PCA Based Geometric Modeling for Automatic Face Detection

The Internet of Things (IoT) represents a paradigm shift in which the Internet is connected to real objects in a range of areas, including home automation, industrial processes, human health, and environmental monitoring. The global market for IoT devices is booming, and it is estimated that there will be 50 billion connected devices by the end of 2025. This explosion of IoT devices, which can be expanded more easily than desktop PCs, has led to an increase in cyber-attacks involving IoT devices. To address this issue, it is necessary to create novel approaches for identifying attacks launched by hacked IoT devices. Due to the possibility that these attacks would succeed, Intrusion Detection Systems (IDS) are required. IDS' feature selection stage is widely regarded as the most essential stage. This stage is extremely time-consuming and labor-intensive. However, numerous machine learning (ML) algorithms have been proposed to enhance this stage to boost an IDS's performance. These approaches, however, did not produce desirable results in terms of accuracy and detection rate (DR). In this paper, we propose a novel hybrid Autoencoder and Modified Particle Swarm Optimization (HAEMPSO) for feature selection and deep neural network (DNN) for classification. The PSO with modification of inertia weight was utilized to optimize the parameters of DNN. The experimental analysis was performed on two realistic UNSW-NB15 and BoT-IoT datasets that are suitable for IoT environment. The findings obtained by analyzing the proposed HAEMPSO against the Generic attack in the UNSW-NB15 dataset gave an accuracy of 98.8%, and a DR of 99.9%. While the benign class revealed an accuracy of 99.9% and DR of 99.7%. In the BoT-IoT dataset, the DDoS HTTP attack revealed an accuracy of 99.22% and DR of 97.79%. While the benign class gave an accuracy of 97.54% and DR of 97.92%. In comparison with the state-of-the-art machine learning schemes, our proposed HAEMPSO-DNN achieved a competitive feat in terms of DR and accuracy.

The Support Vector Machine (SVM) (Cortes and Vapnik, 1995; Vapnik, 1995; Burges, 1998) is intended to generate an optimal separating hyperplane by minimizing the generalization error without the assumption of class probabilities such as Bayesian classifier. The decision hyperplane of SVM is determined by the most informative data instances, called Support Vectors (SVs). In practice, these SVMs are a subset of the entire training data. By now, SVMs have been successfully applied in many applications, such as face detection, handwritten digit recognition, text classification, and data mining. Osuna et al. (1997) applied SVMs for face detection. Heisele et al. (2004) achieved high face detection rate by using 2nd degree SVM. They applied hierarchical classification and feature reduction methods to speed up face detection using SVMs. Feature extraction and reduction are two primary issues in feature selection that is essential in pattern classification. Whether it is for storage, searching, or classification, the way the data are represented can significantly influence performances. Feature extraction is a process of extracting more effective representation of objects from raw data to achieve high classification rates. For image data, many kinds of features have been used, such as raw pixel values, Principle Component Analysis (PCA), Independent Component Analysis (ICA), wavelet features, Gabor features, and gradient values. Feature reduction is a process of selecting a subset of features with preservation or improvement of classification rates. In general, it intends to speed up the classification process by keeping the most important class-relevant features.

Objectives: To present a model which can detect malicious attacks using the URL of the Social Communication Platform using the data imbalance machine learning algorithm. The main objective is to detect the attack and prevent it from happening. Methods: This study presents an efficient feature extraction and selection method addressing feature imbalance problems; and also presents an improved concept drift and machine learning-based classification. This paper extracts the URL of the undesired tweets, identifies them, and filters them for classification. Findings: The experiments have been conducted using the drifted twitter spam dataset. Our model DIA-XGBoost extracts the URL of the undesired tweets, identifies them, and filters them for classification. Further, the attack pattern varies with respect to time. Furthermore, the results show that our DIA-XGBoost attains higher accuracy performance by 1.254%, URL recall performance by 0.14%, and increased Fmeasure performance by 10% when compared with the existing ML techniques (Random Forest, K-Nearest Neighbour, XGBoost). Thus, the existing ML-based classification model achieves poor classification accuracy whereas our model solves this issue. Novelty: Various Machine Learning (ML) techniques have been applied for the classification of URL redirection attacks. However, the spam data generally exhibit feature imbalance. Further, the attack pattern varies with respect to time. Thus, the existing ML-based classification model achieves poor classification accuracy. Hence, our model solves the issue using the DIA-XGBoost algorithm, detects and prevents URL malicious attacks. Keywords: Data Imbalance; Feature Extraction; Concept Drift; URL; Machine Learning; URL Redirection Attack

