The document describes a proposed spatial data mining system called SD-Miner. SD-Miner consists of three main parts: a graphical user interface, an SD-Miner module for processing spatial data mining functions, and a data storage module. The SD-Miner module provides four spatial data mining functionalities: spatial clustering, spatial classification, spatial characterization, and spatio-temporal association rule mining. The document presents the architecture of SD-Miner and provides examples of using it to perform spatial clustering, classification, and characterization on spatial data from a database.
High Performance Computing for Satellite Image Processing and Analyzing – A ...Editor IJCATR
High Performance Computing (HPC) is the recently developed technology in the field of computer science, which evolved
due to meet increasing demands for processing speed and analysing/processing huge size of data sets. HPC brings together several
technologies such as computer architecture, algorithm, programs and system software under one canopy to solve/handle advanced
complex problems quickly and effectively. It is a crucial element today to gather and process large amount of satellite (remote sensing)
data which is the need of an hour. In this paper, we review recent development in HPC technology (Parallel, Distributed and Cluster
Computing) for satellite data processing and analysing. We attempt to discuss the fundamentals of High Performance Computing
(HPC) for satellite data processing and analysing, in a way which is easy to understand without much previous background. We sketch
the various HPC approach such as Parallel, Distributed & Cluster Computing and subsequent satellite data processing & analysing
methods like geo-referencing, image mosaicking, image classification, image fusion and Morphological/neural approach for hyperspectral satellite data. Collective, these works deliver a snapshot, tables and algorithms of the recent developments in those sectors and
offer a thoughtful perspective of the potential and promising challenges of satellite data processing and analysing using HPC
paradigms.
An Improved Way of Segmentation and Classification of Remote Sensing Images U...ijsrd.com
The Ultimate significance of Images lies in processing the digital image which stems from two principal application areas: Advances of pictorial information for human interpretation; and dispensation of image data for storage, communication, and illustration for self-sufficient machine perception. The objective of this research work is to define the meaning and possibility of image segmentation based on remote sensing images which are successively classified with statistical measures. In this paper kernel induced Possiblistic C-means clustering algorithm has been implemented for classifying remote sensing image data with image features. As a final point of the proposed work is to point out that this algorithm works well for segmenting and classifying the image with better accuracy with statistical metrices.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The complexity of landscape pattern mining is well stated due to its non-linear spatial image formation and
inhomogeneity of the satellite images. Land Ex tool of the literature work needs several seconds to answer input
image pattern query. The time duration of content based image retrieval depends on input query complexity. This
paper focuses on designing and implementing a training dataset to train NML (Neural network based Machine
Learning) algorithm to reduce the search time to improve the result accuracy. The performance evolution of
proposed NML CBIR (Content Based Image Retrieval) method will be used for comparison of satellite and natural
images by means of increasing speed and accuracy.
Keywords: Spatial Image, Satellite image, NML, CBIR
High Performance Computing for Satellite Image Processing and Analyzing – A ...Editor IJCATR
High Performance Computing (HPC) is the recently developed technology in the field of computer science, which evolved
due to meet increasing demands for processing speed and analysing/processing huge size of data sets. HPC brings together several
technologies such as computer architecture, algorithm, programs and system software under one canopy to solve/handle advanced
complex problems quickly and effectively. It is a crucial element today to gather and process large amount of satellite (remote sensing)
data which is the need of an hour. In this paper, we review recent development in HPC technology (Parallel, Distributed and Cluster
Computing) for satellite data processing and analysing. We attempt to discuss the fundamentals of High Performance Computing
(HPC) for satellite data processing and analysing, in a way which is easy to understand without much previous background. We sketch
the various HPC approach such as Parallel, Distributed & Cluster Computing and subsequent satellite data processing & analysing
methods like geo-referencing, image mosaicking, image classification, image fusion and Morphological/neural approach for hyperspectral satellite data. Collective, these works deliver a snapshot, tables and algorithms of the recent developments in those sectors and
offer a thoughtful perspective of the potential and promising challenges of satellite data processing and analysing using HPC
paradigms.
An Improved Way of Segmentation and Classification of Remote Sensing Images U...ijsrd.com
The Ultimate significance of Images lies in processing the digital image which stems from two principal application areas: Advances of pictorial information for human interpretation; and dispensation of image data for storage, communication, and illustration for self-sufficient machine perception. The objective of this research work is to define the meaning and possibility of image segmentation based on remote sensing images which are successively classified with statistical measures. In this paper kernel induced Possiblistic C-means clustering algorithm has been implemented for classifying remote sensing image data with image features. As a final point of the proposed work is to point out that this algorithm works well for segmenting and classifying the image with better accuracy with statistical metrices.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The complexity of landscape pattern mining is well stated due to its non-linear spatial image formation and
inhomogeneity of the satellite images. Land Ex tool of the literature work needs several seconds to answer input
image pattern query. The time duration of content based image retrieval depends on input query complexity. This
paper focuses on designing and implementing a training dataset to train NML (Neural network based Machine
Learning) algorithm to reduce the search time to improve the result accuracy. The performance evolution of
proposed NML CBIR (Content Based Image Retrieval) method will be used for comparison of satellite and natural
images by means of increasing speed and accuracy.
