COMPUTER SCIENCE ENGINEERING(CSE)

ABSTRACT
The aim of this research paper is the classification of magnetic resonance brain Images as normal and abnormal
using linear kernel based Support Vector Machine (SVM) for which different texture features are utilized to
characterize the information level contained in the image. The proposed method is compared with k-nearest
neighbor (K-NN) classifier and hidden markov model (HMM) classifier. To evaluate the performance of the
proposed method, classification rate, recall, and precision evaluation metrics are choosen. The comparative
results of the research demonstrates that SVM based on linear kernel provides much higher precision and low
error rates as compared to KNN and HMM classifier.

Nowadays credit cards have become a prerequisite in our daily activities. The misuse of credit card is increasing day by day. There are several ways in which a offender performs a credit card fraud. It is always been advised to exercise and provide additional security to the credit cards in order to elude the frauds. This paper focuses on credit card frauds. It addresses the cyber security challenges faced by the businesses today and it also recommends some mechanisms to allocate number to the credit card so as to avoid and identify possible hacking and spoofing . This paper also reviews of diverse algorithms that can be used to overcome the frauds in plastic digital and virtual currencies.

It is critical in cloud computing to have excellent data accessibility and system performance. To improve system availability, commonly used data should be duplicated to many places, allowing users to access it from a nearby site. Deciding on a sensible number and location for replicas is a difficult problem in cloud computing. Therefore, a novel Data Replication system based on data mining techniques is being proposed in this research work. The data replication is done here by locating commonly utilized data patterns in a node's massive database. This will be accomplished using an optimization-assisted frequent pattern mining approach, with a novel hybrid algorithm performing the best threshold selection. The proposed hybrid algorithm referred to as Greywolves Updated Exploration and Exploitation with Sealion Behaviour (GUEES), hybrids the concept of Sealion Optimization Model (SLnO) and Grey wolf optimization (GWO) algorithms. Apart from this, the mining will be carried out under the defining dual constraints such as (i) Prioritization and (ii) Cost. The prioritization falls under two cases: queuing both high and low-priority data, and the cost relies on the evaluation of storage demand. The highpriority queues are optimized with the GUEES model. Finally, a comparative validation is carried out to validate the efficiency of the adopted model. Accordingly, when the number of requests=1000, the network usage of the proposed model is 35.

Cloud providers frequently utilize two tightly coupled resource management strategies like task scheduling & data replication to boost the performance of the system generally, guaranteeing service level agreement (SLA) compliance, as well as protecting their own financial gain. An Improved Correlation strategy-based Task Scheduling and Data Replication in Cloud (ICTSDC) is what this work aims to give. The suggested system's primary phases are as follows: Management of replication and task scheduling. Initial job scheduling will be optimization-based and take into account goals such bottleneck value, migration cost, VM load, enhanced correlation, and replication, respectively. For this, a brand-new extended DMO algorithm called Self-adaptive Dwarf Mongoose Optimization (SADMO) is presented. In the replication management stage, the potential copies must first be identified based on the prior objective. The suggested SADMO model implements the optimization technique for replica placement throughout the replication management process. The outcomes of the ICTSDC technique are evaluated to other methods using a variety of metrics, like bottleneck value, migration cost, Virtual Machine (VM) load, improved correlation, as well as replication efficiency. A lower mean value of 0.324 is gained with the ICTSDC scheme for fitness.

Many people like to study automated music genre labeling because it is an important part of music information search systems. There is a lot of study on how to group Western music styles. Indian music has a long past that goes back to the Puranas and Vedas. In Indian homes today, music is still an important part of life. The types of music played in India depend on where you live, your society, your religion, and your language. It is hard to make software that can automatically classify different types of music like humans can. There aren't many data sets that can be used to classify Indian music. We made a library to organize and study different types of Indian music, such as Bollywood Rap, Ghazal, Garhwali, Bhajan, Bollywood Romantic, Sufi, and Bhojpuri. We made a database to sort Indian songs into genres like Bollywood Rap, Ghazal, Garhwali, Bhajan, Bollywood Romantic, Sufi, and Bhojpuri. Then, we used SVM, Gradient Boosting, KNN, Light GBM, and Neural Networks to compare how well they worked. With a score of 77.2%, the Light GBM classification is the most accurate.

Data-intensive applications must be run on systems with high-performance processing and enough storage. When compared to conventional distributed systems like the data grid, cloud computing offers these features on a platform that is more flexible, scalable, and inexpensive. Moreover, retrieving data files is crucial to operate these services. Typically, accessing data causes the entire cloud workflow system to experience a bottleneck, thus significantly reducing system performance. Two key strategies that can enhance the efficiency of data-intensive applications are task scheduling and data replication. This research proposes a novel Data replication and scheduling in the cloud. Initially, the workflow management process is performed with 3 phases (1) workflow placement, (2) clustering of tasks, and (3) scheduling and replication. Initially, the workflow placement takes place. Then, the clustering of tasks is performed via an improved K-means algorithm. Finally, the tasks and datasets are replicated during the scheduling and replication phase. Further, the scheduling and replication are performed using the Self Modified Pelican Optimization Algorithm (SM-POA) based on the execution cost, migration cost, storage cost and replication.