bluuebunny/crossref_metadata_2025 huggingface dataset:
AESH au service d’une école inclusive et bienveillante : quelles compétences éthiques ?
Authors: Maguet Ulla , Panissal Nathalie
Published: September 2019

Cet article s’intéresse à la professionnalisation des AESH (Accompagnants des élèves en situation de handicap) dans le cadre de l’inclusion d’élève dans des établissements scolaires ordinaires. Ces agents tendent peu à peu à être reconnus comme de véritables professionnels par les textes officiels et la création de diplômes. Nous explorons un élément de leur professionnalisation à travers leur éthique professionnelle. Des entretiens semi-dirigés font ressortir les soucis ou tensions éthiques qu’ils expriment lors de la prise en charge de dilemmes professionnels qu’ils rencontrent. Cette première approche exploratoire montre que les tensions vécues renvoient à des conflits intérieurs qu’ils éprouvent lors de la rencontre des cas particuliers en situation. Ils agissent selon leurs valeurs personnelles et selon la représentation qu’ils ont de leur mission inclusive.
L’inclusion dans et par l’emploi : contextes et tensions
Authors: Jory Hervé
Published: September 2019

Cette contribution explore la portée et les limites de la mise en œuvre du principe d’inclusion dans et par l’emploi. Elle envisage l’importance des configurations institutionnelles liées à ce principe à propos du dispositif récent Parcours Emploi Compétences (PEC). S’appuyant sur un corpus d’entretiens, la réflexion porte ensuite sur les enjeux de l’inclusion en termes d’innovations sociales dans le champ des politiques de lutte contre le chômage.
Le droit international humanitaire : échecs et potentialités
Authors: David Éric , Taïbi Nadia
Published: September 2019

Dans cet entretien, Éric David, expert depuis 1996 de la commission consultative de droit international humanitaire à Bruxelles, montre que la formation juridique et surtout psychologique des combattants au respect du Droit International Humanitaire est plus efficace que la menace de poursuites pénales devant un Conseil de guerre ou un tribunal international. Il faut donc sans cesse rappeler la valeur absolue de tout être humain en temps de paix pour préparer la guerre.
Siqueiros et les aléas du discours d’autorité
Authors: Plâa Monique
Published: September 2019

Le travail analyse deux formes du discours, celui de Siqueiros qui n’a cessé de définir et de défendre le muralisme et celui d’Octavio Paz, le prestigieux poète et essayiste qui à ce jour reste le critique qui fait autorité quand il s’agit de muralisme. Avec une rhétorique agressive et des formulations d’apparence sectaires, incessamment, des décennies durant et ce encore en 1966, alors même que le Mexique célèbre son œuvre, Siqueiros doit sans encore et toujours, contre tout et contre tous défendre le muralisme comme mouvement uni et cohérent et comme esthétique au service d’une philosophie de l’histoire. Ce faisant, il fait la preuve, que sa manière de voir les choses ne parvient pas à faire autorité. Paz, au nom de l’autorité que lui vaut son statut de poète et d’essayiste prestigieux, impose naturellement une lecture du muralisme qui nie subtilement ce qui, dans le discours et la pratique de Siqueiros, en fait le fondement : philosophie de l’histoire, constante élaboration d’une esthétique au service de la figuration et revendication d’une figuration au service d’un art engagé sont tenus pour de simples et malencontreux travers. Ce faisant, l’autorité de Paz en récusant le monde des idées qui le sous-tend prive le muralisme d’une approche critique qui rende compte de la subtile complexité de son élaboration et de la portée de ses enjeux.
A ciência geográfica no helenismo: I -As mitografias de Gaia.
Authors: Vitte Antonio Carlos , Pereira Marcio Mello
Published: September 2019

O artigo tem por objetivo discutir o papel do mito de Gaia na formação da ciência geográfica. Para tal objetivo, foram analisadas as obras Ilídada e Odisséia de Homero, os Hinos Homéricos, Teogonia – a origem dos deuses de Hesíodo e as obras Órficas, que permitiram entender o papel do mito de Gaia na construção da primeira interpretação geográfica no que tange à origem e a dinâmica de transformação da Terra, permitindo o desenvolvimento da ciência na antiguidade. O foco inicial recaiu na busca da compreensão da fisiologia e da fisiografia de Gaia pelos pré-socráticos, momento que surgiu o conceito de Kosmos, que pôs fim ao mito de Gaia. Foi a partir desta arquitetura que se desenvolveu a ciência geográfica no helenismo, cujo auge foram os trabalhos de Teofrasto e Estrabão, que influenciaram a própria constituição ciência geográfica na modernidade.
Distribuição dos casos de infecção por vírus Zika vírus (ZIKV) na Região Metropolitana de Maceió
Authors: Almeida Lorena Sampaio , Araújo Letícia Alves de , Cota Ana Lídia Soares , Rodrigues Diego Freitas
Published: September 2019

