An Artificial Intelligence Approach for the Kinodynamically Feasible Trajectory Planning of a Car-like Vehicle

Artificial intelligence is becoming more popular between students in higher education helping students in finding answers faster and easier and augment the educational process. Although the educators usually consider artificial intelligence as a threat to educational processes, however artificial intelligence is a leap across creative and innovative thinking in various fields, including mathematics education. Several studies have shown that artificial intelligence allows students to develop and improve more mathematical skills and cognitive skills in learning. It can help students explore more without waiting for an educator. The purpose of the study is to identify what kind of artificial intelligence - based platforms are used by students in undergraduate engineering mathematics studies, to collect the users - students and teachers experience of using them and to perform a comparative analysis of two countries - Latvia and Estonia, identifying the main challenges and considerations for the use of artificial intelligence - based mathematics learning platforms in mathematics studies at universities. Research tasks include identification of AI-based mathematics learning platforms such as Photomath, Desmos, GeoGebra, Maplesoft, Matific, Carnegie Learning, Microsoft Math Solver, ASSISTments, Fishtree, ALEKS, Symbolab, Cognii, Gradescope, Acadly, Maple Calculator as well as chatbot ChatGPT, and analysis of scientific literature on the benefits of their use in mathematics studies. Empirical research includes a survey of undergraduate students in both countries as well as focused interviews of the teaching staff. The research results show that the most popular tools in both countries are Photomath, chatbot Chat GPT as well as Symbolab, complemented by GeoGebra in Estonia and Desmos in Latvia.

Response: On Claims That Answer the Wrong Questions

Deep learning and other kinds of artificial intelligence have shown significant promise in the realm of medicine. Identifying the signs and markers of female perimenopause is one of these applications. By merging deep learning approaches with interpretability and ethical issues, this study contributes to the increasing corpus of research on explainable artificial intelligence models. This project is testing a range of models, including support vector machines (SVM), random forest, deep learning, and logistic regression, to see which artificial intelligence model is most beneficial in diagnosing perimenopausal symptoms. To examine the practicability of these models, a range of measures such as recall, accuracy, precision, F1-score, and ethical judgements are utilised. In terms of recall, accuracy, and precision, the proposed enhanced hybrid deep learning strategy uses deep neural networks and attention processes to outperform traditional models. The study also stresses the importance of ethical considerations while developing AI models, particularly when it comes to data protection, privacy legislation, and preventing prejudice.

The technology of swarm intelligence has been applied to a mechanical vibration monitoring system composed of a network of units equipped with sensors and actuators. The expression of “swarm intelligence” was first used in 1988 in the context of cellular robotic systems, where lots of simple agents may generate self-organized patterns through mutual interactions. There are various examples of the swarm intelligence in the natural environment, a swarm of ants, birds or fish. In this sense, the network of agents in a swarm may have some kind of intelligence or higher function than those appeared in a simple agent, which is defined as the swarm intelligence. The concept of swarm intelligence may be applied in diverse engineering fields such as flexible pattern recognition, adaptive control system, or intelligent monitoring system, because some kind of intelligence may emerge on the network without any special control system. In this study, a simulation model of a five degree-of-freedom lumped mass-spring system was prepared as an example of a mechanical dynamic system. Five units composed of a displacement sensor and a variable damper as actuator were assumed to be placed on each mass of the system. Each unit was connected to each other to exchange the information of state variables measured by sensors on each unit. Because the network of units configured as a mutual connected neural network, a kind of artificial intelligence, the network of units may memorize the several expected vibration-controlled patterns and may produce the signal to the actuators on the unit to reduce the vibration of target system. The simulation results showed that the excited vibration was reduced autonomously by selecting the position where the damping should be applied.

The urgent need to reduce carbon emissions is leading to growing use of renewable electricity, particularly from wind and photovoltaics. However, the intermittent nature of these power sources presents challenges to power systems, which need to ensure high and consistent power quality. Going forward, power systems also need to be able to respond to changes in loads, for example from EV charging. Neither production nor load changes can be predicted precisely, and so there is a degree of uncertainty or fuzziness. One way to meet these challenges is to use a kind of artificial intelligence - fuzzy logic. Fuzzy logic uses variables that may be any real number between 0 and 1, rather than either 0 or 1. It has obvious advantages when used for optimization of alternative and renewable energy systems. The parametric fuzzy algorithm is inherently adaptive because the coefficients can be altered to accommodate requirements and data availability. This book focuses on the use of fuzzy logic and neural networks to control power grids and adapt them to changing requirements. Chapters cover fuzzy inference, fuzzy logic-based control, feedback and feedforward neural networks, competitive and associate neural networks, and applications of fuzzy logic, deep learning and big data in power electronics and systems.

