Fog computing: a cloud to the ground support for smart things and machine-to-machine networks

Cloud services to smart things face latency and intermittent connectivity issues. Fog devices are positioned between cloud and smart devices. Their high speed Internet connection to the cloud, and physical proximity to users, enable real time applications and location based services, and mobility support. Cisco promoted fog computing concept in the areas of smart grid, connected vehicles and wireless sensor and actuator networks. This survey article expands this concept to the decentralized smart building control, recognizes cloudlets as special case of fog computing, and relates it to the software defined networks (SDN) scenarios. Our literature review identifies a handful number of articles. Cooperative data scheduling and adaptive traffic light problems in SDN based vehicular networks, and demand response management in macro station and micro-grid based smart grids are discussed. Security, privacy and trust issues, control information overhead and network control policies do not seem to be studied so far within the fog computing concept.

Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset

For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.

Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models

In this paper, two real-world medical classification problems using electrocardiogram (ECG) and auscultatory blood pressure (Korotkoff) signals are examined. A total of nine machine learning models are applied to perform classification of the medical data sets. A number of useful performance metrics which include accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve are computed. In addition to the original data sets, noisy data sets are generated to evaluate the robustness of the classifiers against noise. The 10-fold cross validation method is used to compute the performance statistics, in order to ensure statistically reliable results pertaining to classification of the ECG and Korotkoff signals are produced. The outcomes indicate that while logistic regression models perform the best with the original data set, ensemble machine learning models achieve good accuracy rates with noisy data sets.

Cutting the cord: Can society over-invest in extremely premature and critically impaired neonates?

This article provides a critical examination of the allocation of scarce public health care funds in relation to extremely premature and sick neonates. Decisions to withdraw or withhold life-sustaining treatment from neonates born extremely premature are generally informed by arbitrary and often subjective considerations of those involved in their care – namely parents and medical practitioners. This article argues for a sharp and immediate focus in decisions to treat such neonates based on the allocation of limited health care resources. Accordingly, decisions to save and preserve the lives of imperilled neonates should not be limited to the immediate financial costs of medical treatment. More explicitly there should be a full appreciation of the cost of disability to the family, requirements for long-term care, and the benefits and associated costs of life, not only to the patient, but also to society.

Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation

Dynamic surface roughness prediction during metal cutting operations plays an important role to enhance the productivity in manufacturing industries. Various machining parameters such as unwanted noises affect the surface roughness, whatever their effects have not been adequately quantified. In this study, a general dynamic surface roughness monitoring system in milling operations was developed. Based on the experimentally acquired data, the milling process of Al 7075 and St 52 parts was simulated. Cutting parameters (i.e., cutting speed, feed rate, and depth of cut), material type, coolant fluid, X and Z components of milling machine vibrations, and white noise were used as inputs. The original objective in the development of a dynamic monitoring system is to simulate wide ranges of machining conditions such as rough and finishing of several materials with and without cutting fluid. To achieve high accuracy of the resultant data, the full factorial design of experiment was used. To verify the accuracy of the proposed model, testing and recall/verification procedures have been carried out and results showed that the accuracy of 99.8 and 99.7 % were obtained for testing and recall processes.

Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression

BACKGROUND: Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study.

METHODS: The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators.

RESULTS: After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001).

CONCLUSION: The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.

BacHbpred: support vector machine methods for the prediction of bacterial hemoglobin-like proteins

The recent upsurge in microbial genome data has revealed that hemoglobin-like (HbL) proteins may be widely distributed among bacteria and that some organisms may carry more than one HbL encoding gene. However, the discovery of HbL proteins has been limited to a small number of bacteria only. This study describes the prediction of HbL proteins and their domain classification using a machine learning approach. Support vector machine (SVM) models were developed for predicting HbL proteins based upon amino acid composition (AC), dipeptide composition (DC), hybrid method (AC + DC), and position specific scoring matrix (PSSM). In addition, we introduce for the first time a new prediction method based on max to min amino acid residue (MM) profiles. The average accuracy, standard deviation (SD), false positive rate (FPR), confusion matrix, and receiver operating characteristic (ROC) were analyzed. We also compared the performance of our proposed models in homology detection databases. The performance of the different approaches was estimated using fivefold cross-validation techniques. Prediction accuracy was further investigated through confusion matrix and ROC curve analysis. All experimental results indicate that the proposed BacHbpred can be a perspective predictor for determination of HbL related proteins. BacHbpred, a web tool, has been developed for HbL prediction.

