A virtualização é uma ciência da nuvem na atualidade?

Introduction: The present research was conducted in 2017 at Raiganj University, India. Cloud Computing and virtualization are significant concepts in today’s computing and information technology world. Cloud Computing is helpful for creating eco-friendly atmospheres, in other words, complete and healthy sustainability. Academic programs in the field of virtualization and Cloud Computing are still rare. This study seeks to learn more about such affairs. Methods: Cloud Computing is actually a kind of virtualization which ultimately helps create virtual platforms, thus it is useful to learn about various educational programs in the field, the methods of general search engine have been used with proper keywords and titles. Result: Cloud Computing and its wider uses resulted in academic programs, centers and departments in many countries around the world. Cloud Computing forms a tool and mechanism of virtualization for complete scientific domain nowadays. Virtualization has become a field and an available study program in international universities and also in Indian academics. Conclusions: This paper highlights several aspects of Cloud Computing from the beginning to growing nature as a field of study with academic and techno-managerial points of view. Originality: In major indexing agencies, studies on Cloud Computing are rarely available in Indian context. Limitation: The study concerned a specific area in India and used search methods conducted from July to September 2017. After that period, work has not been included and analyzed in the study.Introducción: el presente trabajo de investigación se llevó a cabo en el 2017 en la Universidad de Raiganj, India. La computación en nube y la virtualización son conceptos importantes en el mundo de la informática y la tecnología de la información actual. La computación en la nube es útil para crear atmósferas ecológicas, en otras palabras, una sostenibilidad completa y saludable. Los programas académicos en el campo de la virtualización y la computación en la nube aún son escasos. Este estudio busca aprender más sobre tales asuntos. Métodos: la computación en la nube (Cloud Computing) es un tipo de virtualización que ayuda a crear plataformas virtuales, por lo que ahora es útil en diferentes estancias, y para aprender sobre programas educativos en el campo, los métodos de búsqueda general se han hecho con títulos y palabras clave adecuadas. Resultado: la computación en la nube y sus usos más amplios dieron como resultado programas académicos, centros y departamentos en muchos países alrededor del mundo. La computación en la nube genera una herramienta y un mecanismo de virtualización para un dominio científico completo en la actualidad. La virtualización se ha convertido en un campo y un programa de estudio disponible en universidades internacionales y también entre académicos de la India. Conclusiones: este documento destaca varios aspectos de la computación en la nube desde sus inicios, incluyendo su naturaleza en crecimiento como campo de estudio con puntos de vista académicos y tecno-gerenciales. Originalidad: en las principales agencias de indexación, los estudios sobre computación en la nube raramente están disponibles en el contexto de la India. Limitación: el estudio se refiere a un sector específico de India y se utilizaron métodos de búsqueda realizados entre julio y septiembre de 2017, no se incluye y analizan trabajos posteriores en este estudioIntrodução: esta pesquisa foi levada a cabo em 2017, na Universidade de Raiganj, na Índia. A computação na nuvem e a virtualização são conceitos importantes no mundo atual da informática e da tecnologia da informação. A computação na nuvem é útil para criar atmosferas ecológicas, em outras palavras, uma sustentabilidade completa e saudável. Os programas acadêmicos no campo da virtualização e da computação na nuvem ainda são escassos. Este estudo busca aprender mais sobre esses assuntos.Métodos: cloud computing é um tipo de virtualização que ajuda a criar plataformas virtuais, por isso, agora, é útil em diferentes áreas e para aprender sobre os programas educativos no campo, os métodos de busca geral têm sido utilizados com títulos e palavras-chave adequadas.Resultados: a computação na nuvem e os seus usos mais amplos deram como resultado programas acadêmicos, centros e departamentos em muitos países ao redor do mundo. A computação na nuvem gera uma ferramenta e um mecanismo de virtualização para um domínio científico completo na atualidade. A virtualização tornou-se um campo e um programa de estudo disponível em universidades internacionais e, também, entre acadêmicos da Índia.Conclusões: este documento destaca vários aspectos da computação na nuvem desde o princípio, inclusive a sua natureza em crescimento como campo de estudo com pontos de vista acadêmicos e tecnológicos gerenciais.Originalidade: nas principais agências de indexação, os estudos sobre computação na nuvem raramente estão disponíveis no contexto da Índia.Limitação: o estudo se refere a uma área específica na Índia e foram utilizados métodos de busca realizados entre julho e setembro de 2017, um trabalho posterior não foi incluído nem analisado no estud

