Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Evaluating an automated procedure of machine learning parameter tuning for software effort estimation

Software effort estimation requires accurate prediction models. Machine learning algorithms have been used to create more accurate estimation models. However, these algorithms are sensitive to factors such as the choice of hyper-parameters. To reduce this sensitivity, automated approaches for hyper-parameter tuning have been recently investigated. There is a need for further research on the effectiveness of such approaches in the context of software effort estimation. These evaluations could help understand which hyper-parameter settings can be adjusted to improve model accuracy, and in which specific contexts tuning can benefit model performance. The goal of this work is to develop an automated procedure for machine learning hyper-parameter tuning in the context of software effort estimation. The automated procedure builds and evaluates software effort estimation models to determine the most accurate evaluation schemes. The methodology followed in this work consists of first performing a systematic mapping study to characterize existing hyper-parameter tuning approaches in software effort estimation, developing the procedure to automate the evaluation of hyper-parameter tuning, and conducting controlled quasi experiments to evaluate the automated procedure. From the systematic literature mapping we discovered that effort estimation literature has favored the use of grid search. The results we obtained in our quasi experiments demonstrated that fast, less exhaustive tuners were viable in place of grid search. These results indicate that randomly evaluating 60 hyper-parameters can be as good as grid search, and that multiple state-of-the-art tuners were only more effective than this random search in 6% of the evaluated dataset-model combinations. We endorse random search, genetic algorithms, flash, differential evolution, and tabu and harmony search as effective tuners.Los algoritmos de aprendizaje automático han sido utilizados para crear modelos con mayor precisión para la estimación del esfuerzo del desarrollo de software. Sin embargo, estos algoritmos son sensibles a factores, incluyendo la selección de hiper parámetros. Para reducir esto, se han investigado recientemente algoritmos de ajuste automático de hiper parámetros. Es necesario evaluar la efectividad de estos algoritmos en el contexto de estimación de esfuerzo. Estas evaluaciones podrían ayudar a entender qué hiper parámetros se pueden ajustar para mejorar los modelos, y en qué contextos esto ayuda el rendimiento de los modelos. El objetivo de este trabajo es desarrollar un procedimiento automatizado para el ajuste de hiper parámetros para algoritmos de aprendizaje automático aplicados a la estimación de esfuerzo del desarrollo de software. La metodología seguida en este trabajo consta de realizar un estudio de mapeo sistemático para caracterizar los algoritmos de ajuste existentes, desarrollar el procedimiento automatizado, y conducir cuasi experimentos controlados para evaluar este procedimiento. Mediante el mapeo sistemático descubrimos que la literatura en estimación de esfuerzo ha favorecido el uso de la búsqueda en cuadrícula. Los resultados obtenidos en nuestros cuasi experimentos demostraron que algoritmos de estimación no-exhaustivos son viables para la estimación de esfuerzo. Estos resultados indican que evaluar aleatoriamente 60 hiper parámetros puede ser tan efectivo como la búsqueda en cuadrícula, y que muchos de los métodos usados en el estado del arte son solo más efectivos que esta búsqueda aleatoria en 6% de los escenarios. Recomendamos el uso de la búsqueda aleatoria, algoritmos genéticos y similares, y la búsqueda tabú y harmónica.Escuela de Ciencias de la Computación e InformáticaCentro de Investigaciones en Tecnologías de la Información y ComunicaciónUCR::Vicerrectoría de Investigación::Sistema de Estudios de Posgrado::Ingeniería::Maestría Académica en Computación e Informátic

Finding Optimum Resource Allocation to Optimizing Construction Project Time/Cost through Combination of Artificial Agents CPM and GA

In order to plan a construction project, computer simulations are frequently used to predict the performance of the operations through simulating the process flows and resource selection procedure. However, for finding the optimum resource allocation of the construction activities, all possible combinations must be tested through simulation study. If the number of activities and allocated resources are high, the numbers of these combinations become too large, then this process would not be economical task to do. Therefore, simulation analysis is no longer considered through an optimization technique. Using of Genetic Algorithms (GA) is one of the simple and widely used tools for optimizing heavy intensive engineering problems which can covers various areas of research. With keeping this in mind, this study presented a new hybrid model which integrated agent based modeling with CPM and GA to find out the best resource allocation combination for the construction project’s activities. Based on the results obtained, this new hybrid model can eectively find the optimum resource allocation with respect to time, cost, or any combination of time-cost

Novel hybridized computational paradigms integrated with five stand-alone algorithms for clinical prediction of HCV status among patients: A data-driven technique

