Download or read book Parallel Search Algorithms for Robot Motion Planning written by Daniel Joseph Challou and published by . This book was released on 1995 with total page 618 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Molecular Simulation on Cementitious Materials From Computational Chemistry Method to Application written by Dongshuai Hou and published by Frontiers Media SA. This book was released on 2022-02-09 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dissertation Abstracts International written by and published by . This book was released on 2006 with total page 780 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Euro Par 2019 Parallel Processing Workshops written by Ulrich Schwardmann and published by Springer Nature. This book was released on 2020-05-29 with total page 765 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the workshops held at 25th International Conference on Parallel and Distributed Computing, Euro-Par 2019, which took place in Göttingen, Germany, in August 2019. The 53 full papers and 10 poster papers presented in this volume were carefully reviewed and selected from 77 submissions. Euro-Par is an annual, international conference in Europe, covering all aspects of parallel and distributed processing. These range from theory to practice, from small to the largest parallel and distributed systems and infrastructures, from fundamental computational problems to full-edged applications, from architecture, compiler, language and interface design and implementation to tools, support infrastructures, and application performance aspects. Chapter "In Situ Visualization of Performance-Related Data in Parallel CFD Applications" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Download or read book Parallel Problem Solving from Nature PPSN XV written by Anne Auger and published by Springer. This book was released on 2018-08-30 with total page 515 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 11101 and 11102 constitutes the refereed proceedings of the 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018, held in Coimbra, Portugal, in September 2018. The 79 revised full papers were carefully reviewed and selected from 205 submissions. The papers cover a wide range of topics in natural computing including evolutionary computation, artificial neural networks, artificial life, swarm intelligence, artificial immune systems, self-organizing systems, emergent behavior, molecular computing, evolutionary robotics, evolvable hardware, parallel implementations and applications to real-world problems. The papers are organized in the following topical sections: numerical optimization; combinatorial optimization; genetic programming; multi-objective optimization; parallel and distributed frameworks; runtime analysis and approximation results; fitness landscape modeling and analysis; algorithm configuration, selection, and benchmarking; machine learning and evolutionary algorithms; and applications. Also included are the descriptions of 23 tutorials and 6 workshops which took place in the framework of PPSN XV.

Download or read book AIRLINE PASSENGER SATISFACTION Analysis and Prediction Using Machine Learning and Deep Learning with Python written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-08-08 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the project "Airline Passenger Satisfaction Analysis and Prediction Using Machine Learning and Deep Learning with Python," the aim was to analyze and predict passenger satisfaction in the airline industry. The project began with an extensive data exploration phase, wherein the dataset containing various features related to passenger experiences was thoroughly examined. The dataset was then preprocessed, ensuring data cleanliness and preparing it for further analysis. One of the initial steps involved understanding the distribution of categorized features within the dataset. By visualizing the distribution of these features, insights were gained into the prevalence of different categories, providing a preliminary understanding of passenger preferences and experiences. For the prediction aspect, machine learning models were employed, and a Grid Search approach was implemented to fine-tune hyperparameters and optimize model performance. This process allowed the identification of the best-performing model configuration, enhancing the accuracy of passenger satisfaction predictions. The models used are Logistic Regression, Support Vector Machines, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting. Going beyond traditional machine learning, a Deep Learning approach was introduced using an Artificial Neural Network (ANN). This model, designed to capture intricate patterns and relationships within the data, showcased the potential of deep learning for improving predictive accuracy. The evaluation of both machine learning and deep learning models was centered around key metrics. The accuracy score was a primary indicator of model performance, reflecting the ratio of correctly predicted passenger satisfaction outcomes. Additionally, the Classification Report provided a comprehensive overview of precision, recall, and F1-score for each category, shedding light on the model's ability to classify passenger satisfaction levels accurately. Visualizing the results played a pivotal role in the project. The plotted Training and Validation Accuracy and Loss graphs offered insights into the convergence and generalization capabilities of the models. These visualizations helped in understanding potential overfitting or underfitting issues and guided the fine-tuning process. To assess the models' predictive performance, a Confusion Matrix was constructed. This matrix presented a clear breakdown of correct and incorrect predictions, facilitating an understanding of where the model excelled and where it struggled. Furthermore, scatter plots were utilized to visually compare the predicted values against the actual true values, offering a tangible representation of the models' effectiveness. Throughout the project, rigorous data preprocessing and feature engineering were integral to improving model accuracy. Features were appropriately scaled, and categorical variables were transformed using techniques like one-hot encoding, enabling models to efficiently learn from the data. The project also focused on the interpretability of the models, enabling stakeholders to comprehend the factors influencing passenger satisfaction predictions. This interpretability was essential for making informed business decisions based on the model insights. In conclusion, the project showcased a comprehensive approach to analyzing and predicting airline passenger satisfaction. Through meticulous data exploration, feature distribution analysis, machine learning model selection, hyperparameter tuning, and deep learning implementation, the project provided valuable insights for the airline industry. By utilizing a combination of machine learning and deep learning techniques, the project demonstrated a holistic approach to understanding and enhancing passenger experiences and satisfaction levels.

