average processing time and localization coverage has also been performed. The processing time increased by a factor of 2.5s for 500 nodes (can be attributed to more number of iterations and computations and large deployment area with small radio range of nodes) and coverage remained almost equal, albeit slightly low by a factor of 1% to 2%. Apart from these, the article also discusses the impact of adding extra functionalities in the proposed hybrid fuzzy system based localization scheme on processing time and localization accuracy.Lastly, this study also briefs about how the proposed scalable, cooperative and secure localization scheme tackles the type of attacks that pose threat to localization.
INCREMENTAL SEMI-SUPERVISED CLUSTERING METHOD USING NEIGHBOURHOOD ASSIGNMENTIJCSEA Journal
Semi-supervised considering so as to cluster expects to enhance clustering execution client supervision as
pair wise imperatives. In this paper, we contemplate the dynamic learning issue of selecting pair wise
must-connect and can't interface imperatives for semi supervised clustering. We consider dynamic learning
in an iterative way where in every emphasis questions are chosen in light of the current clustering
arrangement and the current requirement set. We apply a general system that expands on the idea of
Neighbourhood, where Neighbourhoods contain "named samples" of distinctive bunches as indicated by
the pair wise imperatives. Our dynamic learning strategy extends the areas by selecting educational
focuses and questioning their association with the areas. Under this system, we expand on the fantastic
vulnerability based rule and present a novel methodology for figuring the instability related with every
information point. We further present a determination foundation that exchanges off the measure of
vulnerability of every information point with the expected number of inquiries (the expense) needed to
determine this instability. This permits us to choose questions that have the most astounding data rate. We
assess the proposed strategy on the benchmark information sets and the outcomes show predictable and
significant upgrades over the current cutting edge.
Ijricit 01-002 enhanced replica detection in short time for large data setsIjripublishers Ijri
Similarity check of real world entities is a necessary factor in these days which is named as Data Replica Detection.
Time is an critical factor today in tracking Data Replica Detection for large data sets, without having impact over quality
of Dataset. In this we primarily introduce two Data Replica Detection algorithms , where in these contribute enhanced
procedural standards in finding Data Replication at limited execution periods.This contribute better improvised state
of time than conventional techniques . We propose two Data Replica Detection algorithms namely progressive sorted
neighborhood method (PSNM), which performs best on small and almost clean datasets, and progressive blocking (PB),
which performs best on large and very grimy datasets. Both enhance the efficiency of duplicate detection even on very
large datasets.
AN EFFICIENT DEPLOYMENT APPROACH FOR IMPROVED COVERAGE IN WIRELESS SENSOR NET...csandit
Wireless Sensor Networks (WSNs) are experiencing a revival of interest and a continuous advancement in various scientific and industrial fields. WSNs offer favorable low cost and readily deployable solutions to perform the monitoring, target tracking, and recognition of physical events. The foremost step required for these types of ad-hoc networks is to deploy all the sensor nodes in their positions carefully to form an efficient network. Such network should satisfy the quality of service (QoS) requirements in order to achieve high performance levels. In
this paper we address the coverage requirement and its relation with WSN nodes placement problems. In fact, we present a new optimization approach based on the Flower Pollination Algorithm (FPA) to find the best placement topologies in terms of coverage maximization. We have compared the performance of the resulting algorithm, called FPACO, with the original practical swarm optimization (PSO) and the genetic algorithm (GA). In all the test instances, FPACO performs better than all other algorithms.
Dynamic Trust Management of Unattended Wireless Sensor Networks for Cost Awar...paperpublications3
Abstract: Unattended Wireless Sensor Networks (UWSNs) are characterized by long periods of disconnected operation and fixed or irregular intervals between sink visits. The absence of an online trusted third party implies that existing WSN trust management schemes are not applicable to UWSNs. In this paper, a trust management scheme for UWSNs to provide dynamic trust management is proposed. For trust data storage, geographic hash table is employed, it also helps to identify storage nodes and to significantly decrease storage cost. In order to mitigate trust fluctuations caused by environmental factors a subjective logic based consensus techniques is used. In this paper a set of trust similarity functions are exploited to detect trust outliers.
