Articoli di riviste: "Hybrid AI"

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Hopgood, A. "Hybrid AI". ITNOW 55, n. 4 (26 novembre 2013): 10–11. http://dx.doi.org/10.1093/itnow/bwt066.

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Ikegaya, Yuji. "Brain-AI hybrid". Proceedings for Annual Meeting of The Japanese Pharmacological Society 97 (2023): 3—B—SL16. http://dx.doi.org/10.1254/jpssuppl.97.0_3-b-sl16.

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Silva, Felipe Leno Da, Silvio Stanzani, Jefferson Fialho, Jorge Mondadori, Muriel Mazzetto, Felipe Sanches Couto e Raphael Cobe. "Designing a Hybrid AI Residency". Proceedings of the AAAI Conference on Artificial Intelligence 35, n. 17 (18 maggio 2021): 15640–46. http://dx.doi.org/10.1609/aaai.v35i17.17842.

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The industry demand for AI experts raised to unprecedented levels in the last years. However, the increasing demand was not met by the number of skilled professionals in this area. As an effort to mitigate this problem, many companies create AI residency programs to provide in-house practical training. However, we argue that the usual dynamics based on one-on-one mentorship in those programs is very hard to scale and insufficient to meet the demand for AI professionals. In this paper, we describe a hybrid AI residency program that connects educational institutions, partner companies, and prospective residents. This program is designed to be funded by partner companies.Residents are exposed to practical projects of industry interest and are instructed on AI techniques and tools. We describe how we implemented our program, the challenges involved, and the lessons learned after the conclusion of the first residency class. Our program was developed to be inclusive and scalable, and resulted in a high employment rate for our alumni. Furthermore, several partner companies invested in in-house AI teams after the residency, resulting in direct benefits for our local AI community.

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Siddique, Nazmul H., Balasundram P. Amavasai e Akira Ikuta. "Editorial: Hybrid Techniques in AI". Artificial Intelligence Review 27, n. 2-3 (marzo 2007): 77–78. http://dx.doi.org/10.1007/s10462-008-9085-2.

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Mateas, Michael. "Expressive AI: A Hybrid Art and Science Practice". Leonardo 34, n. 2 (aprile 2001): 147–53. http://dx.doi.org/10.1162/002409401750184717.

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Expressive AI is a new interdiscipline of AI-based cultural production, combining art practice and AI-research practice. This article explores expressive AI by comparing it with other AI discourses, describing how it borrows notions of interpretation and authorship from both art and AI research practice and providing preliminary desiderata for the practice.

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Monostori, L., Cs Egresits e B. Kádár. "Hybrid AI Approaches to Intelligent Manufacturing". IFAC Proceedings Volumes 29, n. 1 (giugno 1996): 571–76. http://dx.doi.org/10.1016/s1474-6670(17)57723-x.

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Khriapynskyi, Anton, Ihor Khmyrov, Ivo Svoboda, Mykhailo Shevchuk e Vira Iastrebova. "State information security strategies in conditions of hybrid threats". Revista Amazonia Investiga 12, n. 69 (30 settembre 2023): 84–93. http://dx.doi.org/10.34069/ai/2023.69.09.7.

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Hybrid information threats under the conditions of modern development of digital technologies are currently becoming one of the major issues for a modern democracy. The amount of damage that hybrid threats bring to the world economy contributes to the establishment of effective legal mechanisms to combat them. The purpose of the study was to single out the information security strategies under conditions of hybrid threats, including the spread of disinformation and fake news. The application of the comparative analysis method mad it possible to identify the gaps in information security strategies for countering hybrid threats. The strategy of information security in the conditions of hybrid threats is a coordinated action plan aimed at countering and fighting hybrid threats to safeguard cyberspace and preserve a democracy. Information security against hybrid threats is based on such cornerstones as: availability, confidentiality, integrity of information data, and safety. Enhancement of information security under the conditions of hybrid threats should be carried out at the expense of digital transformation, increasing the level of digital literacy of society and establishing a fair responsibility measure for a purposeful spreading of disinformation. The perspective of further research is addressing information security strategies as well as elaborating practical guidelines for the formation of a secure information space.

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Almusaed, Amjad, Asaad Almssad, Ibrahim Yitmen e Raad Z. Homod. "Enhancing Student Engagement: Harnessing “AIED”’s Power in Hybrid Education—A Review Analysis". Education Sciences 13, n. 7 (21 giugno 2023): 632. http://dx.doi.org/10.3390/educsci13070632.

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Hybrid learning is a complex combination of face-to-face and online learning. This model combines the use of multimedia materials with traditional classroom work. Virtual hybrid learning is employed alongside face-to-face methods. That aims to investigate using Artificial Intelligence (AI) to increase student engagement in hybrid learning settings. Educators are confronted with contemporary issues in maintaining their students’ interest and motivation as the popularity of online and hybrid education continues to grow, where many educational institutions are adopting this model due to its flexibility, student-teacher engagement, and peer-to-peer interaction. AI will help students communicate, collaborate, and receive real-time feedback, all of which are challenges in education. This article examines the advantages and disadvantages of hybrid education and the optimal approaches for incorporating Artificial Intelligence (AI) in educational settings. The research findings suggest that using AI can revolutionize hybrid education, as it enhances both student and instructor autonomy while fostering a more engaging and interactive learning environment.

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Yang, J. B. "Hybrid AI system for retaining wall selection". Construction Innovation 4, n. 1 (marzo 2004): 33–52. http://dx.doi.org/10.1108/14714170410814999.

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Yang, J. B. "Hybrid AI system for retaining wall selection". Construction Innovation 4, n. 1 (1 marzo 2004): 33–52. http://dx.doi.org/10.1191/1471417504ci065oa.

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Henshaw, Carl Glen, e Robert M. Sanner. "Hybrid AI/control system interactions and analysis". Journal of Experimental & Theoretical Artificial Intelligence 16, n. 4 (ottobre 2004): 189–208. http://dx.doi.org/10.1080/09528130412331294724.

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James, Alex Pappachen. "A hybrid memristor–CMOS chip for AI". Nature Electronics 2, n. 7 (luglio 2019): 268–69. http://dx.doi.org/10.1038/s41928-019-0274-6.

