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Your search keyword 'LN cat08778a' showing total 13 results
13 results on 'LN cat08778a'

1. Business analytics.

Author

Sahay, Amar

Subjects
Management -- Statistical methods, Decision making -- Statistical methods, Business planning, Strategic planning, Business intelligence, BUSINESS & ECONOMICS -- Industrial Management, BUSINESS & ECONOMICS -- Management, BUSINESS & ECONOMICS -- Management Science, BUSINESS & ECONOMICS -- Organizational Behavior, Electronic books, analytics, business analytics, business intelligence, data analysis, data mining, decision making, descriptive analytics, machine learning, modeling, neural networks, optimization, predictive analytics, predictive modeling, prescriptive analytics, quantitative techniques, regression analysis, simulation, statistical analysis, time-series forecasting
Abstract

Abstract: This book is about Business Analytics (BA)--an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI) that looks into historical data to better understand business performance thereby improving performance, and creating new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics--the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe a number of terms related to business analytics. The second part of the book is about descriptive analytics and its applications. The topics discussed are--Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically--Concepts and Computer Applications. Finally, an overview and a case on descriptive statistics with applications and notes on implementation are presented. The concluding remarks provide information on becoming a certified analytics professional (CAP) and an overview of the second volume of this book which is a continuation of this first volume. It is about predictive analytics which is the application of predictive models to predict future trends. The second volume discusses Prerequisites for Predictive Modeling; Most Widely used Predictive Analytics Models, Linear and Non-linear regression, Forecasting Techniques, Data mining, Simulation, and Data Mining.

Published
2018

2. Business analytics.

Author

Sahay, Amar

Subjects
Management -- Statistical methods, Decision making -- Statistical methods, Business planning, Strategic planning, Business intelligence, BUSINESS & ECONOMICS -- Industrial Management, BUSINESS & ECONOMICS -- Management, BUSINESS & ECONOMICS -- Management Science, BUSINESS & ECONOMICS -- Organizational Behavior, Electronic books, analytics, business analytics, business intelligence, data analysis, data mining, decision making, descriptive analytics, machine learning, modeling, neural networks, optimization, predictive analytics, predictive modeling, prescriptive analytics, quantitative techniques, regression analysis, simulation, statistical analysis, time-series forecasting
Abstract

Abstract: This book is about Business Analytics (BA)--an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI) that looks into historical data to better understand business performance thereby improving performance, and creating new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics--the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe a number of terms related to business analytics. The second part of the book is about descriptive analytics and its applications. The topics discussed are--Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically--Concepts and Computer Applications. Finally, an overview and a case on descriptive statistics with applications and notes on implementation are presented. The concluding remarks provide information on becoming a certified analytics professional (CAP) and an overview of the second volume of this book which is a continuation of this first volume. It is about predictive analytics which is the application of predictive models to predict future trends. The second volume discusses Prerequisites for Predictive Modeling; Most Widely used Predictive Analytics Models, Linear and Non-linear regression, Forecasting Techniques, Data mining, Simulation, and Data Mining.

Published
2018

3. A First Course in Machine Learning.

Author

Rogers, Simon and Girolami, Mark

Subjects
Machine learning, COMPUTERS -- General, Data Mining, Maschinelles Lernen, Machine Learning, Electronic books
Published
2016

4. AI Applications to Power Systems.

Author

Tjing Lie, Tek and Tjing Lie, Tek

Subjects
Technology: general issues, History of engineering & technology, self-healing grid, machine-learning, feature extraction, event detection, optimization techniques, manta ray foraging optimization algorithm, multi-objective function, radial networks, optimal power flow, automatic P2P energy trading, Markov decision process, deep reinforcement learning, deep Q-network, long short-term delayed reward, inter-area oscillations, modal analysis, reduced order modeling, dynamic mode decomposition, machine learning, artificial neural networks, steady-state security assessment, situation awareness, cellular computational networks, load flow prediction, contingency, fuzzy system, change detection, data analytics, data mining, filtering, optimization, power quality, signal processing, total variation smoothing, n/a
Abstract

Summary: Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered.

5. Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation.

Author

Stavrakakis, George S. and Stavrakakis, George S.

Subjects
Technology: general issues, History of engineering & technology, entrained flow coal gasification, ash melting point, operation temperature, Markov process, stochastic optimization model, genetic algorithm, gallium nitride, magnetic-free converters, module-level converters, parallel architecture, partial shading, photovoltaic systems, switched capacitor converters, hybrid energy storage system, supercapacitor, lead-acid battery, energy management system, battery degradation, depth of discharge, techno-economic analysis, hybrid power station, green island, energy storage, remote community, reserves, k-means, probabilistic dimensioning, dynamic dimensioning, balancing, wave energy converters, deep neural networks, renewable energy sources, spatial planning, sentinel satellite imagery, permanent magnet synchronous machines, generators, fault detection, demagnetization, artificial intelligence, data mining, machine learning, advanced deep learning, windspeed forecasting, solar irradiation forecasting, increased RES penetration, smart grid, scalability, replicability, FLEXITRANSTORE, Angolan economy, diversification, strategic alternative, biofuels
Abstract

Summary: An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice.

6. Artificial Intelligence and Cognitive Computing. Methods, Technologies, Systems, Applications and Policy Making.

Author

Lytras, Miltiadis, Visvizi, Anna, and Lytras, Miltiadis

Subjects
Information technology industries, data mining, decision-making system, rough set, mixed integer linear programming, assembly clearance, diesel engine quality, Internet of things, Wireless nodes, Hybrid clustering, Multi-hop routing, Network lifetime, Artificial intelligence, data envelopment analysis, decision making, artificial intelligence, performance, visual analytics, system, air quality, spatiotemporal, multivariate, dimension reduction, clustering, regular patterns, anomalies, speech recognition, Long Short Term Memory (LSTM), speech output correction, most-matching, empirical correlations, rheological properties, real-time, water-based drill-in fluid, artificial neural network, elastic parameters, Poisson's ratio, sandstone, self-adaptive differential evolution, total organic carbon, barnett shale, devonian shale, fishbone multilateral wells, predictive models, well productivity, international research, knowledge map visualization, policy documents quantification, research hotspot, policy keyword, minimum miscibility pressure (MMP), CO2 flooding, new models, face recognition, security, spoofing, histogram of oriented gradients, smart cities, deep learning, LSTM, neural networks, location prediction, trajectories, smart tourism, static Young's modulus, sandstone formations, machine learning, n/a
Abstract

Summary: Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today's world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that.

7. Bioinformatics Applications Based On Machine Learning.

Author

Chamoso, Pablo, Rodriguez, Sara, Mohamad, Mohd, González-Briones, Alfonso, and Chamoso, Pablo

Subjects
Technology: general issues, machine learning, metagenomics, bioinformatics, CTX-M, data mining, cluster, clinical implications, diabetes, epidemiology, forecast, PART, Decision table, Weka, real-life patients, regression, ear detection, computer vision, convolutional neural network, image recognition, video analysis, gene clustering, swarm intelligence, biological functions detection, informative genes, fuel cell, hydrogen energy, intelligent systems, hybrid systems, Artificial Neural Networks, power management, Machine Learning, personality assessment, gradient boosting, Affective Computing, transposable elements, metrics, deep learning, detection, classification, mitochondrial protein, bi-directional LSTM, plasmodium falciparum, Particle Swarm Optimization, Harmony Search, parameter estimation, Arabidopsis thaliana, clinical data, feature selection, genetic programming, evolutionary computation, dynamic models, evolutionary computing, derivative-free optimization, metabolism, glycolysis, yeast
Abstract

Summary: The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems.

