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Добірка наукової літератури з теми "Artificial intelligence (ML/DL)"

Автор: Grafiati

Опубліковано: 29 березня 2025

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Статті в журналах з теми "Artificial intelligence (ML/DL)"

Pelayes, David Eduardo, Jose A. Mendoza, and Anibal Martin Folgar. "Artificial intelligence use in diabetes." Latin American Journal of Ophthalmology 5 (December 10, 2022): 6. http://dx.doi.org/10.25259/lajo_4_2022.

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Diabetic retinopathy (DR) affects the small vessels of the eye and is the leading cause of blindness in people on reproductive age; however, less than half of patients are aware of their condition; therefore, early detection and treatment is essential to combat it. There are currently multiple technologies for DR detection, some of which are already commercially available. To understand how these technologies work, we must know first some basic concepts about artificial intelligence (AI) such as machine learning (ML) and deep learning (DL). ML is the basic process by which AI incorporates new data using different algorithms and thus creates new knowledge on its base, learns from it, and makes determinations and predictions on some subject based on all that information. AI can be presented at various levels. DL is a specific type of ML, which trains a computer to perform tasks as humans do, such as speech recognition, image identification, or making predictions. DL has shown promising diagnostic performance in image recognition, being widely adopted in many domains, including medicine. For general image analysis, it has achieved strong results in various medical specialties such as radiology dermatology and in particular for ophthalmology. We will review how this technology is constantly evolving which are the available systems and their task in real world as well as the several challenges, such as medicolegal implications, ethics, and clinical deployment model needed to accelerate the translation of these new algorithms technologies into the global health-care environment.

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Gokcekuyu, Yasemin, Fatih Ekinci, Mehmet Serdar Guzel, Koray Acici, Sahin Aydin, and Tunc Asuroglu. "Artificial Intelligence in Biomaterials: A Comprehensive Review." Applied Sciences 14, no. 15 (July 28, 2024): 6590. http://dx.doi.org/10.3390/app14156590.

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The importance of biomaterials lies in their fundamental roles in medical applications such as tissue engineering, drug delivery, implantable devices, and radiological phantoms, with their interactions with biological systems being critically important. In recent years, advancements in deep learning (DL), artificial intelligence (AI), machine learning (ML), supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) have significantly transformed the field of biomaterials. These technologies have introduced new possibilities for the design, optimization, and predictive modeling of biomaterials. This review explores the applications of DL and AI in biomaterial development, emphasizing their roles in optimizing material properties, advancing innovative design processes, and accurately predicting material behaviors. We examine the integration of DL in enhancing the performance and functional attributes of biomaterials, explore AI-driven methodologies for the creation of novel biomaterials, and assess the capabilities of ML in predicting biomaterial responses to various environmental stimuli. Our aim is to elucidate the pivotal contributions of DL, AI, and ML to biomaterials science and their potential to drive the innovation and development of superior biomaterials. It is suggested that future research should further deepen these technologies’ contributions to biomaterials science and explore new application areas.

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Drikakis, Dimitris, and Filippos Sofos. "Can Artificial Intelligence Accelerate Fluid Mechanics Research?" Fluids 8, no. 7 (July 19, 2023): 212. http://dx.doi.org/10.3390/fluids8070212.

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The significant growth of artificial intelligence (AI) methods in machine learning (ML) and deep learning (DL) has opened opportunities for fluid dynamics and its applications in science, engineering and medicine. Developing AI methods for fluid dynamics encompass different challenges than applications with massive data, such as the Internet of Things. For many scientific, engineering and biomedical problems, the data are not massive, which poses limitations and algorithmic challenges. This paper reviews ML and DL research for fluid dynamics, presents algorithmic challenges and discusses potential future directions.

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Zhang, Shengzhe. "Artificial Intelligence and Applications in Structural and Material Engineering." Highlights in Science, Engineering and Technology 75 (December 28, 2023): 240–45. http://dx.doi.org/10.54097/9qknfc57.

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The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has become a vital tool attributed to Structural and Material Engineering and developed the way engineers approach design analysis and optimization. This paper explores the principal models of ML and DL, such as the generative adversarial network (GAN) and the artificial neural networks (ANN) and, and discusses their impacts on the applications of material design, structure damage detection (SDD), and archtecture design. It indicates that the high-quality of database is the essential key to training the model. Thus, the data preprocessing is required for expanding the data source and improving the quality of data. In material design process, ML and DL models reduce the time to predict the properties of construction materials, which makes SDD realistic as well. For architecture design, GAN is used to generate image data, such as drawing of the floor plan and this could be helpful to reduce the labor resources. However, some challenges of ML and DL are found while applying the algorithms to real-life applications. For example, sufficient data is needed to train the DL models and the ethic aspect is also a concern when thinking of AI.

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AFTAB, Ifra, Mohammad DOWAJY, Kristof KAPITANY, and Tamas LOVAS. "Artificial Intelligence (AI) – based strategies for point cloud data and digital twins." Nova Geodesia 3, no. 3 (August 19, 2023): 138. http://dx.doi.org/10.55779/ng33138.

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Artificial Intelligence (AI), specifically machine learning (ML) and deep learning (DL), is causing a paradigm shift in coding practices and software solutions across diverse fields. This study focuses on harnessing the potential of ML/DL strategies in the geospatial domain, where geodata possesses characteristics that align with the concept of a “lingual manuscript” in aesthetic theory. By employing ML/DL techniques, such as feature evaluation and extraction from 3D point clouds, we can derive concepts that are specific to software, geographical areas, and tasks. ML/DL-based interpretation of 3D point clouds extends geospatial modelling beyond implicit representations, enabling the resolution of complex heuristic-based reconstructions and abstract concepts. These advancements in artificial intelligence have the potential to optimize and expedite geodata computation and geographic information systems. However, ML/DL encounters notable challenges in this domain, including the need for abundant training data, advanced statistical methods, and the development of effective feature representations. Overcoming these challenges is essential to enhance the performance and efficacy of ML/DL systems. Additionally, ML/DL-based solutions can simplify software engineering processes by replacing certain aspects of current adoption and implementation practices, resulting in reduced complexities in development and management. Through the adoption of ML/DL, many of the existing explicitly coded GIS implementations may gradually be replaced in the long term. Overall, this research illustrates the transformative capabilities of ML/DL in geospatial applications and underscores the significance of addressing associated challenges to drive further advancements in the field.

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Iadanza, Ernesto, Rachele Fabbri, Džana Bašić-ČiČak, Amedeo Amedei, and Jasminka Hasic Telalovic. "Gut microbiota and artificial intelligence approaches: A scoping review." Health and Technology 10, no. 6 (October 26, 2020): 1343–58. http://dx.doi.org/10.1007/s12553-020-00486-7.

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Abstract This article aims to provide a thorough overview of the use of Artificial Intelligence (AI) techniques in studying the gut microbiota and its role in the diagnosis and treatment of some important diseases. The association between microbiota and diseases, together with its clinical relevance, is still difficult to interpret. The advances in AI techniques, such as Machine Learning (ML) and Deep Learning (DL), can help clinicians in processing and interpreting these massive data sets. Two research groups have been involved in this Scoping Review, working in two different areas of Europe: Florence and Sarajevo. The papers included in the review describe the use of ML or DL methods applied to the study of human gut microbiota. In total, 1109 papers were considered in this study. After elimination, a final set of 16 articles was considered in the scoping review. Different AI techniques were applied in the reviewed papers. Some papers applied ML, while others applied DL techniques. 11 papers evaluated just different ML algorithms (ranging from one to eight algorithms applied to one dataset). The remaining five papers examined both ML and DL algorithms. The most applied ML algorithm was Random Forest and it also exhibited the best performances.

