Periodicals of Engineering and Natural Sciences (PEN - International University of Sarajevo)
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RETRACTED: : Neural network to investigate gaming addiction and its impact on health effects during the COVID-19 Pandemic
No full textThis article has been retracted. After a thorough investigation, the listed authors of “Neural network to investigate gaming addiction and its impact on health effects during the COVID-19 Pandemic” committed a serious misconduct of standards of publication ethics, since they submitted a contribution that did not satisfy originality
Computing driver tiredness and fatigue in automobile via eye tracking and body movements
Get PDFThe aim of this paper is to classify the driver tiredness and fatigue in automobile via eye tracking and body movements using deep learning based Convolutional Neural Network (CNN) algorithm. Vehicle driver face localization serves as one of the most widely used real-world applications in fields like toll control, traffic accident scene analysis, and suspected vehicle tracking. The research proposed a CNN classifier for simultaneously localizing the region of human face and eye positioning. The classifier, rather than bounding rectangles, gives bounding quadrilaterals, which gives a more precise indication for vehicle driver face localization. The adjusted regions are preprocessed to remove noise and passed to the CNN classifier for real time processing. The preprocessing of the face features extracts connected components, filters them by size, and groups them into face expressions. The employed CNN is the well-known technology for human face recognition. One we aim to extract the facial landmarks from the frames, we will then leverage classification models and deep learning based convolutional neural networks that predict the state of the driver as \u27Alert\u27 or \u27Drowsy\u27 for each of the frames extracted. The CNN model could predict the output state labels (Alert/Drowsy) for each frame, but we wanted to take care of sequential image frames as that is extremely important while predicting the state of an individual. The process completes, if all regions have a sufficiently high score or a fixed number of retries are exhausted. The output consists of the detected human face type, the list of regions including the extracted mouth and eyes with recognition reliability through CNN with an accuracy of 98.57% with 100 epochs of training and testing
A new method of Poisson regression estimator in the presence of a Multicollinearity problem: Simulation and application
Get PDFA new estimator for the Poisson model is introduced in this study. Poisson regression model is an important log- Linear models which is the tool of modeling the dependent variable when its values are positive and as a form of count data or rates additional to be the appropriate model for analyzing rare events. The maximum likelihood estimator (MLE) suffers from the instability problem in the presence of multicollinearity for a Poisson regression model (PRM). The purpose of this study is to make a comparison of parameters estimation methods for the Poisson regression model when that model suffer from semi multicollinearity problem through the possible methods with proposed method and also propositions for the biased parameter. A Monte Carlo simulation experiment used to generate data follows Poisson regression model and suffer from multicollinearity problem according to variation factors like sample size, the value of simple correlation coefficient and the number of independent variables. So mean squared error and relative efficiency is adopted as a criteria to the comparison of the parameters estimation methods for the model. The simulation results and the real-life application evidenced that the proposed estimator performs better than the rest of the estimators
Comparison of the two hybrid models, Wavelet-ARIMA and Wavelet-ES, to predict the prices of the US dollar index
Get PDFThe US dollar index is one of the most important measures to compare the value of the US dollar against a basket of foreign currencies. The strategic importance of this index lies in avoiding risks and fluctuations in the basket of major global currencies. It is known that the process of accurate prediction must take place after understanding the nature of the data of the phenomenon under study, and accordingly we can employ the most appropriate models to obtain the best predictive values. In this paper, we made a comparison between two models from the hybrid wavelet transform models, namely Wavelet-ARIMA and Wavelet-ES, by applying to data representing the weekly rates of the last price of the US dollar index from 2011 to 2022, in order to get the best predictive values for this indicator. The results of the comparison criteria AIC, RMSE and MAPE indicated the preference of the hybrid Wavelet-ARIMA model, which was used to predict the weekly rates of the index (USDX). These results indicated that there would be no significant changes or fluctuations during the next sixteen weeks, the weekly average of the index price will be (95.24), which will be recorded in the fourteenth week, and that the fifteenth week will record the highest predictive value of the index, as it will amount to ($96.31)
Convolutional neural network in the classification of COVID-19
Get PDFCovid-19 spread out rapidly around the world, forcing many countries to full shutdown, and economical and social consequences. Resulting in rapid need for new and effective methods to deal with this crisis and control it. X-ray lung images is considered one of the most effective and safe method for diagnosing Covid-19, since it could provide solid proof of the existing of the disease, and it has limited effect on the health of the human comparing with other radiography methods. In this proposed work, CNN model is designed and trained to classify Covid-19 X-ray images, by using the COVID-19 Radiography Database, which is published and available online. This database is collected by researchers and experts from various universities around the world. The database contains total of 15153 lung x-ray images, divided into three classes. The classification classes are: Normal, Covid-19, and Viral Pneumonia. The model is trained and tested on publicly available dataset. The dataset is divided into three parts: training, validation, and testing datasets. The model is evaluated based on the three of these datasets. Totally, the evaluation metrics include Accuracy, F1-score, Area Under Curve (AUC), Precision, and Recall, with values of greater than 98% for all of the evaluation metrics. Comparing the results with state of arts publications, which used the same dataset, the proposed method outperformed the state of arts publications depending on the evaluation metrics. The number of the trainable parameters in the proposed CNN model is about 25.4 millions