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Werle, Rodrigo, Jared J. Schmidt, John Laborde, Angela Tran, Cody F. Creech e John L. Lindquist. "Shattercane X ALS-Tolerant Sorghum F1 Hybrid and Shattercane Interference in ALS-Tolerant Sorghum". Journal of Agricultural Science 6, n. 4 (15 marzo 2014): 159. http://dx.doi.org/10.5539/jas.v6n4p159.

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ALS-tolerant grain sorghum cultivars are expected to be available for farmers within the next few years. Knowing that: i) crosses between sorghum and shattercane are likely to occur resulting in crop-to-weed gene flow; ii) ALS-susceptible shattercane X ALS-tolerant grain sorghum F1 hybrids (hybrids) were ultimately resistant to ALS-herbicides under field conditions; and iii) hybrid fitness is equal to, or greater than, the wild parent, we conducted a greenhouse study to compare the competitive effect of shattercane and hybrid on sorghum, and whether or not herbicide application would influence the competitive ability of the hybrid plants. An additive design was used where weed densities varied while that of crop remained constant. The treatment design was a factorial with two weedy genotypes, shattercane and hybrid, with the hybrid being either exposed or not exposed to an ALS-herbicide application (nicosulfuron (26.25 g ai ha-1) + rimsulfuron (13.16 g ai ha-1)), and five weed densities (0, 1, 2, 3, and 4 plants pot-1). Sorghum density was kept at 1 plant pot-1. F-tests were performed to compare differences across treatment levels. Shattercane and hybrid produced similar amounts of total above ground biomass within each density, and herbicide exposure did not decrease hybrid biomass production. Moreover, shattercane and hybrid competed similarly with sorghum, and a herbicide application did not reduce the competitive ability of the hybrid. Sorghum wild relatives must be managed by alternative methods before and during the adoption of ALS-tolerant sorghum technology to avoid gene flow and crop yield loss due to competition.

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Sun, Ziheng, Liping Di, Annie Burgess, Jason A. Tullis e Andrew B. Magill. "Geoweaver: Advanced Cyberinfrastructure for Managing Hybrid Geoscientific AI Workflows". ISPRS International Journal of Geo-Information 9, n. 2 (21 febbraio 2020): 119. http://dx.doi.org/10.3390/ijgi9020119.

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AI (artificial intelligence)-based analysis of geospatial data has gained a lot of attention. Geospatial datasets are multi-dimensional; have spatiotemporal context; exist in disparate formats; and require sophisticated AI workflows that include not only the AI algorithm training and testing, but also data preprocessing and result post-processing. This complexity poses a huge challenge when it comes to full-stack AI workflow management, as researchers often use an assortment of time-intensive manual operations to manage their projects. However, none of the existing workflow management software provides a satisfying solution on hybrid resources, full file access, data flow, code control, and provenance. This paper introduces a new system named Geoweaver to improve the efficiency of full-stack AI workflow management. It supports linking all the preprocessing, AI training and testing, and post-processing steps into a single automated workflow. To demonstrate its utility, we present a use case in which Geoweaver manages end-to-end deep learning for in-time crop mapping using Landsat data. We show how Geoweaver effectively removes the tedium of managing various scripts, code, libraries, Jupyter Notebooks, datasets, servers, and platforms, greatly reducing the time, cost, and effort researchers must spend on such AI-based workflows. The concepts demonstrated through Geoweaver serve as an important building block in the future of cyberinfrastructure for AI research.

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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.

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van Bekkum, Michael, Maaike de Boer, Frank van Harmelen, André Meyer-Vitali e Annette ten Teije. "Modular design patterns for hybrid learning and reasoning systems". Applied Intelligence 51, n. 9 (18 giugno 2021): 6528–46. http://dx.doi.org/10.1007/s10489-021-02394-3.

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AbstractThe unification of statistical (data-driven) and symbolic (knowledge-driven) methods is widely recognized as one of the key challenges of modern AI. Recent years have seen a large number of publications on such hybrid neuro-symbolic AI systems. That rapidly growing literature is highly diverse, mostly empirical, and is lacking a unifying view of the large variety of these hybrid systems. In this paper, we analyze a large body of recent literature and we propose a set of modular design patterns for such hybrid, neuro-symbolic systems. We are able to describe the architecture of a very large number of hybrid systems by composing only a small set of elementary patterns as building blocks. The main contributions of this paper are: 1) a taxonomically organised vocabulary to describe both processes and data structures used in hybrid systems; 2) a set of 15+ design patterns for hybrid AI systems organized in a set of elementary patterns and a set of compositional patterns; 3) an application of these design patterns in two realistic use-cases for hybrid AI systems. Our patterns reveal similarities between systems that were not recognized until now. Finally, our design patterns extend and refine Kautz’s earlier attempt at categorizing neuro-symbolic architectures.

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Brosnan, James T., Gregory K. Breeden, Adam W. Thoms e John C. Sorochan. "Effects of Preemergence Herbicides on the Establishment Rate and Tensile Strength of Hybrid Bermudagrass Sod". Weed Technology 28, n. 1 (marzo 2014): 206–12. http://dx.doi.org/10.1614/wt-d-13-00102.1.

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Use of PRE herbicides for control of annual grassy weeds during commercial sod production has been limited. Research was conducted from 2010 to 2013 evaluating the effects of pendimethalin (3.36 kg ai ha−1), dithiopyr (0.56 kg ai ha−1), prodiamine (0.6 kg ai ha−1), oxadiazon (3.36 kg ai ha−1), prodiamine + sulfentrazone (0.84 + 0.41 kg ai ha−1), dimethenamid-P(1.68 kg ai ha−1), and indaziflam (0.03 and 0.05 kg ai ha−1) applications at sprigging on the establishment rate and tensile strength of ‘Tifway' hybrid bermudagrass sod at harvest (377 d after sprigging). All herbicides increased the days required to reach 50% hybrid bermudagrass cover compared with the nontreated control. Days required to reach 50% hybrid bermudagrass cover were lowest for oxadiazon and prodiamine (39 to 44 d), greatest for both rates of indaziflam (65 to 141 d), with dimethenamid-P, prodiamine plus sulfentrazone, pendimethalin, and dithiopyr ranking intermediate (45 to 63 d). Both rates of indaziflam reduced sod tensile strength at harvest compared with the nontreated control during a single year of the study; no other differences in sod strength due to herbicide treatment were detected at any time.