Keywords: Spatial Image, Satellite image, NML, CBIR
Automated features extraction from satellite images.HimanshuGupta1081
This is the final year civil engineering project presentation in which different features i.e. Buildings, Road Network, Vegetation and Water are extracted automatically from satellite images with the help of Ecognition software. We have done our analysis on satellite images of sikar, rajasthan. In this project object based image analysis (OBIA) approach are used.
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
This paper presents to building identification from satellite images. Because of monitoring illegal land usage. Nowadays rapid urbanization leads to
increase the land usage, in this case of monitoring illegal land usage is very important. This project implemented to building identification from
satellite images, images are provided from Bing maps. Adaptive Neuro Fuzzy Inference System used to check data base information. In this proposed
system, I can identify only building images from the satellite images, To improving the image details effectively.
Enhanced target tracking based on mean shift algorithm for satellite imageryeSAT Journals
Abstract Target tracking in high resolution satellite images is challenging task for computer vision field. In this paper we have proposed a mean shift algorithm based enhanced target tracking system for high resolution satellite imagery. In proposed tracking algorithm, Target modeling is done using spectral features of target object i.e. Mean & Energy density function. Feature Vector space with minimum Euclidean Distance is used for predicting next possible position of target object in consecutive frames. Proposed tracking algorithm has been tested using two high resolution databases i.e. Harbor & Airport region database acquired by WorldView-2 satellite at different times. Recall, Precision & F1 score etc. performance parameters are also calculated for showing the tracking ability of the proposed method in real-time applications and are compared with the results of Regional Operator Design based tracking algorithm proposed in [1]. The results show that our proposed method gives relatively better performance than the other tracking algorithms used in satellite imagery. Keywords- Target tracking, Mean shift algorithm, Energy density function, Feature Vector Space, Frame
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A novel approach for georeferenced data analysis using hard clustering algorithmeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance FeedbackIJMIT JOURNAL
Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database. Image contents are plays significant role for image retrieval. There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design. Each image has three contents such as: color, texture and shape features. Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results. Color histogram and texture features have potential to retrieve similar images on the basis of their properties. As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query. To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solutio
Goal location prediction based on deep learning using RGB-D camerajournalBEEI
In the navigation system, the desired destination position plays an essential role since the path planning algorithms takes a current location and goal location as inputs as well as the map of the surrounding environment. The generated path from path planning algorithm is used to guide a user to his final destination. This paper presents a proposed algorithm based on RGB-D camera to predict the goal coordinates in 2D occupancy grid map for visually impaired people navigation system. In recent years, deep learning methods have been used in many object detection tasks. So, the object detection method based on convolution neural network method is adopted in the proposed algorithm. The measuring distance between the current position of a sensor and the detected object depends on the depth data that is acquired from RGB-D camera. Both of the object detected coordinates and depth data has been integrated to get an accurate goal location in a 2D map. This proposed algorithm has been tested on various real-time scenarios. The experiments results indicate to the effectiveness of the proposed algorithm.
Extraction of Buildings from Satellite ImagesAkanksha Prasad
Buildings are termed as important components for various applications. Building extraction is defined as a sub-problem of Object Recognition. Though, numerous building extraction techniques have been proposed in the literature. But still they often exhibit limited success in the real scenarios. The main purpose of this research is to develop an algorithm which is able to detect and extract buildings from satellite images. In the proposed approach feature-based extraction process is used to extract buildings from satellite images. The overall system is tested and high performance detection is achieved which shows the effectiveness of proposed approach.
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.
An Automatic Neural Networks System for Classifying Dust, Clouds, Water, and ...Waqas Tariq
This paper presents an automatic remotely sensed system that is designed to classify dust, clouds, water and vegetation features from red sea area. Thus provides the system to make the test and classification process without retraining again. This system can rebuild the architecture of the neural network (NN) according to a linear combination among the number of epochs, the number of neurons, training functions, activation functions, and the number of hidden layers. Theproposed system is trained on the features of the provided images using 13 training functions, and is designed to find the best networks that has the ability to have the best classification on data is not included in the training data.This system shows an excellent classification of test data that is collected from the training data. The performances of the best three training functionsare%99.82, %99.64 and %99.28for test data that is not included in the training data.Although, the proposed system was trained on data selected only from one image, this system shows correctly classification of the features in the all images. The designed system can be carried out on remotely sensed images for classifying other features.This system was applied on several sub-images to classify the specified features. The correct performance of classifying the features from the sub-images was calculated by applying the proposed system on some small sections that were selected from contiguous areas contained the features.
