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  • 1
    UID:
    b3kat_BV046324998
    Format: 1 Online-Ressource (XIV, 178 p)
    Edition: 1st ed. 2020
    ISBN: 9789811518232
    Series Statement: Studies in Computational Intelligence 879
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-151-822-5
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-151-824-9
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-151-825-6
    Language: English
    Subjects: Computer Science
    RVK:
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 2
    UID:
    b3kat_BV046878699
    Format: 1 Online-Ressource (XVII, 1537 p. 722 illus., 506 illus. in color)
    Edition: 1st ed. 2021
    ISBN: 9789811553417
    Series Statement: Lecture Notes in Electrical Engineering 668
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-155-340-0
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-155-342-4
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-155-343-1
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 3
    Online Resource
    Online Resource
    Singapore : Springer Singapore | Singapore : Springer
    UID:
    b3kat_BV046083271
    Format: 1 Online-Ressource (XIV, 265 p. 156 illus., 52 illus. in color)
    Edition: 1st ed. 2020
    ISBN: 9789811388217
    Series Statement: Lecture Notes in Electrical Engineering 577
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-138-820-0
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-138-822-4
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-138-823-1
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 4
    Online Resource
    Online Resource
    Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) : IGI Global
    UID:
    b3kat_BV047608855
    Format: 1 Online-Ressource(26 1 online resource (281 pages))
    ISBN: 9781799833291 , 9781799833284
    Content: "This book examines the application of intelligence computational techniques and methods in energy and power systems"--
    Note: Includes bibliographical references and index. - Description based on title screen (IGI Global, viewed 03/06/2021)
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9781799833277
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 5
    UID:
    b3kat_BV046203658
    Format: 1 Online-Ressource
    ISBN: 9781522596851
    Content: "This book examines the use of antennas and sensors in the field of wearable computing"--
    Note: Wearable antenna for energy harvesting application at dual resonant frequency / Bharat Khare, UIT RGPV Bhopal, India -- Relationship between co-axial probe feed and inset feed in rectangular microstrip patch / Ravi Prasad, Bundelkhand Institute of Engineering & Technology Jhansi, India, D. Srivastava, Bundelkhand Institute of Engineering & Technology Jhansi, India, Rishabh Baudh, Bundelkhand Institute of Engineering & Technology, Jhansi, India, J Saini, Bundelkhand Institute of Engineering and Technology, India -- Performance analysis of cascade H-bridge multilevel inverter topology with filter circuit and without filter circuit / Nikhil Agrawal, Ujjain Engineering College, Ujjain
    Additional Edition: Erscheint auch als Druck-Ausgabe, hardcover ISBN 978-1-5225-9683-7
    Additional Edition: Erscheint auch als Druck-Ausgabe, softcover ISBN 978-1-5225-9684-4
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 6
    UID:
    almahu_9949385326902882
    Format: 1 online resource (xvi, 182 pages)
    Edition: First edition.
    ISBN: 9781003110101 , 100311010X , 9781000476613 , 1000476618 , 9781000476590 , 1000476596
    Content: Cloud computing offers subscription-based on-demand services, and it has emerged as the backbone of the computing industry. It has enabled us to share resources among multiple users through virtualization, which creates a virtual instance of a computer system running in an abstracted hardware layer. Unlike early distributed computing models, it offers virtually limitless computing resources through its large scale cloud data centers. It has gained wide popularity over the past few years, with an ever-increasing infrastructure, a number of users, and the amount of hosted data. The large and complex workloads hosted on these data centers introduce many challenges, including resource utilization, power consumption, scalability, and operational cost. Therefore, an effective resource management scheme is essential to achieve operational efficiency with improved elasticity. Machine learning enabled solutions are the best fit to address these issues as they can analyze and learn from the data. Moreover, it brings automation to the solutions, which is an essential factor in dealing with large distributed systems in the cloud paradigm. Machine Learning for Cloud Management explores cloud resource management through predictive modelling and virtual machine placement. The predictive approaches are developed using regression-based time series analysis and neural network models. The neural network-based models are primarily trained using evolutionary algorithms, and efficient virtual machine placement schemes are developed using multi-objective genetic algorithms. Key Features: the first book to set out a range of machine learning methods for efficient resource management in a large distributed network of clouds. predictive analytics is an integral part of efficient cloud resource management, and this book gives a future research direction to researchers in this domain. it is written by leading international researchers. The book is ideal for researchers who are working in the domain of cloud computing.