Final Year IEEE Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE Projects, Academic Final Year IEEE Projects 2013, Academic Final Year IEEE Projects 2014, IEEE MATLAB Projects, 2013 IEEE MATLAB Projects, 2013 IEEE MATLAB Projects in Chennai, 2013 IEEE MATLAB Projects in Trichy, 2013 IEEE MATLAB Projects in Karur, 2013 IEEE MATLAB Projects in Erode, 2013 IEEE MATLAB Projects in Madurai, 2013 IEEE MATLAB Projects in Salem, 2013 IEEE MATLAB Projects in Coimbatore, 2013 IEEE MATLAB Projects in Tirupur, 2013 IEEE MATLAB Projects in Bangalore, 2013 IEEE MATLAB Projects in Hydrabad, 2013 IEEE MATLAB Projects in Kerala, 2013 IEEE MATLAB Projects in Namakkal, IEEE MATLAB Image Processing, IEEE MATLAB Face Recognition, IEEE MATLAB Face Detection, IEEE MATLAB Brain Tumour, IEEE MATLAB Iris Recognition, IEEE MATLAB Image Segmentation, Final Year Matlab Projects in Pondichery, Final Year Matlab Projects in Tamilnadu, Final Year Matlab Projects in Chennai, Final Year Matlab Projects in Trichy, Final Year Matlab Projects in Erode, Final Year Matlab Projects in Karur, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Tirunelveli, Final Year Matlab Projects in Madurai, Final Year Matlab Projects in Salem, Final Year Matlab Projects in Tirupur, Final Year Matlab Projects in Namakkal, Final Year Matlab Projects in Tanjore, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Bangalore, Final Year Matlab Projects in Hydrabad, Final Year Matlab Projects in Kerala.
A Novel Penalized and Compensated Constraints Based Modified Fuzzy Possibilis...ijsrd.com
A cluster is a group of objects which are similar to each other within a cluster and are dissimilar to the objects of other clusters. The similarity is typically calculated on the basis of distance between two objects or clusters. Two or more objects present inside a cluster and only if those objects are close to each other based on the distance between them.The major objective of clustering is to discover collection of comparable objects based on similarity metric. Fuzzy Possibilistic C-Means (FPCM) is the effective clustering algorithm available to cluster unlabeled data that produces both membership and typicality values during clustering process. In this approach, the efficiency of the Fuzzy Possibilistic C-means clustering approach is enhanced by using the penalized and compensated constraints based FPCM (PCFPCM). The proposed PCFPCM approach differ from the conventional clustering techniques by imposing the possibilistic reasoning strategy on fuzzy clustering with penalized and compensated constraints for updating the grades of membership and typicality. The performance of the proposed approaches is evaluated on the University of California, Irvine (UCI) machine repository datasets such as Iris, Wine, Lung Cancer and Lymphograma. The parameters used for the evaluation is Clustering accuracy, Mean Squared Error (MSE), Execution Time and Convergence behavior.
Anomaly Detection using multidimensional reduction Principal Component AnalysisIOSR Journals
Anomaly detection has been an important research topic in data mining and machine learning. Many
real-world applications such as intrusion or credit card fraud detection require an effective and efficient
framework to identify deviated data instances. However, most anomaly detection methods are typically
implemented in batch mode, and thus cannot be easily extended to large-scale problems without sacrificing
computation and memory requirements. In this paper, we propose multidimensional reduction principal
component analysis (MdrPCA) algorithm to address this problem, and we aim at detecting the presence of
outliers from a large amount of data via an online updating technique. Unlike prior principal component
analysis (PCA)-based approaches, we do not store the entire data matrix or covariance matrix, and thus our
approach is especially of interest in online or large-scale problems. By using multidimensional reduction PCA
the target instance and extracting the principal direction of the data, the proposed MdrPCA allows us to
determine the anomaly of the target instance according to the variation of the resulting dominant eigenvector.
Since our MdrPCA need not perform eigen analysis explicitly, the proposed framework is favored for online
applications which have computation or memory limitations. Compared with the well-known power method for
PCA and other popular anomaly detection algorithms
Ensemble based Distributed K-Modes ClusteringIJERD Editor
Clustering has been recognized as the unsupervised classification of data items into groups. Due to the explosion in the number of autonomous data sources, there is an emergent need for effective approaches in distributed clustering. The distributed clustering algorithm is used to cluster the distributed datasets without gathering all the data in a single site. The K-Means is a popular clustering method owing to its simplicity and speed in clustering large datasets. But it fails to handle directly the datasets with categorical attributes which are generally occurred in real life datasets. Huang proposed the K-Modes clustering algorithm by introducing a new dissimilarity measure to cluster categorical data. This algorithm replaces means of clusters with a frequency based method which updates modes in the clustering process to minimize the cost function. Most of the distributed clustering algorithms found in the literature seek to cluster numerical data. In this paper, a novel Ensemble based Distributed K-Modes clustering algorithm is proposed, which is well suited to handle categorical data sets as well as to perform distributed clustering process in an asynchronous manner. The performance of the proposed algorithm is compared with the existing distributed K-Means clustering algorithms, and K-Modes based Centralized Clustering algorithm. The experiments are carried out for various datasets of UCI machine learning data repository.
