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Ultra wideband (UWB) systems are the most appropriate for high data rate wireless transmission with low power consumption. However, the antenna design for UWB has been a challenging task. Moreover, it is always desirable to have more freedom by designing different shape antennas with identical characteristics so that they can be used in either transmitter or receiver depending on other physical constraints such as area. To tackle these issues, in this paper, we have investigated a joint optimization of three different shape-printed monopole antennas, namely, printed square monopole antenna, printed circular monopole antenna and printed hexagonal monopole antenna, for UWB applications. More specifically, we have obtained the optimized geometrical parameters of these antennas by minimizing the mean-square-error for desired lower band edge frequency, quality factor, and bandwidth. The objective of joint optimization is to have identical frequency characteristics for the aforementioned three types of PMA which will give a freedom to interchangeably use them at either side, transmitting or receiving. Moreover, we employ particle swarm optimization (PSO) algorithm for our problem as it is well known in the literature that PSO performs well in electromagnetic and antenna applications. Simulation results are presented to show the performance of the proposed design.

Cyber security is one of the major concerns of today’s connected world. For all the platforms of today’s communication technology such as wired, wireless, local and remote access, the hackers are present to corrupt the system functionalities, circumvent the security measures and steal sensitive information. Amongst many techniques of hackers, port scanning and Distributed Denial of Service (DDoS) attacks are very common. In this paper, the benefits of machine learning are taken into consideration for classification of port scanning and DDoS attacks in a mix of normal and attack traffic. Different machine learning algorithms are trained and tested on a recently published benchmark dataset (CICIDS2017) to identify the best performing algorithms on the data which contains more recent vectors of port scanning and DDoS attacks. The classification results show that all the variants of discriminant analysis and Support Vector Machine (SVM) provide good testing accuracy i.e. more than 90%. According to a subjective rating criterion mentioned in this paper, 9 algorithms from a set of machine learning experiments receive the highest rating (good) as they provide more than 85% classification (testing) accuracy out of 22 total algorithms. This comparative analysis is further extended to observe training performance of machine learning models through k-fold cross validation, Area Under Curve (AUC) analysis of the Receiver Operating Characteristic (ROC) curves, and dimensionality reduction using the Principal Component Analysis (PCA). To the best of our knowledge, a comprehensive comparison of various machine learning algorithms on CICIDS2017 dataset is found to be deficient for port scanning and DDoS attacks while considering such recent features of attack.

|The dog is considered as an important companion animal. The dogs perform a significant role in different departments also. For example police assistance, murder cases and identification of addicted medicines at airports. These functions can be performed only by healthy dogs. The infertility adversely affects the health of female dogs. The infertile bitches are unable to produce healthy and live puppies. The fertility of bitch requires the normal estrus cycle and ovulation within the reproductive tract followed by the union of sperm. The developed embryo must implant in the endometrium of uterus till the completion of pregnancy period and successful parturition. There are many causes responsible for the infertility in female dog. The common categories of infertility are structural, physiological, neoplasm and infectious agents. The objective of this review was to highlight the emerging causes of infertility.

Due to the dynamic nature, chaotic time series are difficult predict. In conventional signal processing approaches signals are treated either in time or in space domain only. Spatio-temporal analysis of signal provides more advantages over conventional uni-dimensional approaches by harnessing the information from both the temporal and spatial domains. Herein, we propose an spatio-temporal extension of RBF neural networks for the prediction of chaotic time series. The proposed algorithm utilizes the concept of time-space orthogonality and separately deals with the temporal dynamics and spatial nonlinearity(complexity) of the chaotic series. The proposed RBF architecture is explored for the prediction of Mackey-Glass time series and results are compared with the standard RBF. The spatio-temporal RBF is shown to out perform the standard RBFNN by achieving significantly reduced estimation error.

Alcoholism is a widely affected disorder that leads to critical brain deficiencies such as emotional and behavioural impairments. One of the prominent sources to detect alcoholism is by analysing Electroencephalogram (EEG) signals. Previously, most of the works have focused on detecting alcoholism using various machine and deep learning algorithms. This paper has used a novel algorithm named Sliding Singular Spectrum Analysis (S-SSA) to decompose and de-noise the EEG signals. We have considered independent component analysis (ICA) to select the prominent alcoholic and non-alcoholic components from the preprocessed EEG data. Later, these components were used to train and test various machine learning models like SVM, KNN, ANN, GBoost, AdaBoost and XGBoost to classify alcoholic and nonalcoholic EEG signals. The sliding SSA-ICA algorithm helps in reducing the computational time and complexity of the machine learning models. To validate the performance of the ICA algorithm, we have compared the computational time and accuracy of ICA with its counterpart, like principal component analysis (PCA). The proposed algorithm is tested on a publicly available UCI alcoholic EEG dataset. To verify the performance of machine learning models, we have calculated various metrics like accuracy, precision, recall and F1 score. Our work reported the highest accuracy of 98.97% with the XGBoost classifier. The validation of the proposed method is done by comparing the classification metrics with the latest state-of-the-art works.

Background: Humans can deliver many emotions during a conversation. Facial expressions show information about emotions. Objectives: This study proposed a Machine Learning (ML) approach based on a statistical analysis of emotion recognition using facial expression through a digital image. Methodology: A total of 600 digital image datasets divided into 6 classes (Anger, Happy, Fear, Surprise, Sad, and Normal) was collected from publicly available Taiwan Facial Expression Images Database. In the first step, all images are converted into a gray level format and 4 Regions of Interest (ROIs) are created on each image, so the total image dataset gets divided in 2400 (600 x 4) sub-images. In the second step, 3 types of statistical features named texture, histogram, and binary feature are extracted from each ROIs. The third step is a statistical feature optimization using the best-first search algorithm. Lastly, an optimized statistical feature dataset is deployed on various ML classifiers. Results: The analysis part was divided into two phases: firstly boosting algorithms-based ML classifiers (named as LogitBoost, AdaboostM1, and Stacking) which obtained 94.11%, 92.15%, and 89.21% accuracy, respectively. Secondly, decision tree algorithms named J48, Random Forest, and Random Committee were obtained with 97.05%, 93.14%, and 92.15% accuracy, respectively. Conclusion: It was observed that decision tree based J48 classifiers gave 97.05% classification accuracy.

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