Methodological Basis for Robust Power System Planning in Capital and Intermediate Cities using Machine Learning and Computational Intelligence Techniques

Integrative economic framework incorporating the Emission Trading Scheme (ETS) for U.S. Residential energy systems

Globally, the supply of fossil fuels is rapidly depleting and the effects of their use on the climate are becoming more noticeable. Recent European initiatives and renewable targets have increased the awareness of carbon emissions and the need for increased use of renewable sources. The UK government is committed to meeting an ambitious target of producing 15 percent of the UK's energy demand from renewable sources by 2020. The aim of the study is to discover how arise in sustainable power generation for the UK domestic sector could be achieved. This is achieved by analysing the current UK electricity consumption within the domestic sector, identifying the current renewable generation methods used and attaining expert views on renewables and their future prospect. Two semi-structured interviews with experts in the field of renewable energy were conducted. It is clear that both believe that the future of renewable energy within the UK will primarily be in the form of wind and solar power however, technologies such as wave and tidal power were also discussed which also have the potential to play a role in the future of UK energy. Through analysing all the research, recommendations have been made for the future of renewables within the UK. It has been suggested that the UK government expands the use of renewables, with the majority of development in solar and wind; increased use of domestic renewable sources and alter consumer behaviour; and finally, a recommendation for the government to establish an initiative to encourage consumers to time-shift when they use electricity to enable the use of energy from intermittent sources when available.

Micro combined heat and power home supply: Prospective and adaptive management achieved by computational intelligence techniques

As people age, there is a growing need to comprehend the intricate mechanisms that occur and identify trustworthy indicators for early diagnosis, intervention, and personalized medical care. Detailed analysis of Artificial Intelligence (AI) for the identification of aging markers is presented in this article. Our ability to analyze large datasets in genomics, proteomics, metabolomics, imaging, and clinical records has been greatly enhanced by the integration of AI techniques like machine learning and data analytics. A holistic view of aging markers is provided by the review through the use of AI that integrates clinical data, lifestyle factors, and environmental information. The examination focuses on the use of AI to estimate an individual’s risk of premature aging by considering various factors. The incorporation of AI into the study and identification of aging markers brings about a fundamental change in research on aging. Through this analysis, it is evident that AI has the potential to revolutionize our understanding of aging and aid in the prevention and management of age-related diseases.

Nature Inspired Computing (NIC) paradigms, namely, swarm intelligence, evolutionary computation and computational intelligence techniques are widely applied to develop versatile and adaptable systems and create computational methods that can assist human to resolve real world complex problems. It can be achieved by transferring knowledge from natural systems to engineering systems. The objective of this paper is to analyze the recent trends and advancement in the application of metaheuristic algorithms to solve various domains, especially, denoising, edge detection and classification problem. This paper provides a comprehensive list of global optimization algorithms that can be applied to develop innovative system and soft computing applications integrates with computational intelligence techniques. Several fitness functions and parameters implemented in the evaluation of global optimization algorithm to find global optimum for solving diverse applications are thoroughly investigated. The implications for the selection of fitness function to solve specific optimization problems and applications are also discussed.

[EN] The electricity sector is currently undergoing a process of liberalization and separation of roles, which is being implemented under the regulatory auspices of each Member State of the European Union and, therefore, with different speeds, perspectives and objectives that must converge on a common horizon, where Europe will benefit from an interconnected energy market in which producers and consumers can participate in free competition.\n\nThis process of liberalization and separation of roles involves two consequences or, viewed another way, entails a major consequence from which other immediate consequence, as a necessity, is derived. The main consequence is the increased complexity in the management and supervision of a system, the electrical, increasingly interconnected and participatory, with connection of distributed energy sources, much of them from renewable sources, at different voltage levels and with different generation capacity at any point in the network. From this situation the other consequence is derived, which is the need to communicate information between agents, reliably, safely and quickly, and that this information is analyzed in the most effective way possible, to form part of the processes of decision taking that improve the observability and controllability of a system which is increasing in complexity and number of agents involved.\n\nWith the evolution of Information and Communication Technologies (ICT), and the investments both in improving existing measurement and communications infrastructure, and taking the measurement and actuation capacity to a greater number of points in medium and low voltage networks, the availability of data that informs of the state of the network is increasingly higher and more complete. All these systems are part of the so-called Smart Grids, or intelligent networks of the future, a future which is not so far.\n\nOne such source of information comes from the energy consumption of customers, measured on a regular basis (every hour, half hour or quarter-hour) and sent to the Distribution System Operators from the Smart Meters making use of Advanced Metering Infrastructure (AMI). This way, there is an increasingly amount of information on the energy consumption of customers, being stored in Big Data systems. This growing source of information demands specialized techniques which can take benefit from it, extracting a useful and summarized knowledge from it.\n\nThis thesis deals with the use of this information of energy consumption from Smart Meters, in particular on the application of data mining techniques to obtain temporal patterns that characterize the users of electrical energy, grouping them according to these patterns in a small number of groups or clusters, that allow evaluating how users consume energy, both during the day and during a sequence of days, allowing to assess trends and predict future scenarios. For this, the current techniques are studied and, proving that the current works do not cover this objective, clustering or dynamic segmentation techniques applied to load profiles of electric energy consumption from domestic users are developed. These techniques are tested and validated on a database of hourly energy consumption values for a sample of residential customers in Spain during years 2008 and 2009. The results allow to observe both the characterization in consumption patterns of the different types of residential energy consumers, and their evolution over time, and to assess, for example, how the regulatory changes that occurred in Spain in the electricity sector during those years influenced in the temporal patterns of energy consumption.