International Journal of Computer Science, Engineering and Information Techno...IJCSEIT Journal
In the field of proteomics because of more data is added, the computational methods need to be more
efficient. The part of molecular sequences is functionally more important to the molecule which is more
resistant to change. To ensure the reliability of sequence alignment, comparative approaches are used. The
problem of multiple sequence alignment is a proposition of evolutionary history. For each column in the
alignment, the explicit homologous correspondence of each individual sequence position is established. The
different pair-wise sequence alignment methods are elaborated in the present work. But these methods are
only used for aligning the limited number of sequences having small sequence length. For aligning
sequences based on the local alignment with consensus sequences, a new method is introduced. From NCBI
databank triticum wheat varieties are loaded. Phylogenetic trees are constructed for divided parts of
dataset. A single new tree is constructed from previous generated trees using advanced pruning technique.
Then, the closely related sequences are extracted by applying threshold conditions and by using shift
operations in the both directions optimal sequence alignment is obtained.
Noise-robust classification with hypergraph neural networknooriasukmaningtyas
This paper presents a novel version of hypergraph neural network method. This method is utilized to solve the noisy label learning problem. First, we apply the PCA dimensional reduction technique to the feature matrices of the image datasets in order to reduce the “noise” and the redundant features in the feature matrices of the image datasets and to reduce the runtime constructing the hypergraph of the hypergraph neural network method. Then, the classic graph based semisupervised learning method, the classic hypergraph based semi-supervised learning method, the graph neural network, the hypergraph neural network, and our proposed hypergraph neural network are employed to solve the noisy label learning problem. The accuracies of these five methods are evaluated and compared. Experimental results show that the hypergraph neural network methods achieve the best performance when the noise level increases. Moreover, the hypergraph neural network methods are at least as good as the graph neural network.
INCREMENTAL SEMI-SUPERVISED CLUSTERING METHOD USING NEIGHBOURHOOD ASSIGNMENTIJCSEA Journal
Semi-supervised considering so as to cluster expects to enhance clustering execution client supervision as
pair wise imperatives. In this paper, we contemplate the dynamic learning issue of selecting pair wise
must-connect and can't interface imperatives for semi supervised clustering. We consider dynamic learning
in an iterative way where in every emphasis questions are chosen in light of the current clustering
arrangement and the current requirement set. We apply a general system that expands on the idea of
Neighbourhood, where Neighbourhoods contain "named samples" of distinctive bunches as indicated by
the pair wise imperatives. Our dynamic learning strategy extends the areas by selecting educational
focuses and questioning their association with the areas. Under this system, we expand on the fantastic
vulnerability based rule and present a novel methodology for figuring the instability related with every
information point. We further present a determination foundation that exchanges off the measure of
vulnerability of every information point with the expected number of inquiries (the expense) needed to
determine this instability. This permits us to choose questions that have the most astounding data rate. We
assess the proposed strategy on the benchmark information sets and the outcomes show predictable and
significant upgrades over the current cutting edge.
Ijricit 01-002 enhanced replica detection in short time for large data setsIjripublishers Ijri
Similarity check of real world entities is a necessary factor in these days which is named as Data Replica Detection.
Time is an critical factor today in tracking Data Replica Detection for large data sets, without having impact over quality
of Dataset. In this we primarily introduce two Data Replica Detection algorithms , where in these contribute enhanced
procedural standards in finding Data Replication at limited execution periods.This contribute better improvised state
of time than conventional techniques . We propose two Data Replica Detection algorithms namely progressive sorted
neighborhood method (PSNM), which performs best on small and almost clean datasets, and progressive blocking (PB),
which performs best on large and very grimy datasets. Both enhance the efficiency of duplicate detection even on very
large datasets.
