Clustering-Based Outlier Detection Technique Using PSO-KNN

In this work, we present an unsupervised machine learning algorithm for outlier detection by integrating Particle Swarm Optimization (PSO) and the K-nearest neighbor (KNN) technique. Initially, the data clustering of the considered datasets was carried out using PSO to obtain optimized clusters. In the optimization process, we have adopted Davies-Bouldin (DB) index as a fitness function. The optimized clusters were pruned to exclude densely packed inliers data. Thereafter, the KNN method was employed to detect outliers present in the datasets. Our proposed algorithm was tested for outlier detection on eight different datasets and compared its performance with PSO+K-means, K-means, Local Outlier Factor (LOF), and Local Distance-based Outlier Factor (LDOF) methods. Our results show that the outlier detection efficiency of the proposed method outperforms than other four techniques. We believe that our proposed technique simple and efficient in finding the outliers in various types of datasets and it could be a promising tool for outlier detection in data mining

A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection

Emerging Approaches for Fluorescence-Based Newborn Screening of Mucopolysaccharidoses

Interest in newborn screening for mucopolysaccharidoses (MPS) is growing, due in part to ongoing efforts to develop new therapies for these disorders and new screening assays to identify increased risk for the individual MPSs on the basis of deficiency in the cognate enzyme. Existing tests for MPSs utilize either fluorescence or mass spectrometry detection methods to measure biomarkers of disease (e.g., enzyme function or glycosaminoglycans) using either urine or dried blood spot (DBS) samples. There are currently two approaches to fluorescence-based enzyme function assays from DBS: (1) manual reaction mixing, incubation, and termination followed by detection on a microtiter plate reader; and (2) miniaturized automation of these same assay steps using digital microfluidics technology. This article describes the origins of laboratory assays for enzyme activity measurement, the maturation and clinical application of fluorescent enzyme assays for MPS newborn screening, and considerations for future expansion of the technology

Digital Microfluidic Platform to Maximize Diagnostic Tests with Low Sample Volumes from Newborns and Pediatric Patients

“Children are not tiny adults” is an adage commonly used in pediatrics to emphasize the fact that children often have different physiological responses to sickness and trauma compared to adults. However, despite widespread acceptance of this concept, diagnostic blood testing is an excellent example of clinical care that is not yet customized to the needs of children, especially newborns. Cumulative blood loss resulting from clinical testing does not typically impact critically ill adult patients, but can quickly escalate in children, leading to iatrogenic anemia and related comorbidities. Moreover, the tests prioritized for rapid, near-patient testing in adults are not always the most clinically relevant tests for children or newborns. This report describes the development of a digital microfluidic testing platform and associated clinical assays purposely curated to address current shortcomings in pediatric laboratory testing by using microliter volumes (<50 µL) of samples. The automated platform consists of a small instrument and single-use cartridges, which contain all reagents necessary to prepare the sample and perform the assay. Electrowetting technology is used to precisely manipulate nanoliter-sized droplets of samples and reagents inside the cartridge. To date, we have automated three disparate types of assays (biochemical assays, immunoassays, and molecular assays) on the platform and have developed over two dozen unique tests, each with important clinical application to newborns and pediatric patients. Cell lysis, plasma preparation, magnetic bead washing, thermocycling, incubation, and many other essential functions were all performed on the cartridge without any user intervention. The resulting assays demonstrate performance comparable to standard clinical laboratory assays and are economical due to the reduced hands-on effort required for each assay and lower overall reagent consumption. These capabilities allow a wide range of assays to be run simultaneously on the same cartridge using significantly reduced sample volumes with results in minutes