SPRING TECHNOLOGY AND USE IN BUSINESS APPLICATION

this paper discusses spring technology and use in business application development. Focus on object creation, dependency satisfaction & life - cycle management must be delegated by spring framework (IOC, AOP, TX, and ORM). Spring does not confine itself to a ny specific domain. It can be use in a simple console application or in a distributed business application. Development point of view Spring reduces 70% of coding efforts [1] . It helps in object creation, transaction - management, connectivity with database Testing and deploying an application

Trusted resource allocation in volunteer edge-cloud computing for scientific applications

Data-intensive science applications in fields such as e.g., bioinformatics, health sciences, and material discovery are becoming increasingly dynamic and demanding with resource requirements. Researchers using these applications which are based on advanced scientific workflows frequently require a diverse set of resources that are often not available within private servers or a single Cloud Service Provider (CSP). For example, a user working with Precision Medicine applications would prefer only those CSPs who follow guidelines from HIPAA (Health Insurance Portability and Accountability Act) for implementing their data services and might want services from other CSPs for economic viability. With the generation of more and more data these workflows often require deployment and dynamic scaling of multi-cloud resources in an efficient and high-performance manner (e.g., quick setup, reduced computation time, and increased application throughput). At the same time, users seek to minimize the costs of configuring the related multi-cloud resources. While performance and cost are among the key factors to decide upon CSP resource selection, the scientific workflows often process proprietary/confidential data that introduces additional constraints of security postures. Thus, users have to make an informed decision on the selection of resources that are most suited for their applications while trading off between the key factors of resource selection which are performance, agility, cost, and security (PACS). Furthermore, even with the most efficient resource allocation across multi-cloud, the cost to solution might not be economical for all users which have led to the development of new paradigms of computing such as volunteer computing where users utilize volunteered cyber resources to meet their computing requirements. For economical and readily available resources, it is essential that such volunteered resources can integrate well with cloud resources for providing the most efficient computing infrastructure for users. In this dissertation, individual stages such as user requirement collection, user's resource preferences, resource brokering and task scheduling, in lifecycle of resource brokering for users are tackled. For collection of user requirements, a novel approach through an iterative design interface is proposed. In addition, fuzzy interference-based approach is proposed to capture users' biases and expertise for guiding their resource selection for their applications. The results showed improvement in performance i.e. time to execute in 98 percent of the studied applications. The data collected on user's requirements and preferences is later used by optimizer engine and machine learning algorithms for resource brokering. For resource brokering, a new integer linear programming based solution (OnTimeURB) is proposed which creates multi-cloud template solutions for resource allocation while also optimizing performance, agility, cost, and security. The solution was further improved by the addition of a machine learning model based on naive bayes classifier which captures the true QoS of cloud resources for guiding template solution creation. The proposed solution was able to improve the time to execute for as much as 96 percent of the largest applications. As discussed above, to fulfill necessity of economical computing resources, a new paradigm of computing viz-a-viz Volunteer Edge Computing (VEC) is proposed which reduces cost and improves performance and security by creating edge clusters comprising of volunteered computing resources close to users. The initial results have shown improved time of execution for application workflows against state-of-the-art solutions while utilizing only the most secure VEC resources. Consequently, we have utilized reinforcement learning based solutions to characterize volunteered resources for their availability and flexibility towards implementation of security policies. The characterization of volunteered resources facilitates efficient allocation of resources and scheduling of workflows tasks which improves performance and throughput of workflow executions. VEC architecture is further validated with state-of-the-art bioinformatics workflows and manufacturing workflows.Includes bibliographical references

Analysis Role of ML and Big Data Play in Driving Digital Marketing's Paradigm Shift

Marketing strategies are being revolutionized by the development of user data and the expanding usability of Machine Learning (ML) as well as Big Data approaches. The wide variety of options that ML and Big Data applications provide in building and sustaining a competitive corporate edge are not fully understood by researchers and marketers. Based on a thorough analysis of academic and commercial literature, we offer a classification of ML and Big Data use cases in marketing in this article. In order to effectively employ ML and Big Data in marketing, we have discovered 11 recurrent use cases that are grouped into 4 homogenous families. These families are: fundamentals of the consumer, the consumer experience, decision-making, and financial impact. We go over the taxonomy's repeating patterns and offer a conceptual framework for understanding and extending it, emphasizing the practical ramifications for marketers and academics

Structural Changes and Ferroelectric Properties of BiFeO₃-PbTiO₃ Thin Films Grown via a Chemical Multilayer Deposition Method

