Abstract Automated analysis of skin lesions in dermoscopy images has gained much attention due to its medical importance in the early detection of melanoma. Detection of lesions has become a challenge due to the strong visual similarity between benign and malignant skin lesions. In this research, a customized deep convolutional neural network (CNN) architecture has been designed to discriminate between benign and malignant lesions. The model is designed carefully with lesser convolution layers, fewer filters, and parameters to achieve better classification performance compared to pretrained VGG16, ResNet50, InceptionV3 models and, ensures state‐of‐the‐art performance. The proposed model is composed of nine trainable layers: eight convolution layers and one fully connected layer. The suggested framework is extensively evaluated on the benchmark ISIC 2016 challenge dataset. The effect of different input transformations over the dataset has been studied. For fair comparison, standard deep learning models such as VGG16, ResNet50, and InceptionV3 have been used for lesion classification using transfer learning approach. The memory requirement of the proposed model is reduced by 388, 68, and 63 times and FLOPs needed are lowered by 95%, 85%, and 84% compared to VGG16‐TrL, ResNet50‐TrL, and InceptionV3‐TrL, respectively. Results show that class balancing with external images improves classification performance.

Abstract Traffic sign recognition (TSR) has been a rising and lucrative field for researchers during the last decades. The high improvement of ADAS (autonomic driving autonomous system) has led researchers worldwide to concentrate on the development of TSR systems. As such, a novel automated multiclass TSR system is proposed. The architecture uses wavelet descriptors to extract the high information density and the traffic signs' edges and curves. The LL band image is directly fed into the classifier to avoid normalization. Three classifiers, CNN, CNN ensemble, and LSTM, are deployed for recognition. The architecture is implemented on IRSDBv1.0, the first available Indian traffic sign database. The architecture is also implemented on the standard traffic sign database GTSRB to investigate its effectiveness. An efficiency of 71.57% and 96.76% are recorded on IRSDBv1.0, and GTSRB, respectively. A list of comparative results is also provided to prove the competence of the architecture. The reasons behind the difference in the achieved accuracy are also discussed.

Android OS being the popular choice of majority users also faces the constant risk of breach of confidentiality, integrity and availability (CIA). Effective mitigation efforts needs to identified in order to protect and uphold the CIA triad model, within the android ecosystem. In this paper, we propose a novel method of android malware classification using Object-Oriented Software Metrics and machine learning algorithms. First, android apps are decompiled and Object-Oriented Metrics are obtained. VirusTotal service is used to tag an app either as malware or benign. Object-Oriented Metrics and malware tag are clubbed together into a dataset. Eighty different machine-learned models are trained over five thousand seven hundred and seventy four android apps. We evaluate the performance and stability of these models using it's malware classification accuracy and AUC (area under ROC curve) values. Our method yields an accuracy and AUC of 99.83% and 1.0 respectively.