International Journal of Computational Intelligence and Applications

Fuzzy weighting exponent $m$ is an important parameter of fuzzy $c$-means (FCM), closely related to the performance of the algorithm. First, an improved fuzzy correlation degree was put forward to measure the relevance between the clusters, based on which a new cluster validity function was defined to evaluate the quality of the fuzzy partition. Then a self-adaptive FCM for the optimal value of $m$ was proposed with the aid of the global search ability of improved particle swarm algorithm to find out both the final clustering centroids and the optimal value of fuzzy weighting exponent automatically. The improved particle swarm algorithm updated the speed and the position based on dynamic inertia weight and learning factors, and introduced mutation of genetic algorithm to keep the diversity of the particles, preventing premature convergence. The experimental results showed that the proposed algorithm automatically calculated the optimal value of $m$ and meanwhile achieved better clustering results.

How to localize objects in images accurately and efficiently is a challenging problem in computer vision. In this paper, a novel top–down fine-grained salient object detection method based on deep-learned features is proposed, which can detect the same object in input image as the query image. The query image and its three subsample images are used as top–down cues to guide saliency detection. We ameliorate convolutional neural network (CNN) using the fast VGG network (VGG-f) pre-trained on ImageNet and re-trained on the Pascal VOC 2012 dataset. Experiment on the FiFA dataset demonstrates that proposed method can localize the saliency region and find the specific object (e.g., human face) as the query. Experiments on the David1 and Face1 sequences conclusively prove that the proposed algorithm is able to effectively deal with many challenging factors including illumination change, shape deformation, scale change and partial occlusion.

Twitter has changed the way people get information by allowing them to express their opinion and comments on the daily tweets. Unfortunately, due to the high popularity of Twitter, it has become very attractive to spammers. Unlike other types of spam, Twitter spam has become a serious issue in the last few years. The large number of users and the high amount of information being shared on Twitter play an important role in accelerating the spread of spam. In order to protect the users, Twitter and the research community have been developing different spam detection systems by applying different machine-learning techniques. However, a recent study showed that the current machine learning-based detection systems are not able to detect spam accurately because spam tweet characteristics vary over time. This issue is called “Twitter Spam Drift”. In this paper, a semi-supervised learning approach (SSLA) has been proposed to tackle this. The new approach uses the unlabeled data to learn the structure of the domain. Different experiments were performed on English and Arabic datasets to test and evaluate the proposed approach and the results show that the proposed SSLA can reduce the effect of Twitter spam drift and outperform the existing techniques.

Face recognition from low quality videos is one of the major challenges prevailing in video surveillance system. Several works have been contributed towards the face recognition, but have suffered due to the fact that the low quality videos have face part with low resolution. Also, using traditional feature extraction schemes make the recognition process to be tough. This paper introduces a novel feature descriptor and classification scheme for recognizing the face from low quality videos. Here, the video frames are sequentially provided to the Viola–Jones algorithm for detecting the face part, and the quality of the face part is improved by applying bi-cubic interpolation based super resolution scheme. Now, the features of enhanced video frame are extracted using proposed local direction feature descriptor, namely scattering wavelet-based local directional pattern (SW-LDP). Then, the extracted features are fed as the input to the actor critic neural network, where training is done using the newly developed fractional calculus based krill–lion (fractional KL) algorithm. The proposed fractional KL-ACNN algorithm is experimented using the standard FAMED database. From the analysis, it is evident that the proposed classifier achieved low FAR of 3.89%, and low FRR of 4.04%, and high accuracy value of 95%, respectively.

This paper proposes Interval type-2 Fuzzy sliding mode controller based on Backstepping (IT2FBSMC), to control the speed of a dual star induction machine (DSIM), in order to get a robust performance machine. An appropriate control strategy based on the coupling of three methods (Backstepping, sliding mode and type-2 Fuzzy controller) is used to build a robust controller used to approximate the discontinuous control eliminating the chattering phenomenon and guaranteeing the stability of the machine. Moreover, it forces the rotor angular speed to follow a desired reference signal. The simulation results obtained using Matlab/Simulink behavior are presented and discussed. The obtained results show that the controller can greatly alleviate the chattering effect and enhance the robustness of control systems with high accuracy.

The accurate analysis and controller design for high-order systems are important issues. In this paper, a design of controller is suggested to Single Machine Infinite Bus (SMIB) System in which its order is reduced by the proposed self-adaptive Firefly Algorithm (FA). First, the dynamic adaption to the dominant parameters of the standard FA is proposed for overcoming its disadvantages and improving its ability in searching the global optimum solution with application to reduce the model order of SMIB. The parameters which are proposed to self-adaption are both of the light absorption based on the mean distance of fireflies’ positions and the step setting based on the fitness information to the status of preceding firefly and the current fireflies. Second, proportional–integral–derivative (PID) controller is suggested to be designed by pole zero cancellation method via the step response characteristics of the reduced-order SMIB to be applied to its high order. The efficiency of the proposed methods is tested on high-order SMIB to get its corresponding low order and to control it. The experimental results prove the efficacy of self-adaptive FA compared to the standard FA and the methods in the literature in terms of step response characteristics using error indices. The proposed methods assure the stability of the reduced order and the ability of the designed controller to handle both high and low-order model.

The paper aims at establishing the output-to-input relationship of the real-life adult depression data using a neural network (NN) model. The said model has been developed to diagnose and detect the associated severity (grade) of the illness. An intelligent NN-based reverse model has been trained through batch mode and put to test on another set of real-life data. Reverse mapping of this model has been developed to isolate significantly contributing input components (factors) for any given case to expedite the preventive procedure for further deterioration as well as the start of treatment.

To solve the problems from using single-layer features in traditional water body identification models, such as the lack of local descriptors, large quantization errors, and the lack of semantic information descriptions, a multimodal model is proposed based on the different levels of feature knowledge. First, based on the multidescriptor hierarchical feature, the middle-level local feature extraction of the water body is achieved, and, combined with the convolutional neural network, the high-order global features of the water body are extracted. Then, the image features are hierarchically normalized, and multimodal RBM self-encoding is used for fusion to reduce the quantization error of each layer feature in the encoding process. Finally, the generative model of the Multimodal Model is used to expand the data and filter the multilayered features after fusion. In addition, the semantic information of a water body is further discovered by using the encoder and decoder of the discriminant model and is classified by employing SoftMax. The results show that compared with the traditional water body identification methods, the proposed method improves the recognition accuracy and image description ability.