Thin films of (1-x)BiFeO3-xPbTiO3 (BF-xPT) with x ~ 0.60 were fabricated on Pt/Si substrates by chemical solution deposition of precursor BF and PT layers alternately in three different multilayer configurations. These multilayer deposited precursor films upon annealing at 700{\deg}C in nitrogen show pure perovskite phase formation. In contrast to the equilibrium tetragonal structure for the overall molar composition of BF:PT::40:60, we find monoclinic structured BF-xPT phase of MA type. Piezo-force microscopy confirmed ferroelectric switching in the films and revealed different normal and lateral domain distributions in the samples. Room temperature electrical measurements show good quality ferroelectric hysteresis loops with remanent polarization, Pr, of up to 18 {\mu}C/cm2 and leakage currents as low as 10-7 A/cm2.Comment: 14 Pages and 6 figure

A Systematic Survey of Classification Algorithms for Cancer Detection

Cancer is a fatal disease induced by the occurrence of a count of inherited issues and also a count of pathological changes. Malignant cells are dangerous abnormal areas that could develop in any part of the human body, posing a life-threatening threat. To establish what treatment options are available, cancer, also referred as a tumor, should be detected early and precisely. The classification of images for cancer diagnosis is a complex mechanism that is influenced by a diverse of parameters. In recent years, artificial vision frameworks have focused attention on the classification of images as a key problem. Most people currently rely on hand-made features to demonstrate an image in a specific manner. Learning classifiers such as random forest and decision tree were used to determine a final judgment. When there are a vast number of images to consider, the difficulty occurs. Hence, in this paper, weanalyze, review, categorize, and discuss current breakthroughs in cancer detection utilizing machine learning techniques for image recognition and classification. We have reviewed the machine learning approaches like logistic regression (LR), Naïve Bayes (NB), K-nearest neighbors (KNN), decision tree (DT), and Support Vector Machines (SVM)

A Review on Cloud Data Security Challenges and existing Countermeasures in Cloud Computing

Cloud computing (CC) is among the most rapidly evolving computer technologies. That is the required accessibility of network assets, mainly information storage with processing authority without the requirement for particular and direct user administration. CC is a collection of public and private data centers that provide a single platform for clients throughout the Internet. The growing volume of personal and sensitive information acquired through supervisory authorities demands the usage of the cloud not just for information storage and for data processing at cloud assets. Nevertheless, due to safety issues raised by recent data leaks, it is recommended that unprotected sensitive data not be sent to public clouds. This document provides a detailed appraisal of the research regarding data protection and privacy problems, data encrypting, and data obfuscation, including remedies for cloud data storage. The most up-to-date technologies and approaches for cloud data security are examined. This research also examines several current strategies for addressing cloud security concerns. The performance of each approach is then compared based on its characteristics, benefits, and shortcomings. Finally, go at a few active cloud storage data security study fields

Polynomial approach modeling among diabetic patients associated with age in rural hilly population of Dehradun district, Uttarakhand

Background: Diabetes mellitus is a form of infections that includes issues with the hormone insulin. It is described by constant rise of blood glucose level surprising ordinary esteem. In this paper, an exertion has been made to fit scientific model to diabetic patients and additionally its total dispersion for both genders related with time of rural population from Dehradun district, Uttarakhand.Methods: For this reason, the information have been taken from field overview in rural hilly population of Dehradun district. In this investigation, an endeavor has been given to demonstrate that the polynomial model is attempted to fit to the conveyance of diabetic patients related with age and also its cumulative distribution.Results: The fitted model provides statistically significant values with R2=0.9997 and ρcv2= 0.994857. This is the polynomial of degree four, i.e. bi-quadratic polynomial model. The polynomial model is assumed for the cumulative distribution of diabetic patients associated with age and the fitted model provides statistically significant values providing R2= 0.99998 and ρcv2= 0.999983 and shrink-age coefficient=0.00001414. This is the polynomial of degree three, i.e. cubic polynomial model. From this statistic we see that the fitted models are highly cross-validated, and their shrinkages are 0.004842857 and 0.00001414 for the models (1) and (2) respectively.Conclusions: It is discovered that the distribution of diabetic patients for both genders related with age takes after bi-quadratic polynomial model. In addition, it is found that cumulative distribution of diabetic patients takes as cubic polynomial model. Cross validity prediction power is utilized to the fitted model to verify the stability of the model in this study