Classification of Disaster Risks in the Philippines using Adaptive Boosting Algorithm with Decision Trees and Support Vector Machine as Based Estimators

This paper employed the intelligent approach based on machine learning categorized as base and ensemble methods in classifying the disaster risk in the Philippines. It focused on the Decision Trees, Support Vector Machine, Adaptive Boosting Algorithm with Decision Trees, and Support Vector Machine as base estimators. The research used the Exponential Regression for missing value imputation and converted the number of casualties, damaged houses, and properties into five (5) risk levels using Quantile Method. The 10-fold cross-validation was used to validate the proposed algorithms. The experiment shows that Decision Trees and Adaptive Decision Trees are the most suitable models for the disaster data with the score of more than 90%, more than 75%, more than 75% in all the classification metrics (accuracy, precision, recall f1-score) when applied to classification risk levels of casualties, damaged houses and damaged properties respectively.

Ground-state Shallow-donor Binding Energy in (In,Ga)N/GaN Double QWs Under Temperature, Size, and the Impurity Position Effects

In this paper, we study the hydrogen-like donor-impurity binding energy of the ground-state change as a function of the well width under the effect of temperature, size, and impurity position. Within the framework of the effective mass approximation, the Schrodinger-Poisson equation has been solved taken account an on-center hydrogen-like impurity in double QWs with rectangular finite confinement potential profile for 10% of indium concentration in the (well region). The eigenvalues and their correspondent eigenvectors have been obtained by the fined element method (FEM). The obtained results are in good agreement with the literature and show that the temperature, size, and the impurity position have a significant impact on the binding energy of a hydrogen-like impurity in symmetric double coupled quantum wells based on non-polar wurtzite (In,Ga) N/GaN core/Shell.

External Environment Dependent Spin and Orbital Exchange Interactions

We present a set of equations expressing the parameters of the magnetic interactions of an electronic system. This allows to establish a mapping between the initial electronic system and a spin model including up to quadratic interactions between the effective spins, with a general interaction (exchange) tensor that accounts for anisotropic exchange, Dzyaloshinskii–Moriya interaction and other symmetric terms such as dipole–dipole interaction. We present the formulas in a format that can be used for computations via Dynamical Mean Field Theory algorithms.

Application of Enhanced Hidden Markov Model in Stock Price Prediction

The main focus of this research is the enhancement of the Hidden Markov Model by using some features of Neural Networks and the forecasted values of predictors by Seasonal Autoregressive Integrated Moving Average. The enhanced method was used to predict the close price of stocks whose predictors are open price, high price, low price, and volume of Apple and Nokia data. The performance of the method was measured using the Mean Absolute Percentage Error of the predicted price. The result was compared against the actual close price by using the paired T-test. The testing of the hypothesis showed that the Enhanced Hidden Markov Model obtained more than 94% accuracy rate. It also shows that in Apple data, the predicted close price of the Enhanced Hidden Markov Model is significantly better than the predicted close price of Neural Networks. Using Nokia data, the test claims that there is no difference between the performance of Enhanced Hidden Markov Model and Neural Network in prediction.

Modelling and Simulation of Below-the-Hook Lifting Device Balanced C-hook for Load to Investigate the Static and Model Analysis for Various Grades of Steels by Numerical Method

Below-the-Hook Lifting Devices will enable easy loading, unloading and transportation of heavy metal coils. A balanced c-hook is one of the widely used Below-the-Hook Lifting Devices works by inserting its lower arm inside the hole of coil. Multiple variations of c hooks are being used based on different load requirements, and different applications like paper roll c hooks to transport heavy paper rolls, spring loaded c hooks to reduce weight by avoiding counter weights etc. Structural and mechanical lifters may be modified or re-assessed, provided that such alterations are analyzed and approved. Balanced c-Hook are to be designed by considering forces imposed by the lifted load, the weights of the device’s parts, since balanced c hooks are subjected repeated loading and unloading, there is probability of failure due stress concentration. To prevent chances of failure, Prior study is required on this; the materials which are generally used for the C hook are considered for a particular loading condition of 10 Tons, Design of the bellow the hooks is done by numerical method. Modeling and Simulations are carried to determine the various factors like deformation, stresses generated and mode shapes. The comparison among all the selected materials is done to check the suitability of the material to use as a balanced C hook. For the generation of CAD model of C -Hook various geometrical features and Dimensions are selected as per the specification from ASME B30.20 standard. To investigate statics stress results and model are obtained from Finite element Method. from the results of the analysis it is observed that results obtained are in close match with each other and maximum stress concentration occurs at inner most surface.

