bluuebunny/crossref_metadata_2025 huggingface dataset:
Examination of the Relationship Between Physical Activity and Cognitive Skills
Authors: Makra Gabriella , Balogh László
Published: September 2019

Introduction: Examining physical activity and cognitive function has become a popular topic of research since the quality of these two components influences our quality of life and performance. It is very important for both sexes, all ages and life situations.

Objective: Determining a type of physical activity (PA) that has an effect on both cognitive functions (CF) and motor skills and performance. Clarifying or promoting measurement methods, or developing new methods that meet current standards and objectives. The results may be used to help to impove performance in all areas.

Method: Primary purpose of our study was to conduct a review, in order to summarize the relevant literature on the following topics: sex, age, fitness level, possible illness.

Conclusions: We found a significant relationship between cognitive functions and physical activity, which can be transposition in a proper way, both in terms of living standards and performance. On the other hand, there are a lack of uniform, accurate measurement methods. Thhere is a need for specific physical activity routines that specifically improve the level of cognitive function, even at an older age.
The Effect of Proprioceptive Training on Core Muscles Strength and Single Leg Balancing Ability in Youth Soccer Players
Authors: Kovács Miklós , József Márton Pucsok
Published: September 2019

Core stability and the ability to balance have both a critical role in today’s modern soccer. Majority of injuries originated from the weakness and inefficient strength of core musculature and the kinetic chain. The purpose of our study was to investigate the effect of a three month proprioceptive training programme on core stability and the ability to balance. We hypothesized that both of the skills might improve by the end of the 12-week exercise program. We set up a group involving 15 players, who regularly participated in a weekly 40 minutes special proprioceptive training routine. The control group hasn’t attended any additional strengthening workout session. The youngsters executed a fitball balancing task, in a seated position, and the so-called Stork-test.  Before the 3-month exercise program, we implemented a pre-, at the end of the program a post-test. According to our results, we may conclude that we had measured a significant difference only in the Fitball-test when players executed the single leg balancing test (Stork-test), we haven’t observed any significant difference.
Developing Module for Entrepreneurship Based Translation and Interpreting Skills Course in English Language Teacher Education
Authors: Siyaswati Siyaswati , Rochmawati Dyah
Published: September 2019

The present study describes the development of a course module on Entrepreneurship-Based Translation and Interpretation Skills for the students of English Language Teacher Education Department. The module was inspired by empirical research findings about the knowledge base of student teachers. It was created and refined during its three-year application will be reflected upon here, including feedback collected from student evaluation sheets. In the end, the participants responded positively to the course module. The students stated that the module was interesting, relevant and valuable for their later profession. They also emphasized that they now felt more competent in the area of Translation and Interpretation Skills and entrepreneurship.
TECHNICAL EFFICIENCY HETEROGENEITY OF TERTIARY INSTITUTIONS IN VIET NAM: A METAFRONTIER DIRECTIONAL TECHNOLOGY APPROACH
Authors: Villano Renato A. , Tran Carolyn-Dung Thi Thanh
Published: September 2019

The higher education system of Viet Nam has been undergoing reform process with the aim of seeking a position in the world’s education market. However, recent changes in the system have made the operational efficiency of the system less stable, thus making it more challenging to improve the country’s universities world rankings. In this paper, we investigate the performance of tertiary education institutions in Viet Nam and evaluate the efficiency gap between colleges and universities. Using the metafrontier directional technology approach, we estimate both the group frontier and metafrontier efficiencies for 112 universities and 141 colleges using data for 2011–2013 and compute their differences imposed by the technology associated with different levels of ownership and locations. The findings showed that the performance of universities were better than that of colleges, at 0.837 0.774, respectively. However, under an unrestricted metafrontier framework, the metatechnology ratios suggest that universities and colleges were operated comparatively well by potentially increasing their performance by 7.8 and 5.0 per cent, respectively. Both urban universities and colleges are found to be more efficient than their rural counterparts, but the effects of ownerships showed mixed results on the performance of universities and colleges. Our results highlight the need for appropriate policies and enabling environment that will enhance the performance of each institution. It is imperative to re-evaluate the specific role and individual contributions of colleges and universities in the national education system and assist rural universities and colleges to explore their full potential to enhance their performance.
Rapid Variability in the SOL2011-08-04 Flare: Implications for Electron Acceleration
Authors: Altyntsev Alexander T. , Meshalkina Nataliia S. , Lysenko Alexandra L. , Fleishman Gregory D.
Published: September 2019