Number of malware detection models has been proposed recently, which still poses major limitations in terms of detection rate. Hence, to overcome this, this paper introduces a new malware detection model with three stages: Feature Extraction, Feature selection and Classification. In feature extraction phase, the Term Frequency-Inverse Document Frequency (TF-IDF) and Information gain (IG) features are extracted. More importantly, the IG feature is subjected with the Holoentropy evaluation. Following the feature extraction phase feature selection is performed using Principle Component Analysis (PCA). Finally, to do the classification process, Deep Belief Network (DBN) is used with optimized activation function. To work out this optimization scenario, this paper intends to propose a new hybrid algorithm that combines the concept of Lion Algorithm (LA) and Glowworm Swarm Algorithm (GSO). The performance of proposed Lion Updated GSO (LU-GSO) is compared over other conventional models with respect to various evaluation measures and proves the betterments over others. Through the performance analysis, it was observed that the proposed model attains high accuracy, which is 10.21%, 10.04%, 9.18% and 6.42% better than LA, GSO, GWO and PSO, respectively.

The eternal enigma in prostatic biopsy access route.

In this paper, propose a method of face detection based on adaptive skin color and structure model for multi-pose color images in a complex background. First, a face skin color model is built, and an adaptive Gamma correction is used in the skin color segmentation, which improve the skin color model to adapt to different illumination and multi-poses of face. Then a triangular structure model is constructed specific to the face features. And finally use the structure model to locate the face precisely on the basis of face images of skin color. The experimental results show the approach raised on this paper can adapt to the face detection under the conditions of different illumination environment, various poses and complex background, and the algorithm is quick and effective.

A simple and efficient filter feature selection method via document-term matrix unitization

Network traffic in cloud computing is characterized by a large volume of data, with potential high dimensionality and high levels of redundancy. “Big data” not only retard the execution process of intrusion detection systems (IDS) but they can also lead to unsatisfactory classification accuracy. The efficient correlation-based feature selection (ECOFS) approach proposed in this paper, can handle linearly and nonlinearly dependent data and it can eliminate redundant and irrelevant features. Its effectiveness has been evaluated through its employment in an intrusion detection system. A Libsvm-IDS has been built to operate using the features selected by the proposed ECOFS algorithm. The performance of the hybrid Libsvm-IDS + ECOFS approach has been evaluated using two well-known intrusion detection evaluation datasets, namely the KDD Cup 99 and the NSL-KDD. The evaluation results show that our ECOFS algorithm selects the smallest number of features, resulting in the lowest computational cost for the Libsvm-IDS, without any performance compromise. In fact, our algorithm has achieved higher accuracy compared with two well established methods.

An efficient wavelet-based feature selection (FS) method is proposed in this paper for subject recognition using ground reaction force measurements. Our approach relies on a local fuzzy evaluation measure with respect to patterns that reveal the adequacy of data coverage for each feature. Furthermore, FS is driven by a fuzzy complementary criterion (FuzCoC) which assures that those features are iteratively introduced, providing the maximum additional contribution with regard to the information content given by the previously selected features. On the basis of the principles of FuzCoC, we develop two novel techniques. At Stage 1, wavelet packet (WP) decomposition of gaits is accomplished to obtain a set of discriminating frequency sub-bands. A computationally simple FS method is then applied at Stage 2, providing a compact set of powerful and complementary features, from WP coefficients. The quality of our approach is validated via comparative analysis against existing methods on gait recognition.