Download or read book Computational Intelligence and Healthcare Informatics written by Om Prakash Jena and published by John Wiley & Sons. This book was released on 2021-09-08 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: COMPUTATIONAL INTELLIGENCE and HEALTHCARE INFORMATICS The book provides the state-of-the-art innovation, research, design, and implements methodological and algorithmic solutions to data processing problems, designing and analysing evolving trends in health informatics, intelligent disease prediction, and computer-aided diagnosis. Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments. This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis. Audience The book is of interest to artificial intelligence and biomedical scientists, researchers, engineers and students in various settings such as pharmaceutical & biotechnology companies, virtual assistants developing companies, medical imaging & diagnostics centers, wearable device designers, healthcare assistance robot manufacturers, precision medicine testers, hospital management, and researchers working in healthcare system.

Download or read book Grammar Based Feature Generation for Time Series Prediction written by Anthony Mihirana De Silva and published by Springer. This book was released on 2015-02-14 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method can be applied to a wide range of machine learning architectures and applications to represent complex feature dependencies explicitly when machine learning cannot achieve this by itself. Industrial applications can use the proposed technique to improve their predictions.

Download or read book ECAI 2023 written by K. Gal and published by IOS Press. This book was released on 2023-10-18 with total page 3328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrate innovative applications and uses of advanced AI technology. ECAI 2023 received 1896 submissions – a record number – of which 1691 were retained for review, ultimately resulting in an acceptance rate of 23%. The 390 papers included here, cover topics including machine learning, natural language processing, multi agent systems, and vision and knowledge representation and reasoning. PAIS 2023 received 17 submissions, of which 10 were accepted after a rigorous review process. Those 10 papers cover topics ranging from fostering better working environments, behavior modeling and citizen science to large language models and neuro-symbolic applications, and are also included here. Presenting a comprehensive overview of current research and developments in AI, the book will be of interest to all those working in the field.

