Bibliografie tematiche / Hybird Artificial intelligence

Letteratura scientifica selezionata sul tema "Hybird Artificial intelligence"

Autore: Grafiati

Pubblicato: 22 giugno 2024

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Articoli di riviste sul tema "Hybird Artificial intelligence":

Zhang, Nian, Yunpeng Han, Quanshen Si e Guiwu Wei. "A novel method for multi-attribute risk decision-making based on regret theory and hybird information". Journal of Intelligent & Fuzzy Systems 39, n. 5 (19 novembre 2020): 6955–64. http://dx.doi.org/10.3233/jifs-200081.

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To consider the decision makers’ regret behavior and describe the hybrid evolution information in the risk decision-making problem, a new approach is proposed based on regret theory in this paper. Firstly, the probable value of different states are calculated by Pignistic probability transformation method. Secondly, the relative closeness formula of hybrid information are established and the utility values of alternatives are computed. Then, decision makers’ utility values are obtained according to the regret theory. Moreover, the overall perceived utility values of alternatives are obtained by weighted arithmetic mean and got the optimal one by the ranking order. Finally, an numerical example is illustrated the method and comparative analysis are offered between the proposed approach and other existed methods to show that is feasible and usable.

Douzi, Samira, Feda A. AlShahwan, Mouad Lemoudden e Bouabid El Ouahidi. "Hybrid Email Spam Detection Model Using Artificial Intelligence". International Journal of Machine Learning and Computing 10, n. 2 (febbraio 2020): 316–22. http://dx.doi.org/10.18178/ijmlc.2020.10.2.937.

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Chen, Liming, Huansheng Ning, Chris D. Nugent e Zhiwen Yu. "Hybrid Human-Artificial Intelligence". Computer 53, n. 8 (agosto 2020): 14–17. http://dx.doi.org/10.1109/mc.2020.2997573.

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Abubakar, A. Mohammed. "Using hybrid SEM – artificial intelligence". Personnel Review 49, n. 1 (19 novembre 2019): 67–86. http://dx.doi.org/10.1108/pr-06-2017-0180.

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Purpose Boreout is a psychological state of intense boredom and apathy. Characterized by the absence of mental stimuli (i.e. menial tasks) required to keep employees conscious about their environment, and this incessant decline in mental stimuli may turn employees into “professional zombies.” The diversity in work needs and preferences across generations has become a key organizational factor, generational differences have been studied in Western countries, not much information is available about generational cohorts and satisfaction (i.e. career, life and job satisfaction) in developing countries. The purpose of this paper is to provide more insights on these phenomena. Design/methodology/approach Drawing upon conservation of resources theory, this paper examines the potential effects of boreout on important job outcomes (i.e. career, life and job satisfaction) conditioned by generation (Gen-Xers and Gen-Yers) in the service industry. Data analyses with Artificial Intelligence technique (i.e. artificial neural network) and structural equation modeling were conducted with data collated from Nigerian service employees. Findings Results revealed that boreout has a negative impact on career, life and job satisfaction. The hypothesized relationships were significantly moderated by generation cohorts as Gen-Xers and Gen-Yers were found to be distinct cohorts. Originality/value This paper advocates that non-western organizations should avoid utmost service standardization and rigid stylization of work processes and procedures.

Feuerecker, Benedikt, Maurice M. Heimer, Thomas Geyer, Matthias P. Fabritius, Sijing Gu, Balthasar Schachtner, Leonie Beyer et al. "Artificial Intelligence in Oncological Hybrid Imaging". Nuklearmedizin - NuclearMedicine 62, n. 05 (ottobre 2023): 296–305. http://dx.doi.org/10.1055/a-2157-6810.

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Background Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes. Methods and Results The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations. Conclusion AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation. Key Points:

González Quirós, José Luis, e David Díaz Pardo de Vera. "Theory of mind: from artificial intelligence to hybrid intelligence". TECHNO REVIEW. International Technology, Science and Society Review 9, n. 2 (18 gennaio 2021): 103–19. http://dx.doi.org/10.37467/gka-revtechno.v9.2816.

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Philosophy of mind has long ceased to be, if indeed it ever was, the exclusive domain of philosophers. In contemporary thought there is increasing interest in these matters from the point of view of technology. This paper gives a critique of the ideas of Ray Kurzweil and briefly reviews some of the recent trends in the treatment of these questions.

Jarrahi, Mohammad Hossein, Christoph Lutz e Gemma Newlands. "Artificial intelligence, human intelligence and hybrid intelligence based on mutual augmentation". Big Data & Society 9, n. 2 (luglio 2022): 205395172211428. http://dx.doi.org/10.1177/20539517221142824.