A Review of BSS Based Digital Image Watermarking and Extraction MethodsIOSR Journals
The field of Signal Processing has witnessed the strong emergence of a new technique, the Blind
Signal Processing (BSP) which is based on sound theoretical foundation. An offshoot of the BSP is known as
Blind Source Separation (BSS). This digital signal processing techniques have a wide and varied potential
applications. The term blind is indicative of the fact that both the source signal and the mixing procedures are
unknown. One of the more interesting applications of BSS is in field of image data security/authentication where
digital watermarking is proposed. Watermarking is a promising technique to help protect data security and
intellectual property rights. The plethora digital image watermarking methods are surveyed and discussed here
with their features and limitations. Thus literature survey is presented in two major categories-Digital image
watermarking methods and BSS based techniques in digital image watermarking and extraction
Thermal Effects in Stokes’ Second Problem for Unsteady Second Grade Fluid Flo...IOSR Journals
In this paper, we investigated the effects of magnetic field and thermal in Stokes’ second problem for unsteady second grade fluid flow through a porous medium. The expressions for the velocity field and the temperature field are obtained analytically. The effects of various pertinent parameters on the velocity field and temperature field are studied through graphs in detail.
In Vivo Assay of Analgesic Activity of Methanolic and Petroleum Ether Extract...IOSR Journals
Abstract: Aims: The main objective of this work was to observe the analgesic activity of Vitex negundo (leaves) on mice. Study Design: Present study was designed to isolate pure compounds as well as to observe pharmacological activities of the isolated pure compounds with crude extracts of the plant Vitex negundo (leaves). The study protocol consisted of the following steps:
Cold extraction at room temperature of the whole plant with distilled methanol.
Filtration of the crude petroleum ether and methanolic extracts by using the Markin cotton cloth and subsequently through the filter paper and solvent evaporation.
Screening of analgesic activity of crude extracts on Swiss Albino mice.
Place and Duration of Study: The study of analgesic activity of Vitex negundo (leaves) on mice was take place in the laboratory of Department of Pharmacy, Southeast University, Bangladesh, between January 2011 and July 2011. Methodology: The analgesic activity was investigated for its peripheral pharmacological actions using acetic acid-induced writhing test in mice. Results: The methanolic and petroleum ether extracts, at the dose of 200 mg/kg body weight, displayed 82.60% & 74.66% pain inhibition which was significant (p<0.001) compared to control. These results indicate that the extracts possess strong analgesic activity. Conclusion: The present study tends to suggest the analgesic activities of the crude methanolic and petroleum ether extract of the leaves of Vitex negundo and justify its use in folkloric remedies
Automated features extraction from satellite images.HimanshuGupta1081
This is the final year civil engineering project presentation in which different features i.e. Buildings, Road Network, Vegetation and Water are extracted automatically from satellite images with the help of Ecognition software. We have done our analysis on satellite images of sikar, rajasthan. In this project object based image analysis (OBIA) approach are used.
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
This paper presents to building identification from satellite images. Because of monitoring illegal land usage. Nowadays rapid urbanization leads to
increase the land usage, in this case of monitoring illegal land usage is very important. This project implemented to building identification from
satellite images, images are provided from Bing maps. Adaptive Neuro Fuzzy Inference System used to check data base information. In this proposed
system, I can identify only building images from the satellite images, To improving the image details effectively.
Enhanced target tracking based on mean shift algorithm for satellite imageryeSAT Journals
Abstract Target tracking in high resolution satellite images is challenging task for computer vision field. In this paper we have proposed a mean shift algorithm based enhanced target tracking system for high resolution satellite imagery. In proposed tracking algorithm, Target modeling is done using spectral features of target object i.e. Mean & Energy density function. Feature Vector space with minimum Euclidean Distance is used for predicting next possible position of target object in consecutive frames. Proposed tracking algorithm has been tested using two high resolution databases i.e. Harbor & Airport region database acquired by WorldView-2 satellite at different times. Recall, Precision & F1 score etc. performance parameters are also calculated for showing the tracking ability of the proposed method in real-time applications and are compared with the results of Regional Operator Design based tracking algorithm proposed in [1]. The results show that our proposed method gives relatively better performance than the other tracking algorithms used in satellite imagery. Keywords- Target tracking, Mean shift algorithm, Energy density function, Feature Vector Space, Frame
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A novel approach for georeferenced data analysis using hard clustering algorithmeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance FeedbackIJMIT JOURNAL
Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database. Image contents are plays significant role for image retrieval. There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design. Each image has three contents such as: color, texture and shape features. Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results. Color histogram and texture features have potential to retrieve similar images on the basis of their properties. As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query. To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solutio
Goal location prediction based on deep learning using RGB-D camerajournalBEEI
In the navigation system, the desired destination position plays an essential role since the path planning algorithms takes a current location and goal location as inputs as well as the map of the surrounding environment. The generated path from path planning algorithm is used to guide a user to his final destination. This paper presents a proposed algorithm based on RGB-D camera to predict the goal coordinates in 2D occupancy grid map for visually impaired people navigation system. In recent years, deep learning methods have been used in many object detection tasks. So, the object detection method based on convolution neural network method is adopted in the proposed algorithm. The measuring distance between the current position of a sensor and the detected object depends on the depth data that is acquired from RGB-D camera. Both of the object detected coordinates and depth data has been integrated to get an accurate goal location in a 2D map. This proposed algorithm has been tested on various real-time scenarios. The experiments results indicate to the effectiveness of the proposed algorithm.