    Note: List of FiguresList of TablesPrefaceAuthor BiosAbbreviationsIntroduction1.1 CLOUD COMPUTING1.2 CLOUD MANAGEMENT1.2.1 Workload Forecasting1.2.2 Load Balancing1.3 MACHINE LEARNING1.3.1 Artificial Neural Network1.3.2 Metaheuristic Optimization Algorithms1.3.3 Time Series Analysis1.4 WORKLOAD TRACES1.5 EXPERIMENTAL SETUP & EVALUATION METRICS1.6 STATISTICAL TESTS1.6.1 Wilcoxon Signed-Rank Test1.6.2 Friedman Test1.6.3 Finner TestTime Series Models2.1 AUTOREGRESSION2.2 MOVING AVERAGE2.3 AUTOREGRESSIVE MOVING AVERAGE2.4 AUTOREGRESSIVE INTEGRATED MOVING AVERAGE2.5 EXPONENTIAL SMOOTHING2.6 EXPERIMENTAL ANALYSIS2.6.1 Forecast Evaluation2.6.2 Statistical AnalysisError Preventive Time Series Models3.1 ERROR PREVENTION SCHEME3.2 PREDICTIONS IN ERROR RANGE3.3 MAGNITUDE OF PREDICTIONS3.4 ERROR PREVENTIVE TIME SERIES MODELS3.4.1 Error Preventive Autoregressive Moving Average3.4.2 Error Preventive Auto Regressive Integrated Moving Average3.4.3 Error Preventive Exponential Smoothing3.5 PERFORMANCE EVALUATION3.5.1 Comparative Analysis3.5.2 Statistical AnalysisMetaheuristic Optimization Algorithms4.1 SWARM INTELLIGENCE ALGORITHMS IN PREDICTIVE MODEL4.1.1 Particle Swarm Optimization4.1.2 Firefly Search Algorithm4.2 EVOLUTIONARY ALGORITHMS IN PREDICTIVE MODEL4.2.1 Genetic Algorithm4.2.2 Differential Evolution4.3 NATURE INSPIRED ALGORITHMS IN PREDICTIVE MODEL4.3.1 Harmony Search4.3.2 Teaching Learning Based Optimization4.4 PHYSICS INSPIRED ALGORITHMS IN PREDICTIVE MODEL4.4.1 Gravitational Search Algorithm4.4.2 Blackhole Algorithm4.5 STATISTICAL PERFORMANCE ASSESSMENTEvolutionary Neural Networks5.1 NEURAL NETWORK PREDICTION FRAMEWORK DESIGN5.2 NETWORK LEARNING5.3 RECOMBINATION OPERATOR STRATEGY LEARNING5.3.1 Mutation Operator5.3.1.1 DE/current to best/15.3.1.2 DE/best/15.3.1.3 DE/rand/15.3.2 Crossover Operator5.3.2.1 Ring Crossover5.3.2.2 Heuristic Crossover5.3.2.3 Uniform Crossover5.3.3 Operator Learning Process5.4 ALGORITHMS AND ANALYSIS5.5 FORECAST ASSESSMENT5.5.1 Short Term Forecast5.5.2 Long Term Forecast5.6 COMPARATIVE ANALYSISSelf Directed Learning6.1 NON-DIRECTED LEARNING BASED FRAMEWORK6.1.1 Non-Directed Learning6.2 SELF-DIRECTED LEARNING BASED FRAMEWORK6.2.1 Self Directed Learning6.2.2 Cluster Based Learning6.2.3 Complexity analysis6.3 FORECAST ASSESSMENT6.3.1 Short Term Forecast6.3.1.1 Web Server Workloads6.3.1.2 Cloud Workloads6.4 LONG TERM FORECAST6.4.0.1 Web Server Workloads6.4.0.2 Cloud Workloads6.5 COMPARATIVE & STATISTICAL ANALYSISEnsemble Learning7.1 EXTREME LEARNING MACHINE7.2 WORKLOAD DECOMPOSITION PREDICTIVE FRAMEWORK7.2.1 Framework Design7.3 ELM ENSEMBLE PREDICTIVE FRAMEWORK7.3.1 Ensemble Learning7.3.2 Expert Architecture Learning7.3.3 Expert Weight Allocation7.4 SHORT TERM FORECAST EVALUATION7.5 LONG TERM FORECAST EVALUATION7.6 COMPARATIVE ANALYSISLoad Balancing8.1 MULTI-OBJECTIVE OPTIMIZATION8.2 RESOURCE EFFICIENT LOAD BALANCING FRAMEWORK8.3 SECURE AND ENERGY AWARE LOAD BALANCING FRAMEWORK8.3.1 Side Channel Attacks8.3.2 Ternary Objective VM Placement8.4 SIMULATION SETUP8.5 HOMOGENEOUS VM PLACEMENT ANALYSIS8.6 HETEROGENEOUS VM PLACEMENT ANALYSISBibliographyIndex
    Language: English
    Keywords: Electronic books. ; Electronic books.
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  • 7
    UID:
    almahu_9949530797002882