Big Techs and startups in pharmaceutical R&D – A 2020 perspective on artificial intelligence

By the method of documentation and logical analysis, based on the data, based on logic and based on the knowledge of three kinds of artificial intelligence in the sports education, the intelligent learning system feedback delay are studied, combined with mobile communication which led to the artificial intelligence online sports games teaching, pattern recognition, and virtual technology combined with innovative teaching interaction and experience. Promoting the development of green PE teaching machine learning can identify the types of PE activities and realize efficient PE learning diagnosis. Intelligent decision support system can identify sports talents and improve the effect of personalized PE teaching evaluation. From the perspective of psychological development and education, the key problems to be solved in the integration of artificial intelligence and physical education are examined. Then, the consistent model predictive control for feedback delay of nonlinear sports learning multiagent system with network induced delay and random communication protocol is studied. Under the communication waiting mechanism designed, each agent has a certain tolerance of delay, and this tolerance can be determined by ensuring the stability of the system. At the same time, a random communication protocol is designed to ensure the ordered communication of the multiagent system. Finally, the effectiveness of the proposed algorithm is verified by numerical simulation. To solve the channel competition access problem of the sports intelligent learning system with special structure feedback delay model predictive control, a dual channel awareness scheduling strategy under the model predictive control framework was proposed, and the distributed threshold strategy of sensors and the priority threshold strategy of controllers were designed. It is proved that the sensor will eventually work at Nash equilibrium point under the policy updating mechanism, and the priority threshold strategy of the controller is better than the traditional independent and identically distributed access strategy. By avoiding the data transmission when the channel status is poor, the channel access of the system is efficient and saves energy.

What artificial intelligence can tell us about the mind and intelligent behavior. What can artificial intelligence teach us about the mind? If AI's underlying concept is that thinking is a computational process, then how can computation illuminate thinking? It's a timely question. AI is all the rage, and the buzziest AI buzz surrounds adaptive machine learning: computer systems that learn intelligent behavior from massive amounts of data. This is what powers a driverless car, for example. In this book, Hector Levesque shifts the conversation to “good old fashioned artificial intelligence,” which is based not on heaps of data but on understanding commonsense intelligence. This kind of artificial intelligence is equipped to handle situations that depart from previous patterns—as we do in real life, when, for example, we encounter a washed-out bridge or when the barista informs us there's no more soy milk. Levesque considers the role of language in learning. He argues that a computer program that passes the famous Turing Test could be a mindless zombie, and he proposes another way to test for intelligence—the Winograd Schema Test, developed by Levesque and his colleagues. “If our goal is to understand intelligent behavior, we had better understand the difference between making it and faking it,” he observes. He identifies a possible mechanism behind common sense and the capacity to call on background knowledge: the ability to represent objects of thought symbolically. As AI migrates more and more into everyday life, we should worry if systems without common sense are making decisions where common sense is needed.

This paper presents the Symbols Research software (SR) – logical model and research tool that provides new opportunities in the field of empirical research of communication. SR collects and reviews the entire communication on social networks in a given time and social space and automatically detects the required content. In real time it sorts the material, creates basic overview, notices certain regularities and presents analytical findings. The model itself is a kind of artificial intelligence because it imitates the human brain: the operators who monitor the effectiveness of the so-called table of symbols (instrument for the content identification, classification and evaluation), continuously control its sensitivity and efficiency during the research process, enter corrections, and then these corrections are applied as a rule for future processing of material, which makes the instrument constantly improved. Almost in parallel, or with the postponement of a few seconds, the second and third level of processing are performed: simple analytics and results presentation, and then more complex analyses –multivariate and regression analysis as well as statistical procedure similar to the structural equation modeling. The SR concept and software can never be regarded as final and complete, or more precisely, they are and they must be flexible as well as the subject of continuous upgrading and improvements. Key words : content analysis, communication, social networks, methodology, advanced research, artificial intelligence

The birth of consciousness: I think, therefore I am?