8. Computation in Complex Networks.

Author

Pizzuti, Clara, Socievole, Annalisa, and Pizzuti, Clara

Subjects
Technology: general issues, city interaction network, evolution model, preferential attachment, WeChat, maximum likelihood, chimera states, coupled map lattice, nilpotent matrix, community detection, membrane algorithm, self-organizing map network, complex networks, optimization, structural balance, minimum memory based sign adjustment, social networks, NW network, convergence, complex system simulation, cloud computing architecture, service-oriented modeling, semantic search framework, QoS-based service selection, cascading failures, network topology, null models, SciSci, knowledge evolution, machine learning, bridging centrality, disjoint nodes, disjunct nodes, node similarity, overlapping nodes, Bayesian networks, entropy, socio-ecological system, complex network, chaotic time series, Gaussian mixture model, maximum mean discrepancy, angiogenesis, network properties, variational inference, graph neural network, variational autoencoder, network embedding, online social networks, social media, information spreading, information diffusion, cross-entropy, cross-domain recommendation, sentiment analysis, latent sentiment review feature, non-linear mapping, dissimilarity spaces, support vector machines, kernel methods, computational biology, systems biology, protein contact networks, data mining, overlapping communities, modularity, literary works, genre classification, stylistic attributes, lemmatization, renormalisation process, network growth, inverse preferential attachment, language networks, language development, multilayer complex networks, stability, spreading control, graph neural networks, node classification, active learning, graph representation learning, n/a
Abstract

Summary: Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue "Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicine

9. Data Science for Economics and Finance. Methodologies and Applications.

Author

Consoli, Sergio, Reforgiato Recupero, Diego, Saisana, Michaela, and Consoli, Sergio

Subjects
Data mining, Machine learning, Business mathematics & systems, Public administration, Information retrieval, Data Mining and Knowledge Discovery, Machine Learning, Business Information Systems, Big Data/Analytics, Computer Appl. in Administrative Data Processing, Information Storage and Retrieval, IT in Business, Computer and Information Systems Applications, Open Access, Data Mining, Big Data, Data Analytics, Decision Support Systems, Semantics and Reasoning, Expert systems / knowledge-based systems, Information technology: general issues, Data warehousing
Abstract

Summary: This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.

10. Machine Learning in Tribology.

Author

Tremmel, Stephan, Marian, Max, and Tremmel, Stephan

Subjects
Technology: general issues, History of engineering & technology, artificial intelligence, machine learning, artificial neural networks, tribology, condition monitoring, semi-supervised learning, random forest classifier, self-lubricating journal bearings, reduced order modelling, dynamic friction, rubber seal applications, tensor decomposition, laser surface texturing, texturing during moulding, digital twin, PINN, reynolds equation, triboinformatics, databases, data mining, meta-modeling, monitoring, analysis, prediction, optimization, fault data generation, Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), bearing fault diagnosis, unbalanced datasets, tribo-testing, tribo-informatics, natural language processing, tribAIn, BERT, amorphous carbon coatings, UHWMPE, total knee replacement, Gaussian processes, rolling bearing dynamics, cage instability, regression, neural networks, random forest, gradient boosting, evolutionary algorithms, rolling bearings, remaining useful life, feature engineering, structure-borne sound, n/a
Abstract

Summary: Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology.

11. Open Data and Energy Analytics.

Author

Nastasi, Benedetto, Manfren, Massimiliano, Noussan, Michel, and Nastasi, Benedetto

Subjects
Research & information: general, data envelopment analysis, Kohonen self-organizing maps, factor analysis, multiple regression, energy efficiency, social media, energy-consuming activities, energy consumption, machine learning, ontology, energy performance certificate, heating energy demand, buildings, data mining, classification, regression, decision tree, support vector machine, random forest, artificial neural network, open data, electrification modelling, Malawi, OnSSET, MESSAGEix, reproducibility, collaborative work, open modelling and data, data-handling, integrated assessment modelling, data pre- and post-processing, space heating, domestic hot water, market assessment, EU28, district heating, data analytics, big data, forecasting, energy, polygeneration, clustering, kNN, pattern recognition, heating, building stock, heat map, spatial analysis, heat density map, building performance simulation, parametric modelling, energy management, model calibration, Passive House, energy planning, energy potential mapping, urban energy atlas, urban energy transition, energy data, data-aware planning, spatial planning, open data analytics, smart cities, open energy governance, urban database, energy mapping, building dataset, energy modelling
Abstract