The Group Method of Data Handling (GMDH) is a typical inductive modeling method that is built on principles of self-organization for modeling complex systems. However, it is known to often under-perform on non-parametric regression tasks, while time series modeling GMDH exhibits a tendency to find very complex polynomials that cannot model well future, unseen oscillations of the series. In order to alleviate these problems, GMDH has been recently hybridized with some computational intelligence (CI) techniques resulting in more robust and flexible hybrid intelligent systems for solving complex, real-world problems. The central theme of this book is to present in a very clear manner hybrids of some computational intelligence techniques and GMDH approach. The hybrids discussed in the book include GP-GMDH (Genetic Programming-GMDH) algorithm, GA-GMDH (Genetic Algorithm-GMDH) algorithm, DE-GMDH (Differential Evolution-GMDH) algorithm, and PSO-GMDH (Particle Swarm Optimization) algorithm. Also included is the description of the recently introduced GAME (Group Adaptive Models Evolution algorithm. The hybrid character of models and their self-organizing ability give these hybrid self-organizing modeling systems an advantage over standard data mining models. The modeling and data mining solutions of several real-life problems in the areas of engineering, bioinformatics, finance, and economics are presented in the chapters. The book will benefit amongst others, people who are working in the areas of neural networks, machine learning, artificial intelligence, complex system modeling and analysis, and optimization.

Artificial Intelligence (AI) is considered a copy of human intelligence, which is executed by computer systems. Nowadays, AI is being used in almost every sector. In healthcare sector, particularly, it assists in designing smart devices and agents, which help in the diagnosis of various diseases. The combination of healthcare and AI has wide potential applications. Healthcare AI market is growing rapidly and showing promising outcomes irrespective of the size and complexity of data. Machine learning and the advancements associated with it in the medical field are remarkable. For non-computer background researchers, open source python libraries are available to develop AI systems. Healthcare data is of different types, such as clinical data and omics data, and it is further categorized as structured and unstructured data, therefore they require different types of computational techniques for further processing. To evaluate the performance of these AI systems different parameters like accuracy, sensitivity, specificity, etc. are also discussed. Additionally, the insights into the challenges of healthcare industry and its scope of improvement are also presented in this study.

The article presents the development of a hybrid approach to quality control in additive manufacturing using the Fused Deposition Modeling (FDM) method, based on the integration of artificial intelligence techniques and statistical analysis. The study substantiates the need to shift from traditional quality control methods to intelligent monitoring systems capable of real-time defect detection and process analysis. The results of the analysis emphasize the effective application of techniques such as ANOVA, PCA, artificial intelligence algorithms, and image-based recognition systems in identifying irregularities and predicting potential issues in additive production. These technologies also play a vital role in fine-tuning critical FDM process parameters. The proposed integrated approach demonstrates considerable advantages in enhancing accuracy, process uniformity, and overall operational stability in 3D printing environments.

Stress perfusion cardiac magnetic resonance imaging (CMR) has gained increasing adoption across North America and Europe for the evaluation of symptomatic suspected or established ischemic heart disease (IHD). Over the past decade, stress perfusion CMR has demonstrated excellent diagnostic and prognostic performance, particularly in patients at intermediate or high risk of IHD or with established coronary artery disease (CAD). After the landmark ISCHEMIA trial, stress CMR may play an important role in selecting patients for invasive management strategies and determination of revascularization technique. Artificial intelligence has streamlined CMR scanning techniques and in-line automation of quantitative pixelated perfusion maps. Quantitative stress CMR can evaluate absolute myocardial blood flow and perfusion reserve that improves risk stratification and detection of coronary microvascular disease (CMD). CMD detection may assist clinicians with diagnosis of chest pain in patients without obstructive CAD and improve prognostication and detection of pathophysiological mechanisms in a variety of cardiomyopathies. Quantitative stress perfusion CMR will play an important clinical role in evaluating patients at risk of IHD and cardiomyopathy with iterative cost and time efficiency owing to continued integration of artificial intelligence techniques. More widespread adoption will likely improve cost effective cardiac care and reduce adverse clinical outcomes.