Particle acceleration in solar flares remains an outstanding problem in solar physics. It is currently unclear which of the acceleration mechanisms dominates and how exactly the excessive magnetic energy is transferred to nonthermal and other forms of energy. We emphasize that the ultimate acceleration mechanism must be capable of efficiently working in the most extreme conditions, such as the shortest detected timescales and the highest acceleration efficiency. Here we focus on a detailed multiwavelength analysis of the initial phase of the SOL2011-08-04 flare, which demonstrated prominent short subpeaks of nonthermal emission during filament eruption associated with the flare. We demonstrate that the three-dimensional configuration of the flare, combined with timing and spectral behavior of the rapidly varying component, put very stringent constraints on the acceleration regime. Specifically, the rapid subpeaks are generated by short injections of nonthermal electrons with a reasonably hard, single power-law spectrum and a relatively narrow spread of pitch-angles along the mean magnetic field. The acceleration site is a compact volume located near the top of the extended coronal loop(s). The electrons are promptly accelerated up to several hundreds of keV, with the characteristic acceleration time shorter than 50 ms. We show that these properties are difficult to reconcile with widely adopted stochastic acceleration models, while the data inescapably require acceleration by a super-Dreicer electric field, whether regular or random.
A Search for Cosmic-Ray Proton Anisotropy with the Fermi Large Area Telescope
Authors: Ajello M. , Baldini L. , Barbiellini G. , Bastieri D. , Bechtol K. , Bellazzini R. , Bissaldi E. , Blandford R. D. , Bonino R. , Bottacini E. , Brandt T. J. , Bruel P. , Buson S. , Cameron R. A. , Caputo R. , Cavazzuti E. , Chen S. , Chiaro G. , Ciprini S. , Cohen-Tanugi J. , Costantin D. , Cuoco A. , Cutini S. , D’Ammando F. , de la Torre Luque P. , de Palma F. , Desai A. , Digel S. W. , Di Lalla N. , Di Venere L. , Domínguez A. , Fegan S. J. , Fukazawa Y. , Funk S. , Fusco P. , Gargano F. , Gasparrini D. , Giglietto N. , Giordano F. , Giroletti M. , Green D. , Grenier I. A. , Guiriec S. , Hayashi K. , Hays E. , Hewitt J. W. , Horan D. , Jóhannesson G. , Kuss M. , Latronico L. , Li J. , Liodakis I. , Longo F. , Loparco F. , Lubrano P. , Maldera S. , Manfreda A. , Martí-Devesa G. , Mazziotta M. N. , Meehan M. , Mereu I. , Meyer M. , Michelson P. F. , Mirabal N. , Mitthumsiri W. , Mizuno T. , Morselli A. , Negro M. , Nuss E. , Omodei N. , Orienti M. , Orlando E. , Paliya V. S. , Paneque D. , Persic M. , Pesce-Rollins M. , Piron F. , Porter T. A. , Principe G. , Rainò S. , Rando R. , Razzano M. , Razzaque S. , Reimer A. , Reimer O. , Serini D. , Sgrò C. , Siskind E. J. , Spandre G. , Spinelli P. , Suson D. J. , Tajima H. , Thayer J. B. , Torres D. F. , Troja E. , Vandenbroucke J. , Yassine M. , Zimmer S. ,   The Fermi-LAT Collaboration
Published: September 2019

The Fermi Large Area Telescope (LAT) has amassed a large data set of primary cosmic-ray protons throughout its mission. In fact, it is the largest set of identified cosmic-ray protons ever collected at this energy. The LAT’s wide field of view and full-sky survey capabilities make it an excellent instrument for studying cosmic-ray anisotropy. As a space-based survey instrument, the LAT is sensitive to anisotropy in both R.A. and decl., while ground-based observations only measure the anisotropy in R.A. We present the results of the first-ever proton anisotropy search using Fermi LAT. The data set was collected over eight years and consists of approximately 179 million protons above 78 GeV, enabling it to probe dipole anisotropy below an amplitude of 10−3, resulting in the most stringent limits on the decl. dependence of the dipole to date. We measure a dipole amplitude δ = 3.9 ± 1.5 × 10−4 with a p-value of 0.01 (pretrials) for protons with energy greater than 78 GeV. We discuss various systematic effects that could give rise to a dipole excess and calculate upper limits on the dipole amplitude as a function of minimum energy. The 95% confidence level upper limit on the dipole amplitude is δ
UL = 1.3 × 10−3 for protons with energy greater than 78 GeV and δ
UL = 1.2 × 10−3 for protons with energy greater than 251 GeV.
Populations state of selected communities of Juniperus communis(Common Juniper)in Vitosha Mt., Bulgaria
Authors: Lambevska-Hristova Aneta , Bancheva Svetlana
Published: September 2019

Juniperus communis L. formations on heaths or calcareous grassland are included in the Bulgarian Biological Diversity Act (code 5130) and some of them are covered by the European Ecological Network Natura 2000 in Bulgaria.

Aim: The purpose of this work is to investigate the status of selected communities with the participation of J. communis in the Special Areas of Conservation (SAC) "Vitosha" with an emphasis on the species richness of plants and fungi and the impact of natural and anthropogenic factors.

Material and methods: The survey was conducted between May and September 2019. Habitat 5130 covers an area of 19.27 ha in the territory of SAC "Vitosha". For the purpose of this study is crawled throughout the mountain where it is registered. The route and stationary methods were used for selected objects. Reporting plots were installed in 5 places, each with an area of 100 sq.m. For the assessment of the state of J. communis’ communities and the influencing factors the appropriate Methodology used for the establishment of the national Natura 2000 network in Bulgaria was applied (Zingstra et al. 2009).

