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The traditional negative selection algorithm with a randomly generated hypersphere detector is unable to satisfy the development needs of continuous learning due to the inherent defects of the detector. This paper proposes a novel negative selection algorithm for hyper-rectangle detectors that overcomes the shortcomings of randomly generated hyper-sphere detectors and lays the foundation for a negative selection algorithm with continuous learning capability. It uses self-sample clusters of equal-sized hypercubes instead of self-samples for training. The hyper-rectangle detectors are generated by cutting the nonself-space along the boundary of the self-sample clusters. The state space is covered without overlapping each other by self-sample clusters and detectors. The anomaly detection performance of the proposed method was demonstrated using Iris data, vowel recognition data (Vowel), and Wisconsin Breast Cancer (BCW) data. The experimental results show that the proposed method outperforms other artificial immune algorithms and clustering algorithms under the same parameter conditions. [ABSTRACT FROM AUTHOR]

Multimodal analysis focuses on the internal and external manifestations of cancer cells to provide physicians, oncologists and surgeons with timely information on personalized diagnosis and treatment for patients. Decision fusion in multimodal analysis reduces manual intervention, and improves classification accuracy facilitating doctors to make quick decisions. Genetic characteristics extracted on biopsies do not, however, provide details on adjacent cells. Images can only provide external observable details of cancer cells. While mammograms can detect breast cancer, region wise details can be obtained from ultrasound images. Hence, different types of imaging techniques are used. Features are extracted using the SelectKbest method in the Wisconsin Breast Cancer, Clinical and gene expression datasets. The features are extracted using Gray Level Co-occurrence Matrix from Histology, Mammogram and Sonogram images. For image datasets, the Convolution Neural Network (CNN) is used as a classifier. The combined features from clinical, gene expression and image datasets are used to train an Integrated Stacking Classifier. The integrated multimodal system's effectiveness is shown by experimental findings. [ABSTRACT FROM AUTHOR]

BACKGROUND: The evidence base supports effectiveness of dental sealants for prevention of childhood caries in school-aged children. OBJECTIVE: This study describes planning, development, usability testing and outcomes following implementation of DentaSeal, a web-based application designed to accurately track unique student data and generate reports for all Wisconsin school-based sealant placement (SP) programs. METHODS: Application software development was informed by a steering committee of representative stakeholders who were interviewed to inform design and provide feedback for design of DentaSeal during development and evaluation. Software development proceeded based on wireframes developed to build architectural design. Usability testing followed and informed any required adjustments to the application. The DentaSeal prototype was beta tested and fully implemented subsequently in the public health sector. RESULTS: The DentaSeal application demonstrated capacity to: 1) track unique student SP data and longitudinal encounter history, 2) generate reports and 3) support administrative tracking. In 2019, DentaSeal captured SP data of 47 school-based programs in Wisconsin that sponsored > 7,000 program visits for 184,000 children from 62 counties. Delivery of > 548,000 SP services were catalogued. CONCLUSIONS: For public health initiatives targeting reduction in caries incidence, web-based applications such as DentaSeal represent useful longitudinal tracking tools for cataloguing SP in school-based program participants. [ABSTRACT FROM AUTHOR]

In data mining, deep learning and machine learning models face class imbalance problems, which result in a lower detection rate for minority class samples. An improved Synthetic Minority Over-sampling Technique (SMOTE) is introduced for effective imbalanced data classification. After collecting the raw data from PIMA, Yeast, E.coli, and Breast cancer Wisconsin databases, the pre-processing is performed using min-max normalization, cleaning, integration, and data transformation techniques to achieve data with better uniqueness, consistency, completeness and validity. An improved SMOTE algorithm is applied to the pre-processed data for proper data distribution, and then the properly distributed data is fed to the machine learning classifiers: Support Vector Machine (SVM), Random Forest, and Decision Tree for data classification. Experimental examination confirmed that the improved SMOTE algorithm with random forest attained significant classification results with Area under Curve (AUC) of 94.30%, 91%, 96.40%, and 99.40% on the PIMA, Yeast, E.coli, and Breast cancer Wisconsin databases. [ABSTRACT FROM AUTHOR]