A preliminary study on machinability of super austenitic stainless steel

Stainless steel is the most widely used alloys of steel. The reputed variety of stainless steel having customised material properties as per the design requirements is Duplex Stainless Steel and Austenitic Stainless Steel. The Austenite Stainless Steel alloy has been developed further to be Super Austenitic Stainless Steel (SASS) by increasing the percentage of the alloying elements to form the half or more than the half of the material composition. SASS (Grade-AL-6XN) is an alloy steel containing high percentages of nickel (24%), molybdenum (6%) and chromium (21%). The chemical elements offer high degrees of corrosion resistance, toughness and stability in a large range of hostile environments like petroleum, marine and food processing industries. SASS is often used as a commercially viable substitute to high cost non-ferrous or non-metallic metals. The ability to machine steel effectively and efficiently is of utmost importance in the current competitive market. This paper is an attempt to evaluate the machinability of SASS which has been a classified material so far with very limited research conducted on it. Understanding the machinability of this alloy would assist in the effective forming of this material by metal cutting. The novelty of research associated with this is paper is reasonable taking into consideration the unknowns involved in machining SASS. The experimental design consists of conducting eight milling trials at combination of two different feed rates, 0.1 and 0.15 mm/tooth; cutting speeds, 100 and 150 m/min; Depth of Cut (DoC), 2 and 3 mm and coolant on for all the trials. The cutting tool has two inserts and therefore has two cutting edges. The trial sample is mounted on a dynamometer (type 9257B) to measure the cutting forces during the trials. The cutting force data obtained is later analyzed using DynaWare supplied by Kistler. The machined sample is subjected to surface roughness (Ra) measurement using a 3D optical surface profilometer (Alicona Infinite Focus). A comprehensive metallography process consisting of mounting, polishing and etching was conducted on a before and after machined sample in order to make a comparative analysis of the microstructural changes due to machining. The microstructural images were capture using a digital microscope. The microhardness test were conducted on a Vickers scale (Hv) using a Vickers microhardness tester. Initial bulk hardness testing conducted on the material show that the alloy is having a hardness of 83.4 HRb. This study expects an increase in hardness mostly due to work hardening may be due to phase transformation. The results obtained from the cutting trials are analyzed in order to judge the machinability of the material. Some of the criteria used for machinability evaluation are cutting force analysis, surface texture analysis, metallographic analysis and microhardness analysis. The methodology followed in each aspect of the investigation is similar to and inspired by similar research conducted on other materials. However, the novelty of this research is the investigation of various aspects of machinability and drawing comparisons between each other while attempting to justify each result obtained to the microstructural changes observed which influence the behaviour of the alloy. Due to the limited scope of the paper, machinability criteria such as chip morphology, Metal Removal Rate (MRR) and tool wear are not included in this paper. All aspects are then compared and the optimum machining parameters are justified with a scope for future investigations

A multiobjective state transition algorithm for single machine scheduling

In this paper, a discrete state transition algorithm is introduced to solve a multiobjective single machine job shop scheduling problem. In the proposed approach, a non-dominated sort technique is used to select the best from a candidate state set, and a Pareto archived strategy is adopted to keep all the non-dominated solutions. Compared with the enumeration and other heuristics, experimental results have demonstrated the effectiveness of the multiobjective state transition algorithm.

