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: Quantum efficiency & Diode. The organization has 618 authors who have published 940 publications receiving 10674 citations.

Profiling drivers based on driver dependent vehicle driving features

Abstract: This work addresses the problem of profiling drivers based on their driving features. A purpose-built hardware integrated with a software tool is used to record data from multiple drivers. The recorded data is then profiled using clustering techniques. k-means has been used for clustering and the results are counterchecked with Fuzzy c-means (FCM) and Model Based Clustering (MBC). Based on the results of clustering, a classifier, i.e., an Artificial Neural Network (ANN) is trained to classify a driver during driving in one of the four discovered clusters (profiles). The performance of ANN is compared with that of a Support Vector Machine (SVM). Comparison of the clustering techniques shows that different subsets of the recorded dataset with a diverse combination of attributes provide approximately the same number of profiles, i.e., four. Analysis of features shows that average speed, maximum speed, number of times brakes were applied, and number of times horn was used provide the information regarding drivers' driving behavior, which is useful for clustering. Both one versus one (SVM) and one versus rest (SVM) method for classification have been applied. Average accuracy and average mean square error achieved in the case of ANN was 84.2 % and 0.05 respectively. Whereas the average performance for SVM was 47 %, the maximum performance was 86 % using RBF kernel. The proposed system can be used in modern vehicles for early warning system, based on drivers' driving features, to avoid accidents.

Gene encoder: a feature selection technique through unsupervised deep learning-based clustering for large gene expression data

Abstract: Cancer is a severe condition of uncontrolled cell division that results in a tumor formation that spreads to other tissues of the body. Therefore, the development of new medication and treatment methods for this is in demand. Classification of microarray data plays a vital role in handling such situations. The relevant gene selection is an important step for the classification of microarray data. This work presents gene encoder, an unsupervised two-stage feature selection technique for the cancer samples’ classification. The first stage aggregates three filter methods, namely principal component analysis, correlation, and spectral-based feature selection techniques. Next, the genetic algorithm is used, which evaluates the chromosome utilizing the autoencoder-based clustering. The resultant feature subset is used for the classification task. Three classifiers, namely support vector machine, k-nearest neighbors, and random forest, are used in this work to avoid the dependency on any one classifier. Six benchmark gene expression datasets are used for the performance evaluation, and a comparison is made with four state-of-the-art related algorithms. Three sets of experiments are carried out to evaluate the proposed method. These experiments are for the evaluation of the selected features based on sample-based clustering, adjusting optimal parameters, and for selecting better performing classifier. The comparison is based on accuracy, recall, false positive rate, precision, F-measure, and entropy. The obtained results suggest better performance of the current proposal.

Influence of Mo addition on the structural and electrochemical performance of Ni-rich cathode material for lithium-ion batteries.

Abstract: Molybdenum modified LiNi0.84Co0.11Mn0.05O2 cathode with different doping concentrations (0–5 wt.%) is successfully prepared and its electrochemical performances are investigated. It is demonstrated that molybdenum in LiNi0.84Co0.11Mn0.05O2 has a positive effect on structural stability and extraordinary electrochemical performances, including improved long-term cycling and high-rate capability. Among all samples, the 1 wt. % molybdenum LiNi0.84Co0.11Mn0.05O2 delivers superior initial discharge capacity of 205 mAh g−1 (0.1 C), cycling stability of 89.5% (0.5 C) and rate capability of 165 mAh g−1 (2 C) compared to those of others. Therefore, we can conclude that the 1 wt. % molybdenum is an effective strategy for Ni-rich LiNi0.84Co0.11Mn0.05O2 cathode used in lithium ion batteries.

Optimality study of China’s crude oil imports through China Pakistan economic corridor using fuzzy TOPSIS and Cost-Benefit analysis

Abstract: With China's increasing role in the international political arena, it needs to focus on the diversification of its energy import routes China is heavily reliant on importing fossil fuels for its industrial and domestic needs, and its traditional trade routes are vulnerable to political, logistical, and security disadvantages China has been importing from American, African, and Middle Eastern countries through Myanmar's and Eastern China's seaports The China Pakistan Economic Corridor (CPEC), if utilized for the crude oil imports, can become a viable alternative, and may result in the reduction of the vulnerabilities faced by existing routes To assess this proposition, this study has focused on identifying China’s most optimal route for crude oil import through a multi-criteria decision making (MCDM) technique; Fuzzy-TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) Furthermore, to confirm the economic viability of the CPEC route, its Cost-Benefit Analysis (CBA) has also been performed Besides, Fuzzy TOPSIS has been used to identify the best transport mechanism from the CPEC’s seaports till Western China MCDM criteria in both cases include time, economic costs, energy consumption, environmental emissions, and security risks Results indicate that the maritime route, passing through Myanmar is the most optimal route, followed by the CPEC route Both these routes are prioritised over the traditional route which passes through Eastern China seaports