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Choudhary, Laxmi, and Jitendra Singh Choudhary. "Deep Learning Meets Machine Learning: A Synergistic Approach towards Artificial Intelligence." Journal of Scientific Research and Reports 30, no. 11 (November 16, 2024): 865–75. http://dx.doi.org/10.9734/jsrr/2024/v30i112614.

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The evolution of artificial intelligence (AI) has progressed from rule-based systems to learning-based models, integrating machine learning (ML) and deep learning (DL) to tackle complex data-driven tasks. This review examines the synergy between ML, which utilizes algorithms like decision trees and support vector machines for structured data, and DL, which employs neural networks for processing unstructured data such as images and natural language. The combination of these paradigms through hybrid ML-DL models has enhanced prediction accuracy, scalability, and automation across domains like healthcare, finance, natural language processing, and robotics. However, challenges such as computational demands, data dependency, and model interpretability remain. This paper discusses the benefits, limitations, and future potential of ML and DL and also provides a review study of a hybrid model makes use of both techniques (machine learning & deep learning) advantages to solve complicated problems more successfully than one could on its own. To boost performance, increase efficiency, or address scenarios where either ML or DL alone would not be able to manage, this approach combines deep learning structures with conventional machine learning techniques.

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Gayatri, T., G. Srinivasu, D. M. K. Chaitanya, and V. K. Sharma. "A Review on Optimization Techniques of Antennas Using AI and ML / DL Algorithms." International Journal of Advances in Microwave Technology 07, no. 02 (2022): 288–95. http://dx.doi.org/10.32452/ijamt.2022.288295.

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In recent years, artificial intelligence (AI) aided communications grabbed huge attention to providing solutions for mathematical problems in wireless communications, by using machine learning (ML) and deep learning (DL) algorithms. This paper initially presents a short background on AI, CEM, and the role of AI / ML / DL in antennas. A study on ML / DL algorithms and the optimization techniques of antenna parameters using various ML / DL algorithms are presented. Finally, the application areas of AI in antennas are illustrated.

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El-den, B. M. El, and Marwa M. Eid. "Watermarking Models and Artificial Intelligence." Journal of Artificial Intelligence and Metaheuristics 1, no. 2 (2022): 24–30. http://dx.doi.org/10.54216/jaim.010203.

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Machine learning and deep learning are good bets for solving various intelligence-related problems. While it has practical applications in watermarking, it performs less well on more standard tasks like prediction, classification, and regression. This article offers the results of a thorough investigation into watermarking using modern tools like AI, ML, and DL. Watermarking's origins, some historical context, and the most fascinating and practical applications are also covered briefly.

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Kuhn, Stefan, Rômulo Pereira de Jesus, and Ricardo Moreira Borges. "Nuclear Magnetic Resonance and Artificial Intelligence." Encyclopedia 4, no. 4 (October 18, 2024): 1568–80. http://dx.doi.org/10.3390/encyclopedia4040102.

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This review explores the current applications of artificial intelligence (AI) in nuclear magnetic resonance (NMR) spectroscopy, with a particular emphasis on small molecule chemistry. Applications of AI techniques, especially machine learning (ML) and deep learning (DL) in the areas of shift prediction, spectral simulations, spectral processing, structure elucidation, mixture analysis, and metabolomics, are demonstrated. The review also shows where progress is limited.

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Дисертації з теми "Artificial intelligence (ML/DL)"

Laguili, Oumaima. "Smart management of combined electric water heaters and self-consumption photovoltaic solar panels (SmartECS)." Electronic Thesis or Diss., Perpignan, 2024. https://theses-public.univ-perp.fr/2024PERP0045.pdf.

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Alors que le secteur du bâtiment se montre de plus en plus économe en énergie, les besoins en eau chaude sanitaire (ECS) augmentent, en particulier dans les logements récents. De ce fait, il apparait nécessaire d'améliorer l'efficacité des solutions mises en œuvre pour la production d'ECS, de mieux comprendre les besoins en ECS et d'impliquer l'usager dans la prise de décision. Le projet traite du développement d'algorithmes pour le contrôle/commande « intelligent » d'installations associant chauffe-eau électrique et panneaux solaires photovoltaïques en autoconsommation. Sera mise en œuvre une stratégie fondée sur la théorie de la commande prédictive, mettant à profit les outils de l'apprentissage automatique. Cette stratégie sera généralisée aux systèmes « multi-chauffe-eau », mutualisant une production solaire photovoltaïque, parle développement d'une commande distribuée et hiérarchisée. Une expérimentation permettra d'évaluer les conditions d'acceptabilité de la solution développée et l'impact de l'information sur la prise de décision
While the building sector is increasingly energy efficient, the needs in domestic hot water (DHW) is increasing, especially in newer homes. Therefore, improvement of efficiency in the production of DHW, a better understanding of the needs in DHW, and user involvement in the decision-making process are necessary. The project deals with the development of algorithms for the smart control of combined electric water heaters and self-consumption photovoltaic solar panels. A model-based predictive control strategy will be developed and implemented, leveraging machine learning tools. The strategy will be generalized to multi-water heater systems, sharing photovoltaic solar production, through the development of a distributed and hierarchical control approach. An experiment will make it possible to assess the conditions of acceptability of the developed solution and the impact of information on decision-making

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Giuliani, Luca. "Extending the Moving Targets Method for Injecting Constraints in Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23885/.

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Informed Machine Learning is an umbrella term that comprises a set of methodologies in which domain knowledge is injected into a data-driven system in order to improve its level of accuracy, satisfy some external constraint, and in general serve the purposes of explainability and reliability. The said topid has been widely explored in the literature by means of many different techniques. Moving Targets is one such a technique particularly focused on constraint satisfaction: it is based on decomposition and bi-level optimization and proceeds by iteratively refining the target labels through a master step which is in charge of enforcing the constraints, while the training phase is delegated to a learner. In this work, we extend the algorithm in order to deal with semi-supervised learning and soft constraints. In particular, we focus our empirical evaluation on both regression and classification tasks involving monotonicity shape constraints. We demonstrate that our method is robust with respect to its hyperparameters, as well as being able to generalize very well while reducing the number of violations on the enforced constraints. Additionally, the method can even outperform, both in terms of accuracy and constraint satisfaction, other state-of-the-art techniques such as Lattice Models and Semantic-based Regularization with a Lagrangian Dual approach for automatic hyperparameter tuning.

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Lundin, Lowe. "Artificial Intelligence for Data Center Power Consumption Optimisation." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447627.

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The aim of the project was to implement a machine learning model to optimise the power consumption of Ericsson’s Kista data center. The approach taken was to use a Reinforcement Learning agent trained in a simulation environment based on data specific to the data center. In this manner, the machine learning model could find interactions between parameters, both general and site specific in ways that a sophisticated algorithm designed by a human never could. In this work it was found that a neural network can effectively mimic a real data center and that the Reinforcement Learning policy "TD3" could, within the simulated environment, consistently and convincingly outperform the control policy currently in use at Ericsson’s Kista data center.

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Karlsson, Frida. "The opportunities of applying Artificial Intelligence in strategic sourcing." Thesis, KTH, Industriell ekonomi och organisation (Inst.), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281306.

