chiranji lal chowdhary (283 Ergebnisse)

Taschenbuch. Zustand: Neu. Neuware -Das System zur Erkennung von Ausreißern entdeckt neue oder seltene Ereignisse, Anomalien, bösartige Handlungen und außergewöhnliche Phänomene. Es ist zwingend erforderlich, diese Anomalien beim Data Mining zu finden, da das Vorhandensein dieser Objekte die Datenbank normalerweise ineffizient macht. Ein Ausreißer ist eine Beobachtung, die so stark von den anderen Beobachtungen abweicht, dass der Verdacht aufkommt, dass sie durch einen anderen Mechanismus erzeugt wurde. Das Auffinden von Objekten, die nicht den wohldefinierten Vorstellungen vom erwarteten Verhalten in einem Datensatz entsprechen, wird als Ausreißererkennung bezeichnet. Die Ausreißererkennung ist ein Vorverarbeitungsschritt zum Auffinden dieser nicht konformen Objekte in Datensätzen. Die Erkennung von Ausreißern ist ein schwieriger Prozess in großen Datenbanken, da sie hochdimensionale Daten mit einer niedrigen Anomalierate enthalten. Hier werden Ausreißer formal definiert und es werden optimierte Methoden zur Erkennung von Ausreißern vorgeschlagen. Die Optimierung der Ausreißererkennung wird durch ein neues Konzept der Holoentropie erreicht, das Entropie und Gesamtkorrelation kombiniert. Es ist ein effektiveres und effizienteres praktisches Phänomen bei Ausreißer-Erkennungsmethoden. Es kann sowohl bei großen als auch bei hochdimensionalen Datensätzen effektiv eingesetzt werden.Books on Demand GmbH, Überseering 33, 22297 Hamburg 104 pp. Deutsch.

Taschenbuch. Zustand: Neu. Neuware -In Computer Vision,Image Processing, Artificial Intelligence and Neural Networks object recognition is one of the most successful applications of image or object analysis and understanding.The recognition system typically involves some sort of sensor,the use of a model database in which all the objects ¿models¿ representations are saved,and a decision-making ability.When a sensor views an object the digitized image is processed so as to represent it in the same way as the models are represented in the databases.Then a recognition algorithm tries to find the model to which the object best matches.For the view-based recognition, the representations take into account the appearance of the object. To achieve 3D Object recognition(3DOR) the pose of objects are also saved in the database.In general two 3DOR techniques. They are Geometric feature-based approach and Appearance- based approach.The geometric feature-based approach uses properties of shape of object i.e. lines, curves,and vertices for object recognition descriptions.But appearance-based 3DOR is the combined effects of objects shape, reflectance properties,pose and the illumination.Books on Demand GmbH, Überseering 33, 22297 Hamburg 76 pp. Englisch.

Taschenbuch. Zustand: Neu. Neuware -Outlier detection system discovers the novel or rare events, anomalies, vicious actions, exceptional phenomena. It is mandatory to find these anomalies in data mining because the presence of these objects usually makes the database inefficient. An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism. Finding objects that do not conform to well-defined notions of expected behaviour in a dataset is called outlier detection. Outlier detection is a pre-processing step for locating these non-conforming objects in data sets. This outlier detection is a challenging process in large scale database since it has high dimensional data with low anomalous rate. Here outliers are defined formally and the optimized ways to detect outliers is also proposed here. Optimization in outlier detection is achieved by a new concept of holoentropy which combines entropy and total correlation. It is a more effective and efficient practical phenomenon in outlier detection methods. It can be used effectively to deal with both large and high-dimensional datasets.Books on Demand GmbH, Überseering 33, 22297 Hamburg 104 pp. Englisch.

