Computation, Volume 10, Issue 11 (November 2022) – 14 articles

Although the variable selection and regularization procedures have been extensively considered in the literature for the quantile regression $\left(QR\right)$ scenario via penalization, many such procedures fail to deal with data aberrations in the design space, namely, high leverage points (X-space outliers) and collinearity challenges simultaneously. Some high leverage points referred to as collinearity influential observations tend to adversely alter the eigenstructure of the design matrix by inducing or masking collinearity. Therefore, in the literature, it is recommended that the problems of collinearity and high leverage points should be dealt with simultaneously. In this article, we suggest adaptive $LASSO$ and adaptive E-$NET$ penalized $QR$ ($QR$-$ALASSO$ and $QR$-$AE$-$NET$) procedures where the weights are based on a $QR$ estimator as remedies. We extend this methodology to their penalized weighted $QR$ versions of $WQR$-$LASSO$, $WQR$-E-$NET$ procedures we had suggested earlier. In the literature, adaptive weights are based on the RIDGE regression ($RR$) parameter estimator. Although the use of this estimator may be plausible at the ${\ell }_1$ estimator ($QR$ at $\tau =0.5$) for the symmetrical distribution, it may not be so at extreme quantile levels. Therefore, we use a $QR$-based estimator to derive adaptive weights. We carried out a comparative study of $QR$-$LASSO$, $QR$-E-$NET$, and the ones we suggest here, $viz.$, $QR$-$ALASSO$, $QR$-$AE$-$NET$, weighted $QR$$ALASSO$ penalized and weighted $QR$ adaptive $AE$-$NET$ penalized ($WQR$-$ALASSO$ and $WQR$-$AE$-$NET$) procedures. The simulation study results show that $QR$-$ALASSO$, $QR$-$AE$-$NET$, $WQR$-$ALASSO$ and $WQR$-$AE$-$NET$ generally outperform their nonadaptive counterparts. At predictor matrices with collinearity inducing points under normality, the $QR$-$ALASSO$ and $QR$-$AE$-$NET$, respectively, outperform the non-adaptive procedures in the unweighted scenarios, as follows: in all 16 cases (100%) with respect to correctly selected (shrunk) zero coefficients; in 88% with respect to correctly fitted models; and in 81% with respect to prediction. In the weighted penalized $WQR$ scenarios, $WQR$-$ALASSO$ and $WQR$-$AE$-$NET$ outperform their non-adaptive versions as follows: in 75% of the time with respect to both correctly fitted models and correctly shrunk zero coefficients and in 63% with respect to prediction. At predictor matrices with collinearity masking points under normality, the $QR$-$ALASSO$ and $QR$-$AE$-$NET$, respectively, outperform the non-adaptive procedures in the unweighted scenarios as follows: in prediction, in $100\%$ and $88\%$ of the time; with respect to correctly fitted models in $100\%$ and $50\%$ (while in $50\%$ equally); and with respect to correctly shrunk zero coefficients in $100\%$ of the time. In the weighted scenario, $WQR$-$ALASSO$ and $WQR$-$AE$-$NET$ outperform their respective non-adaptive versions as follows; with respect to prediction, both in $63\%$ of the time; with respect to correctly fitted models, in $88\%$ of the time while with respect to correctly shrunk zero coefficients in $100\%$ of the time. At predictor matrices with collinearity inducing points under the t-distribution, the $QR$-$ALASSO$ and $QR$-$AE$-$NET$ procedures outperform their respective non-adaptive procedures in the unweighted scenarios as follows: in prediction, in $100\%$ and $75\%$ of the time; with respect to correctly fitted models $88\%$ of the time each; and with respect to correctly shrunk zero $88\%$ and in $100\%$ of the time. Additionally, the procedures $WQR$-$ALASSO$ and $WQR$-$AE$-$NET$ and their unweighted versions result in the former outperforming the latter in all respective cases with respect to prediction whilst there is no clear "winner" with respect to the other two measures. Overall, the $WQR$-$ALASSO$ generally outperforms all other models with respect to all measures. At the predictor matrix with collinearity-masking points under the t-distribution, all adaptive versions outperformed their respective non-adaptive versions with respect to all metrics. In the unweighted scenarios, the $QR$-$ALASSO$ and $QR$-$AE$-$NET$ dominate their non-adaptive versions as follows: in prediction, in $63\%$ and $75\%$ of the time; with respect to correctly fitted models, in $100\%$ and $38\%$ (while in $62\%$ equally); in $100\%$ of the time with respect to correctly shrunk zero coefficients. In the weighted scenarios, all adaptive versions outperformed their non-adaptive versions as follows: $62\%$ of the time in both respective cases with respect to prediction while it is vice-versa with respect to correctly fitted models and with respect to correctly shrunk zero coefficients. In the weighted scenarios, $WQR$-$ALASSO$ and $WQR$-$AE$-$NET$ dominate their respective non-adaptive versions as follows; with respect to correctly fitted models, in $62\%$ of the time while with respect to correctly shrunk zero coefficients in $100\%$ of the time in both cases. At the design matrix with both collinearity and high leverage points under the heavy-tailed distributions (t-distributions with $d\in (1;6)$ degrees of freedom) scenarios, the dominance of the adaptive procedures over the non-adaptive ones is again evident. In the unweighted scenarios, the procedures $QR$-$ALASSO$ and $QR$-$AE$-$NET$ outperform their non-adaptive versions as follows; in prediction, in $75\%$ and $62\%$ of the time; with respect to correctly fitted models, they perform better in $100\%$ and $88\%$ of the time, while with respect to correctly shrunk zero coefficients, they outperform their non-adaptive ones $100\%$ of the time in both cases. In the weighted scenarios, $WQR$-$ALASSO$ and $WQR$-$AE$-$NET$ dominate their non-adaptive versions as follows; with respect to prediction, in $100\%$ of the time in both cases; and with respect to both correctly fitted models and correctly shrunk zero coefficients, they both do $88\%$ of the time. Results from applications of the suggested procedures to real life data sets are more or less in line with the simulation studies results. Full article