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There is little consensus on what artificial intelligence (AI) systems may or may not embrace. Although this may point to multiplicity of interpretations and backgrounds, a lack of conceptual clarity could thwart the development of common ground around the concept among researchers, practitioners and users of AI and pave the way for misinterpretation and abuse of the concept. This article argues that one of the effective ways to delineate the concept of AI is to compare and contrast it with human intelligence. In doing so, the article broaches the unique capabilities of humans and AI in relation to one another (human and machine tacit knowledge), as well as two types of AI systems: one that goes beyond human intelligence and one that is necessarily and inherently tied to it. It finally highlights how humans and AI can augment their capabilities and intelligence through synergistic human–AI interactions (i.e., human-augmented AI and augmented human intelligence), resulting in hybrid intelligence, and concludes with a future-looking research agenda.

Gudova, M. Yu, E. V. Rubtsova e N. A. Simbirtseva. "Communication Trends in the Post-Literacy Era: From Human Creativity to the Creativity of Artificial Intelligence and Human-Machine Hybrids". Izvestia Ural Federal University Journal Series 1. Issues in Education, Science and Culture 27, n. 2 (2021): 235–49. http://dx.doi.org/10.15826/izv1.2021.27.2.048.

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The article is based on the materials of the Fifth International Theoretical Scientific Conference “Communication trends in the post-literacy era: polylingualism, multimodality and polyculturalism as preconditions for new creativity”, which took place at the Institute of Humanities in November 26–28, 2020. The authors analyze the main communication trends that have developed under the influence of the Covid-2019 pandemic in the sociocultural space in 2020. The main trend is the use of artificial intelligence in such areas of socioculture as communication, media, education. The concept of creativity is clarified, the creative possibilities and limits of human and artificial intelligence are considered, the threats and dangers of the artificial intelligence‘s development and its implementation in various spheres of human life are analyzed, such as education, socialization and inculturation, journalism and mass information, contemporary art, museum and exhibition activity. The conclusion is made about the need for further interdisciplinary research of artificial intelligence in the humanitarian sphere.

Fox, Stephen. "Beyond AI: Multi-Intelligence (MI) Combining Natural and Artificial Intelligences in Hybrid Beings and Systems". Technologies 5, n. 3 (22 giugno 2017): 38. http://dx.doi.org/10.3390/technologies5030038.

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Lukyanova, Ekaterina D. "Artificial Intelligence: Achievements and Postponed Risks". Sociologicheskaja nauka i social naja praktika 7, n. 1 (2019): 142–48. http://dx.doi.org/10.19181/snsp.2019.7.1.6275.

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The article considers the problem of the development of artificial intelligence, its ambivalent impact on society and humanity. There are the description and definition of artificial intelligence, the problem field was defined. We conducted a study of the manifest and latent functions of artificial intelligence, which can provoke postponed risks. We considered the vision of artificial intelligence as a complex socio-biotechnological hybrid and a qualitatively new mechanism of social control. The achievements and risk-based role of artificial intelligence in modern complex society was estimated. Particular attention is paid to the nature of knowledge about the phenomenon of artificial intelligence, acquiring an interdisciplinary character. Some deferred risks related to the functioning of artificial intelligence are indicated. The factors contributing to the creation of a socio-biotechnological hybrid in the form of artificial intelligence are considered in the context of the riskological turn. The risks of a new type of irrational rationality are considered, the cause of which, according to the author, is the introduction of IT and artificial intelligence, the consequences of rationality are given. The problem of the risk of deintimization, depriving a person of opportunities and the right to personal information is noted. That problem may entail new manifestations of alienation in the form of the dehumanization of social and natural realities. due to the introduction in many programs of artificial intelligence. The article deals with the problem of the pluralization of knowledge about artificial intelligence, the use of performance mechanisms that produce fakes, absolutizing positive or negative consequences of the functioning of artificial intelligence. The article summarizes the state of the risks of artificial intelligence at the moment. Recommendations for further interdisciplinary study of artificial intelligence are presented. The article substantiates the importance of the implementation of the risk assessment of the emerging programs based on artificial intelligence.

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Tesi sul tema "Hybird Artificial intelligence":

Liu, Ziming. "Méthodes hybrides d'intelligence artificielle pour les applications de navigation autonome". Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4004.