The analysis of video recorded surgical procedures is considered to be a useful extension of the medical curriculum. We can foster the development of video-based e-learning courses by working out a unified description method which would facilitate the exchange of these materials between different platforms. Sophisticated metadata enables a broader integration of artificial intelligence techniques into e-learning. The aim of this paper is to present the possibility of combining the MPEG-7 metadata standard with the MeSH classification for indexing of video recordings in medical e-learning. A tool for metadata descriptions of surgical videos in accordance with the MPEG-7 standard is also presented. This tool is part of a larger architecture for the exchange of medical multimedia objects.KeywordsMPEG-7e-learningmetadatamedical terminologylearning objectsMeSH

Flavor Engineering: A comprehensive review of biological foundations, AI integration, industrial development, and socio-cultural dynamics

Radon anomalies have long been explored as potential geochemical precursors to seismic activity due to their responsiveness to subsurface stress variations. However, before this study, the scientific progression of this research domain had not been systematically examined through a quantitative lens. This study presents a comprehensive bibliometric analysis of 379 articles published between 1977 and 2025 and indexed in Scopus and Web of Science. Utilizing the Bibliometrix R-package and its Biblioshiny interface, the analysis investigates temporal publication trends, leading countries, institutions, international collaboration networks, and thematic evolution. The results reveal a marked increase in research output since 2010, with China, India, and Italy emerging as the most prolific contributors. Thematic mapping indicates a shift from conventional geochemical monitoring toward the integration of artificial intelligence techniques, such as decision trees and neural networks, for anomaly detection and predictive modeling. Notwithstanding this methodological evolution, core research themes remain centered on radon concentration monitoring and the analysis of environmental parameters. Overall, the findings highlight the coexistence of traditional and emerging approaches, emphasizing the importance of standardized methodologies and interdisciplinary collaboration. This bibliometric synthesis provides strategic insights to inform future research and strengthen the role of radon monitoring in seismic early warning systems.

The application of social robots is increasing daily due to their various capabilities. In real settings, social robots have been successfully deployed in multiple domains, such as health, education, and the service industry. However, it is crucial to identify the strengths and limitations of a social robot before it can be employed in a real-life scenario. In this study, we explore and examine the capabilities of a humanoid robot, ‘Pepper’, which can be programmed to interact with humans. The present paper investigates five capabilities of Pepper: mapping and navigation, speech, hearing, object detection, and face detection. We attempt to study each of these capabilities in-depth with the help of experiments conducted in the laboratory. It has been identified that Pepper’s sound and speech recognition capabilities yielded satisfactory results, even with various accents. On the other hand, Pepper’s built-in SLAM navigation is unreliable, making it difficult to reach destinations accurately due to generated maps. Moreover, its object and face detection capabilities delivered inconsistent outcomes. This shows that Pepper has potential for improvement in its current capabilities. However, previous studies showed that with the integration of artificial intelligence techniques, a social robot’s capabilities can be enhanced significantly. In the future, we will focus on such integration in the Pepper robot, and the present study’s exploration will help to establish a baseline comprehension of the in-built artificial intelligence of Pepper. The findings of the present paper provide insights to researchers and practitioners planning to use the Pepper robot in their future work.

Recent trends in communication technologies led to a shift in the already traditional Alternative Dispute Resolution paradigm, giving birth to the Online Dispute Resolution one. In this new paradigm, technologies are used as a way to deliver better, faster and cheaper alternatives to litigation in court. However, the role of technology can be further enhanced with the integration of Artificial Intelligence techniques. In this paper we present UMCourt, a tool that merges concepts from the fields of Law and Artificial Intelligence. The system keeps the parties informed about the possible consequences of their litigation if their problems are to be settled in court. Moreover, it makes use of a Case-based Reasoning algorithm that searches for solutions for the litigation considering past known similar cases, as a way to enhance the negotiation process. When parties have access to all this information and are aware of the consequences of their choices, they can take better decisions that encompass all the important aspects of a litigation process.KeywordsOnline Dispute ResolutionCase-based ReasoningMulti-agent System