    Format: 1 online resource
    Edition: First edition.
    ISBN: 9781003321149 , 1003321143 , 9781000879094 , 1000879097 , 9781000879070 , 1000879070
    Content: "The advancements in intelligent decision-making techniques have elevated the efficiency of manufacturing industries and led to the start of the Industry 4.0 era. Industry 4.0 is revolutionizing the way companies manufacture, improve and distribute their products. Manufacturers are integrating new technologies, including the Internet of Things (IoT), cloud computing and analytics, and Artificial intelligence and machine learning, into their production facilities throughout their operations. In the past few years, intelligent analytics has emerged as a solution that examines both historical and real-time data to uncover performance insights. Because the amount of data that needs analysis is growing daily, advanced technologies are necessary to collect, arrange, and analyze incoming data. This approach enables businesses to detect valuable connections and trends and make decisions that boost overall performance. In Industry 4.0, intelligent analytics has a broader scope in terms of descriptive, predictive, and prescriptive sub-domains. To this end, the book will aim to review and highlight the challenges faced by Intelligent Analytics in Industry 4.0 and present the recent developments done to address those challenges"--
    Note: Analytics approach for intelligent cyber-physical system integration in industrial internet of things (industry 4.0) / Lukman Adewale Ajao and Simon T. Apeh -- Digital twins : a state of the art from industry 4.0 perspective / Deiva Ganesh A And Kalpana P -- Human-centered approach to intelligent analytics in industry 4.0 / Varad Vishwarupe, Milind Pande, Vishal Pawar, Prachi Joshi, Aseem Deshmukh, Shweta Mhaisalkar, Nicole Mathias, Shrey Maheshwari and Neha Mandora -- Advance in robotics industry 4.0 / Muchharla Suresh, Rajesh Kumar Dash, Deepika Padhy, Swastid Dash, Subhransu Kumar Das, and K Nageswara Rao Achary -- A cloud-based real-time healthcare monitoring system for CVD patients / Anjali Kumari, Animesh Kumar, Kanishka Vijayvargiya, Piyush Goyal, Ujjwal Tripathi and Avinash Chandra Pandey -- Assessment of fuzzy logic assessed recommender system: A critical critique / S Gopal Krishna Patro, Brojo Kishore Mishra and Sanjaya Kumar Panda -- Intelligent analytics in big data and cloud / K Aditya Shastry and Manjunatha B A -- Various audio classification models for automatic speaker verification system in industry 4.0 / Sanil Joshi, Mohit Dua and Shelza Dua -- Trending IoT platforms on middleware layer / Neha Katiyar, Surabhi Sakhshi, Priti Kumari and Jyoti Srivastava -- Healthcare IoT: a factual and feasible application of industrial IoT / Subasish Mohapatra, Amlan Sahoo, Munesh Singh and Subhadarshini Mohanty -- IoT based spacecraft anti-collision HUD design formulation / Bibhorr -- Coverage of LoRaWAN in Vijayawada: a practical approach / P. Saleem Akram and M Lakshmana -- Intelligent health care industry for disease detection / Ishaan Deep, Payaswini Singh, Aditi Trishna, Sanskar, Biswa Ranjan Senapati and Rakesh Ranjan Swain -- Challenges with industry 4.0 security / Mohit Sajwan and Simranjit Singh -- Dodging security attacks and