A Multiple Classifier Approach to Improving Classification Accuracy Using Big Data Analytics Tool

At the heart of analytics is data. Data analytics has become an indispensable part of intelligent decision making in the current digital scenario. Applications today generate a large amount of data. Associated with the data deluge, data analytics field has seen an onset of a large number of open source tools and software to expedite large scale analytics. Data science community is robust with numerous options of tools available for storing, processing and analysing data. This research paper makes use of KNIME, one of the popular tools for big data analytics, to perform an investigative study of the key classification algorithms in machine learning. The comparative study shows that the classification accuracy can be enhanced by using a combination of the learning techniques and proposes an ensemble technique on publicly available datasets

Denoising of MST RADAR Signal usingCWT and Overlapping Group Shrinkage

Existing algorithmsare generally denouncing the existence of clusters with large amplitude coefficients. The L1 norm as well as other distinct models of sparsity does not attract a cluster tendency (group sparsity). In the light of a minimisation of convex cost work fusing the blended norm, this work introduces the technique "overlapping group shrinking." The groups are completely overlapping in order to abstain from blocking relics. A basic minimization calculation, in light of progressive replacement, is inferred. A straightforward strategy for setting the regularization boundary, in view of constricting the noise to a predefined level, is portrayed in detail by combining OGS with one of the most powerful mathematical tool wavelet transforms. In fact, the CWT coefficients are processed by OGS to produce a noise-free signal. The CWT coefficients are also processed.The proposed approach is represented on MST RADAR signals, the denoised signals delivered by CWT combined with OGS are liberated from noise

LEADERSHIP DEVELOPMENT AND QUALITY ENHANCEMENT IN HIGHER EDUCATION

Maintaining the profile of students and teachers high is a major challenge for higher educational institutions. Teacher quality influences curriculum, provides leadership, and promote student progression leading to innovation and best practices. The leadership provides clear vision and mission for the institution to advance. The functions of the institution and its academic and administrative units are governed by the principles of participation and transparency. Formulation of development objectives, directives and guidelines with specific plans for implementation by aligning the academic and administrative aspects improves the overall quality of the Institutional provisions. With this in view, institutions resort to a lot of ways by which they can attract, motivate and maintain high standards of excellence among teachers and infuse leadership among students. Efforts taken by the institution in maintaining leadership is reflected in the quality policy of the institution as revealed in the top management philosophy of maintaining high standards. This paper discusses the ways and means of leadership development resulting in quality enhancement in higher education institutions

Optimized Round Robin CPU Scheduling Algorithm

One of the fundamental function of an operating system is scheduling. There are 2 types of uni-processor operating system in general. Those are uni-programming and multi-programming. Uni-programming operating system execute only single job at a time while multiprogramming operating system is capable of executing multiple jobs concurrently. Resource utilization is the basic aim of multiprogramming operating system. There are many scheduling algorithms available for multi-programming operating system. But our work focuses on design and development aspect of new and novel scheduling algorithm for multi-programming operating system in the view of optimization. We developed a tool which gives output in the form of experimental results with respect to some standard and new scheduling algorithms e.g. First come first serve, shortest job first, round robin, optimal and a novel cpu scheduling algorithm etc

A comparative clinical study on Brihmana effect of Ashwagandhaadya Ghrita Snehanapana and Matra Basti in Karshya w.s.r. to Under Weight

Background: Karshya as per classics has Shushkasphik, Udara, Greeva, Dhamanijalasantata, Twagasthishesha, Kshuda, Pipasa, Sheeta, Ushna, Vayu, Varsha, Bharadan Asahishnuta and it is similar to the clinical features of under-weight/under nourishment includes loss of weight, muscle wasting, loss of subcutaneous fat, stress, fatigue and general weakness. Materials and Methods: The study was conducted as a randomized clinical trial and carried out in the Dept. of Panchakarma during the year 2016 - 2018. Patients with Dourbalya and anxious to gain weight were included in the study. Their age group was in between 18-45 years. 30 patients who fulfilled the inclusion and exclusion criteria were selected for the study. Patients were randomly allocated into two groups. Group A and Group B. In patients of Group A, Ashwagandhadya Ghrita as Brimhana Snehana 1 Pala i.e. 48ml daily in two divided doses for 15 days with Ushnodaka. In Group B, Ashwagandhadya Ghrita as Matra Basti i.e. 50ml daily for 15 days. Results: Statistically, Body weight is highly significant at the level of p<0.001. In Matra basti group in comparison to Snehapana group