An IoT Based Predictive Connected Car Maintenance Approach

Internet of Things (IoT) is fast emerging and becoming an almost basic necessity in general life. The concepts of using technology in our daily life is not new, but with the advancements in technology, the impact of technology in daily activities of a person can be seen in almost all the aspects of life. Today, all aspects of our daily life, be it health of a person, his location, movement, etc. can be monitored and analyzed using information captured from various connected devices. This paper discusses one such use case, which can be implemented by the automobile industry, using technological advancements in the areas of IoT and Analytics. ‘Connected Car’ is a terminology, often associated with cars and other passenger vehicles, which are capable of internet connectivity and sharing of various kinds of data with backend applications. The data being shared can be about the location and speed of the car, status of various parts/lubricants of the car, and if the car needs urgent service or not. Once data are transmitted to the backend services, various workflows can be created to take necessary actions, e.g. scheduling a service with the car service provider, or if large numbers of care are in the same location, then the traffic management system can take necessary action. ’Connected cars’ can also communicate with each other, and can send alerts to each other in certain scenarios like possible crash etc. This paper talks about how the concept of ‘connected cars’ can be used to perform ‘predictive car maintenance’. It also discusses how certain technology components, i.e., Eclipse Mosquito and Eclipse Paho can be used to implement a predictive connected car use case

Spiking Activity of a LIF Neuron in Distributed Delay Framework

Evolution of membrane potential and spiking activity for a single leaky integrate-and-fire (LIF) neuron in distributed delay framework (DDF) is investigated. DDF provides a mechanism to incorporate memory element in terms of delay (kernel) function into a single neuron models. This investigation includes LIF neuron model with two different kinds of delay kernel functions, namely, gamma distributed delay kernel function and hypo-exponential distributed delay kernel function. Evolution of membrane potential for considered models is studied in terms of stationary state probability distribution (SPD). Stationary state probability distribution of membrane potential (SPDV) for considered neuron models are found asymptotically similar which is Gaussian distributed. In order to investigate the effect of membrane potential delay, rate code scheme for neuronal information processing is applied. Firing rate and Fano-factor for considered neuron models are calculated and standard LIF model is used for comparative study. It is noticed that distributed delay increases the spiking activity of a neuron. Increase in spiking activity of neuron in DDF is larger for hypo-exponential distributed delay function than gamma distributed delay function. Moreover, in case of hypo-exponential delay function, a LIF neuron generates spikes with Fano-factor less than 1

Nuevo marco para utilizar la minería de datos y reglas de asociación para la clasificación de la gravedad de accidentes de tráfico

Introduction: Traffic accidents are an undesirable burden on society. Every year around one million deaths and more than ten million injuries are reported due to traffic accidents. Hence, traffic accidents prevention measures must be taken to overcome the accident rate. Different countries have different geographical and environmental conditions and hence the accident factors diverge in each country. Traffic accident data analysis is very useful in revealing the factors that affect the accidents in different countries. This article was written in the year 2016 in the Institute of Technology & Science, Mohan Nagar, Ghaziabad, up, India. Methology: We propose a framework to utilize association rule mining (arm) for the severity classification of traffic accidents data obtained from police records in Mujjafarnagar district, Uttarpradesh, India. Results: The results certainly reveal some hidden factors which can be applied to understand the factors behind road accidentality in this region. Conclusions: The framework enables us to find three clusters from the data set. Each cluster represents a type of accident severity, i.e. fatal, major injury and minor/no injury. The association rules exposed different factors that are associated with road accidents in each category. The information extracted provides important information which can be employed to adapt preventive measures to overcome the accident severity in Muzzafarnagar district.Introducción: los accidentes de tránsito son una carga indeseable para la sociedad. Cada año se reportan alrededor de un millón de muertes y más de diez millones de lesiones debido a accidentes de tráfico. Por lo tanto, se deben implementar medidas de prevención de accidentes de tráfico para superar la tasa de accidentalidad. Los países tienen diferentes condiciones geográficas y ambientales y, por ello, las variables que inciden varían en cada país. El análisis de los datos de accidentes de tráfico es muy útil para revelar los factores o variables que inciden en la accidentalidad en diferentes países. Este artículo fue escrito en el 2016 en el Instituto de Tecnología y Ciencia, Mohan Nagar, Ghaziabad, UP, India. Metodología: proponemos un marco para utilizar la minería de datos y reglas de asociación (arm) para la clasificación de severidad de los datos de accidentes de tráfico obtenidos de registros policiales en eldistrito de Mujjafarnagar, Uttarpradesh, India Resultados: los resultados revelan ciertamente algunos factores ocultos que se pueden aplicar para entender las variables detrás de la accidentalidad de tráfico en esta región. Conclusiones: el marco permite establecer tres categorías en el conjunto de datos que representan el tipo de gravedad del accidente: fatal, lesiones graves, y lesiones menores o inexistentes. Las reglas de asociación expusieron diferentes factores relacionados con los accidentes de tráfico en cada categoría. Los datos extraídos proporcionan información importante que se puede emplear para adaptar las medidas preventivas para superar la gravedad de los accidentes de tráfico en el distrito de Muzzafarnagar