AN EFFICIENT DEPLOYMENT APPROACH FOR IMPROVED COVERAGE IN WIRELESS SENSOR NET...csandit
Wireless Sensor Networks (WSNs) are experiencing a revival of interest and a continuous advancement in various scientific and industrial fields. WSNs offer favorable low cost and readily deployable solutions to perform the monitoring, target tracking, and recognition of physical events. The foremost step required for these types of ad-hoc networks is to deploy all the sensor nodes in their positions carefully to form an efficient network. Such network should satisfy the quality of service (QoS) requirements in order to achieve high performance levels. In
this paper we address the coverage requirement and its relation with WSN nodes placement problems. In fact, we present a new optimization approach based on the Flower Pollination Algorithm (FPA) to find the best placement topologies in terms of coverage maximization. We have compared the performance of the resulting algorithm, called FPACO, with the original practical swarm optimization (PSO) and the genetic algorithm (GA). In all the test instances, FPACO performs better than all other algorithms.
Dynamic Trust Management of Unattended Wireless Sensor Networks for Cost Awar...paperpublications3
Abstract: Unattended Wireless Sensor Networks (UWSNs) are characterized by long periods of disconnected operation and fixed or irregular intervals between sink visits. The absence of an online trusted third party implies that existing WSN trust management schemes are not applicable to UWSNs. In this paper, a trust management scheme for UWSNs to provide dynamic trust management is proposed. For trust data storage, geographic hash table is employed, it also helps to identify storage nodes and to significantly decrease storage cost. In order to mitigate trust fluctuations caused by environmental factors a subjective logic based consensus techniques is used. In this paper a set of trust similarity functions are exploited to detect trust outliers.
Final Year IEEE Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE Projects, Academic Final Year IEEE Projects 2013, Academic Final Year IEEE Projects 2014, IEEE MATLAB Projects, 2013 IEEE MATLAB Projects, 2013 IEEE MATLAB Projects in Chennai, 2013 IEEE MATLAB Projects in Trichy, 2013 IEEE MATLAB Projects in Karur, 2013 IEEE MATLAB Projects in Erode, 2013 IEEE MATLAB Projects in Madurai, 2013 IEEE MATLAB Projects in Salem, 2013 IEEE MATLAB Projects in Coimbatore, 2013 IEEE MATLAB Projects in Tirupur, 2013 IEEE MATLAB Projects in Bangalore, 2013 IEEE MATLAB Projects in Hydrabad, 2013 IEEE MATLAB Projects in Kerala, 2013 IEEE MATLAB Projects in Namakkal, IEEE MATLAB Image Processing, IEEE MATLAB Face Recognition, IEEE MATLAB Face Detection, IEEE MATLAB Brain Tumour, IEEE MATLAB Iris Recognition, IEEE MATLAB Image Segmentation, Final Year Matlab Projects in Pondichery, Final Year Matlab Projects in Tamilnadu, Final Year Matlab Projects in Chennai, Final Year Matlab Projects in Trichy, Final Year Matlab Projects in Erode, Final Year Matlab Projects in Karur, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Tirunelveli, Final Year Matlab Projects in Madurai, Final Year Matlab Projects in Salem, Final Year Matlab Projects in Tirupur, Final Year Matlab Projects in Namakkal, Final Year Matlab Projects in Tanjore, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Bangalore, Final Year Matlab Projects in Hydrabad, Final Year Matlab Projects in Kerala.
A Novel Penalized and Compensated Constraints Based Modified Fuzzy Possibilis...ijsrd.com
A cluster is a group of objects which are similar to each other within a cluster and are dissimilar to the objects of other clusters. The similarity is typically calculated on the basis of distance between two objects or clusters. Two or more objects present inside a cluster and only if those objects are close to each other based on the distance between them.The major objective of clustering is to discover collection of comparable objects based on similarity metric. Fuzzy Possibilistic C-Means (FPCM) is the effective clustering algorithm available to cluster unlabeled data that produces both membership and typicality values during clustering process. In this approach, the efficiency of the Fuzzy Possibilistic C-means clustering approach is enhanced by using the penalized and compensated constraints based FPCM (PCFPCM). The proposed PCFPCM approach differ from the conventional clustering techniques by imposing the possibilistic reasoning strategy on fuzzy clustering with penalized and compensated constraints for updating the grades of membership and typicality. The performance of the proposed approaches is evaluated on the University of California, Irvine (UCI) machine repository datasets such as Iris, Wine, Lung Cancer and Lymphograma. The parameters used for the evaluation is Clustering accuracy, Mean Squared Error (MSE), Execution Time and Convergence behavior.