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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:

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Schuering, Ben, e Thomas Schmid. "What Can Computers Do Now? Dreyfus Revisited for the Third Wave of Artificial Intelligence". Proceedings of the AAAI Symposium Series 3, n. 1 (20 maggio 2024): 248–52. http://dx.doi.org/10.1609/aaaiss.v3i1.31207.

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In recent years, artificial intelligence (AI) has seen significant advances that have in fact exceeded even optimistic prognoses. Using data-driven AI, namely deep learning techniques, it has been demonstrated that computers may now be equipped with abilities of remarkable scope and quality, such as solving image and text processing tasks at human level. Large language models, in particular, have sparked debates regarding opportunities and challenges of this rapidly developing area. Will remaining fundamental challenges of data-driven AI, such as factual or logical mistakes, be overcome for good if complemented and hybridized with symbolic AI techniques, such as knowledge representation and reasoning? Will systems of artificial general intelligence (AGI) emerge from this, possessing common sense and in fact completing the decades-old quest for AI that motivated the raise of the field in the 1950s? In the light of these questions, we review the likewise, decades-old philosophical debate about capabilities and limitations of computers from a hybrid AI point of view. Here, we discuss how hybrid AI is coming closer to disproving Hubert Dreyfus’ famous statements regarding what computers can not do. At the same time, we shed light on a lesser discussed challenge for hybrid AI: the possibility that its developers might be its biggest limiters.

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Slater, Louise J., Louise Arnal, Marie-Amélie Boucher, Annie Y. Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing et al. "Hybrid forecasting: blending climate predictions with AI models". Hydrology and Earth System Sciences 27, n. 9 (15 maggio 2023): 1865–89. http://dx.doi.org/10.5194/hess-27-1865-2023.

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Abstract. Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology, and Earth system models – into a final prediction product. They are recognized as a promising way of enhancing the prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at subseasonal to decadal scales, a better appreciation of the strengths of AI, and expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally expensive offline land model, can minimize the effect of biases that exist within dynamical outputs, benefit from the strengths of machine learning, and can learn from large datasets, while combining different sources of predictability with varying time horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities for further research. These include obtaining physically explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.

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Rafner, Janet, Miroslav Gajdacz, Gitte Kragh, Arthur Hjorth, Anna Gander, Blanka Palfi, Aleksandra Berditchevskiaia et al. "Revisiting Citizen Science Through the Lens of Hybrid Intelligence". Human Computation 9, n. 1 (16 novembre 2022): 66–95. http://dx.doi.org/10.15346/hc.v9i1.133.

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Artificial Intelligence (AI) can augment and sometimes even replace human cognition. Inspired by efforts to value human agency alongside productivity, we discuss and categorize the potential of solving Citizen Science (CS) tasks with Hybrid Intelligence (HI), a synergetic mixture of human and artificial intelligence. Due to the unique participant-centered set of values and the abundance of tasks drawing upon both human common sense and complex 21st century skills, we believe that the field of CS offers an invaluable testbed for the development of human-centered AI including HI, while also benefiting CS. In order to investigate this potential, we first relate CS to adjacent computational disciplines. Then, we demonstrate that CS projects can be grouped according to their potential for HI-enhancement by examining two key dimensions: the level of digitization and the amount of knowledge or experience required for participation. Finally, we propose a framework for types of human-AI interaction in CS based on established criteria of HI. This “HI lens” provides the CS community with an overview of ways to utilize the combination of AI and human intelligence in their projects. For AI researchers, this work highlights the opportunity CS presents to engage with real-world data sets and explore new AI methods and applications.

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Montani, Stefania, e Manuel Striani. "Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey". Yearbook of Medical Informatics 28, n. 01 (agosto 2019): 120–27. http://dx.doi.org/10.1055/s-0039-1677911.

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Objectives: This survey analyses the latest literature contributions to clinical decision support systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt Artificial Intelligence (AI) techniques in a broad sense. The goal is to analyse the distribution of data-driven AI approaches with respect to “classical" knowledge-based ones, and to consider the issues raised and their possible solutions. Methods: We included PubMed and Web of ScienceTM publications, focusing on contributions describing clinical DSSs that adopted one or more AI methodologies. Results: We selected 75 papers, 49 of which describe approaches in the data-driven AI area, 20 present purely knowledge-based DSSs, and 6 adopt hybrid approaches relying on both formalized knowledge and data. Conclusions: Recent studies in the clinical DSS area demonstrate a prevalence of data-driven AI, which can be adopted autonomously in purely data-driven systems, or in cooperation with domain knowledge in hybrid systems. Such hybrid approaches, able to conjugate all available knowledge sources through proper knowledge integration steps, represent an interesting example of synergy between the two AI categories. This synergy can lead to the resolution of some existing issues, such as the need for transparency and explainability, nowadays recognized as central themes to be addressed by both AI and medical informatics research.

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Funabashi, M., A. Maeda, Y. Morooka e K. Mori. "Fuzzy and neural hybrid expert systems: synergetic AI". IEEE Expert 10, n. 4 (agosto 1995): 32–40. http://dx.doi.org/10.1109/64.403949.

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Patil, R., A. Shreya, P. Maulik e S. Chaudhury. "Hybrid AI based stroke characterization with explainable model". Journal of the Neurological Sciences 405 (ottobre 2019): 162–63. http://dx.doi.org/10.1016/j.jns.2019.10.753.

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Maletzki, Carsten, Eric Rietzke e Ralph Bergmann. "Empowering Large Language Models in Hybrid Intelligence Systems through Data-Centric Process Models". Proceedings of the AAAI Symposium Series 3, n. 1 (20 maggio 2024): 167–74. http://dx.doi.org/10.1609/aaaiss.v3i1.31196.