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Artificial Intelligence technology has become increasingly important from a business perspective. In strategic sourcing, the technology has not been explored much. However, 67% of CPO:s in a survey showed that AI is one of their top priorities the next 10 years. AI can be used to identify patterns, predict prices and provide support in decision making. A qualitative case study has been performed in a strategic sourcing function at a large size global industrial company where the purpose has been to investigate how applicable AI is in the strategic sourcing process at The Case Company. In order to achieve the purpose of this study, it has been important to understand the strategic sourcing process and understand what AI technology is and what it is capable of in strategic sourcing. Based on the empirical data collection combined with literature, opportunities of applying AI in strategic sourcing have been identified and key areas for an implementation have been suggested. These include Forecasting, Spend Analysis & Savings Tracking, Supplier Risk Management, Supplier Identification & Selection, RFQ process, Negotiation process, Contract Management and Supplier Performance Management. These key areas have followed the framework identified in the literature study while identifying and adding new factors. It also seemed important to consider factors such as challenges and risks, readiness and maturity as well as factors that seems to be important to consider in order to enable an implementation. To assess how mature and ready the strategic sourcing function is for an implementation, some of the previous digital projects including AI technologies have been mapped and analysed. Based on the identified key areas of opportunities of applying AI, use cases and corresponding benefits of applying AI have been suggested. A guideline including important factors to consider if applying the technology has also been provided. However, it has been concluded that there might be beneficial to start with a smaller use case and then scale it up. Also as the strategic sourcing function has been establishing a spend analytics platform for the indirect team, there might be a good start to evaluate that project and then apply AI on top of the existing solution. Other factors to consider are ensuring data quality and security, align with top management as well as demonstrate the advantages AI can provide in terms of increased efficiency and cost savings. The entire strategic sourcing function should be involved in an AI project and the focus should not only be on technological aspect but also on soft factors including change management and working agile in order to successfully apply AI in strategic sourcing.
Artificiell Intelligens har blivit allt viktigare ur ett affärsperspektiv. När det gäller strategiskt inköp har tekniken inte undersökts lika mycket tidigare. Hursomhelst, 67% av alla tillfrågade CPO:er i en enkät ansåg att AI är en av deras topprioriteringar de kommande tio åren. AI kan exempelvis identifiera mönster, förutspå priser samt ge support inom beslutsfattning. En kvalitativ fallstudie har utförts i en strategisk inköpsfunktion hos ett globalt industriföretag där syftet har varit att undersöka hur tillämpbart AI är i strategiskt inköp hos Case-Företaget. För att uppnå syftet med denna studie har det varit viktigt att förstå vad den strategiska inköpsprocessen omfattas av samt vad AI-teknologi är och vad den är kapabel till inom strategiskt inköp. Därför har litteraturstudien gjorts för att undersöka hur man använt AI inom strategiskt inköp tidigare och vilka fördelar som finns. Baserat på empirisk datainsamling kombinerat med litteratur har nyckelområden för att applicera AI inom strategiskt inköp föreslagits inkluderat forecasting, spendanalys & besparingsspårning, riskhantering av leverantörer, leverantörsidentifikation och val, RFQ-processen, förhandlingsprocessen, kontrakthantering samt uppföljning av leverantörsprestation. Dessa nyckelområden har följt det ramverk som skapats i litteraturstudien samtidigt som nya faktorer har identifierats och lagts till då de ansetts som viktiga. För att tillämpa AI i strategiska inköpsprocessen måste Case-Företaget överväga andra aspekter än var i inköpsprocessen de kan dra nytta av AI mest. Faktorer som utmaningar och risker, beredskap och mognad samt faktorer som ansetts viktiga att beakta för att möjliggöra en implementering har identifierats. För att bedöma hur mogen och redo den strategiska inköpsfunktionen hos Case-Företaget är för en implementering har några av de tidigare digitala projekten inklusive AI-teknik kartlagts och analyserats. Det har emellertid konstaterats att det kan vara fördelaktigt för strategiskt inköp att börja med ett mindre användningsområde och sedan skala upp det. Eftersom strategiska inköpsfunktionen har implementerat en spendanalys plattform kan det vara en bra start att utvärdera det projektet och sedan tillämpa AI ovanpå den befintliga lösningen. Andra faktorer att beakta är att försäkra datakvalitet och säkerhet, involvera ledningen samt lyfta vilka fördelar AI kan ge i form av ökad effektivitet och kostnadsbesparingar. Därtill är det viktigt att inkludera hela strategiska inköps-funktionen samt att inte endast beakta den tekniska aspekten utan också mjuka faktorer så som change management och agila metoder.

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Djaidja, Taki Eddine Toufik. "Advancing the Security of 5G and Beyond Vehicular Networks through AI/DL." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCK009.

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L'émergence des réseaux de cinquième génération (5G) et des réseaux véhiculaire (V2X) a ouvert une ère de connectivité et de services associés sans précédent. Ces réseaux permettent des interactions fluides entre les véhicules, l'infrastructure, et bien plus encore, en fournissant une gamme de services à travers des tranches de réseau (slices), chacune adaptée aux besoins spécifiques de ceux-ci. Les générations futures sont même censées apporter de nouvelles avancées à ces réseaux. Cependant, ce progrès remarquable les expose à une multitude de menaces en matière de cybersécurité, dont bon nombre sont difficiles à détecter et à atténuer efficacement avec les contre mesures actuelles. Cela souligne la nécessité de mettre en oeuvre de nouveaux mécanismes avancés de détection d'intrusion pour garantir l'intégrité, la confidentialité et la disponibilité des données et des services.Un domaine suscitant un intérêt croissant à la fois dans le monde universitaire qu'industriel est l'Intelligence Artificielle (IA), en particulier son application pour faire face aux menaces en cybersécurité. Notamment, les réseaux neuronaux (RN) ont montré des promesses dans ce contexte, même si les solutions basées sur l'IA sont accompagnées de défis majeurs.Ces défis peuvent être résumés comme des préoccupations concernant l'efficacité et l'efficience. Le premier concerne le besoin des Systèmes de Détection d'Intrusions (SDI) de détecter avec précision les menaces, tandis que le second implique d'atteindre l'efficacité en termes de temps et la détection précoce des menaces.Cette thèse représente l'aboutissement de nos recherches sur l'investigation des défis susmentionnés des SDI basés sur l'IA pour les systemes 5G en général et en particulier 5G-V2X. Nous avons entamé notre recherche en réalisant une revue de la littérature existante. Tout au long de cette thèse, nous explorons l'utilisation des systèmes d'inférence floue (SIF) et des RN, en mettant particulièrement l'accent sur cette derniere technique. Nous avons utilisé des techniques de pointe en apprentissage, notamment l'apprentissage profond (AP), en intégrant des réseaux neuronaux récurrents et des mécanismes d'attention. Ces techniques sont utilisées de manière innovante pour réaliser des progrès significatifs dans la résolution des préoccupations liées à l'amélioration de l'efficacité et de l'efficience des SDI. De plus, nos recherches explorent des défis supplémentaires liés à la confidentialité des données lors de l'utilisation des SDIs basés sur l'AP. Nous y parvenons en exploitant les algorithmes d'apprentissage fédéré (AF) les plus récents
The emergence of Fifth Generation (5G) and Vehicle-to-Everything (V2X) networks has ushered in an era of unparalleled connectivity and associated services. These networks facilitate seamless interactions among vehicles, infrastructure, and more, providing a range of services through network slices, each tailored to specific requirements. Future generations are even expected to bring further advancements to these networks. However, this remarkable progress also exposes them to a myriad of security threats, many of which current measures struggle to detect and mitigate effectively. This underscores the need for advanced intrusion detection mechanisms to ensure the integrity, confidentiality, and availability of data and services.One area of increasing interest in both academia and industry spheres is Artificial Intelligence (AI), particularly its application in addressing cybersecurity threats. Notably, neural networks (NNs) have demonstrated promise in this context, although AI-based solutions do come with inherent challenges. These challenges can be summarized as concerns about effectiveness and efficiency. The former pertains to the need for Intrusion Detection Systems (IDSs) to accurately detect threats, while the latter involves achieving time efficiency and early threat detection.This dissertation represents the culmination of our research findings on investigating the aforementioned challenges of AI-based IDSs in 5G systems in general and 5G-V2X in particular. We initiated our investigation by conducting a comprehensive review of the existing literature. Throughout this thesis, we explore the utilization of Fuzzy Inference Systems (FISs) and NNs, with a specific emphasis on the latter. We leveraged state-of-the-art NN learning, referred to as Deep Learning (DL), including the incorporation of recurrent neural networks and attention mechanisms. These techniques are innovatively harnessed to making significant progress in addressing the concerns of enhancing the effectiveness and efficiency of IDSs. Moreover, our research delves into additional challenges related to data privacy when employing DL-based IDSs. We achieve this by leveraging and experimenting state-of-the-art federated learning (FL) algorithms