Anomaly Detection using multidimensional reduction Principal Component AnalysisIOSR Journals
Anomaly detection has been an important research topic in data mining and machine learning. Many
real-world applications such as intrusion or credit card fraud detection require an effective and efficient
framework to identify deviated data instances. However, most anomaly detection methods are typically
implemented in batch mode, and thus cannot be easily extended to large-scale problems without sacrificing
computation and memory requirements. In this paper, we propose multidimensional reduction principal
component analysis (MdrPCA) algorithm to address this problem, and we aim at detecting the presence of
outliers from a large amount of data via an online updating technique. Unlike prior principal component
analysis (PCA)-based approaches, we do not store the entire data matrix or covariance matrix, and thus our
approach is especially of interest in online or large-scale problems. By using multidimensional reduction PCA
the target instance and extracting the principal direction of the data, the proposed MdrPCA allows us to
determine the anomaly of the target instance according to the variation of the resulting dominant eigenvector.
Since our MdrPCA need not perform eigen analysis explicitly, the proposed framework is favored for online
applications which have computation or memory limitations. Compared with the well-known power method for
PCA and other popular anomaly detection algorithms
Ensemble based Distributed K-Modes ClusteringIJERD Editor
Clustering has been recognized as the unsupervised classification of data items into groups. Due to the explosion in the number of autonomous data sources, there is an emergent need for effective approaches in distributed clustering. The distributed clustering algorithm is used to cluster the distributed datasets without gathering all the data in a single site. The K-Means is a popular clustering method owing to its simplicity and speed in clustering large datasets. But it fails to handle directly the datasets with categorical attributes which are generally occurred in real life datasets. Huang proposed the K-Modes clustering algorithm by introducing a new dissimilarity measure to cluster categorical data. This algorithm replaces means of clusters with a frequency based method which updates modes in the clustering process to minimize the cost function. Most of the distributed clustering algorithms found in the literature seek to cluster numerical data. In this paper, a novel Ensemble based Distributed K-Modes clustering algorithm is proposed, which is well suited to handle categorical data sets as well as to perform distributed clustering process in an asynchronous manner. The performance of the proposed algorithm is compared with the existing distributed K-Means clustering algorithms, and K-Modes based Centralized Clustering algorithm. The experiments are carried out for various datasets of UCI machine learning data repository.
International Journal of Computer Science, Engineering and Information Techno...IJCSEIT Journal
In the field of proteomics because of more data is added, the computational methods need to be more
efficient. The part of molecular sequences is functionally more important to the molecule which is more
resistant to change. To ensure the reliability of sequence alignment, comparative approaches are used. The
problem of multiple sequence alignment is a proposition of evolutionary history. For each column in the
alignment, the explicit homologous correspondence of each individual sequence position is established. The
different pair-wise sequence alignment methods are elaborated in the present work. But these methods are
only used for aligning the limited number of sequences having small sequence length. For aligning
sequences based on the local alignment with consensus sequences, a new method is introduced. From NCBI
databank triticum wheat varieties are loaded. Phylogenetic trees are constructed for divided parts of
dataset. A single new tree is constructed from previous generated trees using advanced pruning technique.
Then, the closely related sequences are extracted by applying threshold conditions and by using shift
operations in the both directions optimal sequence alignment is obtained.
Noise-robust classification with hypergraph neural networknooriasukmaningtyas
This paper presents a novel version of hypergraph neural network method. This method is utilized to solve the noisy label learning problem. First, we apply the PCA dimensional reduction technique to the feature matrices of the image datasets in order to reduce the “noise” and the redundant features in the feature matrices of the image datasets and to reduce the runtime constructing the hypergraph of the hypergraph neural network method. Then, the classic graph based semisupervised learning method, the classic hypergraph based semi-supervised learning method, the graph neural network, the hypergraph neural network, and our proposed hypergraph neural network are employed to solve the noisy label learning problem. The accuracies of these five methods are evaluated and compared. Experimental results show that the hypergraph neural network methods achieve the best performance when the noise level increases. Moreover, the hypergraph neural network methods are at least as good as the graph neural network.
http://kasanpro.com/projects-list/ieee-2014-java-projects, List of IEEE Java Projects. It Contains the IEEE Projects in the Language Java with ieee publication year 2014
A fully distributed scheme for discovery of semantic relationshipsNexgen Technology
Ecruitment Solutions (ECS) is one of the leading Delhi based Software Development & HR Consulting Firm, which is assessed at the level of ISO 9001:2008 standard. ECS offers an awesome project and product based solutions to many customers around the globe.
