การใช้โครงร่างแบบอิงเทคนิคการทำเหมืองบนเว็บเพื่อการปรับปรุงระบบอีเลิร์นนิง

The objectives of this research were 1) to synthesis the monitoring, evaluation, usability and learning pattern via the Internet by using data mining techniques and 2) to develop the system model using the pattern synthesized. The development step used the web data mining, called “Cross-Industry Standard Process (CRISP-DM)”, to develop the framework for improving the e-learning system. In this research, that collected the data from Claroline Thai e-learning and used these techniques; data classification, data clustering and data association, to synthesis the model for improving the e-learning system. The research results were as follows: pattern of web usage for downloading content and learning document were high level, the linkages of usage and contents were less level. Then the results that used to improve the pattern of e-learning system. Those were web pages and web data structure and included learning activities in course developing , which supported usability of the system The results showed that the improvement of e-learning system by using the web data mining had more suitable

Knowledge-Based Information Resource Management System for Materials of Sodium-Cooled Fast Reactor

In the development of advanced fast reactors, materials and coolant/ material interactions pose a critical barrier for higher temperature and longer core life designs. For sodium-cooled fast reactors (SFRs) such as the Experimental Breeder Reactors in Idaho and the Fast Flux Test Facility in Hanford, experience has shown that qualified structural materials and fuel cladding severely limits their economic performance. Liquid sodium has been selected as the primary coolant candidate for the Advanced Burner Reactor (ABR) of the Global Nuclear Partnership (GNEP). Materials improvement has been identified as a major thrust to improve fast reactor economics. Researchers from universities, national laboratories, and related industrial participants have been continuously generating data and knowledge about materials and their interactions with coolants for the past few decades. Considering cost and time, the paradigm of designing and implementing a successful advanced nuclear system can be shifted and updated via the integration of information and internet technologies. Such efforts can be better visualized by implementing collective (centralized or distributed) data storage to serve the community with organized material data sets. This project proposes to create a modularized web-based information system with models to systematically catalog and analyze existing data, and guide the new development and testing to acquire new data. Technically speaking, information retrieval and knowledge discovery tools will be implemented for researchers with both information look-up options from material databases and technology/development gap analysis from intelligent agent and reporting components. The goal of the system is not only to provide another database, but to also create a distributable and expandable, platform-free, location-free online system for research institutes and industrial partners. Such knowledge discovery and data mining processes generally include data integration, preparation and transformation, data mining and evaluation, and data visualization. Parallel to the development of these front-end analysis tools, web-based data updating and portal administration interfaces will also be designed and developed. Data collection will start during the early stage of the project due to its time consuming nature

Rosetta: Large scale system for text detection and recognition in images

In this paper we present a deployed, scalable optical character recognition (OCR) system, which we call Rosetta, designed to process images uploaded daily at Facebook scale. Sharing of image content has become one of the primary ways to communicate information among internet users within social networks such as Facebook and Instagram, and the understanding of such media, including its textual information, is of paramount importance to facilitate search and recommendation applications. We present modeling techniques for efficient detection and recognition of text in images and describe Rosetta's system architecture. We perform extensive evaluation of presented technologies, explain useful practical approaches to build an OCR system at scale, and provide insightful intuitions as to why and how certain components work based on the lessons learnt during the development and deployment of the system.Comment: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) 2018, London, United Kingdo

Opinion mining in Machine Learning for High Perfomance using Sentimental Analysis

Opinion mining refers to the use of the natural language processing in which it is used for linguistics to identify and extract information .Opinion mining has been an indispensible part of present scenario. Due to large amount of online app development and processing of all data through internet Opinion has become one of the major part in reviewing through online. A various kinds of probabilistic topic modeling technique are available to analyze and extract the idea behind the probability distribution over words. In proposed review system, a review of a particular product that brought in is Amazon, opinion review dataset of a particular product by UPC database and it is pre-processed to give a result by machine learning to get specific opinion word using sentimental analyses. LDA model is applied into the machine learning technique to analyses. It also determine the large amount of time required for determining the opinion of a particular product that is purchased. Experimental evaluation shows that our proposed techniques are efficient and perform better than previously proposed technique, however, the proposed technique can be used by any other languages