Extraction of Buildings from Satellite ImagesAkanksha Prasad
Buildings are termed as important components for various applications. Building extraction is defined as a sub-problem of Object Recognition. Though, numerous building extraction techniques have been proposed in the literature. But still they often exhibit limited success in the real scenarios. The main purpose of this research is to develop an algorithm which is able to detect and extract buildings from satellite images. In the proposed approach feature-based extraction process is used to extract buildings from satellite images. The overall system is tested and high performance detection is achieved which shows the effectiveness of proposed approach.
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.
An Automatic Neural Networks System for Classifying Dust, Clouds, Water, and ...Waqas Tariq
This paper presents an automatic remotely sensed system that is designed to classify dust, clouds, water and vegetation features from red sea area. Thus provides the system to make the test and classification process without retraining again. This system can rebuild the architecture of the neural network (NN) according to a linear combination among the number of epochs, the number of neurons, training functions, activation functions, and the number of hidden layers. Theproposed system is trained on the features of the provided images using 13 training functions, and is designed to find the best networks that has the ability to have the best classification on data is not included in the training data.This system shows an excellent classification of test data that is collected from the training data. The performances of the best three training functionsare%99.82, %99.64 and %99.28for test data that is not included in the training data.Although, the proposed system was trained on data selected only from one image, this system shows correctly classification of the features in the all images. The designed system can be carried out on remotely sensed images for classifying other features.This system was applied on several sub-images to classify the specified features. The correct performance of classifying the features from the sub-images was calculated by applying the proposed system on some small sections that were selected from contiguous areas contained the features.
A Review of BSS Based Digital Image Watermarking and Extraction MethodsIOSR Journals
The field of Signal Processing has witnessed the strong emergence of a new technique, the Blind
Signal Processing (BSP) which is based on sound theoretical foundation. An offshoot of the BSP is known as
Blind Source Separation (BSS). This digital signal processing techniques have a wide and varied potential
applications. The term blind is indicative of the fact that both the source signal and the mixing procedures are
unknown. One of the more interesting applications of BSS is in field of image data security/authentication where
digital watermarking is proposed. Watermarking is a promising technique to help protect data security and
intellectual property rights. The plethora digital image watermarking methods are surveyed and discussed here
with their features and limitations. Thus literature survey is presented in two major categories-Digital image
watermarking methods and BSS based techniques in digital image watermarking and extraction
Thermal Effects in Stokes’ Second Problem for Unsteady Second Grade Fluid Flo...IOSR Journals
In this paper, we investigated the effects of magnetic field and thermal in Stokes’ second problem for unsteady second grade fluid flow through a porous medium. The expressions for the velocity field and the temperature field are obtained analytically. The effects of various pertinent parameters on the velocity field and temperature field are studied through graphs in detail.
In Vivo Assay of Analgesic Activity of Methanolic and Petroleum Ether Extract...IOSR Journals
Abstract: Aims: The main objective of this work was to observe the analgesic activity of Vitex negundo (leaves) on mice. Study Design: Present study was designed to isolate pure compounds as well as to observe pharmacological activities of the isolated pure compounds with crude extracts of the plant Vitex negundo (leaves). The study protocol consisted of the following steps:
Cold extraction at room temperature of the whole plant with distilled methanol.
Filtration of the crude petroleum ether and methanolic extracts by using the Markin cotton cloth and subsequently through the filter paper and solvent evaporation.
Screening of analgesic activity of crude extracts on Swiss Albino mice.
