An Enhanced Integrated Method for Healthcare Data Classification with Incompleteness

In numerous real-world healthcare applications,handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification tasks.Traditional approaches often rely on statistical methods for imputation,which may yield suboptimal results and be computationally intensive.This paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved accuracy.Conventional classification methods are ill-suited for incomplete medical data.To enhance efficiency without compromising accuracy,this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete data.Initially,the linear interpolation imputation method alongside an iterative Fuzzy c-means clustering method is applied and followed by a classification algorithm.The effectiveness of the proposed approach is evaluated using multiple performance metrics,including accuracy,precision,specificity,and sensitivity.The encouraging results demonstrate that our proposed method surpasses classical approaches across various performance criteria.

MRMR Based Feature Vector Design for Efficient Citrus Disease Detection

In recent times,the images and videos have emerged as one of the most important information source depicting the real time scenarios.Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane.The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition.One of the application fields pertains to detection of diseases occurring in the plants,which are destroying the widespread fields.Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests.This is a tedious and time consuming process and does not suffice the accuracy levels.This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading.The digital images captured from the field’s forms the dataset which trains the machine learning models to predict the nature of the disease.The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images,appropriate segmentation methodology,feature vector development and the choice of machine learning algorithm.To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages.Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection.The training vector thus developed is capable of presenting the relationship between the feature values and the target class.In this article,a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach is proposed.The overall improvement in terms of accuracy is measured and depicted.

Distributed Differences Structures Underlie Gating between the Kin Channel KAT1 and the Kout Channel SKOR

The family of voltage-gated （Shaker-like） potassium channels in plants includes both inward-rectifying （Kin） channels that allow plant cells to accumulate K＋ and outward-rectifying （Kout） channels that mediate K＋ efflux. Despite their dose structural similarities, Kin and Kout channels differ in their gating sensitivity towards voltage and the extracellular K＋ concentration. We have carried out a systematic program of domain swapping between the Kout channel SKOR and the Kin channel KAT1 to examine the impacts on gating of the pore regions, the S4, S5, and the S6 helices. We found that, in particular, the N-terminal part of the S5 played a critical role in KAT1 and SKOR gating. Our findings were supported by molecular dynamics of KAT1 and SKOR homology models. In silico analysis revealed that during channel opening and closing, displacement of certain residues, especially in the S5 and S6 segments, is more pronounced in KAT1 than in SKOR. From our analysis of the S4-S6 region, we conclude that gating （and K＋-sensing in SKOR） depend on a number of structural elements that are dispersed over this -145-residue sequence and that these place additional constraints on configurational rearrangement of the channels during gating.