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Mala, R.
- An Effective Process Schedule by Clustered Servers
Authors
1 Department of Computer Science, Cauvery College for Women, Trichy, IN
2 EBET Group of Institutions, Kanagayam Taulk, Tripur, IN
Source
Networking and Communication Engineering, Vol 2, No 5 (2010), Pagination: 140-144Abstract
With the tremendous growth of the network services, the problem rate also gets increased. In order to overcome these obstacles, enormous technologies have been developed. In this article, a better solution is proposed to increase the server performance. In networking, the client sends the request to server; the server processes the request and sends back the response to the client. When the number of client request increases, the burden for server also gets increased which leads to lower performance. To overcome this problem, we use the distributed networking techniques. With the help of this concept, the client‘s work is distributed among several servers and thus the performance increases. This can be done by using large number of techniques such as software based, hardware based and network based technology. But these techniques have several problems such as high cost requirement and some implementation problems. In our article, we use a special technique to distribute the work to clustered server and calculate the execution time and the time to be consumed. We also implement the constraint to search whether the server already executes the request. If so, the response is stored in memory and it is retrieved in short time. This project is used to handle the client request efficiently by balancing the work load across the replicated web server dynamically.Keywords
Clustered Server, Distributed Networking Techniques, Replicated Web Server, Server Performance, Tremendous Growth.
- Detecting Noise Data in Face Recognition Using Geometric Moment Analysis Algorithm
Authors
Source
Digital Image Processing, Vol 6, No 7 (2014), Pagination: 289-294Abstract
Face detection and recognition is an effective and popular technique for authorizing the users and securing the data at the time of transactions. However, there exists some other traditional face recognition approaches with some disadvantages when opted to various types of input conditions. They are Geometric, Photometric and Principal Component Analysis etc.
The geometric approach deals with the geometric features that are generated by a sector, edges and regions of some shapes formed by many points. The recognition result is compared by analyzing the featured set. A calculation is done between the features in the template image and every image in the database. Hence this method is tough. But the main complication is locating a point automatically. Also the other problem arises if the image is of poor quality.
On the other hand the photometric approach refines an image into values and that value is compared with templates to remove variances. It depends on the input image and the geometric location of different angles. While the photometric transformation is employed on the source image, it does not consider the photometric change which is nothing but the changes in the pixel. This approach requires multiple registered images of the same person. If any images which are not present in the dataset are subjected to processing, this approach considers that image as a new image which is specified as unauthenticated.
Principal Component Analysis is an orthogonal linear transformation which maps the data to another coordinates. It uses Eigen faces. This approach processes the images of equal sizes. Also this approach reduces the sizes of data using data compression technique. The images get disintegrated to form unrelated blocks that are stored in one dimension array know as Eigen faces. The face images can be signified as a sum of Eigen faces.
In order to overcome this incompetence, a new face recognition scheme based on invariant moment features which assure a secure transaction is proposed. Also the proposed scheme deals with an effective preprocessing using Short Time Fourier Transform (STFT), image enhancement techniques, extraction of local and global information using Region of Interest (ROI) calculation by the method of subdividing the determined ROI region into multiple sub ROIs. The ROI mainly focuses on the local features in face such as eyes, nose and lips. Face values in different angles were observed by calculating the area and centroid of the face using the above parameters which results in higher matching accuracy in the experimental results.Keywords
Eigen Faces, Geometric Moment Analysis, Invariant Moment, Photometric Approach, Principal Component Analysis, Region of Interest, STFT.- An Efficient Rule Based Association Analysis for Business Data Base
Authors
1 Department of Computer Science, H & H Rajah's College, Pudukkotai, IN
2 Department of IT, St. Joseph's College (Auto.), Trichy, IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 2 (2013), Pagination: 51-58Abstract
In this Business world, everything is made computerized to make the process efficiently and to improve the business. In Business, information is valuable and need to be maintained. To do this, the database can be useful. In that type of organization, the database is used as OLAP (Online Line Analytical Processing). i.e., the database maintains historical data about the organization. In this situation, the size of the database grows large. These kinds of database in which large volumes of data are stored are termed as Data Warehouse. Extracting the data from this data warehouse is termed as Data Mining.When the database size grows large, mining the data becomes time consuming. To reduce the delay, some characteristics are needed. One such characteristic is called Association Analysis. This Association Analysis is used to mine the data based upon the analysis result of the data. The analysis is made by proposing such techniques. In this paper, the association rule is created to mine the data from the large amount of data based upon some characteristics. This paper is proposed to implement on the E-commerce organization. In that kind of organization, the main purpose of the organization is to provide satisfaction for the upcoming user. It can be done by extraction of the data from the database is through the customer behavior. That is, the rule is developed to mine the data with respect to the target customer behavior, there by, the performance of the server is enhanced. Specifically if the client enters into the site, the server has to search for the previous request for that site that was made by the customers. If the server detects the previous request then the customer is provided with the response depending upon the previous transaction. With the help of the customer behavior, the association rule is created and the better response is given to them.