Artificial intelligence has the potential to be trained to sort through vast amounts of information and develop conclusions that may inform strategic decision-making. This has the potential to enhance the efficiency and effectiveness of decision-making inside enterprises. One of the most well-known ways that AI is being used to boost productivity is via the automation of repetitive chores. By using automation, businesses may free up employees’ time to focus on other, potentially more fruitful tasks, such as brainstorming or strategic planning. An increasing number of business procedures are using some kind of artificial intelligence (AI). Still, when applied to increasing productivity in businesses, it really shines. Insights from various AI technologies are offered in this chapter to help executives avoid cognitive biases, acquire insights from enormous volumes of data, and make strategic choices more quickly.

Index

The exponential rise of urban areas and the associated surge in transportation congestion. Consequently, this study offers a thorough method for vehicle recognition and counting via the use of machine learning, as well as an effective system for real-time traffic monitoring, with the aim of reducing traffic. The first step is to develop a model that can identify and follow moving cars in still photos or video. This research delves into the topic of teaching a computer to count automobiles using machine learning, a kind of artificial intelligence. The purpose of this study is to provide a computational model for intelligent vehicle detection and tracking at a given location and time of day, using real-time images of passing cars. This approach uses OpenCV to evaluate the model's car detection and counting capabilities, and convolutional neural networks (CNNs) for object recognition and classification. The techniques laid the groundwork for early improvements, which were often enhanced using machine learning classifiers such as random forests and support vector machines (SVMs). To automate the process and get useful information regarding traffic patterns and management.

Solar energy is one of the most preferred energy sources among renewable energy sources. Very short-term power forecasting has an important role in the voltage and frequency control of solar energy. However, it provides stability to energy by correcting energy fluctuations in the energy market. In this study, long short term memory (LSTM), support vector machines (SVM) and hybrid LSTM-SVM model were used to estimate PV power in the very short term. The inputs of the models were 60-minute pressure, humidity, temperature, cloudiness and wind speed of Şanlıurfa province in 2022.At the output of the models, the 60-minute power value of the PV panel was obtained. The performances of hybrid LSTM-SVM, LSTM and SVM were compared using mean square error (MSE), root mean square error (RMSE), normalized root mean square error (NRMSE), mean absolute error (MAE) and correlation coefficient (R). In the very short term, PV panel power Hybrid LSTM-SVM, SVM, and LSTM predicted 0.9649, 0.8836 and 0.7255, respectively. The proposed hybrid LSTM-SVM model outperformed the classical LSTM and SVM. The performance metrics of the hybrid LSTM-SVM model, MSE, RMSE, NRMSE, MAE and R, were 9.0098e-04, 0.0300, 0.0318, 0.011 and 0.9823, respectively. The hybrid LSTM-SVM model had high stability and accuracy in very short-term solar power forecasting. Hybrid LSTM-SVM can be used as an alternative method for very short-term solar power forecasting.

The integration of Artificial Intelligence (AI) into Structural Health Monitoring (SHM) systems has emerged as a transformative solution for predictive failure analysis in pressure systems such as pressure vessels, pipelines, and industrial reactors. This study aims to systematically examine the role of AI-powered SHM frameworks in enhancing the reliability, safety, and operational efficiency of these high-risk infrastructures. A total of 63 peer-reviewed journal articles and conference papers published between 2000 and 2023 were reviewed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. The selected studies were analyzed in terms of AI techniques applied, types of sensors integrated, fusion architectures, model performance metrics, validation methods, and real-world industrial applications. The review reveals that AI models—especially machine learning and deep learning algorithms—have significantly improved the early detection of faults, classification accuracy, and remaining useful life (RUL) prediction when supported by multi-sensor fusion frameworks. Models such as support vector machines (SVM), convolutional neural networks (CNN), and long short-term memory (LSTM) networks were frequently used and demonstrated strong performance, often achieving accuracy levels exceeding 90% across varied industrial scenarios. Furthermore, many of these systems have been successfully deployed in operational environments, leading to measurable improvements in maintenance scheduling, reduced downtime, and heightened safety. However, the review also identifies critical implementation challenges, including data scarcity, limited model interpretability, system integration constraints, and cybersecurity vulnerabilities. These barriers highlight the need for standardized practices, improved data governance, and interdisciplinary collaboration.