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Autonomous Surveillance of Infants’ Needs Using CNN Model for Audio Cry Classification

Journal of Data Analysis and Information Processing 2022年第4期10卷 198-219页

作者： Geofrey Owino anthony Waititu anthony wanjoya John Okwiri Department of Statistics and Actuarial Sciences Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya

Infants portray suggestive unique cries while sick, having belly pain, discomfort, tiredness, attention and desire for a change of diapers among other needs. There exists limited knowledge in accessing the infants’ needs as they only relay information through suggestive cries. Many teenagers tend to give birth at an early age, thereby exposing them to be the key monitors of their own babies. They tend not to have sufficient skills in monitoring the infant’s dire needs, more so during the early stages of infant development. Artificial intelligence has shown promising efficient predictive analytics from supervised, and unsupervised to reinforcement learning models. This study, therefore, seeks to develop an android app that could be used to discriminate the infant audio cries by leveraging the strength of convolution neural networks as a classifier model. Audio analytics from many kinds of literature is an untapped area by researchers as it’s attributed to messy and huge data generation. This study, therefore, strongly leverages convolution neural networks, a deep learning model that is capable of handling more than one-dimensional datasets. To achieve this, the audio data in form of a wave was converted to images through Mel spectrum frequencies which were classified using the computer vision CNN model. The Librosa library was used to convert the audio to Mel spectrum which was then presented as pixels serving as the input for classifying the audio classes such as sick, burping, tired, and hungry. The study goal was to incorporate the model as an android tool that can be utilized at the domestic level and hospital facilities for surveillance of the infant’s health and social needs status all time round.

关键词： Convolutional Neural Network (CNN) Mel Frequency Cepstral Coefficients (MFCCs) Rectified Linear Unit (ReLU) Activation Function Audio Analytics Deep Neural Network (DNN)

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A Hybrid DNN-RBFNN Model for Intrusion Detection System

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Journal of Data Analysis and Information Processing 2023年第4期11卷 371-387页

作者： Wafula Maurice Oboya anthony Waititu Gichuhi anthony wanjoya Department of Statistics and Actuarial Sciences Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya

Intrusion Detection Systems (IDS) are pivotal in safeguarding computer networks from malicious activities. This study presents a novel approach by proposing a Hybrid Dense Neural Network-Radial Basis Function Neural Network (DNN-RBFNN) architecture to enhance the accuracy and efficiency of IDS. The hybrid model synergizes the strengths of both dense learning and radial basis function networks, aiming to address the limitations of traditional IDS techniques in classifying packets that could result in Remote-to-local (R2L), Denial of Service (Dos), and User-to-root (U2R) intrusions.

关键词： Dense Neural Network (DNN) Radial Basis Function Neural Network (RBFNN) Intrusion Detection System (IDS) Denial of Service (DoS) Remote to Local (R2L) User-to-Root (U2R)

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Spatio-Temporal Variation of HIV Infection in Kenya

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Open Journal of Statistics 2018年第5期8卷 811-830页

作者： Benard Tonui Samuel Mwalili anthony wanjoya Department of Mathematics & Computer Science University of Kabianga Kericho Kenya Department of Statistics & Actuarial Science Jomo Kenyatta University of Agriculture & Technology Juja Kenya

Disease mapping is the study of the distribution of disease relative risks or rates in space and time, and normally uses generalized linear mixed models (GLMMs) which includes fixed effects and spatial, temporal, and spatio-temporal random effects. Model fitting and statistical inference are commonly accomplished through the empirical Bayes (EB) and fully Bayes (FB) approaches. The EB approach usually relies on the penalized quasi-likelihood (PQL), while the FB approach, which has increasingly become more popular in the recent past, usually uses Markov chain Monte Carlo (McMC) techniques. However, there are many challenges in conventional use of posterior sampling via McMC for inference. This includes the need to evaluate convergence of posterior samples, which often requires extensive simulation and can be very time consuming. Spatio-temporal models used in disease mapping are often very complex and McMC methods may lead to large Monte Carlo errors if the dimension of the data at hand is large. To address these challenges, a new strategy based on integrated nested Laplace approximations (INLA) has recently been recently developed as a promising alternative to the McMC. This technique is now becoming more popular in disease mapping because of its ability to fit fairly complex space-time models much more quickly than the McMC. In this paper, we show how to fit different spatio-temporal models for disease mapping with INLA using the Leroux CAR prior for the spatial component, and we compare it with McMC using Kenya HIV incidence data during the period 2013-2016.

