Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

The widespread acceptance and increase of the Internet and mobile technologies have revolutionized our existence. On the other hand, the world is witnessing and suffering due to technologically aided crime methods. These threats, including but not limited to hacking and intrusions and are the main concern for security experts. Nevertheless, the challenges facing effective intrusion detection methods continue closely associated with the researcher’s interests. This paper’s main contribution is to present a host-based intrusion detection system using a C4.5-based detector on top of the popular Consolidated Tree Construction (CTC) algorithm, which works efficiently in the presence of class-imbalanced data. An improved version of the random sampling mechanism called Supervised Relative Random Sampling (SRRS) has been proposed to generate a balanced sample from a high-class imbalanced dataset at the detector’s pre-processing stage. Moreover, an improved multi-class feature selection mechanism has been designed and developed as a filter component to generate the IDS datasets’ ideal outstanding features for efficient intrusion detection. The proposed IDS has been validated with state-of-the-art intrusion detection systems. The results show an accuracy of 99.96% and 99.95%, considering the NSL-KDD dataset and the CICIDS2017 dataset using 34 features.

Supervised learning and pattern recognition is a crucial area of research in information retrieval, knowledge engineering, image processing, medical imaging, and intrusion detection. Numerous algorithms have been designed to address such complex application domains. Despite an enormous array of supervised classifiers, researchers are yet to recognize a robust classification mechanism that accurately and quickly classifies the target dataset, especially in the field of intrusion detection systems (IDSs). Most of the existing literature considers the accuracy and false-positive rate for assessing the performance of classification algorithms. The absence of other performance measures, such as model build time, misclassification rate, and precision, should be considered the main limitation for classifier performance evaluation. This paper’s main contribution is to analyze the current literature status in the field of network intrusion detection, highlighting the number of classifiers used, dataset size, performance outputs, inferences, and research gaps. Therefore, fifty-four state-of-the-art classifiers of various different groups, i.e., Bayes, functions, lazy, rule-based, and decision tree, have been analyzed and explored in detail, considering the sixteen most popular performance measures. This research work aims to recognize a robust classifier, which is suitable for consideration as the base learner, while designing a host-based or network-based intrusion detection system. The NSLKDD, ISCXIDS2012, and CICIDS2017 datasets have been used for training and testing purposes. Furthermore, a widespread decision-making algorithm, referred to as Techniques for Order Preference by Similarity to the Ideal Solution (TOPSIS), allocated ranks to the classifiers based on observed performance reading on the concern datasets. The J48Consolidated provided the highest accuracy of 99.868%, a misclassification rate of 0.1319%, and a Kappa value of 0.998. Therefore, this classifier has been proposed as the ideal classifier for designing IDSs.

A compact textile ultra-wideband (UWB) antenna with an electrical dimension of 0.24λo × 0.24λo × 0.009λo with microstrip line feed at lower edge and a frequency of operation of 2.96 GHz is proposed for UWB application. The analytical investigation using circuit theory concepts and the cavity model of the antenna is presented to validate the design. The main contribution of this paper is to propose a wearable antenna with wide impedance bandwidth of 118.68 % (2.96–11.6 GHz) applicable for UWB range of 3.1 to 10.6 GHz. The results present a maximum gain of 5.47 dBi at 7.3 GHz frequency. Moreover, this antenna exhibits Omni and quasi-Omni radiation patterns at various frequencies (4 GHz, 7 GHz and 10 GHz) for short-distance communication. The cutting notch and slot on the patch, and its effect on the antenna impedance to increase performance through current distribution is also presented. The time-domain characteristic of the proposed antenna is also discussed for the analysis of the pulse distortion phenomena. A constant group delay less than 1 ns is obtained over the entire operating impedance bandwidth (2.96–11.6 GHz) of the textile antenna in both situations, i.e., side by side and front to front. Linear phase consideration is also presented for both situations, as well as configurations of reception and transmission. An assessment of the effects of bending and humidity has been demonstrated by placing the antenna on the human body. The specific absorption rate (SAR) value was tested to show the radiation effect on the human body, and it was found that its impact on the human body SAR value is 1.68 W/kg, which indicates the safer limit to avoid radiation effects. Therefore, the proposed method is promising for telemedicine and mobile health systems.

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