This paper continues the research of the previous work, regarding PandaSays mobile application, having its main purpose to detect the affective state of the child from his drawings, using MobileNet neural network. Children diagnosed with autism spectrum disorder, have difficulties in expressing their feelings and communicating with others. The purpose of PandaSays mobile application, is to help parents and tutors that have children diagnosed with autism, to communicate better with them and to understand their feelings. The main goal was to improve the model’s accuracy, trained with MobileNet neural network, which reached the value of 84.583%. For training the model, it was used Python programming language. The study focuses further on accessibility and its importance to children diagnosed with autism. Relevant screenshots of the mobile application are presented, in order to indicate that the application follows the accessibility guidelines and rules. Finally, there is presented the interaction with Marty robot and the efficiency of mobile application’s drawing prediction. Full article

In this paper we study deep neural network algorithms for solving linear and semilinear parabolic partial integro-differential equations with boundary conditions in high dimension. Our method can be considered as an extension of the deep splitting method for PDEs to equations with non-local terms. To show the viability of our approach, we discuss several case studies from insurance and finance. Full article

The documents similarity metric is a substantial tool applied in areas such as determining topic in relation to documents, plagiarism detection, or problems necessary to capture the semantic, syntactic, or structural similarity of texts. Evaluated results of the similarity measure depend on the types of word represented and the problem statement and can be time-consuming. In this paper, we present a problem-independent algorithm of the similarity metric greedy texts similarity mapping (GTSM), which is computationally efficient to be applied for large datasets with any preferred word vectorization models. GTSM maps words in two texts based on a decision rule that evaluates word similarity and their importance to the texts. We compare it with the well-known word mover’s distance (WMD) algorithm in the k-nearest neighbors text classification problem and find that it leads to similar or better results. In the correlation evaluation task of similarity measures with human-judged scores, we demonstrate its higher correlation scores in comparison with WMD and sentence mover’s similarity (SMS) and show that GTSM is a decent alternative for both word-level and sentence-level tasks. Full article

Shoplifting is a major problem for shop owners and many other parties, including the police. Video surveillance generates huge amounts of information that staff cannot process in real time. In this article, the problem of detecting shoplifting in video records was solved using a classifier, which was a hybrid neural network. The hybrid neural network included convolutional and recurrent ones. The convolutional network was used to extract features from the video frames. The recurrent network processed the time sequence of the video frames features and classified the video fragments. In this work, gated recurrent units were selected as the recurrent network. The well-known UCF-Crime dataset was used to form the training and test datasets. The classification results showed a high accuracy of 93%, which was higher than the accuracy of the classifiers considered in the review. Further research will focus on the practical implementation of the proposed hybrid neural network. Full article

