IoT Assistive Technology for People with Disabilities
Soulease: A Mind-Refreshing Application for Mental Well-Being
AI-Powered Weather System with Disaster Prediction
AI Driven Animal Farming and Livestock Management System
Advances in AI for Automatic Sign Language Recognition: A Comparative Study of Machine Learning Approaches
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
Multi-processor scheduling problem has been shown to be NP-hard and therefore, no exact optimal solution algorithms could be constructed. In this study, we present the computational experience and performance evaluation of some greedy based algorithms to solve the multi-processor scheduling problem. These algorithms are approximation algorithms in which we compute lower bounds and percentage gaps to show that our solutions are close to relevant optimal solutions. We shall also show that the performance of these algorithms improves as the problem size grows. We make use of the principle of cumulative moving averages to show that the algorithms can be applied to large scale problem instances as well. These algorithms are very fast so that they can be applied to solve large scale problems found in practice, without much computational burden.
In this epoch Medical Image segmentation is one of the most challenging problems in the research field of MRI scan image classification and analysis. The importance of image segmentation is to identify various features of the image that are used for analyzing, interpreting and understanding of images. Image segmentation for MRI of brain is highly essential due to accurate detection of brain tumor. This paper presents an efficient image segmentation technique that can be used for detection of tumor in the Brain. This innovative method consists of three steps. First is Image enhancement to improve the quality of the tumor image by eliminating noise and to normalize the image. Second is fuzzy logic which produce optimal threshold to avoid the fuzziness in the image and makes good regions regarding Image and tumor part of the Image. Third is novel OTSU technique applied for separating the tumor regions in the MRI. This method has produced better results than traditional extended OTSU method.
The compressive sensing (or compressive sampling, CS) theorem states that a sparse signal can be perfectly reconstructed even though it is sampled at a rate lower than the Nyquist rate. It has gained an increasing interest due to its promising results in various applications. There are two popular reconstruction methods for CS: basis pursuit (BP) and matching pursuit (MP).Introductory papers on CS often concentrated either on mathematical fundamentals or reconstruction algorithms for CS. Newcomers in this field are required to study a number of papers to fully understand the idea of CS. This paper aims to provide both the basic idea of CS and how to implement BP and MP, so that newcomers no longer need to survey multiple papers to understand CS and can readily apply CS for their works.
This work provides an epistemological rational for the transformative process of qualitative data into quantitative outcomes through the Tri–Squared Test as an efficient technique for the rapid testing of software applications. The design of fixed-parameter algorithms for software problems can prove to be beneficial to the engineer who has to efficiently and rapidly design a tool. This novel methodology provides elegance and has a wide range of applicability to practically address important and relevant problems in the “trial and error” process of application development. This research is the continuation of a dynamic mixed methods approach that is a transformative process which changes qualitative data into quantitative outcomes through the Tri–Squared statistical measure first introduced in i-Manager’s Journal of Mathematics.
The data world has become completely digitized and information moves through channels from one place to other. In this scenario data security has caught everybody’s attention. Since private network is not the solution of data security all the time and none of the medium used for data transfer is fully secured which encouraged information hiding techniques like Encryption and Steganography. The advantage of steganography over encryption is that messages do not attract attention to themselves. Plainly visible encrypted messages—no matter how unbreakable—will arouse suspicion, hence cryptography protects the contents of a message only, whereas steganography can protect both messages and communicating parties. In all existing Steganographic techniques, there is a restriction with respect to payload capacity is that the amount of data to be embedded into cover file can go beyond the size of cover file. In this paper we are going to check the enhancement of the payload capacity of steganography in any covert communication by using Mobile Apps as cover file and a small modification in approach of hiding data into cover file. Millions of mobile applications are downloaded on mobile phones daily from all part of world. This technique has overcome the problem of other Steganographic techniques like limited payload capacity with added advantage of mobility and portability as it runs on mobile phones instead of PC. This technique is developed in J2ME platform, Mobile Application is a Maze game, payload type is image having various file formats and tested on Nokia Supernova 7610 series phones.