data leakage prevention for cloud and IoT environments / Ishu Gupta, Ankit Tiwari, Priya Agarwal, Sloni Mittal and Ashutosh Kumar Singh -- Role of blockchain in industry 4.0 / Keshav Kaushik and Bhavana Kaushik -- Blockchain and bitcoin security in industry 4.0 / Yadunath Pathak and Praveen Pawar -- Technology in industry 4.0 / Rashmy Moray and Amar Patnaik -- Intelligent analytics in cyber physical systems / Manjushree Nayak, Priyanka P Pratihari, Sanjana Mahapatra and Shyam Sundar Pradhan -- An overlook on security challenges in industry 4.0 / Ramya. R, S. Sharmila Devi and Y. Adline Jancy.
    Additional Edition: Print version: Intelligent analytics for industry 4.0 applications Boca Raton : CRC Press, 2023 ISBN 9781032342412
    Language: English
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  • 8
    UID:
    almahu_BV046415610
    Format: xiv, 178 Seiten : , Illustrationen, Diagramme (überwiegend farbig).
    ISBN: 978-981-151-822-5
    Series Statement: Studies in computational intelligence volume 879
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-981-151-823-2
    Language: English
    Subjects: Computer Science
    RVK:
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  • 9
    UID:
    almahu_9949567980002882
    Format: 1 online resource (xii, 240 pages) : , illustrations.
    Edition: First edition.
    ISBN: 9781003361633 , 1003361633 , 9781000910377 , 1000910377 , 9781000910360 , 1000910369
    Series Statement: Materials, devices, and circuits: design and reliability
    Content: "This book provides a composite solution for optimal logic designs for quantum-dot cellular automata based circuits. It includes the basics of new logic functions and novel digital circuit designs, quantum computing with QCA, new trends in quantum and quantum-inspired algorithms and applications, and algorithms to support QCA designers. Futuristic Developments in Quantum-Dot Cellular Automata Circuits for Nanocomputing includes QCA-based new nanoelectronics architectures that help in improving the logic computation and information flow at physical implementation level. The book discusses design methodologies to obtain an optimal layout for some of the basic logic circuits considering key metrics such as wire delays, cell counts, and circuit area that help in improving the logic computation and information flow at physical implementation level. Examines several challenges toward QCA technology like clocking mechanism, floorplan which would facilitate manufacturability, Electronic Design Automation (EDA) tools for design and fabrication like simulation, synthesis, testing etc. The book is intended for students and researchers in electronics and computer disciplines who are interested in this rapidly changing field under the umbrella of courses such as emerging Nano-technologies and its architecture, low-power digital design. The work will also help the manufacturing companies/industry professionals, in nanotechnology and semiconductor engineers in the development of low power quantum computers"--
    Additional Edition: Print version: Quantum-dot cellular automata circuits for nanocomputing applications Boca Raton : CRC Press, [2024] ISBN 9781032420189
    Language: English
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