M. Ramakrishna Murty

• M.Tech,Ph.D
• Professor at GITAM University

One of the main causes of blindness, diabetic retinopathy, requires sophisticated diagnostic methods for prompt and precise detection. This abstract presents a novel diagnostic approach that optimizes the diagnostic process by combining medical image analysis with Artificial Bee Colony (ABC) algorithms, inspired by the collective intelligence of be...

Reshaping the future of learning disabilities in higher education with AI holds immense promise for creating inclusive, supportive, and empowering educational experiences. It is a vital step towards ensuring that students with learning disabilities have equal access to education and opportunities for success. This highlights the transformative role...

In autoimmune disorders, your immune system attacks your body. Included in this category are rheumatoid arthritis, Crohn's disease, and a few thyroid diseases. Typically, the immune system defends against pathogens and viruses. As soon as it detects these unfamiliar intruders, it dispatches a group of fighter cells to absorb them. It takes expertis...

Diabetic retinopathy (DR) is one of the disorders which generally occurs among diabetic patients, which can even affect the eye gradually. This disorder has to be identified at the beginning stage, or else this can damage the eyesight permanently. Since the fundus oculi are so easily visible, retinopathy is the most commonly recorded chronic compli...

In this chapter, a combination of swarm intelligence algorithms is used to diagnose pneumonia from a patient's x-ray report of lungs conditions. The ability of swarm intelligent algorithms to solve a wide range of problems. For the classification of the disease for this research, a feed forward neural network with swarm intelligent algorithms had b...

This book highlights a collection of high-quality peer-reviewed research papers presented at the Sixth International Conference on Information System Design and Intelligent Applications (INDIA 2019), held at Lendi Institute of Engineering & Technology, Vizianagaram, Andhra Pradesh, India, from 1 to 2 November 2019. It covers a wide range of topics...

This book features high-quality papers presented at the International Conference on Computational Intelligence and Informatics (ICCII 2018), which was held on 28–29 December 2018 at the Department of Computer Science and Engineering, JNTUH College of Engineering, Hyderabad, India. The papers focus on topics such as data mining, wireless sensor netw...

In this paper, we study how to handle current issues encountered once training classifiers at intervals within the defined framework, i.e., the cascades of boosted ensembles (CoBEs). This CoBEs framework became more popular ones they are more successful in the face detector. From there on researchers started improvising the present procedure by imp...

The most important goal of the software industry is to build high quality software products. Defective software modules lead to software system failure. The aim of reliable software is to minimize the number of failures that occur when software program runs. Software fault prediction is an important area to develop quality software. By using essent...

Abdominal CT scan images are widely used in detection of kidney lesions. This paper study is conducted for pre-processing abdominal CT scan images so as to segment the kidney for further analysis of lesion detection. Various noise filters and segmentation techniques have been experimented to select the best filter and segmentation techniques for pr...

Association rule mining is one of the vital data mining tasks to extract knowledge from the data. In the process of association rule mining the foremost step is to find the frequent itemset. The frequent itemset is used to generate association rules. In general brute -force approach is expensive because there are exponentially many rules that can b...

Abstract—Big data is a collection of data sets. It is so enormous and complex that it becomes difficult to processes and analyse using normal database management tools or traditional data processing applications. Big data is having many challenges. The main problem of the big data is store and retrieve of the data from the search engines. Document...

Abstract. Clustering is useful in several machine learning and data mining applications such as information retrieval, market analysis, web analysis etc. The most popular partitioning clustering algorithms are K-means. The performance of these algorithms converges to local minima depends highly on initial cluster centroids. In order to overcome loc...

Finding the optimal number of clusters has remained to be a challenging problem in data mining research community. Several approaches have been suggested which include evolutionary computation techniques like genetic algorithm, particle swarm optimization, differential evolution etc. for addressing this issue. Many variants of the hybridization...

