Processing of Wisconsin Breast Cancer Data using Ebola Optimization Algorithm with Mixture Kernel SVM

Being a deadly disease, breast cancer is becoming the more progressive one in providing higher mortality for females around the world. Thereby, the need for an appropriate strategy is always required for earlier breast cancer diagnosis. The physicians utilize the Computer-Aided Diagnosis (CAD) tool for effective and tireless detection of such cancers. In this regard, the work is intended to design a CAD system for breast cancer diagnosis in a timely manner. The implementation starts with the use of Wisconsin Breast Cancer dataset. After performing preprocessing and visual analysis of the input dataset, feature selection is performed to improve the efficiency of the CAD system. This can be done by using the recently evolved Ebola Optimization Algorithm (EOA). This algorithm is based on an effective approach used in the propagation of the Ebola virus among individuals. After feature selection, the dominant features are then classified with the aid of a mixture Kernel Support Vector Machine (mK-SVM) algorithm. Additionally, the work utilized the Linear SVM, and KNN algorithms for the experimental analysis and comparison. As a result, the mK-SVM together with EOA provides maximum accuracy of 97.19% in classifying the input as either benign severity or malignant case. © 2022 IEEE.

ModViz: A Modular and Extensible Architecture for Drill-Down and Visualization of Complex Data

Analysis of data sets that may be changing often or in real-time, consists of at least three important synchronized components: i) figuring out what to infer (objectives), ii) analysis or computation of those objectives, and iii) understanding of the results which may require drill-down and/or visualization. There is considerable research on the first two of the above components whereas understanding actionable inferences through visualization has not been addressed properly. Visualization is an important step towards both understanding (especially by non-experts) and inferring the actions that need to be taken. As an example, for Covid-19, knowing regions (say, at the county or state level) that have seen a spike or are prone to a spike in the near future may warrant additional actions with respect to gatherings, business opening hours, etc. This paper focuses on a modular and extensible architecture for visualization of base as well as analyzed data. This paper proposes a modular architecture of a dashboard for user interaction, visualization management, and support for complex analysis of base data. The contributions of this paper are: i) extensibility of the architecture providing flexibility to add additional analysis, visualizations, and user interactions without changing the workflow, ii) decoupling of the functional modules to ease and speed up development by different groups, and iii) supporting concurrent users and addressing efficiency issues for display response time. This paper uses Multilayer Networks (or MLNs) for analysis. To showcase the above, we present the architecture of a visualization dashboard, termed CoWiz++ (for Covid Wizard), and elaborate on how web-based user interaction and display components are interfaced seamlessly with the back-end modules. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Leveraging Transfer Learning to Identify Food Categories

In today's scenario, recognition of pictured food dishes automatically has significant importance. During the COVID-19 pandemic, there was a decline in people visiting restaurants for their dietary requirements. So many restaurants started offering their services online. This situation caused a demand for better categorization of food into various categories on a large scale by companies that facilitated these services. It is challenging to congregate a large dataset of food categories, so it is complex to build a generalized architecture. To solve this issue, In this paper, domain-specific transfer learning is used to build the model using some standard architectures like VGGNET, RESNET, and EFFICIENTNET family, which are trained on popular benchmark datasets such as IMAGENET, COCO, etc. The similarity between the source and target datasets is calculated to find the best source dataset, and the one with the highest similarity is chosen for transfer learning. The solution proposed in this paper outperforms some of the existing works on categorizing food items.

CoWiz: Interactive Covid-19 Visualization Based On Multilayer Network Analysis

Covid Wizard or CoWiz is a Covid-19 visualization dashboard based on Multilayer Network (MLN) analysis underneath(1). Online dashboards typically plot/visualize statistical information gleaned from raw data, such as daily cases, deaths, recoveries, tests, etc. However, for a better understanding, we need aggregate analysis (e.g., community, centrality) and its visualization which is the purpose of CoWiz. As an example, grouping counties across a country/region based on similarity of increase/decrease in cases, deaths, hospitalizations over intervals is not possible without aggregate analysis. This is where CoWiz utilizes community and other concepts over MLNs that are inferred from Covid and other relevant data sets for visualization. This demo presents a flexible, interactive dashboard which is capable of visualizing various aspects of Covid-19 data, including composition of Covid data with demographics (population density, education level, average earning, vehicle movements, and change in purchase patterns) at the granularity of county for USA. This paper elaborates on the types of analysis, underlying model, and how a flexible visualization dashboard has been developed using open source software and data sets. As new data becomes available, they can be incorporated into the visualization with no manual intervention.