National University of Computer and Emerging Sciences

About: National University of Computer and Emerging Sciences is a education organization based out in Islamabad, Pakistan. It is known for research contribution in the topics: Computer science & The Internet. The organization has 1506 authors who have published 2438 publications receiving 26786 citations.

Fake reviews classification using deep learning ensemble of shallow convolutions

Abstract: Online reviews have a decisive impact on consumers’ purchasing decisions This opens the doors for spammers and scammers to post fake reviews for promoting non-existent products or undermine competitor products to affect social behavior Thus, the identification of reviews as fake and real has become ever more important Traditional approaches for text classification use a bag-of-words model to represent text which causes sparsity and word representations learnt from neural networks with limited ability to handle unknown words In this paper, we propose a technique based on three different models trained on the idea of a multi-view learning technique and create an ensemble of all models by employing an aggregation technique for generating final predictions The core idea of our methodology is to extract rich information from the text of reviews by combining bag-of-n-grams and parallel convolution neural networks(CNNs) By using an n-gram embedding layer with small kernel sizes we can use local context with the same computation power as required to train deep and complex CNNs Our CNN-based architecture consumes n-gram embeddings as input and uses the parallel convolutional blocks to extract richer feature representations from text Our approach for the detection of fake reviews also combines textual linguistic features and non-textual features related to reviewer behavior We evaluate our approach on publically available Yelp Filtered Dataset and achieve F1 scores of up to 92% for classifying fake reviews

Conventional and Metaheuristic Optimization Algorithms for Solving Short Term Hydrothermal Scheduling Problem: A Review

Abstract: Short term hydrothermal scheduling (STHTS) is a non-linear, multi-modal and very complex constrained optimization problem which has been solved using several conventional and modern metaheuristic optimization algorithms A number of research articles have been published addressing STHTS using different techniques This article presents a comprehensive review of research published for solving the STHTS problem in the last four decades

A load balance multi-scheduling model for OpenCL kernel tasks in an integrated cluster

Abstract: Nowadays, embedded systems are comprised of heterogeneous multi-core architectures, i.e., CPUs and GPUs. If the application is mapped to an appropriate processing core, then these architectures provide many performance benefits to applications. Typically, programmers map sequential applications to CPU and parallel applications to GPU. The task mapping becomes challenging because of the usage of evolving and complex CPU- and GPU-based architectures. This paper presents an approach to map the OpenCL application to heterogeneous multi-core architecture by determining the application suitability and processing capability. The classification is achieved by developing a machine learning-based device suitability classifier that predicts which processor has the highest computational compatibility to run OpenCL applications. In this paper, 20 distinct features are proposed that are extracted by using the developed LLVM-based static analyzer. In order to select the best subset of features, feature selection is performed by using both correlation analysis and the feature importance method. For the class imbalance problem, we use and compare synthetic minority over-sampling method with and without feature selection. Instead of hand-tuning the machine learning classifier, we use the tree-based pipeline optimization method to select the best classifier and its hyper-parameter. We then compare the optimized selected method with traditional algorithms, i.e., random forest, decision tree, Naive Bayes and KNN. We apply our novel approach on extensively used OpenCL benchmarks, i.e., AMD and Polybench. The dataset contains 653 training and 277 testing applications. We test the classification results using four performance metrics, i.e., F-measure, precision, recall and $$R^2$$ . The optimized and reduced feature subset model achieved a high F-measure of 0.91 and $$R^2$$ of 0.76. The proposed framework automatically distributes the workload based on the application requirement and processor compatibility.

Improved Opposition-Based PSO for Feedforward Neural Network Training

Abstract: In this study we present an improved opposition- based PSO and apply it to feedforward neural network training. The improved opposition-based PSO utilizes opposition-based initialization, opposition-based generation jumping and opposition-based velocity calculation. The opposition-based PSO is first tested on some unimodal and multimodal problems and its performance is compared with standard PSO. We then test the performance of the improved opposition-based PSO for training feedforward neural network and also present a comparison with standard PSO.