Data mining for the diagnosis of type 2 diabetes

Diabetes is the most common disease nowadays in all populations and in all age groups. diabetes contributing to heart disease, increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important classification problem. Different techniques of artificial intelligence has been applied to diabetes problem. The purpose of this study is apply the artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining (DM) technique for the diabetes disease diagnosis. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with decision tree (DT), Bayesian classifier (BC) and other algorithms, recently proposed by other researchers, that were applied to the same database. The robustness of the algorithms are examined using classification accuracy, analysis of sensitivity and specificity, confusion matrix. The results obtained by AMMLP are superior to obtained by DT and BC.

Mining recurring concepts in a dynamic feature space

Most data stream classification techniques assume that the underlying feature space is static. However, in real-world applications the set of features and their relevance to the target concept may change over time. In addition, when the underlying concepts reappear, reusing previously learnt models can enhance the learning process in terms of accuracy and processing time at the expense of manageable memory consumption. In this paper, we propose mining recurring concepts in a dynamic feature space (MReC-DFS), a data stream classification system to address the challenges of learning recurring concepts in a dynamic feature space while simultaneously reducing the memory cost associated with storing past models. MReC-DFS is able to detect and adapt to concept changes using the performance of the learning process and contextual information. To handle recurring concepts, stored models are combined in a dynamically weighted ensemble. Incremental feature selection is performed to reduce the combined feature space. This contribution allows MReC-DFS to store only the features most relevant to the learnt concepts, which in turn increases the memory efficiency of the technique. In addition, an incremental feature selection method is proposed that dynamically determines the threshold between relevant and irrelevant features. Experimental results demonstrating the high accuracy of MReC-DFS compared with state-of-the-art techniques on a variety of real datasets are presented. The results also show the superior memory efficiency of MReC-DFS.

Facing the climate change challenges in the mining and mineral industry as providers of a sustainable manufacturing process

A sustainable manufacturing process must rely on an also sustainable raw materials and energy supply. This paper is intended to show the results of the studies developed on sustainable business models for the minerals industry as a fundamental previous part of a sustainable manufacturing process. As it has happened in other economic activities, the mining and minerals industry has come under tremendous pressure to improve its social, developmental, and environmental performance. Mining, refining, and the use and disposal of minerals have in some instances led to significant local environmental and social damage. Nowadays, like in other parts of the corporate world, companies are more routinely expected to perform to ever higher standards of behavior, going well beyond achieving the best rate of return for shareholders. They are also increasingly being asked to be more transparent and subject to third-party audit or review, especially in environmental aspects. In terms of environment, there are three inter-related areas where innovation and new business models can make the biggest difference: carbon, water and biodiversity. The focus in these three areas is for two reasons. First, the industrial and energetic minerals industry has significant footprints in each of these areas. Second, these three areas are where the potential environmental impacts go beyond local stakeholders and communities, and can even have global impacts, like in the case of carbon. So prioritizing efforts in these areas will ultimately be a strategic differentiator as the industry businesses continues to grow. Over the next forty years, world?s population is predicted to rise from 6.300 million to 9.500 million people. This will mean a huge demand of natural resources. Indeed, consumption rates are such that current demand for raw materials will probably soon exceed the planet?s capacity. As awareness of the actual situation grows, the public is demanding goods and services that are even more environmentally sustainable. This means that massive efforts are required to reduce the amount of materials we use, including freshwater, minerals and oil, biodiversity, and marine resources. It?s clear that business as usual is no longer possible. Today, companies face not only the economic fallout of the financial crisis; they face the substantial challenge of transitioning to a low-carbon economy that is constrained by dwindling natural resources easily accessible. Innovative business models offer pioneering companies an early start toward the future. They can signal to consumers how to make sustainable choices and provide reward for both the consumer and the shareholder. Climate change and carbon remain major risk discontinuities that we need to better understand and deal with. In the absence of a global carbon solution, the principal objective of any individual country should be to reduce its global carbon emissions by encouraging conservation. The mineral industry internal response is to continue to focus on reducing the energy intensity of our existing operations through energy efficiency and the progressive introduction of new technology. Planning of the new projects must ensure that their energy footprint is minimal from the start. These actions will increase the long term resilience of the business to uncertain energy and carbon markets. This focus, combined with a strong demand for skills in this strategic area for the future requires an appropriate change in initial and continuing training of engineers and technicians and their awareness of the issue of eco-design. It will also need the development of measurement tools for consistent comparisons between companies and the assessments integration of the carbon footprint of mining equipments and services in a comprehensive impact study on the sustainable development of the Economy.