Download or read book DATA SCIENCE WORKSHOP Heart Failure Analysis and Prediction Using Scikit Learn Keras and TensorFlow with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-08-18 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this "Heart Failure Analysis and Prediction" data science workshop, we embarked on a comprehensive journey through the intricacies of cardiovascular health assessment using machine learning and deep learning techniques. Our journey began with an in-depth exploration of the dataset, where we meticulously studied its characteristics, dimensions, and underlying patterns. This initial step laid the foundation for our subsequent analyses. We delved into a detailed examination of the distribution of categorized features, meticulously dissecting variables such as age, sex, serum sodium levels, diabetes status, high blood pressure, smoking habits, and anemia. This critical insight enabled us to comprehend how these features relate to each other and potentially impact the occurrence of heart failure, providing valuable insights for subsequent modeling. Subsequently, we engaged in the heart of the project: predicting heart failure. Employing machine learning models, we harnessed the power of grid search to optimize model parameters, meticulously fine-tuning algorithms to achieve the best predictive performance. Through an array of models including Logistic Regression, KNeighbors Classifier, DecisionTrees Classifier, Random Forest Classifier, Gradient Boosting Classifier, XGB Classifier, LGBM Classifier, and MLP Classifier, we harnessed metrics like accuracy, precision, recall, and F1-score to meticulously evaluate each model's efficacy. Venturing further into the realm of deep learning, we embarked on an exploration of neural networks, striving to capture intricate patterns in the data. Our arsenal included diverse architectures such as Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, Self Organizing Maps (SOMs), Recurrent Neural Networks (RNN), Deep Belief Networks (DBN), and Autoencoders. These architectures enabled us to unravel complex relationships within the data, yielding nuanced insights into the dynamics of heart failure prediction. Our approach to evaluating model performance was rigorous and thorough. By scrutinizing metrics such as accuracy, recall, precision, and F1-score, we gained a comprehensive understanding of the models' strengths and limitations. These metrics enabled us to make informed decisions about model selection and refinement, ensuring that our predictions were as accurate and reliable as possible. The evaluation phase emerges as a pivotal aspect, accentuated by an array of comprehensive metrics. Performance assessment encompasses metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Cross-validation and learning curves are strategically employed to mitigate overfitting and ensure model generalization. Furthermore, visual aids such as ROC curves and confusion matrices provide a lucid depiction of the models' interplay between sensitivity and specificity. Complementing our advanced analytical endeavors, we also embarked on the creation of a Python GUI using PyQt. This intuitive graphical interface provided an accessible platform for users to interact with the developed models and gain meaningful insights into heart health. The GUI streamlined the prediction process, making it user-friendly and facilitating the application of our intricate models to real-world scenarios. In conclusion, the "Heart Failure Analysis and Prediction" data science workshop was a journey through the realms of data exploration, feature distribution analysis, and the application of cutting-edge machine learning and deep learning techniques. By meticulously evaluating model performance, harnessing the capabilities of neural networks, and culminating in the creation of a user-friendly Python GUI, we armed participants with a comprehensive toolkit to analyze and predict heart failure with precision and innovation.

Download or read book Soft Computing Approach for Mathematical Modeling of Engineering Problems written by Ali Ahmadian and published by CRC Press. This book was released on 2021-09-02 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes different mathematical modeling and soft computing techniques used to solve practical engineering problems. It gives an overview of the current state of soft computing techniques and describes the advantages and disadvantages of soft computing compared to traditional hard computing techniques. Through examples and case studies, the editors demonstrate and describe how problems with inherent uncertainty can be addressed and eventually solved through the aid of numerical models and methods. The chapters address several applications and examples in bioengineering science, drug delivery, solving inventory issues, Industry 4.0, augmented reality and weather forecasting. Other examples include solving fuzzy-shortest-path problems by introducing a new distance and ranking functions. Because, in practice, problems arise with uncertain data and most of them cannot be solved exactly and easily, the main objective is to develop models that deliver solutions with the aid of numerical methods. This is the reason behind investigating soft numerical computing in dynamic systems. Having this in mind, the authors and editors have considered error of approximation and have discussed several common types of errors and their propagations. Moreover, they have explained the numerical methods, along with convergence and consistence properties and characteristics, as the main objectives behind this book involve considering, discussing and proving related theorems within the setting of soft computing. This book examines dynamic models, and how time is fundamental to the structure of the model and data as well as the understanding of how a process unfolds • Discusses mathematical modeling with soft computing and the implementations of uncertain mathematical models • Examines how uncertain dynamic systems models include uncertain state, uncertain state space and uncertain state’s transition functions • Assists readers to become familiar with many soft numerical methods to simulate the solution function’s behavior This book is intended for system specialists who are interested in dynamic systems that operate at different time scales. The book can be used by engineering students, researchers and professionals in control and finite element fields as well as all engineering, applied mathematics, economics and computer science interested in dynamic and uncertain systems. Ali Ahmadian is a Senior Lecturer at the Institute of IR 4.0, The National University of Malaysia. Soheil Salahshour is an associate professor at Bahcesehir University.