Place and Duration of Study: The study of analgesic activity of Vitex negundo (leaves) on mice was take place in the laboratory of Department of Pharmacy, Southeast University, Bangladesh, between January 2011 and July 2011. Methodology: The analgesic activity was investigated for its peripheral pharmacological actions using acetic acid-induced writhing test in mice. Results: The methanolic and petroleum ether extracts, at the dose of 200 mg/kg body weight, displayed 82.60% & 74.66% pain inhibition which was significant (p<0.001) compared to control. These results indicate that the extracts possess strong analgesic activity. Conclusion: The present study tends to suggest the analgesic activities of the crude methanolic and petroleum ether extract of the leaves of Vitex negundo and justify its use in folkloric remedies
The Evaluation of p-type doping in ZnO taking Co as dopantIOSR Journals
P-type doping is excessively difficult in wide-band gap semiconductors, such as GaN, ZnSe, and
indeed ZnO. Practically it is very difficult to obtain stable and true p-type ZnO. Under standard conditions there
are reports on decaying or even completely absent p-type conductivity. Several hypotheses have been
formulated to explain this unusual behaviour. The commonly accepted theory is that the elements assume
undesired (non-substitutional) positions or form complexes, resulting in self-compensation of the p-type
conductivity. Note that p-dopants can be compensated by low energy native defects, such as Zni or VO, or
background impurities, such as aforementioned H. Deep impurity levels can cause significant resistance to the
formation of a shallow acceptor level. In this work the p-type doped Zno based samples are prepared where
incorporation of Co into the ZnO crystal lattice is done and various measurements like Sheet resistivity, Hall
Voltage, Hall mobility and 2d hole concentration are made. From the results it has been clearly found that the
p-type doping takes place successfully and can be used for various future applications
Simple and Effective Method of the Synthesis of Nanosized Fe2O 3particlesIOSR Journals
Abstract: Nanosized Iron oxide is prepared by using precipitation method from iron nitrate and liquid ammonia. Thermal analysis shows that synthesized iron oxide shows some weight loss and oxide undergoing decomposition, dehydration or any physical change from TGA curve we observe that Iron oxide shows stable weight loss above 4000C. In DTA curve also, there is exothermic and endothermic peak. Which shows phase transition, solid state reaction or any chemical reaction occurred during heating treatment. Morphology is observed by scanning electron microscopy (SEM) shows particles are nanosized. Further morphology observation by Transmission Electron Microscopy (TEM) revels that Iron Oxide has the corundum (Al2O3) structure. Magnetic measurements shows that iron oxide has five unpaired electron and strongly paramagnetic character.
Effects of Addition of Sprint, Strength and Agility Training On Cardiovascula...IOSR Journals
Abstract: To find training related physiological and cardiovascular changes and adaptations in field hockey
players, this interventional study was conducted with 30 male field hockey trainees (age: 15.7±1.55 years,
range: 13-20 years) undergoing training in preparation phase. In addition to the their usual routine of 2 hours
per day, 6 times a week of aerobic, anaerobic and skill training session of field hockey; a specialized additional
training of 2 hours per day- 3 times a week, consisting of sprint, strength-power and agility was incorporated in
the schedule. Selected variables measured before and after the 6 week training were compared with paired t
test. Upon analysis we found that lean body mass (LBM) of the participants showed significant improvement
(P<0.05)><0.05)><0.05) in the echocardiographic parameters – increase in left ventricular
posterior wall thickness, left ventricular ejection fraction; and decrease in left ventricular end-systolic volume.
However when these parameters were expressed relative to LBM, no significant change was seen. Left
ventricular end-diastolic diameter and volume, body weight and body mass index too did not change
significantly after the training. This 6 week specialized additional training resulted in improvement of body
composition and cardiovascular functions of the participants, indicating favorable physiological, morphological
and functional adaptations.
Key words: field hockey; physiological adaptations; sports physiology; body composition; echocardiography;
lean body mass.
Feature Extraction Based Estimation of Rain Fall By Cross Correlating Cloud R...IOSR Journals
In this paper we present the feature extraction based estimation of rain fall by cross correlating
cloud RADAR Data. The idea is to select a square box of around 200x200 pixels around the point of interest and
take the cross correlation between the last picture and one that is 5 or 10 minutes older. We then determine the
wind direction and speed by finding the highest point in the correlation. Last step is to interpolate the data
acquired in a tagged format to the latest data in the up-wind direction to get a prediction for the near future.
The basic principle works, but it is hard to get a good estimate of the wind direction.
Effects of Harness Running, Sand Running, Weight - Jacket Running and Weight ...IOSR Journals
Abstract: Background: The purpose of the study was to find the effects of Harness Running, Sand Running,
Weight-Jacket Running and Weight training on the performance of Dribbling and kicking among the Burdwan
Distrist School going soccer players.
Method-100 male students from the different schools of the Burdwan distrist were randomly selected as subjects
and there age were 14-18 years served as Harness Running group (HRG), second group served as Sand
Running group (SRG), third group served as Weight-Jacket Running group (WJRG), fourth group served as
Weight training group (WTG) and the fifth group served as Control group (CTG).Ten weeks training were given
for experiment accordingly. The control group was not given any training except of their routine. The selected
subjects were measure of the following soccer skills of Kicking and Dribbling. ANCOVA was calculation for
statistical treatment.
