Multimodal Deep Neural Networks for Digitized Document Classification

As digital technologies have advanced more rapidly,the number of paper documents recently converted into a digital format has exponentially increased.To respond to the urgent need to categorize the growing number of digitized documents,the classification of digitized documents in real time has been identified as the primary goal of our study.A paper classification is the first stage in automating document control and efficient knowledge discovery with no or little human involvement.Artificial intelligence methods such as Deep Learning are now combined with segmentation to study and interpret those traits,which were not conceivable ten years ago.Deep learning aids in comprehending input patterns so that object classes may be predicted.The segmentation process divides the input image into separate segments for a more thorough image study.This study proposes a deep learning-enabled framework for automated document classification,which can be implemented in higher education.To further this goal,a dataset was developed that includes seven categories:Diplomas,Personal documents,Journal of Accounting of higher education diplomas,Service letters,Orders,Production orders,and Student orders.Subsequently,a deep learning model based on Conv2D layers is proposed for the document classification process.In the final part of this research,the proposed model is evaluated and compared with other machine-learning techniques.The results demonstrate that the proposed deep learning model shows high results in document categorization overtaking the other machine learning models by reaching 94.84%,94.79%,94.62%,94.43%,94.07%in accuracy,precision,recall,F-score,and AUC-ROC,respectively.The achieved results prove that the proposed deep model is acceptable to use in practice as an assistant to an office worker.

Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning

Video analytics is an integral part of surveillance cameras. Comparedto video analytics, audio analytics offers several benefits, includingless expensive equipment and upkeep expenses. Additionally, the volume ofthe audio datastream is substantially lower than the video camera datastream,especially concerning real-time operating systems, which makes it lessdemanding of the data channel’s bandwidth needs. For instance, automaticlive video streaming from the site of an explosion and gunshot to the policeconsole using audio analytics technologies would be exceedingly helpful forurban surveillance. Technologies for audio analytics may also be used toanalyze video recordings and identify occurrences. This research proposeda deep learning model based on the combination of convolutional neuralnetwork (CNN) and recurrent neural network (RNN) known as the CNNRNNapproach. The proposed model focused on automatically identifyingpulse sounds that indicate critical situations in audio sources. The algorithm’saccuracy ranged from 95% to 81% when classifying noises from incidents,including gunshots, explosions, shattered glass, sirens, cries, and dog barking.The proposed approach can be applied to provide security for citizens in openand closed locations, like stadiums, underground areas, shopping malls, andother places.

Socio-economic development of countries based on the Composite Country Development Index(CCDI)

World experience indicates the existence of significant imbalances in the development of countries.The problem of assessing the rational development of the regional and national economy is becoming urgent,since such assessments can prevent development imbalances across countries.The aim of this study is to elaborate a methodology to assess the countries’socio-economic development by integraring 12 modern indices of socio-economic development into the Composite Country Development Index(CCDI).The methodology of this research was based on a set of key indices that described socio-economic development level in four fields(social development,digital development,economic development,and environmental security)and then these indices were integrated into the CCDI.The study further applied factor analysis and R-Studio software to define the gaps of social and economic development in 59 selected countries using the trigonometric function of the angle sine.The correlation analysis confirmed the existence of a close interrelation among the studied countries.This paper noted that due to the emergence of new priorities,it is necessary to revise the assessment methodology of socio-economic development level and expand them to cover the decisive factors.This was confirmed by the results obtained,demonstrating various combinations of the development level in the four fields and their impact on the CCDI.The scientific contribution of this research is to form a methodology(e.g.,the CCDI)for evaluating the socio-economic development level of countries in the world.

Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology.The latest advancements in communication technology have significantly surpassed the conventional constraints for communication with regards to time and location.These new platforms have ushered in a new age of user-generated content,online chats,social network and comprehensive data on individual behavior.However,the abuse of communication software such as social media websites,online communities,and chats has resulted in a new kind of online hostility and aggressive actions.Due to widespread use of the social networking platforms and technological gadgets,conventional bullying has migrated from physical form to online,where it is termed as Cyberbullying.However,recently the digital technologies as machine learning and deep learning have been showing their efficiency in identifying linguistic patterns used by cyberbullies and cyberbullying detection problem.In this research paper,we aimed to evaluate shallow machine learning and deep learning methods in cyberbullying detection problem.We deployed three deep and six shallow learning algorithms for cyberbullying detection problems.The results show that bidirectional long-short-term memory is the most efficient method for cyberbullying detection,in terms of accuracy and recall.

Experimental study on mitigating wind erosion of calcareous desert sand using spray method for microbially induced calcium carbonate precipitation

Wind erosion is one of the significant natural calamities worldwide, which degrades around one-third of global land. The eroded and suspended soil particles in the environment may cause health hazards, i.e.allergies and respiratory diseases, due to the presence of harmful contaminants, bacteria, and pollens.The present study evaluates the feasibility of microbially induced calcium carbonate precipitation(MICP)technique to mitigate wind-induced erosion of calcareous desert sand(Thar desert of Rajasthan province in India). The temperature during biotreatment was kept at 36℃ to stimulate the average temperature of the Thar desert. The spray method was used for bioaugmentation of Sporosarcina(S.) pasteurii and further treatment using chemical solutions. The chemical solution of 0.25 pore volume was sprayed continuously up to 5 d, 10 d, 15 d, and 20 d, using two different concentration ratios of urea and calcium chloride dihydrate viz 2:1 and 1:1. The biotreated samples were subjected to erosion testing(in the wind tunnel) at different wind speeds of 10 m/s, 20 m/s, and 30 m/s. The unconfined compressive strength of the biocemented crust was measured using a pocket penetrometer. The variation in calcite precipitation and microstructure(including the presence of crystalline minerals) of untreated as well as biotreated sand samples were determined through calcimeter, scanning electron microscope(SEM), and energydispersive X-ray spectroscope(EDX). The results demonstrated that the erosion of untreated sand increases with an increase in wind speeds. When compared to untreated sand, a lower erosion was observed in all biocemented sand samples, irrespective of treatment condition and wind speed. It was observed that the sample treated with 1:1 cementation solution for up to 5 d, was found to effectively resist erosion at a wind speed of 10 m/s. Moreover, a significant erosion resistance was ascertained in15 d and 20 d treated samples at higher wind speeds. The calcite content percentage, thickness of crust,bulk density, and surface strength of biocemented sand were enhanced with the increase in treatment duration. The 1:1 concentration ratio of cementation solution was found effective in improving crust thickness and surface strength as compared to 2:1 concentration ratio of cementation solution. The calcite crystals formation was observed in SEM analysis and calcium peaks were observed in EDX analysis for biotreated sand.

Numerical investigations to design a novel model based on the fifth order system of Emden–Fowler equations

The aim of the present study is to design a new fifth order system of Emden–Fowler equations and related four types of the model.The standard second order form of the Emden–Fowler has been used to obtain the new model.The shape factor that appear more than one time discussed in detail for every case of the designed model.The singularity atη=0 at one point or multiple points is also discussed at each type of the model.For validation and correctness of the new designed model,one example of each type based on system of fifth order Emden–Fowler equations are provided and numerical solutions of the designed equations of each type have been obtained by using variational iteration scheme.The comparison of the exact results and present numerical outcomes for solving one problem of each type is presented to check the accuracy of the designed model.

Geotechnical problems on freezing ground soil and experimental investigation in Kazakhstan

Frost susceptibility should be considered in the design and construction of foundations and retaining-wall structures in regions with the seasonally freezing-soil ground condition.When planning construction that goes deep into this soil,one has to understand the impact of horizontal forces to an underground wall and realize the potential effect of frost heaving upon,deep foundations.This article presents a few soil tests for frost heaving and the results of those tests show dangerous data for retaining structures under the soil conditions in Kazakhstan.The main parameters of the soil include frost penetration and heaving rate and amount.So,in designing underground structures,one must understand and consider that frost heaving occurs in various directions;this factor is very important for predicting on the restriction of deformations of structures in the seasonally freezing-soil ground condition.