关键词： HIV INLA McMC Leroux CAR Prior Disease Mapping Spatio-Temporal Models

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Statistical Modeling of Malaria Incidences in Apac District, Uganda

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Open Journal of Statistics 2017年第6期7卷 901-919页

作者： Ayo Eunice anthony wanjoya Livingstone Luboobi Pan African University Institute of Basic Sciences Technology and Innovation Nairobi Kenya Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya Strathmore University Nairobi Kenya

Malaria is a major cause of morbidity and mortality in Apac district, Northern Uganda. Hence, the study aimed to model malaria incidences with respect to climate variables for the period 2007 to 2016 in Apac district. Data on monthly malaria incidence in Apac district for the period January 2007 to December 2016 was obtained from the Ministry of health, Uganda whereas climate data was obtained from Uganda National Meteorological Authority. Generalized linear models, Poisson and negative binomial regression models were employed to analyze the data. These models were used to fit monthly malaria incidences as a function of monthly rainfall and average temperature. Negative binomial model provided a better fit as compared to the Poisson regression model as indicated by the residual plots and residual deviances. The Pearson correlation test indicated a strong positive association between rainfall and malaria incidences. High malaria incidences were observed in the months of August, September and November. This study showed a significant association between monthly malaria incidence and climate variables that is rainfall and temperature. This study provided useful information for predicting malaria incidence and developing the future warning system. This is an important tool for policy makers to put in place effective control measures for malaria early enough.

关键词： Malaria Incidence Climate Variables Poisson Regression Negative Binomial Regression Generalized Linear Model Apac District

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A Simulation Study of Hierarchical Bayesian Fusion Spatial Small Area Model for Binary Outcome under Spatial Misalignment

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Open Journal of Statistics 2021年第6期11卷 993-1009页

作者： Kindie Fentahun Muchie anthony Kibira wanjoya Samuel Musili Mwalili Institute for Basic Sciences Technology and Innovation Pan African University Nairobi Kenya Department of Epidemiology and Biostatistics School of Public Health College of Medicine and Health Sciences Bahir Dar University Bahir Dar Ethiopia Department of Statistics and Actuarial Sciences Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya

Simulation (stochastic) methods are based on obtaining random samples θ5 from the desired distribution p(θ) and estimating the expectation of any function h(θ). Simulation methods can be used for high-dimensional distributions, and there are general algorithms which work for a wide variety of models. Markov chain Monte Carlo (MCMC) methods have been important in making Bayesian inference practical for generic hierarchical models in small area estimation. Small area estimation is a method for producing reliable estimates for small areas. Model based Bayesian small area estimation methods are becoming popular for their ability to combine information from sever

关键词： Simulation Small Area Estimation Hierarchical Bayesian Spatial Misalign-ment Fusion Process

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Bayesian Non-Parametric Mixture Model with Application to Modeling Biological Markers

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Journal of Data Analysis and Information Processing 2019年第4期7卷 141-152页

作者： Mercy K. Peter Levi Mbugua anthony wanjoya Department of Mathematics Pan African University Institute for Basic Sciences Technology and Innovation Nairobi Kenya Department of Statistics and Actuarial science Technical University of Kenya Nairobi Kenya Department of Statistics and Actuarial science Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya

The effect of treatment on patient’s outcome can easily be determined through the impact of the treatment on biological events. Observing the treatment for patients for a certain period of time can help in determining whether there is any change in the biomarker of the patient. It is important to study how the biomarker changes due to treatment and whether for different individuals located in separate centers can be clustered together since they might have different distributions. The study is motivated by a Bayesian non-parametric mixture model, which is more flexible when compared to the Bayesian Parametric models and is capable of borrowing information across different centers allowing them to be grouped together. To this end, this research modeled Biological markers taking into consideration the Surrogate markers. The study employed the nested Dirichlet process prior, which is easily peaceable on different distributions for several centers, with centers from the same Dirichlet process component clustered automatically together. The study sampled from the posterior by use of Markov chain Monte carol algorithm. The model is illustrated using a simulation study to see how it performs on simulated data. Clearly, from the simulation study it was clear that, the model was capable of clustering data into different clusters.

关键词： Bayesian Non-Parametric Nested Dirichlet Process Biomarker Clustering Surrogate Markers Dirichlet Process Markov Chain Monte Carlo

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Mixed-Effects Parametric Proportional Hazard Model with Generalized Log-Logistic Baseline Distribution

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Journal of Data Analysis and Information Processing 2023年第2期11卷 81-102页

作者： Maryrose Wausi Peter Samuel Musili Mwalili anthony Kibira wanjoya Abdsalam Hassan Muse Pan African University Institute for Basic Sciences Technology and Innovation (PAUSTI) Nairobi Kenya Jomo kenyatta University of Agriculture and Technology (JKUAT) Nairobi Kenya