A Novel Approach on Visual Question Answering by Parameter Prediction using Faster Region Based Convolutional Neural Network

Visual Question Answering (VQA) is a stimulating process in the field of Natural Language Processing (NLP) and Computer Vision (CV). In this process machine can find an answer to a natural language question which is related to an image. Question can be open-ended or multiple choice. Datasets of VQA contain mainly three components; questions, images and answers. Researchers overcome the VQA problem with deep learning based architecture that jointly combines both of two networks i.e. Convolution Neural Network (CNN) for visual (image) representation and Recurrent Neural Network (RNN) with Long Short Time Memory (LSTM) for textual (question) representation and trained the combined network end to end to generate the answer. Those models are able to answer the common and simple questions that are directly related to the image’s content. But different types of questions need different level of understanding to produce correct answers. To solve this problem, we use faster Region based-CNN (R-CNN) for extracting image features with an extra fully connected layer whose weights are dynamically obtained by LSTMs cell according to the question. We claim in this paper that a single R-CNN architecture can solve the problems related to VQA by modifying weights in the parameter prediction layer. Authors trained the network end to end by Stochastic Gradient Descent (SGD) using pretrained faster R-CNN and LSTM and tested it on benchmark datasets of VQA

Análisis comparativo sobre modelos de redes neuronales profundas para la detección de ciberbullying en redes sociales

Social media usage has been increased and it consists of both positive and negative effects. By considering the misusage of social media platforms by various cyberbullying methods like stalking, harassment there should be preventive methods to control these and to avoid mental stress. These extra words will expand the size of the vocabulary and influence the performance of the algorithm. Therefore, we come up with variant deep learning models like LSTM, BI-LSTM, RNN, BI-RNN, GRU, BI-GRU to detect cyberbullying in social media. These models are applied on Twitter, public comments data and performance were observed for these models and obtained improved accuracy of 90.4%.Introducción: el uso de las redes sociales se ha incrementado y tiene efectos tanto positivos como negativos. Al considerar el uso indebido de las plataformas de redes sociales a través de varios métodos de acoso cibernético, como el acecho y el acoso, debe haber métodos preventivos para controlarlos y evitar el estrés mental.Problema: estas palabras adicionales ampliarán el tamaño del vocabulario e influirán en el rendimiento del algoritmo.Objetivo: Detectar el ciberacoso en las redes sociales.Metodología: en este documento, presentamos variantes de modelos de aprendizaje profundo como la memoria a largo plazo (LSTM), memoria bidireccional a largo plazo (BI-LSTM), redes neuronales recurrentes (RNN), redes neuronales recurrentes bidireccionales (BI-RNN), unidad recurrente cerrada (GRU) y unidad recurrente cerrada bidireccional (BI-GRU) para detectar el ciberacoso en las redes sociales.Resultados: El mecanismo propuesto ha sido realizado, analizado e implementado sobre datos de Twitter con Accuracy, Precision, Recall y F-Score como medidas. Los modelos de aprendizaje profundo como LSTM, BI-LSTM, RNN, BI-RNN, GRU y BI-GRU se aplican en Twitter a los datos de comentarios públicos y se observó el rendimiento de estos modelos, obteniendo una precisión mejorada del 90,4 %.Conclusiones: Los resultados indican que el mecanismo propuesto es eficiente en comparación con los es-quemas del estado del arte.Originalidad: la aplicación de modelos de aprendizaje profundo para realizar un análisis comparativo de los datos de las redes sociales es el primer enfoque para detectar el ciberacoso.Restricciones: estos modelos se aplican solo en comentarios de datos textuales. El trabajo propio no se ha concentrado en datos multimedia como audio, video e imágenes