Download or read book Deep learning techniques and their applications to the healthy and disordered brain during development through adulthood and beyond written by Amir Shmuel and published by Frontiers Media SA. This book was released on 2023-02-07 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Mastering Azure Machine Learning written by Christoph Korner and published by Packt Publishing Ltd. This book was released on 2022-05-10 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Supercharge and automate your deployments to Azure Machine Learning clusters and Azure Kubernetes Service using Azure Machine Learning services Key Features Implement end-to-end machine learning pipelines on Azure Train deep learning models using Azure compute infrastructure Deploy machine learning models using MLOps Book Description Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project life cycle that ML professionals, data scientists, and engineers can use in their day-to-day workflows. This book covers the end-to-end ML process using Microsoft Azure Machine Learning, including data preparation, performing and logging ML training runs, designing training and deployment pipelines, and managing these pipelines via MLOps. The first section shows you how to set up an Azure Machine Learning workspace; ingest and version datasets; as well as preprocess, label, and enrich these datasets for training. In the next two sections, you'll discover how to enrich and train ML models for embedding, classification, and regression. You'll explore advanced NLP techniques, traditional ML models such as boosted trees, modern deep neural networks, recommendation systems, reinforcement learning, and complex distributed ML training techniques - all using Azure Machine Learning. The last section will teach you how to deploy the trained models as a batch pipeline or real-time scoring service using Docker, Azure Machine Learning clusters, Azure Kubernetes Services, and alternative deployment targets. By the end of this book, you'll be able to combine all the steps you've learned by building an MLOps pipeline. What you will learn Understand the end-to-end ML pipeline Get to grips with the Azure Machine Learning workspace Ingest, analyze, and preprocess datasets for ML using the Azure cloud Train traditional and modern ML techniques efficiently using Azure ML Deploy ML models for batch and real-time scoring Understand model interoperability with ONNX Deploy ML models to FPGAs and Azure IoT Edge Build an automated MLOps pipeline using Azure DevOps Who this book is for This book is for machine learning engineers, data scientists, and machine learning developers who want to use the Microsoft Azure cloud to manage their datasets and machine learning experiments and build an enterprise-grade ML architecture using MLOps. This book will also help anyone interested in machine learning to explore important steps of the ML process and use Azure Machine Learning to support them, along with building powerful ML cloud applications. A basic understanding of Python and knowledge of machine learning are recommended.

Download or read book Computational Methods in Predicting Complex Disease Associated Genes and Environmental Factors written by Yudong Cai and published by Frontiers Media SA. This book was released on 2021-06-11 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Intelligent Systems Concepts Methodologies Tools and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2018-06-04 with total page 2390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ongoing advancements in modern technology have led to significant developments in intelligent systems. With the numerous applications available, it becomes imperative to conduct research and make further progress in this field. Intelligent Systems: Concepts, Methodologies, Tools, and Applications contains a compendium of the latest academic material on the latest breakthroughs and recent progress in intelligent systems. Including innovative studies on information retrieval, artificial intelligence, and software engineering, this multi-volume book is an ideal source for researchers, professionals, academics, upper-level students, and practitioners interested in emerging perspectives in the field of intelligent systems.