The emergence of health informatics opens new opportunities and doors for different disease diagnoses. The current work proposed the implementation of five different stand-alone techniques coupled with four different novel hybridized paradigms for the clinical prediction of hepatitis C status among patients, using both sociodemographic and clinical input variables. Both the visualized and quantitative performances of the stand-alone algorithms present the capability of the Gaussian process regression (GPR), Generalized neural network (GRNN), and Interactive linear regression (ILR) over the Support Vector Regression (SVR) and Adaptive neuro-fuzzy inference system (ANFIS) models. Hence, due to the lower performance of the stand-alone algorithms at a certain point, four different novel hybrid data intelligent algorithms were proposed, including: interactive linear regression-Gaussian process regression (ILR-GPR), interactive linear regression-generalized neural network (ILR-GRNN), interactive linear regression-Support Vector Regression (ILR-SVR), and interactive linear regression-adaptive neuro-fuzzy inference system (ILR-ANFIS), to boost the prediction accuracy of the stand-alone techniques in the clinical prediction of hepatitis C among patients. Based on the quantitative prediction skills presented by the novel hybridized paradigms, the proposed techniques were able to enhance the performance efficiency of the single paradigms up to 44% and 45% in the calibration and validation phases, respectively.Operational Research Centre in Healthcare, Near East University, North Cyprus, Mersin-10, Turkiy

Efficient Learning Machines

Computer scienc

Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach

Global Navigation Satellite System-Reflectometry (GNSS-R) as a microwave remote sensing technique can retrieve the Earth’s surface parameters using the GNSS reflected signal from the surface. These reflected signals convey the surface features and therefore can be utilized to detect certain physical properties of the reflecting surface such as soil moisture content (SMC). Up to now, a serial of electromagnetic models (e.g., bistatic radar and Fresnel equations, etc.) are employed and solved for SMC retrieval. However, due to the uncertainty of the physical characteristics of the sites, complexity, and nonlinearity of the inversion process, etc., it is still challenging to accurately retrieve the soil moisture. The popular machine learning (ML) methods are flexible and able to handle nonlinear problems. It can dig out and model the complex interactions between input and output and ultimately make good predictions. In this paper, two typical ML methods, specifically, random forest (RF) and support vector machine (SVM), are employed for SMC retrieval from GNSS-R data of self-designed experiments (in situ and airborne). A comprehensive simulated dataset involving different types of soil is constructed firstly to represent the complex interactions between the variables (reflectivity, elevation angle, dielectric constant, and SMC) for the requirement of training ML regression models. Correspondingly, the main task of soil moisture retrieval (regression) is addressed. Specifically, the post-processed data (reflectivity and elevation angle) from sensor acquisitions are used to make predictions by these two adopted ML methods and compared with the commonly used GNSS-R retrieval method (electromagnetic models). The results show that the RF outperforms the SVM method, and it is more suitable for handling the inversion problem. Moreover, the RF regression model built by the comprehensive dataset demonstrates satisfactory accuracy and strong universality, especially when the soil type is not uniform or unknown. Furthermore, the typical task of detecting water/soil (classification) is discussed. The ML algorithms demonstrate a high potential and efficiency in SMC retrieval from GNSS-R data

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Relationship of TRIM5 and TRIM22 polymorphisms with liver disease and HCV clearance after antiviral therapy in HIV/HCV coinfected patients