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Nystad, Marcus, and Lukas Lindblom. "Artificial Intelligence in the Pulp and Paper Industry : Current State and Future Trends." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279574.

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The advancements in Artificial Intelligence (AI) have received large attention in recent years and increased awareness has led to massive societal benefits and new opportunities for industries able to capitalize on these emerging technologies. The pulp and paper industry is going through one of the most considerable transformations into Industry 4.0. Integrating AI technology in the manufacturing process of the pulp and paper industry has shown great potential, but there are uncertainties which direction companies are heading. This study is an investigation of the pulp and paper industry in collaboration with IBM that aims to fill a gap between academia and the progress companies are making. More specifically, this thesis is a multiple case study of the current state and barriers of AI technology in the Swedish pulp and paper industry, the future trends and expectations of AI and the way organizations are managing AI initiatives Semi-structured interviews were conducted with 11 participants from three perspectives and the data was thematically coded. Our analysis shows that the use of AI varies, and companies are primarily experimenting with a still immature technology. Several trends and areas with future potential were identified and it was shown that digital innovation management is highly regarded. We conclude that there are several barriers hindering further use of AI. However, continued progress with AI will provide large benefit long term in areas such as predictive maintenance and process optimization. Several measures taken to support initiatives with AI were identified and discussed. We encourage managers to take appropriate actions in the continued work toward AI integration and encourage further research in the area of potential reworks in R&D.
Framgångarna inom Artificiell Intelligens (AI) har fått stor uppmärksamhet de senaste åren och ökad medvetenhet har lett till stora fördelar för samhället liksom nya möjligheter för industrier som tar vara på dessa nya teknologier. Pappers- och massa industrin genomgår en av de mest omfattande transformationerna mot Industri 4.0. Integreringen av AI-teknologi i industrins tillverkningsprocesser has visat stor potential, men också osäkerhet kring vilken riktning företag är på väg mot. Denna studie är en undersökning av den svenska pappers- och massaindustrin, i samarbete med IBM, som syftar till att minska gapet mellan akademin och framstegen företag inom industrin tar. Mer specifikt är denna uppsats en kombinerad fallstudie av det nuvarande läget, barriärerna till AI-teknik i den svenska pappers- och massa industrin, de framtida trenderna och förväntningarna på AI och metoderna företag använder för att stötta AI-initiativ. Semi-strukturerade intervjuer genomfördes med 11 deltagare från tre olika perspektiv och datan var tematiskt kodad. Vår analys visar att användning av AI varierar och företag experimenterar huvudsakligen med omogen teknik. Flera trender och områden med potential för framtiden identifierades och det visades att digital innovationshantering är högt ansedd. Vi sammanfattar med att det finns flera barriärer som hindrar fortsatt användning av AI. Fortsatt arbete med AI-tekniken kommer leda till stora fördelar på lång sikt inom områden som prediktivt underhåll och fortsatt processoptimering. Flera åtgärder för att stötta AI-initiativ var identifierade och diskuterades. Vi uppmuntrar industrin att genomföra lämpliga åtgärder i det fortsatta arbetet mot AI-integration och uppmuntrar fortsatt forskning inom potentiella omstruktureringar inom FoU.

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Hanski, Jari, and Kaan Baris Biçak. "An Evaluation of the Unity Machine Learning Agents Toolkit in Dense and Sparse Reward Video Game Environments." Thesis, Uppsala universitet, Institutionen för speldesign, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444982.

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In computer games, one use case for artificial intelligence is used to create interesting problems for the player. To do this new techniques such as reinforcement learning allows game developers to create artificial intelligence agents with human-like or superhuman abilities. The Unity ML-agents toolkit is a plugin that provides game developers with access to reinforcement algorithms without expertise in machine learning. In this paper, we compare reinforcement learning methods and provide empirical training data from two different environments. First, we describe the chosen reinforcement methods and then explain the design of both training environments. We compared the benefits in both dense and sparse rewards environments. The reinforcement learning methods were evaluated by comparing the training speed and cumulative rewards of the agents. The goal was to evaluate how much the combination of extrinsic and intrinsic rewards accelerated the training process in the sparse rewards environment. We hope this study helps game developers utilize reinforcement learning more effectively, saving time during the training process by choosing the most fitting training method for their video game environment. The results show that when training reinforcement agents in sparse rewards environments the agents trained faster with the combination of extrinsic and intrinsic rewards. And when training an agent in a sparse reward environment with only extrinsic rewards the agent failed to learn to complete the task.

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Klingvall, Emelie. "Artificiell intelligens som ett beslutsstöd inom mammografi : En kvalitativ studie om radiologers perspektiv på icke-tekniska utmaningar." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18768.

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Artificiell intelligence (AI) har blivit vanligare att använda för att stödja människor i deras beslutsfattande. Maskininlärning (ML) är ett delområde inom AI som har börjat användas mer inom hälso-och sjukvården. Patientdata ökar inom vården och ett AI-system kan behandla denna ökade datamängd, vilket vidare kan utveckla ett beslutsstöd som hjälper läkarna. AI-tekniken blir vanligare att använda inom radiologin och specifikt inom mammografin som ett beslutsstöd. Användning av AI-teknik inom mammografin medför fördelar men det finns även utmaningar som inte har något med tekniken att göra.Icke-tekniska utmaningar är viktiga att se över för att generera en lyckad praxis. Studiens syfte var därför att undersöka icke-tekniska utmaningar vid användning av AI som ett beslutsstöd inom mammografi ur ett radiologiskt perspektiv. Radiologer med erfarenhet av mammografi intervjuades i syfte att öka kunskapen kring deras syn på användningen.Resultatet från studien identifierade och utvecklade de icke-tekniska utmaningarna utifrån temana: ansvar, mänskliga förmågor, acceptans, utbildning/kunskap och samarbete. Resultatet indikerade även på att inom dessa teman finns icke-tekniska utmaningar med tillhörande aspekter som är mer framträdande än andra. Studien ökar kunskaperna kring radiologers syn på användningen och bidrar till framtida forskning för samtliga berörda aktörer. Forskning kan ta hänsyn till dessa icke-tekniska utmaningar redan innan tekniken är implementerad i syfte att minska risken för komplikationer.
Artificial intelligence (AI) has become more commonly used to support people when making decisions. Machine learning (ML) is a sub-area of AI that has become more frequently used in health care. Patient data is increasing in healthcare and an AI system can help to process this increased amount of data, which further can develop a decision support that can help doctors. AI technology is becoming more common to use in radiology and specifically in mammography, as a decision support. The usage of AI technology in mammography has many benefits, but there are also challenges that are not connected to technology.Non-technical challenges are important to consider and review in order to generate a successful practice. The purpose of this thesis is therefore to review non-technical challenges when using AI as a decision support in mammography from a radiological perspective. Radiologists with experience in mammography were interviewed in order to increase knowledge about their views on the usage.The results identified and developed the non-technical challenges based on themes: responsibility, human abilities, acceptance, education/knowledge and collaboration. The study also found indications within these themes that there are non-technical challenges with associated aspects that are more prominent than others. This study emphasizes and increases the knowledge of radiologists views on the usage of AI and contributes to future research for all the actors involved. Future research can address these non-technical challenges even before the technology is implemented to reduce the risk of complications.

