Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
Ensuring Software Quality in Engineering Environments
New 3D Face Matching Technique for an Automatic 3D Model Based Face Recognition System
Algorithmic Cost Modeling: Statistical Software Engineering Approach
Prevention of DDoS and SQL Injection Attack By Prepared Statement and IP Blocking
Major Indian population about 65-70% follows agriculture as their profession for their living. New opportunities are being created by using smartphone technology for farmers. A mobile application is a software designed specifically for use on small, wireless devices, such as smartphones and tablets. Android is an open source development platform that it is easily available to any programmer who has the knowledge of Java and Android SDK. This particular software helps the farmers to gain facilities which were not available to them before. Now-a-days, the smartphone technology is being used in many applications like health monitoring, weather - predictions, etc. It is also used in the field of agriculture. Kisan Nestham is an android-based application, which provides information to farmers regarding different crops and other agricultural products.
One had to perceive the trend topic in Twitter concentrated on how the particular topic is trending suddenly. Based on factors like that coverage, popularity and reputation, etc., Kalman filter method is used for finding how the topic is trending. Existing methods concentrated on trend detection, trend taxonomy. But the disadvantage is based on Google trend manipulation, where a malicious user have the facility to manipulate Twitter trends. To overcome the disadvantage of existing method dynamic factors are provided for knowing a trending popular topic. A brief description about related terms used in Twitter was also depicted. In this work, the admin has the facility to view the end users and friend requests by the new users, new tweet given by the user, also display the hash tags of the number and also detecting the positive and negative words along with the number of words, to find top frequent tags detecting fake tweets given by the people, and also capturing IP address of the particular system.
It is required to study images by using computer vision for modern intelligent applications. But it is often found that it is very difficult to apply computer vision on daily appliances as computer vision requires high processing power and consumes heavy hardware when under use. Thus it becomes difficult for a normal system to work on the schematics of computer vision for processing the environment. Hence the authors propose a novel model of image segmentation and classification by using the principles of operating systems and parallel processing to speed up the current image segmentation algorithms and reduce the current CPU usage.
Record Matching is the task of identifying records which are present across different databases. Some approximate techniques should be available to match the records if unique identifiers are not present. The record matching consists of mainly two approaches, such as sanitization approach and cryptographic approach. The sanitization technique mainly relies on methods, such as K-anonymization and random noise addition. The cryptographic technique uses Secure Multiparty Computation (SMC). It will provide accurate results, but the cost involved will be very high. The hybrid technique combines both the sanitization and cryptographic approaches. The major advantages of combining these approaches are it uses effective decision rule that matches the input values. Based on this, the privacy provided will be high. But the cost involved will be very high and is not effective. Hence the proposed method uses the two party protocols which involve record matching only between the data parties. There will be no trusted third party involved and the secure matching is done by using Binning method.
The intrusion detection system plays an important role in securing our system, by preventing our system from intruders. However, traditional intrusion detection, such as user authentication, encryption, and firewall have failed to completely protect networks and systems from the increasing and sophisticated attacks and malwares. The presented new method classifies network behaviour as normal or abnormal while reducing misclassification. Ant Colony Optimization (ACO) algorithms can be applied to the data mining field to extract a set of rules for detection and classification. Support Vector Machine (SVM) is a technique for detecting intrusions in the system, which can provide real-time detection capability and it can deal with large dimensionality of data. SVM can learn a larger set of patterns and be able to scale better because the classification complexity does not depend on the dimensionality of the feature space. In this paper, Active learning Support Vector Machine and Ant Colony clustering are combined to detect the network intrusion. Combining SVM and Ant Colony (CSVAC) uses both the algorithm while avoiding their weaknesses. This algorithm is implemented and evaluated using standard benchmark KDDCUP99 data set.