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Hybrid intelligence systems aim to leverage synergies in closely collaborating teams of humans and artificial intelligence (AI). To guide the realization of such teams, recent research proposed design patterns that capture role-based knowledge on human-AI collaborations. Building on these patterns requires hybrid intelligence systems to provide mechanisms that orchestrate human and AI contributions accordingly. So far, it is unclear if such mechanisms can be provided based on shared representations of the required knowledge. In this regard, we expect ontology-based data-centric process modeling to be a promising direction for hybrid intelligence systems that aim to support knowledge-intensive processes (KiPs). We illustrate this through exemplary process models (realized with our ontology- and data-driven business process model -- ODD-BP) that reflect the team design patterns for hybrid intelligence systems. We point out that relying on such process models enables multiple actors to fulfill roles jointly and allows them to address individual shortcomings. This is examined by discussing integrating large language models (LLMs) into the process models and describing how complementary AI actors could help to empower LLMs to fulfill their role in human-AI collaboration more comprehensively. Future work will extend the provided concepts while their evaluation initially focuses on the KiP of medical emergency call handling.

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Brito, Thawane Rodrigues, David Pessoa Guedes, José Reinaldo Moreira da Silva, Edy Eime Pereira Baraúna e Juarez Benigno Paes. "Influence of chromated copper arsenate type-C concentration on the quality of treated fence posts". Research, Society and Development 11, n. 4 (16 marzo 2022): e21411427088. http://dx.doi.org/10.33448/rsd-v11i4.27088.

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This study aimed to reduce the concentration of chromated copper arsenate type-C (CCA-C) in preservative treatments of Eucalyptus sp. for use in applications with ground contact. The genetic materials used were Eucalyptus cloeziana of seminal origin and the clonal hybrid (E. urophylla x E. camaldulensis), which was derived from six year old crops at commercial plantations. The basic density, moisture content, percentage of sapwood, and wood treatable volume were determined, and the anatomical structures were characterized. The genetic materials were treated with concentrations of 1.2%, 1.3%, 1.4%, 1.5%, and 1.6% active ingredients (AI) of CCA-C. The quality of the preservative treatment was evaluated via penetration and retention tests. The treated wood volume of E. cloeziana exhibited 44% irregular partial penetration, 40% vascular penetration, 12% peripheral partial penetration, and 4% no penetration. For the hybrid, the wood volume exhibited 68% total penetration, 24% peripheral partial penetration, and 8% irregular partial penetration. The concentrations of 1.4% AI and 1.5% AI for E. cloeziana and of 1.4% AI and 1.6% AI for hybrid reached the minimum retention required by standard.

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Musanga, Vengai, Colin Chibaya e Serestina Viriri. "A scoping review of literature on deep learning and symbolic AI-based framework for detecting Covid-19 using computerized tomography scans". International Journal of Research in Business and Social Science (2147- 4478) 13, n. 2 (3 aprile 2024): 412–19. http://dx.doi.org/10.20525/ijrbs.v13i2.2955.

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This scoping review aims to explore various Deep Learning and Symbolic Artificial Intelligence (AI) models that can be integrated into explainable hybrid AI for the purpose of detecting COVID-19 based on Computerized Tomography (CT) scans. We followed the PRISMA-ScR framework as the foundation for our scoping review protocol. Our approach included a thorough search across 13 databases, complemented by an additional random internet search for relevant articles. Due to the voluminous number of articles returned, the search was further narrowed using the keywords: Deep Learning, Symbolic AI and Hybrid AI. These keywords were used because they are more visible in the earmarked literature. A screening of all articles by title was performed to remove duplicates. The final screening process centered on the publication year, ensuring that all considered articles fell within the range of 2019 to 2023, inclusive. Subsequently, abstract or text synthesis was conducted. Our search query retrieved a total of 3,312 potential articles from the thirteen databases, and an additional 12 articles from a random internet search, resulting in a cumulative count of 3,324 identified articles. After the deduplication and screening steps, 260 articles met our inclusion criteria. These articles were categorized based on the year of publication, the type of aim, and the type of AI used. An analysis of the year of publication revealed a linear trend, indicating growth in the field of Hybrid AI. Out of the five aim categories identified, we deliberately excluded articles that lacked a specified aim. It's noteworthy that 3% of the articles focused on the integration of AI models. The low percentage value suggests that the integration aspect is overlooked, thereby transcripting the integration of Deep Learning and symbolic AI into hybrid AI as an area worth exploring. This scoping review gives an overview of how a Deep Learning and Symbolic AI-based framework has been used in the detection of COVID-19 based on CT scans.

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R.Deepika e Dr.M.Vaneedharan. "__________________________________________________________________________ THE FUTURE OF WORK: ADAPTING HR POLICIES AND PROCEDURES FOR A HYBRID WORKFORCE IN PRIVATE SECTOR BANKS WITH AI". International Journal Of Trendy Research In Engineering And Technology 08, n. 01 (2024): 25–30. http://dx.doi.org/10.54473/ijtret.2024.8104.

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In the era of digital transformation and evolving work dynamics, the integration of artificial intelligence (AI) has emerged as a transformative tool in reshaping human resource management practices within private sector banks. As private sector banks navigate the evolving landscape of work arrangements, the integration of AI technologies into HR policies and procedures becomes imperative to effectively manage a hybrid workforce. This paper examines how private sector banks are adapting their HR practices to accommodate remote, hybrid, and on-site employees through the utilization of AI tools. By exploring the intersection of AI and HR in the context of a hybrid workforce, this study investigates the impact on employee engagement, productivity, and well-being. Key considerations include the development of inclusive policies, training programs for AI implementation, data privacy and security measures, and strategies for fostering collaboration and communication among dispersed teams. Insights from this research aim to guide private sector banks in harnessing the potential of AI to create a cohesive and agile workforce that thrives in the future of work. By exploring the practical implications of AI adoption in HR functions, this study aims to offer actionable recommendations for private sector banks looking to leverage technology to navigate the complexities of managing a diverse and distributed workforce. Through a comprehensive analysis of the evolving landscape of work arrangements and the role of AI in facilitating efficient HR practices, this research contributes to the ongoing dialogue on shaping the future of work in the banking sector.

