Syed Moshfeq Salaken

A deep-structural medical image classification for a Radon-based image retrieval

Content-based image retrieval is an effective and efficient technique to retrieve images from a big dataset with similar images. To have a robust retrieval system, a proper and accurate classification scheme is required to categorise the information of shape, texture, and colours. In this paper, a deep convolutional neural network is proposed to classify the information of radiology images. Deep networks need millions of data, but the lack of availability of balanced large datasets in medical domain motivates us to trust on even the second prediction category rather than just the best one. Hence the best predicted categories are considered for a query test, followed by a similarity-based search technique. This results in a proper classification performance. Moreover, as Radon transformation is famous in medical domain, this conversion technique is utilized for a similarity-based search scheme, after measuring by a k-nearest neighbours algorithm. The experimental results and comparison show that this strategy not only improve the performance, but also save the computational costs.

Extreme learning machine based transfer learning algorithms: A survey

Succinctly and concisely summarizes the current works in extreme learning based transfer learning tasks.Works as a starting point to find future opportunities in this domain.Provides recommendation for future experiments. Extreme learning machine (ELM) has been increasingly popular in the field of transfer learning (TL) due to its simplicity, training speed and ease of use in online sequential learning process. This paper critically examines transfer learning algorithms formulated with ELM technique and provides state of the art knowledge to expedite the learning process ELM based TL algorithms. As this article discusses available ELM based TL algorithm in detail, it provides a holistic overview of current literature, serves as a starting point for new researchers in ELM based TL algorithms and facilitates identification of future research direction in concise manner.

Modification on enhanced Karnik-Mendel algorithm

New initialization proposed for enhanced Karnik-Mendel type reduction algorithm.Modification is proposed for right switch point initialization only.Proposed change is supported by convergence speed, iteration savings & time saving.Control surface remains same with the modified algorithm. Karnik-Mendel(KM) algorithm and its enhancements are among the most popular type reduction algorithms in the literature. Enhanced KM (EKM) algorithm is computationally fast and can quickly locate left and right switch points in just a few iteration. This paper proposes a subtle yet very effective modification to EKM algorithm to further improve its computational requirement. The modification relates to how the initial right switch point is determined. Comprehensive simulation results for different cases and scenarios provide the statistical proof for the validity of conclusions drawn on the superiority of the proposed initialization compared to initialization used in the original EKM algorithm. The superiority is quantitatively measured in the number of saved iterations and convergence speed.

Multiclass EEG data classification using fuzzy systems

This paper presents an approach to analysis of multiclass EEG data obtained from the brain computer interface (BCI) applications. The proposed approach comprises two stages including feature extraction using the common spatial pattern (CSP) and classification using fuzzy logic systems (FLS). CSP is used to extract significant features that are then fed into FLS as inputs for classification. The metaheuristic population-based particle swarm optimization method is used to train parameters of the FLS. The multiclass motor imagery dataset IIa from the BCI competition IV is used for experiments to highlight the superiority of the proposed approach against competing methods, which include linear discriminant analysis, naïve bayes, k-nearest neighbour, ensemble learning AdaBoost and support vector machine. Results from experiments show the great accuracy of the combination of CSP and FLS. Therefore, the proposed approach can be implemented effectively in the practical BCI systems, which would be helpful for people with impairments and rehabilitation.

Prediction interval-based neural network controller for nonlinear processes

Prediction interval (PI) has been extensively used to predict the forecasts for nonlinear systems as PI-based forecast is superior over point-forecast to quantify the uncertainties and disturbances associated with the real processes. In addition, PIs bear more information than point-forecasts, such as forecast accuracy. The aim of this paper is to integrate the concept of informative PIs in the control applications to improve the tracking performance of the nonlinear controllers. In the present work, a PI-based controller (PIC) is proposed to control the nonlinear processes. Neural network (NN) inverse model is used as a controller in the proposed method. Firstly, a PI-based model is developed to construct PIs for every sample or time instance. The PIs are then fed to the NN inverse model along with other effective process inputs and outputs. The PI-based NN inverse model predicts the plant input to get the desired plant output. The performance of the proposed PIC controller is examined for a nonlinear process. Simulation results indicate that the tracking performance of the PIC is highly acceptable and better than the traditional NN inverse model-based controller.

