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Machine Learning Techniques and Analytics for Cloud Security
covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions
Artificial neural networks with Java : Tools for Building Neural Network Applications
Covers the internals of front and back propagation and helps you understand the main principles of neural network processing. You also will learn how to prepare the data to be used in neural network development and you will be able to suggest various techniques of data preparation for many unconventional tasks. You will learn: Use Java for the development of neural network applications / Prepare data for many different tasks / Carry out some unusual neural network processing / Use a neural network to process non-continuous functions / Develop a program that recognizes handwritten digits
Artificial neural networks : Recent advances, new perspectives and applications
This book explores the potential of ANNs for applications in different fields. Itincludes eight chapters that discuss deep learning, ANN tools, and other cutting-edgetechnologies. It also suggests avenues for further research into ANN techniques formedical imaging to detect breast tumors, classification of COVID-19 surveillancedatasets, health management, estimation of materials processing parameters, solarenergy management, and control of a petrochemical unit.
Artificial neural network-based optimized design of reinforced concrete structures
Introduces AI-based Lagrange optimization techniques that can enable more rational engineering decisions for concrete structures while conforming to codes of practice. It shows how objective functions including cost, CO2 emissions, and structural weight of concrete structures are optimized either separately or simultaneously while satisfying constraining design conditions using an ANN-based Lagrange algorithm. Any design target can be adopted as an objective function. Many optimized design examples are verified by both conventional structural calculations and big datasets. Uniquely applies the new powerful tools of AI to concrete structural design and optimization Multi-objective functions of concrete structures optimized either separately or simultaneously Design requirements imposed by codes are automatically satisfied by constraining conditions Heavily illustrated in color with practical design examples
Artificial intelligence and data mining approaches in security frameworks
Offers solutions to the problems of security, outlining the concepts behind allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts. provides state of the art approaches of artificial intelligence and data mining in these areas. It includes areas of detection, prediction, as well as future framework identification, development, building service systems and analytical aspects. In all these topics, applications of AI and data mining, such as artificial neural networks, fuzzy logic, genetic algorithm and hybrid mechanisms, are explained and explored. This book is aimed at the modeling and performance prediction of efficient security framework systems, bringing to light a new dimension in the theory and practice. This groundbreaking new volume presents these topics and trends, bridging the research gap on AI and data mining to enable wide-scale implementation. Whether for the veteran engineer or the student, this is a must-have for any library. This groundbreaking new volume: Clarifies the understanding of certain key mechanisms of technology helpful in the use of artificial intelligence and data mining in security frameworks ; Covers practical approaches to the problems engineers face in working in this field, focusing on the applications used every day ; Contains numerous examples, offering critical solutions to engineers and scientists ; Presents these new applications of AI and data mining that are of prime importance to human civilization as a whole
Algorithms for a New World : When Big Data and Mathematical Models Meet
Algorithms, artificial neural networks, and machine learning help us discover the opportunities and pitfalls of a world governed by mathematics and artificial intelligence.
AI in drug discovery
Constitutes the refereed proceedings of the First international workshop on ai in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024, in Lugano, Switzerland, on September 19, 2024. These papers focus on various aspects of the rapidly evolving field of Artificial Intelligence (AI)-driven drug discovery in chemistry, including Big Data and advanced Machine Learning, eXplainable AI (XAI), Chemoinformatics, Use of deep learning to predict molecular properties, Modeling and prediction of chemical reaction data and Generative models.
Adaptive Business Intelligence
In the modern information era, managers must recognize the competitive opportunities represented by decision-support tools. Adaptive business intelligence systems combine prediction and optimization techniques to assist decision makers in complex, rapidly changing environments. These systems address the fundamental questions: What is likely to happen in the future? And what is the best decision right now? Adaptive Business Intelligence includes elements of data mining, predictive modeling, forecasting, optimization, and adaptability.
Artificial intelligence in mechatronics and civil engineering : Bridging the gap
Recent studies highlight the application of artificial intelligence, machine learning, and simulation techniques in engineering. This book covers the successful implementation of different intelligent techniques in various areas of engineering focusing on common areas between mechatronics and civil engineering. The power of artificial intelligence and machine learning techniques in solving some examples of real-life problems in engineering is highlighted in this book. The implementation process to design the optimum intelligent models is discussed in this book.
Challenges for Computational Intelligence
Computational Intelligence (CI) is used as a name to cover many existing branches of science, with artificial neural networks, fuzzy systems and evolutionary computation forming its core. In recent years CI has been extended by adding many other subdisciplines and it became quite obvious that this new field also requires a series of challenging problems that will give it a sense of direction. Without setting up clear goals and yardsticks to measure progress on the way many research efforts are wasted.The book written by top experts in CI provides such clear directions and the much-needed focus on the most important and challenging research issues, showing a roadmap how to achieve ambitious goals.
Artificial neural networks in Vehicular Pollution Modelling
Artificial neural networks (ANNs), which are parallel computational models, comprising of interconnected adaptive processing units (neurons) have the capability to predict accurately the dispersive behavior of vehicular pollutants under complex environmental conditions. This book aims at describing step-by-step procedure for formulation and development of ANN based VP models considering meteorological and traffic parameters. The model predictions are compared with existing line source deterministic/statistical based models to establish the efficacy of the ANN technique in explaining frequent dispersion complexities in urban areas. The book is very useful for hardcore professionals and researchers working in problems associated with urban air pollution management and control.
Artificial neural networks for the Modelling and Fault Diagnosis of Technical Processes
In this book, a wide spectrum of fault diagnosis techniques have been developed. A major category of fault diagnosis techniques is the model based one, where an analytical model of the plant to be monitored is assumed to be available.
Advances in statistical methods for the health sciences : Applications to cancer and AIDS studies, genome sequence analysis, and survival analysis
This volume, an outgrowth of an "International Conference on Statistical Methods in Health Sciences," covers a wide range of topics pertaining to new statistical methods and novel applications in the health sciences.
Advances in Cardiac Signal Processing
Deals with the acquisition and extraction of the various morphological features of the electrocardiogram signals.In the first chapters the book first presents data fusion and different data mining techniques that have been used for the cardiac state diagnosis. The second part deals with heart rate variability (HRV), a non-invasive measurement of cardiovascular autonomic regulation. Next, visualization of ECG data is discussed, an important part of the display in life threatening state. Here, the handling of data is discussed which were acquired during several hours. In the following chapters the book discusses aortic pressure measurement which is of significant clinical importance. It presents non-invasive methods for analysis of the aortic pressure waveform, indicating how it can be employed to determine cardiac contractility, arterial compliance, and peripheral resistance. In addition, the book demonstrates methods to extract diagnostic parameters for assessing cardiac function. Further the measurement strategies for contractile effort of the left ventricle are presented. Finally, the book concludes about the future of cardiac signal processing leading to next generation research topics which directly impacts the cardiac health care.