Clustered survival data are widely observed in a variety of setting. Most survival models incorporate clustering and grouping of data accounting for between-cluster variability that creates correlation in order to prevent underestimate of the standard errors of the parameter estimators but do not include random effects. In this study, we developed a mixed-effect parametric proportional hazard (MEPPH) model with a generalized log-logistic distribution baseline. The parameters of the model were estimated by the application of the maximum likelihood estimation technique with an iterative optimization procedure (quasi-Newton Raphson). The developed MEPPH model’s performance was evaluated using Monte Carlo simulation. The Leukemia dataset with right-censored data was used to demonstrate the model’s applicability. The results revealed that all covariates, except age in PH models, were significant in all considered distributions. Age and Townsend score were significant when the GLL distribution was used in MEPPH, while sex, age and Townsend score were significant in MEPPH model when other distributions were used. Based on information criteria values, the Generalized Log-Logistic Mixed-Effects Parametric Proportional Hazard model (GLL-MEPPH) outperformed other models.

关键词： Survival Analysis Generalized Log-Logistic Parametric Proportional Hazard Mixed-Effects Monte Carlo Maximum Likelihood Estimation

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Effects of Andrade and Burgers rheologies on glacial isostatic adjustment modeling in Antarctica

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Geodesy and Geodynamics 2024年第5期15卷 429-440页

作者： Alexandre Boughanemi anthony Mémin UniversitéCôte d'Azur CNRSObservatoire de la Côte d'AzurIRDGéoazur250 rue Albert EinsteinSophia Antipolis 06560 ValbonneFrance

Variations in ice mass deform the Earth and modify its gravity field,a process known as Glacial Isostatic Adjustment(GIA).GIA in Antarctica remains poorly constrained due to the cumulative effect of past and present ice-mass changes,the unknown history of the past ice-mass change,and the uncertainties on the mechanical properties of the *** paper investigates the effect of using Andrade and Burgers viscoelastic rheologies,rather than the commonly used Maxwell rheology,to model GIA-induced deformation in *** Love number and Green's function formalism are used to compute the radial surface displacements and the gravity changes induced by the past and present ice-mass *** consider an Earth model whose elastic properties and radial structure are averaged from the Preliminary Reference Earth Model and two viscosity profiles to account for the recently published results on the present ice-mass *** the three rheological laws affects the temporal response of the Earth differently,leading to smaller discrepancies than those induced by the two viscosity *** differences are the largest between Maxwell and Burgers rheologies during the 100-1000 years following the beginning of the surface-mass *** show that using the Andrade and Burgers rheologies allows the Earth to respond on decennial to centennial time scales,up to 10 m more than *** only the recent ice-mass changes,the deformation rates derived from Burgers and Andrade rheologies are several times larger than those estimated by Maxwell rheology.

关键词： GIA Antarctica Modeling Rheology Displacement Viscosity GNSS

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Numerical modeling of external light gauge steel framed wall systems exposed to bushfire flame zone conditions

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Frontiers of Structural and Civil Engineering 2024年第9期18卷 1424-1444页

作者： Sahani HENDAWITHARANA anthony ARIYANAYAGAM Mahen MAHENDRAN Faculty of Engineering Queensland University of TechnologyBrisbaneQLD 4000Australia

Bushfire-related building losses cause adverse economic impacts to countries prone to *** materials and components play a vital role in reducing these ***,due to high costs of experimental studies and lack of numerical studies,the heat transfer behavior of building’s external components in bushfire-prone areas has not been adequately *** large-scale heat transfer models are developed using Computational Fluid Dynamics(CFD)tools,and the availability of CFD models for heat transfer in building components improves the understanding of the behavior of systems and systems of ***,this paper uses a numerical modeling approach to investigate the bushfire/wildfire resistance of external Light gauge Steel Framed(LSF)wall *** full-scale and small-scale heat transfer models were developed for the LSF wall *** results of six internal and external LSF wall systems with varying plasterboard thickness and cladding material were used to validate the developed *** study was then extended to investigate the bushfire resistance of seven external wall systems under two different bushfire flame zone *** results illustrate the significant effects of fire curves,LSF wall components and configuration on the heat transfer across the *** have shown 1)the favorable performance of steel cladding and Autoclaved Aerated Concrete(AAC)panels when used on the external side of wall systems and 2)the adequacy of thin-walled steel studs’load-bearing capacity during bushfire *** study has shown that most of the investigated external LSF walls could be reused with cost-effective retrofitting such as replacing the Fire Side(FS)steel cladding after bushfire ***,this study has advanced the understanding of the behavior of external light steel framed walls under bushfire flame zone conditions.

关键词： bushfires flame zone external light gauge steel framed walls heat transfer analyses numerical models

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A Simulation Study of Hierarchical Bayesian Fusion Spatial Small Area Model for Binary Outcome under Spatial Misalignment

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Open Journal of Statistics（统计学期刊） 2021年第6期

作者： Kindie Fentahun Muchie anthony Kibira wanjoya Samuel Musili Mwalili

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