E-healthcare is warm area of research and a number of algorithms have been applied to classify healthcare data. In the healthcare field, a large amount of clinical data is generated through MRI, CT... Show More

E-healthcare is warm area of research and a number of algorithms have been applied to classify healthcare data. In the healthcare field, a large amount of clinical data is generated through MRI, CT scans, and other diagnostic tools. Healthcare analytics are used to analyze the clinical data of patient records, disease diagnosis, cost, hospital management, etc. Analytical techniques and data visualization are used to get the real time information. Further, this information can be used for decision making. Also, this information is useful for the better treatment of patients. In this work, an improved big bang-big crunch (BB-BC) based clustering algorithm is applied to analyze healthcare data. Cluster analysis is an important task in the field of data analysis and can be used to understand the organization of data. In this work, two healthcare datasets, CMC and cancer, are used and the proposed algorithm obtains better results when compared to MEBB-BC, BB-BC, GA, PSO and K-means algorithms. The performance of the improved BB-BC algorithm is also examined against benchmark clustering datasets. The simulation results showed that proposed algorithm improves the clustering results significantly when compared to other algorithms.

Whenever there is a copywrite protection an article or official material it is usually encrypted with specific information that can only be decrypted by the person who created the document and the... Show More

Whenever there is a copywrite protection an article or official material it is usually encrypted with specific information that can only be decrypted by the person who created the document and the owner can claim the ownership of the original document. This type of encryption is called watermarking. This can be used by different e-commerce sites like Netflix, Amazon Prime, who can protect their media through the process of watermarking. As computational power is increasing along with applied mathematics, the application area is now available for multimedia files. This article focuses on encryption of video files so that the owner can claim the ownership of the created video file. The technology also enables the secrecy of the encryption between the sender and the receiver so that receiver can decrypt the file and receive the ownership that is being transferred from the user. In another way, any e-commerce site can claim the ownership of their media if it is pirated.

Cloud computing is the cutting edge and has become inevitable in all forms of computing. This is due to its nature of elasticity, cost-effectiveness, availability, etc. The online applications like... Show More

Cloud computing is the cutting edge and has become inevitable in all forms of computing. This is due to its nature of elasticity, cost-effectiveness, availability, etc. The online applications like e-commerce, and e-healthcare applications are moving to the cloud to reduce their operational cost. These applications have the vulnerability of a HTTP flooding Distributed Denial of Service attack in the cloud. This flooding attack aims to overload the application, making it unable to process genuine requests and bring it down. So, these applications need to be secured and safeguarded against such attacks. This HTTP flooding attack is one of the key challenging issues as it shows normal behaviour with regard to all lower networking layers like TCP 3-way handshaking by mimicking genuine requests and it is even harder in the cloud due to the cloud properties. This article offers a solution for detecting a HTTP flooding attack in the cloud by using the novel TriZonal Linear Prediction (TLP) model. The solution was implemented using OpenStack and the FIFA Worldcup '98 data set for experimentation.

Cloud computing represents a new era of using high quality and a lesser quantity of resources in a number of premises. In cloud computing, especially infrastructure base resources (IAAS), cost... Show More

Cloud computing represents a new era of using high quality and a lesser quantity of resources in a number of premises. In cloud computing, especially infrastructure base resources (IAAS), cost denotes an important factor from the service provider. So, cost reduction is the major challenge but at the same time, the cost reduction increases the time which affects the quality of the service provider. This challenge in depth is related to the balance between time and cost resulting in a complex decision-based problem. This analysis helps in motivating the use of learning approaches. In this article, the proposed multi-tasking convolution neural network (M-CNN) is used which provides learning of task-based deadline and cost. Further, provides a decision for the process of task scheduling. The experimental analysis uses two types of dataset. One is the tweets and the other is Genome workflow and the comparison of the method proposed has been done with the use of distinct approaches such as PSO and PSO-GA. Simulated results show significant improvement in the use of both the data sets.

Image recognition plays a vital role in image-based product searches and false logo identification on e-commerce sites. For the efficient recognition of images, image segmentation is a very... Show More

Image recognition plays a vital role in image-based product searches and false logo identification on e-commerce sites. For the efficient recognition of images, image segmentation is a very important and is an essential phase. This article presents a physics-inspired electromagnetic field optimization (EFO)-based image segmentation method which works using an automatic clustering concept. The proposed approach is a physics-inspired population-based metaheuristic that exploits the behavior of electromagnets and results into a faster convergence and a more accurate segmentation of images. EFO maintains a balance of exploration and exploitation using the nature-inspired golden ratio between attraction and repulsion forces and converges fast towards a globally optimal solution. Fixed length real encoding schemes are used to represent particles in the population. The performance of the proposed method is compared with recent state of the art metaheuristic algorithms for image segmentation. The proposed method is applied to the BSDS 500 image data set. The experimental results indicate better performance in terms of accuracy and convergence speed over the compared algorithms.

In the software maintenance activity, regression testing is performed for validing modified source code. Regression testing ensures that the modified code would not affect the earlier tested... Show More

In the software maintenance activity, regression testing is performed for validing modified source code. Regression testing ensures that the modified code would not affect the earlier tested program. Due to a constraint of resources and time, regression testing is a time-consuming process and it is a very expensive activity. During the regression testing, a set of the test case and the existing test cases are reused. To minimize the cost of regression testing, the researchers proposed a test case prioritization based on clustering techniques. In recent years, research on regression testing has made significant progress for object-oriented software. The empirical results show the importance of K-mean clustering algorithm used to achieve an effective result. They found from experimental results that their proposed approach achieves the highest faults detected value than others.