Finding: From the finding implies that the Weight Jacket Group was more effective in decreasing the time taken
and increasing the distance then all other training programs after ten weeks of training on Dribbling and
kicking.
Conclusions: In dribbling Weight Jacket Group showed higher adjusted post-test mean difference with Control
Group in comparison to other three training groups which is 0.8, higher than the critical difference 0.51
required being significant at 0.05 levels.
In kicking Weight Jacket Running Group showed higher adjusted post-test mean difference with Control Group
in comparison to other three training groups which are 2.50, higher than the critical difference 1.60 required
being significant at 0.05 levels.
Keywords: Harness Running, Sand Running, Weight-Jacket Running, Weight training, agility, Dribbling and
kicking
Effect of astaxanthin on ethylene glycol induced nephrolithiasisIOSR Journals
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SD-miner System to Retrieve Probabilistic Neighborhood Points in Spatial Data Mining
1. IOSR Journal of Computer Engineering (IOSRJCE)
ISSN: 2278-0661 Volume 4, Issue 6 (Sep.-Oct. 2012), PP 01-05
www.iosrjournals.org
www.iosrjournals.org 1 | P a g e
SD-miner System to Retrieve Probabilistic Neighborhood Points
in Spatial Data Mining
Asst. Prof. S. G. Kulkarni1
, Asst. Prof. Padma D2
, Mr. Manjunath R. H3
1,2,3
Department of Computer Science, Gogte Institute of Technology, Belgaum.
Abstract: In GIS or Geographic Information system technology, a vast volume of spatial data has been
accumulated, thereby incurring the necessity of spatial data mining techniques. Displaying and visualizing such
data items are important aspects. But no RDBMS software is loaded with displaying the spatial result over a
MAP overlay or answer spatial queries like “all the points within” certain Neighborhood.
In this project, we propose a new spatial data mining system named SD-Miner. SD-Miner consists of
three parts: A Graphical User Interface for inputs and outputs, a Data Mining Module that processes spatial
data mining functionalities, a Data Storage Model that stores and manages spatial as well as non-spatial data
by using a DBMS. In particular, the data mining module provides major spatial data mining functionalities such
as spatial clustering, spatial classification, spatial characterization, and spatio-temporal association rule
mining. SD-Miner has its own characteristics:
(1) It supports users to perform non-spatial data mining functionalities as well as spatial data mining
functionalities intuitively and effectively.
(2) It provides users with spatial data mining functions as a form of libraries, thereby making applications
conveniently use those functions.
(3) It inputs parameters for mining as a form of database tables to increase flexibility. Result shows that
significantly reduced and precise data items are displayed through the result of this technique.
I. Introduction
Due to the development of information technology, a vast volume of data is accumulated on many
fields. Since automated methods for filtering/analyzing the data and also explaining the results are required, a
variety of data mining techniques finding new knowledge by discovering hidden rules from vast amount of data
are developed. In the field of geography, due to the development of technology for remote sensing, monitoring,
geographical information systems, and global positioning systems, a vast volume of spatial data is accumulated.
Also, there have been many studies of discovering meaningful knowledge from the spatial data. Since the spatial
data has its own characteristics different from the non-spatial data, direct using of general data mining
techniques incurs many difficulties. So there have been many studies of spatial data mining techniques
considering the characteristics of the spatial data. However, commercial tools for spatial data mining have not
been provided. Currently, many commercial data mining tools are available, but these tools not support the
spatial data mining functionalities. Also, while some academic spatial data mining tools such as Geo-Miner are
available, there are almost no commercial spatial data mining tools. So, for easy using of spatial data mining for
real spatial data applications, developments of spatial data mining tools are needed. In this paper, author
proposes a new spatial data mining system named SD-Miner. SD-Miner supports four important spatial data
mining functionalities: spatial clustering, spatial classification, spatial characterization, and spatio-temporal
association rule mining. We first analyze characteristics of previous spatial data mining techniques and suggest
techniques to improve their efficiency in developing SD-Miner [1].
II. Motivation
Spatial data mining that is, discovery of interesting, implicit knowledge in spatial databases is a highly
demanding field because very large amount of spatial data have been collected in various applications, ranging
from remote sensing, to Geographical Information Systems (GIS), computer cartography, environmental
assessment and planning etc. Spatial data has its own characteristic different from non spatial data. Direct use of
general data mining techniques incur many difficulties, so we need a spatial data mining tool for easy using of
spatial data, for spatial data application, which can be easily done by SD-miner. The SD-miner supports four
important spatial data mining functionalities as spatial clustering, spatial classification, spatial characterization
and spatial temporal association rule mining [1].