Como se comportou a distribuição espacial da infecção por ZIKV na Região Metropolitana de Maceió (RMM)? Esse artigo tem como objetivo analisar a distribuição espacial dos casos de ZIKV na RMM no período de 2016 a 2017. Trata-se de um estudo quantitativo com delineamento ecológico transversal. A população estudada consiste nos casos notificados de ZIKV. A unidade de análise é a notificação dos casos de infecção por ZIKV na Região RMM, com seus indicadores populacionais, sociais e econômicos advindos da agregação das informações dos setores censitários do censo do Instituto Brasileiro de Geografia e Estatística (IBGE). Também foram considerados alguns indicadores em saúde ambiental: presença de esgotamento sanitário; coleta de lixo e resíduos depositados em áreas urbanas. Para a Zona Macro-Urbana, que apresentou maior número de registro de casos de Infecção por ZIKV, foi realizada a distribuição espacial de acordo com suas regiões administrativas. De um total de 3474 casos no município de Maceió, 165 foram registrados como ignorado/branco, o que destaca o problema da subnotificação no território, podendo mascarar os dados e impedir o direcionamento das ações. Os três bairros que mais apresentaram casos de infecção por ZIKV foram: Tabuleiro dos Martins, Clima Bom e Jacintinho, nos quais, possuem características em comum, como a distribuição populacional, possuem alta densidade populacional, com população acima dos 36.000 habitantes, apresentam desigualdade na distribuição de renda e os serviços de abastecimento de água e esgotamento sanitário promovem a reprodução do vetor.
AI and Individualized Education in Phys Ed and Sport
Authors: Patrascu Adrian , Patrascu Monica
Published: September 2019

The development of AI technologies in recent years has opened the doors to a wide array of application domains. One field of study in particular has great potential to use this knowledge: physical education and sport. New devices and software have made their way into the field of physical activity, motivating the investigation of AI-driven technologies as a tool in developing a new type of educational system. The general scope of this chapter is to analyze the use of AI individualized education in one major field of study: physical education and sport. The use of AI technologies in training future teachers is also discussed and analyzed. The implementation of AI software in sport is brought to attention, along with relevant case studies. Limitations and hurdles in constructing intelligent support systems for training were identified. Accessibility of an AI-driven educational system was detailed, pointing out barriers with regard to culture, social environment, and age differences.
Virtual Laboratory to Face New Challenges in the Industry
Authors: Soriano Arturo , Ponce Pedro , Molina Arturo
Published: September 2019

The fast-growing field of technology allows for the development, design, and implementation of smart solutions to several industries such as smart grid, manufacturing, automotive, and robotics. Trends in smart solutions are based on providing a certain degree of intelligence to the process and products through intelligent artificial and machine learning algorithms. It also involves further use of devices based on power electronics elements, fast microcontrollers, intelligent algorithms, and processing of information via local or cloud. Hence, a new workforce that possesses multidisciplinary skills (i.e., dealing with different fields of knowledge in order to find solutions to complex problems) is required. Recently, virtual laboratories have emerged so as to aid in the improvement of forming engineers. In this chapter, a virtual co-simulation laboratory is proposed to deal with the lack of engineers with multidisciplinary skills in mind; additionally, a new framework based in co-simulation, co-modeling, and co-design is proposed in order to maximize the use of the laboratory proposed.
Towards a Utility Theory of Privacy and Information Sharing and the Introduction of Hyper-Hyperbolic Discounting in the Digital Big Data Age
Authors: Puaschunder Julia
Published: September 2019

Today enormous data storage capacities and computational power in the e-big data era have created unforeseen opportunities for big data hoarding corporations to reap hidden benefits from individuals' information sharing, which occurs bit by bit in small tranches over time. Behavioral economics describes human decision-making fallibility over time but has—to this day—not covered the problem of individuals' decision to share information about themselves in tranches on social media and big data administrators being able to reap a benefit from putting data together over time and reflecting the individual's information in relation to big data of others. The decision-making fallibility inherent in individuals having problems understanding the impact of their current information sharing in the future is introduced as hyper-hyperbolic discounting decision-making predicament.
Breaking the Formal Financing Barriers Facing Entrepreneurs
Authors: Raimi Lukman
Published: September 2019

Financing programs at both start-up and growth phases are confronted with a number of institutional barriers. However, the digital era with its attendant benefits of interconnectedness has provided an alternative financing option called crowdfunding for internet-savvy entrepreneurs. Crowdfunding is a digital-based tool used to raise funds for different projects contributing to socio-economic development. The chapter discusses crowdfunding as an alternative financing option for enterprise development in Nigeria. Using a desk research technique, this chapter highlights the prospects of crowdfunding and strategies for leveraging crowdfunding as a viable alternative financing alternative in Nigeria. As an internet-driven process, this chapter identifies a number of challenges, but the most fundamental is the absence of regulatory environment to protect investors, a development that is in contrast to developed countries where crowdfunding is guided by enabling legislation. The chapter concludes with a number of research implications and suggestions.
Adoption and Use of Human Information System Digital Technology for Organizational Competitiveness
Authors: Narayana Alamuri Surya , Bhusal Roshee Lamichhane
Published: September 2019