Everyday terabytes of data is generated in the real world and most of the mare stored in electronic devices, thus offering great potential for data analysis. Data is not only growing in volume, but also expanding in its varieties like text, commercial or business, medical, images, multimedia from various sources, internet being one among them. Mos...

Several validity indices have been designed to evaluate solutions obtained by clustering algorithms. Traditional indices are generally designed to evaluate center-based clustering, where clusters are assumed to be of globular shapes with defined centers or representatives. Therefore they are not suitable to evaluate clusters of arbitrary shapes, si...

Teaching Learning Based Optimization (TLBO) is being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces. This paper presents an effect of variation of a teaching factor TF in traditional TLBO algorithm and then proposed a value for teaching factor TF. The traditional TLBO algori...

Teaching Learning Based Optimization (TLBO) is being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces. This paper presents an effect of variation of a teaching factor TF in traditional TLBO algorithm and then proposed a value for teaching factor TF. The traditional TLBO algori...

Several validity indices have been designed to evaluate solutions obtained by clustering algorithms. Traditional indices are generally designed to evaluate center-based clustering, where clusters are assumed to be of globular shapes with defined centers or representatives. Therefore they are not suitable to evaluate clusters of arbitrary shapes, si...

The k-means algorithm is well-known for its efficiency in clustering large data sets and it is restricted to the numerical data types. But the real world is a mixture of various data typed objects. In this paper we implemented algorithms which extend the k-means algorithm to categorical domains by using Modified k-modes algorithm and domains with m...

Clustering is unsupervised approach to group the data items. Each group is called a cluster which contains similar types of data items and dissimilar data items in other clusters. The goal of clustering is to find the best cluster results. Traditional clustering procedures may not reach the objective of clustering. This paper addresses advance issu...

Text Mining is important, emerging, research area, because plenty of text resources growing rapidly through the internet and digital world. In the text data analysis text categorization is one of the vital techniques. Traditional text categorization methods are not able to handle well with learning across different domains. Cross-domain classificat...

In the text document analysis process keywords are often represented in bag-of-words or vector space model. This representation is high-dimensional and sparse. Keyword extraction is considered as core technology of all automatic processing for text materials. Keywords represent in condensed from the essential content of a document. In this paper we...

In the text document analysis process keywords are often represented in bag-of-words or vector space model. This representation is high-dimensional and sparse. Keyword extraction is considered as core technology of all automatic processing for text materials. Keywords represent in condensed from the essential content of a document. In this paper we...

Grouping large number of text documents is a challenging task due to high dimensional representation of the vector space model. Higher dimensionality of text data leads to computational burden and inefficient cluster results. In this work we improve the quality of the text document clustering using Singular Value Decomposition technique along with...

Clustering of text documents is a discovered process that groups a set of documents such that the intra-cluster similarity is maximized and the inter-cluster similarity is minimized. The document pre-processing is a vital and critical step in document clustering process. It is a challenge to efficiently pre-process very large document collection....

Grouping large number of text documents is a challenging task due to high dimensional representation of the vector space model. Higher dimensionality of text data leads to computational burden and inefficient cluster results. In this work we improve the quality of the text document clustering using Singular Value Decomposition technique along with...

Text documents are very significant in the contemporary organizations; moreover their constant accumulation enlarges the scope of document storage. Standard text mining and information retrieval techniques of text document usually rely on word matching. An alternative way of information retrieval is clustering. In which document pre-processing is a...

This paper presents an unsupervised document clustering method with enhanced preprocessing of document. Given a large unlabeled document collection, it is often helpful to organize this collection into clusters of related documents. By using a vector space model, text data can be treated as high-dimensional but sparse numerical data vectors. It is...

Due to rapid growth of on-line information, text classification has become one of key technique for handling and organizing text data. One of the reasons to build taxonomy of documents is to make it easier to find relevant documents, content filtering and topic tracking. LS-SVM is the classifier, used in this paper for efficient classification of t...