In this research work, we present a simple and efficient passive microfluidic device for plasma separation from pure blood. The microdevice has been fabricated using conventional photolithography technique on a single layer of polydimethylsiloxane, and has been extensively tested on whole blood and enhanced (upto 62%) hematocrit levels of human blood. The microdevice employs elevated dimensions of about 100 μm; such elevated dimensions ensure clog-free operation of the microdevice and is relatively easy to fabricate. We show that our microdevice achieves almost 100% separation efficiency on undiluted blood in the flow rate range of 0.3 to 0.5 ml/min. Detailed biological characterization of the plasma obtained from the microdevice is carried out by testing: proteins by ultra-violet spectrophotometric method, hCG (human chorionic gonadotropin) hormone, and conducting random blood glucose test. Additionally, flow cytometry study has also been carried on the separated plasma. These tests attest to the high quality of plasma recovered. The microdevice developed in this work is an outcome of extensive experimental research on understanding the flow behavior and separation phenomenon of blood in microchannels. The microdevice is compact, economical and effective, and is particularly suited in continuous flow operations.
The widespread acceptance and increase of the Internet and mobile technologies have revolutionized our existence. On the other hand, the world is witnessing and suffering due to technologically aided crime methods. These threats, including but not limited to hacking and intrusions and are the main concern for security experts. Nevertheless, the challenges facing effective intrusion detection methods continue closely associated with the researcher’s interests. This paper’s main contribution is to present a host-based intrusion detection system using a C4.5-based detector on top of the popular Consolidated Tree Construction (CTC) algorithm, which works efficiently in the presence of class-imbalanced data. An improved version of the random sampling mechanism called Supervised Relative Random Sampling (SRRS) has been proposed to generate a balanced sample from a high-class imbalanced dataset at the detector’s pre-processing stage. Moreover, an improved multi-class feature selection mechanism has been designed and developed as a filter component to generate the IDS datasets’ ideal outstanding features for efficient intrusion detection. The proposed IDS has been validated with state-of-the-art intrusion detection systems. The results show an accuracy of 99.96% and 99.95%, considering the NSL-KDD dataset and the CICIDS2017 dataset using 34 features.
Big data development in biomedical and medical service networks provides a research on medical data benefits, early ailment detection, patient care and network administrations.e-Health applications are particularly important for the patients who are unfit to see a specialist or any health expert. The objective is to encourage clinicians and families to predict disease using Machine Learning (ML) procedures. In addition, diverse regions show important qualities of certain provincial ailments, which may hinder the forecast of disease outbreaks. The objective of this work is to predict the different kinds of diseases using Grey Wolf optimization and auto encoder based Recurrent Neural Network (GWO+RNN). The features are selected using GWO and the diseases are predicted by using RNN method. Initially the GWO algorithm avoids the irrelevant and redundant attributes significantly, after the features are forwarded to the RNN classifier. The experimental result proved that the performance of GWO+RNN algorithm achieved better than existing method like Group Search Optimizer and Fuzzy Min-Max Neural Network (GFMMNN) approach. The GWO-RNN method used the medical UCI database based on various datasets such as Hungarian, Cleveland, PID, mammographic masses, Switzerland and performance was measured with the help of efficient metrics like accuracy, sensitivity and specificity. The proposed GWO+RNN method achieved 16.82% of improved prediction accuracy for Cleveland dataset.
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