Staying competitive in the current digitized workplace era requires, among other things, an adequate and efficient use of modern technology. Human resource information system (HRIS) is one of several tools that helps organizations remain sustainable by providing technology that can help to acquire, store, generate, analyze, and disseminate timely and accurate employee information and activities. Of late, HRIS is slowly gaining prominence in Nepal. A generic model for conditions that are necessary for successful adoption and use of HRIS in Nepali organizations is designed as the models proposed by earlier researchers in a developed context may not work well in a developing context. This sets fertile ground to carry out scholarly inquiry into the domain of HRIS in the Nepalese context. The limitations of present study are mentioned and practical/research implications of the same are discussed towards the end. Researchers are of the opinion that the findings of this preliminary study can be taken up to the next level for carrying out quantitative research in HRIS domain in Nepal.
Optimization Problem
Authors: Voronin Albert N.
Published: September 2019

A systemic approach to solving multicriteria optimization problems is proposed. The system approach allowed uniting the models of individual schemes of compromises into a single integrated structure that adapts to the situation of adopting a multi-criteria solution. The advantage of the concept of non-linear scheme of compromises is the possibility of making a multicriteria decision formally, without the direct participation of a person. The apparatus of the non-linear scheme of compromises, developed as a formalized tool for the study of control systems with conflicting criteria, makes it possible to solve practically multicriteria problems of a wide class.
Optimization Models for Calculation of Personalized Strategies
Authors: Nastenko Ievgen Arnoldovich , Pavlov Volodymyr Anatolevich , Nosovets Olena Konstantinovna , Davydko Oleksandr , Pavlov Oleksander
Published: September 2019

The chapter considers the problem of calculating the best individual strategy based on models obtained from observations of a given sample object's reaction to the applied control actions. In order to improve the calculation efficiency, the construction of the optimization problem with line dependence on the control variables is offered. To ensure the calculation adequacy, the object state models of optimal complexity, nonlinear with respect to the initial conditions and parameters, are considered. Examples of optimal personalized treatment strategies calculation are given. The proposed approach can be extended to other practical areas to solve the decision making, provided the development of adequate the object state models in the optimization field.
Process-Oriented Organizations
Authors: Janeš Aleksander , Novak Rajko
Published: September 2019

The main purpose of the chapter is to represent practical approach on the empirically-evaluated business process orientation (BPO) based on the research of the Slovenian power supply business. Within the empirical investigation, the level of BPO maturity was measured in the 19 organizations. The survey was focused on the top, middle, and lower managers. As a measuring instrument, a questionnaire for the extended concept of process orientation was used. The results of the BPO measurement shows that, despite this long-standing engagement with processes, quality management system and the IT portfolio management (PoM) of operations, process orientation, and appurtenant IT PoM maturity is not very high. Results suggested the opportunities for improvement, particularly for better use and to take advantage of IT. One important reason for performing the research in the power supply business is the importance of its activities for the socio-economic and environmental impact of the whole society and therefore better understanding of the recognized “soft factors.”
Technology and Organizational Change
Authors: Attaran Mohsen , Attaran Sharmin , Kirkland Diane
Published: September 2019

This chapter explores the changing dimensions of the workplace and highlights the relationship between technology and organizational change. The chapter begins by briefly reviewing some key perspectives that have emerged in the information systems (IS) literature to account for the relationship between technology and organizational change. It highlights the importance of smart workplace technologies, identifies determinants of successful workplace transformation, proposes a conceptual model for implementation, identifies key factors to consider, and covers some of the potential benefits. The chapter argues that digital transformation is more than just implementing digital technologies. Successful digital transformation occurs when business strategies or major sections of an organization are altered.
Consumer Online Behavior, Data Sharing, and Ethics
Authors: Miori Virginia M. , Herschel Richard T.
Published: September 2019

This chapter reports the results of a survey that examines how a sampling of millennials describes their online activity, their social engagement, and their priorities when they are asked to value their online activity. It also explores whether there are tenets of a specific ethical perspective that shape their thinking about what is moral behavior online. Results indicate that the online behavior of the study participants involves extensive use of social media with a variety of platforms employed. Degree of engagement is not dependent on whether the individual is introvert or extrovert. Their online priority focuses first on a concern for their privacy, followed by their appreciation for time saving technology and opportunities for money savings and promotions. No single ethical theory dominates their expressed moral values, though there is a clear pattern that is consistent with consequentialism.
The Use of Google Analytics for Measuring Website Performance of Non-Formal Education Institution
Authors: Domazet Ivana S. , Simović Vladimir M.
Published: September 2019