Results: A remarkable species richness of plants and fungi was found in the selected communities of Common Juniper at SAC "Vitosha". A new lignicolous species for the Bulgarian mycota, Peniophora junipericola J. Erikss on single host J. communis, was found. The explored plant communities were in good condition and have not been established factors threatening their existence.

Conclusion: The habitat occurs at various altitudes and includes species with various ecological characteristics. A tendency for restoration and increasing of the areas covered by the Common juniper has been observed recently as a result of abandonment of arable land.
Relative clutch mass of Basiliscus vittatus Wiegmann, 1828 (Squamata, Corytophanidae): female morphological constraints
Authors: Suárez-Varón Gabriel , Suárez-Rodríguez Orlando , Granados-González Gisela , Villagrán-Santa Cruz Maricela , Gribbins Kevin M. , Cortez-Quezada Diego , Hernández-Gallegos Oswaldo
Published: September 2019

Clutch size (CS) and relative clutch mass (RCM) are considered important features in life history descriptions of species within Squamata. Variations in these two characteristics are caused by both biotic and abiotic factors. The present study provides the first account related to CS and RCM ofBasiliscus vittatusin Mexico within a population that inhabits an open riverbed juxtapositioned to tropical rainforest habitat in Catemaco, Veracruz, Mexico (170 m a.s.l.). Twenty-nine gravid females were collected and kept in captivity under favorable conditions that promote oviposition. The CS within this population was 6.2 ± 0.2 and was correlated positively with snout vent-length (SVL); while the RCM was 0.17 ± 0.006 and was correlated positively with both CS and width of egg. Factors, such as female morphology and environmental conditions, should influence these reproductive traits inB. vittatus. The data collected in this study could provide a framework for comparisons of the life history traits across populations ofB. vittatusin Mexico and within other species of the family Corytophanidae and provide a model for testing how abiotic and biotic factors may influence the CS and RCM in basilisk lizards throughout their range.
Le paradigme de l’inclusion ou la société des singularités pour horizon
Authors: Pillant Yves
Published: September 2019

La notion d’inclusion indique une nouveauté qui semble recouper certains travaux sociologiques sur la « société des singularités ». Penser une société inclusive offre une occasion de reconsidérer l’égalité démocratique à l’aune de l’unicité de chacun de ses membres. Cet article se propose de croiser cette recherche sociologique sur l’égalité avec la philosophie de Levinas sur l’altérité. La notion d’intégration comme assimilation de l’altérité fera place à celle d’inclusion au service d’une quête d’un commun d’humanité. Renversant l’antériorité de l’identité sur l’altérité, les tendances à l’homogénéisation et à l’identitarisme sont revisitées pour orienter des chemins qui pensent la diversité comme le ferment de notre production du commun.
Inclusion et hétérotopie : monographie d’un atelier djembé pratiqué dans la mixité
Authors: Vigneulle Marianne
Published: September 2019

À travers une enquête sur un atelier de djembé partagé entre personnes dites « handicapées » et personnes dites « valides », l’article interroge l’inclusion dans un cadre très local, en étudiant les conditions de cette pratique commune constituée autour de deux enjeux initiaux potentiellement contradictoires : la synchronisation rythmique et la synchronisation sociale. L’observation ethnographique conduit à formuler l’hypothèse d’un espace hétérotopique, un monde social qui se distingue de l’espace courant dans son organisation, ses règles et ses modes de domination. Le sentiment de singularité de l’expérience vécue apparaît alors comme une composante essentielle de l’investissement des participants dans cet atelier.
Proximal Humeral Fractures: A Correlation of Residual Deformity With Long-Term Function
Authors: Keene J S , Huizenga R E , Engber W D , Rogers S C
Published: September 2019

ABSTRACT: Twenty-six patients with 27 fractures of the proximal humerus were evaluated by radiographic and clinical examination (Neer rating scale) 13 months to 12 years after injury. Proximal humeral fractures that had healed with less than 55° of angulation and less than 1.5 cm of displacement of the major fracture segments had good or excellent functional results. These fractures were successfully treated with a sling or cuff and collar immobilization. Two- and three-part fractures with greater than 55° of angulation or greater than 1.5 cm of displacement had fair or poor functional results. These fractures should be reduced by closed or open methods if good or excellent results are required. Minimal (0 to 2 mm) displacement of greater tuberosity fractures was often associated with rotator cuff tears. Neither good nor excellent results were obtained in four-part, Group IV fractures treated nonsurgically.
Time‐varying band‐pass filtering GPR data by self‐inverse filtering
Authors: Economou Nikos
Published: September 2019

