Robust filtering schemes for machine learning systems to defend Adversarial Attack

Robust filtering schemes for machine learning systems to defend Adversarial Attac

Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial Attacks

Developing secure machine learning models from adversarial examples is challenging as various methods are continually being developed to generate adversarial attacks. In this work, we propose an evolutionary approach to automatically determine Image Processing Techniques Sequence (IPTS) for detecting malicious inputs. Accordingly, we first used a diverse set of attack methods including adaptive attack methods (on our defense) to generate adversarial samples from the clean dataset. A detection framework based on a genetic algorithm (GA) is developed to find the optimal IPTS, where the optimality is estimated by different fitness measures such as Euclidean distance, entropy loss, average histogram, local binary pattern and loss functions. The "image difference" between the original and processed images is used to extract the features, which are then fed to a classification scheme in order to determine whether the input sample is adversarial or clean. This paper described our methodology and performed experiments using multiple data-sets tested with several adversarial attacks. For each attack-type and dataset, it generates unique IPTS. A set of IPTS selected dynamically in testing time which works as a filter for the adversarial attack. Our empirical experiments exhibited promising results indicating the approach can efficiently be used as processing for any AI model

CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space

Predicting if red blood cells (RBC) are infected with the malaria parasite is an important problem in Pathology. Recently, supervised machine learning approaches have been used for this problem, and they have had reasonable success. In particular, state-of-the-art methods such as Convolutional Neural Networks automatically extract increasingly complex feature hierarchies from the image pixels. While such generalized automatic feature extraction methods have significantly reduced the burden of feature engineering in many domains, for niche tasks such as the one we consider in this paper, they result in two major problems. First, they use a very large number of features (that may or may not be relevant) and therefore training such models is computationally expensive. Further, more importantly, the large feature-space makes it very hard to interpret which features are truly important for predictions. Thus, a criticism of such methods is that learning algorithms pose opaque black boxes to its users, in this case, medical experts. The recommendation of such algorithms can be understood easily, but the reason for their recommendation is not clear. This is the problem of non-interpretability of the model, and the best-performing algorithms are usually the least interpretable. To address these issues, in this paper, we propose an approach to extract a very small number of aggregated features that are easy to interpret and compute, and empirically show that we obtain high prediction accuracy even with a significantly reduced feature-space.Comment: Accepted in The 2020 International Joint Conference on Neural Networks (IJCNN 2020) At Glasgow (UK

Study of Different Deep Learning Approach with Explainable AI for Screening Patients with COVID-19 Symptoms: Using CT Scan and Chest X-ray Image Dataset

The outbreak of COVID-19 disease caused more than 100,000 deaths so far in the USA alone. It is necessary to conduct an initial screening of patients with the symptoms of COVID-19 disease to control the spread of the disease. However, it is becoming laborious to conduct the tests with the available testing kits due to the growing number of patients. Some studies proposed CT scan or chest X-ray images as an alternative solution. Therefore, it is essential to use every available resource, instead of either a CT scan or chest X-ray to conduct a large number of tests simultaneously. As a result, this study aims to develop a deep learning-based model that can detect COVID-19 patients with better accuracy both on CT scan and chest X-ray image dataset. In this work, eight different deep learning approaches such as VGG16, InceptionResNetV2, ResNet50, DenseNet201, VGG19, MobilenetV2, NasNetMobile, and ResNet15V2 have been tested on two dataset-one dataset includes 400 CT scan images, and another dataset includes 400 chest X-ray images studied. Besides, Local Interpretable Model-agnostic Explanations (LIME) is used to explain the model's interpretability. Using LIME, test results demonstrate that it is conceivable to interpret top features that should have worked to build a trust AI framework to distinguish between patients with COVID-19 symptoms with other patients.Comment: This is a work in progress, it should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the fiel

Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection

Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real world problems. QML has the potential to address cybersecurity related challenges. Considering the novelty and complex architecture of QML, resources are not yet explicitly available that can pave cybersecurity learners to instill efficient knowledge of this emerging technology. In this research, we design and develop QML-based ten learning modules covering various cybersecurity topics by adopting student centering case-study based learning approach. We apply one subtopic of QML on a cybersecurity topic comprised of pre-lab, lab, and post-lab activities towards providing learners with hands-on QML experiences in solving real-world security problems. In order to engage and motivate students in a learning environment that encourages all students to learn, pre-lab offers a brief introduction to both the QML subtopic and cybersecurity problem. In this paper, we utilize quantum support vector machine (QSVM) for malware classification and protection where we use open source Pennylane QML framework on the drebin215 dataset. We demonstrate our QSVM model and achieve an accuracy of 95% in malware classification and protection. We will develop all the modules and introduce them to the cybersecurity community in the coming days

Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance

Heart disease, one of the main reasons behind the high mortality rate around the world, requires a sophisticated and expensive diagnosis process. In the recent past, much literature has demonstrated machine learning approaches as an opportunity to efficiently diagnose heart disease patients. However, challenges associated with datasets such as missing data, inconsistent data, and mixed data (containing inconsistent missing data both as numerical and categorical) are often obstacles in medical diagnosis. This inconsistency led to a higher probability of misprediction and a misled result. Data preprocessing steps like feature reduction, data conversion, and data scaling are employed to form a standard dataset—such measures play a crucial role in reducing inaccuracy in final prediction. This paper aims to evaluate eleven machine learning (ML) algorithms—Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Naive Bayes (NB), Support Vector Machine (SVM), XGBoost (XGB), Random Forest Classifier (RF), Gradient Boost (GB), AdaBoost (AB), Extra Tree Classifier (ET)—and six different data scaling methods—Normalization (NR), Standscale (SS), MinMax (MM), MaxAbs (MA), Robust Scaler (RS), and Quantile Transformer (QT) on a dataset comprising of information of patients with heart disease. The result shows that CART, along with RS or QT, outperforms all other ML algorithms with 100% accuracy, 100% precision, 99% recall, and 100% F1 score. The study outcomes demonstrate that the model’s performance varies depending on the data scaling method.Open Access fees paid for in whole or in part by the University of Oklahoma Libraries.Ye

Introduction of Medical Imaging Modalities

The diagnosis and treatment of various diseases had been expedited with the help of medical imaging. Different medical imaging modalities, including X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Nuclear Imaging, Ultrasound, Electrical Impedance Tomography (EIT), and Emerging Technologies for in vivo imaging modalities is presented in this chapter, in addition to these modalities, some advanced techniques such as contrast-enhanced MRI, MR approaches for osteoarthritis, Cardiovascular Imaging, and Medical Imaging data mining and search. Despite its important role and potential effectiveness as a diagnostic tool, reading and interpreting medical images by radiologists is often tedious and difficult due to the large heterogeneity of diseases and the limitation of image quality or resolution. Besides the introduction and discussion of the basic principles, typical clinical applications, advantages, and limitations of each modality used in current clinical practice, this chapter also highlights the importance of emerging technologies in medical imaging and the role of data mining and search aiming to support translational clinical research, improve patient care, and increase the efficiency of the healthcare system.Comment: 19 pages, 7 figures, 1 table; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging

Development of genic-SSR markers by deep transcriptome sequencing in pigeonpea [Cajanus cajan (L.) Millspaugh]