The purpose of this chapter is to measure the performance of a non-formal educational institution's website using Google Analytics data and data obtained in a separate research on specific non-formal educational institution's users. The goal was to determine the best performing acquisition channel for non-formal educational institutions and the average user profile of this kind of educational programs by means of user acquisition and behavior data. Key parameters used to measure the performance of different acquisition channels were conversion rate, average session duration, and bounce rate. User gender data and age group data were used on a side of user specific data. The findings presented in this research may be applicable to other non-formal educational institutions as well.
Artificial Neural Network What-If Theory
Authors: Buscema Paolo Massimo , Tastle William J.
Published: September 2019

Data sets collected independently using the same variables can be compared using a new artificial neural network called Artificial neural network What If Theory, AWIT. Given a data set that is deemed the standard reference for some object, i.e. a flower, industry, disease, or galaxy, other data sets can be compared against it to identify its proximity to the standard. Thus, data that might not lend itself well to traditional methods of analysis could identify new perspectives or views of the data and thus, potentially new perceptions of novel and innovative solutions. This method comes out of the field of artificial intelligence, particularly artificial neural networks, and utilizes both machine learning and pattern recognition to display an innovative analysis.
Ant Colony Optimization Applied to the Training of a High Order Neural Network with Adaptable Exponential Weights
Authors: Abdelbar Ashraf M. , Elnabarawy Islam , Wunsch II Donald C. , Salama Khalid M.
Published: September 2019

High order neural networks (HONN) are neural networks which employ neurons that combine their inputs non-linearly. The HONEST (High Order Network with Exponential SynapTic links) network is a HONN that uses neurons with product units and adaptable exponents. The output of a trained HONEST network can be expressed in terms of the network inputs by a polynomial-like equation. This makes the structure of the network more transparent and easier to interpret. This study adapts ACOℝ, an Ant Colony Optimization algorithm, to the training of an HONEST network. Using a collection of 10 widely-used benchmark datasets, we compare ACOℝ to the well-known gradient-based Resilient Propagation (R-Prop) algorithm, in the training of HONEST networks. We find that our adaptation of ACOℝ has better test set generalization than R-Prop, though not to a statistically significant extent.
Prediction of Biosorption Capacity Using Artificial Neural Network Modeling and Genetic Algorithm
Authors: Mishra Prakash Chandra , Giri Anil Kumar
Published: September 2019

Artificial neural network model is applied for the prediction of the biosorption capacity of living cells of Bacillus cereus for the removal of chromium (VI) ions from aqueous solution. The maximum biosorption capacity of living cells of Bacillus cereus for chromium (VI) was found to be 89.24% at pH 7.5, equilibrium time of 60 min, biomass dosage of 6 g/L, and temperature of 30 ± 2 °C. The biosorption data of chromium (VI) ions collected from laboratory scale experimental set up is used to train a back propagation (BP) learning algorithm having 4-7-1 architecture. The model uses tangent sigmoid transfer function at input to hidden layer whereas a linear transfer function is used at output layer. The data is divided into training (75%) and testing (25%) sets. Comparison between the model results and experimental data gives a high degree of correlation R2 = 0.984 indicating that the model is able to predict the sorption efficiency with reasonable accuracy. Bacillus cereus biomass is characterized using AFM and FTIR.
An Adaptive Second Order Neural Network with Genetic-Algorithm-based Training (ASONN-GA) to Forecast the Closing Prices of the Stock Market
Authors: Nayak Sarat Chandra , Misra Bijan Bihari , Behera Himansu Sekhar
Published: September 2019

Successful prediction of stock indices could yield significant profit and hence require an efficient prediction system. Higher order neural networks (HONN) have several advantages over traditional neural networks such as stronger approximation, higher fault tolerance capacity and faster convergence characteristics. This paper proposes an adaptive single layer second order neural network with genetic algorithm based training (ASONN-GA) applied to forecast daily closing prices of the stock market. For comparative study of performance, two conventional neural based models such as a recurrent neural network (RNN) and a multilayer perceptron (MLP) have been developed. The optimal network parameters for all the three models are tuned by genetic algorithm (GA). The efficiencies of the models have been evaluated by forecasting the one-day-ahead closing prices of real stock markets. From simulation studies, it is revealed that the ASONN-GA model achieve better forecasting accuracy over other two models.
Efficient Imbalanced Multimedia Concept Retrieval by Deep Learning on Spark Clusters
Authors: Yan Yilin , Chen Min , Sadiq Saad , Shyu Mei-Ling
Published: September 2019

The classification of imbalanced datasets has recently attracted significant attention due to its implications in several real-world use cases. The classifiers developed on datasets with skewed distributions tend to favor the majority classes and are biased against the minority class. Despite extensive research interests, imbalanced data classification remains a challenge in data mining research, especially for multimedia data. Our attempt to overcome this hurdle is to develop a convolutional neural network (CNN) based deep learning solution integrated with a bootstrapping technique. Considering that convolutional neural networks are very computationally expensive coupled with big training datasets, we propose to extract features from pre-trained convolutional neural network models and feed those features to another full connected neutral network. Spark implementation shows promising performance of our model in handling big datasets with respect to feasibility and scalability.
A Deep Learning Solution for Multimedia Conference System Assisted by Cloud Computing
Authors: Zhang Wei , Shi Huiling , Lu Xinming , Zhou Longquan
Published: September 2019

