Tiarrah Computing: The Next Generation of Computing

The evolution of Internet of Things (IoT) brought about several challenges for the existing Hardware, Network and Application development. Some of these are handling real-time streaming and batch bigdata, real- time event handling, dynamic cluster resource allocation for computation, Wired and Wireless Network of Things etc. In order to combat these technicalities, many new technologies and strategies are being developed. Tiarrah Computing comes up with integration the concept of Cloud Computing, Fog Computing and Edge Computing. The main objectives of Tiarrah Computing are to decouple application deployment and achieve High Performance, Flexible Application Development, High Availability, Ease of Development, Ease of Maintenances etc. Tiarrah Computing focus on using the existing opensource technologies to overcome the challenges that evolve along with IoT. This paper gives you overview of the technologies and design your application as well as elaborate how to overcome most of existing challenge

EapGAFS: Microarray Dataset for Ensemble Classification for Diseases Prediction

Microarray data stores the measured expression levels of thousands of genes simultaneously which helps the researchers to get insight into the biological and prognostic information. Cancer is a deadly disease that develops over time and involves the uncontrolled division of body cells. In cancer, many genes are responsible for cell growth and division. But different kinds of cancer are caused by a different set of genes. So to be able to better understand, diagnose and treat cancer, it is essential to know which of the genes in the cancer cells are working abnormally. The advances in data mining, machine learning, soft computing, and pattern recognition have addressed the challenges posed by the researchers to develop computationally effective models to identify the new class of disease and develop diagnostic or therapeutic targets. This paper proposed an Ensemble Aprior Gentic Algorithm Feature Selection (EapGAFS) for microarray dataset classification. The proposed algorithm comprises of the genetic algorithm implemented with aprior learning for the microarray attributes classification. The proposed EapGAFS uses the rule set mining in the genetic algorithm for the microarray dataset processing. Through framed rule set the proposed model extract the attribute features in the dataset. Finally, with the ensemble classifier model the microarray dataset were classified for the processing. The performance of the proposed EapGAFS is conventional classifiers for the collected microarray dataset of the breast cancer, Hepatities, diabeties, and bupa. The comparative analysis of the proposed EapGAFS with the conventional classifier expressed that the proposed EapGAFS exhibits improved performance in the microarray dataset classification. The performance of the proposed EapGAFS is improved ~4 – 6% than the conventional classifiers such as Adaboost and ensemble

A NOVEL APPROACH FOR MULTI VARIANT CLASSIFICATION OF MEDICAL DATA IN SHORT TEXT

Data Mining Techniques has attained its momentum in several areas, and its efficient performance in decision support has outperformed and made it a reliable choice. The medical world is one such empirical domain in which a perfect decision at right time would turn out to be a lifesaver. Medical data figures out to be majorly multi-dimensional, where relevant feature extraction is a challenging factor. Several classification approaches like SVM, Decision Trees, and Naive Based are considered to handle these profound challenges. One such challenge discussed in our paper emphasizing on Medical decision support system with Machine Learning Methodology considering diseases and treatments with their semantic relations in the document of Pub med abstracts. The proposed Multi variant classification framework aims at reducing data into attributes using PCA Transformation infusion with an efficient classification Algorithm - CNB. Our computed results are comparatively successful in attaining ultimate outcomes concerning performance metrics like Accuracy, Precision, Recall, and Time. The strength of our work lies in presenting an efficient approach for elevating enhanced decisions in Health care

A Novel Approach for Multi Variant Classification of Medical Data in Short Text

457-462Data Mining Techniques has attained its momentum in several areas, and its efficient performance in decision support has outperformed and made it a reliable choice. The medical world is one such empirical domain in which a perfect decision at right time would turn out to be a lifesaver. Medical data figures out to be majorly multi-dimensional, where relevant feature extraction is a challenging factor. Several classification approaches like SVM, Decision Trees, and Naive Based are considered to handle these profound challenges. One such challenge discussed in our paper emphasizing on Medical decision support system with Machine Learning (ML) Methodology considering diseases and treatments with their semantic relations in the document of Pub med abstracts. The proposed Multi variant classification framework aims at reducing data into attributes using PCA Transformation infusion with an efficient classification Algorithm - CNB. Our computed results are comparatively successful in attaining ultimate outcomes concerning performance metrics like Accuracy, Precision, Recall, and Time. The strength of our work lies in presenting an efficient approach for elevating enhanced decisions in Health care

Evolutionary Optimization Algorithm for Classification of Microarray Datasets with Mayfly and Whale Survival

In the field of bioinformatics, a vast amount of biological data has been generated thanks to the digitalization of high-throughput devices at a reduced cost. Managing such large datasets has become a challenging task for identifying disease-causing genes. Microarray technology enables the simultaneous monitoring of gene expression levels, thereby improving disease diagnosis accuracy for conditions like diabetes, hepatitis, and cancer. As these complex datasets become more accessible, innovative data analytics approaches are necessary to extract meaningful knowledge. Machine learning and data mining techniques can be employed to leverage big and heterogeneous data sources, facilitating biomedical research and healthcare delivery. Data mining has emerged as a vital tool in the medical field, providing insights into illnesses and treatments and enhancing the efficiency of healthcare systems. This thesis aims to present a novel hybrid technique for feature selection using amalgamation wrappers. The proposed approach combines the Mayfly and whale survival strategies, leveraging the strengths of both algorithms. The model was evaluated using various datasets and assessment criteria, including precision, accuracy, recall, F1-score, and specificity. The simulation results demonstrated that the proposed integrated optimization model exhibits improved classification performance with 12% higher accuracy in disease diagnosis