Taschenbuch. Zustand: Neu. Neuware -Le système de détection des valeurs aberrantes permet de découvrir les événements nouveaux ou rares, les anomalies, les actions vicieuses et les phénomènes exceptionnels. Il est indispensable de trouver ces anomalies dans le cadre de l'exploration des données, car la présence de ces objets rend généralement la base de données inefficace. Une valeur aberrante est une observation qui s'écarte tellement des autres observations que l'on soupçonne qu'elle a été générée par un mécanisme différent. La détection des objets qui ne sont pas conformes aux notions bien définies de comportement attendu dans un ensemble de données s'appelle la détection des valeurs aberrantes. La détection des valeurs aberrantes est une étape de prétraitement permettant de localiser ces objets non conformes dans les ensembles de données. La détection des valeurs aberrantes est un processus difficile dans les bases de données à grande échelle, car elles contiennent des données de grande dimension avec un faible taux d'anomalies. Les valeurs aberrantes sont définies de manière formelle et des méthodes optimisées de détection des valeurs aberrantes sont également proposées ici. L'optimisation de la détection des valeurs aberrantes est réalisée par un nouveau concept d'holoentropie qui combine l'entropie et la corrélation totale. Il s'agit d'un phénomène pratique plus efficace dans les méthodes de détection des valeurs aberrantes.Books on Demand GmbH, Überseering 33, 22297 Hamburg 104 pp. Französisch.

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Taschenbuch. Zustand: Neu. Neuware -O sistema de deteção de anomalias descobre os acontecimentos novos ou raros, as anomalias, as acções viciosas e os fenómenos excepcionais. É obrigatório encontrar estas anomalias na extração de dados porque a presença destes objectos torna geralmente a base de dados ineficaz. Um outlier é uma observação que se desvia tanto das outras observações que levanta suspeitas de que foi gerada por um mecanismo diferente. A deteção de objectos que não estão em conformidade com noções bem definidas de comportamento esperado num conjunto de dados é designada por deteção de outliers. A deteção de outliers é uma etapa de pré-processamento para localizar estes objectos não conformes em conjuntos de dados. A deteção de outliers é um processo difícil em bases de dados de grande escala, uma vez que estas têm dados de elevada dimensão com baixa taxa de anomalias. Aqui os outliers são definidos formalmente e as formas optimizadas de deteção de outliers também são propostas aqui. A otimização na deteção de outliers é conseguida através de um novo conceito de holoentropia que combina a entropia e a correlação total. Trata-se de um fenómeno prático mais eficaz e eficiente nos métodos de deteção de outliers. Pode ser utilizado eficazmente para lidar com conjuntos de dados de grande dimensão e de elevada dimensão.Books on Demand GmbH, Überseering 33, 22297 Hamburg 108 pp. Portugiesisch.

Taschenbuch. Zustand: Neu. Neuware -El sistema de detección de anomalías descubre los sucesos novedosos o raros, las anomalías, las acciones viciosas, los fenómenos excepcionales. Es obligatorio encontrar estas anomalías en la minería de datos porque la presencia de estos objetos suele hacer ineficiente la base de datos. Un valor atípico es una observación que se desvía tanto de las demás observaciones como para despertar sospechas de que ha sido generada por un mecanismo diferente. Encontrar objetos que no se ajustan a las nociones bien definidas de comportamiento esperado en un conjunto de datos se denomina detección de valores atípicos. La detección de valores atípicos es un paso de preprocesamiento para localizar estos objetos no conformes en los conjuntos de datos. Esta detección de valores atípicos es un proceso difícil en bases de datos a gran escala, ya que tiene datos de alta dimensión con una baja tasa de anomalías. Aquí se definen formalmente los valores atípicos y se proponen formas optimizadas de detectarlos. La optimización en la detección de valores atípicos se consigue mediante un nuevo concepto de holoentropía que combina la entropía y la correlación total. Se trata de un fenómeno práctico más eficaz y eficiente en los métodos de detección de valores atípicos. Puede utilizarse eficazmente para tratar conjuntos de datos grandes y de alta dimensión.Books on Demand GmbH, Überseering 33, 22297 Hamburg 104 pp. Spanisch.

