Ghulam Ishaq Khan Institute of Engineering Sciences and Technology

About: Ghulam Ishaq Khan Institute of Engineering Sciences and Technology is a education organization based out in Topi, Pakistan. It is known for research contribution in the topics: Thin film & Quantum efficiency. The organization has 618 authors who have published 940 publications receiving 10674 citations.

Synthesis of copper coated carbon nanotubes for aluminium matrix composites

Abstract: In this investigation copper coated carbon nanotubes (CNTs) were prepared to enhance the interfacial bonding between CNTs and aluminum matrix by the molecular-level mixing process. In optimized plating bath of (1:1) by wt. CNT with Cu, thickness of coated CNTs is reduced to 100 nm to promote uniform distribution of Cu nanoparticle on the surface of pretreated CNTs. The mixing of CNTs was accomplished by ultrasonication and ball milling. Scanning electron microscope analysis revealed the homogenous dispersion of Cu-coated CNTs in nanocomposites samples compared to the uncoated CNTs. The samples were pressureless sintered under vacuum. The densification increased with the increase in the CNTs content and is more pronounced in Cu-coated CNT nanocomposites.

Humidity sensing properties of CNT-OD-VETP nanocomposite films

Abstract: In this study, the blend of orange dye (OD), C17H17N5O2 (5 wt%), vinyl-ethynyl-trimethyl-piperidole (VETP), C12H19NO, (5 wt%) and carbon nanotube (CNT) powder (10 wt%) in a mixture of distilled water (80 wt%) and spirit were drop-casted on glass substrates with pre-deposited surface-type silver electrodes to fabricate CNT–OD–VETP nanocomposite thin films. In the process of thin films deposition, 2 V DC was applied to Ag electrodes. The thicknesses of the CNT–OD–VETP films were in the range of 10–15 μm. The I–V characteristics of the surface-type Ag/CNT–OD–VETP/Ag samples showed rectification behavior. The effect of humidity on the electrical properties of the nanocomposite films was investigated by measurement of the capacitance and dissipation of the samples at two different frequencies of the applied voltage: 120 Hz and 1 kHz. The resistance of the samples was determined from values of dissipation. It was observed that at 120 Hz and 1 kHz, under humidity of up to 90% RH, the capacitance of the cell increased by 7.4×103 and 740 times and resistance decreased by 2.3×104 and 3.8×104 times, accordingly, with respect to 40% RH conditions. The average response and recovery times of the films were obtained by capacitance-time measurements to evaluate the dynamics of the water vapor absorption and desorption processes. The experimental results have been supported by the simulation of the capacitance–humidity relationship. It is assumed that the humidity response of the cell is associated with diffusion of water vapors and doping of the semiconductor nanocomposite by water molecules.

Density-based clustering of big probabilistic graphs

Abstract: Clustering is a machine learning task to group similar objects in coherent sets. These groups exhibit similar behavior with-in their cluster. With the exponential increase in the data volume, robust approaches are required to process and extract clusters. In addition to large volumes, datasets may have uncertainties due to the heterogeneity of the data sources, resulting in the Big Data. Modern approaches and algorithms in machine learning widely use probability-theory in order to determine the data uncertainty. Such huge uncertain data can be transformed to a probabilistic graph-based representation. This work presents an approach for density-based clustering of big probabilistic graphs. The proposed approach deals with clustering of large probabilistic graphs using the graph’s density, where the clustering process is guided by the nodes’ degree and the neighborhood information. The proposed approach is evaluated using seven real-world benchmark datasets, namely protein-to-protein interaction, yahoo, movie-lens, core, last.fm, delicious social bookmarking system, and epinions. These datasets are first transformed to a graph-based representation before applying the proposed clustering algorithm. The obtained results are evaluated using three cluster validation indices, namely Davies–Bouldin index, Dunn index, and Silhouette coefficient. This proposal is also compared with four state-of-the-art approaches for clustering large probabilistic graphs. The results obtained using seven datasets and three cluster validity indices suggest better performance of the proposed approach.