Weldability and machinability of duplex stainless steel

This chapter investigates two important processing methods, such as welding and machine of duplex stainless steel. The welding process welding generally degrades the properties of these materials by redistributing the phases during melting and solidification. On the other hand, the redistribution during machining mainly take place combined effect of stress, strain rate and temperature. Mechanism of machining process and several welding methods has been analysed in details. It was found that outcomes of welding processes depend on the welding methods. Most of the cases an appropriate annealing process can be used to restore the expected properties of the weld joints though the parameters of annealing process are different in different welding methods. Nonmetallic inclusions and the low carbon content of duplex stainless steel reduce the machinability of duplex stainless steel. SEM and optical microscopic details of the frozen cutting zone and chips revealed that the harder austenite phase dissipates in the advancement of the cutting tool, being effectively squeezed out of the softer ferrite phase. Abrasion and adhesion were the most common wear modes developed on the flank and rake faces. Adhesion wear being the most prevalent on the flank face, appeared to be initiated by built-up edge formation.

Sparse adaptive multi-hyperplane machine

The Adaptive Multiple-hyperplane Machine (AMM) was recently proposed to deal with large-scale datasets. However, it has no principle to tune the complexity and sparsity levels of the solution. Addressing the sparsity is important to improve learning generalization, prediction accuracy and computational speedup. In this paper, we employ the max-margin principle and sparse approach to propose a new Sparse AMM (SAMM). We solve the new optimization objective function with stochastic gradient descent (SGD). Besides inheriting the good features of SGD-based learning method and the original AMM, our proposed Sparse AMM provides machinery and flexibility to tune the complexity and sparsity of the solution, making it possible to avoid overfitting and underfitting. We validate our approach on several large benchmark datasets. We show that with the ability to control sparsity, the proposed Sparse AMM yields superior classification accuracy to the original AMM while simultaneously achieving computational speedup.

A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting

This paper presents a novel design of interval type-2 fuzzy logic systems (IT2FLS) by utilizing the theory of extreme learning machine (ELM) for electricity load demand forecasting. ELM has become a popular learning algorithm for single hidden layer feed-forward neural networks (SLFN). From the functional equivalence between the SLFN and fuzzy inference system, a hybrid of fuzzy-ELM has gained attention of the researchers. This paper extends the concept of fuzzy-ELM to an IT2FLS based on ELM (IT2FELM). In the proposed design the antecedent membership function parameters of the IT2FLS are generated randomly, whereas the consequent part parameters are determined analytically by the Moore-Penrose pseudo inverse. The ELM strategy ensures fast learning of the IT2FLS as well as optimality of the parameters. Effectiveness of the proposed design of IT2FLS is demonstrated with the application of forecasting nonlinear and chaotic data sets. Nonlinear data of electricity load from the Australian National Electricity Market for the Victoria region and from the Ontario Electricity Market are considered here. The proposed model is also applied to forecast Mackey-glass chaotic time series data. Comparative analysis of the proposed model is conducted with some traditional models such as neural networks (NN) and adaptive neuro fuzzy inference system (ANFIS). In order to verify the structure of the proposed design of IT2FLS an alternate design of IT2FLS based on Kalman filter (KF) is also utilized for the comparison purposes.

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013.

Sexual blinging of women : Alice Walker's african character tashi and issue of female genital cutting

This thesis provides a reading of the different forms of representation that can be attributed to the character Tashi, the protagonist of the novel Possessing the Secret of Joy (1992), written by the African American writer Alice Walker. Before this work Tashi had already appeared in two previous novels by Walker, first, in The Color Purple (1982) and then, as a mention, in The Temple of My Familiar (1989). With Tashi, the author introduces the issue of female circumcision, a ritual Tashi submits herself to at the beginning of her adult life. The focus of observation lies in the ways in which the author’s anger is transformed into a means of creative representation. Walker uses her novel Possessing the Secret of Joy openly as a political instrument so that the expression “female mutilation” (term used by the author) receives ample attention from the media and critics in general. The aim of this investigation is to evaluate to what extent Walker’s social engagement contributes to the development of her work and to what extent it undermines it. For the analysis of the different issues related to “female genital cutting”, the term I use in this thesis, the works of feminist critics and writers such as Ellen Gruenbaum, Lightfoot-Klein, Nancy Hartsock, Linda Nicholson, Efrat Tseëlon and the Egyptian writer and doctor Nawal El Saadawi will be consulted. I hope that this thesis can contribute as an observation about Alice Walker’s use of her social engagement in the creation of her fictional world.