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This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selecta

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Springer Proceedings in Mathematics & Statistics

Dieky Adzkiya

Kistosil Fahim

Applied and Computational Mathematics

ICoMPAC 2023, Sukolilo, Indonesia, September 30

&lt;p&gt;Part 1: Applied Mathematics (Control Systems, Mathematical Simulation, and Modeling).- H. Margaretha, N. Satyahadi and F. V. Ferdinand, Simulation of Investment Behaviors using Risk-Influenced Utility Models of Health and Wealth.- R. K. Dewi, I. Magdalena and I. Marcela, Wave Attenuation by a Combination of Mangroves and Reefball.- R. M. Valerio and I. Magdalena, Resonance Detection in a Semi-Closed Basin with Constant Height and Triangular Width.- R. Rahmawati, L. Harsyiah and Z. W. Baskara, Comparison of Ridge Regression and Principal Component Regression in Overcoming Multicolinierity of Factors Affecting Poverty in Indonesia.- Ahmadin, J. Naiborhu, Fatmawati and Windarto, Identification on Class Nonlinear Control System with Relative Degree Two.- A. F. Khariri, P. K. Intan, Moh. Hafiyusholeh, A. H. Asyhar and A. Fanani, Implementation of K-Means Particle Swarm Optimisation for Clustering Football Players in The Top Five European Football Leagues.- M. Widianto, C. Imron and B. Widodo, A short review of numerical modelling for photocatalytic degradation in dye systems.- S. Subchan, H. Purnawan, T. Asfihani and L. R. Amalia, Reference Tracking of Quadrotor using Modified Nonlinear Model Predictive Control based on Nonlinear Disturbance Observer.- D. Savitri, M. Jakfar and L. Putri, Numerical Simulation and Dynamics of Sedimentation Rate with Seagrass Growth Considering Dredging Factors.- H. Nuha, N. Siswanto and E. Widodo, A Mixed Integer Nonlinear Programming for Allocation Polyethylene Terephthalate (PET) Waste Management in Reverse Logistic Network.- A. Zaki Al Muntazhar, D. Ratna Sulistyaningrum and Subiono, Quantum K-Nearest Neighbors For Object Recognition.- T. Asfihani, F. N. Sihan Novita Lutfiani, A. Maulana Syafi'I, Subchan Subchan and A. Hasan, Adaptive Kalman Filter for Automated Actuator Fault Diagnosis in Unmanned Surface Vehicle.- E. R M Putri, A. Wahyu Widianto, A. Hakam, V. Ryan Tjahjono and H. Susanto, A moving average genetic algorithm (MA-GA)for estimating the COVID-19 dynamic based on a stochastic SIRD model.- A. Hakam, E. R M Putri and L. Mardiyanto, A general solution of Black-Scholes equations on some Rainbow options.- F. Ardhiansyah, R. W. Prastianto, M. Murdjito, K. Suastika and S. Nugroho, Dynamic Response of Floating Crane During Lifting Operation: A Parametric Study.- Part 2: Computational Mathematics (Formal Methods and Data Science): M. T. Ammar, M. Arzaki and G. S. Wulandari, Efficient SAT-Based Approach for Solving Juosan Puzzles.- J. E. Sakti, M. Arzaki and G. S. Wulandari, Modeling Path Puzzles as SAT Problems and How to Solve Them.- H. Tanabe and M. Kimura, A trustworthiness scoring model for reviews using a graph structure.- M. Iqbal, A. Afandy and N. Hidayat, Non-Linear Reward Deep Q Networks for Smooth Action in A Car Game.- Arthur de Clairval, L. A. Erwin Schuler, M. F. Rellier, M. Isa Irawan, I. Mukhlash and M. Iqbal, College Student Activity Recognition from Smartwatch Dataset.- A. Taryana, D. Adzkiya, M. Syifa'ul Mufid, I. Mukhlash and A. Abate, LTL Model Checking for Verification of Electronic Medical Record (EMR) Design.- G. I. Permata Sari, Hsing-Kuo Pao, R. C. Hario Pribadi and M. Iqbal, Multi-Feature Subgraph Fusion with Text Knowledge on Citation Link Prediction.- I Komang Pande Prajadhita Wibawa Putra, I. Irhamah, Nur Iriawan and K. Fithriasari, Predicting Poverty Percentage Based on Satellite Imagery and Point of Interest Using Support Vector Regression and Random Forest Regression (Case Study of Central Java Province).- M. Hutapea, G. Wulandari and M. Arzaki, Preliminary Analysis of Isabelle Proof Assistant in Graph Programming Verification with GP2.- Part 3: Pure Mathematics (Graph Theory and Analysis): T. Irvan Haryadi, R. Rinurwati, D. Winda Setyawati, S. Soleha, I. Herisman, K. Baihaqi and S. Sadjidon, Nonlocal Edge Metric Dimension on Corona Multiproduct Graphs.- N. Ain Supu, I. M. Alamsyah and E. Suwastika, The Vertex Degree of Relative g-noncommuting Graph of the Dihedral Groups.- S. Wibowo and C. R. Indrati, On Conformable Fractional Riesz Bounded p-Variation of Order- α.- H. K. Ningrum, E. Y. Asmarani, I Gede Adhitya Wisnu Wardhana, S. Salwa and Z. Y. Awanis, The Wiener Index, The Harmonic Index, and The Graph Representation of the Prime Ideal Graph for the Integers Modulo Rings with Prime Power Order.- R. Rinurwati, M. Chalid Fansury, A. Hevita G.K. Saefulloh and T. I. Haryadi, Multiplicative Sum Nano Zagreb Index of Various 2-Power Corona Graphs.&lt;br&gt;&lt;/p&gt;