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Bengtsson, Theodor, and Jonas Hägerlöf. "Stora mängder användardata för produktutveckling : Möjligheter och utmaningar vid integrering av stora mängder användardata i produktutvecklingsprocesser." Thesis, KTH, Integrerad produktutveckling, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297966.

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Teknikutvecklingen har bidragit till ökad mängd användardata företag har tillgång till och väntas fortsätta öka. Företag som integrerar användardata i sina produktutvecklingsprocesser väntas uppnå konkurrensfördelar. Arbetets syfte handlar om att undersöka möjligheter och utmaningar vid integrering av stora mängder användardata. Genom att besvara två frågeställningar fastställer undersökningen arbetets syfte, där även konsekvenser för beslutsfattande behandlas. Arbetsprocessen inleddes med en litteraturstudie som låg till grund för både problematiseringen och syftet som identifierar ett gap i forskningen kring användardata i produktutvecklingsprocesser. Genom litteraturstudien skapades en bredare förståelse för ämnet. Den empiriska delen utgjordes av en kvalitativ semistrukturerad intervjustudie med fyra deltagande företag och lika många respondenter med kunskap inom området. Genom kodning av materialet identifierades områden bland respondenterna som bidrog med insikter som behandlats för att bidra till forskningsområdet. Resultaten belyser möjligheter och utmaningar företag står inför vid integrering av storamängder användardata i produktutvecklingsprocesser. Studien framhåller användaren som central i produktutvecklingen, där ökad data möjliggör komplexa dataanalyser. Effektivanalys av data möjliggör snabbare itereringsprocesser och repetitiva jobb kan ersättas av mer stimulerande. Därtill blir beslutsunderlag mer omfattande och kan generera nya strategier och utformningar av erbjudanden. Studien fastställer även att ökad mängd data ställer krav på företag, där relevansen i datan är viktig och processer för hantering måste kunna definiera relevant data. Vidare måste företag mogna i rollen att integrera användardata. För att beslutsunderlag från användardata ska vara säkert bör kvalitativa och kvantitativa analyser främjas att samverka för att bekräfta varandras identifierade mönster. Integrering av stora mängder användardata i produktutvecklingsprocesser fastställs av denna studie kräva att kompetens erhålls för att i processer för hantering av data kunna säkerställa relevans genom att definiera vilken data som ska samlas in. Vid lyckad integrering uppnår företag som integrerar användardata konkurrensfördelar och kapitaliseringsmöjligheter som är långsiktigt gynnsamma.
The technology development has contributed to an increased amount of user data companies have access to and is expected to continue to increase. Companies that integrate user data into their product development processes are expected to gain competitive advantages. The purpose of the work is to investigate opportunities and challenges when integrating large amounts of user data. By answering two questions, the study determines the purpose of the work, where the consequences for decision making also are addressed. The work process began with a literature study that formed the basis for both the problematization and the purpose that identifies a gap in the research about user data in product development processes. The literature study created a broader understanding of the subject. The empirical part consisted of a qualitative semistructured interview study with four participating companies and an equal number of respondents with knowledge in the field. Coding of the material identified areas among the respondents which contributed within sights that were processed to contribute to the research area.The results highlight opportunities and challenges companies face when integrating large amounts of user data into product development processes. The study highlights the user as central to product development, where increased data enables complex data analysis. Efficient analysis of data enables faster iteration processes and repetitive jobs can be replaced by more stimulating. In addition, the basis for decision-making becomes more extensive and can generate new strategies and designs for offers. The study also determines that increased data places demands on companies, where the relevance of the data is important and processes for handling must be able to define the relevant data. Furthermore, companies need to mature in the role of integrating user data. In order to ensure the safe basis for decisionmaking from user data, qualitative and quantitative analyses should be promoted to work together to confirm each other’s identified patterns. The integration of large amounts of user data into product development processes is determined by this study to require the acquisition of competence in order to ensure relevance in data management processes by defining which data to collect. With successful integration, companies that integrate user data achieve competitive advantages and capitalization opportunities that are long-term beneficial.

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Pouy, Léo. "OpenNas : un cadre adaptable de recherche automatique d'architecture neuronale." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG089.

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Lors de la création d'un modèle de réseau de neurones, l'étape dite du "fine-tuning" est incontournable. Lors de ce fine-tuning, le développeur du réseau de neurones doit ajuster les hyperparamètres et l'architecture du réseau pour que ce-dernier puisse répondre au cahier des charges. Cette étape est longue, fastidieuse, et nécessite de l'expérience de la part du développeur. Ainsi, pour permettre la création plus facile de réseaux de neurones, il existe une discipline, l'"Automatic Machine Learning" (Auto-ML), qui cherche à automatiser la création de Machine Learning. Cette thèse s'inscrit dans cette démarche d'automatisation et propose une méthode pour créer et optimiser des architectures de réseaux de neurones (Neural Architecture Search). Pour ce faire, un nouvel espace de recherche basé sur l'imbrication de blocs à été formalisé. Cet espace permet de créer un réseau de neurones à partir de blocs élémentaires connectés en série ou en parallèle pour former des blocs composés qui peuvent eux-mêmes être connectés afin de former un réseau encore plus complexe. Cet espace de recherche à l'avantage d'être facilement personnalisable afin de pouvoir influencer la recherche automatique vers des types d'architectures (VGG, Inception, ResNet, etc.) et contrôler le temps d'optimisation. De plus il n'est pas contraint à un algorithme d'optimisation en particulier. Dans cette thèse, la formalisation de l'espace de recherche est tout d'abord décrite, ainsi que des techniques dîtes d'"encodage" afin de représenter un réseau de l'espace de recherche par un entier naturel (ou une liste d'entiers naturels). Puis, des stratégies d'optimisation applicables à cet espace de recherche sont proposées. Enfin, des expérimentations de recherches d'architectures neuronales sur différents jeux de données et avec différents objectifs en utilisant l'outil développé (nommé OpenNas) sont présentées
When creating a neural network, the "fine-tuning" stage is essential. During this fine-tuning, the neural network developer must adjust the hyperparameters and the architecture of the network so that it meets the targets. This is a time-consuming and tedious phase, and requires experience on the part of the developer. So, to make it easier to create neural networks, there is a discipline called Automatic Machine Learning (Auto-ML), which seeks to automate the creation of Machine Learning. This thesis is part of this Auto-ML approach and proposes a method for creating and optimizing neural network architectures (Neural Architecture Search, NAS). To this end, a new search space based on block imbrication has been formalized. This space makes it possible to create a neural network from elementary blocks connected in series or in parallel to form compound blocks which can themselves be connected to form an even more complex network. The advantage of this search space is that it can be easily customized to influence the NAS for specific architectures (VGG, Inception, ResNet, etc.) and control the optimization time. Moreover, it is not constrained to any particular optimization algorithm. In this thesis, the formalization of the search space is first described, along with encoding techniques to represent a network from the search space by a natural number (or a list of natural numbers). Optimization strategies applicable to this search space are then proposed. Finally, neural architecture search experiments on different datasets and with different objectives using the developed tool (named OpenNas) are presented

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Книги з теми "Artificial intelligence (ML/DL)"

Hartanto, Ronny. A Hybrid Deliberative Layer for Robotic Agents: Fusing DL Reasoning with HTN Planning in Autonomous Robots. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.