2. SD-miner System to Retrieve Probabilistic Neighborhood Points in Spatial Data Mining
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III. Proposed Approach
This work attempts to develop a spatial data mining system, the SD-miner which consists of three
parts: A graphical user interface for inputs and outputs, a data mining module that processes spatial data mining
functionalities, a data storage model that stores and manages spatial as well as non spatial data by using a
DBMS.
Fig 1: Architecture of SD-Miner
SD-Miner is composed of three main parts (Figure 1): Graphical User Interface (GUI), SD-Miner
Module, and Data Storage Module (also, called DBMS management module) [1].
Graphical User Interface
A GUI is a type of computer human interface on a computer. It solves the blank screen problem that
confronted early computer users. These early users sat down in front of a computer and faced a blank screen,
with only a prompt. The computer gave the user no indication what the user was to do next. GUI is an attempt to
solve this blank screen problem.
Graphical user interface (GUI), a computer program that enables a person to communicate with a
computer through the use of symbols, visual metaphors, and pointing devices. Best known for its
implementation in Apple Inc.’s Macintosh and Microsoft Corporation’s Windows operating system, the GUI has
replaced the arcane and difficult textual interfaces of earlier computing with a relatively intuitive system that has
made computer operation not only easier to learn but more pleasant and natural. The GUI is now the standard
computer interface, and its components have themselves become unmistakable cultural artifacts.
Snap1- GUI
At a conceptual level, a computer human interface is a means by which “people and computers
communicate with each other". In computer science terms, the GUI is a visual operating display that the monitor
presents on the monitor to the computer operator. More specifically, a GUI is a specification for the look and
feel of the computer system. GUI usually have common characteristic such as windows, icons, menus, and
push-buttons (WIMP). Collectively, WIMP (Windows, Icons, Mouse, Pointer) are pictures that bring forth a
certain action or an action space. The user issues commands via the GUI to computer applications. GUI usually
has three major components. These three components are: a windowing system, an imaging model, and an
application program interface (API). The windowing system builds the windows, menus, and dialog boxes that
appear on the screen. The imaging model defines the fonts and graphics that appear on the screen. WIMP is
products of both the windowing system and imaging model. Finally, the API is the means in which the user
specifies how and what windows and graphics appear on the screen.
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The GUI contains some of the buttons and combo boxes which performs actions like:
1.Fetch tables- Which fetches the tables from the database.
2.View column-Which displays columns according to the query written.
3.Simple select query-which specifies the condition in term of query.
4.Select spatial co-ordinates- this button helps us to select the required longuted and latitude.
5.Select dimensions.
SD-miner Module
The SD-Miner module processes respective data mining functions and transfers the results to the data
storage module. This module provides four data mining functions as shown in fig-1.
Four important characteristics of SD-miner system:
Spatial Clustering
Spatial clustering classifies spatial objects as multiple groups according to its positional or
geographical characteristics. This proposed work uses K means clustering.
K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem.
The procedure follows a simple and easy way to classify a given data set through a certain number of clusters
(assume k clusters). The main idea is to define k centroids, one for each cluster [2].
Spatial Classification
Usually, in the spatial classification, the objects are classified with considering of spatial and non-
spatial attributes. The spatial classification also uses the decision tree. A big difference between the spatial
classification and the classification is that the aggregation value of the spatial objects in a near region is used in
the spatial classification.
For making of a decision tree, the technique additionally uses predicates on relationship among spatial
objects as decision criteria. For this, as the first step, the spatial attributes are represented as spatial predicates
and then the possible useful predicates are extracted. For the second step, the decision tree is constructed with
the predicates. The benefits of this technique are as follows. Since the decision tree is constructed after
discarding ineffective predicates, the tree construction cost is greatly reduced. Speedy and correct classification
can be made [3].
Spatial Characterization
Spatial characterization extracts a global outline of data classes for a given spatial region by using the
spatial objects of the region. It gives simple and clear abstract information of the region. Spatial characterization
evaluates whether the characteristics of given spatial objects are expanded to near region. To do this, the objects
are defined as a neighbor of each other with considering of their distance or direction
The neighbor information is managed by using the neighbor table. The region handled by spatial
characterization can be expanded with a spatial expansion algorithm using the neighbor table [4].
Spatio-Temporal Association Rule Mining
By using spatial association rule mining, proposed system can represent topological relationship and
distance relationship of spatial objects via analyzing the relationship among spatial data and between spatial and
non-spatial data. Also, by adding of temporal data analysis, proposed system can use spatio-temporal association
rule mining.SD-Miner uses spatio-temporal association rule mining. In order to use spatial association rule
mining, the spatial relationships between spatial objects must be defined. The relationships are represented as
spatial predicates. The predicates defined by a user are stored as concept hierarchy data in the database of SD-
Miner. If the spatial relationship is defined as the predicates, the predicates can be regarded as non-spatial
attributes. So spatio-temporal association rules can be extracted by using the well-known Apriori algorithm.