A new symmetric block cipher algorithm called AL02 has been developed. The algorithm scheme provides five-round encryption of 128-bit blocks, while the data size at the input and output of the S-box is 8 bits. The main transformation is the F transformation. The difference between the proposed algorithm and the classical scheme is that the F transformation provides the maximum possible dependence of the output vector bits on the input bits and is based on “modulo 2 addition” and a substitution S-box. To assess the strength of the AL02 algorithm, it was programmatically implemented in the C programming language. During the analysis, the cryptographic properties of the developed encryption algorithm were tested. The algorithm was tested for statistical security. For an experimental assessment, in order to ensure that the ciphertext is not inferior to a random sequence in its properties, the well-known sets of statistical tests by NIST (National Institute of Standards and Technology) and Donald Knuth were used. The property of the avalanche effect was also checked. The strength was evaluated using the methods of differential and linear cryptanalysis. Full article

When an indoor disaster occurs, the disaster site can become very difficult to escape from due to the scenario or building. Most people evacuate when a disaster situation occurs, but there are also disaster victims who cannot evacuate and are isolated. Isolated disaster victims often cannot move quickly because they do not have all the necessary information about the disaster, and secondary damage can occur. Rescue workers must rescue disaster victims quickly, before secondary damage occurs, but it is not always easy to locate isolated victims within a disaster site. In addition, rescue operators can also suffer from secondary damage because they are exposed to disaster situations. We present a HHD technique that can detect isolated victims in indoor disasters relatively quickly, especially when covered by fire smoke, by merging one-stage detectors YOLO and RetinaNet. HHD is a technique with a high human detection rate compared to other techniques while using a 1-stage detector method that combines YOLO and RetinaNet. Therefore, the HHD of this paper can be beneficial in future indoor disaster situations. Full article

This paper aims to identify the economic factors that significantly affect the demand for and supply of housing in three major cities in Turkey, such as Istanbul, Ankara, and Izmir. This study uses monthly data ranges from January 2010 to December 2020 because of the limited housing price data from each city. For smooth measurement, the logarithm of all data except measurements of nominal interest rate, real interest rate and inflation is used. This research uses the Co-integration Analysis and Vector Error Correction Model (VECM) to investigate the macroeconomic variables’ effects on the demand and supply. Mortgage credit volume, as a dependent variable, is influenced by real per capita GDP, real house prices, projected inflation, and nominal interest rates. On the contrary, the building site is used as a dependent variable on the supply side that is determined by the real housing price, the real interest rate, and the real cost of construction. In the VECM model, the mortgage credit volume and constriction cost were dominated by error correction variables, showing the adjustment of disequilibrium towards an equilibrium point. In the case of Ankara, supply-side variables have a long-term relationship. Both housing demand and supply-related factors have a long-term impact on the housing market in Istanbul and Izmir. Given a significant p-value, the coefficient of C1 derived from system equations is negative. Full article

Currently, telemedicine has gained more strength and its use allows establishing areas that acceptably guarantee patient care, either at the level of control or event monitors. One of the systems that adapt to the objectives of telemedicine are fall detection systems, for which artificial vision or artificial intelligence algorithms are used. This work proposes the design and development of a fall detection model with the use of artificial intelligence, the model can classify various positions of people and identify when there is a fall. A Kinect 2.0 camera is used for monitoring, this device can sense an area and guarantees the quality of the images. The measurement of position values allows to generate the skeletonization of the person and the classification of the different types of movements and the activation of alarms allow us to consider this model as an ideal and reliable assistant for the integrity of the elderly. This approach analyzes images in real time and the results showed that our proposed position-based approach detects human falls reaching 80% accuracy with a simple architecture compared to other state-of-the-art methods. Full article

This paper addresses the modeling and the control of an autonomous bicycle robot where the reference point is the center of gravity. The controls are based on the wheel heading’s angular velocity and the steering’s angular velocity. They have been developed to drive the autonomous bicycle robot from a given initial state to a final state, so that the total running cost is minimized. To solve the problem, the following approach was used: after having computed the control system Hamiltonian, Pontryagin’s Minimum Principle was applied to derive the feasible controls and the costate system of ordinary differential equations. The feasible controls, derived as functions of the state and costate variables, were substituted into the combined nonlinear state–costate system of ordinary differential equations and yielded a control-free, state–costate system of ordinary differential equations. Such a system was judiciously vectorized to easily enable the application of any computer program written in Matlab, Octave or Scilab. A Matlab computer program, set as the main program, was developed to call a Runge–Kutta function coded into Matlab to solve the combined control-free, state–costate system of ordinary differential equations coded into a Matlab function. After running the program, the following results were obtained: seven feasible state functions from which the feasible trajectory of the robot is derived, seven feasible costate functions, and two feasible control functions. Computational simulations were developed and provided in order to persuade the readers of the effectiveness and the reliability of the approach. Full article