With the development of information technology, more and more people use multimedia conference system to communicate or work across regions. In this article, an ultra-reliable and low-latency solution based on Deep Learning and assisted by Cloud Computing for multimedia conference system, called UCCMCS, is designed and implemented. In UCCMCS, there are two-tiers in its data distribution structure which combines the advantages of cloud computing. And according to the requirements of ultra-reliability and low-latency, a bandwidth optimization model is proposed to improve the transmission efficiency of multimedia data so as to reduce the delay of the system. In order to improve the reliability of data distribution, the help of cloud computing node is used to carry out the retransmission of lost data. the experimental results show UCCMCS could improve the reliability and reduce the latency of the multimedia data distribution in multimedia conference system.
Analysis of Gait Flow Image and Gait Gaussian Image Using Extension Neural Network for Gait Recognition
Authors: Arora Parul , Srivastava Smriti , Singhal Shivank
Published: September 2019

This paper proposes a new technique to recognize human gait by combining model free feature extraction approaches and a classifier. Gait flow image (GFI) and gait Gaussian image (GGI) are the two feature extraction techniques used in combination with ENN. GFI is a gait period based technique, uses optical flow features. So it directly focuses on dynamic part of human gait. GGI is another gait period based technique, computed by applying Gaussian membership function on human silhouettes. Next, ENN has been used as a classifier which combines the extension theory and neural networks. All the study has been done on CASIA-A and OU-ISIR treadmill B databases. The results derived using ENN are compared with SVM (support vector machines) and NN (Nearest neighbor) classifiers. ENN proved to give good accuracy and less iteration as compared to other traditional methods.
A Hybrid NIDS Model Using Artificial Neural Network and D-S Evidence
Authors: Lu Chunlin , Li Yue , Ma Mingjie , Li Na
Published: September 2019

Artificial Neural Networks (ANNs), especially back-propagation (BP) neural network, can improve the performance of intrusion detection systems. However, for the current network intrusion detection methods, the detection precision, especially for low-frequent attacks, detection stability and training time are still needed to be enhanced. In this paper, a new model which based on optimized BP neural network and Dempster-Shafer theory to solve the above problems and help NIDS to achieve higher detection rate, less false positive rate and stronger stability. The general process of the authors' model is as follows: firstly dividing the main extracted feature into several different feature subsets. Then, based on different feature subsets, different ANN models are trained to build the detection engine. Finally, the D-S evidence theory is employed to integration these results, and obtain the final result. The effectiveness of this method is verified by experimental simulation utilizing KDD Cup1999 dataset.
Speckle Noise Filtering Using Back-Propagation Multi-Layer Perceptron Network in Synthetic Aperture Radar Image
Authors: Amitab Khwairakpam , Kandar Debdatta , Maji Arnab K.
Published: September 2019

Synthetic Aperture Radar (SAR) are imaging Radar, it uses electromagnetic radiation to illuminate the scanned surface and produce high resolution images in all-weather condition, day and night. Interference of signals causes noise and degrades the quality of the image, it causes serious difficulty in analyzing the images. Speckle is multiplicative noise that inherently exist in SAR images. Artificial Neural Network (ANN) have the capability of learning and is gaining popularity in SAR image processing. Multi-Layer Perceptron (MLP) is a feed forward artificial neural network model that consists of an input layer, several hidden layers, and an output layer. We have simulated MLP with two hidden layer in Matlab. Speckle noises were added to the target SAR image and applied MLP for speckle noise reduction. It is found that speckle noise in SAR images can be reduced by using MLP. We have considered Log-sigmoid, Tan-Sigmoid and Linear Transfer Function for the hidden layers. The MLP network are trained using Gradient descent with momentum back propagation, Resilient back propagation and Levenberg-Marquardt back propagation and comparatively evaluated the performance.
Optimization of Cutting Parameters for AISI H13 Tool Steel by Taguchi Method and Artificial Neural Network
Authors: Pathak Hrishikesh , Das Sanghamitra , Doley Rakesh , Kashyap Satadru
Published: September 2019

In the present study an attempt has been made to investigate the effect of cutting parameters (cutting speed, feed rate, and depth of cut) on surface roughness and material removal rate (MRR) during dry turning operation of AISI H13 tool steel as per Taguchi's experimental design technique using an L9 orthogonal array. Signal to noise ratio (S/N) results and Analysis of Variance (ANOVA) were employed in order to investigate the optimal and significant cutting characteristics of H13 tool steel respectively. This paper focuses on optimizing the cutting parameters for minimum surface roughness and maximum MRR of H13 tool steel using high speed steel (HSS) as the cutting tool during turning. The results indicated that feed has a significant influence on surface finish and depth of cut on MRR when turning operation was carried out with HSS cutting tool. An artificial neural network model and regression equations were also developed to obtain minimum surface roughness and maximum MRR at different cutting conditions.
Spam Detection on Social Media Using Semantic Convolutional Neural Network
Authors: Jain Gauri , Sharma Manisha , Agarwal Basant
Published: September 2019

