Relevant bibliographies by topics / Intelligence artificielle (ML/DL)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Intelligence artificielle (ML/DL)'

Author: Grafiati
Published: 29 March 2025

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Journal articles on the topic "Intelligence artificielle (ML/DL)"

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Brouchet, Edouard, François de Brondeau, Marie-José Boileau, and Masrour Makaremi. "Apport de l’intelligence artificielle dans la prévision de croissance mandibulaire : revue systématique de la littérature." Revue d'Orthopédie Dento-Faciale 58, no. 2 (2024): 185–209. http://dx.doi.org/10.1051/odf/2024021.

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L’orthodontiste intervient principalement auprès d’enfants en cours de croissance. L’examen clinique initial ne fournit qu’une image statique qui doit être interprétée en tenant compte de son évolution potentielle. Une prédiction précise de la croissance mandibulaire, permettrait au praticien d’améliorer le diagnostic, la planification du traitement et ainsi la prise en charge du patient. De nombreux travaux de recherche ont été menés, basés sur des signes structuraux, des analyses céphalométriques et des valeurs d’agrandissement moyen, mais restent imprécis. Les limites rapportées comprennent
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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 (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
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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 (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 he
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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
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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 (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:
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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 (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 predictiv
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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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Drikakis, Dimitris, and Filippos Sofos. "Can Artificial Intelligence Accelerate Fluid Mechanics Research?" Fluids 8, no. 7 (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 poten
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An, Ruopeng, Jing Shen, and Yunyu Xiao. "Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies." Journal of Medical Internet Research 24, no. 12 (2022): e40589. http://dx.doi.org/10.2196/40589.

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Background Obesity is a leading cause of preventable death worldwide. Artificial intelligence (AI), characterized by machine learning (ML) and deep learning (DL), has become an indispensable tool in obesity research. Objective This scoping review aimed to provide researchers and practitioners with an overview of the AI applications to obesity research, familiarize them with popular ML and DL models, and facilitate the adoption of AI applications. Methods We conducted a scoping review in PubMed and Web of Science on the applications of AI to measure, predict, and treat obesity. We summarized an
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Ali, Zulfiqar, Asif Muhammad, Nangkyeong Lee, Muhammad Waqar, and Seung Won Lee. "Artificial Intelligence for Sustainable Agriculture: A Comprehensive Review of AI-Driven Technologies in Crop Production." Sustainability 17, no. 5 (2025): 2281. https://doi.org/10.3390/su17052281.

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Smart farming leverages Artificial Intelligence (AI) to address modern agricultural sustainability challenges. This study investigates the application of machine learning (ML), deep learning (DL), and time series analysis in agriculture through a systematic literature review following the PRISMA methodology. The review highlights the critical roles of ML and DL techniques in optimizing agricultural processes, such as crop selection, yield prediction, soil compatibility classification, and water management. ML algorithms facilitate tasks like crop selection and soil fertility classification, wh
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Dissertations / Theses on the topic "Intelligence artificielle (ML/DL)"

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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
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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 nom
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Taghavian, Masoud. "VNF placement in 5G Networks using AI/ML." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2024. http://www.theses.fr/2024IMTA0421.

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La transition inévitable des dispositifs matériels physiques vers des modules logiciels légers et réutilisables dans le cadre de la virtualisation des fonctions de réseau (NFV) offre d’innombrables possibilités tout en présentant plusieurs défis sans précédent. La satisfaction des attentes de la NFV dans les réseaux post-5G dépend fortement du placement efficace des services de réseau.L’allocation dynamique des ressources physiques pour les demandes de services en ligne exigeant des ressources hétérogènes selon des exigences de qualité de service spécifiques représente l’une des étapes les plu
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Delestrac, Paul. "Advanced Profiling Techniques For Evaluating GPU Computing Efficiency Executing ML Applications." Electronic Thesis or Diss., Université de Montpellier (2022-....), 2024. http://www.theses.fr/2024UMONS014.

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L'augmentation en complexité des applications d'Intelligence Artificielle (IA) entraîne une demande accrue en puissance de calcul et en énergie pour entraîner et exécuter des modèles d'apprentissage automatique (ML). Les processeurs graphiques (GPU), forts d'architectures améliorées (e.g., ajout de cœurs dédiés à l'IA en 2017), sont devenus le système de prédilection pour de telles tâches. Concevoir des systèmes plus efficients pour l'IA n'est possible qu'avec une connaissance approfondie des limites des systèmes existants, où matériel et logiciel sont étroitement couplés. Mais l'abstraction d
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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 da
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Bouraoui, Zied. "Inconsistency and uncertainty handling in lightweight description logics." Thesis, Artois, 2015. http://www.theses.fr/2015ARTO0408/document.

