Machine learning (ML) can enhance a dermatologist’s work, from diagnosis to customized care. The development of ML algorithms in dermatology has been supported lately regarding links to digital data processing (e.g., electronic medical records, Image Archives, omics), quicker computing and cheaper data storage. This article describes the fundamentals of ML-based implementations, as well as future limits and concerns for the production of skin cancer detection and classification systems. We also explored five fields of dermatology using deep learning applications: (1) the classification of diseases by clinical photos, (2) der moto pathology visual classification of cancer, and (3) the measurement of skin diseases by smartphone applications and personal tracking systems. This analysis aims to provide dermatologists with a guide that helps demystify the basics of ML and its different applications to identify their possible challenges correctly. This paper surveyed studies on skin cancer detection using deep learning to assess the features and advantages of other techniques. Moreover, this paper also defined the basic requirements for creating a skin cancer detection application, which revolves around two main issues: the full segmentation image and the tracking of the lesion on the skin using deep learning. Most of the techniques found in this survey address these two problems. Some of the methods also categorize the type of cancer too.

Ball screw electro-mechanical actuators are commonly found in high precision motion control applications including aerospace systems as well as automated setups for industries. These actuators perform flight / application critical job and ball screw drives are responsible to provide precise linear motion while carrying thrust loading. A failure in ball screw drive may disturb positioning accuracy of overall system. At present, few techniques are available to monitor electro-mechanical actuators for aerospace and industrial systems. This paper provides a deep learning based intelligent technique to monitor condition of ball screw actuators. The proposed scheme utilizes modified residual learning scheme to extract features from two-dimensional transformed motor current signals. The current signal data was collected under different load domains in terms of magnitude and direction reversal. A 2D-Remanant-CNN (2D-Rem-CNN) model was developed for features extraction with proposed optimized softmax for classification of mechanical faults. The proposed technique was validated against different ball screw fault cases. The testing results prove the superiority of 2D-Rem-CNN model against different state of the art techniques. The proposed framework was also tested for system’s stability under different load domains.

Reliability of high precision linear motion system is one of the main concerns in industrial and military systems. The performance and repeatability of these systems are influenced by their respective linear drives and load bearings. A fault in these members severely affects the safe working of overall system. This paper gives a reliable intelligent approach to detect and classify faults for linear motion systems based on deep learning methods. Accuracy in faults identification is highly dependent on improved features extraction. For this purpose, a novel Residual Twin CNN (ResT-CNN) is proposed that uses combination of 1-D and 2-D CNN in parallel learning which improves features extraction performance; followed by knowledge base-Remnant-PCA (Kb-Rem-PCA) architecture in combination with multi-class support vector machine (Mc-SVM). This novel hybrid combination proved very effective in accurate faults identification. The performance of proposed methodology was also validated by IMS-UC (Intelligent Maintenance Systems – University of Cincinnati) public bearing dataset. The results confirm the effectiveness of proposed scheme in comparison to existing state of the art techniques.

Precise and reliable segmentation of vertebral column is crucial for accurate computer aided diagnostic system (CADx). It assists the radiologists and doctors in identifying various pathologies in the vertebrae with better visualizations. Accurate segmentation of vertebral disks and bones from the medical images is a tough job and becomes more challenging when dealing with various deformities and pathologies. While remarkable success was achieved by deep convolutional neural networks (DCNNs) in medical image segmentation, it is still a difficult task for DCNNs to handle the medical image segmentation problems with various deformities and anatomical complexities. In this paper, we propose a novel and efficient framework to address the subject problem by integrating a parametric level set approach in deep convolutional neural networks. The proposed scheme utilizes the probability map of pre-trained deep network to initialize the level set and it refines the output iteratively under the action of various forces to fine-tune the training of deep network. Thus the learning of the network is improved and the network is able to accommodate high topological shape variations in the vertebrae. This proposed method was evaluated on two different datasets. The first one is 20 publically available 3D spine MRI dataset to perform disc segmentation and the second one is 173 computed tomography scans for thoracolumbar (thoracic and lumbar) vertebrae segmentation. The dice score was found to be 90.37 ± 0.9 percent for disks segmentation and 94.7 ± 1.1 with ASSD of 0.1 ± 0.04 mm for thoracolumbar vertebrae segmentation. The results reveal that our proposed method is robust over multiple segmentations and outperformed the recently published state of art methods.

Over the last couple of decades, researchers have searched for patterns in the occurrence of crimes to predict crimes using intelligent methods based on Deep Learning accurately. To find out the variety of Deep Learning algorithms applied to the tasks of anticipating crime, this current review paper analyzes more than 150 studies. Researchers are given access to datasets that they use to predict crime; the paper discusses the techniques often implemented in algorithms as well as various patterns and components related to criminality that employ Deep Learning. The review also outlines strategies and potential challenges that must be addressed to improve the efficiency of crime prediction. To finish, the detailed literature review on the application of Deep Learning for crime prediction presented in this paper should be useful to scholars interested in this field. Crime estimation strategies can be raised among authorities to deter and prevent criminal actions even more effectively.