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Evolutionary optimization algorithms
This comprehensive reference text discusses evolutionary optimization techniques, to find optimal solutions for single and multi-objective problems. The text presents each evolutionary optimization algorithm along with its history and other working equations. It also discusses variants and hybrids of optimization techniques. The text presents step-by-step solution to a problem and includes software's like MATLAB and Python for solving optimization problems. It covers important optimization algorithms including single objective optimization, multi objective optimization, Heuristic optimization techniques, shuffled frog leaping algorithm, bacteria foraging algorithm and firefly algorithm. Aimed at senior undergraduate and graduate students in the field of electrical engineering, electronics engineering, mechanical engineering, and computer science and engineering, this text: Provides step-by-step solution for each evolutionary optimization algorithm. Provides flowcharts and graphics for better understanding of optimization techniques. Discusses popular optimization techniques include particle swarm optimization and genetic algorithm. Presents every optimization technique along with the history and working equations. Includes latest software like Python and MATLAB.
Book
A novel integrated fuzzy DEA–artificial intelligence approach for assessing environmental efficiency and predicting CO2 emissions
Undesirable output of industrial economic activities—carbon dioxide (CO
2
) and other pollutants—has been become global concern because of their harmful effects on the climate, especially for environmentally sustainable production systems which attempts to generate less undesirable outputs, as well as achieve higher levels of production and economic growth. This study proposes a novel environmental efficiency data envelopment analysis (DEA) in conjunction with predicting artificial intelligence algorithms. The proposed model—fuzzy undesirable slacks-based measure DEA model (FUNSBM)—measures environmental efficiency in terms of the directional distance function and weak disposability, and its combined approaches (artificial neural network (ANN), ANN + particle swarm optimization (PSO) and artificial immune system (AIS)) predict optimal values of inefficient decision-making units (DMUs) so that they become more efficient considering the possible reduction of CO
2
emissions in their production process. The FUNSBM model is applied to a dataset of 30 Iranian forest management units. The findings show that almost 47% DMUs are operating at low efficiency levels with a weak efficiency dispersion; however, these inefficient DMUs could improve their efficiency border via following the combined approaches. This analysis shows that the FUNSBM-AIS approach, by 53% reduction of CO
2
emission, is the best approach to predict and/or control CO
2
emission in optimal way while FUNSBM-ANN and FUNSBM-ANN + PSO are reduced CO
2
emission by 15% and 32%, respectively. As the major conclusion, the FUNSBM-AIS approach exhibits a high degree of reliability to predict the lowest amount of CO
2
emission and can help improve the inefficient DMUs by following their predicted optimal values.
Journal Article
Artificial Intelligence Applied to Drone Control: A State of the Art
The integration of Artificial Intelligence (AI) tools and techniques has provided a significant advance in drone technology. Besides the military applications, drones are being increasingly used for logistics and cargo transportation, agriculture, construction, security and surveillance, exploration, and mobile wireless communication. The synergy between drones and AI has led to notable progress in the autonomy of drones, which have become capable of completing complex missions without direct human supervision. This study of the state of the art examines the impact of AI on improving drone autonomous behavior, covering from automation to complex real-time decision making. The paper provides detailed examples of the latest developments and applications. Ethical and regulatory challenges are also considered for the future evolution of this field of research, because drones with AI have the potential to greatly change our socioeconomic landscape.
Journal Article
Flow starvation during square-flow assisted ventilation detected by supervised deep learning techniques
This work was funded by projects RTC‑2017‑6193‑1 (AEI/FEDER EU) and 202118 (413/C/2021), CIBER‑Consorcio Centro de Investigación Biomédica en RED‑CB06/06/1097, Instituto de Saludo Carlos III, Ministerio de Ciencia e Innovación and Unión Europea – European Regional Development Fund, CERCA Program/Generalitat de Catalunya and Fundació Institut d’Investigació i Innovació Parc Taulí‑I3PT. C. de Haro is granted with a Contrato para la intensificación de la actividad investigadora en el sistema nacional de salud (INT20/00030), AES 2020, by Instituto de Salud Carlos III. L. Sarlabous is supported by Pla Estratègic de Recerca i Innovació en Salut program from the Health Department of Generalitat de Catalunya, Spain.