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Cette thèse étudie la dynamique des croyances et la gestion de l’incertitude dans DL-Lite, une des plus importantes familles des logiques de description légères. La première partie de la thèse porte sur la gestion de l’incertitude dans DL-Lite. En premier lieu, nous avons proposé une extension des principaux fragments de DL-Lite pour faire face à l’incertitude associée aux axiomes en utilisant le cadre de la théorie des possibilités. Cette extension est réalisée sans engendrer des coûts calculatoires supplémentaires. Nous avons étudié ensuite la révision des bases DL-Lite possibilistes en prés
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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 m
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Hmedoush, Iman. "Connectionless Transmission in Wireless Networks (IoT)." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS143.pdf.

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L'origine concernant l'idée d'ajouter de l'intelligence aux objets de base et de les faire communiquer n'est pas connue précisément. Mais ces derniers temps, l'émergence d'Internet en tant que réseau de communication global a aussi motivé l'utilisation de son architecture et de ses protocoles pour connecter des objets. C'est par exemple le cas célèbre du distributeur automatique de sodas connecté à l'ARPANET dans les années 1980. Au cours des deux dernières décennies, de nombreuses améliorations technologiques ont été développées pour rendre possible l'Internet des objets (IoT). Un scénario d'
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Kaplan, Caelin. "Compromis inhérents à l'apprentissage automatique préservant la confidentialité." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4045.

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À mesure que les modèles d'apprentissage automatique (ML) sont de plus en plus intégrés dans un large éventail d'applications, il devient plus important que jamais de garantir la confidentialité des données des individus. Cependant, les techniques actuelles entraînent souvent une perte d'utilité et peuvent affecter des facteurs comme l'équité et l'interprétabilité. Cette thèse vise à approfondir la compréhension des compromis dans trois techniques de ML respectueuses de la vie privée : la confidentialité différentielle, les défenses empiriques, et l'apprentissage fédéré, et à proposer des méth
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Callebert, Lucile. "Activités collaboratives et génération de comportements d'agents : moteur décisionnel s'appuyant sur un modèle de confiance." Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2299/document.

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Lorsqu’ils travaillent en équipe, les humains ont rarement des comportements optimaux : ils peuvent faire des erreurs, manquer de motivation ou de compétence. Dans les domaines des environnements virtuels ou des systèmes multi-agents, de nombreux travaux ont cherché à reproduire les comportements d’équipes humaines : un agent représente alors un membre de l’équipe. Cependant, ces travaux ont très souvent pour objectif la performance de l’équipe, et non la fidélité des comportements produits. Pour former un apprenant en environnement virtuel à prêter attention et à s’adapter aux autres, nous av
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Book chapters on the topic "Intelligence artificielle (ML/DL)"

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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. 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. 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. 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. 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. 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. 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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Das, Pritam, Hakam Singh, Nilamadhab Mishra, et al. "The Impact and Evolution of Deep Learning in Contemporary Real-World Predictive Applications." In Advances in Computational Intelligence and Robotics. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-6230-3.ch001.

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Deep learning (DL) is making a significant impact on the lives of human beings, either directly or indirectly; we benefit from Artificial Intelligence (AI), Machine Learning (ML), DL and other technologies/networks in our day-to-day lives. DL not only can mimic a human brain and carry out tasks like a human being but has also outworked the approaches of ML, making itself the most efficient technology used nowadays. Maybe this is the reason why various fields like Cybersecurity, Medical Treatments, Traffic Control, Weather Forecast, Bioinformatics, Fraud Detection, Robotics, Vocal AI, Computer Vision, Autonomous Vehicles, E-Commerce and so on are using DL techniques/algorithms rather than the traditional ML approach. This review will illuminate the History of DL, i.e. what DL is, how and why it has evolved so far, and what the various parts of DL are. Though DL is considered the most efficient, it has some limitations, and we will investigate them gradually. We will also study all pre-researched DL Techniques and their implications for contemporary real-world predictive applications.
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Nagula, Jagan Mohan, Murugan R., and Tripti Goel. "Role of Machine and Deep Learning Techniques in Diabetic Retinopathy Detection." In Multidisciplinary Applications of Deep Learning-Based Artificial Emotional Intelligence. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5673-6.ch003.

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Machine learning (ML) and deep learning (DL) techniques play a significant role in diabetic retinopathy (DR) detection via grading the severity levels or segmenting the retinal lesions. High sugar levels in the blood due to diabetes causes DR, a leading cause of blindness. Manual detection or grading of the DR requires ophthalmologists' expertise and consumes time prone to human errors. Therefore, using fundus images, the ML and DL algorithms help automatic DR detection. The fundus imaging analysis helps the early DR detection, controlling, and treatment evaluation of DR conditions. Knowing the fundus image analysis requires a strong knowledge of the system and ML and DL functionalities in computer vision. DL in fundus imaging is a rapidly expanding research area. This chapter presents the fundus images, DR, and its severity levels. Also, this chapter explains the performance analysis of the various ML DL-based DR detection techniques. Finally, the role of ML and DL techniques in DR detection or severity grading is discussed.
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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. 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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Mishra, Alok, and Preeti Mishra. "MACHINE AND DEEP LEARNING APPLICATIONS: ADVANCEMENTS, CHALLENGES, AND FUTURE DIRECTIONS." In Futuristic Trends in Artificial Intelligence Volume 3 Book 1. Iterative International Publisher, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bfai1p1ch8.