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La navigation autonome est une tâche difficile qui a un large éventail d'applications dans le monde réel. Le système de navigation autonome peut être utilisé sur différentes plateformes, telles que les voitures, les drones et les robots. Ces systèmes autonomes réduiront considérablement le travail humain et amélioreront l'efficacité du système de transport actuel. Certains systèmes autonomes ont été utilisés dans des scénarios réels, comme les robots de livraison et les robots de service. Dans le monde réel, les systèmes autonomes doivent construire des représentations de l'environnement et se localiser pour interagir avec l'environnement. Différents capteurs peuvent être utilisés pour atteindre ces objectifs. Parmi eux, le capteur caméra est le meilleur choix entre le coût et la fiabilité. Actuellement, la navigation autonome visuelle a connu des améliorations significatives grâce à l'apprentissage profond. Les méthodes d'apprentissage profond présentent des avantages pour la perception de l'environnement. Cependant, elles ne sont pas robustes pour la localisation visuelle où les méthodes basées sur des modèles ont des résultats plus fiables. Afin d'utiliser les avantages des méthodes basées sur les données et sur les modèles, une méthode hybride d'odométrie visuelle est étudiée dans cette thèse. Tout d'abord, des méthodes d'optimisation efficaces sont essentielles pour les méthodes basées sur les modèles et les méthodes basées sur les données qui partagent la même théorie d'optimisation. Actuellement, la plupart des réseaux d'apprentissage profond sont encore formés avec des optimiseurs de premier ordre inefficaces. Par conséquent, cette thèse propose d'étendre les méthodes d'optimisation efficaces basées sur les modèles pour former les réseaux d'apprentissage profond. La méthode Gaussienne-Newton et les méthodes efficaces de second ordre sont appliquées pour l'optimisation de l'apprentissage profond. Deuxièmement, la méthode d'odométrie visuelle basée sur un modèle repose sur des informations préalables sur la profondeur, l'estimation robuste et précise de la profondeur est essentielle pour la performance du module d'odométrie visuelle. Sur la base de la théorie traditionnelle de la vision par ordinateur, la vision stéréo peut calculer la profondeur avec l'échelle correcte, ce qui est plus fiable que les solutions monoculaires. Toutefois, les réseaux stéréoscopiques 2D-3D actuels à deux niveaux présentent des problèmes d'annotations de profondeur et d'écart entre les domaines de disparité. En conséquence, un réseau stéréo supervisé par la pose et un réseau stéréo adaptatif sont étudiés. Toutefois, les performances des réseaux en deux étapes sont limitées par la qualité des caractéristiques 2D qui construisent le volume de coût de l'appariement stéréo. Au lieu de cela, un nouveau réseau stéréo 3D en une étape est proposé pour apprendre les caractéristiques et l'appariement stéréo implicitement en une seule étape. Troisièmement, pour assurer la robustesse du système, le réseau stéréo et le module d'odométrie visuelle directe dense sont combinés pour créer une odométrie visuelle directe dense hybride stéréo (HDVO). L'odométrie visuelle directe dense est plus fiable que la méthode basée sur les caractéristiques, car elle est optimisée à partir des informations globales de l'image. L'HDVO est optimisée avec la perte de minimisation photométrique. Cependant, cette perte souffre de bruits provenant de la zone d'occlusion, de la zone de texture homogène et des objets dynamiques. Cette thèse étudie la suppression des valeurs de perte bruitées à l'aide de masques binaires. De plus, pour réduire les effets des objets dynamiques, les résultats de la segmentation sémantique sont utilisés pour améliorer ces masques. Enfin, pour être généralisée à un nouveau domaine de données, une méthode d'entraînement test-temps pour l'odométrie visuelle est explorée
Autonomous driving is a challenging task that has a wide range of applications in the real world. The autonomous driving system can be used in different platforms, such as cars, drones, and robots. These autonomous systems will reduce a lot of human labor and improve the efficiency of the current transportation system. Some autonomous systems have been used in real scenarios, such as delivery robots, and service robots. In the real world, autonomous systems need to build environment representations and localize themselves to interact with the environment. There are different sensors can be used for these objectives. Among them, the camera sensor is the best choice between cost and reliability. Currently, visual autonomous driving has achieved significant improvement with deep learning. Deep learning methods have advantages for environment perception. However, they are not robust for visual localization where model-based methods have more reliable results. To utilize the advantages of both data-based and model-based methods, a hybrid visual odometry method is explored in this thesis. Firstly, efficient optimization methods are critical for both model-based and data-based methods which share the same optimization theory. Currently, most deep learning networks are still trained with inefficient first-order optimizers. Therefore, this thesis proposes to extend efficient model-based optimization methods to train deep learning networks. The Gaussian-Newton and the efficient second-order methods are applied for deep learning optimization. Secondly, the model-based visual odometry method is based on the prior depth information, the robust and accurate depth estimation is critical for the performance of visual odometry module. Based on traditional computer vision theory, stereo vision can compute the depth with the correct scale, which is more reliable than monocular solutions. However, the current two-stage 2D-3D stereo networks have the problems of depth annotations and disparity domain gap. Correspondingly, a pose-supervised stereo network and an adaptive stereo network are investigated. However, the performance of two-stage networks is limited by the quality of 2D features that build stereo-matching cost volume. Instead, a new one-stage 3D stereo network is proposed to learn features and stereo-matching implicitly in a single stage. Thirdly, to keep robust, the stereo network and the dense direct visual odometry module are combined to build a stereo hybrid dense direct visual odometry (HDVO). Dense direct visual odometry is more reliable than the feature-based method because it is optimized with global image information. The HDVO is optimized with the photometric minimization loss. However, this loss suffers noises from the occlusion area, homogeneous texture area, and dynamic objects. This thesis explores removing noisy loss values with binary masks. Moreover, to reduce the effects of dynamic objects, semantic segmentation results are used to improve these masks. Finally, to be generalized for a new data domain, a test-time training method for visual odometry is explored. These proposed methods have been evaluated on public autonomous driving benchmarks, and show state-of-the-art performances