This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, especially in case of text within the limited number of characters. Effective spam detection requires more number of efficient features to be learned. In the current article, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an added semantic layer on the top of it. The resultant model is known as a semantic convolutional neural network (SCNN). A semantic layer is composed of training the random word vectors with the help of Word2vec to get the semantically enriched word embedding. WordNet and ConceptNet are used to find the word similar to a given word, in case it is missing in the word2vec. The architecture is evaluated on two corpora: SMS Spam dataset (UCI repository) and Twitter dataset (Tweets scrapped from public live tweets). The authors' approach outperforms the-state-of-the-art results with 98.65% accuracy on SMS spam dataset and 94.40% accuracy on Twitter dataset.
Neural Network (NN) Based Route Weight Computation for Bi-Directional Traffic Management System
Authors: Akhter Shamim , Rahman Rahatur , Islam Ashfaqul
Published: September 2019

Low-cost, flexible, easily maintainable and secure traffic management support systems are in demand. Internet-based real time bi-directional communication provides significant benefits to monitor road traffic conditions. Dynamic route computation is a vital requirement to make the traffic management system more realistic and reliable. Therefore, an integrated approach with multiple data feeds and Backpropagation (BP) Neural Network (NN) with Levenberg-Marquardt (LM) optimization is applied to predict the road weights. The results indicate that the proposed traffic system/tool with NN based dynamic weights computation is much more effective to find the optimal routes. The BP NN with LM optimization achieves 96.67% accuracy.
Optimal Model Parameters of Inverse Kinematics Solution of a 3R Robotic Manipulator Using ANN Models
Authors: Karkalos Nikolaos E. , Markopoulos Angelos P. , Dossis Michael F.
Published: September 2019

Solution of inverse kinematics equations of robotic manipulators constitutes usually a demanding problem, which is also required to be resolved in a time-efficient way to be appropriate for actual industrial applications. During the last few decades, soft computing models such as Artificial Neural Networks (ANN) models were employed for the inverse kinematics problem and are considered nowadays as a viable alternative method to other analytical and numerical methods. In the current paper, the solution of inverse kinematics equations of a planar 3R robotic manipulator using ANN models is presented, an investigation concerning optimum values of ANN model parameters, namely input data sample size, network architecture and training algorithm is conducted and conclusions concerning models performance in these cases are drawn.
Case Studies in Amalgamation of Deep Learning and Big Data
Authors: Jeyakumar Balajee , Durai M.A. Saleem , Lopez Daphne
Published: September 2019

Deep learning is now more popular research domain in machine learning and pattern recognition in the world. It is widely success in the far-reaching area of applications such as Speech recognition, Computer vision, Natural language processing and Reinforcement learning. With the absolute amount of data accessible nowadays, big data brings chances and transformative possible for several sectors, on the other hand, it also performs on the unpredicted defies to connecting data and information. The size of the data is getting larger, and deep learning is imminent to play a vital role in big data predictive analytics solutions. In this paper, we make available a brief outline of deep learning and focus recent research efforts and the challenges in the fields of science, medical and water resource system.
Use of Images of Leaves and Fruits of Apple Trees for Automatic Identification of Symptoms of Diseases and Nutritional Disorders
Authors: Nachtigall Lucas Garcia , Araujo Ricardo Matsumura , Nachtigall Gilmar Ribeiro
Published: September 2019

Rapid diagnosis of symptoms caused by pest attack, diseases and nutritional or physiological disorders in apple orchards is essential to avoid greater losses. This paper aimed to evaluate the efficiency of Convolutional Neural Networks (CNN) to automatically detect and classify symptoms of diseases, nutritional deficiencies and damage caused by herbicides in apple trees from images of their leaves and fruits. A novel data set was developed containing labeled examples consisting of approximately 10,000 images of leaves and apple fruits divided into 12 classes, which were classified by algorithms of machine learning, with emphasis on models of deep learning. The results showed trained CNNs can overcome the performance of experts and other algorithms of machine learning in the classification of symptoms in apple trees from leaves images, with an accuracy of 97.3% and obtain 91.1% accuracy with fruit images. In this way, the use of Convolutional Neural Networks may enable the diagnosis of symptoms in apple trees in a fast, precise and usual way.
Using Functional Link Artificial Neural Network (FLANN) for Bank Credit Risk Assessment
Authors: Jena Saroj Kanta , Dwivedy Maheshwar , Kumar Anil
Published: September 2019