Since the proposed method is implemented in disconnected Architecture, it gives fast response to the user. A snapshot about this technique is explained briefly in this paper with suitable algorithm.
Keywords
Association Analysis, Association Rule, Customer Behavior, Database, Data Mining, Data Warehouse, Frequent Item Set Mining, OLAP, Historical Data.- Smart Information Management for Smarter Decision Making
Authors
1 Department of Computer Science, Cauvery College for Women, Trichy, IN
2 EBET Group of Institutions, Kanagayam Taulk, Tripur, IN
Source
Data Mining and Knowledge Engineering, Vol 2, No 9 (2010), Pagination: 227-232Abstract
In this Internet world, Data is the most valuable resource of an enterprise. In this competitive world, it is a tedious process to make business decisions when using the large database. The retrieval of information is difficult and time consuming. Also it is the responsibility of the user to allocate enough memory to store the information.
To search for an item, it takes the framework for a particular user to extract the information from the server. With this tremendous growth of network services, the problem rate also gets increased. To overcome this problem, suitable techniques are applied in this article.
The information is extracted by sending the request to the server and waits for the response. This method of request/reply is better only for some time. In some situation, when the lack of users are sending the sending the request to the server, the burden of the server gets increased and it automatically goes down. So, to reduce the server burden, we need a special technique that is also reliable to the user.
In this paper, we propose the Disconnected Architecture, which is user-friendly to access and extract the information from the database. This Architecture maintains a temporary memory to store the items which are frequently accessed by the user. This temporary memory is called as Log. With the help of this log, the server burden is reduced and the performance of the server gets raised.
When the server receives the request, it search the log whether this kind of request is already processed by it and if so, it sends back the same reply as before. Otherwise, it processes the new request and sends the reply to the user.
In this article, we can extract the information from the database using the Mining Techniques. This paper also describes the practicalities and the constraints in Data Mining and its Advancements.
Keywords
Business Decisions, Competitive World, Disconnected Architecture, Log, Temporary Memory, Tremendous Growth of Network Services.- Human Age Estimation Based on Bio-Content Features on Facial Images Via Gender Based Classification
Authors
1 Bharathiar University, Coimbatore, Tamil Nadu, IN
2 Alagappa University Model Conpituent College of Arts & Science, Paramakudi, Tamil Nadu, IN
Source
Digital Image Processing, Vol 9, No 5 (2017), Pagination: 95-102Abstract
Age estimation is achieved better using facial images. Normally, the age estimation is categorized in two ways such as age estimation and age group classification. The age estimation is performed to estimate the exact age of a given test image. Whereas the age group classification is done to classify the age group of a person such as adults, junior adults and senior adults. The age group classification is supported by the features and patterns which is clearly defined in craniofacial growth. The wrinkles and ratios play an important role in age group classification. These similar features can be also applied for age estimation of an individual. Our proposed methodology estimates the age of a person in three steps. 1. Face detection, 2. Feature extraction, 3. Gender based classification, 4. Age estimation.
Face detection is achieved using Viola-Jones algorithm. After pre-processing and contract enhancement, the proposed facial feature called Bio-Content Feature (BCF) is extracted. The proposed feature is extracted using angle-based and dimension-based measurements and dimension reduction methods. The proposed extraction methodology handles the problems like image misalignment, high dimensionality problems, shape variations and geometrical transformations.
The extracted feature is subjected to multi-linear regression to find the relationship between the test and the trained image. The third step performs gender based classification of the images based on the result of the regression. The fourth step delivers the estimated age of the test image. This proposed work shows its accuracy up to 92% via gender-based classification on age estimation. The advantage of the proposed methodology for gender-based classification is proved by vast experiments on the public available FG-NET database. The approach could be widely used in real world applications, crime investigation, and human-computer interaction.
Keywords
Human Age Estimation, Face Detection, Multi Linear Regression, Feature Extraction, Bio-Content Feature, Gender Based Classification, Contrast Enhancement.References
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