Deep Feature Representation and Similarity Matrix based Noise Label Refinement Method for Efficient Face Annotation

Face annotation is a naming procedure that assigns the correct name to a person emerging from an image. Faces that are manually annotated by people in online applications include incorrect labels, giving rise to the issue of label ambiguity. This may lead to mislabelling in face annotation. Consequently, an efficient method is still essential to enhance the reliability of face annotation. Hence, in this work, a novel method named the Similarity Matrix-based Noise Label Refinement (SMNLR) is proposed, which effectively predicts the accurate label from the noisy labelled facial images. To enhance the performance of the proposed method, the deep learning technique named Convolutional Neural Networks (CNN) is used for feature representation. Several experiments are conducted to evaluate the effectiveness of the proposed face annotation method using the LFW, IMFDB and Yahoo datasets. The experimental results clearly illustrate the robustness of the proposed SMNLR method in dealing with noisy labelled faces

Drug Target Interaction Prediction Using Machine Learning Techniques – A Review

Drug discovery is a key process, given the rising and ubiquitous demand for medication to stay in good shape right through the course of one’s life. Drugs are small molecules that inhibit or activate the function of a protein, offering patients a host of therapeutic benefits. Drug design is the inventive process of finding new medication, based on targets or proteins. Identifying new drugs is a process that involves time and money. This is where computer-aided drug design helps cut time and costs. Drug design needs drug targets that are a protein and a drug compound, with which the interaction between a drug and a target is established. Interaction, in this context, refers to the process of discovering protein binding sites, which are protein pockets that bind with drugs. Pockets are regions on a protein macromolecule that bind to drug molecules. Researchers have been at work trying to determine new Drug Target Interactions (DTI) that predict whether or not a given drug molecule will bind to a target. Machine learning (ML) techniques help establish the interaction between drugs and their targets, using computer-aided drug design. This paper aims to explore ML techniques better for DTI prediction and boost future research. Qualitative and quantitative analyses of ML techniques show that several have been applied to predict DTIs, employing a range of classifiers. Though DTI prediction improves with negative drug target pairs (DTP), the lack of true negative DTPs has led to the use a particular dataset of drugs and targets. Using dynamic DTPs improves DTI prediction. Little attention has so far been paid to developing a new classifier for DTI classification, and there is, unquestionably, a need for better ones

A SURVEY ON MULTICAST ROUTING PROTOCOLS FOR PERFORMANCE EVALUATION IN WIRELESS SENSOR NETWORK

Multicast is a process used to transfer same message to multiple receivers at the same time. This paper presents the simulation and analysis of the performance of six different multicast routing protocols for Wireless Sensor Network (WSN). They are On Demand Multicast Routing Protocol (ODMRP), Protocol for Unified Multicasting through Announcement (PUMA), Multicast Adhoc On demand Distance Vector Protocol (MAODV), Overlay Boruvka-based Adhoc Multicast Protocol (OBAMP), Application Layer Multicast Algorithm (ALMA) and enhanced version of ALMA (ALMA-H) for WSN. Among them, ODMRP, MAODV and PUMA are reactive protocols while OBAMP, ALMA and ALMA-H are proactive protocols. This paper compares the performance of these protocols with common parameters such as Throughput, Reliability, End-to-End delay and Packet Delivery Ratio (PDR) with increasing the numbers of nodes and increasing the speed of the nodes. The main objective of this work is to select the efficient multicast routing protocol for WSN among six multicast routing protocol based on relative strength and weakness of each protocol. The summary of above six multicast routing protocols is presented with a table of different performance characteristics. Experimental result shows that ODMRP attains higher throughput, reliability and higher packet delivery ratio than other multicast routing protocol, while incurring far less end-to-end delay

TEXTURE BASED LAND COVER CLASSIFICATION ALGORITHM USING GABOR WAVELET AND ANFIS CLASSIFIER

Texture features play a predominant role in land cover classification of remotely sensed images. In this study, for extracting texture features from data intensive remotely sensed image, Gabor wavelet has been used. Gabor wavelet transform filters frequency components of an image through decomposition and produces useful features. For classification of fuzzy land cover patterns in the remotely sensed image, Adaptive Neuro Fuzzy Inference System (ANFIS) has been used. The strength of ANFIS classifier is that it combines the merits of fuzzy logic and neural network. Hence in this article, land cover classification of remotely sensed image has been performed using Gabor wavelet and ANFIS classifier. The classification accuracy of the classified image obtained is found to be 92.8%

VALIDATING THE PERFORMANCE OF PERSONALIZATION TECHNIQUES IN SEARCH ENGINE

User profiling is an important and basic component in personalized search engine. Search engines respond to a user’s query by using the bag-of-words model, which matches keyword between the query and web documents but ignore contexts and users’ preferences. Personalized search greatly improves the search results as of the results provided by the search engine without personalization. In this paper, the performance of personalized search based on content analysis and personalized search based on user group have been evaluated. In personalized search based on content analysis the contents are traced by finding the user’s browsed documents and search history, which reduce the users search time. In user profile only user preference alone is taken into consideration. The experimental results show that the personalized search based on user group method having higher precision and recall rate than the content analysis method