Erscheinungsdatum: 25.09.2014, Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: BrainAGE, Titelzusatz: A novel machine learning approach for identifying abnormal age-related brain changes, Autor: Franke, Katja, Verlag: Südwestdeutscher Verlag für Hochschulschriften AG Co. KG, Sprache: Englisch, Rubrik: Medizin // Allgemeines, Lexika, Seiten: 168, Informationen: Paperback, Gewicht: 267 gr, Verkäufer: averdo
Erscheint auf schwarzem Vinyl im klaren Slipcase. Printed Album Art. Printed Inner Sleeve. 24x36" Poster und Downloadcode. Mit "The New Abnormal" legt die amerikanische Rockformation The Strokes endlich ein neues Album vor. Sieben Jahre sind seit ihrem letzten Longplayer "Comedown Machine" vergangen während vor vier Jahren mit "Future Present Past" die letzte EP erschienen ist. Neun fantastische Songs haben Julian Casablancas, Albert Hammond Jr. und Co aufgenommen, und zwar mit Star-Producer Rick Rubin."
Computing terminology ab 23.99 € als Taschenbuch: Hacker Computer program Control unit Word processing Nibble Data stream Killer application Automated information system Interoperability Security kernel System integrity Machine-readable medium Indirection Abnormal end Abort. Aus dem Bereich: Bücher, Taschenbücher, Wirtschaft & Soziales,
You are about to discover how to leverage the power of autophagy to detox your body, promote muscle growth, prevent diseases, reverse the aging process, and boost your energy!Your body has in-build systems for defending itself against all manner of chronic diseases. And I’m not referring to the immune system. I’m referring to a secret system that the body uses to get rid of old and damaged cells and replacing these cells with new healthy ones. That’s not all - this system also destroys bacteria and viruses that may lead to infection and prevents normal, healthy cells from becoming cancerous. And with such a system, you could literally deal with all manner of diseases like cancer, dementia, chronic inflammation, arthritis, abnormal aging, chronic weight gain, chronic fatigue, muscle loss, and much more! This system is "autophagy".And while this system occurs naturally, there are steps you can take to maximize its effectiveness so that you get all the benefits that come with autophagy! But what exactly does autophagy do that makes it bring about each one of these benefits and more? What activities or habits enhance autophagy? How do you make your body an autophagy-inducing machine? What’s the science behind autophagy? What studies have been done to prove the effectiveness and efficiency of autophagy in bringing about various benefits?If you have these and other related questions about autophagy, this audiobook is for you, so keep reading. Autophagy covers the ins and outs of this somewhat "magical" system that can turn your health around if you make it part of your everyday life.More precisely, the audiobook will teach you:The basics of autophagy, including what it is, how it works, along with the many benefits that autophagy can bring to your body and overall well-beingThe factors responsible for increased lifespan and longevity in humans and other species, including the place of aut 1. Language: English. Narrator: Layce Gardner. Audio sample: http://samples.audible.de/bk/acx0/181214/bk_acx0_181214_sample.mp3. Digital audiobook in aax.
Electricity consumer dishonesty is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This research study presents a new approach towards nontechnical loss (NTL) detection in power utilities using support vector machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) in peninsular Malaysia to reduce its NTLs, due to abnormalities and fraud activities, such as electricity theft. The fraud detection model developed in this study preselects suspected customers to be inspected onsite fraud based on irregularities in consumption behavior. This provides a method of data mining, which involves feature extraction from historical customer consumption data. This SVM based approach uses customer load profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. Feedback from TNB Distribution (TNBD) for onsite inspection of shortlisted customers indicates that the proposed fraud detection model developed in this research study is more effective compared to the current actions taken by them.
The use of hematological analyzers has become routine in clinical practice, but the sheer volume of data produced by these devices often makes manual inspection of all the results an unwieldy task. For this reason, automated pattern analysis through the use of machine learning has been used in these types of situations, to save time and to provide invaluable aid to medical professionals in this area of diagnostic medicine. Toward this end, Artificial Neural Networks (ANNs) are often relied upon in the field of machine learning, because of their ability to distill representative feature components from large amounts of input data. This paper details an approach in which the scatterplots of cells that were produced by a hematological device were used as inputs. The data were separated into two classes, one containing clinically Normal samples, and the second containing abnormal samples that contained Variant Lymphocytes. Statistical features were extracted from these data using Principal Component Analysis (PCA) and then a Perceptron ANN was employed to differentiate between the two classes of data. The accuracy of pattern classification using this method was then discussed.
Lead poisoning is a serious public health concern in Nigeria and it exerts toxic effects in several body organs such as kidney, heart, brain, ear and sex organs. Occupational exposure particularly tricycle repairers in Nigeria is vague. The survey reports on the nature and extent to which tricycle repairers in Nigeria exposure to lead at workplace. Twenty tricycle repairers were randomly recruited and their anthropometric parameters were collected and recorded in a Quantum Health Machine (QHM). The automated QHM examined each repairer's body and blood lead contents immediately each repairer held the QHM sensitive handle and results displayed on the detector screen. Repairers were mainly exposed to lead through petrol additive lead tetraethyl and through paint pigment lead carbonate. Generally, 85% of studied repairers had moderate 40% to 48% abnormal contents of lead in their bodies while 15% repairers were of mild abnormal lead contents. The overall mean and median levels of lead in studied repairers was 162.8 and 165.1 (range:100.2-196.4)µg/dL respectively. These levels exceeded the Food and Drug Administration Provisional Total Tolerable Intake Levels of 75µg/dL/day for adults.