In addition, ECS has also widened its wings by the way consummating academic projects especially for the final year professional degree students in India. ECS consist of a technical team that has solved many IEEE papers and delivered world-class solutions .
Novel Class Detection Using RBF SVM Kernel from Feature Evolving Data Streamsirjes
In the data mining field the classification of data stream creates many problems. The challenges
faces in the data stream are infinite length, concept drift, concept evaluation and feature evolution. Most of the
existing system focuses on the only first two challenges. We propose a framework in which each classifier is
prepared with the novel class detector for addressing the two challenges concept drift and concept evaluation
and for addressing the feature evolution feature set homogeneous technique is proposed. We improved the
novel class detection module by building it more adaptive to evolving the stream. SVM based feature extraction
for RBF kernel method is also proposed for detecting the novel class from the steaming data. By using the
concept of permutation and combination RBF kernel extracts the features and find out the relation between
them. This improves the novel class detect technique and provide more accuracy for classifying the data
A Mixture Model of Hubness and PCA for Detection of Projected OutliersZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm .This combination not only
provides the great boost to the detection of outliers but also enhances its accuracy and precision.
A Mixture Model of Hubness and PCA for Detection of Projected OutliersZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm .
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Deep vs diverse architectures for classification problemsColleen Farrelly
Deep learning study, comparing deep learning methods with wide learning methods; applications include simulation data and real industry problems. Pre-print of paper found here: https://arxiv.org/ftp/arxiv/papers/1708/1708.06347.pdf
A Novel Clustering Method for Similarity Measuring in Text DocumentsIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Vidya Pratishthan’s College of Engineering, Baramati
is going to organize “VPiDEA’12” is a state level
idea presentation which provides a forum for Post
Graduate and Under Graduate students to discuss
and enhance their project ideas. It will also help them
to improve their knowledge, ability of learning new
things and helps in having up-to-date knowledge on
the technology.
For more details visit: www.vpidea.org
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
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Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
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• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• 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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
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This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
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Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
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And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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Adaptive cluster distance bounding
1. For Diploma, BE, ME, M Tech, BCA, MCA, PHD Project Guidance, Please Visit: www.ocularsystems.in Or
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Adaptive Cluster Distance Bounding for High
Dimensional Indexing+
Abstract:
We consider approaches for similarity search in correlated, high-dimensional data
sets, which are derived within a clustering framework. We note that indexing by
“vector approximation” (VA-File), which was proposed as a technique to combat the
“Curse of Dimensionality,” employs scalar quantization, and hence necessarily
ignores dependencies across dimensions, which represents a source of sub
optimality. Clustering, on the other hand, exploits inter dimensional correlations
and is thus a more compact representation of the data set. However, existing
methods to prune irrelevant clusters are based on bounding hyperspheres and/or
bounding rectangles, whose lack of tightness compromises their efficiency in exact
nearest neighbor search. We propose a new cluster-adaptive distance bound based
on separating hyperplane boundaries of Voronoi clusters to complement our cluster
based index. This bound enables efficient spatial filtering, with a relatively small
preprocessing storage overhead and is applicable to euclidean and Mahalanobis
similarity measures. Experiments in exact nearest-neighbor set retrieval, conducted
on real data sets, show that our indexing method is scalable with data set size and
data dimensionality and outperforms several recently proposed indexes. Relative to
the VA-File, over a wide range of quantization resolutions, it is able to reduce
random IO accesses, given (roughly) the same amount of sequential IO operations,
by factors reaching 100X and more.
Ocular Systems, Shop No:1, Swagat Corner Building, Near Narayani Dham Temple, Katraj, Pune-46
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Existing System:
However, existing methods to prune irrelevant clusters are based on bounding
hyperspheres and/or bounding rectangles, whose lack of tightness compromises
their efficiency in exact nearest neighbor search.
Spatial queries, specifically nearest neighbor queries, in high-dimensional spaces
have been studied extensively. While several analyses have concluded that the
nearest neighbor search, with Euclidean distance metric, is impractical at high
dimensions due to the notorious “curse of dimensionality”, others have suggested
that this may be over pessimistic. Specifically, the authors of have shown that
what Determines the search performance (at least for R-tree-like structures) is the
intrinsic dimensionality of the data set and not the dimensionality of the address
space (or the embedding dimensionality).