This companion volume to Artificial Intelligence: A Guide for Everyone offers a comprehensive exploration of AI analytics, catering to individuals of all backgrounds and expertise levels. It seeks to demystify AI analytics by exploring core concepts, explaining various stages (Descriptive, Diagnostic, Predictive, and Prescriptive), and highlighting their limitations. Through domain-specific applications, it illustrates how AI is utilized across industries for innovation and efficiency, providing dedicated subsections for healthcare, finance, customer support, and more.Artificial Intelligence: Analytics, Platforms, and Risks thoroughly examines the AI infrastructure and ecosystem, offering insights into frameworks, platforms, tools, key players, and the critical hardware and software components supporting AI applications. It also addresses the multifaceted risks and challenges of AI, such as bad data, model selection bias, job displacement, weaponization, and ethical dilemmas, along with strategies to mitigate these risks.&nbsp;Additionally, it discusses the hurdles in AI adoption, including roadblocks, myths, planning complexities, resource requirements, and data and model challenges. Looking ahead, the book explores the future of AI, highlighting catalysts for technological progress, the transformative impact across industries, and emerging trends shaping the field. With the aim of empowering readers to navigate the complexities of AI, harness its potential, and contribute to its responsible and ethical advancement, this book serves as a comprehensive guide to AI technologies.

This companion volume to Artificial Intelligence for Everyone offers a comprehensive exploration of AI analytics, catering to individuals of all backgrounds and expertise levels. It seeks to demystify AI analytics by exploring core concepts, explaining various stages (descriptive, predictive, prescriptive), and highlighting their limitations. Through domain-specific applications, it illustrates how AI is utilized across industries for innovation and efficiency, providing dedicated subsections for healthcare, finance, customer support, and more.Artificial Intelligence: Analytics, Platforms, and Risks thoroughly examines the AI infrastructure and ecosystem, offering insights into frameworks, platforms, tools, key players, and the critical hardware and software components supporting AI applications. It also addresses the multifaceted risks and challenges of AI, such as bad data, model selection bias, job displacement, weaponization, and ethical dilemmas, along with strategies to mitigate these risks. Additionally, it discusses the hurdles in AI adoption, including roadblocks, myths, planning complexities, resource requirements, and data and model challenges. Looking ahead, the book explores the future of AI, highlighting catalysts for technological progress, the transformative impact across industries, and emerging trends shaping the field. With the aim of empowering readers to navigate the complexities of AI, harness its potential, and contribute to its responsible and ethical advancement, this book serves as a comprehensive guide to AI technologies.

Artificial Intelligence: Analytics, Platforms, and Risks

An Introduction to String Algorithms

A comprehensive, up-to-date introduction to active inference and the free energy principle for an engineering-focused audience. Active inference, which uses machine learning to model brain function and behavior, emerged from decades of cross-disciplinary research in computational neuroscience, resulting in a vast literature but no unifying treatment. Filling this gap, Sanjeev Namjoshi provides comprehensive coverage of the foundational material needed to understand and navigate this fast-moving field from first principles. Using a simple, conversational style free of proofs, lemmas, and theorems, Namjoshi brings together theory and technical material in one self-contained text. The book begins with an explanation of the general statistical framework used in active inference models that describes the relationship between artificial agents and their environments. It then introduces fundamental concepts in machine learning and statistics and connects them to the active inference perspective. Featuring worked examples, simulations, and detailed walkthroughs of concepts, this user-friendly text aims to expand the readership of active inference to an engineering-focused audience.&nbsp; <ul><li>Provides a one-stop-shop for understanding the foundations and applications of active inference and the free energy principle </li><li>Makes a complex, interdisciplinary subject accessible to students and professionals beyond the neurosciences</li><li>Covers discrete and continuous state-space formulations of active inference as well as state-of-the-art extensions to base active inference methods</li><li>Features extensive appendices and supplemental resources</li></ul>