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Mukherjee, Sudipta. ML. NET Revealed: Simple Tools for Applying Machine Learning to Your Applications. Apress L. P., 2020.

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Majumder, Abhilash. Deep Reinforcement Learning in Unity: With Unity ML Toolkit. Apress L. P., 2020.

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Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare. MDPI, 2022. http://dx.doi.org/10.3390/books978-3-0365-3741-2.

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Kshirsagar, Ameya, Jainam Panchal, and Manan Shah. Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry. Taylor & Francis Group, 2022.

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Kshirsagar, Ameya, Jainam Panchal, and Manan Shah. Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry. Taylor & Francis Group, 2022.

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Kshirsagar, Ameya, Jainam Panchal, and Manan Shah. Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry. CRC Press LLC, 2022.

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Kshirsagar, Ameya, Jainam Panchal, and Manan Shah. Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry. CRC Press LLC, 2022.

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Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry. Taylor & Francis Group, 2022.

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Rahman, Mahmudur, ed. Artificial Intelligence (AI) and Machine Learning (ML) in Medical Imaging Informatics towards Diagnostic Decision Making. MDPI, 2023. http://dx.doi.org/10.3390/books978-3-0365-8129-3.

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Частини книг з теми "Artificial intelligence (ML/DL)"

Rajendran, Sindhu, Alen Aji John, B. Suhas, and B. Sahana. "Role of ML and DL in Detecting Fraudulent Transactions." In Artificial Intelligence for Societal Issues, 59–82. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-12419-8_4.

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Wittenberg, Thomas, Thomas Lang, Thomas Eixelberger, and Roland Grube. "Acquisition of Semantics for Machine-Learning and Deep-Learning based Applications." In Unlocking Artificial Intelligence, 153–75. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64832-8_8.

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AbstractFor the development, training, and validation of machine learning (ML) and deep learning (DL) based methods, such as, e.g., image analysis, prediction of critical events, extraction or reconstruction of information from disrupted data streams, searching similarities in data collections, or planning of procedures, a lot of data is needed. Additionally to this data (images, bio-signals, vital-signs, text records, machine states, trajectories, antenna data, ...) adequate supplementary information about the meaning encoded in the data is required. Only with this additional information – the meaning or knowledge – a tight relation between the raw data and the human-understandable concepts – the semantics – from the real world can be established. Nevertheless, as the amount of data needed to develop robust ML or DL methods is strongly increasing, the assessment and acquisition of the related knowledge becomes more and more challenging. Within this chapter, an overview of concepts of knowledge acquisition applied to the different examples of applications is described and evaluated. Six main groups of knowledge acquisition related to AI-based technologies have been identified, namely (1) manual annotation methods, (2) data augmentation, (3) generative networks or simulation techniques, (4) synchronized sensors, (5)Active Learning approaches, and (6) explicit knowledge modeling using semantic networks.

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Gadri, Said, and Erich Neuhold. "Building Best Predictive Models Using ML and DL Approaches to Categorize Fashion Clothes." In Artificial Intelligence and Soft Computing, 90–102. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61401-0_9.

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Das, Priya, and Sohail Saif. "Intrusion Detection in IoT-Based Healthcare Using ML and DL Approaches: A Case Study." In Artificial Intelligence and Cyber Security in Industry 4.0, 271–94. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2115-7_12.

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Kotios, Dimitrios, Georgios Makridis, Silvio Walser, Dimosthenis Kyriazis, and Vittorio Monferrino. "Personalized Finance Management for SMEs." In Big Data and Artificial Intelligence in Digital Finance, 215–32. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-030-94590-9_12.

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AbstractThis chapter presents Business Financial Management (BFM) tools for Small Medium Enterprises (SMEs). The presented tools represent a game changer as they shift away from a one-size-fits-all approach to banking services and put emphasis on delivering a personalized SME experience and an improved bank client’s digital experience. An SME customer-centric approach, which ensures that the particularities of the SME are taken care of as much as possible, is presented. Through a comprehensive view of SMEs’ finances and operations, paired with state-of-the-art ML/DL models, the presented BFM tools act as a 24/7 concierge. They also operate as a virtual smart advisor that delivers in a simple, efficient, and engaging way business insights to the SME at the right time, i.e., when needed most. Deeper and better insights that empower SMEs contribute toward SMEs’ financial health and business growth, ultimately resulting in high-performance SMEs.

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Trocin, Cristina, Jan Gunnar Skogås, Thomas Langø, and Gabriel Hanssen Kiss. "Operating Room of the Future (FOR) Digital Healthcare Transformation in the Age of Artificial Intelligence." In Digital Transformation in Norwegian Enterprises, 151–72. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05276-7_9.

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AbstractNew technologies are emerging under the umbrella of digital transformation in healthcare such as artificial intelligence (AI) and medical analytics to provide insights beyond the abilities of human experts. Because AI is increasingly used to support doctors in decision-making, pattern recognition, and risk assessment, it will most likely transform healthcare services and the way doctors deliver those services. However, little is known about what triggers such transformation and how the European Union (EU) and Norway launch new initiatives to foster the development of such technologies. We present the case of Operating Room of the Future (FOR), a research infrastructure and an integrated university clinic which investigates most modern technologies such as artificial intelligence (AI), machine learning (ML), and deep learning (DL) to support the analysis of medical images. Practitioners can benefit from strategies related to AI development in multiple health fields to best combine medical expertise with AI-enabled computational rationality.

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Parkash, Surya, Ravinder Singh, and Shubham Badola. "Assessing Landslide Disaster Risk Reduction and Resilience: Case Studies and Insights, India." In Progress in Landslide Research and Technology, 323–39. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-72736-8_22.

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AbstractThe purpose of this paper is to highlight the issues of landslide disaster risk reduction in India by presenting real case studies of landslide incidences happened in the past. It also focuses upon the resilience measures and policies required for reducing landslide risk. This study highlights and give insight on the few important case studies of past landslides/mass-movement incidences such as Phuktal landslide dammed reservoir, Kargil-Ladakh (2015), Idukki-Kerala (2018), landslide at Noney district-Manipur (2022), Rock-ice avalanche and debris flow, Chamoli (2021), land subsidence in Joshimath-Uttarakhand (2022), South Lhonak Glacial Lake Outburst Floods-Sikkim (2023) etc. These disaster events are influenced by the intrinsic factors (i.e., geo-tectonic, drainage/hydrology, land uses) and extrinsic factors (i.e., climate change, anthropogenic activities, climate variability, natural and socio-economic development).The landslide disaster events induced by geo-tectonics, heavy rainfall, Glacial Lake Outburst Floods (GLOFs), anthropogenic activities etc. have been causing severe losses and damages to lives and properties in different regions of India. The extreme weather events and climate change increases the frequency of disaster events in hilly regions of India. As a result, the necessity for stringent policies and strategies (e.g., landuse, construction practices, enact/revise regulations etc.) for reducing disaster risks was extremely felt. Simultaneously, the advancement of innovative technology and tools such as Deep Learning (DL), data mining, Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IOT) are required to generate reliable, field validated models of multi-hazards based early warning, risk assessment, mitigation, response etc., which may reduce impacts of future disasters and its occurrences.

