Diagnosis of Melanoma Using Deep Learning

When compared to other types of skin cancer, melanoma is the deadliest. However, those who are diagnosed early on have a better prognosis for the purpose of providing a supplementary opinion to experts; various methods of spontaneous melanoma recognition and diagnosis have been investigated by different researchers. Because of the imbalance between classes, building models from existing information has proven difficult. Machine learning algorithms paired with imbalanced basis training approaches are being evaluated for their performance on the melanoma diagnosis challenge in this study. There were 200 dermoscopic photos in which patterns of skin lesions could be extracted using the VGG16, VGG19, Inception, and ResNet convolutional neural network architectures with the ABCD rule. After employing attribute selection with GS and training data balance using Synthetic Minority Oversampling Technique and Edited Nearest Neighbor rule, the random forest classifier had a sensitivity of nearly 93% and a kappa index ( k − index ) of 78%.

Quality-related fault diagnosis based on k-nearest neighbor rule for non-linear industrial processes

The fault diagnosis approaches based on k-nearest neighbor rule have been widely researched for industrial processes and achieve excellent performance. However, for quality-related fault diagnosis, the approaches using k-nearest neighbor rule have been still not sufficiently studied. To tackle this problem, in this article, we propose a novel quality-related fault diagnosis framework, which is made up of two parts: fault detection and fault isolation. In the fault detection stage, we innovatively propose a novel non-linear quality-related fault detection method called kernel partial least squares- k-nearest neighbor rule, which organically incorporates k-nearest neighbor rule with kernel partial least squares. Specifically, we first employ kernel partial least squares to establish a non-linear regression model between quality variables and process variables. After that, the statistics and thresholds corresponding to process space and predicted quality space are appropriately designed by adopting k-nearest neighbor rule. In the fault isolation stage, in order to match our proposed non-linear quality-related fault detection method kernel partial least squares- k-nearest neighbor seamlessly, we propose a modified variable contributions by k-nearest neighbor (VCkNN) fault isolation method called modified variable contributions by k-nearest neighbor (MVCkNN), which elaborately introduces the idea of the accumulative relative contribution rate into VC k-nearest neighbor, such that the smearing effect caused by the normal distribution hypothesis of VC k-nearest neighbor can be mitigated effectively. Finally, a widely used numerical example and the Tennessee Eastman process are employed to verify the effectiveness of our proposed approach.

Construction and Optimization of Biconnected and Wide-Coverage Topology Based on Node Mobility

Constructing a communications topology with fault tolerance and effective coverage plays an important role in wireless sensor networks. This paper is aimed at constructing and maintaining a biconnected topology, while minimizing the movement distance of the nodes and maximizing the coverage of the field of interest. First, it presents a new model with the motion constraint. If the nodes move at distance within the limit value calculated by the model, the topology is always connected, whether the neighbors of nodes are dynamic or static. Secondly, it improves the coverage strategy based on the nearest neighbor rule (NNR) and finds a rule of nodes’ spreading so that the nodes are distributed evenly and the spacing of the adjacent nodes is controllable. In addition, the nodes move only when necessary according to the added judgment conditions. Consequently, the movement distance is reduced. The simulation results prove the feasibility and effectiveness of the Localized Topology Optimized Method (LTOM) proposed by this paper. The connected indicators of the system’s topology during implementing LTOM are consistent, and the transformation of topology by LTOM is symmetric. Compared with the other distributed algorithm, NNR, LTOM reduces the movement distance of nodes, improves the connected probability, and maximizes the coverage of the topological structures under the biconnected conditions.

Recent (Dis)similarity Measures Between Histograms for Recognizing Many Classes of Plant Leaves

The accurate identification of plant species is crucial in botanical taxonomy as well as in related fields such as ecology and biodiversity monitoring. In spite of the recent developments in DNA-based analyses for phylogeny and systematics, visual leaf recognition is still commonly applied for species identification in botany. Histograms, along with the well-known nearest neighbor rule, are often a simple but effective option for the representation and classification of leaf images. Such an option relies on the choice of a proper dissimilarity measure to compare histograms. Two state-of-the-art measures—called weighted distribution matching (WDM) and Poisson-binomial radius (PBR)—are compared here in terms of classification performance, computational cost, and non-metric/non-Euclidean behavior. They are also compared against other classical dissimilarity measures between histograms. Even though PBR gives the best performance at the highest cost, it is not significantly better than other classical measures. Non-Euclidean/non-metric nature seems to play an important role.