Experimental investigations of freezing soils at ground conditions of Astana, Kazakhstan

Kazakhstan regions is seasonal climatic with transient freezing of soil ground during the winter. Roadbed integrity is important to resist the sustained load transmitted by traffic on the road surface. Freezing of soil ground could significantly influence roadbed integrity in the seasonal freezing climate of Kazakhstan. The proper determination magnitude of frost heave and heaving pressure by the influence of freezing temperatures during the winter season are necessary for design and construction of highways. Thus, experimental tests were conducted on specimens obtained from Astana （Kazakhstan） to determine the freezing pressure and magnitude of frost heaving.

Erratum to “The Riemann Hypothesis-Millennium Prize Problem” [Advances in Pure Mathematics 6 (2016) 915-920]

The original online version of this article (Durmagambetov, A.A. (2016) The Riemann Hypothesis-Millennium Prize Problem. Advances in Pure Mathematics, 6, 915-920. 10.4236/apm.2016.612069) unfortunately contains a mistake. The author wishes to correct the errors in Theorem 2 of the result part.

Information Security and the Theory of Unfaithful Information

In this article, the theory of information security is written as a context of national security. Article is devoted to an actual problem of legal support of information security in the Republic of Kazakhstan. The author analyzes modern problems and threats of information security in the conditions of globalization and considers aspects of information security. This article focuses on issues of spreading harmful information, which negatively affects the psyche, behavior, health, society and destabilizes the government administration. The article makes the case for improving the legislation of the Republic of Kazakhstan in strengthening informational security of individuals, society, the state, and measures to prevent the destructive impact of harmful information.

COMPLEX INTERPOLATION OF NONCOMMUTATIVE HARDY SPACES ASSOCIATED WITH SEMIFINITE VON NEUMANN ALGEBRAS

We proved a complex interpolation theorem of noncommutative Hardy spaces associated with semi-finite von Neumann algebras and extend the Riesz type factorization to the semi-finite case.

OUTER OPERATORS FOR THE NONCOMMUTATIVE SYMMETRIC HARDY SPACES ASSOCIATED WITH FINITE SUBDIAGONAL ALGEBRA

In this article, we extended main results on outer operators of [6] to the symmetric Hardy spaces, when associated subdiagonal algebra is finite.

Indoor Air Quality Control Using Backpropagated Neural Networks

Providing comfortable indoor air quality control in residential construction is an exceedingly important issue.This is due to the structure of the fast response controller of air quality.The presented work shows the breakdown and creation of a mathematical model for an interactive,nonlinear system for the required comfortable air quality.Furthermore,the paper refers to designing traditional proportional integral derivative regulators and proportional,integral,derivative regulators with independent parameters based on a backpropagation neural network.In the end,we perform the experimental outputs of a suggested backpropagation neural network-based proportional,integral,derivative controller and analyze model results by applying the proposed system.The obtained results demonstrated that the proposed controller can provide the required level of clean air in the room.The proposed developed model takes into consideration international Heating,Refrigerating,and air conditioning standards as ASHRAE AND ISO.Based on the findings,we concluded that it is possible to implement a proposed system in homes and offer equivalent indoor air quality with continuous mechanical ventilation without a profuse amount of energy.