Download or read book DATA SCIENCE WORKSHOP Alzheimer s Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-08-21 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the "Data Science Workshop: Alzheimer's Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI," the project aimed to address the critical task of Alzheimer's disease prediction. The journey began with a comprehensive data exploration phase, involving the analysis of a dataset containing various features related to brain scans and demographics of patients. This initial step was crucial in understanding the data's characteristics, identifying missing values, and gaining insights into potential patterns that could aid in diagnosis. Upon understanding the dataset, the categorical features' distributions were meticulously examined. The project expertly employed pie charts, bar plots, and stacked bar plots to visualize the distribution of categorical variables like "Group," "M/F," "MMSE," "CDR," and "age_group." These visualizations facilitated a clear understanding of the demographic and clinical characteristics of the patients, highlighting key factors contributing to Alzheimer's disease. The analysis revealed significant patterns, such as the prevalence of Alzheimer's in different age groups, gender-based distribution, and cognitive performance variations. Moving ahead, the project ventured into the realm of predictive modeling. Employing machine learning techniques, the team embarked on a journey to develop models capable of predicting Alzheimer's disease with high accuracy. The focus was on employing various machine learning algorithms, including K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Gradient Boosting, Light Gradient Boosting, Multi-Layer Perceptron, and Extreme Gradient Boosting. Grid search was applied to tune hyperparameters, optimizing the models' performance. The evaluation process was meticulous, utilizing a range of metrics such as accuracy, precision, recall, F1-score, and confusion matrices. This intricate analysis ensured a comprehensive assessment of each model's ability to predict Alzheimer's cases accurately. The project further delved into deep learning methodologies to enhance predictive capabilities. An arsenal of deep learning architectures, including Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, Feedforward Neural Networks (FNN), and Recurrent Neural Networks (RNN), were employed. These models leveraged the intricate relationships present in the data to make refined predictions. The evaluation extended to ROC curves and AUC scores, providing insights into the models' ability to differentiate between true positive and false positive rates. The project also showcased an innovative Python GUI built using PyQt. This graphical interface provided a user-friendly platform to input data and visualize the predictions. The GUI's interactive nature allowed users to explore model outcomes and predictions while seamlessly navigating through different input options. In conclusion, the "Data Science Workshop: Alzheimer's Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI" was a comprehensive endeavor that involved meticulous data exploration, distribution analysis of categorical features, and extensive model development and evaluation. It skillfully navigated through machine learning and deep learning techniques, deploying a variety of algorithms to predict Alzheimer's disease. The focus on diverse metrics ensured a holistic assessment of the models' performance, while the innovative GUI offered an intuitive platform to engage with predictions interactively. This project stands as a testament to the power of data science in tackling complex healthcare challenges.

Download or read book High Performance Medical Image Processing written by Sanjay Saxena and published by CRC Press. This book was released on 2022-07-07 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: The processing of medical images in a reasonable timeframe and with high definition is very challenging. This volume helps to meet that challenge by presenting a thorough overview of medical imaging modalities, its processing, high-performance computing, and the need to embed parallelism in medical image processing techniques to achieve efficient and fast results. With contributions from researchers from prestigious laboratories and educational institutions, High-Performance Medical Image Processing provides important information on medical image processing techniques, parallel computing techniques, and embedding parallelism in different image processing techniques. A comprehensive review of parallel algorithms in medical image processing problems is a key feature of this book. The volume presents the relevant theoretical frameworks and the latest empirical research findings in the area and provides detailed descriptions about the diverse high-performance techniques. Topics discussed include parallel computing, multicore architectures and their applications in image processing, machine learning applications, conventional and advanced magnetic resonance imaging methods, hyperspectral image processing, algorithms for segmenting 2D slices for 3D viewing, and more. Case studies, such as on the detection of cancer tumors, expound on the information presented. Key features: Provides descriptions of different medical imaging modalities and their applications Discusses the basics and advanced aspects of parallel computing with different multicore architectures Expounds on the need for embedding data and task parallelism in different medical image processing techniques Presents helpful examples and case studies of the discussed methods This book will be valuable for professionals, researchers, and students working in the field of healthcare engineering, medical imaging technology, applications in machine and deep learning, and more. It is also appropriate for courses in computer engineering, biomedical engineering and electrical engineering based on artificial intelligence, parallel computing, high performance computing, and machine learning and its applications in medical imaging.