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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 Nature Proceedings excluding Computer Science

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;

The vast potential of AI technology remains untapped in areas like audio, video, and complex data analysis. In fact, many of today's professionals find it challenging to apply AI innovations across these diverse domains due to a lack of guidance and practical implementations.This comprehensive guide, tailored especially for intermediate to advanced ML engineers, data scientists, and researchers, fills the gap. Author Nicole Koenigstein guides readers through the versatile applications of transformer models, not only deepening theoretical understanding but also emphasizing actionable strategies for real-world applications. The book equips you with a grand unified theory for transformers--foundational insights that keep you on the cutting edge regardless of state-of-the-art model evolutions. You'll discover how to apply: <ul><li>Transformers in nontext domains like image, video, and music generation</li> <li>Reasoning models, coding agents, and multi-agent architectures</li> <li>Training-time and test-time optimization strategies</li> <li>Production deployment, runtime engineering, and hardware efficiency</li></ul>

Applications Beyond Nlp

Transformers: The Definitive Guide

India Studies in Business and Economics

This book presents a comprehensive study that conducts a data-driven performance benchmarking exercise for regional school education systems in India, with a clear focus on highlighting disparities and challenges within these systems, as well as providing targeted policy recommendations for potential improvements. The book conducts an in-depth examination of the educational environment, current status, policies, and interventions across schools. It explores why, despite various national initiatives aimed at improving access to and quality of school education, significant gaps persist across Indian states, particularly in the two most populous states, Uttar Pradesh and Bihar, which face a range of economic challenges.The book evaluates rankings and efficiency variations in school education development and develops a novel nonparametric efficiency-effectiveness framework to analyze the performance of the school education system. In addition to employing a state-of-the-art meta-BoD-RDM framework for constructing a school education development index, the empirical analysis employs a two-stage network Data Envelopment Analysis (NDEA) model using survey-based data from schools in India to compute school-wise efficiency and effectiveness scores. These scores are then integrated with a machine learning-based Random Forest algorithm to identify the key factors behind the observed inter-school variations in school efficiency and school effectiveness. The book also discusses progress in school education development during the COVID-19 pandemic. It identifies several key areas that require policy intervention and urgent attention from educationists and governments. This data-enabled benchmarking and analytical study not only contributes to understanding the current landscape but also supports the formulation of future educational policies aimed at effectively addressing the evolving needs of the school education system in India.

Data-Driven Benchmarking of School Education Systems in India

Integrated Mega-Models and Systems Science

This open access book offers a systematic introduction to operational thinking as a distinctive Chinese cognitive framework for understanding and governing complex systems. Drawing on five millennia of Chinese civilization, it traces how strategic wisdom evolved from Yu the Great’s flood control, Dujiangyan and the Three Gorges Project to today’s South–North Water Transfer, “Dual Carbon” strategy, smart cities and AI-enabled decision-making. This book provides an original “trinity” framework of systems thinking, coordinated planning thinking, and balancing thinking, explaining how structural cognition, process integration, and conflict coordination work together to solve multi-objective, multi-constraint problems. Through rich historical and contemporary cases—from classical texts such as The Art of War, Qimin Yaoshu and Discourses on Salt and Iron to modern mega-projects, digital platforms and resilient supply chains—it shows how Chinese governance wisdom has been continuously transformed into modern methodologies for optimization, risk control and strategic planning. Methodologically, this book develops a complete toolkit of “structural cognition–process integration–conflict coordination,” supported by cognitive models, case analysis, and digital-intelligent teaching tools. It demonstrates how operational thinking can guide national governance modernization, regional coordinated development, industrial upgrading, and personal time- and life-management (for example, via the “Four Quadrants Rule”). Special chapters translate Party innovation theories and Marxist views of practice into operable methods, realizing the integration of “Great Ideology and Politics + digital intelligence + practical training” in general education and professional curricula. By integrating traditional culture, systems science, management science and AI, this book builds an independent knowledge system of operational thinking with Chinese characteristics, contemporary value and global relevance. It helps readers move from “cognitive mapping” to “practical navigation,” and from intuitive judgment to data- and model-driven decision-making.

The Mind of Operations

Turn raw data into real traction—and real revenue.
This hands-on guide is built for the realities of startup life—where time is short, resources are limited, and every decision counts. Whether you're just getting started or scaling fast, this book is your essential toolkit for building a data-driven startup ready for growth and innovation—and for turning insight into impact, and data into dollars.
Drawing from years of experience as the first data hire at multiple high-growth startups across the US and Europe, author Piotr Sidoruk shares practical insights into building data systems that are both affordable and scalable, agile and reliable. You’ll explore how to create a strong data foundation free from the rigidity of traditional corporate models, and how to use analytics and AI to inform product development, customer insights, and investor communication—ultimately turning data into measurable business outcomes and revenue growth.
What sets this book apart is its unified approach. Instead of siloing data engineering, analytics, and machine learning, it brings them together into one actionable playbook tailored for startup environments. With real-world examples from companies such as Spotify, Airbnb, and Stripe, you’ll see how data can become a strategic asset from day one. From your first data hire to your next funding round, this book is your manual for building a startup where data drives every decision.
What You Will Learn
<ul>
<li>Develop a data-driven strategy aligned with startup goals, using rapid, iterative methods to support fast growth</li>
<li>Set up and manage data warehouses, build pipelines, and integrate diverse data sources seamlessly</li>
<li>Design and scale flexible, cost-effective data infrastructure tailored to startup constraints</li>
<li>Use data to drive decision making that directly impacts customer retention and revenue</li>
<li>Build dashboards and reports in BI tools to track the metrics that matter most for customer engagement, retention, and growth</li>
<li>Master data storytelling to influence stakeholders and secure investor funding</li>
<li>Demystify and apply advanced techniques in AI and machine learning to enhance innovation and competitiveness</li>
<li>Calculate and monitor essential startup metrics, such as Customer Acquisition Cost (CAC), Lifetime Value (LTV), retention, and growth KPIs</li>
<li>Combine quantitative data with qualitative insights to deeply understand user behavior and optimize product experience</li>
</ul>
&nbsp;