We extend our distance bounding technique to the Mahalanobis distance metric,
and note large gains over existing indexes.
Proposed System:
We propose a new cluster-adaptive distance bound based on separating hyperplane
boundaries of Voronoi clusters to complement our cluster based index. This bound
enables efficient spatial filtering, with a relatively small pre-processing storage
overhead and is applicable to Euclidean and Mahalanobis similarity measures.
Experiments in exact nearest-neighbor set retrieval, conducted on real data-sets,
show that our indexing method is scalable with data-set size and data
dimensionality and outperforms several recently proposed indexes.
Ocular Systems, Shop No:1, Swagat Corner Building, Near Narayani Dham Temple, Katraj, Pune-46
E-Mail: info@ocularsystems.in
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we outline our approach to indexing real high-dimensional data-sets. We focus on
the clustering paradigm for search and retrieval. The data-set is clustered, so that
clusters can be retrieved in decreasing order of their probability of containing
entries relevant to the query.
We note that the Vector Approximation (VA)-file technique implicitly assumes
independence across dimensions, and that each component is uniformly distributed.
This is an unrealistic assumption for real data-sets that typically exhibit significant
correlations across dimensions and non-uniform distributions. To approach
optimality, an indexing technique must take these properties into account. We
resort to a Voronoi clustering framework as it can naturally exploit correlations
across dimensions (in fact, such clustering algorithms are the method of choice in
the design of vector quantizers). Moreover, we show how our clustering procedure
can be combined with any other generic clustering method of choice (such as
BIRCH ) requiring only one additional scan of the data-set. Lastly, we note that the
sequential scan is in fact a special case of clustering based index i.e. with only one
cluster.
Several index structures exist that facilitate search and retrieval of multi-
dimensional data. In low dimensional spaces, recursive partitioning of the space
with hyper-rectangles hyper-spheres or a combination of hyper-spheres and
hyper-rectangles have been found to be effective for nearest neighbor search and
retrieval. While the preceding methods specialize to Euclidean distance (l2 norm),
M-trees have been found to be effective for metric spaces with arbitrary distance
functions (which are metrics).
Such multi-dimensional indexes work well in low dimensional spaces, where they
outperform sequential scan. But it has been observed that the performance
degrades with increase in feature dimensions and, after a certain dimension
threshold, becomes inferior to sequential scan. In a celebrated result,
Ocular Systems, Shop No:1, Swagat Corner Building, Near Narayani Dham Temple, Katraj, Pune-46
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Weber et. Al have shown that whenever the dimensionality is above 10, these
methods are outperformed by simple sequential scan. Such performance
degradation is attributed to Bellman’s ‘curse of dimensionality’, which refers to the
exponential growth of hyper-volume with dimensionality of the space.
Module Description:
1. A New Cluster Distance Bound
2. Adaptability to Weighted Euclidean or Mahalanobis Distances
3. An Efficient Search Index
4. Vector Approximation Files
5. Approximate Similarity Search
A New Cluster Distance Bound
Crucial to the effectiveness of the clustering-based search strategy is efficient
bounding of query-cluster distances. This is the mechanism that allows the
elimination of irrelevant clusters. Traditionally, this has been performed with
bounding spheres and rectangles. However, hyperspheres and hyperrectangles are
generally not optimal bounding surfaces for clusters in high dimensional spaces. In
fact, this is a phenomenon observed in the SR-tree, where the authors have used a
combination spheres and rectangles, to outperform indexes using only bounding
spheres (like the SS-tree) or bounding rectangles (R∗-tree).
The premise herein is that, at high dimensions, considerable improvement in
efficiency can be achieved by relaxing restrictions on the regularity of bounding
surfaces (i.e., spheres or rectangles). Specifically, by creating Voronoi clusters,
withpiecewise-linear boundaries, we allow for more general convex polygon
Ocular Systems, Shop No:1, Swagat Corner Building, Near Narayani Dham Temple, Katraj, Pune-46
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structures that are able to efficiently bound the cluster surface. With the
construction of Voronoi clusters under the Euclidean distance measure, this is
possible. By projection onto these hyperplane boundaries and complementing with
the cluster-hyperplane distance, we develop an appropriate lower bound on the
distance of a query to a cluster.