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Morton, Cathy A., e R. Gordon Harvey. "Sweet Corn (Zea mays) Hybrid Tolerance to Nicosulfuron". Weed Technology 6, n. 1 (marzo 1992): 91–96. http://dx.doi.org/10.1017/s0890037x00034357.

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Tolerance of eight processing sweet corn hybrids to nicosulfuron was evaluated in field studies conducted at Arlington, Wis. in 1988 and 1989. Magnitude of sweet corn vigor reduction varied between years. Greater vigor reduction occurred to ‘Jubilee’ sweet corn than to the other seven hybrids. Injury to the hybrids increased as nicosulfuron rate increased. In 1988, some of the hybrids were stunted slightly by all of the herbicide rates, but within a hybrid there was no significant difference among herbicide rates. In 1989 none of the hybrids, including Jubilee, was stunted by even 140 g ai ha–1nicosulfuron compared with the corresponding check. Effects of nicosulfuron on yields of the eight hybrids in 1988 and in 1989 are best described by a quadratic regression equation.

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Sharma, Ritu, Raginee Sharma e Dr Achala Jain. "A Comparative Analysis of a Hybrid System with Hybrid Methodologies". International Journal of Innovative Technology and Exploring Engineering 11, n. 7 (30 giugno 2022): 17–20. http://dx.doi.org/10.35940/ijitee.g9969.0611722.

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Economic Load Dispatch (ELD) is an important optimization problem in the energy system. Economic Dispatch (ED) is a short-term determination of the optimal performance of a set of power generation assets to meet the system load at the lowest possible cost, taking into account transmission and operational constraints. Economic dispatch problems are solved by dedicated computer software that needs to take into account the operational and system limitations of available resources and corresponding transmission functions. Economic load balancing provides optimal cost savings for power plant operations where methodologies can be applied in a variety of ways, from traditional to advanced. To achieve this, traditional methods have been used from the last few years to the 90's, but in the last few decades AI methods have met their needs and validated satisfactory results. Some advanced hybrid techniques used are the Modified Salp Swarm Optimization Algorithm (MSSA) with Artificial Intelligent (AI) technique aided with Particle Swarm Optimization (PSO) technique, Improved Moth-Fly Optimization Algorithm (IMFOA) with the Recurrent Neural Network (RNN), the Improved Fruit Fly Optimization Algorithm (IFOA) with Artificial Neural Network (ANN) system and Lightning Search Algorithm (LSA) with Genetic Algorithm (GA) which will encourage the researches for providing better solution for economic load dispatch problem is presented in this paper.

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Stadelmann, Thilo, Julian Keuzenkamp, Helmut Grabner e Christoph Würsch. "The AI-Atlas: Didactics for Teaching AI and Machine Learning On-Site, Online, and Hybrid". Education Sciences 11, n. 7 (25 giugno 2021): 318. http://dx.doi.org/10.3390/educsci11070318.

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We present the “AI-Atlas” didactic concept as a coherent set of best practices for teaching Artificial Intelligence (AI) and Machine Learning (ML) to a technical audience in tertiary education, and report on its implementation and evaluation within a design-based research framework and two actual courses: an introduction to AI within the final year of an undergraduate computer science program, as well as an introduction to ML within an interdisciplinary graduate program in engineering. The concept was developed in reaction to the recent AI surge and corresponding demand for foundational teaching on the subject to a broad and diverse audience, with on-site teaching of small classes in mind and designed to build on the specific strengths in motivational public speaking of the lecturers. The research question and focus of our evaluation is to what extent the concept serves this purpose, specifically taking into account the necessary but unforeseen transfer to ongoing hybrid and fully online teaching since March 2020 due to the COVID-19 pandemic. Our contribution is two-fold: besides (i) presenting a general didactic concept for tertiary engineering education in AI and ML, ready for adoption, we (ii) draw conclusions from the comparison of qualitative student evaluations (n = 24–30) and quantitative exam results (n = 62–113) of two full semesters under pandemic conditions with the result of previous years (participants from Zurich, Switzerland). This yields specific recommendations for the adoption of any technical curriculum under flexible teaching conditions—be it on-site, hybrid, or online.

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Rafner, Janet, Dominik Dellermann, Arthur Hjorth, Dóra Verasztó, Constance Kampf, Wendy Mackay e Jacob Sherson. "Deskilling, Upskilling, and Reskilling: a Case for Hybrid Intelligence". Morals & Machines 1, n. 2 (2021): 24–39. http://dx.doi.org/10.5771/2747-5174-2021-2-24.

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Advances in AI technology affect knowledge work in diverse fields, including healthcare, engineering, and management. Although automation and machine support can increase efficiency and lower costs, it can also, as an unintended consequence, deskill workers, who lose valuable skills that would otherwise be maintained as part of their daily work. Such deskilling has a wide range of negative effects on multiple stakeholders -- employees, organizations, and society at large. This essay discusses deskilling in the age of AI on three levels - individual, organizational and societal. Deskilling is furthermore analyzed through the lens of four different levels of human-AI configurations and we argue that one of them, Hybrid Intelligence, could be particularly suitable to help manage the risk of deskilling human experts. Hybrid Intelligence system design and implementation can explicitly take such risks into account and instead foster upskilling of workers. Hybrid Intelligence may thus, in the long run, lower costs and improve performance and job satisfaction, as well as prevent management from creating unintended organization-wide deskilling.

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Zaresefat, Mojtaba, e Reza Derakhshani. "Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review". Water 15, n. 9 (2 maggio 2023): 1750. http://dx.doi.org/10.3390/w15091750.

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Developing precise soft computing methods for groundwater management, which includes quality and quantity, is crucial for improving water resources planning and management. In the past 20 years, significant progress has been made in groundwater management using hybrid machine learning (ML) models as artificial intelligence (AI). Although various review articles have reported advances in this field, existing literature must cover groundwater management using hybrid ML. This review article aims to understand the current state-of-the-art hybrid ML models used for groundwater management and the achievements made in this domain. It includes the most cited hybrid ML models employed for groundwater management from 2009 to 2022. It summarises the reviewed papers, highlighting their strengths and weaknesses, the performance criteria employed, and the most highly cited models identified. It is worth noting that the accuracy was significantly enhanced, resulting in a substantial improvement and demonstrating a robust outcome. Additionally, this article outlines recommendations for future research directions to enhance the accuracy of groundwater management, including prediction models and enhance related knowledge.