Getting started with Data Analytics and AI in Startups

From Data to Dollars

This book introduces the subject of artificial intelligence (AI), its structures, uses, application to the artificial neural network, technology applications, and the advantages and disadvantages of its use, including its potential weaknesses and drawbacks. AI, in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. Such machines may be called AIs. AI technology is widely used throughout industry, government, and science.Some high-profile applications include advanced web search engines (e.g., Google Search); recommendation systems (used by platforms such as YouTube, Amazon, and Netflix); interacting via human speech (e.g., Google Assistant, Siri, and Alexa); autonomous vehicles (e.g., Waymo); generative and creative tools (e.g., ChatGPT, Apple Intelligence, and AI art); and superhuman play and analysis in strategy games (e.g., chess and Go). However, many AI applications are not perceived as AI. It would appear that much of specific AI has filtered into general applications, often without being called AI because once something becomes useful and common enough, it is no longer labelled as AI.The growing use of AI in the 21st century is influencing a societal and economic shift towards increased automation, data-driven decision-making, and the integration of AI systems into various economic and commercial sectors and areas of everyday life. Its impact is felt across job markets, healthcare, government, industry, education, propaganda, and disinformation and deception, whether deliberate or otherwise. This raises questions about the long-term effects, ethical implications, and risks of AI, prompting discussions about regulatory policies to ensure the safety and benefits of the technology.

Advantages, Disadvantages, Benefits, and Threats

Artificial Intelligence

ICISAS 2025 is the signature research conference of Curtin University Dubai. It brings together leading experts to discuss how innovation, sustainability, and applied sciences can shape the future of business and society.

Integrating Sustainable Technologies with Data-Driven Solutions

The Future of Business and Society

What AI Can’t Do Without You

The Human Advantage

Toward a deeper understanding of minds, whether natural or artificial, and of how they are studied and built across AI, AGI, and cognitive science. Minds are functional entities that enable intelligent behavior. In On Minds, Paul Rosenbloom offers reflections and perspectives on the study of minds arising from a multi-decade career focused largely on cognitive architectures—hypotheses concerning the fixed structures and processes that yield a mind, whether natural or artificial. Cognitive architectures must be integrative in accommodating the full range of capabilities required of a mind. They must also be general, in applying across many, if not all, problems and domains. This breadth of investigation, across a sequence of three such architectures—XAPS, Soar, and Sigma—and over many years, has inspired a set of reflections on both cognitive architectures and three core disciplines within which such work occurs—artificial intelligence (AI), artificial general intelligence (AGI), and cognitive science. The book’s reflections are of three types. The first explores four concepts critical to understanding minds—architectures, symbols, intelligence, and theories—to provide new perspectives and insights on them. The second rethinks the structure of the space of technologies that underlies work on minds, yielding multidimensional maps that clarify their similarities and differences plus detailed insights into the tradeoffs that exist across both individual technologies and integrated systems—whether symbolic, neural, or hybrid—built from them. The third traces the author’s decades of work on architectures—including cognitive architectures, the Common Model of Cognition, and the nature and structure of computing as a great scientific domain—interwoven with personal reflections and anecdotes.

Reflections of a Cognitive Architect

On Minds

Platform Supply Chains in the AI Era

Risk Management

Platform Supply Chains 5

Machine learning has become an essential component of modern business analytics -- driving insights, optimizing operations, and supporting strategic, data-informed decisions. This textbook offers a hands-on and accessible introduction to applying machine learning in business settings using R, with a strong emphasis on the tidy ecosystem. Key packages such as tidyverse, tidymodels, and tidytext provide a cohesive and efficient framework for data analysis, modeling, and communication.Balancing theoretical foundations with applied practice, the book introduces mathematical ideas in a clear, intuitive manner, always anchored in realistic business scenarios. Suitable for the classroom and independent research, readers will gain the confidence and tools to apply the tidy ecosystem effectively, from data preparation to modeling to interpretation, enabling reproducible, interpretable, and impactful analytics in dynamic business environments.Bowei Chen
 is a Professor of Business Analytics and Artificial Intelligence at the Adam Smith Business School, University of Glasgow, UK, and the Founding Programme Director of the MSc in Business Analytics programme. He received his PhD in Computer Science from the University College London. His research interests include business analytics, artificial intelligence, machine learning, and data science applications in management, finance, and the social sciences. He has published in leading academic conferences, journals, and books in these fields, and has undertaken international research collaborations and academic appointments across different institutions.
 Tianyuan Huang
 is a Lecturer of Data Science and Big Data Technologies at the School of Data Science, Zhejiang University of Finance &amp; Economics, China. He earned his PhD from Fudan University and has authored three R programming books in China. His current research mainly focuses on quantitative science studies, and he is also dedicated to leveraging data science to rapidly build domain expertise and advance scientific knowledge discovery. In addition to his publications, he supports the R community by maintaining several CRAN packages, such as akc for automated knowledge classification and tidyfst for efficient tidy data manipulation.

A Tidy Approach

Business Analytics with R

How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

The Deep Learning Revolution

Applied and Computational Mathematics

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