Even though ground penetrating radar data signal processing has already been studied by many researchers, more research is needed and expected from automatic ground penetrating radar data analysis. An automatic band‐pass filtering procedure can lead to sufficient real‐time data interpretation as signal buried in noise could be amplified. Ground penetrating radar traces are highly non‐stationary, requiring time‐varying processing techniques. An algorithm, based on self‐inverse filtering, which is a special case of inverse filtering, was implemented. It is a ground penetrating radar trace filtering approach and is implemented by applying inverse filtering in each time sample in the time‐frequency domain. Applied on a synthetic trace, this algorithm performed better than a stationary band‐pass filter and empirical mode decomposition family methods, whereas its application on real ground penetrating radar data from two different sites enhanced reflections buried in noise without the need to test different high‐frequency band stops and with minimum distortion of the signal and the initial temporal resolution of the data.
GPR analysis of clayey soil behaviour in unsaturated conditions for pavement engineering and geoscience applications
Authors: Tosti Fabio , Benedetto Andrea , Ciampoli Luca Bianchini , Lambot Sébastien , Patriarca Claudio , Slob Evert C.
Published: September 2019

Clay content is one of the primary causes of pavement damages, such as subgrade failures, cracks, and pavement rutting, thereby playing a crucial role in road safety issues as an indirect cause of accidents. In this paper, several ground‐penetrating radar methods and analysis techniques were used to nondestructively investigate the electromagnetic behaviour of sub‐asphalt compacted clayey layers and subgrade soils in unsaturated conditions. Typical road materials employed for load‐bearing layers construction, classified as A1, A2, and A3 by the American Association of State Highway and Transportation Officials soil classification system, were used for the laboratory tests. Clay‐free and clay‐rich soil samples were manufactured and adequately compacted in electrically and hydraulically isolated formworks. The samples were tested at different moisture conditions from dry to saturated. Measurements were carried out for each water content using a vector network analyser spanning the 1 GHz–3 GHz frequency range, and a pulsed radar system with ground‐coupled antennas, with 500‐MHz centre frequency. Different theoretically based methods were used for data processing. Promising insights are shown to single out the influence of clay in load‐bearing layers and subgrade soils, and its impact on their electromagnetic response at variable moisture conditions.
Ground‐penetrating radar measurement of the distribution of thixotropic slurry behind large‐diameter segments in long‐distance pipe‐jacking construction
Authors: Zhao Yonghui , Wu Jiansheng , Xie Xiongyao , Feng Kunwei , Zeng Chenchao
Published: September 2019

Pipe‐jacking engineering requires monitoring of the spatial distribution of thixotropic slurry behind the pipe segments, but timely thickness measurements are difficult during construction. Ground‐penetrating radar was used here during sewage pipe jacking, to evaluate the distribution of the slurry by considering its dielectric contrast with the surrounding soil. A series of circular ground‐penetrating radar data was collected along the inner surfaces of the pipe segments using an antenna with a frequency of 900 MHz. To enable accurate interpretation of the ground‐pene‐trating radar images, the dielectric parameters of the slurry were first measured by time‐domain reflectometry. Statistical analyses revealed the slurry as a special medium with extremely high dielectric constant and conductivity. Numerical simulation indicated that the ground‐penetrating radar images can be considerably improved by applying Karhunen–Loeve transformation followed by f–k Stolt migration, which facilitates identification of the reflections from the slurry–soil interface. Based on all the processed ground‐penetrating radar field data, the thickness and the distribution of the slurry at different pipe segments were calculated using the two‐way travel time and the radar signal propagation velocity in the slurry. The results show that ground‐penetrating radar is indeed effective for estimating the quality of slurry injected during pipe jacking.
Mapping of hydrocarbon‐ and scrap‐metal‐contaminated soil using volatile organic compounds and electromagnetic profiling methods
Authors: Delgado‐Rodríguez Omar
Published: September 2019

The electromagnetic profiling method detects lateral changes in soil conductivity up to a certain depth. Volatile organic compounds are most commonly found in sites contaminated by hydrocarbons. This work presents the results of the application of electromagnetic profiling and volatile organic compound methods for detecting hydrocarbon and buried scrap metal contamination zones at a 48‐ha site that was used as an industrial waste dump for many years. Potentially contaminated zones were configured based on a joint analysis of low resistivity anomalies (mature pollution) and high concentrations of volatile organic compounds, which reduced the amount of soil sampling through optimal location of the drilling points. Chemical analysis in 54 soil samples showed the presence of three hydrocarbon‐contaminated zones that produced total petroleum hydrocarbon values higher than 10,000 ppm. Buried scrap metal was detected by anomalous in‐phase measurements obtained by an electromagnetic profiling survey, facilitating future removal works.
Pore radius distribution and fractal dimension derived from spectral induced polarization
Authors: Zhang Zeyu , Weller Andreas , Kruschwitz Sabine
Published: September 2019