Investigating prediction modelling of academic performance for students in rural schools in Kenya

Academic performance prediction modelling provides an opportunity for learners' probable outcomes to be known early, before they sit for final examinations. This would be particularly useful for education stakeholders to initiate intervention measures to help students who require high intervention to pass final examinations. However, limitations of infrastructure in rural areas of developing countries, such as lack of or unstable electricity and Internet, impede the use of PCs. This study proposed that an academic performance prediction model could include a mobile phone interface specifically designed based on users' needs. The proposed mobile academic performance prediction system (MAPPS) could tackle the problem of underperformance and spur development in the rural areas. A six-step Cross-Industry Standard Process for Data Mining (CRISP-DM) theoretical framework was used to support the design of MAPPS. Experiments were conducted using two datasets collected in Kenya. One dataset had 2426 records of student data having 22 features, collected from 54 rural primary schools. The second dataset had 1105 student records with 19 features, collected from 11 peri-urban primary schools. Evaluation was conducted to investigate: (i) which is the best classifier model among the six common classifiers selected for the type of data used in this study; (ii) what is the optimal subset of features from the total number of features for both rural and peri-urban datasets; and (iii) what is the predictive performance of the Mobile Academic Performance Prediction System in classifying the high intervention class. It was found that the system achieved an F-Measure rate of nearly 80% in determining the students who need high intervention two years before the final examination. It was also found that the system was useful and usable in rural environments; the accuracy of prediction was good enough to motivate stakeholders to initiate strategic intervention measures. This study provides experimental evidence that Educational Data Mining (EDM) techniques can be used in the developing world by exploiting the ubiquitous mobile technology for student academic performance prediction

Weak signal identification with semantic web mining

We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization's environment and that they appear in different contexts. We use internet information to represent organization's environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Extracting consumers needs for new products a web mining approach

Here we introduce a web mining approach for automatically identifying new product ideas extracted from web logs. A web log - also known as blog - is a web site that provides commentary, news, and further information on a subject written by individual persons. We can find a large amount of web logs for nearly each topic where consumers present their needs for new products. These new product ideas probably are valuable for producers as well as for researchers and developers. This is because they can lead to a new product development process. Finding these new product ideas is a well-known task in marketing. Therefore, with this automatic approach we support marketing activities by extracting new and useful product ideas from textual information in internet logs. This approach is implemented by a web-based application named Product Idea Web Log Miner where users from the marketing department provide descriptions of existing products. As a result, new product ideas are extracted from the web logs and presented to the users

Text Analytics for Android Project

Most advanced text analytics and text mining tasks include text classification, text clustering, building ontology, concept/entity extraction, summarization, deriving patterns within the structured data, production of granular taxonomies, sentiment and emotion analysis, document summarization, entity relation modelling, interpretation of the output. Already existing text analytics and text mining cannot develop text material alternatives (perform a multivariant design), perform multiple criteria analysis, automatically select the most effective variant according to different aspects (citation index of papers (Scopus, ScienceDirect, Google Scholar) and authors (Scopus, ScienceDirect, Google Scholar), Top 25 papers, impact factor of journals, supporting phrases, document name and contents, density of keywords), calculate utility degree and market value. However, the Text Analytics for Android Project can perform the aforementioned functions. To the best of the knowledge herein, these functions have not been previously implemented; thus this is the first attempt to do so. The Text Analytics for Android Project is briefly described in this article

Measuring the Use of the Active and Assisted Living Prototype CARIMO for Home Care Service Users: Evaluation Framework and Results

To address the challenges of aging societies, various information and communication technology (ICT)-based systems for older people have been developed in recent years. Currently, the evaluation of these so-called active and assisted living (AAL) systems usually focuses on the analyses of usability and acceptance, while some also assess their impact. Little is known about the actual take-up of these assistive technologies. This paper presents a framework for measuring the take-up by analyzing the actual usage of AAL systems. This evaluation framework covers detailed information regarding the entire process including usage data logging, data preparation, and usage data analysis. We applied the framework on the AAL prototype CARIMO for measuring its take-up during an eight-month field trial in Austria and Italy. The framework was designed to guide systematic, comparable, and reproducible usage data evaluation in the AAL field; however, the general applicability of the framework has yet to be validated