Adaptability to Weighted Euclidean or Mahalanobis Distances
While the Euclidean distance metric is popular within the multimedia indexing
community it is by no means the “correct” distance measure, in that it may be a
poor approximation of user perceived similarities. The Mahalanobis distance
measure has more degrees of freedom than the Euclidean distance and by proper
updation (or relevance feedback), has been found to be a much better estimator of
user perceptions and more recently) . We extend our distance bounding technique
to the Mahalanobis distance metric, and note large gains over existing indexes.
An Efficient Search Index
The data set is partitioned into multiple Voronoi clusters and for any kNN query, the
clusters are ranked in order of the hyperplane bounds and in this way, the
irrelevant clusters are filtered out. We note that the sequential scan is a special
case of our indexing, if there were only one cluster. An important feature of our
search index is that we do not store the hyperplane boundaries (which form the
faces of the bounding polygons), but rather generate them dynamically, from the
cluster centroids. The only storage apart from the centroids are the cluster-
hyperplane boundary distances (or the smallest cluster-hyperplane distance). Since
our bound is relatively tight, our search algorithm is effective in spatial filtering of
Ocular Systems, Shop No:1, Swagat Corner Building, Near Narayani Dham Temple, Katraj, Pune-46
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irrelevant clusters, resulting in significant performance gains. We expand on the
results and techniques initially presented in , with comparison against several
recently proposed indexing techniques.
Vector Approximation Files
A popular and effective technique to overcome the curse of dimensionality is the
vector approximation file (VA-File). VA-File partitions the space into hyper-
rectangular cells, to obtain a quantized approximation for the data that reside inside
the cells. Non-empty cell locations are encoded into bit strings and stored in a
separate approximation file, on the hard-disk. During a nearest neighbor search,
the vector approximation file is sequentially scanned and upper and lower bounds
on the distance from the query vector to each cell are estimated. The bounds are
used to prune irrelevant cells. The final set of candidate vectors are then read from
the hard disk and the exact nearest neighbors are determined. At this point, we
note that the terminology “Vector Approximation” is somewhat confusing, since
what is actually being performed is scalar quantization, where each component of
the feature vectors separately and uniformly quantized (in contradistinction with
vector quantization in the signal compression literature).
VA-File was followed by several more recent techniques to overcome the curse of
dimensionality. In the VA+-File, the data-set is rotated into a set of uncorrelated
dimensions, with more approximation bits being provided for dimensions with
higher variance. The approximation cells are adaptively spaced according to the
data distribution. Methods such as LDR and the recently proposed non-linear
approximations aim to outperform sequential scan by a combination of clustering
and dimensionality reduction. There also exist a few hybrid methods, such as the A-
Tree, and IQ-Tree, which combine VA-style approximations within a tree based
index.
Approximate Similarity Search
Ocular Systems, Shop No:1, Swagat Corner Building, Near Narayani Dham Temple, Katraj, Pune-46
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Lastly, it has been argued that the feature vectors and distance functions are often
only approximations of user perception of similarity. Hence, even the results of an
exact similarity search is inevitably perceptually approximate, with additional
rounds of query refinement necessary. Conversely, by performing an approximate
search, for a small penalty in
accuracy, considerable savings in query processing time would be possible.
Examples of such search strategies are MMDR probabilistic searches and locality
sensitive hashing .The reader is directed to for a more detailed survey of
approximate similarity search. The limits of approximate indexing i.e. the optimal
tradeoffs between search quality and search time has also been studied within an
information theoretic framework.
System Architecture:
Ocular Systems, Shop No:1, Swagat Corner Building, Near Narayani Dham Temple, Katraj, Pune-46
E-Mail: info@ocularsystems.in
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Hardware System Requirement
Processor - Pentium –III
Speed - 1.1 Ghz
RAM - 256 MB(min)
Hard Disk - 20 GB
Floppy Drive - 1.44 MB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
S/W System Requirement
Operating System : Windows 95/98/2000/NT4.0.
Application Server : Tomcat6.0
Front End : HTML, Java.
Scripts : JavaScript.
Server side Script : Java Server Pages.
Database : Mysql.
Database Connectivity : JDBC.
Ocular Systems, Shop No:1, Swagat Corner Building, Near Narayani Dham Temple, Katraj, Pune-46
E-Mail: info@ocularsystems.in