A Deep Learning-Based Approach for Road Surface Damage Detection

Timely detection and elimination of damage in areas with excessive vehicle loading can reduce the risk of road accidents.Currently,various methods of photo and video surveillance are used to monitor the condition of the road surface.The manual approach to evaluation and analysis of the received data can take a protracted period of time.Thus,it is necessary to improve the procedures for inspection and assessment of the condition of control objects with the help of computer vision and deep learning techniques.In this paper,we propose a model based on Mask Region-based Convolutional Neural Network(Mask R-CNN)architecture for identifying defects of the road surface in the real-time mode.It shows the process of collecting and the features of the training samples and the deep neural network(DNN)training process,taking into account the specifics of the problems posed.For the software implementation of the proposed architecture,the Python programming language and the TensorFlow framework were utilized.The use of the proposed model is effective even in conditions of a limited amount of source data.Also as a result of experiments,a high degree of repeatability of the results was noted.According to the metrics,Mask R-CNN gave the high detection and segmentation results showing 0.9214,0.9876,0.9571 precision,recall,and F1-score respectively in road damage detection,and Intersection over Union(IoU)-0.3488 and Dice similarity coefficient-0.7381 in segmentation of road damages.

Association of Xenobiotic Detoxification Enzymes Gene Polymorphism in Predisposition of Bronchial Asthma and Chronic Obstructive Pulmonary Disease

Glutathione-S-transferase （GST） family enzymes are implicated in the pathopbysiology of bronchial asthma （BA） and chronic obstructive pulmonary disease （COPD）. In some cases both illnesses exhibit similar pathomorphologic and clinical features, indicating common genetic basis of predisposing to development of disease. To assess genetic susceptibility we conducted association analysis of glutathione-S-transferases Mu （M）, Theta （T） and Pi （P） gene polymorphism with disease development in 85 adult asthma, 60 COPD subjects and 64 control subjects. Present investigation of GST gene polymorphisms indicates that GSTM1 and GSTT null alleles are associated with predisposition for COPD and they do not appear to play a substantial role in susceptibility to BA. However, homozygote ＋/＋ and heterozygote ＋/0 genotypes of GSTT1 revealed to be associated with increasing of IgE level in serum in BA patients. Our findings suggest that the 105 Val variant of GSTP1 contributed to the increasing risk of developing of both diseases, and more likely for COPD.

Riemann Hypothesis

This work represents the development and detailing of works in [1] [2], and work is dedicated to the promotion of the results Abels obtained modifying zeta functions. The properties of zeta functions are studied;these properties lead to new regularities of zeta functions. The choice of a special type of modified zeta functions allows estimating the Riemann’s zeta function and solving Riemann Problem.

The Protective Role of Succinic Acid on the NF kB Signaling Pathway of Mouse Fibroblast Cells under the Influence of Chromium Oxide and Sodium Arsenite

Antioxidant and anti-inflammatory therapy approaches have been in the focus of attention in the treatment of different cancer diseases where oxidative stress has been implicated. Succinic acid has been previously reported to possess radical scavenger, iron chelating and anti-inflammatory properties in the mouse fibroblast. The purpose of this study was to investigate potential therapeutic effects of succinic acid and possible signal pathway involved in the mouse fibroblast. We demonstrated highly potent antioxidant-radical scavenging activities of succinic acid.

Verbal Images Paradigm in Different Lingual Cultures

This article is aimed at the analysis of the images of consciousness that the representatives of the Kazakh and Russian nations possess. The analysis has been carried on the man-horseparadigm. It has been discovered that the ability to associatively connect the objects and phenomena of the world around bring together the Kazakh and Russian writers as well as does the ability to see common features of different objects. At the same time in the Russian literature sometimes a person is identified with a horse, which does physical work, and characterizes people exhausted by hard work which is not typical for the Kazakhs. But the Russians do not compare their child with a foal whereas for the Kazakhs it is the kindest term of endearment. It is supposed that interesting image paradigms with ＂horse＂ element can also be discovered in the English language

Russian Culture in Kazakh Prose

The article draws attention to the linguistic consciousness of a bilingual Kazakh writer D. Nakipov formed under the influence of Russian culture. The peculiar way of thinking about reality is fixed in a work of fiction by means of special speech techniques, verbal images of not only his native language culture, but the culture he grew up in.