Identification of Protein Alignment for Elder Health Care

Over many years protein sequence alignment problem has grabbed attention of biologists as it implicates, more than two biological sequences. It states all the important aspects of big data and how medical and health informatics, translational bioinformatics will benefit personalized health care both structured and unstructured, covering genomics, proteomics, metabolism. The system develop approach for biological sequence alignment to increase efficiency of analysis operation that speed up the calculation of alignment for huge real time sequences, to develop distributed scan approach in Smith-waterman algorithm for presenting fast solution and optimize the Smith Waterman(SW) alignment algorithm using Distributed approach

Prediction Sequence Patterns of Tourist from the Tourism Website by Hybrid Deep Learning Techniques

Tourism is an important industry that generates incomes and jobs in the country where this industry contributes considerably to GDP. Before traveling, tourists usually need to plan an itinerary listing a sequence of where to visit and what to do. To help plan, tourists usually gather information by reading blogs and boards where visitors who have previously traveled posted about traveling places and activities. Text from traveling posts can infer travel itinerary and sequences of places to visit and activities to experience. This research aims to analyze text postings using 21 deep learning techniques to learn sequential patterns of places and activities. The three main techniques are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and a combination of these techniques including their adaptation with batch normalization. The output is sequential patterns for predicting places or activities that tourists are likely to go and plan to do. The results are evaluated using mean absolute error (MAE) and mean squared error (MSE) loss metrics. Moreover, the predicted sequences of places and activities are further assessed using a sequence alignment method called the Needleman–Wunsch algorithm (NW), which is a popular method to estimate sequence matching between two sequences

A STATE OF THE ART SURVEY ON POLYMORPHIC MALWARE ANALYSIS AND DETECTION TECHNIQUES

Nowadays, systems are under serious security threats caused by malicious software, commonly known as malware. Such malwares are sophisticatedly created with advanced techniques that make them hard to analyse and detect, thus causing a lot of damages. Polymorphism is one of the advanced techniques by which malware change their identity on each time they attack. This paper presents a detailed systematic and critical review that explores the available literature, and outlines the research efforts that have been made in relation to polymorphic malware analysis and their detection