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Machine Learning Based Feature Reduction for Network Intrusion Detection
No full textThe security of networked systems has become a critical universal issue. The rate of attacks against networked systems has increased dramatically, and the tactics used by the attackers are continuing to evolve. Intrusion detection is one of the solutions against these attacks. A common and effective approach for designing Intrusion Detection Systems (IDS) is Machine Learning. The performance of an IDS is significantly improved when the features are more discriminative and representative. This study uses two feature dimensionality reduction approaches: i) Auto-Encoder (AE): an instance of deep learning, for dimensionality reduction, and ii) Principle Component Analysis (PCA). The resulting low-dimensional features from both techniques are then used to build various classifiers such as Random Forest (RF), Bayesian Network, Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) for designing an IDS. The experimental findings with low-dimensional features in binary and multi-class classification show better performance in terms of Detection Rate (DR), F-Measure, False Alarm Rate (FAR), and Accuracy. This research effort is able to reduce the CICIDS2017 dataset's feature dimensions from 81 to 10, while maintaining a high accuracy of 99.6%. Furthermore, we propose a Multi-Class Combined performance metric CombinedMc with respect to class distribution to compare various multi-class and binary classification systems through incorporating FAR, DR, Accuracy, and class distribution parameters. In addition, we developed a uniform distribution based balancing approach to handle the imbalanced distribution of the minority class instances in the CICIDS2017 network intrusion dataset
Personalized Nutrition: Translating the Science of NutriGenomics Into Practice: Proceedings From the 2018 American College of Nutrition Meeting
No full textAdverse reactions to foods and adverse drug reactions are inherent in product defects, medication errors, and differences in individual drug exposure. Pharmacogenetics is the study of genetic causes of individual variations in drug response and pharmacogenomics more broadly involves genome-wide analysis of the genetic determinants of drug efficacy and toxicity. The similarity of nutritional genomics and pharmacogenomics stems from the innate goal to identify genetic variants associated with metabolism and disease. Thus, nutrigenomics can be thought of as encompassing gene–diet interactions involving diverse compounds that are present in even the simplest foods. The advances in the knowledge base of the complex interactions among genotype, diet, lifestyle, and environment is the cornerstone that continues to elicit changes in current medical practice to ultimately yield personalized nutrition recommendations for health and risk assessment. This information could be used to understand how foods and dietary supplements uniquely affect the health of individuals and, hence, wellness. The individual’s gut microbiota is not only paramount but pivotal in embracing the multiple-functional relationships with complex metabolic mechanisms involved in maintaining cellular homeostasis. The genetic revolution has ushered in an exciting era, one in which many new opportunities are expected for nutrition professionals with expertise in nutritional genomics. The American College of Nutrition’s conference focused on “Personalized Nutrition: Translating the Science of NutriGenomics Into Practice” was designed to help to provide the education needed for the professional engagement of providers in the personalized medicine era.https://doi.org/10.1080/07315724.2019.158298
An Overview of Block Chain Technology and Application
No full textCryptocurrency, and its underlying technologies, has been gaining popularity for transaction management beyond financial transactions. Transaction information is maintained in the block-chain, which can be used to audit the integrity of the transaction. The focus on this poster is the potential availability of block-chain technology of other transactional uses. Block-chain is one of the most stable open ledgers that preserves transaction information, and is difficult to forge. Since the information stored in block-chain is not related to personally identify information, it has the characteristics of anonymity. Also, the block-chain allows for transparent transaction verification since all information in the block-chain is open to the public. These characteristics are the same as the requirements for a voting system. That is, strong robustness, anonymity, and transparency. In this paper, we propose an electronic voting system as an application of block-chain, and describe block-chain based voting at a national level through examples
How Does Superoxide Dismutase 1 Cause Familial Amyotrophic Lateral Sclerosis and Treatment Options?