<p>Abstract</p> <p>Background</p> <p>Pigeonpea [<it>Cajanus cajan </it>(L.) Millspaugh], one of the most important food legumes of semi-arid tropical and subtropical regions, has limited genomic resources, particularly expressed sequence based (genic) markers. We report a comprehensive set of validated genic simple sequence repeat (SSR) markers using deep transcriptome sequencing, and its application in genetic diversity analysis and mapping.</p> <p>Results</p> <p>In this study, 43,324 transcriptome shotgun assembly unigene contigs were assembled from 1.696 million 454 GS-FLX sequence reads of separate pooled cDNA libraries prepared from leaf, root, stem and immature seed of two pigeonpea varieties, Asha and UPAS 120. A total of 3,771 genic-SSR loci, excluding homopolymeric and compound repeats, were identified; of which 2,877 PCR primer pairs were designed for marker development. Dinucleotide was the most common repeat motif with a frequency of 60.41%, followed by tri- (34.52%), hexa- (2.62%), tetra- (1.67%) and pentanucleotide (0.76%) repeat motifs. Primers were synthesized and tested for 772 of these loci with repeat lengths of ≥18 bp. Of these, 550 markers were validated for consistent amplification in eight diverse pigeonpea varieties; 71 were found to be polymorphic on agarose gel electrophoresis. Genetic diversity analysis was done on 22 pigeonpea varieties and eight wild species using 20 highly polymorphic genic-SSR markers. The number of alleles at these loci ranged from 4-10 and the polymorphism information content values ranged from 0.46 to 0.72. Neighbor-joining dendrogram showed distinct separation of the different groups of pigeonpea cultivars and wild species. Deep transcriptome sequencing of the two parental lines helped <it>in silico </it>identification of polymorphic genic-SSR loci to facilitate the rapid development of an intra-species reference genetic map, a subset of which was validated for expected allelic segregation in the reference mapping population.</p> <p>Conclusion</p> <p>We developed 550 validated genic-SSR markers in pigeonpea using deep transcriptome sequencing. From these, 20 highly polymorphic markers were used to evaluate the genetic relationship among species of the genus <it>Cajanus</it>. A comprehensive set of genic-SSR markers was developed as an important genomic resource for diversity analysis and genetic mapping in pigeonpea.</p

Harmful and beneficial aspects of Parthenium hysterophorus: an update

Parthenium hysterophorus is a noxious weed in America, Asia, Africa and Australia. This weed is considered to be a cause of allergic respiratory problems, contact dermatitis, mutagenicity in human and livestock. Crop production is drastically reduced owing to its allelopathy. Also aggressive dominance of this weed threatens biodiversity. Eradication of P. hysterophorus by burning, chemical herbicides, eucalyptus oil and biological control by leaf-feeding beetle, stem-galling moth, stem-boring weevil and fungi have been carried out with variable degrees of success. Recently many innovative uses of this hitherto notorious plant have been discovered. Parthenium hysterophorus confers many health benefits, viz remedy for skin inflammation, rheumatic pain, diarrhoea, urinary tract infections, dysentery, malaria and neuralgia. Its prospect as nano-medicine is being carried out with some preliminary success so far. Removal of heavy metals and dye from the environment, eradication of aquatic weeds, use as substrate for commercial enzyme production, additives in cattle manure for biogas production, as biopesticide, as green manure and compost are to name a few of some other potentials. The active compounds responsible for hazardous properties have been summarized. The aim of this review article is to explore the problem P. hysterophorus poses as a weed, the effective control measures that can be implemented as well as to unravel the latent beneficial prospects of this weed

Robust filtering schemes for machine learning systems to defend Adversarial Attacks

Defenses against adversarial attacks are essential to ensure the reliability of machine learning models as their applications are expanding in different domains. Existing ML defense techniques have several limitations in practical use. I proposed a trustworthy framework that employs an adaptive strategy to inspect both inputs and decisions. In particular, data streams are examined by a series of diverse filters before sending to the learning system and then crossed checked its output through a diverse set of filters before making the final decision. My experimental results illustrated that the proposed active learning-based defense strategy could mitigate adaptive or advanced adversarial manipulations both in input and after with the model decision for a wide range of ML attacks by higher accuracy. Moreover, the output decision boundary inspection using a classification technique automatically reaffirms the reliability and increases the trustworthiness of any ML-Based decision support system. Unlike other defense strategies, my defense technique does not require adversarial sample generation, and updating the decision boundary for detection makes the defense systems robust to traditional adaptive attacks