The increasing use of social media and information sharing has given major benefits to humanity. However, this has also given rise to a variety of challenges including the spreading and sharing of hate speech messages. Thus, to solve this emerging issue in social media sites, recent studies employed a variety of feature engineering techniques and machine learning algorithms to automatically detect the hate speech messages on different datasets. However, to the best of our knowledge, there is no study to compare the variety of feature engineering techniques and machine learning algorithms to evaluate which feature engineering technique and machine learning algorithm outperform on a standard publicly available dataset. Hence, the aim of this paper is to compare the performance of three feature engineering techniques and eight machine learning algorithms to evaluate their performance on a publicly available dataset having three distinct classes. The experimental results showed that the bigram features when used with the support vector machine algorithm best performed with 79% off overall accuracy. Our study holds practical implication and can be used as a baseline study in the area of detecting automatic hate speech messages. Moreover, the output of different comparisons will be used as state-of-art techniques to compare future researches for existing automated text classification techniques.

A new impulsive noise removal filter, adaptive dynamically weighted median filter (ADWMF), is proposed. A popular method for removing impulsive noise is a median filter whereas the weighted median filter and center weighted median filter were also investigated. ADWMF is based on weighted median filter. In ADWMF, instead of fixed weights, weightages of the filter are dynamically assigned with the results of noise detection. A simple and efficient noise detection method is also used to detect noise candidates and dynamically assign zero or small weights to the noise candidates in the window. This paper proposes an adaptive method which increases the window size according to the amounts of impulsive noise. Simulation results show that the AMWMF works better for both images with low and high density of impulsive noise than existing methods work.

With optimized design and modern brushless operation, wound rotor synchronous machines are resurrecting as a strong contender in many applications presently dominated by permanent magnet machines. Considering the notion, this paper introduces a novel brushless synchronous machine topology that utilizes sub-harmonic magnetomotive force (MMF) of its stator winding for desirable brushless operation. It is significant to state that the sub-harmonic MMF component that is used in this novel topology is one-fourth of the fundamental MMF component, whereas, in previous practices, it was half. Moreover, the stator of the machine uses a new winding arrangement of two sets of balanced three-phase windings wound in two layers to produce the fundamental and the sub-harmonic MMF. To achieve the brushless excitation, the rotor utilizes an additional winding that is used to induce the electromotive force (EMF) by the sub-harmonic MMF component of the stator. This novel two-layer stator winding topology permits the utilization of maximum allowable space in the stator to house conductors in all of its 48 slots, which was not the case in previous papers. To validate the performance, and feasibility, an 8-pole 48-slot brushless wound rotor synchronous motor is designed, and a 2-D finite element analysis simulation is conducted, where the topology shows immense potential in terms of better torque performance.

This paper presents an image interpolation method that provides superior performance with low complexity. Applying a simple linear method achieves time-efficient interpolation, but often produces artifacts; however, applying other complex methods reduce unwanted artifacts at the cost of high computation time. The proposed interpolation scheme is based on the improvement of an algorithm called “Edge Slope Tracing (EST)„ that predicts the slope based on the information of the adjacent slopes. Predicted slopes are used in slope-based line averaging to perform the interpolation. Slope-based line averaging is followed by post-processing of an interpolated image using two-way interpolation and thin edge correction to avoid the production of unwanted artifacts at the cost of low complexity. The proposed algorithm is very simple and simulation results indicate that it provides better or nearly equal results compared to conventional state-of-the-art algorithms and that it also reduces complexity to a great extent compared to conventional methods.

This paper introduces an image interpolation method that provides performance superior to that of the state-of-the-art algorithms. The simple linear method, if used for interpolation, provides interpolation at the cost of blurring, jagging, and other artifacts; however, applying complex methods provides better interpolation results, but sometimes they fail to preserve some specific edge patterns or results in oversmoothing of the edges due to postprocessing of the initial interpolation process. The proposed method uses a new gradient-based approach that makes an intelligent decision based on the edge direction using the edge map and gradient map of an image and interpolates unknown pixels in the predicted direction using known intensity pixels. The input image is subjected to the efficient hysteresis thresholding-based edge map calculation, followed by interpolation of low-resolution edge map to obtain a high-resolution edge map. Edge map interpolation is followed by classification of unknown pixels into obvious edges, uniform regions, and transitional edges using the decision support system. Coefficient-based interpolation that involves gradient coefficient and distance coefficient is applied to obvious edge pixels in the high-resolution image, whereas transitional edges in the neighborhood of an obvious edge are interpolated in the same direction to provide uniform interpolation. Simple line averaging is applied to pixels that are not detected as an edge to decrease the complexity of the proposed method. Applying line averaging to smooth pixels helps to control the complexity of the algorithm, whereas applying gradient-based interpolation preserves edges and hence results in better performance at reasonable complexity.

Data storage is always an issue, especially for video data from CCTV cameras that require huge amounts of storage. Moreover, monitoring past events is a laborious task. This paper proposes a motion detection method that requires fewer calculations and reduces the required data storage up to 70%, as it stores only the informative frames, enabling the security personnel to retrieve the required information more quickly. The proposed method utilized a histogram-based adaptive threshold for motion detection, and therefore it can work in variable luminance conditions. The proposed method can be applied to streamed frames of any CCTV camera to efficiently store and retrieve informative frames.