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Guérin, Eric, Orhun Aydin, and Ali Mahdavi-Amiri. "Artificial Intelligence." In Manual of Digital Earth, 357–85. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9915-3_10.

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Abstract In this chapter, we provide an overview of different artificial intelligence (AI) and machine learning (ML) techniques and discuss how these techniques have been employed in managing geospatial data sets as they pertain to Digital Earth. We introduce statistical ML methods that are frequently used in spatial problems and their applications. We discuss generative models, one of the hottest topics in ML, to illustrate the possibility of generating new data sets that can be used to train data analysis methods or to create new possibilities for Digital Earth such as virtual reality or augmented reality. We finish the chapter with a discussion of deep learning methods that have high predictive power and have shown great promise in data analysis of geospatial data sets provided by Digital Earth.

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Pop, Emilia-Loredana, and Augusta Raţiu. "Human-Computer Interaction in Artificial Intelligence with Applications in Healthcare: A Review." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia241213.

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In this paper, we provide an overview of Human-Computer Interaction (HCI) in the context of Artificial Intelligence (AI), focusing on Machine Learning (ML), Deep Learning (DL), Artificial Neural Networks (ANN), and applications of these techniques in the field of Healthcare. This review highlights the critical role of the human factor in AI-driven systems with discussions on AI ethics and expectations. Artificial Intelligence techniques, including ML and DL enhance gesture and speech recognition in HCI, while ANN models are particularly effective for hand gesture recognition. In Human-Computer Interaction, AI techniques bring value and understanding to Healthcare in real life. Despite the benefits that Artificial Intelligence brings to HCI, challenges remain. The future promises new applications and perspectives in HCI, where AI with ML, DL, and ANN have an effective impact.

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Andrae, Silvio. "The Use of Artificial Intelligence to Curb Deforestation in the Brazilian Rainforest." In Artificial Intelligence and Data Science for Sustainability, 81–122. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-6829-9.ch004.

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Tropical rainforests like the Amazon are invaluable ecosystems for human society and biodiversity. However, they are facing unprecedented threats, primarily from deforestation. This chapter explores the use of machine learning (ML) and deep learning (DL) to address this pressing environmental problem. By analyzing different ML/DL methods, we show how these tools can be used to understand deforestation patterns in the Brazilian Amazon better. Specifically, we discuss how ML/DL can help identify the drivers of deforestation, improve remote sensing-based monitoring, and predict future deforestation trends. Our results, particularly the role of ML/DL in providing actionable insights, empower decision-makers and policymakers with the knowledge to make informed choices. Ultimately, these strategies contribute to more effective forest conservation measures and sustainable land use, reassuring the audience about the reliability of our research.

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Тези доповідей конференцій з теми "Artificial intelligence (ML/DL)"

Muniraj, Inbarasan. "Investigating the efficacy of deep learning networks for 3D imaging and processing." In 3D Image Acquisition and Display: Technology, Perception and Applications, DW1H.4. Washington, D.C.: Optica Publishing Group, 2024. http://dx.doi.org/10.1364/3d.2024.dw1h.4.

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Artificial intelligence techniques, such as machine learning (ML) and deep learning (DL), are now widely used in various vision-based applications. Here, we summarize some of the most recent advances in Computational Integral Imaging using DL networks.

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Kommineni, Sivaram, Sanvitha Muddana, and Rajiv Senapati. "Explainable Artificial Intelligence based ML Models for Heart Disease Prediction." In 2024 3rd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO), 160–64. IEEE, 2024. http://dx.doi.org/10.1109/iccmso61761.2024.00042.

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Grisez, Laure, Shreshtha Sharma, and Paolo Pileggi. "ML-Based Virtual Sensing for Groundwater Monitoring in the Netherlands." In 17th International Conference on Agents and Artificial Intelligence, 175–84. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013101100003890.

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Dai, Yuyang, and Yilin Yan. "Volatility Forecasting: Can ML Beat Multivariate HAR Models?" In 2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI), 32–37. IEEE, 2024. http://dx.doi.org/10.1109/icecai62591.2024.10674893.

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Wiesbrock, Hans-Werner, and Jurgen Groβmann. "Outline of an Independent Systematic Blackbox Test for ML-based Systems." In 2024 IEEE International Conference on Artificial Intelligence Testing (AITest), 1–10. IEEE, 2024. http://dx.doi.org/10.1109/aitest62860.2024.00009.

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Talpini, Jacopo, Nicolò Civiero, Fabio Sartori, and Marco Savi. "A Federated Approach to Enhance Calibration of Distributed ML-Based Intrusion Detection Systems." In 17th International Conference on Agents and Artificial Intelligence, 840–48. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013376600003890.

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Grube, Nicolas, Mozhdeh Massah, Michael Tebbe, Paul Wancura, Hans-Werner Wiesbrock, Jürgen Grossmann, and Sami Kharma. "On a Systematic Test of ML-Based Systems: Experiments on Test Statistics." In 2024 IEEE International Conference on Artificial Intelligence Testing (AITest), 11–20. IEEE, 2024. http://dx.doi.org/10.1109/aitest62860.2024.00010.

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Du, Hebing, Chunling Wu, Pan He, and Hongyang Li. "TCAE-DL-RGCN Based Detection of Twitter Robots." In 2024 IEEE 7th International Conference on Big Data and Artificial Intelligence (BDAI), 193–98. IEEE, 2024. http://dx.doi.org/10.1109/bdai62182.2024.10692365.

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Allam, Aly, and Benaoumeur Senouci. "Platform Based DL Applications Design: Autonomous Vehicles Case Study." In 2025 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 0901–4. IEEE, 2025. https://doi.org/10.1109/icaiic64266.2025.10920817.

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Pamidimukkala, Jaanaki Swaroop, Tarun Teja P, Suman Paul K, Divya Sri Kosaraju, and Naveenkumar Mahamkali. "Comparative Study of ML Techniques for Classification of Crop Pests." In 2024 4th International Conference on Artificial Intelligence and Signal Processing (AISP), 1–5. IEEE, 2024. https://doi.org/10.1109/aisp61711.2024.10870737.

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Звіти організацій з теми "Artificial intelligence (ML/DL)"

Alhasson, Haifa F., and Shuaa S. Alharbi. New Trends in image-based Diabetic Foot Ucler Diagnosis Using Machine Learning Approaches: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0128.

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Review question / Objective: A significant amount of research has been conducted to detect and recognize diabetic foot ulcers (DFUs) using computer vision methods, but there are still a number of challenges. DFUs detection frameworks based on machine learning/deep learning lack systematic reviews. With Machine Learning (ML) and Deep learning (DL), you can improve care for individuals at risk for DFUs, identify and synthesize evidence about its use in interventional care and management of DFUs, and suggest future research directions. Information sources: A thorough search of electronic databases such as Science Direct, PubMed (MIDLINE), arXiv.org, MDPI, Nature, Google Scholar, Scopus and Wiley Online Library was conducted to identify and select the literature for this study (January 2010-January 01, 2023). It was based on the most popular image-based diagnosis targets in DFu such as segmentation, detection and classification. Various keywords were used during the identification process, including artificial intelligence in DFu, deep learning, machine learning, ANNs, CNNs, DFu detection, DFu segmentation, DFu classification, and computer-aided diagnosis.