The pore size distribution provides a suitable description of the pore space geometry that can be used to investigate the fractal nature of a pore space or to determine the fractal dimension. The fractal dimension describes the size of the geometric objects as a function of resolution. It can be integrated into the models that are used for permeability prediction. We investigated the fractal dimension of the pore volume of 11 Eocene sandstone samples from China. This study describes an approach to use spectral induced polarization spectra to estimate the pore size distribution and to determine the fractal dimension of the pore volume. Additionally, the fractal dimension was derived from data of the capillary pressure curves from mercury intrusion and the transversal relaxation time distribution of nuclear magnetic resonance. For samples with an effective pore radius larger than 1 μm, a good agreement exists between the values of the fractal dimension derived from the three different methods, which implies an identification of similar pore structures. Spectral induced polarization can be a non‐invasive laboratory technique for the estimation of the pore size distribution, but the application of the methodology to field measurements remains a challenging problem considering the limited frequency range.
Seismic survey on an open pingo system in Adventdalen Valley, Spitsbergen, Svalbard
Authors: Rossi Giuliana , Accaino Flavio , Boaga Jacopo , Petronio Lorenzo , Romeo Roberto , Wheeler Walter
Published: September 2019

We present the results of a seismic survey on an open‐system pingo, the Innerhytta pingo, located in the Adventdalen Valley, Spitsbergen Island of the Svalbard Archipelago, Norway. To evaluate the temporal evolution of the permafrost, we need detailed knowledge of its heterogeneities, of which pingos are the most visible markers. The seismic velocity contrasts and inversions peculiar to these features can be a problem for a successful seismic survey. The present paper focusses on the limits and benefits of various seismic methods for imaging the subsurface structure of the pingo, with the aim of defining the best practice for shallow seismic exploration. We performed several tests, using classical reflection/refraction arrays, seismic tomography, and surface wave analysis. We tested different kinds of seismic sources and receivers. The combined use of several sources and receivers sets and patterns, and the use of surface wave analysis and body wave tomography allowed constructing a three‐dimensional model of the velocities. For this particular pingo, beneath an approximately 15‐m thick high‐velocity layer, low velocities are found to be present at an anomalously shallow depth. This low‐velocity layer is interpreted to host the ground water circulation that controls the pingo’s structural evolution. Such observations suggest feeding water flow from depth rather than from the near surface, as commonly supposed for open‐system pingos.
INVERSE DYNAMIC ROBUST CONTROL OF SIT-TO-STAND MOVEMENT
Authors: Nematollahi Mohammad Hassan , Haghpanah Seyyed Arash , Taghvaei Sajjad
Published: September 2019

Sit-to-stand (STS) motion is one of the most common movements of a person during daily life. The motion, as a prerequisite for other movements, such as running and climbing, can be a very challenging task for the elderly or disabled people. Assistant tools, such as auxiliary walkers and exoskeletons, are commonly used to help these individuals have independent or at least semi-independent life. In order to design efficient rehabilitation treatments and also develop assistive devices for STS motion, it is necessary to have a validated dynamic model with an appropriate embedded controller on the ancillary instrument to reach the desired posture of the human body. In this paper, a linkage-based dynamic model of STS motion using Lagrange’s equation is extracted. The dynamic of the model is simulated using anthropometric parameters associated with the subject. Moreover, two robust controller types are designed and implemented in the presence of sinusoidal bounded disturbances in the dynamic system on the model in order to track the desired trajectory acquired from experimental kinematic data. The simulation results of the controlled dynamic model are compared with experimental data from a healthy subject and demonstrate the performance of the controller. The proposed model can be utilized in the design and control of an assistance device for STS motion.
Improving Application Decoupling in Virtual Enterprise Integration
Authors: Delgado José Carlos Martins
Published: September 2019

The interaction of applications belonging to several enterprise information systems (EIS), forming a collaborative network in a virtual enterprise (VE) context, raises an application integration problem that is even more stringent than within a single EIS, since a VE has a temporary nature and therefore integration requirements can change more frequently. Current integration technologies, such as web services and RESTful APIs, solve the interoperability problem but usually entail more coupling than required by the interacting applications. This is caused by sharing data schemas between applications, even if not all features of those schemas are actually exercised. The fundamental problem of application integration is therefore how to provide at most the minimum coupling possible while ensuring at least the minimum interoperability requirements. This chapter proposes compliance and conformance as the concepts to achieve this goal, by sharing only the subset of the features of the data schema that are actually used.
Fiscal Policy and Social Optimization for Developing Nations
Authors: Saiz-Alvarez Jose Manuel , Calleja-Leal Guillermo
Published: September 2019

From the public sector's perspective, one of the healthiest ways to optimize the social and organizational dynamics of a country in the digital era is to adapt the national fiscal policy to the socioeconomic needs of the population. The objective of this work is to analyze what key factors and which strategical proposal nations can follow to improve tax efficiency and, once a fair and equitable redistribution of the collected financial resources is achieved, to offer higher levels of welfare for the entire population in the digital era.
Introduction to Machine Learning
Authors: Tiwari Arvind Kumar
Published: September 2019

