Machine learning

4.3 Deep Learning Technique Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task [12]. Deep learning is another Machine Learning (ML) algorithm

Continuous Auditing (CA hereafter) is a representative of the progressively automated world of auditing, and it is a trend of auditing in the information explosion era. Machine Learning can help to implement CA to improve companies’ decision-making and risk management. 1) Machine Learning and audit analytics fits the original motivation of CA The Auditing Concepts Committee (American Accounting Association [AAA] 1972) defined auditing as “a systematic process of objectively obtaining and evaluating

Deep Learning to replicate the activities of traders in the Automated Systems? This research considers the use of Deep Learning techniques for generating artificial traders and test their efficiency in the Automated Systems. These traders are developed using Keras, a high level neural network toolkit wrapped up by Tensorflow. The data is generated from Bristol Stock Exchange developed by Cliff[21] which is an Automated market. This generated data is then trained and tested using deep learning neural

Hierarchical Document Clustering based on Cosine Similarity measure Ms. Shraddha K.Popat* Ms. Vishakha A. Metre Asst.Professor, Asst.Professor, Department of computer Engineering, Department of computer Engineering, D.Y.Patil, College of Engineering, Akurdi, Pune, India D.Y.Patil, College of Engineering, Akurdi, Pune, India shraddhakp21@gmail.com vishakha.metre@gmail.com Abstract- Clustering is one of the prime topics in data mining. Clustering partitions the data and classifies the data into

Artificial intelligence is the utilization of computers to imitate human brains in restricted domains. This is a consequence of developing computer which carry on logically, reason rationally and can adequately decipher its surroundings continuously in real time. Artificial Intelligence is no longer a fictionist concept, the superior computing prowess of artificial intelligence has enabled them to surpass humans in problem solving. They have made it conceivable to recreate complex activities that

always been an inquisitive person, a voracious reader who has raised the question “Why does this happen?” With this desire to learn and after an eventful journey, I wish to pursue my graduate studies in Computer Science with a concentration on Machine Learning at University of Texas at Austin. Having taken Computer Science as a high school elective and after securing a good rank in the highly competitive All India Engineering Entrance Exam (AIEEE), I joined National Institute of Technology, Rourkela

however due to the pervasive and ubiquitous adoption of powerful computational tools in our contemporary society, ANNs have had a sort of renaissance, much to the avail of scientists, engineers, and consumers. The current cutting-edge in deep-learning and ANNs focuses highly on both their ability to model and interpret complex data, and their ability to be put to scale due through optimization and parallelization. The current framework for designing ANNs is one that is widely available, with

as compare to previous research work on diabetes using ADAP which gives 76% recognition rate. 1.0 INTRODUCTION Diabetes Mellitus is one of the major medical conditions that it presence in patients has been predicted using various types of learning algorithms such as ADAP, and which results showed a performance of 76% (recognition rate). It is evident that for medical applications, a much higher performance is invariably required. The idea of neural computing grew out of desire to capture

data. These data sets are often so large and complex that it becomes difficult to process using on-hand database management tools. There are several techniques which are widely used in implementation of Big Data. 1. Association rule learning: Association rule learning is a method for discovering interesting correlations between variables in large databases. It was first used by major supermarket chains to discover interesting relations between products, using data from supermarket point-of-sale (POS)

cancers. Finally, a robust MDP (RMDP) model is presented to study the sensitivity of MDP optimal strategies under uncertainty. In what follows, I present a summary of findings in Chapters 2, 3, and 4 of this dissertation. In Chapter 2, four machine learning classifiers are used to find the likelihood of having BRCA mutation based on detailed personal and family history of cancer information. The data used for validation of the models emerges from a recent nation-wide survey study (ABOUT) of those