Skin cancer occurrences increase exponentially worldwide due to the lack of awareness of significant populations and skin specialists. Medical imaging can help with early detection and more accurate diagnosis of skin cancer. The physicians usually follow the manual diagnosis method in their clinics but nonprofessional dermatologists sometimes affect the accuracy of the results. Thus, the automated system is required to assist physicians in diagnosing skin cancer at early stage precisely to decrease the mortality rate. This article presents an automatic skin lesions detection through a microscopic hybrid feature set and machine learning-based classification. The employment of deep features through AlexNet architecture with local optimaloriented pattern can accurately predict skin lesions. The proposed model is tested on two open-access datasets PAD-UFES-20 and MED-NODE comprising melanoma and nevus images. Experimental results on both datasets exhibit the efficacy of hybrid features with the help of machine learning. Finally, the proposed model achieved 94.7% accuracy using an ensemble classifier.
Research highlights• The deep features accurately predicted skin lesions through AlexNet architecture with local optimal-oriented pattern.• Proposed model is tested on two datasets PAD-UFES-20, MED-NODE comprising melanoma, nevus images and exhibited high accuracy.
Depression is a global prevalent ailment for possible mental illness or mental disorder globally. Recognizing depressed early signs is critical for evaluating and preventing mental illness. With the progress of machine learning, it is possible to make intelligent systems capable of detecting depressive symptoms using speech analysis. This study presents a hybrid model to identify and predict mental illness from Arabic speech analysis due to depression. The proposed hybrid model comprises convolutional neural network (CNN) and a support vector machine (SVM) to identify and predict mental disorders. Experiments are performed on the Arabic speech benchmark data set of 200 speeches. A total of 70% of data were reserved for training, while 30% of data were to test the proposed model. The hybrid model (CNN + SVM) attained a 90.0% and 91.60% accuracy rate to predict the depression from Arabic speech analysis for training and testing stages. To authenticate the results of a proposed hybrid model, recurrent neural network (RNN) and CNN are also applied to the same data set individually, and the results are compared with each other. The RNN achieved an 80.70% and 81.60% accuracy rate to predict depression while speaking in the training and testing stages. The CNN predicted the depression in the training and testing stages with 88.50% and 86.60% accuracy rates. Based on the analysis, the proposed hybrid model secured better prediction results than individual RNN and CNN models on the same data set. Furthermore, the suggested model had a lower FPR, FNR, and higher accuracy, AUC, sensitivity, and specificity rate than individual RNN, CNN model performance in predicting depression. Finally, the achieved findings will be helpful to classify depression while speaking Arabic/speech and will be beneficial for physicians, psychiatrists, and psychologists in the detection of depression.
Cloud computing helps organizations to dynamically increase the resource needs as and when needed, without the need to purchase them. Security is a basic concern in cloud computing, and threats can occur both internally and externally. Users can access the cloud infrastructure for software, operating system and network infrastructure provided by the Cloud Service Providers (CSP). Evaluating the user behavior in the cloud computing infrastructure is becoming more and more important for both Cloud Users (CSs) as well as Cloud Service Providers The CSPs must ensure the safety of users accessing the cloud. Since user authentication alone is not enough to ensure the safety of users, user behavior trust plays a critical role in ensuring the authenticity of the user as well as safety. In this paper, we present the importance of user behavior in modeling trust, associated evaluation principles and comparison between different trust models.
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