Wen, Chien-Hsien. "Applying artificial intelligence hybrid techniques in wastewater treatment". Ohio : Ohio University, 1997. http://www.ohiolink.edu/etd/view.cgi?ohiou1184357721.

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Castorina, Giovanni. "Artificial intelligence based hybrid systems for financial forecasting". Thesis, University of the West of England, Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365146.

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Abstract (sommario):

Current research carried out on financial forecasting has highlighted some limitations of classical econometric methods based on the assumption that the investigated time series can be described as stationary stochastic processes with Gaussian probability density functions. Chaotic behaviour, fractal characteristics and non-linear dynamics have been emerging in different aspects of the financial forecasting problem. The objective of this thesis is to take a system level perspective of the financial forecasting problem and to explore a number of approaches to enhance more 'traditional' decision making flows for stock market forecasting, with particular emphasis on stock selection and timing. To achieve this purpose, a number of stock selection and timing computational 'modules' are investigated. From a computational point of view, the investigation performed in this work encompass techniques such as artificial neural networks, genetic algorithms, chaos theory and fractal geometry, as well as more traditional methods such as clustering, screening, ranking, and statistics based models. From a financial data point of view, this research takes advantage of both fundamental and technical information to enhance the stock selection and timing processes and to cover several investment horizons. Three computational modules are proposed. First, a multivariate stock ranking module which uses fundamental information and is optimised through genetic algorithms. Second, a multivariate forecasting module which uses technical information and is based on artificial neural networks. Third, a univariate price time series forecasting module based on artificial neural networks. In addition, an integrated flow that takes advantage of some synergies and complementary properties of the devised modules is proposed. The effectiveness of the developed modules and the viability of the proposed integrated flow are evaluated over a number of investment horizons using (out-of-sample) historical data.

Rodic, Daniel. "A Hybrid search heuristic-exhaustive search approach for rule extraction". Pretoria : [s.n.], 2000. http://upetd.up.ac.za/thesis/available/etd-05292006-110006/.

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Natsheh, Emad Maher. "Hybrid power systems energy management based on artificial intelligence". Thesis, Manchester Metropolitan University, 2013. http://e-space.mmu.ac.uk/314015/.

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This thesis presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers.  The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels, based on Levenberg Marquardt learning algorithm. The statistical analysis of the results indicates that the R2 value for the testing set was 0.99.  The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprises PV panels, wind turbine, battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. Moreover, perturb and observe (P&O) algorithm with two different controller techniques - the linear PI and the non-linear passivity-based controller (PBC) - are provided for a comparison with the proposed MPPT controller system. The comparison revealed the robustness of the proposed PV control system for solar irradiance and load resistance changes. Real-time measured parameters and practical load profiles are used as inputs for the developed management system. The proposed model and its control strategy offer a proper tool for optimizing the hybrid power system performance, such as the one used in smart-house applications. The research work also led to a new approach in monitoring PV power stations. The monitoring system enables system degradation early detection by calculating the residual difference between the model predicted and the actual measured power parameters. Measurements were taken over 21 month’s period; using hourly average irradiance and cell temperature. Good agreement was achieved between the theoretical simulation and the real time measurement taken the online grid connected solar power plant.

Abbas, Syed Murtuza. "Advanced Hybrid Simulation Model based on Phenomenology and Artificial Intelligence". University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1427963095.

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Chhabra, Rupanshi. "Control Power Optimization using Artificial Intelligence for Hybrid Wing Body Aircraft". Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/56580.

