Academic literature on the topic 'Hybird Artificial intelligence'
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Journal articles on the topic "Hybird Artificial intelligence":
Zhang, Nian, Yunpeng Han, Quanshen Si, and Guiwu Wei. "A novel method for multi-attribute risk decision-making based on regret theory and hybird information." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 6955–64. http://dx.doi.org/10.3233/jifs-200081.
Douzi, Samira, Feda A. AlShahwan, Mouad Lemoudden, and Bouabid El Ouahidi. "Hybrid Email Spam Detection Model Using Artificial Intelligence." International Journal of Machine Learning and Computing 10, no. 2 (February 2020): 316–22. http://dx.doi.org/10.18178/ijmlc.2020.10.2.937.
Chen, Liming, Huansheng Ning, Chris D. Nugent, and Zhiwen Yu. "Hybrid Human-Artificial Intelligence." Computer 53, no. 8 (August 2020): 14–17. http://dx.doi.org/10.1109/mc.2020.2997573.
Abubakar, A. Mohammed. "Using hybrid SEM – artificial intelligence." Personnel Review 49, no. 1 (November 19, 2019): 67–86. http://dx.doi.org/10.1108/pr-06-2017-0180.
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, no. 05 (October 2023): 296–305. http://dx.doi.org/10.1055/a-2157-6810.
González Quirós, José Luis, and David Díaz Pardo de Vera. "Theory of mind: from artificial intelligence to hybrid intelligence." TECHNO REVIEW. International Technology, Science and Society Review 9, no. 2 (January 18, 2021): 103–19. http://dx.doi.org/10.37467/gka-revtechno.v9.2816.
Jarrahi, Mohammad Hossein, Christoph Lutz, and Gemma Newlands. "Artificial intelligence, human intelligence and hybrid intelligence based on mutual augmentation." Big Data & Society 9, no. 2 (July 2022): 205395172211428. http://dx.doi.org/10.1177/20539517221142824.
Gudova, M. Yu, E. V. Rubtsova, and 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, no. 2 (2021): 235–49. http://dx.doi.org/10.15826/izv1.2021.27.2.048.
Fox, Stephen. "Beyond AI: Multi-Intelligence (MI) Combining Natural and Artificial Intelligences in Hybrid Beings and Systems." Technologies 5, no. 3 (June 22, 2017): 38. http://dx.doi.org/10.3390/technologies5030038.
Lukyanova, Ekaterina D. "Artificial Intelligence: Achievements and Postponed Risks." Sociologicheskaja nauka i social naja praktika 7, no. 1 (2019): 142–48. http://dx.doi.org/10.19181/snsp.2019.7.1.6275.
Dissertations / Theses on the topic "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.
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.
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.
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/.
Natsheh, Emad Maher. "Hybrid power systems energy management based on artificial intelligence." Thesis, Manchester Metropolitan University, 2013. http://e-space.mmu.ac.uk/314015/.
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.
Chhabra, Rupanshi. "Control Power Optimization using Artificial Intelligence for Hybrid Wing Body Aircraft." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/56580.
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.
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.
Wakelam, Mark. "Intelligent hybrid approach for integrated design." Thesis, University of Nottingham, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263942.
Books on the topic "Hybird Artificial intelligence":
Graña Romay, Manuel, Emilio Corchado, and M. Teresa Garcia Sebastian, eds. Hybrid Artificial Intelligence Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13769-3.
Corchado, Emilio, Manuel Graña Romay, and Alexandre Manhaes Savio, eds. Hybrid Artificial Intelligence Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13803-4.
Corchado, Emilio, Ajith Abraham, and Witold Pedrycz, eds. Hybrid Artificial Intelligence Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87656-4.
Corchado, Emilio, Xindong Wu, Erkki Oja, Álvaro Herrero, and Bruno Baruque, eds. Hybrid Artificial Intelligence Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02319-4.
Polycarpou, Marios, André C. P. L. F. de Carvalho, Jeng-Shyang Pan, Michał Woźniak, Héctor Quintian, and Emilio Corchado, eds. Hybrid Artificial Intelligence Systems. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07617-1.
Sanjurjo González, Hugo, Iker Pastor López, Pablo García Bringas, Héctor Quintián, and Emilio Corchado, eds. Hybrid Artificial Intelligent Systems. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86271-8.
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., eds. Hybrid Artificial Intelligent Systems. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15471-3.
Martínez-Álvarez, Francisco, Alicia Troncoso, Héctor Quintián, and Emilio Corchado, eds. Hybrid Artificial Intelligent Systems. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32034-2.
de la Cal, Enrique Antonio, José Ramón Villar Flecha, Héctor Quintián, and Emilio Corchado, eds. Hybrid Artificial Intelligent Systems. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61705-9.
Corchado, Emilio, Václav Snášel, Ajith Abraham, Michał Woźniak, Manuel Graña, and Sung-Bae Cho, eds. Hybrid Artificial Intelligent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28942-2.
Book chapters on the topic "Hybird Artificial intelligence":
Quiza, Ramón, Omar López-Armas, and 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.
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.
Herrero, Álvaro, and 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.
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.
Wang, Guan, Weidong Wang, and 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.
Guan, Bochen, Yanli Liu, Jinnian Zhang, William A. Sethares, Fang Liu, Qinwen Xu, Weiyi Li, and 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.
Su, Yuxin, Ziling Miao, and 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.
Nolle, Lars, Frederic Stahl, and 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.
Schuchter, Florian, Katharina Bause, and 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.
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.
Conference papers on the topic "Hybird Artificial intelligence":
Tang, Shijie, and 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.
Fan, Wangwei, Chenghu Du, and 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.
Hung, Che-Lun, Ren-You Yan, and 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.
Williams, Jessica, Rhyse Bendell, Stephen Fiore, and 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.
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.
Henshaw, P. D., and A. B. Todtenkopf. "Artificial Intelligence Applications Of Fast Optical Memory Access." In Optical and Hybrid Computing, edited by Harold H. Szu. SPIE, 1986. http://dx.doi.org/10.1117/12.964029.
Casasent, David. "Scene Analysis Research: Optical Pattern Recognition And Artificial Intelligence." In Optical and Hybrid Computing, edited by Harold H. Szu. SPIE, 1986. http://dx.doi.org/10.1117/12.964030.
Kalam, Shams, Sidqi A. Abu-Khamsin, Mohammad Rasheed Khan, Asiya Abbasi, Abdul Asad, and 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.
Rupprecht, Patrick, and 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.
Grünbichler, Rudolf, and 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.
Reports on the topic "Hybird Artificial intelligence":
Reifman, J., T. Y. C. Wei, J. E. Vitela, C. A. Applequist, and T. M. Chasensky. Prodiag--a hybrid artificial intelligence based reactor diagnostic system for process faults. Office of Scientific and Technical Information (OSTI), March 1996. http://dx.doi.org/10.2172/224950.
Perdigão, Rui A. P. Earth System Dynamic Intelligence - ESDI. Meteoceanics, April 2021. http://dx.doi.org/10.46337/esdi.210414.
Perdigão, Rui A. P. Earth System Dynamic Intelligence with Quantum Technologies: Seeing the “Invisible”, Predicting the “Unpredictable” in a Critically Changing World. Meteoceanics, October 2021. http://dx.doi.org/10.46337/211028.
Davies, Will. Improving the engagement of UK armed forces overseas. Royal Institute of International Affairs, January 2022. http://dx.doi.org/10.55317/9781784135010.