Principles, Algorithms, and Applications of the Free Energy Principle for Engineers

Fundamentals of Active Inference

Expert reference on building surrogate models, optimization using them, prediction uncertainty associate with them, and their potential failure, with practical implementation in MATLAB Surrogate Modeling and Optimization explains the meaning of different surrogate models and provides an in-depth understanding of such surrogates, emphasizing how much uncertainty is associated with them, and when and how a surrogate model can fail in approximating complex functions and helping readers understand theory through practical implementation in MATLAB. This book enables readers to obtain an accurate approximate function using as few samples as possible, thereby allowing them to replace expensive computer simulations and experiments during design optimization, sensitivity analysis, and/or uncertainty quantification. The book is organized into three parts. Part I introduces the basics of surrogate modeling. Part II reviews various theories and algorithms of design optimization. Part III presents advanced topics in surrogate modeling, including the Kriging surrogate, neural network models, multi-fidelity surrogates, and efficient global optimization using Kriging surrogates. The book is divided into 10 chapters. Each chapter contains about 10 examples and 20 exercise problems. Lecture slides and a solution manual for exercise problems are available for instructors on a companion website. Sample topics discussed in Surrogate Modeling and Optimization include: <ul> <li>Various designs of experiments, such as those developed for linear and quadratic polynomial response surfaces (PRS) in a boxlike design space</li> <li>Criteria for constrained and unconstrained optimization and the most important optimization theories</li> <li>Various numerical algorithms for gradient-based optimization</li> <li>Gradient-free optimization algorithms, often referred to as global search algorithms, which do not require gradient or Hessian information</li> <li>Detailed explanations and implementation on Kriging surrogate, often referred to as Gaussian Process, especially when samples include noise</li> <li>The combination of a small number of high-fidelity samples with many low-fidelity samples to improve prediction accuracy</li> <li>Neural network models, focusing on training uncertainty and its effect on prediction uncertainty</li> <li>Efficient global optimization using either polynomial response surfaces or Kriging surrofates</li> </ul> Surrogate Modeling and Optimization is an essential learning companion for senior-level undergraduate and graduate students in all engineering disciplines, including mechanical, aerospace, civil, biomedical, and electrical engineering. The book is also valuable for industrial practitioners who apply the surrogate model to solve their optimization problems.

Theories, Applications, and Limitations

Surrogate Modeling and Optimization

This unique compendium develops a general approach to building models of economic and financial processes, with a focus on statistical learning techniques that scale to large data sets. It introduces the key elements of a parametric statistical model: likelihood, prior, and posterior, and show how to use them to make predictions.The book covers classical techniques such as multiple regression and the Kalman filter in a clear, accessible style that has been popular with students, but also includes detailed treatments of state-of-the-art models, highlighting tree-based methods, support vector machines and kernel methods, deep learning, and reinforcement learning. Theories are supplemented by real-world examples.This reference text is useful for undergraduate, graduate and even PhD students in quantitative finance, and also to practitioners who are facing the reality that data science and machine learning are disrupting the industry.

Statistical Machine Learning With Applications In Finance

Series On Intelligence Science

This popular science compendium provides an approximate, holistic but first ever solution to the four Holy Grail questions: How does a brain work? How does the brain learn? How does its consciousness arise? How does learning require consciousness?The volume explains how human brains require and learn consciousness and why the new AI will overcome the current lack of conscious learning algorithms in AI. For human societies, it suggests how governments can make their taxpayers safer, more prosper and happier. For future AI, it calls for a thorough investigation in scientific infrastructures like government and private funding agencies, publication venues, professional societies and administrators that evaluate research. Why did a conscious learning algorithm not have a healthy environment to study? Why could rampant misconducts of data deletion in deep learning be allowed to grossly exaggerate AI performances for so long? The author's real-life accounts reveal deep reasons.The useful reference text benefits laymen in all walks of life, as well as professionals, researchers, academics and students in any areas.

Conscious Learning: Humans And Machines

Advanced Intelligence Methods for Data Science and Optimization covers the latest research trends and applications of AI topics such as deep learning, reinforcement learning, evolutionary algorithms, Bayesian optimization, and swarm intelligence. The book is a comprehensive guide that provides readers with theoretical concepts and case studies for applying advanced intelligence methods to real-world problems. Authored by a team of renowned experts in the field, the book offers a holistic approach to understanding and applying intelligence methods across various domains. It explores the fundamental concepts of data science and optimization, providing a strong foundation for readers to build upon, and will be a welcomed resource for AI researchers, data scientists, engineers, and developers on key topics such as evolutionary optimization techniques, reinforcement learning, Natural Language Processing, Bayesian optimization, advanced analytics for large-scale data, fuzzy logic, quantum computing, graph theory, convex optimization, differential evolution, and more.

Applied and Computational Mathematics

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