Fastest-growing source prediction of US electricity production based on a novel hybrid model using wavelet transform

People communicate their views, arguments and emotions about their everyday life on social media (SM) platforms (e.g. Twitter and Facebook). Twitter stands as an international micro-blogging servic...

A gradient boosted decision tree-based sentiment classification of twitter data

To understand daily events accurately, adaptive pose estimation (APE) systems require a robust context-aware model and optimal feature selection methods. In this paper, we propose a novel gait event detection (GED) system that consists of saliency silhouette detection, a robust body parts model and a 2D stick-model followed by a hierarchical optimization algorithm. Furthermore, the most prominent context-aware features such as energy, 0–180° intensity and distinct moveable features are proposed by focusing on invariant and localized characteristics of human postures in different event classes. Finally, we apply Grey Wolf optimization and a genetic algorithm to discriminate complex postures and to provide appropriate labels to each event. In order to evaluate the performance of proposed GED, two public benchmark datasets, UCF101 and YouTube, are examined via the n-fold cross validation method. For the two benchmark datasets, our proposed method detects the human body key points with 82.4% and 83.2% accuracy respectively. Also, it extracts the context-aware features and finally recognizes the gait events with 82.6% and 85.0% accuracy, respectively. Compared with other well-known statistical and state-of-the-art methods, our proposed method outperforms other similarly tasked methods in terms of posture detection and recognition accuracy.

Adaptive Pose Estimation for Gait Event Detection Using Context-Aware Model and Hierarchical Optimization

Routing and hotspot are considered among the key issues in Wireless Sensor Networks (WSNs). Several routing protocols have been proposed; yet they suffer from the lack of fault tolerance, the uneven load balancing and the local optimal solution issues. To fix these issues, we propose in this paper an algorithm named Energy-efficient Cluster-based Routing Protocol using Unequal Clustering and improved Ant Colony Optimization (ACO) techniques (ECRP-UCA). ECRP-UCA divides the network into unequal clusters based on residual energy, distance to the sink, number of neighbor nodes, and a new parameter named number of backward relay nodes in previous round to properly balance the load among Cluster Heads (CHs). We propose also a batch-based clustering method that allows the network to function several rounds without requiring control overhead for its configuration. Additionally, we devise an improved ACO based routing technique for efficient and reliable inter-cluster routing from CHs to the sink. In this algorithm, the heuristic function is formulated considering the energy of next hop sensor node, distance from the current sensor node to the next sensor node and the latter to the destination, and the new parameter Number of Preferred Probable Relay Nodes (NPPRN). By using NPPRN, the ants have the probability to follow other paths, which improves the algorithm’s ability to obtain the optimal global solution and protect the algorithm from converging quickly into the optimal local solution. Moreover, the ant searching direction is improved. The proposed routing protocol is intensively experimented and compared with recent and relevant existing protocols. The simulation results show that the proposed ECRP-UCA outperforms these protocols in terms of various interesting metrics.

An energy-efficient cluster-based routing protocol using unequal clustering and improved ACO techniques for WSNs

Learning features from covariance matrix of gabor wavelet for face recognition under adverse conditions

In this paper, we propose a quadtree based approach to capture the spatial information of medical images for explaining nonlinear SVM prediction. In medical image classification, interpretability becomes important to understand why the adopted model works. Explaining an SVM prediction is difficult due to implicit mapping done in kernel classification is uninformative about the position of data points in the feature space and the nature of the separating hyperplane in the original space. The proposed method finds ROIs which contain the discriminative regions behind the prediction. Localization of the discriminative region in small boxes can help in interpreting the prediction by SVM. Quadtree decomposition is applied recursively before applying SVMs on sub images and model identified ROIs are highlighted. Pictorial results of experiments on various medical image datasets prove the effectiveness of this approach. We validate the correctness of our method by applying occlusion methods.

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Interpreting SVM for medical images using Quadtree.

Machine learning algorithms are inherently not interpretable, and this poses a problem in risk-averse applications of machine learning. Mammographic images are widely used tool to predict breast cancer. Various machine learning algorithms like SVM, RBFNN are used to detect the mass in the mammographic images and classify for cancer, but the classification by SVMs are not intuitive. Our aim is to counter this problem by employing a novel method of using multiple SVMs to elucidate the area affected by cancer. We also color-code the patches for further clarification.

Abhishek Verma

Papers

Interpreting SVM for medical images using Quadtree.

An Interpretable SVM Based Model for Cancer Prediction in Mammograms