Machine learning refers to the changes in systems that perform tasks associated with artificial intelligence. This chapter presents introduction types and application of machine learning. This chapter also presents the basic concepts related to feature selection techniques such as filter, wrapper and hybrid methods and various machine learning techniques such as artificial neural network, Naive Bayes classifier, support vector machine, k-nearest-neighbor, decision trees, bagging, boosting, random subspace method, random forests, k-means clustering and deep learning. In the last the performance measure of the classifier is presented.
A Model Augmenting Credit Risk Management in the Banking Industry
Authors: Kogeda Okuthe Paul , Vumane Nicknolt N.
Published: September 2019

A lack of reliable credit risk measurements and poor control of credit risks has caused massive financial losses across a wide spectrum of business. Financial institutions like banks have not been able to control and contain the rapid increases of the credit defaulting. In this paper, we address the credit lending challenges by eliminating credit defaulting faced by the banking industry. Data from bank of previously accepted and rejected loan applicants was used to construct a credit risk evaluation network. The artificial neural network technique with back-propagation algorithm was applied to develop a model that supports the banks in the credit granting decision-making. The model was trained to categorize applicants as either good (credit granted) or bad (credit denied) based on the credit record. The model was able to predict whether a particular applicant is likely or unlikely to repay the credit. The training of neural network model and validation testing was done using data obtained from the bank. The results show a greater performance, classification and prediction accuracy.
Evaluation of Recurrent Neural Network and its Variants for Intrusion Detection System (IDS)
Authors: Vinayakumar R , Soman K.P. , Poornachandran Prabaharan
Published: September 2019

This article describes how sequential data modeling is a relevant task in Cybersecurity. Sequences are attributed temporal characteristics either explicitly or implicitly. Recurrent neural networks (RNNs) are a subset of artificial neural networks (ANNs) which have appeared as a powerful, principle approach to learn dynamic temporal behaviors in an arbitrary length of large-scale sequence data. Furthermore, stacked recurrent neural networks (S-RNNs) have the potential to learn complex temporal behaviors quickly, including sparse representations. To leverage this, the authors model network traffic as a time series, particularly transmission control protocol / internet protocol (TCP/IP) packets in a predefined time range with a supervised learning method, using millions of known good and bad network connections. To find out the best architecture, the authors complete a comprehensive review of various RNN architectures with its network parameters and network structures. Ideally, as a test bed, they use the existing benchmark Defense Advanced Research Projects Agency / Knowledge Discovery and Data Mining (DARPA) / (KDD) Cup ‘99' intrusion detection (ID) contest data set to show the efficacy of these various RNN architectures. All the experiments of deep learning architectures are run up to 1000 epochs with a learning rate in the range [0.01-0.5] on a GPU-enabled TensorFlow and experiments of traditional machine learning algorithms are done using Scikit-learn. Experiments of families of RNN architecture achieved a low false positive rate in comparison to the traditional machine learning classifiers. The primary reason is that RNN architectures are able to store information for long-term dependencies over time-lags and to adjust with successive connection sequence information. In addition, the effectiveness of RNN architectures are shown for the UNSW-NB15 data set.
A Decision Making Method Based on Society of Mind Theory in Multi-Player Imperfect Information Games
Authors: Wakatsuki Mitsuo , Fujimura Mari , Nishino Tetsuro
Published: September 2019

The authors are concerned with a card game called Daihinmin (Extreme Needy), which is a multi-player imperfect information game. Using Marvin Minsky's “Society of Mind” theory, they attempt to model the workings of the minds of game players. The UEC Computer Daihinmin Competitions (UECda) have been held at the University of Electro-Communications since 2006, to bring together competitive client programs that correspond to players of Daihinmin, and contest their strengths. In this paper, the authors extract the behavior of client programs from actual competition records of the computer Daihinmin, and propose a method of building a system that determines the parameters of Daihinmin agencies by machine learning.
Learning from Unbalanced Stream Data in Non-Stationary Environments Using Logistic Regression Model
Authors: Kulkarni Pallavi Digambarrao , Ade Roshani
Published: September 2019

There are several deep learning approaches that can be applied for analyzing situations in real world problems and inventing their solution in a scientific technique. Supervised data mining methods that predicts instance values, using previously obtained results from already collected data are pretty popular due to their intelligence in machine learning area. Stream data is continuous form of data which can be handled by using incremental learning approach. Stream data learning may face several challenges in real world like concept drift or class imbalance. Concept drift occurs in non-stationary environment where data distribution generation function is dynamic in nature and has no fixed formula to predict the future data distribution nature. Neural network techniques are intelligent enough to improve performance of algorithmic systems that work in such problem domains. This chapter briefly describes how MLP technique is integrated in system so that the system becomes a complete framework for handling unbalanced data with concept drift in the incremental learning strategies.
Digital Investigation of Cybercrimes Based on Big Data Analytics Using Deep Learning
Authors: Hemdan Ezz El-Din , H. Manjaiah D.
Published: September 2019