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Traditional methods of control allocation optimization have shown difficulties in exploiting the full potential of controlling a large array of control surfaces. This research investigates the potential of employing artificial intelligence methods like neurocomputing to the control allocation optimization problem of Hybrid Wing Body (HWB) aircraft concepts for minimizing control power, hinge moments, and actuator forces, while keeping the system weights within acceptable limits. The main objective is to develop a proof-of-concept process suitable to demonstrate the potential of using neurocomputing for optimizing actuation power for aircraft featuring multiple independently actuated control surfaces and wing flexibility. An aeroelastic Open Rotor Engine Integration and Optimization (OREIO) model was used to generate a database of hinge moment and actuation power characteristics for an array of control surface deflections. Artificial neural network incorporating a genetic algorithm then performs control allocation optimization for an example aircraft. The results showed that for the half-span model, the optimization results (for the sum of the required hinge moment) are improved by more than 11%, whereas for the full-span model, the same approach improved the result by nearly 14% over the best MSC Nastran solution by using the neural network optimization process. The results were improved by 23% and 27% over the case where only the elevator is used for both half-span and full-span models, respectively. The methods developed and applied here can be used for a wide variety of aircraft configurations.
Master of Science

Schlobach, Klaus Stefan. "Knowledge discovery in hybrid knowledge representation systems". Thesis, King's College London (University of London), 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.272023.

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Viademonte, da Rosa Sérgio I. (Sérgio Ivan) 1964. "A hybrid model for intelligent decision support : combining data mining and artificial neural networks". Monash University, School of Information Management and Systems, 2004. http://arrow.monash.edu.au/hdl/1959.1/5159.

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Wakelam, Mark. "Intelligent hybrid approach for integrated design". Thesis, University of Nottingham, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263942.

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Libri sul tema "Hybird Artificial intelligence":

Graña Romay, Manuel, Emilio Corchado e M. Teresa Garcia Sebastian, a cura di. Hybrid Artificial Intelligence Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13769-3.

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Corchado, Emilio, Manuel Graña Romay e Alexandre Manhaes Savio, a cura di. Hybrid Artificial Intelligence Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13803-4.

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Corchado, Emilio, Ajith Abraham e Witold Pedrycz, a cura di. Hybrid Artificial Intelligence Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87656-4.

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Corchado, Emilio, Xindong Wu, Erkki Oja, Álvaro Herrero e Bruno Baruque, a cura di. Hybrid Artificial Intelligence Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02319-4.

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Polycarpou, Marios, André C. P. L. F. de Carvalho, Jeng-Shyang Pan, Michał Woźniak, Héctor Quintian e Emilio Corchado, a cura di. Hybrid Artificial Intelligence Systems. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07617-1.

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Sanjurjo González, Hugo, Iker Pastor López, Pablo García Bringas, Héctor Quintián e Emilio Corchado, a cura di. Hybrid Artificial Intelligent Systems. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86271-8.

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García Bringas, Pablo, Hilde Pérez García, Francisco Javier Martínez de Pisón, José Ramón Villar Flecha, Alicia Troncoso Lora, Enrique A. de la Cal, Álvaro Herrero et al., a cura di. Hybrid Artificial Intelligent Systems. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15471-3.

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Martínez-Álvarez, Francisco, Alicia Troncoso, Héctor Quintián e Emilio Corchado, a cura di. Hybrid Artificial Intelligent Systems. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32034-2.

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de la Cal, Enrique Antonio, José Ramón Villar Flecha, Héctor Quintián e Emilio Corchado, a cura di. Hybrid Artificial Intelligent Systems. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61705-9.

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Corchado, Emilio, Václav Snášel, Ajith Abraham, Michał Woźniak, Manuel Graña e Sung-Bae Cho, a cura di. Hybrid Artificial Intelligent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28942-2.

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Capitoli di libri sul tema "Hybird Artificial intelligence":

Quiza, Ramón, Omar López-Armas e J. Paulo Davim. "Artificial Intelligence Tools". In Hybrid Modeling and Optimization of Manufacturing, 39–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28085-6_3.

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Abraham, Ajith. "Hybrid Artificial Intelligence Systems". In Advances in Soft Computing, XVI. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74972-1_1.

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Herrero, Álvaro, e Emilio Corchado. "Visualisation, Artificial Intelligence, and Security". In Mobile Hybrid Intrusion Detection, 3–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18299-0_2.

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Azizi, Aydin. "Hybrid Artificial Intelligence Optimization Technique". In Applications of Artificial Intelligence Techniques in Industry 4.0, 27–47. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2640-0_4.

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Wang, Guan, Weidong Wang e Dian Li. "A Hybrid Pattern Knowledge Graph-Based API Recommendation Approach". In Artificial Intelligence, 465–76. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20503-3_37.

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Guan, Bochen, Yanli Liu, Jinnian Zhang, William A. Sethares, Fang Liu, Qinwen Xu, Weiyi Li e Shuxue Quan. "Hybrid Domain Convolutional Neural Network for Memory Efficient Training". In Artificial Intelligence, 227–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93046-2_20.