Recent advances and emerging challenges of feature selection in the context of big data

Face detection is a key problem in human-computer interaction. In this paper, we present an algorithm for rotation invariant face detection in color images of cluttered scenes. It is a hierarchical approach, which combines a skin color model, a neural network, and an upright face detector. Firstly, the skin color model is used to process the color image to segment the face-like regions from the background. Secondly, the neural network computing and an operation for locating irises are performed to acquire rotation angle of each input window in the face-like regions. Finally, we provide an upright face detector to determine whether or not the rotated window is a face. Those techniques are integrated into a face detection system. The experiments show that the algorithm is robust to different face sizes and various lighting conditions.

Depression is most common mental disorder that is affecting approximately 280 million individuals in the world. The stigma and lack of acceptance and awareness is still influencing people and that is making its diagnosis challenging for the medical practitioners. For tackling this situation technologies are being used in various domains. Speech processing is one such domain that is being used for detection of depression. In that, detection severity of depression with high accuracy remains an active area of research. The work presented in the paper provides an efficient method for feature extraction by spectrogram based CNN, feature selection by a two-level approach of ANOVA and ABC optimizer and multi-level classification by SVM (linear). This hybrid framework provides classification accuracy of 80% which validates its potential for application in real-world scenarios.

The face verification systems gained significant attention in the last few years due to the increased security concern in public and private places. Face detection is the most important and initial stage in the automatic face verification system. It helps to determine the existence of faces in an image and return the position and location of the face. The face verification system's accuracy depends on face detection. The human faces are not always frontal and have many variations, therefore, face detection is challenging in unconstrained scenarios. One main challenge of face detection is occlusion. The proposed work is an attempt to illustrate the cognitive informatics approach using machine learning and present an occluded face detection method. The proposed method uses Adaboost[1] machine learning approach. The Viola-Jones[2] algorithm along with free rectangular features[3] has been adopted in the proposed approach in order to detect faces. the machine learning methods require two operation namely training and testing. Two cascade classifiers are used in which one is trained on holistic faces and the second is trained on half occluded faces; both of the classifiers are used in parallel to work in unconfined scene. Additionally, for improvement the correctness and adeptness of the system, the skin color models are applied which are used for removing of the false positive detection. The experiment has been performed on FDDB[4] dataset. The results shows that the proposed method achieve desirable results in the detection of half occluded faces.

For face detection and tracking in a video sequence, many systems are now applying a technique to extract skin-color areas of faces. In these systems, the skin-color model should be built beforehand. However the skin-color changes when the lighting condition varies, when an input video camera is changed, and when the white-balance of the camera is readjusted, etc. We describe a method to a build a skin-color model for operation while a face detection system is running. When it has no skin-color model, it uses the differences between adjacent frames to extract moving objects. Once the system recognizes that the moving object is a face, it analyses the color histograms of the moving area and extract skin-color distribution. We applied this method to our face detection and tracking system. Experiments showed good results.

This paper proposed a method to enhance the performance of face detection and recognition systems. Our method basically consists of two main parts: firstly, we detect faces and then recognize the detected faces. In detection step we used the skin color segmentation with Gaussian skin color model combined with AdaBoost algorithm, which is fast and also more accurate compared to the other known methods. Also, we use a series of morphological operators to improve the face detection performance. Recognition part consists of four steps: Gabor features extraction, dimension reduction using PCA, feature selection using LDA, and SVM based classification. Combination of PCA and LDA is used for improving the capability of LDA when a few samples of images are available. We test the system on the face databases. Experimental results show that system is robust enough to detect faces in different lighting conditions, scales, poses, and skin colors from various races. Also, system is able to recognize face with less misclassification compared to the previous methods.