Credit scoring is the most important and critical component conducted by the credit providers to decide whether to grant a loan to the applicant or not. Therefore credit scoring models are generally used to predict the potentiality of the loan applicant. A proper evaluation of the credit can help the service provider to determine whether to grant or to reject the credit. The objective of the study is to predict banking credit scoring assessment using a data mining technique i.e. Functional Link Artificial Neural Network (FLANN) classifier. Credit approval datasets: Australian credit and German credit have been used to do this analysis. The output of the study shows that the proposed model used for classification works better on credit dataset. Secondly, we have applied our proposed model on the two credit approval dataset to check the performance of the model for the classification accuracy. A proper evaluation of the credit using the proposed FLANN approach can help the service provider to accurately and quickly ascertain whether to grant credit or to reject.
Neural Network Modeling for Organizational Psychology
Authors: Pessa Eliano
Published: September 2019

The nature itself of organizational psychology makes the study and modeling of emergence processes the key topic of this science. In this regard we can distinguish between two kinds of emergence: the one related to individual constructs and the other to collective constructs. In the former case the presence of suitable individual and contextual features gives rise to the emergence of suitable individual attitudes of holistic nature. In the latter case the features of single individuals belonging to a group, and reciprocally interacting, give rise to the occurrence of collective features and phenomena. In the last years both kinds of emergence have been studied through computational models. In this chapter we focus on the contribution of Artificial Neural Network (ANN) models to this modeling activity. As regards the emergence of individual constructs there is a consistent number of ANN-based models, most of which formulated in terms of recurrent networks. A review of their successes and failures constitutes a first part of the chapter. Instead, the emergence of collective constructs has been so far modelled by resorting to agent-based models. However, in recent times the ANN models have begun to be used with increasing frequency in this field. Namely, each agent can be modelled in an easier way by representing its structure through a suitable neural network. The final part of the chapter is, therefore, devoted to the problems underlying the use of ANNs as constituents of agent models.
A Hybrid Approach Based on Genetic Algorithm and Particle Swarm Optimization to Improve Neural Network Classification
Authors: Hewahi Nabil M. , Abu Hamra Enas
Published: September 2019

Artificial Neural Network (ANN) has played a significant role in many areas because of its ability to solve many complex problems that mathematical methods failed to solve. However, it has some shortcomings that lead it to stop working in some cases or decrease the result accuracy. In this research the authors propose a new approach combining particle swarm optimization algorithm (PSO) and genetic algorithm (GA), to increase the classification accuracy of ANN. The proposed approach utilizes the advantages of both PSO and GA to overcome the local minima problem of ANN, which prevents ANN from improving the classification accuracy. The algorithms start with using backpropagation algorithm, then it keeps repeating applying GA followed by PSO until the optimum classification is reached. The proposed approach is domain independent and has been evaluated by applying it using nine datasets with various domains and characteristics. A comparative study has been performed between the authors' proposed approach and other previous approaches, the results show the superiority of our approach.
Research on Methodology of Correlation Analysis of Sci-Tech Literature Based on Deep Learning Technology in the Big Data
Authors: Zeng Wen , Xu Hongjiao , Li Hui , Li Xiang
Published: September 2019

In the big data era, it is a great challenge to identify high-level abstract features out of a flood of sci-tech literature to achieve in-depth analysis of data. The deep learning technology has developed rapidly and achieved applications in many fields, but has rarely been utilized in the research of sci-tech literature data. This article introduced the presentation method of vector space of terminologies in sci-tech literature based on the deep learning model. It explored and adopted a deep AE model to reduce the dimensionality of input word vector feature. Also put forward is the methodology of correlation analysis of sci-tech literature based on deep learning technology. The experimental results showed that the processing of sci-tech literature data could be simplified into the computation of vectors in the multi-dimensional vector space, and the similarity in vector space could be used to represent similarity in text semantics. The correlation analysis of subject contents between sci-tech literatures of the same or different types can be made using this method.
Modified Differential Evolution Algorithm Based Neural Network for Nonlinear Discrete Time System
Authors: Singh Uday Pratap , Jain Sanjeev , Singh Rajeev Kumar , Parmar Mahesh
Published: September 2019

Two main important features of neural networks are weights and bias connection, which is still a challenging problem for researchers. In this paper we select weights and bias connection of neural network (KN) using modified differential evolution algorithm (MDEA) i.e. MDEA-NN for uncertain nonlinear systems with unknown disturbances and compare it with KN using differential evolution algorithm (DEA) i.e. DEA-KN. In this work, MDEA is based on exploitation and exploration of capability, we have implemented differential evolution algorithm and modified differential evolution algorithm, which are based on the consideration of the three main operator's mutation, crossover and selection. MDEA-KN is applied on two different uncertain nonlinear systems, and one benchmark problem known as brushless dc (BDC) motor. Proposed method is validated through statistical testing's methods which demonstrate that the difference between target and output of proposed method are acceptable.
Vector Optimization of Neural Network Classifiers
Authors: Voronin Albert
Published: September 2019

