A collection of papers from multiple authors. In 2019 and 2020 Smarandache [1, 2, 3, 4] generalized the classical Algebraic Structures to NeutroAlgebraic Structures (or NeutroAlgebras) {whose operations and axioms are partially true, partially indeterminate, and partially false} as extensions of Partial Algebra, and to AntiAlgebraic Structures (or AntiAlgebras) {whose operations and axioms are totally false}. The NeutroAlgebras & AntiAlgebras are a new field of research, which is inspired from our real world. In classical algebraic structures, all axioms are 100%, and all operations are 100% well-defined, but in real life, in many cases these restrictions are too harsh, since in our world we have things that only partially verify some laws or some operations. Using the process of NeutroSophication of a classical algebraic structure we produce a NeutroAlgebra, while the process of AntiSophication of a classical algebraic structure produces an AntiAlgebra.

To understand the catastrophic processes of forest fire danger, different deterministic, probabilistic, and empiric models must be used. Simulating various surface and crown forest fires using predictive information technology could lead to the improvement of existing systems and the examination of the ecological and economic effects of forest fires in other countries. Predicting, Monitoring, and Assessing Forest Fire Dangers and Risks provides innovative insights into forestry management and fire statistics. The content within this publication examines climate change, thermal radiation, and remote sensing. It is designed for fire investigators, forestry technicians, emergency managers, fire and rescue specialists, professionals, researchers, meteorologists, computer engineers, academicians, and students invested in topics centered around providing conjugate information on forest fire danger and risk.

Because trainees need to learn about the underlying technologies to use automation safely and efficiently, the development of automated aviation systems training is a growing challenge. Task analysis has been singled out as the basis of the training, but it can be more time-consuming than traditional development techniques. Cases on Modern Computer Systems in Aviation is an essential reference source that covers new information technology use in aviation systems to streamline the cybersecurity, decision-making, planning, and design processes within the aviation industry. Featuring coverage on a broad range of topics such as computer systems in aviation, artificial intelligence, software-defined networking (SDN), air navigation systems, decision support systems (DSS), and more, this publication is ideally designed for aviation specialists and industry professionals, technicians, practitioners, researchers, and academicians seeking current research on modern modeling approaches to streamline management in aviation.

In this paper, the idea bipolar single-valued neutrosophic (BSVN) set was introduced. We also introduce bipolar single-valued neutrosophic topological space and some of its properties were characterized. Comparing Bipolar single-valued neutrosophic sets with score function, certainty function and accuracy function.

This eleventh volume of Collected Papers includes 90 papers comprising 988 pages on Physics, Artificial Intelligence, Health Issues, Decision Making, Economics, Statistics, written between 2001-2022 by the author alone or in collaboration with the following 84 co-authors (alphabetically ordered) from 19 countries: Abhijit Saha, Abu Sufian, Jack Allen, Shahbaz Ali, Ali Safaa Sadiq, Aliya Fahmi, Atiqa Fakhar, Atiqa Firdous, Sukanto Bhattacharya, Robert N. Boyd, Victor Chang, Victor Christianto, V. Christy, Dao The Son, Debjit Dutta, Azeddine Elhassouny, Fazal Ghani, Fazli Amin, Anirudha Ghosha, Nasruddin Hassan, Hoang Viet Long, Jhulaneswar Baidya, Jin Kim, Jun Ye, Darjan Karabašević, Vasilios N. Katsikis, Ieva Meidutė-Kavaliauskienė, F. Kaymarm, Nour Eldeen M. Khalifa, Madad Khan, Qaisar Khan, M. Khoshnevisan, Kifayat Ullah,, Volodymyr Krasnoholovets, Mukesh Kumar, Le Hoang Son, Luong Thi Hong Lan, Tahir Mahmood, Mahmoud Ismail, Mohamed Abdel-Basset, Siti Nurul Fitriah Mohamad, Mohamed Loey, Mai Mohamed, K. Mohana, Kalyan Mondal, Muhammad Gulfam, Muhammad Khalid Mahmood, Muhammad Jamil, Muhammad Yaqub Khan, Muhammad Riaz, Nguyen Dinh Hoa, Cu Nguyen Giap, Nguyen Tho Thong, Peide Liu, Pham Huy Thong, Gabrijela Popović, Surapati Pramanik, Dmitri Rabounski, Roslan Hasni, Rumi Roy, Tapan Kumar Roy, Said Broumi, Saleem Abdullah, Muzafer Saračević, Ganeshsree Selvachandran, Shariful Alam, Shyamal Dalapati, Housila P. Singh, R. Singh, Rajesh Singh, Predrag S. Stanimirović, Kasan Susilo, Dragiša Stanujkić, Alexandra Şandru, Ovidiu Ilie Şandru, Zenonas Turskis, Yunita Umniyati, Alptekin Ulutaș, Maikel Yelandi Leyva Vázquez, Binyamin Yusoff, Edmundas Kazimieras Zavadskas, Zhao Loon Wang.

Applied mathematics, modelling, and computer simulation are central to many aspects of engineering and computer science, and continue to be of intrinsic importance to the development of modern technologies. This book presents the proceedings of AMMCS 2023, the 3rd International Conference on Applied Mathematics, Modeling and Computer Simulation, held on 12 and 13 August 2023 in Wuhan, China. The conference provided an ideal opportunity for scholars and researchers to communicate important recent developments in their areas of specialization to their colleagues, and to scientists in related disciplines. More than 250 submissions were received for the conference, of which 133 were selected for presentation at the conference and inclusion here after a thorough peer-review process. These range from the theoretical and conceptual to strongly pragmatic papers addressing industrial best practice, and cover topics such as mathematical modeling and application; engineering applications and scientific computations; and the simulation of intelligent systems. The book explores practical experiences and enlightening ideas, and will be of interest to researchers, practitioners, and to all those working in the fields of applied mathematics, modeling and computer simulation.

This book aims to solve the discrete implementation problems of continuous-time neural network models while improving the performance of neural networks by using various Zhang Time Discretization (ZTD) formulas. The authors summarize and present the systematic derivations and complete research of ZTD formulas from special 3S-ZTD formulas to general NS-ZTD formulas. These finally lead to their proposed discrete-time Zhang neural network (DTZNN) algorithms, which are more efficient, accurate, and elegant. This book will open the door to scientific and engineering applications of ZTD formulas and neural networks, and will be a major inspiration for studies in neural network modeling, numerical algorithm design, prediction, and robot manipulator control. The book will benefit engineers, senior undergraduates, graduate students, and researchers in the fields of neural networks, computer mathematics, computer science, artificial intelligence, numerical algorithms, optimization, robotics, and simulation modeling.