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Su, Yuxin, Ziling Miao e Hong Liu. "Audio-Visual Multi-person Keyword Spotting via Hybrid Fusion". In Artificial Intelligence, 327–38. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20500-2_27.

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Nolle, Lars, Frederic Stahl e Tarek El-Mihoub. "On Explanations for Hybrid Artificial Intelligence". In Artificial Intelligence XL, 3–15. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47994-6_1.

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Schuchter, Florian, Katharina Bause e Albert Albers. "Intelligent Data Analytics with Artificial Intelligence for Hybrid Engine Restart". In Proceedings, 61–72. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-37011-4_6.

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Averkin, Alexey. "Hybrid Intelligent Systems Based on Fuzzy Logic and Deep Learning". In Artificial Intelligence, 3–12. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33274-7_1.

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Atti di convegni sul tema "Hybird Artificial intelligence":

Tang, Shijie, e Donglei Zhang. "Railway freight volume forecast based on Hybird Algorithms". In 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI). IEEE, 2022. http://dx.doi.org/10.1109/iwecai55315.2022.00061.

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Fan, Wangwei, Chenghu Du e Li Liu. "CA-FashionNet: Light-Weight Hybird Model for Fashion Style Classification". In 2023 2nd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR). IEEE, 2023. http://dx.doi.org/10.1109/aihcir61661.2023.00041.

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Hung, Che-Lun, Ren-You Yan e Hsiao-Hsi Wang. "Parallel image dehazing algorithm based on GPU using fuzzy system and hybird evolution algorithm". In 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE, 2016. http://dx.doi.org/10.1109/snpd.2016.7515962.

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Williams, Jessica, Rhyse Bendell, Stephen Fiore e Florian Jentsch. "Artificial Social Intelligence in Action: Lessons Learned from Human-Agent Hybrid Search and Rescue". In AHFE 2023 Hawaii Edition. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004190.

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Abstract (sommario):

Socially intelligent artificial agents have recently shown some evidence of improving team performance when advising human teammates during the execution of time-pressured, complex missions. These agents, imbued with a form of social intelligence supported by Artificial Theory of Mind, have also demonstrated some negative outcomes associated with their approaches to delivering advice and motivating teammates to succeed. Here, we closely examine team performance outcomes associated with a simulated team Urban Search and Rescue mission in the context of interventions delivered by artificial socially intelligent agents that served as advisors to the human teammates engaged in task execution. The task studied here required some individual taskwork effectiveness as well as a notable amount of interdependent teamwork coordination. The interdependent activities provided the advising artificially intelligent teammates an opportunity to observe and intervene to improve aspects of team process. Some of the interventions delivered by the socially intelligent agents were found to positively impact performance, notably those that targeted objective data and the dissemination of information to the right individual at appropriate timepoints; however, other interventions negatively impacted team outcomes. Results showed that Motivation interventions aimed solely at bolstering the motivation of team members did not yield positive outcomes; in fact, they were found to have adverse effects on overall team performance and task execution.

Villata, Serena. "Artificial Argumentation for Humans". In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/818.

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The latest years have seen an increasing interest in the topic of Artificial Intelligence (AI), the challenges it is facing, and the recent advances it has achieved, e.g., intelligent personal assistants. Differently from the past, where research on AI was mainly confined in research labs, the topic is now attracting interest from a wider audience, including policy-makers, information technology companies, and philosophers. Alas, these advances have also raised a number of concerns on AI’s social, economic, and legal impact. Hence, the definition of design principles and automated methods to support transparent intelligent machine deliberation is highly desirable. Argumentation is important for handling conflicting beliefs, assumptions, opinions, goals, and many other mental attitudes. Argumentation pervades human intelligent behavior, and I believe that it is a mandatory element to conceive autonomous artificial machines that can exploit argumentation models and tools in the cognitive tasks they are required to carry out. Results in this area will allow reducing the gap between humans and machines towards a good AI hybrid society.

Henshaw, P. D., e A. B. Todtenkopf. "Artificial Intelligence Applications Of Fast Optical Memory Access". In Optical and Hybrid Computing, a cura di Harold H. Szu. SPIE, 1986. http://dx.doi.org/10.1117/12.964029.

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Casasent, David. "Scene Analysis Research: Optical Pattern Recognition And Artificial Intelligence". In Optical and Hybrid Computing, a cura di Harold H. Szu. SPIE, 1986. http://dx.doi.org/10.1117/12.964030.

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Kalam, Shams, Sidqi A. Abu-Khamsin, Mohammad Rasheed Khan, Asiya Abbasi, Abdul Asad e Rizwan Ahmed Khan. "Data Driven Intelligent Modeling to Estimate Adsorption of Methane Gas in Shales". In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22101-ms.