The problem content and statement of the problem of vector optimization of neural network classifiers is given. Method of solution is proposed. Vector optimization of a neural network texts classifier method is proposed.
Towards Developing the Piece-Wise Linear Neural Network Algorithm for Rule Extraction
Authors: Chan Veronica , Chan Christine W.
Published: September 2019

This paper discusses development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems. The algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. The objective of the algorithm is to “open up” the black box of a neural network model so that rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN). The preliminary results showed that the algorithm gives high fidelity and satisfactory results on sixteen of the nineteen tested datasets. By analyzing the values of R2 given by the PWL approximation on the hidden neurons and the overall output, it is evident that in addition to accurate approximation of each individual node of a given ANN model, there are more factors affecting the fidelity of the PWL-ANN algorithm Nevertheless, the algorithm shows promising potential for domains when better understanding about the problem is needed.
Lifestyle, body mass index and wellness in youth: Strengthens and weakness in Italian youth
Authors: Di Giacomo Dina , Ranieri Jessica , Fiasca Fabiana , Mattei Antonella
Published: September 2019

This study investigated the psychological wellbeing and body perception among young Italian people. Drinking, eating, and sexual behaviors have been examined in relation to body mass index (BMI) and psychological well-being by EPOCH framework. This was a cross-sectional observational study conducted on Italian adolescent and young adult population through an online survey. Qualitative and quantitative data on lifestyle, BMI classes, and psychological characteristics of wellness in youth were obtained from a sample of 1221 Italian participants using a self-report questionnaire on body perception and habits, a socio-demographics inventory, and the EPOCH measure. Findings suggest that being underweight or overweight is associated with various factors, including gender, self-control regarding food, perception of one’s body, and the happiness domain. EPOCH framework revealed the psychological wellness of girls and young women did not seem strongly affected by BMI, though Perseverance and Happiness seemed reduced with increasing BMI. The Engagement, Optimism, and Connectedness domains were not significantly affected by the BMI variable. On the contrary, Perseverance and Happiness seemed reduced with increasing BMI and were related to physical shape. Our study offers a new perspective: promoting the development of positive psychological characteristics since adolescence to boost quality of life by improving wellness, by EPOCH approach could be functional reinforcing specific psychological aspects in young generations regarding future wellness, improving the positive perception and management of their own health.
The Future of Physiotherapy
Authors: Editor The
Published: September 2019

No abstract available
Industrial/Occupational physiotherapy: An international perspective
Authors: Jaholkowski B. M.
Published: September 2019

An overview of the development and practice of Industrial/Occupational Physiotherapy in Great Britain, Australia, New Zealand, Finland, Sweden and Denmark is given. Training facilities and courses available in each country are mentioned. Barbara McPhee’s recommendations for the development of this field of physiotherapy are quoted as suitable for South Africa.
Occupational therapy-Work assessment and preparation
Authors: Jack-Bleach Mary-Ann
Published: September 2019

This paper presents aspects of the role of the occupational therapist in the assessment and preparation of patients for work. The roles of the occupational therapist in working with two broad categories of workers, viz. the worker in industry who is experiencing problems related directly or indirectly to his work situation, and the disabled person who wishes to work, are described.
Book Review: Malaria - What you should know about it
Authors: Welfare & Pensions Department of Health,
Published: September 2019

No abstract available
The Fund's Income Position for FY2007 - Midyear Review
Authors:   International Monetary Fund
Published: September 2019

In March and April this year, Executive Directors had wide-ranging discussions on the Fund’s income outlook, which has changed significantly following the recent sharp decline in credit outstanding. Directors supported the proposal for a two-pronged strategy that would involve, first, immediate steps to address the projected income shortfall in FY2007 under current policies and within the framework of the FY2007 budget and, second, development of a broader work plan on the options to ensure a stable and sustainable income base.
Progress Report on the Activities of the Independent Evaluation Office
Authors:   International Monetary Fund
Published: September 2019

Since its April 2006 Report, the IEO has continued work on three ongoing evaluations. It has considered and discussed a broad list of possible future evaluations with a wide range of stakeholders and announced its decision to add four of these to the IEO work program in the financial year 2007. IEO has also started to follow up on the recommendations of the report of the independent external evaluation of the IEO.
Quotas and Voice - A Possible Package of Reforms
Authors:   International Monetary Fund
Published: September 2019

On July 5, the Executive Board had an informal discussion of reforms of quotas and voice as part of the process of formulating reform proposals for the Annual Meetings in Singapore. That discussion confirmed the importance attached by the membership to reaching agreement on a package of quota and voice reforms in Singapore for the Fund’s continued credibility and effectiveness. The discussion also revealed broad support for a reform package along the lines suggested in the staff paper. The support for quota and voice reform across a broad spectrum of the membership indicates that these reforms are not seen as a zero sum game, but rather as change that will strengthen the Fund and from which all members will benefit.