Significant Feature Selection Method for Health Domain using Computational Intelligence- A Case Study for Heart Disease

In the medical field, the diagnosing of cardiovascular disease is that the most troublesome task. The diagnosis of heart disease is difficult as a decision relied on grouping of large clinical and pathological data. Due to this complication, the interest increased in a very vital quantity between the researchers and clinical professionals regarding the economical and correct heart disease prediction. In case of heart disease, the correct diagnosis in early stage is important as time is the very important factor. Heart disease is the principal supply of deaths widespread, and the prediction of Heart Disease is significant at an untimely phase. Machine learning in recent years has been the evolving, reliable and supporting tools in medical domain and has provided the best support for predicting disease with correct case of training and testing. The main idea behind this work is to find relevant heart disease feature among the large number of feature using rough computational Intelligence approach. The proposed feature selection approach performance is better than traditional feature selection approaches. The performances of the rough computation approach is tested with different heart disease data sets and validated with real-time data sets

Opinion mining in Machine Learning for High Perfomance using Sentimental Analysis

Opinion mining refers to the use of the natural language processing in which it is used for linguistics to identify and extract information .Opinion mining has been an indispensible part of present scenario. Due to large amount of online app development and processing of all data through internet Opinion has become one of the major part in reviewing through online. A various kinds of probabilistic topic modeling technique are available to analyze and extract the idea behind the probability distribution over words. In proposed review system, a review of a particular product that brought in is Amazon, opinion review dataset of a particular product by UPC database and it is pre-processed to give a result by machine learning to get specific opinion word using sentimental analyses. LDA model is applied into the machine learning technique to analyses. It also determine the large amount of time required for determining the opinion of a particular product that is purchased. Experimental evaluation shows that our proposed techniques are efficient and perform better than previously proposed technique, however, the proposed technique can be used by any other languages

ROLE BASED SECURED ACCESS OF DATA IN CLOUDS

In mobile wireless sensor network, coverage and energyCloud computing is a type of internet-based computing that provides shared computer processing resources and data to computers and other devices on demand. It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources e.g., computer networks, servers, storage, applications and services, which can be rapidly provisioned and released with minimal management effort. Attribute-based access control defines an access control paradigm whereby access rights are granted to users through the use of policies which combine attributes together. The policies can use any type of attributes such as user attributes, resource attributes, object and environment attributes etc. This model supports Boolean logic, in which rules contain "if-then" statements about who is making the request, the resource and the action. The main problem in attribute–based access control is not having user-centric approach for authorization rules. In ABAC model role hierarchy and object hierarchy is not achieved and restriction in level of expressiveness in access control rules.Secured role-based access control allows managing authorization based on rule-based approach where rules are under the control of data owner and provides enriched role-based expressiveness including role and object hierarchies. Data user without the knowledge of data owner cannot use the cloud server where privilege is provided to data user by data owner. Access control computations are delegated to the cloud service provider, being this not only unable to access the data, but also unable to release it to unauthorized parties. A identity-based proxy re-encryption scheme has been used in order to provide a comprehensive and feasible solution for data centric-approach. Semantic web technologies have been exposed for the representation and evaluation of the authorization model

Level-Headedness in Wireless Sensor Networks

Well-groomed surroundings be a sign of the next evolutionary step in construction, utilities, industrial, residence, shipboard, and haulage system automation. The well-groomed atmosphere needs information about its environment as well as about its internal workings. Sensor networks are the input to assembly the information needed by well-groomed surroundings, whether in buildings, utilities, industrial, home, shipboard, transportation systems, automation, or elsewhere. Localization is one of the basic challenges and it plays vital role. In this paper we are discussing various Application Requirements, probable Approaches, Position unearthing Approaches, Localization Techniques and QOS