International journal of engineering and technology
About: International journal of engineering and technology is an academic journal. The journal publishes majorly in the area(s): Cloud computing & Fuzzy logic. It has an ISSN identifier of 0975-4024. It is also open access. Over the lifetime, 8965 publication(s) have been published receiving 23351 citation(s).
Topics: Cloud computing, Fuzzy logic, Wireless sensor network, Cluster analysis, Artificial neural network
TL;DR: The paper is to find an efficient way of storing unstructured data and appropriate approach of fetching data and the public tweets of Twitter are targeted in this work to organize.
Abstract: Nowadays, most of information saved in companies are unstructured models. Retrieval and extraction of the information is essential works and importance in semantic web areas. Many of these requirements will be depend on the unstructured data analysis. More than 80% of all potentially useful business information is unstructured data, in kind of sensor readings, console logs and so on. The large number and complexity of unstructured data opens up many new possibilities for the analyst. Text mining and natural language processing are two techniques with their methods for knowledge discovery from textual context in documents. This is an approach to organize a complex unstructured data and to retrieve necessary information. The paper is to find an efficient way of storing unstructured data and appropriate approach of fetching data. Unstructured data targeted in this work to organize, is the public tweets of Twitter. Building an Big Data application that gets stream of public tweets from twitter which is latter stored in the HBase using Hadoop cluster and followed by data analysis for data retrieved from HBase by REST calls is the pragmatic approach of this project. Keyword: Unstructured Data, Hadoop, HBase, Data Mining
TL;DR: The paper proposes that learning analytics is dependent on personalised approach for both educators and students, and defines the characterising features that represents the relationship between learning analytics and personalised learning environment.
Abstract: This paper presents learning analytics as a mean to improve students’ learning. Most learning analytics tools are developed by in-house individual educational institutions to meet the specific needs of their students. Learning analytics is defined as a way to measure, collect, analyse and report data about learners and their context, for the purpose of understanding and optimizing learning. The paper concludes by highlighting framework of learning analytics in order to improve personalised learning. In addition, it is an endeavour to define the characterising features that represents the relationship between learning analytics and personalised learning environment. The paper proposes that learning analytics is dependent on personalised approach for both educators and students. From a learning perspective, students can be supported with specific learning process and reflection visualisation that compares their respective performances to the overall performance of a course. Furthermore, the learners may be provided with personalised recommendations for suitable learning resources, learning paths, or peer students through recommending system. The paper’s contribution to knowledge is in consider ing personalised learning within the context framework of learning analytics
TL;DR: An improved framework for computer aided detection of brain tumor which consists of contrast improvement of cerebral MRI features followed by segmentation of targeted region of interest (ROI) will aid in the accurate diagnosis of tumor patients.
Abstract: Brain tumor is an abnormal mass of tissue with uncoordinated growth inside the skull which may invade and damage nerves and other healthy tissues. Non-homogeneities of the brain tissues result in inaccurate detection of tumor boundaries with the existing methods for contrast enhancement and segmentation of magnetic resonance images (MRI).This paper presents an improved framework for computer aided detection of brain tumor. This involves enhancement of cerebral MRI features by incorporating enhancement approaches of both the frequency and spatial domain. The proposed method requires de-noising in wavelet domain followed by enhancement using a non-linear enhancement function. Further an iterative enhancement algorithm is applied for enhancing the edges using the morphological filter. Segmentation of the brain tumor is finally obtained by employing large sized structuring elements along with thresholding. Simulation results along with the estimates of quality metrics portray significant improvement of contrast, enhancement of edges along with detection of boundaries in comparison to other recently developed methods. comprehensive survey indicates the exponential increase in the magnitude of research going on in the medical world for brain cancer indicating the fatal traits of brain tumor. An efficient image contrast enhancement module followed by edge enhancement and segmentation is the primary requirement of any computer aided detection system employed for medical diagnosis. In this paper, a new method for computer aided detection of brain tumor is proposed which consists of contrast improvement of cerebral MRI features followed by segmentation of targeted region of interest (ROI). The proposed framework will aid in the accurate diagnosis of tumor patients. This paper is structured as follows: section I gives a brief introduction of brain tumor. Existing image enhancement techniques have been discussed in the section-III, while an overview of wavelet transform has been given in the third section. Section-IV explains the proposed method. The objective evaluation parameters have been described in the fifth section and the experimental results discussed under section-VI. Seventh section draws the conclusion, whereas the scope for future improvement is given under section VIII.
TL;DR: In this paper, an experimental study of an enhancement of pre-formed foamed concrete, 1300-1900 kg/m3, by utilising two types of additives, silica fume and fly ash, to partially replace Portland cement and fine sand.
Abstract: This paper describes an experimental study of an enhancement of pre-formed foamed concrete, 1300-1900 kg/m3, by utilising two types of additives, silica fume and fly ash, to partially replace Portland cement and fine sand. It focuses on consistency, mechanical and thermal properties as well as presenting a comparison with normal weight, lightweight and foamed concretes from the literature. In addition to conventional foamed concrete mixes (FC), foamed concrete mixes with high flowability and strength (FCa) were also manufactured in this study. The FC mixes had 28-day compressive strengths from 6 to 23 MPa and corresponding thermal conductivities in the dry state from 0.475 to 0.951 W/mK, whereas for the same density range, the FCa mixes gave 19-47 MPa and 0.498-0.962 W/mK, respectively. Compared to other studies on foamed concrete, the results from the mixes investigated in this study showed higher strengths (for a given density), higher tensile to compressive strength ratios and higher moduli of elasticity.
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