Summary: Open data and policy implications coming from data-aware planning entail collection and pre- and postprocessing as operations of primary interest. Before these steps, making data available to people and their decision-makers is a crucial point. Referring to the relationship between data and energy, public administrations, governments, and research bodies are promoting the construction of reliable and robust datasets to pursue policies coherent with the Sustainable Development Goals, as well as to allow citizens to make informed choices. Energy engineers and planners must provide the simplest and most robust tools to collect, process, and analyze data in order to offer solid data-based evidence for future projections in building, district, and regional systems planning. This Special Issue aims at providing the state-of-the-art on open-energy data analytics; its availability in the different contexts, i.e., country peculiarities; and its availability at different scales, i.e., building, district, and regional for data-aware planning and policy-making. For all the aforementioned reasons, we encourage researchers to share their original works on the field of open data and energy analytics. Topics of primary interest include but are not limited to the following: 1. Open data and energy sustainability; 2. Open data science and energy planning; 3. Open science and open governance for sustainable development goals; 4. Key performance indicators of data-aware energy modelling, planning, and policy; 5. Energy, water, and sustainability database for building, district, and regional systems; 6. Best practices and case studies.

12. Process Mining Workshops. ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23-28, 2022, Revised Selected Papers.

Author

Montali, Marco, Senderovich, Arik, Weidlich, Matthias, and Montali, Marco

Subjects
Data mining, Business mathematics & systems, Machine learning, Information technology: general issues, Health & safety aspects of IT, process mining, process discovery, process analytics, process querying, conformance checking, predictive process monitoring, data science, knowledge graphs, event data, streaming analytics, machine learning, deep learning, business process management, health informatics
Abstract

Summary: This open access book constitutes revised selected papers from the International Workshops held at the 4th International Conference on Process Mining, ICPM 2022, which took place in Bozen-Bolzano, Italy, during October 23-28, 2022. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 42 papers included in this volume were carefully reviewed and selected from 89 submissions. They stem from the following workshops: - 3rd International Workshop on Event Data and Behavioral Analytics (EDBA) - 3rd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) - 3rd International Workshop on Responsible Process Mining (RPM) (previously known as Trust, Privacy and Security Aspects in Process Analytics) - 5th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) - 3rd International Workshop on Streaming Analytics for Process Mining (SA4PM) - 7th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) - 1st International Workshop on Education meets Process Mining (EduPM) - 1st International Workshop on Data Quality and Transformation in Process Mining (DQT-PM)

13. Special Issue of the Manufacturing Engineering Society 2019 (SIMES-2019)

Author

Rubio, Eva M., Camacho, Ana M., and Rubio, Eva M.

Subjects
History of engineering & technology, 2D positioning control, nanopositioning, Halbach linear motors, positioning uncertainty, hyperspectral imaging, pigment analysis, cultural heritage, conservation, cultural management, visual enhancement, rock art, cartography, 3D printing, additive manufacturing, fused deposition modeling (FDM), minor surgery, primary care, surgical training, initial population, data mining, multi-variety, machine learning, production planning, capacity planning, digital twin, forecasting, viable system model, simulation, system dynamics, customer demand, demand planning, automotive, military logistics, lean management, DMAIC, VSM-VSD, engineer-to-order, order management process, production planning and control, maintenance management, order-to-delivery, agent-based simulation, metallurgical industry, Inconel 718, Inco718, springback, bauschinger, autoform, stack drilling, CFRP/CFRP laminates, multiple sensor monitoring, tool wear evaluation, dry turning, surface roughness, microhardness, UNS A97075, aluminum alloys, forming, machining, metrology, technological and industrial heritage, industry 4.0, green manufacturing
Abstract

Summary: This Special Issue of the Manufacturing Engineering Society 2019 (SIMES-2019) has been launched as a joint issue of the journals Applied Sciences and Materials. The 10 contributions published in this Special Issue of Applied Sciences present cutting-edge advances in the field of manufacturing engineering, focusing on production planning, sustainability, metrology, cultural heritage, and materials processing, with experimental and numerical results. It is worth mentioning that the topic "production planning" has attracted a great number of contributions in this journal, due to their applicative approach.

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