Validation and Optimization of Thermophysical Properties for Thermal Conductivity and Viscosity of Nanofluid Engine Oil using Neural Network

In this study, the thermophysical properties of thermal conductivity and viscosity of a motor oil nanofluid were investigated using experimental data and artificial neural network. NSGA II optimization algorithm was used to maximize thermal conductivity and minimum viscosity with changes in temperature and volume fraction of nanofluids. Also, to obtain the viscosity and thermal conductivity values in terms of nanofluid temperature and volume fraction with 174 experimental data, neural network modeling was performed. Input data include temperature and volume fraction, and output is viscosity and thermal conductivity. Various indices such as R squared and Mean Square Error (MSE) have been used to evaluate the accuracy of modeling in the prediction of viscosity and thermal conductivity of nanofluids. The coefficient of determination R squared is 0.9989 indicating acceptable agreement with the experimental data. In order to optimize and finally results as an objective function, the optimization algorithm is presented and the Parto front and its corresponding optimum points are presented where the maximum optimization results of thermal conductivity and viscosity occur at 1% volume fraction.

Mathematical Analysis of the Role of Detection Rate on Dynamical Spread of Ebola Virus Disease

In this paper, a non-linear mathematical model of the Ebola virus disease with case detection rate is proposed and analyzed. The whole population under consideration is divided into five compartments e.g. susceptible, latently infected, infected undetected, infected detected, and recovered to study the transmission dynamics of the Ebola virus disease. Based on the immunity level, susceptible individuals move to exposed class or directly to infected detected class once they come into contact with an infective. This has been incorporated through the progression rate which is slow. The equilibria of the model and the basic reproduction number R0 are computed. It is observed that the disease-free equilibrium of the model is locally asymptotically stable when R0<1. The model exhibits forward bifurcation under certain restrictions on parameters, which indicate that the model has a single endemic equilibrium for R0<1. This suggests that an accurate estimation of parameters and the level of control measures are required to reduce the infection prevalence of the Ebola virus in the endemic region and just R0<1 is enough to eliminate the disease from the population. R0needs to be lowered much below one to confirm the global stability of the disease-free equilibrium. Numerical simulation is performed to demonstrate the analytical results. It is found that the increase in the rate of case detection rate leads to a decrease in the threshold value of R0. Numerical simulations have been carried out to support the analytic results.

Analytical Investigation of the Vibrational and Dynamic Response of Nano-Composite Cylindrical Shell Under Thermal Shock and Mild Heat Field by DQM Method

In this paper, the vibrations and dynamic response of an orthotropic thin-walled composite cylindrical shell with epoxy graphite layers reinforced with carbon nanotubes under heat shock and heat field loading are investigated. the carbon nanotubes were uniformly distributed along the thickness of the composite layer. The problem is that at first there is a temperature change due to the thermal field in the cylinder and the cylinder is coincident with the thermal field, then the surface temperature of the cylinder rises abruptly. Partial derivative equations of motion are coupled to heat equations. The differential quadrature method (DQM) is used to solve the equations. In this study, the effects of length, temperature, thickness and radius parameters on the natural frequencies and mid-layer displacement are investigated. The results show that increasing the outside temperature reduces the natural frequency and increases the displacement of the system. Radial displacement results were also compared with previous studies and were found to be in good agreement with previous literature. Increasing the percentage of carbon nanotubes also increased the natural frequency of the system and decreased the mobility of the middle layer.

Using Artificial Intelligence Techniques for Prediction and Estimation of Photovoltaic System Output Power

Two artificial intelligence methods, namely, support vector machines (SVM) and gene expression programming (GEP), were explored for prediction and estimation of the Photovoltaic (PV)output power. Measured values of temperature T (°C) and irradiance E (kWh/㎡) were used as inputs (independent variables) and PV output power P (Kw) was used as output (dependent variable). The statistical metrics were used to assess the predictive performances of the methods. The results of the two models were estimated and compared. The results showed that the two techniques performances are better and similar. Using GEP technique, the relationships between the two parameters and output power were established. Importance of each parameter as contributor to PV output power was also investigated. The results indicated that the SVM and GEP would become the powerful tools that could help estimate the PV output power capacity reserve.

Multi-objective Optimization to Increase Nusselt Number and Reduce Friction Coefficient of Water/Carbon Nanotubes via NSGA II using Response Surface Methodology

Heat transfer science is one of the most important and most applied engineering sciences, with the importance of energy management and energy conservation being doubled. Because of their properties, nanofluids have been widely used in various industries, making them particularly important to study. In this paper, the Nusselt number and coefficient of friction with volume fraction ranging from 0 to 0.1 at approximately Reynolds numbers of 200 to 5000 are studied experimentally. Higher thermal conductivity, better stability, lower pressure drop was observed using nanoparticles of solid particles. NSGA II algorithm was used to maximize Nusselt number and minimum friction coefficient by changing temperature and volume fraction of nanoparticles. To obtain Nusselt number and friction coefficient based on the temperature and volume fraction of the nanoparticles, the experimental data response surface methodology was used and with increasing Reynolds number, the Nusselt number increased and the friction coefficient decreased. In order to evaluate the objective functions in the optimization, the response surface methodology is attached to the optimization algorithm. At the end, the Pareto Front and its corresponding optimal points are presented.