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Nishtar, Zuhaib, e Jamil Afzal. "A Review of Real-Time Monitoring of Hybrid Energy Systems by Using Artificial Intelligence and IoT". Pakistan Journal of Engineering and Technology 6, n. 3 (21 settembre 2023): 8–15. http://dx.doi.org/10.51846/vol6iss3pp8-15.

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This research focuses on the invention of real-time monitoring of hybrid energy systems using artificial intelligence (AI) and the Internet of Things (IoT). The study aims to develop a monitoring system that provides real-time insights, anomaly detection, fault diagnosis, and energy optimization. The research methodology involves the integration of AI algorithms and IoT devices to collect, analyze, and visualize system data. The results demonstrate the effectiveness of the developed monitoring system in improving system performance, sustainability, and cost savings. The practical implementation and scalability of the system are also addressed, along with future research opportunities. This research contributes to the advancement of monitoring systems for hybrid energy applications, promoting efficiency and sustainability in energy management. This research provides significant contributions to the field of real-time monitoring of hybrid energy systems. The article focuses on addressing key problems related to the real-time monitoring of hybrid energy systems using AI and IoT technologies. The lack of real-time insights provided by conventional methods also limits timely decision-making and responsiveness to dynamic changes in the system.

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Ganguly, Kaushik. "Quantum AI: Deep Learning optimization using Hybrid Quantum Filters". International Journal for Research in Applied Science and Engineering Technology 10, n. 9 (30 settembre 2022): 1720–33. http://dx.doi.org/10.22214/ijraset.2022.46914.

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Abstract: Deep learning algorithms have shown promising results for different image processing tasks, particularly in remote sensing & image recognition. Till now many studies have been carried out on image processing, which brings a new paradigm of innovative capabilities under the umbrella of intelligent remote sensing and computer vision. Accordingly, quantum processing algorithms have proved to efficiently solve some issues that are undetectable to classical algorithms and processors. Keeping that in mind, a Quantum Convolutional Neural Network (QCNN) architecture along with Hybrid Quantum filters would be utilized supported by cloud computing infrastructures and data centers to provide a broad range of complex AI services and high data availability. This research summaries the conventional techniques of Classical and Quantum Deep Learning and it’s research progress on realworld problems in remote sensing image processing as a comparative demonstration. Last but not least, we evaluate our system by training on Street View House Numbers datasets in order to highlight the feasibility and effectiveness of using Quantum Deep Learning approach in image recognition and other similar applications. Upcoming challenges and future research areas on this spectrum are also discussed.

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Sreekala, Keshetti, N. Rajkumar, R. Sugumar, K. V. Daya Sagar, R. Shobarani, K. Parthiban Krishnamoorthy, A. K. Saini, H. Palivela e A. Yeshitla. "Skin Diseases Classification Using Hybrid AI Based Localization Approach". Computational Intelligence and Neuroscience 2022 (29 agosto 2022): 1–7. http://dx.doi.org/10.1155/2022/6138490.

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One of the most prevalent diseases that can be initially identified by visual inspection and further identified with the use of dermoscopic examination and other testing is skin cancer. Since eye observation provides the earliest opportunity for artificial intelligence to intercept various skin images, some skin lesion classification algorithms based on deep learning and annotated skin photos display improved outcomes. The researcher used a variety of strategies and methods to identify and stop diseases earlier. All of them yield positive results for identifying and categorizing diseases, but proper disease categorization is still lacking. Computer-aided diagnosis is one of the most crucial methods for more accurate disease detection, although it is rarely used in dermatology. For Feature Extraction, we introduced Spectral Centroid Magnitude (SCM). The given dataset is classified using an enhanced convolutional neural network; the first stage of preprocessing uses a median filter, and the final stage compares the accuracy results to the current method.

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Kasabov, Nikola K. "Hybrid Connectionist Fuzzy Production System: Towards Building Comprehensive AI". Intelligent Automation & Soft Computing 1, n. 4 (gennaio 1995): 355–64. http://dx.doi.org/10.1080/10798587.1995.10750641.

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Liu, Hong-Lin. "A hybrid AI optimization method applied to industrial processes". Chemometrics and Intelligent Laboratory Systems 45, n. 1-2 (gennaio 1999): 101–4. http://dx.doi.org/10.1016/s0169-7439(98)00093-8.

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Zheng, Nanning, Shaoyi Du, Jianji Wang, He Zhang, Wenting Cui, Zijian Kang, Tao Yang et al. "Predicting COVID-19 in China Using Hybrid AI Model". IEEE Transactions on Cybernetics 50, n. 7 (luglio 2020): 2891–904. http://dx.doi.org/10.1109/tcyb.2020.2990162.

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Monostori, L., B. Kádár e Cs Egresits. "Virtual Manufacturing using a Hierarchically Coupled Hybrid AI System". IFAC Proceedings Volumes 28, n. 24 (ottobre 1995): 287–92. http://dx.doi.org/10.1016/s1474-6670(17)46564-5.

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H R, Prakruthi, Lisha Ponnamma, Rakshana M, N. Vishal Krishna Bhat, Meghashyam S. Yambari e Dr Anusha Preetham. "Survey of Literature on Various Hybrid Intelligence Approaches in AuI". International Journal of Engineering Research in Computer Science and Engineering 9, n. 10 (13 ottobre 2022): 37–41. http://dx.doi.org/10.36647/ijercse/09.10.art008.

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To enhance benefits while minimising flaws, Augmented Intelligence combines Artificial Intelligence and Human Intelligence. When human and device capabilities are integrated, efficiency is improved when compared to independent HI and AI techniques. We give a review of the literature to see how AuI has been used in the past, including the functions of HI and AI, AI techniques, characteristics, and operations. We present a check of expert analysis to answer four main concerns to understand the experts' suggestions on AuI, including a) the description of AuI and importance of HI with AuI b) human intelligence in AuI’s functions c) AuI's present and future operations in industry, research, and the general public, and also its benefits and drawbacks.