BACKGROUND AND AIMS: TRIM5 and TRIM22 are restriction factors involved in innate immune response and exhibit anti-viral activity. Single nucleotide polymorphisms (SNPs) at TRIM5 and TRIM22 genes have shown to influence several viral infections such as human immunodeficiency virus (HIV), hepatitis B, as well as measles and rubella vaccination. The aim of this study is to analyze whether TRIM5 and TRIM22 polymorphisms are associated with liver fibrosis inflammation-related biomarkers and response to pegylated-interferon-alpha plus ribavirin (pegIFNα/RBV) therapy in HIV/hepatitis C virus (HCV) coinfected patients. METHODS: A retrospective study was performed in 319 patients who started pegIFNα/RBV therapy. Liver fibrosis stage was characterized in 288 patients. TRIM5 rs3824949 and TRIM22 polymorphisms (rs1063303, rs7935564, and rs7113258) were genotyped using the GoldenGate assay. The primary outcomes were: a) significant liver fibrosis (≥F2) evaluated by liver biopsy or transient elastography (liver stiffness values ≥7.1 Kpa); b) sustained virological response (SVR) defined as no detectable HCV viral load (<10 IU/mL) at week 24 after the end of the treatment. The secondary outcome variable was plasma chemokine levels. RESULTS: Patients with TRIM5 rs3824949 GG genotype had higher SVR rate than patients with TRIM5 rs3824949 CC/CG genotypes (p = 0.013), and they had increased odds of achieving SVR (adjusted odds ratio (aOR = 2.58; p = 0.012). Patients with TRIM22 rs1063303 GG genotype had higher proportion of significant liver fibrosis than patients with rs1063303 CC/CG genotypes (p = 0.021), and they had increased odds of having significant hepatic fibrosis (aOR = 2.19; p = 0.034). Patients with TRIM22 rs7113258 AT/AA genotype had higher SVR rate than patients with rs7113258 TT genotypes (p = 0.013), and they had increased odds of achieving SVR (aOR = 1.88; p = 0.041). The TRIM22 haplotype conformed by rs1063303_C and rs7113258_A was more frequent in patients with SVR (p = 0.018) and was significantly associated with achieving SVR (aOR = 2.80; p = 0.013). The TRIM5 rs3824949 GG genotype was significantly associated with higher levels of GRO-α (adjusted arithmetic mean ratio ((aAMR) = 1.40; p = 0.011) and MCP-1 (aAMR = 1.61; p = 0.003). CONCLUSIONS: TRIM5 and TRIM22 SNPs are associated to increased odds of significant liver fibrosis and SVR after pegIFNα/RBV therapy in HIV/HCV coinfected patients. Besides, TRIM5 SNP was associated to higher baseline levels of circulating biomarkers GRO and MCP-1.The authors wish to thank the Spanish National Genotyping Center (CeGen) for providing the genotyping services (http://www.cegen.org). We also acknowledge the patients in this study for their participation.S

Global Nonlinear Kernel Prediction for Large Dataset with a Particle Swarm Optimized Interval Support Vector Regression

A new global nonlinear predictor with a particle swarm-optimized interval support vector regression (PSO-ISVR) is proposed to address three issues (viz., kernel selection, model optimization, kernel method speed) encountered when applying SVR in the presence of large data sets. The novel prediction model can reduce the SVR computing overhead by dividing input space and adaptively selecting the optimized kernel functions to obtain optimal SVR parameter by PSO. To quantify the quality of the predictor, its generalization performance and execution speed are investigated based on statistical learning theory. In addition, experiments using synthetic data as well as the stock volume weighted average price are reported to demonstrate the effectiveness of the developed models. The experimental results show that the proposed PSO-ISVR predictor can improve the computational efficiency and the overall prediction accuracy compared with the results produced by the SVR and other regression methods. The proposed PSO-ISVR provides an important tool for nonlinear regression analysis of big data

Improving Prediction of Systemic Statin Exposure Using Concomitant Medications, Non-Linear Modelling and Novel SNP Discovery

Introduction: Statin drugs are a highly efficacious treatment for hypercholesterolemia and adherent treatment with statins reduces the risk of cardiovascular disease. Although statins are generally well tolerated, myalgia (muscle pain) is a common side effect and can lead to non-compliance with treatment. Increased systemic exposure may contribute to the development of myalgia. Drug-drug interactions inhibiting statin metabolism and impaired drug transporter function may lead to decreased statin clearance. Establishing accurate predictive models is an important step towards preventing adverse drug events by titrating statin dosing to limit systemic exposure. Objectives: 1) To develop an algorithm to select concomitant medications for incorporation into the existing systemic exposure model and assess their predictive impact; 2) to apply nonlinear techniques to model systemic statin exposure, and assess their effectiveness and feasibility; 3) to identify novel genes and corresponding single nucleotide polymorphisms by NGS in patients whose statin plasma concentrations were under-predicted using the original systemic exposure model to guide future biological research. Methods: Data from a previously-collected prospective cohort of 130 patients prescribed rosuvastatin and 128 patients prescribed atorvastatin were used in this analysis. Concomitant medications were selected using penalized. Stable feature selection was achieved by repeated cross validation. Generalized additive models (GAMs) and support vector regression (SVR) were assessed as candidate nonlinear models. Candidate patients were chosen for NGS sequencing based on the proportional difference between their true and predicted values from the original systemic exposure model. Variant prioritization used the Sequence Kernel Association Test. Results: Atorvastatin linear model fit was statistically significantly improved by incorporating the selected concomitant medications, but rosuvastatin model fit was not. Predictive performance was not improved using GAMs or SVR compared to linear regression, likely due to small sample size. Three candidate genes and corresponding observed variants were identified and discussed as potential predictors of systemic rosuvastatin exposure. Conclusion: Linear modelling of systemic atorvastatin exposure can be improved by incorporating concomitant medications. The feasibility of using nonlinear predictive models is limited by small sample size. Future research on newly identified interacting medication and genetic variants may provide new insights regarding underlying molecular mechanisms affecting systemic statin exposure