Using Machine Learning Algorithms for Cancer Image Dataset: A Predictive and Prescriptive Analysis | Chapter 3 | Research Updates in Mathematics and Computer Science Vol. 2

 This paper focuses on the problem of using machine learning techniques on cancer/tumor prediction. One of the biggest applications of big data and machine learning is in the field of medical domain. Consequently, a health care organization that uses the techniques of machine learning and big data to treat patients see fewer mishaps or gets enough time to deal with them in advance. Classification and prediction of the images are fairly easy task for humans, but it takes more effort for a machine to do the same. Machine learning helps to attain this goal. It automates the task of classifying a large collection of images into different classes by labelling the incoming data and recognizes patterns in it, which is subsequently translated into valuable insights. Furthermore, the prediction of Osteosarcoma case for one of the four classes of tumor namely Non tumor, Non-Viable tumor, viable tumor, Viable: Non-Viable tumor has to be done. The quantitative analysis is done using various machine learning libraries of python. The three classification algorithms used for image analysis are random forest, SVM, and logistic regression. The metrics used for performing perspective analysis are precision, recall and F1 Score. The domain of medical imaging helps providing important information on anatomy and organ function subsequently detecting disease states. Although the characteristics of medical data make its analysis a big challenge notwithstanding that machine learning techniques could make the task easier. The results show that the random forest algorithm has performed best amongst the three classification algorithms when given with less complicated scenario, with prediction accuracy, precision, recall and f1 score of 100%. But the performance of every classification algorithm degrades when provided with the cases of Osteosarcoma which has got more complicated scatter graph. However, the logistic regression retains its performance by predicting tumor cases with 99% accuracy. For future scope, various other machine learning algorithms can be applied to observe their performance on the same set of features extracted from the cancer image data set.

Author(s) Details:

Divya Chauhan,
Department of Computer Applications, Government College Rampur, Himachal Pradesh, India.

Kishori Lal Bansal,
Department of Computer Science, Himachal Pradesh University, Shimla, India.

Please see the link here: https://stm.bookpi.org/RUMCS-V2/article/view/13942

Evolution of Distributed File System and Hadoop: A Mathematical Appraisal | Chapter 11 | Recent Advances in Mathematical Research and Computer Science Vol. 2

 The rapid advancement of technology has had a significant impact on human life. Many conventional systems are being phased out or being replaced by their electronic counterparts. For example, the conventional postal system is being phased out in favour of mobile phones and email. The electronic system offers more features than its traditional counterparts. People can communicate with one another through social media, sharing their views and memories in the form of messages, photographs, or videos. On the other hand, many research efforts are pushed forward in order to improve technologies and knowledge, and data from many sources is gathered in vast quantities for further analysis. In short, today's world is surrounded by a massive amount of data in many forms. This necessitated proper handling of these billions of gigabytes of electronic data, sometimes referred to as BIG DATA. Effective management must be founded on mathematical concepts that have been proved to lower the risk of casualties. The goal of this research is to provide a mathematical evaluation for the evolution of file data distribution and to explain some basic solutions to primitive difficulties using probability theory. If we try to solve an issue like Big Data, probability theory can assist us formalise the basic tree-like architecture of a distributed file system.

Author(S) Details

Atul Saurabh
Department of Java Software Engineer, Rishabh Software, Vadodara, Gujarat, India.

Swapnil M. Parikh
Department of CSE, Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India.

View Book:- https://stm.bookpi.org/RAMRCS-V2/article/view/4458

An Experimental Implementation of Map Reduce on the Hadoop for Analyzing Big Data | Chapter 7 | Theory and Practice of Mathematics and Computer Science Vol. 1

 

The Speedy development of Internet has led to huge quantities of digital data available online and vast capacity

of digital data is increasing and successfully stored. In demand to the process, analyzed, and linked huge volume

of stored data to achieve correct Information, some computation is required. Even efficient processing and

implementation is needed for scientific data performance analysis. The already existing MapReduce Technique

had been compared with Hadoop to afford high performance and efficiency for large volume of dataset. Hadoop

distributed architecture with MapReduce programming is analysis here.

Author (s) Details

Gul Shaira Banu Jahangeer

Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, India, Srivilliputhur, Tamil Nadu, India.

T. Dhiliphan Rajkumar

Faculty of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Tamilnadu, India.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/237

CSFC: A New Centroid Based Clustering Method to Improve the Efficiency of Storing and Accessing Small Files in Hadoop: Recent Advancement | Chapter 4 | Recent Studies in Mathematics and Computer Science Vol. 2

In day to day life, the computer plays a major role, due to this advancement of technology collection of data from various fields are increasing. A large amount of data is produced by various fields because of IOT sensors for every second and is not easy to process. This large amount of data is called as Big data. A large number of small files also considered as Big data. It's not easy to process and store the small files in Hadoop. In the existing methods Merging technologies and Clustering Techniques are used to combine smaller files to large files up to 128 MB before sending it to HDFS in Hadoop. In the Proposed system CSFC (Clustering Small Files based on Centroid) Clustering Technique is used without mentioning the number of Clusters previously because if the clusters are mentioned before, all the files are clubbed within the limited number of clusters. In proposing system clusters are generated by depending on the number of related files in the dataset. The relevant files are combined up to 128 MB in a cluster. If any file is not relevant to the existing cluster or if the memory size reached 128MB then-new cluster will be generated and the file will be stored. It is easy to process the related files, comparing two relevant files. By using this method fetching data from the data node, it produces efficient result when comparing with other clustering techniques. 

Author (s) Details

R. Rathidevi, Research scholar
Department of Computer Science, Vels Institute of Science, Technology and Advanced studies, Chennai, India.

Dr. R. Parameswari, Associate professor
Department of Computer Science, Vels Institute of Science, Technology and Advanced studies, Chennai, India.

View Book :- http://bp.bookpi.org/index.php/bpi/catalog/book/182