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Regufe, Maria João, Vinicius V. Santana, Alexandre F. P. Ferreira, Ana M. Ribeiro, José M. Loureiro e Idelfonso B. R. Nogueira. "A Hybrid Modeling Framework for Membrane Separation Processes: Application to Lithium-Ion Recovery from Batteries". Processes 9, n. 11 (29 ottobre 2021): 1939. http://dx.doi.org/10.3390/pr9111939.

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This study proposed a hybrid modeling framework for membrane separation processes where lithium from batteries is recovered. This is a pertinent problem nowadays as lithium batteries are popularized in hybrid and electric vehicles. The hybrid model is based on an artificial intelligence (AI) structure to model the mass transfer resistance of several experimental separations found in the literature. It is also based on a phenomenological model to represent the transient system regime. An optimization framework was designed to perform the AI model training and simultaneously solve the Ordinary Differential Equation (ODE) system representing the phenomenological model. The results demonstrate that the hybrid model can better represent the experimental validation sets than the phenomenological model alone. This strategy opens doors for further investigations of this system.

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Soni, Pooja, R. Naveena Bhargavi, Vikramaditya Dave e Hemani Paliwal. "Artificial Intelligence-Enabled Techno-Economic Analysis and Optimization of Grid-Tied Solar PV-Fuel Cell Hybrid Power Systems for Enhanced Performance". E3S Web of Conferences 472 (2024): 03012. http://dx.doi.org/10.1051/e3sconf/202447203012.

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The incorporation of energy from renewable sources into the power grid is crucial for achieving sustainable and environmentally friendly power generation. This study proposes an artificial intelligence (AI)-enabled methodology for the analysis & optimization of “grid-tied solar photovoltaic (PV)-fuel cell hybrid power systems.” The research aims to demonstrate how AI techniques can assist in decision-making, improve system performance, and achieve higher levels of energy efficiency and financial viability. The study presents the results of a project focusing on a renewable energy system that feeds into the grid and powers a university building. The hybrid power system’s performance and cost were evaluated using unified approaches to modeling, simulation, optimization, and control. The findings indicate that the AI-optimized “solar PV-fuel cell hybrid system connected to the grid” offers excellent performance, meeting 74% of the building’s energy needs through renewable sources. The system also achieved a low levelled price for energy and minimise CO2 emissions, further enhancing its environmental sustainability. The proposed AI-enabled approach proves to be a promising solution for creating grid-connected renewable energy systems with significant benefits for energy efficiency, cost-effectiveness, and environmental impact.

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Wirth, Norbert. "Hello marketing, what can artificial intelligence help you with?" International Journal of Market Research 60, n. 5 (settembre 2018): 435–38. http://dx.doi.org/10.1177/1470785318776841.

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Artificial intelligence based applications are emerging in a broad range of expert domains. News about AI based solutions in medicine, industrial production processes, logistics, mobility and digital marketing trigger discussions and a lot of speculation. The market research industry seems hesitant and at the same time eager to embrace this new technology. In this article the author provides a definition of artificial intelligence and its different forms: narrow AI, hybrid AI and strong AI. He concludes his reflection on the question whether it’s feasible to develop AI based marketing insights solutions with the recommendation: it’s time to embrace AI.

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DVORAK, J., e H. BUNKE. "CONCEPT AND REALIZATION OF A HYBRID AI TOOL APPLIED TO COMPUTER VISION". International Journal on Artificial Intelligence Tools 03, n. 04 (dicembre 1994): 451–66. http://dx.doi.org/10.1142/s0218213094000261.

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Computer vision includes a variety of tasks of different natures, and there are many applications that have a strong need for knowledge representation and use. Typical knowledge representation methods used in computer vision include frames, rules, logic, constraints, and attributed prototype graphs. Although the advantages of hybrid approaches to knowledge representation have been recognized, no hybrid tool for high-level computer vision is available yet. In this paper we first present a general framework for a hybrid knowledge representation tool. It is based on object-oriented programming and offers distinctive features such as high flexibility, coherence, and a clean integration of a collection of knowledge-based techniques. Then we give a brief overview of our computer vision tool VISTO, which was created along the framework discussed in the first part of the paper. With an application example we illustrate the use of VISTO and the advantages of hybrid knowledge representation in comparison to non-hybrid approaches.

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Ramana, P. V. "Hybrid Artificial Intelligence Network in Taxation of Upheaval Damaged Structures". Proceedings of the 12th Structural Engineering Convention, SEC 2022: Themes 1-2 1, n. 1 (19 dicembre 2022): 1687–97. http://dx.doi.org/10.38208/acp.v1.706.

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The knowledge base of the new ANN is reconstructed through the use of the Fuzzy Set Theory, which permits to formalization ion the procedures in a non-deterministic way. The uncertainty and the imprecision of the gathered data are managed with techniques topical to Fuzzy Logic. Moreover, the System permits the user to modify the same knowledge base or insert new basheadquartersd use the most appropriate one in specific situations. The gathered data and the evaluations' results are loaded on databases and databases, successively, for further elaborations and new evaluations with different knowledge bases. Implementing and fleshing out several psychological and neuroscience theories of cognition, the AI-ANN conceptual model aims at being a cognitive “theory of everything.” With modules or processes for perception, working memory, episodic memories, “consciousness,” procedural memory, action selection, perceptual learning, serial learning, deliberation, volition, and non-routine problem solving, the AI-ANN model is ideally suited to provide a functional ontology that would allow for the discussion, design, and comparison of AGI systems. The AI-ANN architecture is based on the cognitive cycle, a “cognitive atom.” The more elementary cognitive modules and processes play a role in each mental cycle. Higher-level functions are performed over multiple cycles. In addition to giving a quick overview of the AI-ANN conceptual model and its underlying computational technology, one can argue for the AI-ANN architecture’s role as a foundational architecture for an AGI. Finally, lessons For AGI researchers drawn from the model and its architecture are discussed.