The control of permanent magnet synchronous motor drive based on the space vector pulse width modulation and fractional order PID controller
Get PDFThis study explains a new way to speed control for PMSMs based on the FOC and SVPWM techniques employed in the building of the permanent magnet synchronous motors (PMSMs). When it comes to current control, two inner and one outside feedback loops were used. Feedback control with FOPID controllers is used to optimize the performance of PMSM motor design. FOPID parameters were optimized using genetic algorithms in MATLAB/Simulink simulations. Good dynamic and static qualities are demonstrated through simulation results. There is also a comparison of PMSM PID and FOPID controllers included
An overview of technologies deployed in GCC Countries to combat COVID-19
Get PDFSince December 2019, COVID-19 and all of its variants continue to ravage the planet with consequent negative impact that has completely changed our lives within a short period of time after the outbreak of the Virus. On March 11, 2020, COVID-19 was declared a global pandemic by the World Health Organization. Since then, a group of new COVID-19 variants has emerged posing a greater danger to humanity. By the start of August 2021, the reported COVID-19 related death toll across the globe has rocketed to 4,233,139. To deal with the COVID-19 pandemic, countries across the world have rushed to develop various techniques in order to embrace an array of solutions to the problem. Covid-19 negatively affected countries in several sectors including industry, business, health, and education to name a few. The Gulf Cooperation Council (GCC) countries are among the top countries which use cutting-edge technologies in several sectors. This significantly helped these countries to overcome the spread of this virus. In this paper, we present an overview of the technologies, techniques, solutions, and strategies deployed by the Gulf Cooperation Council (GCC) countries to combat the COVID-19 pandemic in order to safeguard their citizens and speedy return of life to normalcy
Selective laser melting of Inconel 601 alloy using nanosecond fibre laser
Get PDFThe paper describes the impact of selective laser melting factors including using a nanosecond fibre laser, including laser powers, scanning speed, and thickness of layer, on the relative density and micro hardness Vickers of IN 601 samples was studied. Selective laser melting (SLM) is a commonly used powder bed fusion metal additive manufacturing (AM). Recent advances in additive manufacturing have attracted significant industrial interest, especially for producing metallic parts. Scanning electron microscopy (SEM), EDX and other techniques were utilized for studying the effect of speed of scanning and power of laser on densification behaviour, microstructural evolution and micro hardness of Inconel v alloy that was processed by SLM. With a VED of 3200 J/mm3, a scan speed of 250 mm/s with a 80 W, micro cracks of about 79-93 µm and voids of about (4.5 µm – 5.7) µm in diameter were realised. Moreover, the best hardness of 394 HV was attained with 80 W laser power. However, increasing the energy to more than the required values increased the porosity and decreased hardness. Using microsecond laser in SLM leads to the achievement of full density (almost 99.5%), whereas using nanosecond laser allows the achievement of less density (75-95%), which will be very useful for functions that require a porous structure and less weight, such as those in the aerospace applications, automotive industry and so on
The need for sustainable local management to solve the reality of increasing traffic congestions in Iraq
Get PDFThe need to find solutions based on sustainable methods for the transportation sector in Iraq has become an urgent necessity due to the ever-increasing volume of traffic congestion caused mainly by the increase in the number of vehicles and the total dependence on them for transportation in the absence of other alternatives. This increase in traffic congestion drains all of the fossil energy in addition to losses in time, health and comfort of users and those close to the roads. Also, it increases the costs in maintaining roads and vehicles, as well as the harmful effects on the environment. A number of literatures have been studied to explain the effects and causes of traffic congestion, especially in countries whose conditions are similar to Iraq. The roads and intersections in the city of Baghdad were monitored, and a number of experts were requested to discuss the causes. A sample questionnaire was conducted that included several levels and ages of Iraqi society to determine the sample\u27s impression on a number of points affecting the local traffic congestion in Baghdad city. It was revealed through the questionnaire that there are four main causes for traffic congestion and there is a strong desire among Iraqis to own and drive vehicles, which led to a significant and continuous increase in their numbers, and this is one of the main causes of congestion
Using nano silica to enhance the performance of recycled asphalt mixtures
Get PDFUnderstanding how the physical properties of Nano-silica material affect the ultimate implementation of the asphalt binder is an essential study area that has been disregarded previously. The current investigation aims to determine whether or not it was possible to change the asphalt binder with Nano-silica (NS) depending on the qualities of the asphalt binder under consideration. Using nano-silica (2, 4, and 6 percentage by weight of asphalt), a penetration grade asphalt cement with 60/70 was developed. Nano silica and asphalt cement were first tested for their qualities. The NS modified asphalt binder was ready for use in the experiment after being heated to 160°C and mixed with a shear mixer at 2000 rpm for 60 minutes. The softening point temperature and penetration index of the NS modified asphalt binder, as well as the Brookfield rotational viscosity and ductility data, were also evaluated. Based on the rheological performance of the NS modified asphalt binder, increasing Nano silica content increases stiffness while decreasing temperature sensitivity. The addition of 4% Nano silica (NS) asphalt binder improved its basic properties and allowed it to be used in hot weather. By adding 4% NS to the hot recycle asphalt mixture, the Marshall stability is increased by 32.5%, the flow is reduced by 21.4%, the unit weight is maintained, and the amount of air voids in the mix, as well as other mix qualities, are kept at acceptable ranges. It\u27ll also boost the ITS by 37.8%. In general, adding NS to asphalt mixtures improves their qualities