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Advancements in the realm of artificial intelligence (AI) have been nothing short of extraordinary, and the fields of machine learning (ML) and deep learning (DL) have been at the forefront, driving transformative changes in various sectors such as computer vision, natural language processing, healthcare, finance, and autonomous systems. This research paper presents a comprehensive survey of the most current applications of ML and DL techniques, engages in a discourse regarding the difficulties tied to their execution, and explores the future trajectories within this domain. It encompasses a detailed examination of essential ML and DL algorithms, architectures, and methodologies, underscoring their pragmatic utility and societal ramifications. By conducting an exhaustive review of pertinent literature and research endeavors, the core objective of this scholarly endeavor is to elucidate the progress, obstacles, and untapped potential of ML and DL in catalyzing innovation and addressing complex challenges.
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Conference papers on the topic "Intelligence artificielle (ML/DL)"

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Arunachalam, N., S. Rukmani Devi, Niyas Ahamed A, Nazrin Salma S, and M. Meikandan. "IoT Wearable Medical Device for Heart Disease Recognition Based Ml and Dl: A Bovw Based MDCNN Classification Approach." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721749.

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Muniraj, Inbarasan. "Investigating the efficacy of deep learning networks for 3D imaging and processing." In 3D Image Acquisition and Display: Technology, Perception and Applications. 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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Khan, Ibrahim, and Zahid Ahmed. "ML and DL Classifications of Route Conditions Using Accelerometers and Gyroscope Sensors." In 2023 3rd International Conference on Artificial Intelligence (ICAI). IEEE, 2023. http://dx.doi.org/10.1109/icai58407.2023.10136666.

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Sekhar, Ch, K. Pavani, and M. Srinivasa Rao. "Comparative analysis on Intrusion Detection system through ML and DL Techniques: Survey." In 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA). IEEE, 2021. http://dx.doi.org/10.1109/iccica52458.2021.9697291.

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Wang, Han, and Zefeng Li*. "The application of machine learning and deep learning to Ophthalmology: A bibliometric study (2000-2021)." In Human Interaction and Emerging Technologies (IHIET-AI 2022) Artificial Intelligence and Future Applications. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe100885.

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Machine learning (ML) and deep learning (DL) are an advanced technology for the latest 20 years, which has been applied for multiple fields. This study utilizes methods of text mining and bibliometric analysis to explore applications of ML and DL to Ophthalmology. 50-ML-related and 60-DL-related papers from Web of Science (WOS), 15-ML-related and 38-DL-related articles from China Nation-al Knowledge Infrastructure (CNKI) are explored from 2000 to 2021. A descriptive analysis of major article, developing trends, journal releasing, topic mapping and quotation relationships is implemented in this
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Palei, Shantilata, Rakesh Kumar Lenka, Swoyam Siddharth Nayak, Rohan Mohanty, Biswajit Jena, and Sanjay Saxena. "Precision Agriculture: ML and DL-Based Detection and Classification of Agricultural Pests." In 2023 2nd International Conference on Ambient Intelligence in Health Care (ICAIHC). IEEE, 2023. http://dx.doi.org/10.1109/icaihc59020.2023.10431427.

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Rani, Jyoti, Jaswinder Singh, and Jitendra Virmani. "Mammographic mass Classification using DL based ROI segmentation and ML based Classification." In 2023 International Conference on Device Intelligence, Computing and Communication Technologies, (DICCT). IEEE, 2023. http://dx.doi.org/10.1109/dicct56244.2023.10110098.

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Shankar, T., Gummadapu Sreelekha, Challa Sai Tejaswini, P. Sivasankar, N. Lavanya, and J. Murali. "Prediction of Parkinson’s disease with various ML and DL techniques on speech data." In 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT). IEEE, 2024. http://dx.doi.org/10.1109/aiiot58432.2024.10574537.

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Shukla, Jyoti S., and Rahul Jashvantbhai Pandya. "Predictive Modeling of Vegetative Drought Using ML/DL Approach on Temporal Satellite Data." In 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT). IEEE, 2023. http://dx.doi.org/10.1109/iaict59002.2023.10205851.

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El-Attar, Noha E., and Yehia A. El-Mashad. "Artificial intelligence models for genomics analysis: review article." In Agria Média 2023 és ICI-17 Információ- és Oktatástechnológiai konferencia. Eszterházy Károly Katolikus Egyetem Líceum Kiadó, 2024. http://dx.doi.org/10.17048/am.2023.134.

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Artificial intelligence (AI) including machine learning (ML), and deep learning (DL) models have become powerful tools for analyzing genomics data in recent years. These models can process large amounts of data and identify complex patterns that may not be apparent through traditional statistical methods. ML and DL models have been used for a wide range of genomics applications, including gene expression analysis, variant detection, and drug discovery. One popular approach for using ML and DL models in genomics is to train these models on large datasets of genomic information. These datasets m
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Reports on the topic "Intelligence artificielle (ML/DL)"

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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, 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 database
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