Apresentando e demonstrando novo algoritmo para a melhor alocação de recursos na computação em nuvem com base na filtragem de Kalman

In recent decades lots of attention attracted toward Cloud Computing, and that’s only because of services and powers which present by cloud providers. They present different kind of services, such as: processing ability, storage capacity, Platform as a Service, Software as a Service. According to experts and managers of different industry opinions, two main difficulties in front of organizations and companies in order to migrate to cloud computing is security and accessible resources. In this paper we want to introduce new allocation algorithm for cloud resources. This algorithm is smart algorithm, which is based on Kalman filtering and allocates resources and powers to the users based on their usage backgrounds and their present demand.En las últimas décadas, se ha prestado mucha atención a la computación en la nube, y eso solo se debe a los servicios y poderes que ofrecen los proveedores de la nube. Presentan diferentes tipos de servicios, tales como: capacidad de procesamiento, capacidad de almacenamiento, plataforma como servicio, software como servicio. Según expertos y gerentes de diferentes opiniones de la industria, dos de las principales dificultades frente a las organizaciones y empresas para migrar a la computación en la nube son la seguridad y los recursos accesibles. En este documento queremos introducir un nuevo algoritmo de asignación para los recursos de la nube. Este algoritmo es un algoritmo inteligente, que se basa en el filtrado de Kalman y asigna recursos y poderes a los usuarios en función de sus antecedentes de uso y su demanda actual.Nas últimas décadas, muita atenção atraiu a computação em nuvem, e isso é apenas por causa dos serviços e poderes apresentados pelos provedores de nuvem. Apresentam diferentes tipos de serviços, tais como: capacidade de processamento, capacidade de armazenamento, Plataforma como Serviço, Software como Serviço. De acordo com especialistas e gerentes de diferentes opiniões do setor, duas principais dificuldades na frente de organizações e empresas para migrar para a computação em nuvem são a segurança e os recursos acessíveis. Neste artigo, queremos introduzir um novo algoritmo de alocação para recursos da nuvem. Esse algoritmo é um algoritmo inteligente, que é baseado na filtragem de Kalman e aloca recursos e poderes aos usuários com base em seus históricos de uso e demanda atual

O efeito da agilidade do cliente sobre o desempenho de empresas de software no Irã

Agility consists of two distinctive capabilities of customer sensing and customer responding. As alignment of the two capabilities is greatly important, the present research tried to determine the effect the capabilities’ alignment on business performance and to investigate the effect of responding as moderator variable. This is a descriptive study conducted through a correlation-survey method. Research statistical population included software companies. Research sample included 77 first- and second-ranked software companies in Iran. Research data were collected through questionnaires and were analyzed using regression and correlation tests. Relying on the data collected from software firms directors, research hypotheses were examined in term of alignment. Findings showed that both capabilities significantly influence business performance. If the two capabilities are alignment, business performance would be the best comparing in alignment capabilities.La agilidad involucra dos capacidades distintivas: detección y respuesta del cliente. Como la alineación de las dos capacidades es muy importante, la presente investigación intentó determinar el efecto de la alineación de las capacidades en el rendimiento del negocio e investigar el efecto de la respuesta como variable moderadora. Este es un estudio descriptivo realizado a través de un método de correlación-encuesta. La población estadística de investigación incluyó empresas de software. La muestra de investigación incluyó 77 compañías de software clasificadas primero y segundo en Irán. Los datos de investigación se recolectaron a través de cuestionarios y se analizaron mediante pruebas de regresión y correlación. Basándose en los datos recopilados de los directores de las empresas de software, las hipótesis de investigación se examinaron en términos de alineación. Los resultados mostraron que ambas capacidades influyen significativamente en el rendimiento del negocio. Si las dos capacidades son la alineación, el rendimiento del negocio sería la mejor comparación en capacidades de alineación.A agilidade consiste em duas capacidades distintas de detecção do cliente e resposta do cliente. Como o alinhamento das duas capacidades é muito importante, a presente investigação procurou determinar o efeito do alinhamento das capacidades no desempenho empresarial e investigar o efeito da resposta como variável moderadora. Trata-se de um estudo descritivo realiz ado por meio de um método de levantamento de correlação. A população de pesquisa estatística incluiu empresas de software. A amostra da pesquisa incluiu 77 empresas de software em primeiro e segundo lugar no Irã. Os dados da pesquisa foram coletados por meio de questionários e analisados por testes de regressão e correlação. Com base nos dados coletados dos diretores das empresas de software, as hipóteses de pesquisa foram examinadas em termos de alinhamento. Os resultados mostraram que ambas as capacidades influenciam significativamente o desempenho dos negócios. Se os dois recursos forem de alinhamento, o desempenho do negócio será a melhor comparação em recursos de alinhamento

Application of artificial intelligence techniques for automated detection of myocardial infarction: A review

Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG as well as other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and other biophysical signals.Comment: 16 pages, 8 figure

Evaluating the Organizational Interoperability Maturity Level in ICT Research Center

Interoperability refers to the ability to provide services and to accept services from other systems or devices. Collaborative enterprises face additional challenges to interoperate seamlessly within a networked organization. The major task here is to assess the maturity level of interoperating organizations. For this purpose the maturity models for enterprise were reviewed based on vendors’ reliability and advantages versus disadvantages. Interoperability maturity model was deduced from ATHENA project as European Integrated Project in 2005, this model named as EIMM was examined in Iran information and Communication Institute as a leading Telecommunication organization. 115 questionnaires were distributed between staff of 4 departments: Information Technology, Communication Technology, Security and Strategic studies regarding six areas of concern: Enterprise Modeling, Business Strategy Process, Organization and Competences, Products and Services, Systems and Technology, Legal Environment, Security and Trust at five maturity levels: Performed, Modeled , Integrated, Interoperable and Optimizing maturity. The findings showed different levels of maturity in this Institute. To achieve Interoperability level, appropriate practices are proposed for promotion to the higher levels

A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning

The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. The performance of the proposed approach is evaluated on different real datasets. The experimental results show the effectiveness of the proposed approach and its higher classification accuracy and lower time cost in comparison to other recent relevant methods

A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning

The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. The performance of the proposed approach is evaluated on different real datasets. The experimental results show the effectiveness of the proposed approach and its higher classification accuracy and lower time cost in comparison to other recent relevant methods