Tera Hertz radiation is currently the most researched and useful area in almost all fields of science and industry. The additional challenge is expressed in the form of radiation, pulses of femto-seconds in length are supposed to pass through a transmission line (TL) most efficiently, at a wide range of frequencies. These are complex beams, which make up the electromagnetic (EM) field, represented in the frequency domain in terms of cavity eigenmodes. A simulation allows to describe of the phase-amplitude and spectral characteristics of multimode radiation free-electron laser (FEL) operating in various operational parameters. The analysis is performed through the transmission of optical rays accurately, with each ray being characterized by amplitude, position, and angle in 3D space. A light field representation of a complex EM field is obtained via Wigner Distribution Function, which allows to describe of the dynamics of field evolution in future propagation by a ray tracing (RT) method. The final diagnostics will determine the design of the TL to be assembled in an innovative accelerator under construction at the Schlesinger Family Center for Compact Accelerators, Radiation Sources, and Applications. Full article

In the submitted scientific work, the existing types of stator fastening design of turbogenerators and the main causes of the stressed state of the stator suspensions are considered. A detailed calculation of the complex stressed state of the turbogenerator stator suspension was carried out for a number of electrical sheet steels, taking into consideration the unevenness of the heat distribution along the horizontal axis of the unit. It is proposed that the calculation of the mechanical stress is carried out by means of the mechanical and thermal calculation, coordinated with the electrical one. The possibility of replacing steel 38Х2Н2ВА with steel 34CrNiMo6 and 40NiCrMo7 is indicated, subject to compliance with GOST 8479-70 for the same strength group. Full article

The purpose of this study is to describe a methodology for assessing the geotechnical stability of agricultural facilities, enabling prediction of the state of the geotechnical system, taking into account the influence of external factors and combinations of reactions of the geotechnical system under study. According to the methodology, the heterogeneous geotechnical monitoring data obtained are used in an adjusted geotechnical system model, allowing a bifurcation analysis to be carried out. The bifurcation analysis determines critical values of influencing factors, and the limits of stability of the geotechnical system studied parameters are adjusted. The developed methodology was used to assess and predict the geotechnical stability of agricultural facilities during the processing of geoelectric, resistive acoustic, accelerometric and strain-gauge control data obtained in the period from 2016 to 2021. A feature of the geotechnical system under study is the periodic flooding of the building basement caused by the processes of reclamation and irrigation, leading to changes in the groundwater level. The results show that the permissible calculated elastic limit of the foundation elements (32.2–35.1 MPa) before the loss of stability should be significantly reduced with a change in the water content coefficient (W) of the soil base: at W = 0.15 Eb = 30.7–32.0 MPa; at W = 0.35 Eb = 26.8–28.2 MPa; at W = 0.55 Eb = 24.9–25.3 MPa. Full article

Hyperthermia, an alternative medical approach aiming at locally increasing the temperature of a tumor, can cause the “death” of cancer cells or the sensitization of them to chemotherapeutic drugs and radiation. In contrast with the conventional treatments, hyperthermia provokes no injury to normal tissues. In particular, magnetic hyperthermia can utilize iron oxide nanoparticles, which can be administered intravenously to heat tumors under an alternating magnetic field. Currently, there is no theoretical model in the relative literature for the effective thermal conductivity of blood and magnetic nanoparticles. The scope of the present study is twofold: (a) development of a theoretical relationship, based on experimental findings and blood structure and (b) study of the laminar natural convection in a simplified rectangular porous enclosure, by using the asymptotic expansions method for deriving ordinary differential equations of the mass, momentum and energy balances, as a first approach of investigating heat transfer and providing theoretical guidelines. In short, the thermal conductivity of the resulting bio-nanofluid tends to increase by both increasing the concentration of the nanoparticles and the temperature. Furthermore, the heat transfer is enhanced for more intense internal heating (large Rayleigh numbers) and more permeable media (large Darcy numbers), while larger nanoparticle concentrations tend to suppress the flow. Full article