Medical datasets frequently include vast feature sets with numerous features that are related to one another. As a result, the curse of dimensionality affects learning from a medical dataset to discover significant characteristics, making it necessary to minimize the feature set. Feature selection (FS) is a major step in classification and also in reducing the dimension. This study attempts a novel Binary Multi-objective Chimp Optimization Algorithm (BMOChOA) with dual archive and k-nearest neighbors (KNN) classifier for mining relevant aspects from medical data. In this research, 12 versions of BMOChOA are implemented based on the group information and types of chaotic functions used. The best Pareto front obtained from suggested BMOChOA variations is compared with three benchmark multi-objective FS methods by taking 14 popular medical datasets of variable dimensions. By analyzing the experimental outputs using four multi-objective performance evaluators, it is found that the proposed FS method is superior in finding the best trade-off between the two objective functions: the number of features and classification performance.
Emerging and newly proposed devices integrate various materials at different scales (nano to submicron), revealing sensor response. Prefab simulation is in great demand to elucidate fundamental biosensor phenomena based on transistors. Numerous high electron mobility transistor (HEMT)-based biosensors have been developed, however metal oxide semiconductor HEMT (MOSHEMT) deserves to be further investigated. Sensitivity analysis with neutral and charged biomolecules was carried out using single gate high-κ dielectric MOSHEMT through computer aided design simulation. Device performance was evaluated through shift (sensing action) of device parameters like two-dimensional electron gas, on-current, transconductance, drain current, and output conductance due to immobilization of biomolecules in cavity created under gate. For neutral biomolecule (κ=8), fluctuation in on-current, transconductance, drain current, and output conductance is determined to be 330.3 μA/μm,102.0 μS/μm,319.0μA/μm,and 534.9 μS/μm, respectively. Positively charged biomolecules were more sensitive to device than negatively charged ones. Analysis was done on response to the fill rates of the biomolecules in the cavity. For vertical, horizontal, and tapered profiles, the transconductance sensitivities are 0.65, 0.17, and 0.32 and drain current sensitivities are 0.25, 0.16, and 0.27 at lowest fill (25%). Therefore, AlGaN/ GaN dielectric modulated MOSHEMT assures that the device can be used in sensitive intelligent biomedical applications.
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