Big Data Analytics has become an important paradigm that can help digital investigators to investigate cybercrimes as well as provide solutions to malware and threat prediction, detection and prevention at an early stage. Big Data Analytics techniques can use to analysis enormous amount of generated data from new technologies such as Social Networks, Cloud Computing and Internet of Things to understand the committed crimes in addition to predict the new coming severe attacks and crimes in the future. This chapter introduce principles of Digital Forensics and Big Data as well as exploring Big Data Analytics and Deep Learning benefits and advantages that can help the digital investigators to develop and propose new techniques and methods based on Big Data Analytics using Deep Learning techniques that can be adapted to the unique context of Digital Forensics as well as support performing digital investigation process in forensically sound and timely fashion manner.
Feature Extraction of Video Using Artificial Neural Network
Authors: Hayakawa Yoshihiro , Oonuma Takanori , Kobayashi Hideyuki , Takahashi Akiko , Chiba Shinji , Fujiki Nahomi M
Published: September 2019

In deep neural networks, which have been gaining attention in recent years, the features of input images are expressed in a middle layer. Using the information on this feature layer, high performance can be demonstrated in the image recognition field. In the present study, we achieve image recognition, without using convolutional neural networks or sparse coding, through an image feature extraction function obtained when identity mapping learning is applied to sandglass-style feed-forward neural networks. In sports form analysis, for example, a state trajectory is mapped in a low-dimensional feature space based on a consecutive series of actions. Here, we discuss ideas related to image analysis by applying the above method.
Application of Artificial Neural Networks in Predicting the Degradation of Tram Tracks Using Maintenance Data
Authors: Moridpour Sara , Mazloumi Ehsan , Hesami Reyhaneh
Published: September 2019

The increase in number of passengers and tramcars will wear down existing rail structures faster. This is forcing the rail infrastructure asset owners to incorporate asset management strategies to reduce total operating cost of maintenance whilst improving safety and performance. Analysing track geometry defects is critical to plan a proactive maintenance strategy in short and long term. Repairing and maintaining the correctly selected tram tracks can effectively reduce the cost of maintenance operations. The main contribution of this chapter is to explore the factors influencing the degradation of tram tracks (light rail tracks) using existing geometric data, inspection data, load data and repair data. This chapter also presents an Artificial Neural Networks (ANN) model to predict the degradation of tram tracks. Predicting the degradation of tram tracks will assist in understanding the maintenance needs of tram system and reduce the operating costs of the system.
Meteorological Data Forecast using RNN
Authors: Balluff Stefan , Bendfeld Jörg , Krauter Stefan
Published: September 2019

Gathering knowledge not only of the current but also the upcoming wind speed is getting more and more important as the experience of operating and maintaining wind turbines is increasing. Not only with regards to operation and maintenance tasks such as gearbox and generator checks but moreover due to the fact that energy providers have to sell the right amount of their converted energy at the European energy markets, the knowledge of the wind and hence electrical power of the next day is of key importance. Selling more energy as has been offered is penalized as well as offering less energy as contractually promised. In addition to that the price per offered kWh decreases in case of a surplus of energy. Achieving a forecast there are various methods in computer science: fuzzy logic, linear prediction or neural networks. This paper presents current results of wind speed forecasts using recurrent neural networks (RNN) and the gradient descent method plus a backpropagation learning algorithm. Data used has been extracted from NASA's Modern Era-Retrospective analysis for Research and Applications (MERRA) which is calculated by a GEOS-5 Earth System Modeling and Data Assimilation system. The presented results show that wind speed data can be forecasted using historical data for training the RNN. Nevertheless, the current set up system lacks robustness and can be improved further with regards to accuracy.
Automated Whale Blow Detection in Infrared Video
Authors: Santhaseelan Varun , Asari Vijayan K.
Published: September 2019

In this chapter, solutions to the problem of whale blow detection in infrared video are presented. The solutions are considered to be assistive technology that could help whale researchers to sift through hours or days of video without manual intervention. Video is captured from an elevated position along the shoreline using an infrared camera. The presence of whales is inferred from the presence of blows detected in the video. In this chapter, three solutions are proposed for this problem. The first algorithm makes use of a neural network (multi-layer perceptron) for classification, the second uses fractal features and the third solution is using convolutional neural networks. The central idea of all the algorithms is to attempt and model the spatio-temporal characteristics of a whale blow accurately using appropriate mathematical models. We provide a detailed description and analysis of the proposed solutions, the challenges and some possible directions for future research.
Comparing Predictive Ability of Classifiers in Forecasting Online Buying Behaviour
Authors: Prashar Sanjeev , Mitra S.K.
Published: September 2019