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Abstract Artificial intelligence is a smart tool widely used in Petroleum engineering. Adaptive Neuro-Fuzzy Inference System (ANFIS) is an artificial intelligence technique that is a hybrid between Artificial Neural Networks (ANN) and fuzzy logic. In this paper, both ANN and ANFIS were applied to propose a new methodology based on intelligent algorithms to predict adsorption of methane gas in shale. Feed-Forward Neural Network and subtractive clustering were applied to correlate adsorption with several parameters. These include temperature, pressure, moisture content, and total organic content (TOC). A real data set collected from the literature, which includes about 350 data points, was used in the development of a new empirical correlation. The set was divided into a 70:30 ratio for training and testing, respectively. The average absolute percentage error, correlation coefficient, and mean squared error were considered in the error metrics to obtain the best possible model. The results show that methane adsorption can be efficiently correlated with the inputs using both machine learning tools. Using ANN, the correlation coefficient for both testing and training data was more than 99%. A detailed sensitivity analysis for the ANN model is also provided in this paper.

Rupprecht, Patrick, e Walter Mayrhofer. "Hybrid Intelligence - An Approach towards the Symbiosis of Artificial and Human Creativity and Interaction in the Design and Innovation Process in SMEs". In 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1004718.

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In a world where Artificial Intelligence (AI) is accelerating technological change, small and medium-sized enterprises (SMEs) are challenged to remain competitive and innovative. SMEs, which often are faced with having only limited time and financial resources. particularly struggle to test emerging technologies and develop innovations. In this context, recent studies suggest that the combination of artificial intelligence and the creativity of SME employees may provide a viable solution. According to latest findings, the combination of human and artificial intelligence, also referred to as “Hybrid Intelligence”, has significant potential to enhance innovation processes and increase productivity and creativity. The primary objective of this paper is to investigate how Generative Artificial Intelligence (Generative AI) can enhance creativity and efficiency in established design and innovation processes, such as Design Thinking. In addition, the paper aims to explore the possible symbiotic interaction between humans and artificial intelligence during the different phases of the innovation process in general and within innovation workshops in particular.In a first approach, an overview of human and AI capabilities and the use of Generative AI throughout the innovation process is explored. In addition, several perspectives on the use of symbiotic Human-AI interaction in creative innovation workshops are presented. Finally, the importance of interdisciplinary collaboration and the integration of Hybrid Intelligence in SMEs to enhance creativity and efficiency in the innovation process are highlighted.

Grünbichler, Rudolf, e Raphael Krebs. "Using AI in SMEs to Prevent Corporate Insolvencies: Identification of Frequently Used Algorithms Based on a Literature Review". In Seventh International Scientific-Business Conference LIMEN Leadership, Innovation, Management and Economics: Integrated Politics of Research. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2021. http://dx.doi.org/10.31410/limen.s.p.2021.27.

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Digitization in enterprises enables the application of artificial intelligence, especially machine learning. One area of use for artificial intelligence is in the creation of an insolvency forecast for companies. With a literature review, the current status on the usage of artificial intelligence in insolvency forecasting is presented. For this purpose, the two databases Scopus and Web of Science are searched for scientific papers on the topic of artificial intelligence and corporate insolvencies to get an up-to-date impression of the status quo. A particular focus is placed on small and medium-sized companies. It is shown that artificial intelligence methods provide better results compared to classical methods. The research reveals that the most important algorithms related to the prediction of potential corporate insolvency are artificial neural networks, decision trees and support vector machines as well as hybrid models.

Rapporti di organizzazioni sul tema "Hybird Artificial intelligence":

Reifman, J., T. Y. C. Wei, J. E. Vitela, C. A. Applequist e T. M. Chasensky. Prodiag--a hybrid artificial intelligence based reactor diagnostic system for process faults. Office of Scientific and Technical Information (OSTI), marzo 1996. http://dx.doi.org/10.2172/224950.

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Perdigão, Rui A. P. Earth System Dynamic Intelligence - ESDI. Meteoceanics, aprile 2021. http://dx.doi.org/10.46337/esdi.210414.

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Earth System Dynamic Intelligence (ESDI) entails developing and making innovative use of emerging concepts and pathways in mathematical geophysics, Earth System Dynamics, and information technologies to sense, monitor, harness, analyze, model and fundamentally unveil dynamic understanding across the natural, social and technical geosciences, including the associated manifold multiscale multidomain processes, interactions and complexity, along with the associated predictability and uncertainty dynamics. The ESDI Flagship initiative ignites the development, discussion and cross-fertilization of novel theoretical insights, methodological developments and geophysical applications across interdisciplinary mathematical, geophysical and information technological approaches towards a cross-cutting, mathematically sound, physically consistent, socially conscious and operationally effective Earth System Dynamic Intelligence. Going beyond the well established stochastic-dynamic, information-theoretic, artificial intelligence, mechanistic and hybrid techniques, ESDI paves the way to exploratory and disruptive developments along emerging information physical intelligence pathways, and bridges fundamental and operational complex problem solving across frontier natural, social and technical geosciences. Overall, the ESDI Flagship breeds a nascent field and community where methodological ingenuity and natural process understanding come together to shed light onto fundamental theoretical aspects to build innovative methodologies, products and services to tackle real-world challenges facing our planet.