Journal Article
Algorithms, humans, and interactions : how do algorithms interact with people? designing meaningful AI experiences
\"Amidst the rampant use of algorithmization enabled by AI, the common theme of AI systems is the human factor. Humans play an essential role in designing, developing, and operationalizing AI systems. We have a remit to warrant those systems run transparently, perform equitably, value our privacy, and effectively fulfill human needs. This book takes an interdisciplinary approach to contribute to the ongoing development of human-AI interaction with a particular focus on the \"human\" dimension and provides insights to improve the design of AI that could be genuinely beneficial and effectively used in society. The readers of this book will benefit by gaining insights into various perspectives about how AI has impacted people and society and how it will do so in future, and understanding how we can design algorithm systems that are beneficial, legitimate, usable by humans, and designed considering and respecting human values. This book provides a horizontal set of guidelines and insight into how humans can be empowered by making choices about AI designs that allow them meaningful control over AI. Designing meaningful AI experiences has garnered great attention to address responsibility gaps and mitigate them by establishing conditions that enable the proper attribution of responsibility to humans. This book helps us understand the possibilities of what AI systems can do and how they can and should be integrated into our society\"-- Provided by publisher.
BOOK
How Clinicians Perceive Artificial Intelligence–Assisted Technologies in Diagnostic Decision Making: Mixed Methods Approach
With the rapid development of artificial intelligence (AI) and related technologies, AI algorithms are being embedded into various health information technologies that assist clinicians in clinical decision making.
This study aimed to explore how clinicians perceive AI assistance in diagnostic decision making and suggest the paths forward for AI-human teaming for clinical decision making in health care.
This study used a mixed methods approach, utilizing hierarchical linear modeling and sentiment analysis through natural language understanding techniques.
A total of 114 clinicians participated in online simulation surveys in 2020 and 2021. These clinicians studied family medicine and used AI algorithms to aid in patient diagnosis. Their overall sentiment toward AI-assisted diagnosis was positive and comparable with diagnoses made without the assistance of AI. However, AI-guided decision making was not congruent with the way clinicians typically made decisions in diagnosing illnesses. In a quantitative survey, clinicians reported perceiving current AI assistance as not likely to enhance diagnostic capability and negatively influenced their overall performance (β=-0.421, P=.02). Instead, clinicians' diagnostic capabilities tended to be associated with well-known parameters, such as education, age, and daily habit of technology use on social media platforms.
This study elucidated clinicians' current perceptions and sentiments toward AI-enabled diagnosis. Although the sentiment was positive, the current form of AI assistance may not be linked with efficient decision making, as AI algorithms are not well aligned with subjective human reasoning in clinical diagnosis. Developers and policy makers in health could gather behavioral data from clinicians in various disciplines to help align AI algorithms with the unique subjective patterns of reasoning that humans employ in clinical diagnosis.
Journal Article
Intelligent system algorithms and applications in science and technology
\"Intelligent System Algorithms and Applications in Science and Technology explores the application of intelligent techniques in various fields of engineering and technology. The volume addresses a selection of diverse topics in such areas as machine learning based intelligent systems for healthcare, applications of artificial intelligence and the Internet of Things, intelligent data analytics techniques, intelligent network systems and applications, and inequalities and process control systems. The authors explore the full breadth of the field, which encompasses data analysis, image processing, speech processing and recognition, medical science and health care monitoring, smart irrigation systems, insurance and banking, robotics and process control, etc. The 21st century has witnessed massive changes around the world in intelligence systems in order to become smarter, energy efficient, reliable, and cheaper. This collection of peer-reviewed book chapters, contributed by renowned experts in the field, will help keep readers up to date; it sheds light on the culture of intelligent techniques in the field of engineering and technology\"-- Provided by publisher.
Book
Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns
Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)-infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work.
This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems.
This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI.
We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern.
This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines.
PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590.
Journal Article