With Internet invading geographic boundaries and diverse demographic strata, online shopping is growing at exponential rate. Expected to grow by 45 per cent to $7.69 billion by the end of 2015, India's ecommerce market has emerged as one of the most anticipated destinations for both multinational and domestic retailers. Since their success will depend on their ability to attract shoppers to buy online, it becomes relevant for them to decipher Indian consumers' attitude and behaviour towards online shopping and to predict online buying potential in India. The effectiveness of marketing and promotional strategies and action plans also will have to be pivoted around the potential available in the market. This empirical study explores the accuracy, precision and recall of four different classifying techniques used in predicting online buying. The forecasting ability of logistic regression (LR), artificial neural network (ANN), support vector machines (SVM) and random forest (RF) in the context of willingness of shoppers' to buy online has been compared. Analysis of the data supported most of the predictions albeit with varying level of accuracy. The outcome of the study reflects the superiority of artificial neural network over the other three models in terms of the predicting power. This paper adds to the knowledge body for online retailers in reducing their vulnerability with respect to market demand and improves their preparedness to handle the market response. Managerial implications of the findings and scope for future research have been deliberated.
Neural Network Based Automated System for Diagnosis of Cervical Cancer
Authors: Singh Seema , Tejaswini V. , Murthy Rishya P. , Mutgi Amit
Published: September 2019

Cervical Cancer is one of the most common cancers among women worldwide. Few concerns have arisen such as the shortage of skilled pathologists leading to increase in burden on them. This requires a need for efficient and accurate method that diagnoses cervical cancer without human intervention. In this paper, an automated system is developed for diagnosis of cervical cancer using image processing techniques and neural networks. The system is developed using Cytology images taken from Bangalore based cancer pathologist. MATLAB image processing toolbox is used to extract features from cytology images that are used for discriminating various stages of cervical cancer. The dominant features used for diagnosis are Nucleus to cytoplasm ratio, shape, and color intensity along with nucleus area, perimeter and eccentricity. These features are used to train the neural network using Back-propagation algorithm of supervised training method. The cytology cells were then successfully classified as non-cancerous, low- grade and high-grade cancer cells.
Phased Method for Solving Multi-Objective MPM Job Shop Scheduling Problem
Authors: Tselios Dimitrios C , Savvas Ilias K. , Kechadi M-Tahar
Published: September 2019

The project portfolio scheduling problem has become very popular in recent years since many modern organizations operate in multi-project and multi-objective environment. Current project oriented organizations have to design a plan in order to execute a set of projects sharing common resources such as personnel teams. This problem can be seen as an extension of the job shop scheduling problem; the multi-purpose job shop scheduling problem. In this paper, the authors propose a hybrid approach to deal with a bi-objective optimisation problem; Makespan and Total Weighted Tardiness. The approach consists of three phases; in the first phase they utilise a Genetic Algorithm (GA) to generate a set of initial solutions, which are used as inputs to recurrent neural networks (RNNs) in the second phase. In the third phase the authors apply adaptive learning rate and a Tabu Search like algorithm with the view to improve the solutions returned by the RNNs. The proposed hybrid approach is evaluated on some well-known benchmarks and the experimental results are very promising.
Research on O2O Platform and Promotion Algorithm of Sports Venues Based on Deep Learning Technique
Authors: Han Kaiyan , Wang Qin
Published: September 2019

In the era of big data, intelligent sports venues have a practical significance to provide personalized service for users and build up a platform for stadium management. This article proposes a new parallel big data promotion algorithm based on the latest achievements of big data analysis. The proposed algorithm calculates the optimal value by using the observed variables Y, the hidden variable data Z, the joint distribution P (Y, Z | θ) and distribution conditions P (Z | Y | θ). The experimental results show that the proposed algorithm has higher accuracy of big data analysis, and can serve the intelligent sports venues better.
Software Defect Prediction Using Genetic Programming and Neural Networks
Authors: Akour Mohammed , Melhem Wasen Yahya
Published: September 2019

This article describes how classification methods on software defect prediction is widely researched due to the need to increase the software quality and decrease testing efforts. However, findings of past researches done on this issue has not shown any classifier which proves to be superior to the other. Additionally, there is a lack of research that studies the effects and accuracy of genetic programming on software defect prediction. To find solutions for this problem, a comparative software defect prediction experiment between genetic programming and neural networks are performed on four datasets from the NASA Metrics Data repository. Generally, an interesting degree of accuracy is detected, which shows how the metric-based classification is useful. Nevertheless, this article specifies that the application and usage of genetic programming is highly recommended due to the detailed analysis it provides, as well as an important feature in this classification method which allows the viewing of each attributes impact in the dataset.
Analytical Modeling and Analysis of the Matter Flow during Friction Stir Welding
Authors: Harchouche Zineelabidine , Zemri Mokhtar , Lousdad Abdelkader
Published: September 2019

Friction stir welding is a solid-phase welding process based on the mixing of the pasty material in the stirred zone. The main advantage of this technique is the ability to weld metal alloys which are generally difficult to weld by conventional welding processes. In this paper an analytical model is proposed for the description in 2D the distribution of the material (fluid) flow in the vicinity of the tool pin during friction stir welding process "FSW". For this reason, the analytical solutions are built on the basis of traditional problem of mechanics of the fluids which is used to solve the equation associated with this problem. Furthermore, the aim is to make an analytical study of these aspects for a better understanding of this phenomenon. This method provides a reduction in computational time compared to those required for finite or differential elements methods. Moreover, it highlights on the effects of the different parameters on the material flow during welding.