Linear approximation of Karnik-Mendel type reduction algorithm

Karnik-Mendel (KM) algorithm is the most used and researched type reduction (TR) algorithm in literature. This algorithm is iterative in nature and despite consistent long term effort, no general closed form formula has been found to replace this computationally expensive algorithm. In this research work, we demonstrate that the outcome of KM algorithm can be approximated by simple linear regression techniques. Since most of the applications will have a fixed range of inputs with small scale variations, it is possible to handle those complexities in design phase and build a fuzzy logic system (FLS) with low run time computational burden. This objective can be well served by the application of regression techniques. This work presents an overview of feasibility of regression techniques for design of data-driven type reducers while keeping the uncertainty bound in FLS intact Simulation results demonstrates the approximation error is less than 2%. Thus our work preserve the essence of Karnik-Mendel algorithm and serves the requirement of low computational complexities.

Forecasting Bike Sharing Demand Using Fuzzy Inference Mechanism

Forecasting bike sharing demand is of paramount importance for management of fleet in city level. Rapidly changing demand in this service is due to a number of factors including workday, weekend, holiday and weather condition. These nonlinear dependencies make the prediction a difficult task. This work shows that type-1 and type-2 fuzzy inference-based prediction mechanisms can capture this highly variable trend with good accuracy. Wang-Mendel rule generation method is utilized to generate rulebase and then only current information like date related information and weather condition is used to forecast bike share demand at any given point in future. Simulation results reveal that fuzzy inference predictors can potentially outperform traditional feedforward neural network in terms of prediction accuracy.

Effect of different initializations on EKM algorithm

As an integral part of interval type-2 fuzzy logic system (IT2FLS), type reduction (TR) plays a vital role in determining the performance of IT2FLS. Out of many type reduction algorithms, only Karnik-Mendel type TR algorithms capture the essence of interval type-2 fuzzy sets in type reduction. Enhanced Karnik-Mendel (EKM) algorithm is the most commonly used TR algorithm. In this work, we propose three new initializations for EKM algorithm. It is shown they are performing better than EKM and one of the proposed initializations significantly outperforms others. The performance gain can be upto 40% as per comprehensive simulation results demonstrated in this paper. Our findings are justified by computational time savings and iteration requirement for switch point search.

Seeded transfer learning for regression problems with deep learning

Abstract The difference in data distributions among related, but different domains is a long standing problem for knowledge adaptation. A new method to transform the source domain knowledge to fit the target domain is proposed in this work. The proposed method uses deep learning method and limited number of samples from target domain to transform the source domain dataset. It treats the limited samples of target domain as seeds for initiating the transfer of source knowledge. Comprehensive experiments are conducted using different computational intelligence models and different datasets. Obtained results reveal that prediction models trained using the proposed method demonstrate the best performance in comparison with the same models trained with only source knowledge or deep learned features. Experiments show that models trained using proposed method have outperformed the baseline methods by at least 50% in 14 experiments out of a total of 18.

A collision avoidance system with fuzzy danger level detection

Collision avoidance is an essential component in advanced driving assistance systems, as it ensures the safety of the vehicle in near crash or crash scenarios. In this study, a collision avoidance system for lane change events is proposed which plans the trajectory based on the level of danger. The danger level is computed by a fuzzy inference system developed with naturalistic driving data to better capture the real-world factors, which may cause an accident. In addition, a fault determination classifier is introduced in order to determine the responsible driver in a near crash event. This system is evaluated on simulated naturalistic near crash events and the results demonstrate good performance of the proposed system.