Spatial association rule mining can be applied whether the predicates are spatial or non-spatial. So association
rules can be extracted among spatial predicates or among spatial and non-spatial predicates. This technique can
be applied hierarchically by using level-based predicates if exist. So, proposed system can extract detailed as
well as abstracted association rules in this case [5].
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IV. RESULTS
STEP 1: Load Data Base
Snap 2: Illustrates Loading of Tables.
STEP 2: In Spatial Characterization phase, target objects will be set with respect to the database containing
them as a description of the spatial properties.
In this proposed approach spatial properties like longitude, latitude and MRDS (Mineral Resources
Data Systems) are set.
Snap 3: Illustrates Selecting of Required Characteristics.
Snap 4: Illustrates list of selected characteristics in the form of columns and rows
STEP 3: In Classification phase, the objects (MRDS) are assigned to a respective classes from a given set of
classes based on the attribute values like longitude and latitude of the object.
Snap 5: Illustrates The Classification Processes.
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STEP 4: clustering classifies spatial objects (MRDS) as multiple groups according to specified longitude and
latitude.
Table 1: Illustrating number of clusters vs. Range
Graph: Number clusters vs. Range
The above table and graph shows us the variations of clusters according to the given range. As and
when we change the parameters longitude and latitude the number of clusters will change, because it searches
for the specified characteristic in the given range.
V. Conclusion
Spatial data has positional and topological data that do not exist in general data, and its structure is different
according to the kinds of spatial data. Also, the objects on space affect each other and the relationship of objects is also
different according to the kinds of objects. There have been many researches on spatial data mining considering these
characteristics of spatial data.
In this work, author explains the concept of spatial clustering, spatial classification, spatial characterization, and
spatio-temporal association rule mining. We present our experiences in developing a spatial data mining system called SD-
Miner that provides proper spatial data mining techniques with improved effectiveness and efficiency for real applications.
SD-Miner adopts following techniques. For spatial clustering, proposed system adapt NN Superseeding algorithm. For
spatial characterization, proposed system uses a binary decision tree and the RELIEF algorithm for efficiency. For spatial
characterization, we use the neighbor table for spatial extension of the current characterization method. For spatio-temporal
association rule mining, we use temporal data with spatial association rule mining using the spatial concept layer. SD-Miner
uses spatial data mining functions extended from general mining functions. So, it can be applied to both of spatial and non-
spatial data. Without special intervention of a user, it automatically recognizes which type of data is used. All functions are
developed in library style, and the functions can be reused in another system.
References
[1] SD-Miner: A SPATIAL DATA MINING SYSTEM Duck-Ho Bae, Ji-Haeng Baek, Hyun-Kyo Oh, Ju-Won Song, Sang-Wook Kim
[2] J. Sander et al., “Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications,” Data Mining
and Knowledge Discovery, Vol. 2, No. 2, pp. 169-194, 1998
[3] M. Ester et al., “Algorithms for Characterization and Trend Detection in Spatial Databases,” In Proc. Int'l. Conf. on Knowledge
Discovery and Data Mining, KDD, pp. 44-50, 1998.
[4] K. Koperski, J. Han, and N. Stefanovic, “An Efficient Two-Step Method for Classification of Spatial Data,” In Proc. Int'l. Symp. On
Spatial Data Handling, SDH, pp. 45-54, 1998.
[5] Spatio-Temporal Data Mining On Moving Objects In DBMS Yahaya Bin Abd Rahim GIS Section OTB Research Institute for
Housing, Urban and Mobility Studies Delft University of Technology November 15 2007.
[6] M. Ester et al., “Spatial Data Mining: Database Primitives, Algorithms and Efficient DBMS Support,” Data Mining and Knowledge
Discovery, Vol. 4, pp. 193-216, 2000.
[7] M. Ester, H. Kriegel, and J. Sander, “Algorithms and Applications for Spatial Data Mining,” Geographic Data Mining and
Knowledge discovery, 2001.
[8] J. Han, K. Koperski, and N. Stefanovic, “GeoMiner: A System Prototype for Spatial Data Mining,” In Proc. ACM Int'l. Conf. on
Management of Data, ACM SIGMOD, pp. 553-556, 1997.
[9] W. Lu, J. Han, and B. Ooi, “Discovery of General Knowledge in Large Spatial Databases,” In Proc. Far East Workshop on
Geographic Information Systems, pp. 275-289, 1993.
[10] E. Knorr and R. Ng, “Finding Aggregate Proximity Relationships and Commonalities in Spatial Data Mining,” IEEE Trans. On
Knowledge and Data Engineering, IEEE TKDE, Vol. 8, pp. 884-897, 1996.
Number of
clusters
Range in meters
1 1000
4 2000
8 3000
25 4000
30 5000
38 6000
45 7000
52 8000
65 9000
71 10000