Zeeshan Khawar Malik

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In this paper, we aim to develop novel learning approaches for extracting invariant features from time series. Specifically, we implement an existing method of solving the generalized eigenproblem and use this to firstly implement the biologically inspired technique of slow feature analysis (SFA) originally developed by Wiskott and Sejnowski (Neural Comput(More)
As demonstrated earlier, the learning accuracy of the single-layer-feedforward-network (SLFN) is generally far lower than expected, which has been a major bottleneck for many applications. In fact, for some large real problems, it is accepted that after tremendous learning time (within finite epochs), the network output error of SLFN will stop or reduce(More)
Thoracic splenosis (TS) is autoimplantation of ectopic splenic tissue in the thoracic cavity that occurs following splenic injury. Most cases of TS are asymptomatic and are diagnosed during the course of an evaluation of incidentally discovered pulmonary lesions. Some cases may be difficult to diagnose, especially if features suggesting TS are not(More)
This paper presents a novel online version of laplacian eigenmap termed as generalized incremental laplacian eigenmap (GENILE), one of the most popular manifold-based dimensionality reduction technique performed by solving the generalized eigenvalue problem. We have used swiss roll and s-curve dataset, the most popular datasets used for manifold-based(More)
BACKGROUND Intra-abdominal filarial infection is extremely rare. METHODS Case report and review of the literature. RESULTS A 63-year-old man presented with a painful swelling in the peri-umbilical area. An urgent laparotomy was performed for suspected strangulated peri-umbilical hernia. A live intraperitoneal worm was retrieved during the repair of the(More)
In this paper, we present a novel architecture and learning algorithm for a multilayered echo state machine (ML-ESM). Traditional echo state networks (ESNs) refer to a particular type of reservoir computing (RC) architecture. They constitute an effective approach to recurrent neural network (RNN) training, with the (RNN-based) reservoir generated randomly,(More)
Wikipedia’s category graph is a network of 300,000 interconnected category labels, and can be a powerful resource for many classification tasks. However, its size and the lack of order can make it difficult to navigate. In this paper, we present a new algorithm to efficiently exploit this graph and accurately rank classification labels given user-specified(More)
In this paper, we consider the challenging problem of finding shared information in multiple data streams simultaneously. The standard statistical method for doing this is the well-known canonical correlation analysis (CCA) approach. We begin by developing an online version of the CCA and apply it to reservoirs of an echo state network in order to capture(More)
Faecal Calprotectin (FC) is a surrogate marker for intestinal inflammation, termed Inflammatory Bowel Disease (IBD), but not for cancer. In this retrospective study of 804 patients, an enhanced benchmark predictive model for analyzing FC is developed, based on a novel state-of-the-art Echo State Network (ESN), an advanced dynamic recurrent neural network(More)
Deep Neural Networks, and specifically fullyconnected convolutional neural networks are achieving remarkable results across a wide variety of domains. They have been trained to achieve state-of-the-art performance when applied to problems such as speech recognition, image classification, natural language processing and bioinformatics. Most of these deep(More)