Conceptual Model for Smart Cities: Irrigation and Highway Lamps using IoT

Keeping in mind the need to preserve energy as well as utilize the available at its best the need was felt to develop a module that would be able to sort out the problem where resources such as water and electricity were wasted, in urban as well as rural area. Resource (electricity) was wasted as beside the point operation of Highway & High Mast Lamp; while wastage of water followed by improper trends and methodologies imparted for watering of city park, road side plantation and highway plantation. Thus as per Energy survey statistics of a City (Lucknow, India) it was found that major portion of resources (water and electricity) were being wasted due to negligent activities of officials who were in charge of resource management. So to facilitate energy saving trends and to completely modernize it to autonomous system, module below is proposed which incorporates modern technological peripheral and has its base ingrained in IoT (Internet of Things) which when put into consideration would result in large scale resource and energy saving.This developed module incorporates the peripherals such as Arduino, Texas Instruments ultra low power kits etc. in accordance with software technology including Lab View which help to monitor as well as control the various operation from the base station, located far away from the site. Lab View Interface interacts with all the module located at various city parks, subways and highway lighting modules. Later below in several section a detailed pattern and application frame has been put up

Data Mining Approach of Accident Occurrences Identification with Effective Methodology and Implementation

Data mining is used in various domains of research to identify a new cause for tan effect in the society over the globe. This article includes the same reason for using the data mining to identify the Accident Occurrences in different regions and to identify the most valid reason for happening accidents over the globe. Data Mining and Advanced Machine Learning algorithms are used in this research approach and this article discusses about hyperline, classifications, pre-processing of the data, training the machine with the sample datasets which are collected from different regions in which we have structural and semi-structural data. We will dive into deep of machine learning and data mining classification algorithms to find or predict something novel about the accident occurrences over the globe. We majorly concentrate on two classification algorithms to minify the research and task and they are very basic and important classification algorithms. SVM (Support vector machine), CNB Classifier. This discussion will be quite interesting with WEKA tool for CNB classifier, Bag of Words Identification, Word Count and Frequency Calculation

Comparative Study on Ant Colony Optimization (ACO) and K-Means Clustering Approaches for Jobs Scheduling and Energy Optimization Model in Internet of Things (IoT)

The concept of Internet of Things (IoT) was proposed by Professor Kevin Ashton of the Massachusetts Institute of Technology (MIT) in 1999. IoT is an environment that people understand in many different ways depending on their requirement, point of view and purpose. When transmitting data in IoT environment, distribution of network traffic fluctuates frequently. If links of the network or nodes fail randomly, then automatically new nodes get added frequently. Heavy network traffic affects the response time of all system and it consumes more energy continuously. Minimization the network traffic/ by finding the shortest path from source to destination minimizes the response time of all system and also reduces the energy consumption cost. The ant colony optimization (ACO) and K-Means clustering algorithms characteristics conform to the auto-activator and optimistic response mechanism of the shortest route searching from source to destination. In this article, ACO and K-Means clustering algorithms are studied to search the shortest route path from source to destination by optimizing the Quality of Service (QoS) constraints. Resources are assumed in the active and varied IoT network atmosphere for these two algorithms. This work includes the study and comparison between ant colony optimization (ACO) and K-Means algorithms to plan a response time aware scheduling model for IoT. It is proposed to divide the IoT environment into various areas and a various number of clusters depending on the types of networks. It is noticed that this model is more efficient for the suggested routing algorithm in terms of response time, point-to-point delay, throughput and overhead of control bits

Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data

In the Internet of Things (IoT), Cyber-Physical Systems (CPS), and sensor technologies huge and variety of streaming sensor data is generated. The unification of streaming sensor data is a challenging problem. Moreover, the huge amount of raw data has implied the insufficiency of manual and semi-automatic annotation and leads to an increase of the research of automatic semantic annotation. However, many of the existing semantic annotation mechanisms require many joint conditions that could generate redundant processing of transitional results for annotating the sensor data using SPARQL queries. In this paper, we present an Incremental Clustering Driven Automatic Annotation for IoT Streaming Data (IHC-AA-IoTSD) using SPARQL to improve the annotation efficiency. The processes and corresponding algorithms of the incremental hierarchical clustering driven automatic annotation mechanism are presented in detail, including data classification, incremental hierarchical clustering, querying the extracted data, semantic data annotation, and semantic data integration. The IHCAA-IoTSD has been implemented and experimented on three healthcare datasets and compared with leading approaches namely- Agent-based Text Labelling and Automatic Selection (ATLAS), Fuzzy-based Automatic Semantic Annotation Method (FBASAM), and an Ontology-based Semantic Annotation Approach (OBSAA), yielding encouraging results with Accuracy of 86.67%, Precision of 87.36%, Recall of 85.48%, and F-score of 85.92% at 100k triple data