Perdigão, Rui A. P. Earth System Dynamic Intelligence with Quantum Technologies: Seeing the “Invisible”, Predicting the “Unpredictable” in a Critically Changing World. Meteoceanics, ottobre 2021. http://dx.doi.org/10.46337/211028.

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We hereby embark on a frontier journey articulating two of our flagship programs – “Earth System Dynamic Intelligence” and “Quantum Information Technologies in the Earth Sciences” – to take the pulse of our planet and discern its manifold complexity in a critically changing world. Going beyond the traditional stochastic-dynamic, information-theoretic, artificial intelligence, mechanistic and hybrid approaches to information and complexity, the underlying fundamental science ignites disruptive developments empowering complex problem solving across frontier natural, social and technical geosciences. Taking aim at complex multiscale planetary problems, the roles of our flagships are put into evidence in different contexts, ranging from I) Interdisciplinary analytics, model design and dynamic prediction of hydro-climatic and broader geophysical criticalities and extremes across multiple spatiotemporal scales; to II) Sensing the pulse of our planet and detecting early warning signs of geophysical phenomena from Space with our Meteoceanics QITES Constellation, at the interface between our latest developments in non-linear dynamics and emerging quantum technologies.

Davies, Will. Improving the engagement of UK armed forces overseas. Royal Institute of International Affairs, gennaio 2022. http://dx.doi.org/10.55317/9781784135010.

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The UK government’s Integrated Review of security, defence, development and foreign policy, published in March 2021 alongside a supporting defence command paper, set a new course for UK national security and highlighted opportunities for an innovative approach to international engagement activity. The Integrated Review focused principally on the state threats posed by China’s increasing power and by competitors – including Russia – armed with nuclear, conventional and hybrid capabilities. It also stressed the continuing risks to global security and resilience due to conflict and instability in weakened and failed states. These threats have the potential to increase poverty and inequality, violent extremism, climate degradation and the forced displacement of people, while presenting authoritarian competitors with opportunities to enhance their geopolitical influence. There are moral, security and economic motives to foster durable peace in conflict-prone and weakened regions through a peacebuilding approach that promotes good governance, addresses the root causes of conflict and prevents violence, while denying opportunities to state competitors. The recent withdrawal from Afghanistan serves to emphasize the complexities and potential pitfalls associated with intervention operations in complex, unstable regions. Success in the future will require the full, sustained and coordinated integration of national, allied and regional levers of power underpinned by a sophisticated understanding of the operating environment. The UK armed forces, with their considerable resources and global network, will contribute to this effort through ‘persistent engagement’. This is a new approach to overseas operations below the threshold of conflict, designed as a pre-emptive complement to warfighting. To achieve this, the UK Ministry of Defence (MOD) must develop a capability that can operate effectively in weak, unstable and complex regions prone to violent conflict and crises, not least in the regions on the eastern and southern flanks of the Euro-Atlantic area. The first step must be the development of a cohort of military personnel with enhanced, tailored levels of knowledge, skills and experience. Engagement roles must be filled by operators with specialist knowledge, skills and experience forged beyond the mainstream discipline of combat and warfighting. Only then will individuals develop a genuinely sophisticated understanding of complex, politically driven and sensitive operating environments and be able to infuse the design and delivery of international activities with practical wisdom and insight. Engagement personnel need to be equipped with: An inherent understanding of the human and political dimensions of conflict, the underlying drivers such as inequality and scarcity, and the exacerbating factors such as climate change and migration; - A grounding in social sciences and conflict modelling in order to understand complex human terrain; - Regional expertise enabled by language skills, cultural intelligence and human networks; - Familiarity with a diverse range of partners, allies and local actors and their approaches; - Expertise in building partner capacity and applying defence capabilities to deliver stability and peace; - A grasp of emerging artificial intelligence technology as a tool to understand human terrain; - Reach and insight developed through ‘knowledge networks’ of external experts in academia, think-tanks and NGOs. Successful change will be dependent on strong and overt advocacy by the MOD’s senior leadership and a revised set of personnel policies and procedures for this cohort’s selection, education, training, career management, incentivization, sustainability and support.