Probabilistic meta-analysis of risk from the exposure to Hg in artisanal gold mining communities in Colombia

Colombia is one of the largest per capita mercury polluters in the world as a consequence of its artisanal gold mining activities. The severity of this problem in terms of potential health effects was evaluated by means of a probabilistic risk assessment carried out in the twelve departments (or provinces) in Colombia with the largest gold production. The two exposure pathways included in the risk assessment were inhalation of elemental Hg vapors and ingestion of fish contaminated with methyl mercury. Exposure parameters for the adult population (especially rates of fish consumption) were obtained from nation-wide surveys and concentrations of Hg in air and of methyl-mercury in fish were gathered from previous scientific studies. Fish consumption varied between departments and ranged from 0 to 0.3 kg d?1. Average concentrations of total mercury in fish (70 data) ranged from 0.026 to 3.3 lg g?1. A total of 550 individual measurements of Hg in workshop air (ranging from menor queDL to 1 mg m?3) and 261 measurements of Hg in outdoor air (ranging from menor queDL to 0.652 mg m?3) were used to generate the probability distributions used as concentration terms in the calculation of risk. All but two of the distributions of Hazard Quotients (HQ) associated with ingestion of Hg-contaminated fish for the twelve regions evaluated presented median values higher than the threshold value of 1 and the 95th percentiles ranged from 4 to 90. In the case of exposure to Hg vapors, minimum values of HQ for the general population exceeded 1 in all the towns included in this study, and the HQs for miner-smelters burning the amalgam is two orders of magnitude higher, reaching values of 200 for the 95th percentile. Even acknowledging the conservative assumptions included in the risk assessment and the uncertainties associated with it, its results clearly reveal the exorbitant levels of risk endured not only by miner-smelters but also by the general population of artisanal gold mining communities in Colombia.

Mining mobile apps reviews to support release planning

The mobile apps market is a tremendous success, with millions of apps downloaded and used every day by users spread all around the world. For apps’ developers, having their apps published on one of the major app stores (e.g. Google Play market) is just the beginning of the apps lifecycle. Indeed, in order to successfully compete with the other apps in the market, an app has to be updated frequently by adding new attractive features and by fixing existing bugs. Clearly, any developer interested in increasing the success of her app should try to implement features desired by the app’s users and to fix bugs affecting the user experience of many of them. A precious source of information to decide how to collect users’ opinions and wishes is represented by the reviews left by users on the store from which they downloaded the app. However, to exploit such information the app’s developer should manually read each user review and verify if it contains useful information (e.g. suggestions for new features). This is something not doable if the app receives hundreds of reviews per day, as happens for the very popular apps on the market. In this work, our aim is to provide support to mobile apps developers by proposing a novel approach exploiting data mining, natural language processing, machine learning, and clustering techniques in order to classify the user reviews on the basis of the information they contain (e.g. useless, suggestion for new features, bugs reporting). Such an approach has been empirically evaluated and made available in a web-­‐based tool publicly available to all apps’ developers. The achieved results showed that the developed tool: (i) is able to correctly categorise user reviews on the basis of their content (e.g. isolating those reporting bugs) with 78% of accuracy, (ii) produces clusters of reviews (e.g. groups together reviews indicating exactly the same bug to be fixed) that are meaningful from a developer’s point-­‐of-­‐view, and (iii) is considered useful by a software company working in the mobile apps’ development market.

Prehistoric man, Hall of the Stone Age of the Old World / by Henry Field, Assistant Curator of Physical Anthropology. Foreword by Berthold Laufer -- 8 plates in photogravure and 1 map.

no.31(1933)

Cursor mundi (The cursur o the world). A Northumbrian poem of the XIVth century in four versions.

"The four versions of the Cursor are from 1. Cotton ms. Vesp. A3 in the ... British Museum; 2. Fairfax ms. 14 in the Bodleian Library; 3. Ms. Theol. 107 in the Göttingen University Library; 4, Ms. R. 3. 8 in the library of Trinity College, Cambridge; supplemented by mss. Laud 416, Cotton Galba E9, and mss. in the College of Arms, the Edinburgh College of Physicians, and the Bedford Library."

Military mining.

Part of the maps, plans and diagrams folded.

The Mining magazine.

Description based on: Vol. 38, no. 1 (Jan. 1928); title from caption.

Literary museum, an annual volume of the useful and entertaining, including the wonders of nature and art; tales of all countries and all ages; travels, adventures, etc...

Mode of access: Internet.

Engineering world.

"A weekly technical journal of civil, mechanical, electrical, mining and architectural engineering and construction."

Anthropological papers of the American Museum of Natural History.

Some volumes issued in parts.

Oceanic ichthyology, a treatise on the deep-sea and pelagic fishes of the world, based chiefly upon the collections made by the steamers Blake, Albatross, and Fish Hawk in the northwestern Atlantic, with an atlas containing 417 figures.

Published at the joint expense of the Smithsonian institution and the United States National museum.

Bulletin - Philadelphia Museum of Art.

Some vols. include the museum's Annual report.