Data Mining Approach for Amino Acid Sequence Classification

Computerized applications are employed all around the world, an enormous amount of data is collected. The essential information contained in large amounts of data is attracting scholars from a variety of disciplines to examine how to extract the hidden knowledge inside them. The technique of obtaining or mining usable and valuable knowledge from enormous amounts of data is known as data mining. Text mining, picture mining, sequential pattern mining, web mining, and so on are all examples of data mining fields. Sequencing mining is one of the most important technologies in this field, as it aids in the discovery of sequential connections in data. Sequence mining is used in a variety of applications, including customers' buying trends analysis, web access trends analysis, atmospheric observation, amino acid sequences, Gene sequencing, and so on. Sequence mining techniques are utilized in protein and DNA analysis for sequence alignment, pattern searching, and pattern categorization. Researchers are exhibiting an interest in the subject of amino acid sequence categorization in the field of amino acid sequence analysis. It has the ability to find recurrent patterns in homologous proteins. This study describes the numerous methods used by numerous studies to categories proteins and gives an overview of the most important sequence classification techniques

A Study of Dengue Infection Segmentation, Feature Extraction and Classification

Aedesaegypti mosquito spared the dengue viral illnesses. The world�s greatest developing outbreak is dengue fever. Day �by day the rate of dengue has become significantly around the globe increases. Dengue infections are of three forms: Dengue fever additionally perceived as �break bone� fever, Dengue Haemorrhagic Fever (DHF), Dengue Shock Syndrome (DSS) which are life debilitating. Doctors need to capture approximately 20 to 50 pictures of white blood cell from different angle to identify the disease. The platelet count is estimated using various segmentation techniques and morphological operations with the help of the platelets count dengue fever infection is �detected. A technique used for segmentation are mainly thresholding based that is not segment exact part of defected platelet. But, the result was not so efficient in providing the spatial detail information of the actual disease part. So here we are going to use Fuzzy based algorithm to segment WBC Platelets. There are different feature extraction methods are apply platelet are size, shape and area. But it was not giving the exact results. So here we are going to use Haarlick Features for WBC platelets. And any machine learning method SVM, ANN, Decision Tree will be used for the classification of dengue infection types

Privacy Preservation using T-Closeness with Numerical Attributes

Data mining is a process that is used to retrieve the knowledgeable data from the large dataset. Information imparting around two associations will be basic done a large number requisition zones. As people are uploading their personal data over the internet, however the data collection and data distribution may lead to disclosure of their privacy. So, preserving the privacy of the sensitive data is the challenging task in data mining. Many organizations or hospitals are analyzing the medical data to predict the disease or symptoms of disease. So, before sharing data to other organization need to protect the patient personal data and for that need privacy preservation. In the recent year�s privacy preserving data mining has being received a large amount of attention in the research area. To achieve the expected goal various methods have been proposed. In this paper, to achieve this goal a pre-processing technique i.e. k-means clustering along with anonymization technique i.e. k-anonymization and t-closeness and done analysis which techniques achieves more information gain

DETECTION OF DYNAMIC HAND GESTURE USING SUPPORT VECTOR MACHINE

During past few years, human hand gesture for interaction with computing devices has continues to be thriving area of research. Hand gesture Recognition system received great attention in recent years because it provides human computer interaction and sign language. Hand gesture recognition is contain three stages: Pre-Processing, Features Extraction, classification. Most current approaches is based on the static hand gesture recognition Hand gesture recognition is often too sensitive to poor resolution ,environment of background, occultation among other prevalent problems and recognition dynamic hand gesture. So, proposed work investigates dynamic hand gesture recognition using Conditional Random Field. Result shows dynamic hand gesture recognition under complex background and achieve better recognition rate

C Programming Mastery- Job Interview Oriented Preparation

<p>C Programming Mastery- Job Interview Oriented Preparation</p&gt