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JOSI, Editor. Artificial Intelligence and Machine Learning: Transforming Industrial Optimization. Industrial Engineering Department, Faculty of Engineering, Universitas Andalas, March 2025. https://doi.org/10.25077/03032025.

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The integration of artificial intelligence (AI) and machine learning (ML) into industrial systems is rapidly reshaping the way industries operate, optimize, and innovate. As industries grow more complex and data-driven, the ability to harness AI and ML technologies offers unprecedented efficiency, precision, and adaptability. In the pursuit of optimization, these technologies are no longer just experimental tools but have become essential drivers of transformation.

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Bacci, Marcelo Rodrigues, Catarina Viggiani Bicudo Minczuk, and Fernando Luiz Affonso Fonseca. A systematic review of artificial intelligence models for acute kidney injury prediction. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2023. http://dx.doi.org/10.37766/inplasy2023.3.0025.

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Review question / Objective: We performed a systematic review of the use of AI and ML to build AKI prediction models in hospitalized patients. Condition being studied: Acute kidney injury prediction models efficacy. Eligibility criteria: Manuscripts written in english language with abstract available until the 6th of March. The search strategy should adress the MesH terms in the title and abstract sections.

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Alharbi, Shuaa S., and Haifa F. Alhasson. Toward the Identification of Applications of Artificial Intelligence for Dental Image Detection: Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0023.

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Review question / Objective: The purpose of this systematic review is to understand and compare the current applications of machine learning in the care of dental patients. This will enable us to assess their diagnostic and prognostic accuracy. As part of the study, we will identify areas of development for ML applications in the dental care field. In addition, we will suggest improvements to research methodology that will facilitate the implementation of ML technologies in services and improve clinical treatment guidelines based on the results of future studies. Condition being studied: This study rationally focused on reviewing the current state of Artificial Intelligence (AI) in dentistry and state-of-the-art applications, including the recognition of teeth cavities, filled teeth, crown predictions, oral surgery, and endodontic therapy.

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Ramuhalli, Pradeep, Alex Huning, Askin Guler Yigitoglu, and Abhinav Saxena. Status Report on Regulatory Criteria Applicable to the Use of Artificial Intelligence (AI) and Machine Learning (ML). Office of Scientific and Technical Information (OSTI), September 2023. http://dx.doi.org/10.2172/2007715.

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Niles, Kenneth, Emily Leathers, Joe Tom, Chandler Armstrong, Osama Ennasr, Brandon Dodd, Theresa Coumbe, et al. Leveraging artificial intelligence and machine learning (AI/ML) for levee culvert Inspections in USACE Flood Control Systems (FCS). Engineer Research and Development Center (U.S.), September 2024. http://dx.doi.org/10.21079/11681/49210.

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Levee inspections are essential in preventing flooding within populated regions. Risk assessments of structures are performed to identify potential failure modes to maintain the safety and health of the structure. The data collection and defect coding parts of the inspection process can be labor-intensive and time-consuming. The integration of machine learning (ML) and artificial intelligence (AI) techniques may increase accuracy of assessments and reduce time and cost. To develop a foundation for a fully autonomous inspection process, this research investigates methods to gather information for levees, structures, and culverts as well as methods to identify indicators of future failures using AI and ML techniques. Robotic platform and instrumentation options that can be used in the data collection process are also explored, and a platform-agnostic solution is proposed.

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Rodriguez, Simon, Tim Hwang, and Rebecca Gelles. Comparing Corporate and University Publication Activity in AI/ML. Center for Security and Emerging Technology, January 2021. http://dx.doi.org/10.51593/20200067.

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Based on news coverage alone, it can seem as if corporations dominate the research on artificial intelligence and machine learning when compared to the work of universities and academia. Authors Simon Rodriguez, Tim Hwang and Rebecca Gelles analyze the data over the past decade of research publications and find that, in fact, universities are the more dominant producers of AI papers. They also find that while corporations do tend to generate more citations to the work they publish in the field, these “high performing” papers are most frequently cross-collaborations with university labs

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Christie, Lorna. Interpretable machine learning. Parliamentary Office of Science and Technology, October 2020. http://dx.doi.org/10.58248/pn633.

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Machine learning (ML, a type of artificial intelligence) is increasingly being used to support decision making in a variety of applications including recruitment and clinical diagnoses. While ML has many advantages, there are concerns that in some cases it may not be possible to explain completely how its outputs have been produced. This POSTnote gives an overview of ML and its role in decision-making. It examines the challenges of understanding how a complex ML system has reached its output, and some of the technical approaches to making ML easier to interpret. It also gives a brief overview of some of the proposed tools for making ML systems more accountable.

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Pasupuleti, Murali Krishna. Mathematical Modeling for Machine Learning: Theory, Simulation, and Scientific Computing. National Education Services, March 2025. https://doi.org/10.62311/nesx/rriv125.

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Abstract Mathematical modeling serves as a fundamental framework for advancing machine learning (ML) and artificial intelligence (AI) by integrating theoretical, computational, and simulation-based approaches. This research explores how numerical optimization, differential equations, variational inference, and scientific computing contribute to the development of scalable, interpretable, and efficient AI systems. Key topics include convex and non-convex optimization, physics-informed machine learning (PIML), partial differential equation (PDE)-constrained AI, and Bayesian modeling for uncertainty quantification. By leveraging finite element methods (FEM), computational fluid dynamics (CFD), and reinforcement learning (RL), this study demonstrates how mathematical modeling enhances AI-driven scientific discovery, engineering simulations, climate modeling, and drug discovery. The findings highlight the importance of high-performance computing (HPC), parallelized ML training, and hybrid AI approaches that integrate data-driven and model-based learning for solving complex real-world problems. Keywords Mathematical modeling, machine learning, scientific computing, numerical optimization, differential equations, PDE-constrained AI, variational inference, Bayesian modeling, convex optimization, non-convex optimization, reinforcement learning, high-performance computing, hybrid AI, physics-informed machine learning, finite element methods, computational fluid dynamics, uncertainty quantification, simulation-based AI, interpretable AI, scalable AI.

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Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, March 2025. https://doi.org/10.62311/nesx/rr225.

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Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance human productivity, AI-driven sustainability practices for energy and resource efficiency, and predictive maintenance models that reduce downtime. Addressing ethical challenges, the Article highlights the importance of data privacy, unbiased algorithms, and the environmental responsibility of intelligent automation. Through case studies across manufacturing, healthcare, and energy sectors, readers gain insights into real-world applications of AI and ML, showcasing their impact on efficiency, quality, and safety. The Article concludes with future directions, emphasizing emerging technologies like quantum computing, human-machine synergy, and the sustainable vision for Industry 5.0, where intelligent automation not only drives innovation but also aligns with ethical and social values for a resilient industrial future. Keywords: Industry 5.0, intelligent automation, AI, machine learning, sustainability, human- machine collaboration, cobots, predictive maintenance, quality control, ethical AI, data privacy, Industry 4.0, computer vision, natural language processing, energy efficiency, adaptive logistics, environmental responsibility, industrial ecosystems, quantum computing.

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