Taschenbuch. Zustand: Neu. Neuware -Il sistema di rilevamento degli outlier scopre eventi nuovi o rari, anomalie, azioni viziose, fenomeni eccezionali. È obbligatorio trovare queste anomalie nell'estrazione dei dati perché la presenza di questi oggetti di solito rende il database inefficiente. Un outlier è un'osservazione che si discosta talmente tanto dalle altre osservazioni da destare il sospetto che sia stata generata da un meccanismo diverso. L'individuazione di oggetti che non sono conformi a nozioni ben definite di comportamento atteso in un set di dati è chiamata rilevamento degli outlier. Il rilevamento degli outlier è una fase di pre-elaborazione per individuare questi oggetti non conformi nei set di dati. L'individuazione degli outlier è un processo impegnativo nei database di grandi dimensioni, poiché i dati hanno un'elevata dimensionalità e un basso tasso di anomalie. In questa sede vengono definiti formalmente gli outlier e vengono proposte modalità ottimizzate per il loro rilevamento. L'ottimizzazione del rilevamento degli outlier è ottenuta grazie al nuovo concetto di oloentropia, che combina entropia e correlazione totale. Si tratta di un fenomeno pratico più efficace ed efficiente nei metodi di rilevamento degli outlier. Può essere utilizzato efficacemente per gestire insiemi di dati grandi e ad alta dimensionalità.Books on Demand GmbH, Überseering 33, 22297 Hamburg 104 pp. Italienisch.

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 328 | Sprache: Englisch | Produktart: Bücher.

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Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 294 | Sprache: Englisch | Produktart: Bücher.

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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kishore Kumar D.Kishore Kumar D received his M.S in Software Engg from VIT University Vellore in the year 2015. He has done many projects. He is working in Cognizant Tech. Sols. Second Author, Chiranji Lal Chowdhary obtained his B.E.

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Taschenbuch. Zustand: Neu. Neuware -This edited book provides information on emerging fields of next-generation healthcare informatics with a special emphasis on emerging developments and applications of artificial intelligence, deep learning techniques, computational intelligence methods, Internet of medical things (IoMT), optimization techniques, decision making, nanomedicine, and cloud computing. The book provides a conceptual framework and roadmap for decision-makers for this transformation. The chapters involved in this book cover challenges and opportunities for diabetic retinopathy detection based on deep learning applications, deep learning accelerators in IoT and IoMT, health data analysis, deep reinforcement-based conversational AI agent in healthcare systems, examination of health data performance, multisource data in intelligent medicine, application of genetic algorithms in health care, mental disorder, digital healthcare system with big data analytics, encryption methods in healthcare data security, computation and cognitive bias in healthcare intelligence and pharmacogenomics, guided imagery therapy, cancer detection and prediction techniques, medical image processing for coronavirus, and imbalance learning in health care.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 328 pp. Englisch.

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Buch. Zustand: Neu. Neuware -This book delves into the transformative potential of the Metaverse in the healthcare industry, addressing the challenges and opportunities presented by diverse biomedical data and digital healthcare solutions. The book explores how the convergence of technologies like Artificial Intelligence, Augmented Reality, and Virtual Reality can revolutionize healthcare delivery, enhancing patient outcomes and medical education. Chapters cover topics such as the fundamentals of the Metaverse in healthcare, intelligent healthcare systems architecture, ethical considerations, and the integration of IoT devices. Case studies and real-world applications showcase the Metaverse's role in disease prevention, mental health treatment, medical education, and elderly care, offering valuable insights for researchers, practitioners, and professionals in AI, digital health, and healthcare informatics.Designed as a comprehensive reference for academia, research institutions, and healthcare organizations, the book aims to guide the ethical integration of Metaverse technologies into the healthcare ecosystem to unlock their full potential while ensuring patient privacy and security.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 304 pp. Englisch.

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Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 380 | Sprache: Englisch | Produktart: Bücher.

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