Heritage Institute of Technology

About: Heritage Institute of Technology is a based out in . It is known for research contribution in the topics: Support vector machine & Transconductance. The organization has 581 authors who have published 1045 publications receiving 8345 citations.

Retracted: Energy Efficient, Chain Based Clustering Routing Protocol for Wireless Sensor Networks

Abstract: In this paper we try to improve the LEACH [1] protocol by implementing a chain among the cluster head (CH) nodes. Initially, the clusters are formed and the sensor nodes are kept under any one of the cluster heads according to the LEACH protocol. In LEACH, after sensing the environment, all the sensor nodes transmit the sensed data to its own CH at their time schedule. The CHs fuse all data and send them to base station (BS). The BS get information from all the cluster heads. In our approach, all the CHs form a chain among themselves and only one CH (called leader) nearest to BS sends data to this base station by implementing the concept of PEGASIS [2] protocol. This chain is started from the farthest CH from BS and ending at CH nearest to BS. Each CH gets data from its previous CH in the chain, fuses its own data and sends it to its next CH in the chain. The CH, nearest to BS within this chain only sends data to BS. LEACH, a clustering based protocol uses randomization among the CHs to distribute the energy load among the sensors and thus it reduces energy dissipation. In our protocol, in addition to this energy reduction, the CHs decrease the data transmission and energy among them by incorporating a chain based clustering approach.

Understanding Fabrication and Properties of Magnesium Matrix Nanocomposites

Abstract: Magnesium-based metal matrix nanocomposites (MMNCs) are new class materials which can be used widely in aerospace, biomedical, electronics and automobile industries due to their low density, sustainability, good specific strength and better tribological properties. Performance of MMNCs depends on several factors, i.e., composition and combination of reinforcement, processing methods, etc. Present study tries to review available literatures to discuss about the role of those factors on mechanical properties, tribological properties and corrosion behaviors of magnesium-based MMNCs. In this study, liquid metallurgy-based primary processing methods and secondary methods are discussed in details with the help of available literatures. Roles of ultrasonic treatment, cavitation and acoustic streaming on distribution of nanoparticles are discussed in details. Strengthening mechanisms between particle and matrix metal are also presented. Effects of particles like Al2O3, SiC, WC, TiB2, CNT on mechanical, tribological and corrosion behavior are discussed. Mechanical properties (UTS, YS, microhardness, creep behavior) are mainly discussed and available literatures revealed that the presence of nanoparticles normally enhance these properties. Literature on tribological behavior yielded that nanoparticles help to enhance wear and friction behavior of Mg-MMNCs at room and elevated temperatures. Effects of tribological parameters (load, sliding speed, sliding distance) are also discussed. But researchers are split into two groups about corrosion characteristics of magnesium composites. Some researchers reported that corrosion resistance is decreased due to presence of reinforcement while others concluded that corrosion resistance is enhanced due to reinforcing particles.

Identification of miRNA signature using Next-Generation Sequencing data of prostate cancer

Abstract: MicroRNAs (miRNAs) are a class of ∼22-nucleotide endogenous noncoding RNAs which have critical functions across various biological processes. It is quite well-known that the miRNAs are playing a crucial role for regulating the expression of target gene via repressing translation or promoting messenger RNAs degradation. Therefore, identification of discriminative and differentially expressed miRNA as a signature is an important task for cancer therapy. In this regard, Next-Generation Sequencing (NGS) data of miRNAs, available at The Cancer Research Atlas (TCGA) repository, is analyzed here for prostate cancer. This cancer type is a serious threat to the health of men as found in the literature. Hence, finding miRNA signature using NGS based miRNA expression data for prostate cancer is an important research direction. Generally by motivating this fact, a new miRNA signature identification method for prostate cancer is proposed. The proposed method uses a global optimization technique, called Simulated Annealing (SA), Principal Component Analysis (PCA) and Support Vector Machine (SVM) classifier. Here SA encodes L number of features, in this case miRNAs. Similar number of top L key principal components of the original dataset is extracted using PCA. Thereafter, such components are multiplied with the reduced subset of data so that the classification task can be done on diverse dataset using SVM. Here the classification accuracy of SVM is considered as an underlying objective to optimize using SA. The proposed method can be seen as feature section technique in order to find potential miRNA signature. Finally, the experimental results provide a set of miRNAs with optimal classification accuracy. However, due to the stochastic nature of this algorithm a list of miRNAs is prepared. From the top 15 miRNAs of that list, four miRNAs, hsa-mir-152, hsa-mir-23a, hsa-mir-302f and hsa-mir-101-1, are associated with prostate cancer. Moreover, the performance of the proposed method has also been compared with other widely used state-of-the-art techniques. Furthermore, the obtained results have been justified by means of statistical test along with biological significance tests for the selected miRNAs.

Feature Extraction for IRIS Recognition

Abstract: In this paper we propose a new biometric-based Iris feature extraction system. The system automatically acquires the biometric data in numerical format (Iris Images) by using a set of properly located sensors. We are considering camera as a high quality sensor. Iris Images are typically color images that are processed to gray scale images. Then the Feature extraction algorithm is used to detect “IRIS Effective Region (IER)” and then extract features from “IRIS Effective Region (IER)” that are numerical characterization of the underlying biometrics. Later on this work will be helping to identify an individual by comparing the feature obtained from the feature extraction algorithm with the previously stored feature by producing a similarity score. This score will be indicating the degree of similarity between a pair of biometrics data under consideration. Depending on degree of similarity, individual can be identified.