National Institute of Technology, Meghalaya

About: National Institute of Technology, Meghalaya is a education organization based out in Shillong, India. It is known for research contribution in the topics: Control theory & Computer science. The organization has 503 authors who have published 1062 publications receiving 6818 citations. The organization is also known as: NIT Meghalaya & NITM.

Multiobjective hybrid monarch butterfly optimization for imbalanced disease classification problem

Abstract: Datasets obtained from the real world are far from balanced, particularly for disease datasets, since such datasets are usually highly skewed having a few minority classes apart from one or more prominent majority classes. In this research, we put forward the novel hybrid architecture to handle imbalanced binary disease datasets that arrives upon the efficient combination of Support vector machine (SVM) classifier’s sensitive parameter values for improved performance of SVM by means of an Evolutionary algorithm (EA), namely monarch butterfly optimization (MBO). In this paper, MBO is used to enumerate three objectives, namely prediction accuracy (PAC), sensitivity (SEN), specificity (SPE). Additionally, we propose a Totally uni-modular matrix (TUM) and limit points based non-dominated solutions selection for deciding local and global search and to generate an efficient initial population respectively. Since these two greatly affect the performance of EAs, the performance of the proposed hybrid architecture is tested on 18 disease datasets having binary class labels and the results obtained demonstrate improvements using the proposed method. For the majority of the datasets, either 100% sensitivity and/or specificity were attained. Moreover, pertinent statistical tests were carried out to ascertain the performances obtained.

An Improved Control Strategy for Single-Phase Single-Stage Grid-Tied PV System With Current Reference MPPT Control

Abstract: This paper presents a competent single-phase single-stage grid-tied photovoltaic (PV) power conditioning system (SSGPS) integrated with maximum power point tracking (MPPT) and closed-loop current control scheme. The shortcoming of the conventional MPPT technique to track global power under varying environmental conditions is addressed by developing a simple and efficient current reference control (CRC) based MPPT technique. The CRC MPPT technique enables faster tracking of the global maximum power and the current controller injects pure sinusoidal current into the grid at unity power factor under transient conditions. PWM pulses for the inverter are generated using Hysteresis band controller (HBC). The viability of the control scheme for the SSGPS is validated with simulation and experimental analysis under dynamic test conditions. A real time implementation of the proposed system is carried out in National instruments (NI) LabVIEW environment.

Intelligent Data Placement in Heterogeneous Hadoop Cluster

Abstract: The MapReduce programming model and Hadoop has become the de facto standard for data-intensive applications. Hadoop tasks are mapped to certain nodes within the Hadoop cluster with data required by tasks. Such a strategy is intuitively appealing for a homogeneous cluster, both in terms of computation and storage capabilities. However most commonplace clusters are indeed heterogeneous, since nodes are added over a prolonged period. This necessitates the use of an intelligent data placement strategy among cluster nodes that accounts for the inherent heterogeneity, which otherwise incurs performance bottleneck. In this paper, we propose to have a performance based clustering of Hadoop nodes and subsequently place data among the nodes. Performance based profiling of nodes can be achieved by running multiple benchmarks in an offline manner and segregating dividing the cluster nodes into two subsets namely low and high performance nodes. Additionally, execution process of Hadoop tasks is monitored using Hadoop’s task speculation mechanism and computations are dynamically migrated for slow running tasks based on a prior knowledge of data block regarding the task. Experiments conducted demonstrates that the proposed intelligent data placement improve network utilization and cluster performance.

Blockchain Based Smart Healthcare System for Chronic –Illness Patient Monitoring

Abstract: Chronic disease exists over a period of time and it is very crucial because it can affect the health of disability people more. Normally people think that they are not suffering or affected by the disease because they do not have symptoms. But it does not necessarily mean a person with no symptom has no chronic disease. This paper presents design and prototype implementation of a blockchain system which remotely monitors patient health records. The patient monitoring system is a big challenge and other security threats arises as the health care data transferred from one to another and authorization to create and read transactions, is a major concern related to data loss. The data loss of the patient protected health information which are generated from the devices should be handled carefully. So in this paper we proposed a solidity based smart contract run over a Ethereum virtual machine to facilitate the secure analysis and storing of transactions from and to the sensors. Using the Ethereum protocol, based on a private blockchain and writing the smart contracts to keep track of the patient health information. We have created an automatic system through which the sensors collects the data, then communicates with the decentralized application via the smart contract. The method inside the smart contract collects data when they are called, also reads and writes transactions of all events on the Ethereum virtual network. This solidity based smart contract system along with injected web3, metamask, and truffle environment would support alert transaction to the patient and medical professionals.