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Wang, Ke, Ziyi Ying, Shankha Shubhra Goswami, Yongsheng Yin e Yafei Zhao. "Investigating the Role of Artificial Intelligence Technologies in the Construction Industry Using a Delphi-ANP-TOPSIS Hybrid MCDM Concept under a Fuzzy Environment". Sustainability 15, n. 15 (1 agosto 2023): 11848. http://dx.doi.org/10.3390/su151511848.

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The construction business is always changing, and with the introduction of artificial intelligence (AI) technology it is undergoing substantial modifications in a variety of areas. The purpose of this research paper is to investigate the function of AI tools in the construction industry using a hybrid multi-criteria decision-making (MCDM) framework based on the Delphi method, analytic network process (ANP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) under a fuzzy scenario. The ANP framework offers a systematic approach to quantifying the relative importance of AI technologies based on expert opinions gathered during the Delphi process, whereas the fuzzy TOPSIS methodology is used to rank and select the most appropriate AI technologies for the construction industry. The final results from the ANP revealed that the technological factors are the most crucial, followed by the environmental factors, which highly influence the AI environment. In addition, TOPSIS identified robotics and automation as the best AI alternative among the three options, followed by building information modeling (BIM), whereas computer vision was the least preferred among the list. The proposed hybrid MCDM framework enables a comprehensive evaluation and selection process that takes into account the interdependencies between AI technologies and uncertainties in decision-making.

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Singh, Prabhdeep. "Enhancing Performance of Hybrid Electric Vehicle using Optimized Energy Management Methodology". International Journal of Data Informatics and Intelligent Computing 2, n. 3 (24 settembre 2023): 1–10. http://dx.doi.org/10.59461/ijdiic.v2i3.74.

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The fuel consumption and the fuel management strategy (PMS) of the hybrid electric vehicle are closely linked (HEV). In this study, a hybrid power management technique and an adaptive neuro-fuzzy inference (ANFIS) method are established. Artificial intelligence represents a huge improvement in electricity management across different energy sources (AI). The main energy source of the hybrid power supply is a proton exchange membrane fuel cell (PEMFC), while its electrical storage devices are a battery bank and an ultracapacitor. The hybrid electric vehicle's power management strategy (PMS) and fuel consumption are closely related (HEV). In this paper, an adaptive neuro-fuzzy inference and hybrid power management strategy (ANFIS) approach is developed. A significant advance in electricity management across multiple energy sources is artificial intelligence (AI). The proton exchange membrane fuel cell (PEMFC) serves as the primary energy source of the hybrid power supply, and the ultracapacitor and battery bank serve as its electrical storage components.

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Kraus, Michael A., Rafael Bischof, Walter Kaufmann e Karel Thoma. "Artificial intelligence - finite element method - hybrids for efficient nonlinear analysis of concrete structures". Acta Polytechnica CTU Proceedings 36 (18 agosto 2022): 99–108. http://dx.doi.org/10.14311/app.2022.36.0099.

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Realistic structural analyses and optimisations using the non-linear finite element method are possible today yet suffer from being very time-consuming, particularly in case of reinforced concrete plates and shells. Hence such investigations are currently dismissed in the vast majority of cases in practice. The "Artificial Intelligence - Finite Element - Hybrids" project addresses the current unsatisfactory situation with an approach that combines non-linear finite element models for reinforced concrete shells with scientific machine learning algorithms to create hybrid AI-FEM models. The AI-based surrogate material model provides the material stiffness as well as the stress tensor for given concrete design parameters and the strain tensor. This paper reports on the current status of the project and findings of the calibration of the AI-based reinforced concrete material model. We successfully calibrated and evaluated k-nearest-neighbour, LGBM and ResNet algorithms and report their predictive capabilities. Finally, some light is shed on the future work of integrating the AI surrogate material models back into the finite element method in the course of the numerical analysis of reinforced concrete structures.

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Elbagoury, Bassant M., Luige Vladareanu, Victor Vlădăreanu, Abdel Badeeh Salem, Ana-Maria Travediu e Mohamed Ismail Roushdy. "A Hybrid Stacked CNN and Residual Feedback GMDH-LSTM Deep Learning Model for Stroke Prediction Applied on Mobile AI Smart Hospital Platform". Sensors 23, n. 7 (27 marzo 2023): 3500. http://dx.doi.org/10.3390/s23073500.

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Artificial intelligence (AI) techniques for intelligent mobile computing in healthcare has opened up new opportunities in healthcare systems. Combining AI techniques with the existing Internet of Medical Things (IoMT) will enhance the quality of care that patients receive at home remotely and the successful establishment of smart living environments. Building a real AI for mobile AI in an integrated smart hospital environment is a challenging problem due to the complexities of receiving IoT medical sensors data, data analysis, and deep learning algorithm complexity programming for mobile AI engine implementation AI-based cloud computing complexities, especially when we tackle real-time environments of AI technologies. In this paper, we propose a new mobile AI smart hospital platform architecture for stroke prediction and emergencies. In addition, this research is focused on developing and testing different modules of integrated AI software based on XAI architecture, this is for the mobile health app as an independent expert system or as connected with a simulated environment of an AI-cloud-based solution. The novelty is in the integrated architecture and results obtained in our previous works and this extended research on hybrid GMDH and LSTM deep learning models for the proposed artificial intelligence and IoMT engine for mobile health edge computing technology. Its main goal is to predict heart–stroke disease. Current research is still missing a mobile AI system for heart/brain stroke prediction during patient emergency cases. This research work implements AI algorithms for stroke prediction and diagnosis. The hybrid AI in connected health is based on a stacked CNN and group handling method (GMDH) predictive analytics model, enhanced with an LSTM deep learning module for biomedical signals prediction. The techniques developed depend on the dataset of electromyography (EMG) signals, which provides a significant source of information for the identification of normal and abnormal motions in a stroke scenario. The resulting artificial intelligence mHealth app is an innovation beyond the state of the art and the proposed techniques achieve high accuracy as stacked CNN reaches almost 98% for stroke diagnosis. The GMDH neural network proves to be a good technique for monitoring the EMG signal of the same patient case with an average accuracy of 98.60% to an average of 96.68% of the signal prediction. Moreover, extending the GMDH model and a hybrid LSTM with dense layers deep learning model has improved significantly the prediction results that reach an average of 99%.

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