No full textThe purpose of this study was to evaluate how superoxide dismutase 1 (SOD1) mutation causes familial amyotrophic lateral sclerosis (fALS). This study also evaluates the mechanism of action of approved treatments for ALS and evaluates the action of plants that can also be used in treatment. Basic Procedures- The database used for searching was PubMed. Research focused on papers showing SOD1 mutations in ALS, mechanisms of pathology found in the research such as protein aggregates, ubiquitin/proteasome pathway, valosin-containing protein, rough endoplasmic reticulum stress, and ALS reversals. Papers chosen began in 1993 with the discovery of superoxide dismutase 1 as a causation of ALS. The plants chosen were previously researched for classes of botanical medicine in 2016-2017 of Naturopathic Medical School. Main Findings- Many factors contribute to SOD1 mutation causing familial amyotrophic lateral sclerosis including proper metalation of the enzyme, protein misfolding, rough endoplasmic reticulum stress, flaws in the ubiquitin/proteasome pathway, decreased autophagy, and neuroinflammation. Treatments address excitotoxicity of neurons, oxidative stress, and neuroinflammation
The Crural Interosseous Membrane Re-visited: a Histological and Microscopic Study
No full textThe aim of this study was to characterize the microscopic structure and sensory nerve endings of the crural interosseous membrane (IM). 13 IMs from 7 cadavers were used to analyze the organization of the collagen fibers, IM’s thickness, distribution of elastic fibers and nerve elements. The IM is mainly a two-layer collagen fascicle structure with the collagen fibers of adjacent layers orientated along different directions, forming angles of 30.5 +/- 1.7° at proximal and 26.6 +/- 2.1° at distal part (P>0.05). The percentage of elastic fibers between the two layers and inside the collagen fascicle layer is 10.1 +/- 0.5% and 2.2 +/- 0.1% (P<0.001). The IM’s thickness at proximal, middle, and distal parts is 268.5 +/- 18.6μm; 293.2 +/- 12.5μm; 365.3 +/- 19.3 μm, respectively (Proximal vs Distal: P<0.001; Middle vs Distal: P<0.05). Nerve elements were present and located both inside and on the surface of the IM, whereas the mechanoreceptors are mainly located on the surface of the IM. Free nerve endings (33.3 +/- 5.0/cm2) and Ruffini corpuscles (3.4 +/- 0.6/cm2) were the predominant sensory elements, while Pacinian corpuscles (1.3 +/- 0.7/cm2) were rarely found. The type of mechanoreceptors found suggests that the IM may play a role in proprioception.https://doi.org/10.4081/ejtm.2019.834
Experimental and Computational Study of a Closed Loop Cooling System for High Performance Electronics
No full textThis paper is on the experimental and numerical studies of a closed loop liquid cooling system for a high performance electronic device. The closed loop cooling system consists of a cold plate, a heat exchanger, a pump, a reservoir, and hoses connecting the components. Computational fluid dynamics (CFD) models of the cold plate and the heat exchanger have been developed to study the heat transfer and fluid flow in these devices. Experimental and CFD studies have been conducted to understand the heat transfer and fluid flow in the cold plate and the heat exchanger and their influence on the electronics device, and to study the effect of coolant flow rate on the dissipation of heat in the closed loop system
Review of Deep Learning Algorithms and Architectures
No full textDeep learning (DL) is playing an increasingly important role in our lives. It has already made a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars, predictive forecasting, and speech recognition. The painstakingly handcrafted feature extractors used in traditional learning, classification, and pattern recognition systems are not scalable for large-sized data sets. In many cases, depending on the problem complexity, DL can also overcome the limitations of earlier shallow networks that prevented efficient training and abstractions of hierarchical representations of multi-dimensional training data. Deep neural network (DNN) uses multiple (deep) layers of units with highly optimized algorithms and architectures. This paper reviews several optimization methods to improve the accuracy of the training and to reduce training time. We delve into the math behind training algorithms used in recent deep networks. We describe current shortcomings, enhancements, and implementations. The review also covers different types of deep architectures, such as deep convolution networks, deep residual networks, recurrent neural networks, reinforcement learning, variational autoencoders, and others.https://doi.org/10.1109/ACCESS.2019.291220
Agent Based Modeling in Design Management: Building Agent Profiles for New Product Development
No full textAgent-Based Modeling (ABM) methods have been used extensively in different disciplines like ecology, biology and economics. However, applications on agents with smaller boundaries or within an organization are still on early stage of development compared to game-theory-based modelling or system dynamics simulation. The purpose of this work is laying-out a framework for the modeling of the internal and external organizational interactions regarding design outputs in different stages and actors involved in the New Product Development (NPD) process. A refined framework is proposed based on a set of previously validated agents and their interactions were characterized using a semi-structured survey
Segmentation Of Retinal Blood Vessels Using A Novel Fuzzy Logic Algorithm
No full textIn this work, a rule-based method is presented for blood vessel segmentation in
digital retinal images. This method can be used in computer analyses of retinal images,
e.g., in automated screening for diabetic retinopathy. Diabetic retinopathy is the most
common diabetic eye disease and a leading cause of blindness. Diagnosis of diabetic
retinopathy at an early stage can be done through the segmentation of the blood vessels of
retina. Many studies have been carried out in the last decade in order to obtain accurate
blood vessel segmentation in retinal images including supervised and rule-based methods.
This method uses eight feature vectors for each pixel. These features are means and
medians of intensity values of pixel itself, first and second nearest neighbor at four
directions. Features are used in fuzzy logic algorithm as crisp input. The final segmentation
is obtained using a thresholding method. The method was tested on the publicly available
database DRIVE and its results are compared with distinguished published methods. Our
method achieved an average accuracy of 93.82% and an area under the receiver operating
characteristic curve of 94.19% for DRIVE database. Our results demonstrated an average
sensitivity of 72.28% and a specificity of 97.04%. The calculated sensitivity and specificity values for DRIVE database also state that the proposed segmentation method is effective
and robust
Can Ukraine’s wheat market provide success to its farmers in the current economic and financial environment?
No full textBeginning with the economic aspects of the futures markets like storage and transportation, an investigation into trade costs, operational efficiencies (Federico 2011), and political hurdles to integration is explored. Price convergence illustrates market integration (O’Rourke and Williamson 2004) but trade must be allowed to occur on an equal financial footing for that to happen. Transportation infrastructure and the political climate are considered, but the currency crisis is now the apparent main obstacle to increasing revenues to Ukraine, despite the country being the sixth largest exporter of wheat in the world. The hryvnia lost nearly 70% of its value against the dollar from 2014 to 2015 and has been generally flat since. This makes international sales attractive but puts plans for growth in doubt. GDP growth is one consideration for domestic demand, and that has stalled in the recent two years. With a devalued currency domestically, farmers can no longer access cheap credit, and costs to run their operations are increasing. Resources outside the country have risen due to this currency play, and plantings may decline further at the start of the next growing season