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Abbott, R. (n.d.). Beyond Traditional SMS–Can Resilience Engineering and Deep Learning Neural Networks Be Used to Anticipate Disruptions in the NAS?
Abdel Azim, R., & Aljehani, A. (2022). Neural Network Model for Permeability Prediction from Reservoir Well Logs. Processes, 10(12), 2587.
Abhijith, G. S. V., & Gundad, A. K. V. (2023). Data Mining for Emotional Analysis of Big Data. International Journal of Intelligent Systems and Applications in Engineering, 11(3s), 271–279.
Abraham, S., Huynh, C., & Vu, H. (2019). Classification of soils into hydrologic groups using machine learning. Data, 5(1), 2.
Acero, A., & Zhang, H. (2022). Low-latency intelligent automated assistant. Google Patents.
Acero, A., & Zhang, H. (2024). Low-latency intelligent automated assistant. Google Patents.
Ailio, H. (2020). Koneoppiminen uutisvirran suodatuksessa.
Akubo, R. E. (2019). Design of a Neural Network Architecture for Traffic Light Detection in Autonomous Vehicles [Phdthesis].
Akula, A., Ghosh, R., Guleria, N., Kumar, S., & Sardana, H. (2018). Towards an optimal bag-of-features representation for vehicle type classification in thermal infrared imagery. Optics and Photonics for Information Processing XII, 10751, 191–207.
Alaskar, H., & Saba, T. (2021). Machine learning and deep learning: a comparative review. Proceedings of Integrated Intelligence Enable Networks and Computing: IIENC 2020, 143–150.
Aldén, F., & Juopperi, E. (2018). UX-verktyg för prototyputveckling med AI-baserat automationsstöd för omvandling av skisser till gränssnittskomponenter.
Alder, C., Asani, B., Barmettler, G., Blumenthal, P., Brügger, D., Burgunder, M., & Müller, K. (2018). Studie über Einsatzpotentiale und Beispiele für Conversational Interfaces. Forschungs-, Praxis-und Venture Projekt im Bereich «Digitale Kommunikation ….
Alemayehu, B., & Johnsons, F. (2018). Maskininlärning inom kommersiella fastigheter: Prediktion av framtida hyresvakanser.
Algarni, A. F. H. (2017). A Machine Learning Framework for Optimising File Distribution Across Multiple Cloud Storage Services [Phdthesis]. University of York.
Alghamdi, J. M. A. (n.d.). Auto-Labelling of Text Data Using Unsupervised Learning.
Alsalman, L., & Alotaibi, E. (2021). A balanced routing protocol based on machine learning for underwater sensor networks. IEEE Access, 9, 152082–152097.
Al-Shabi, M., & Abuhamdah, A. (2022). Using deep learning to detecting abnormal behavior in internet of things. International Journal of Electrical and Computer Engineering, 12(2), 2108.
Altamimi, H. A. (2018). The Intersection of Artificial Intelligence and Healthcare. The Future of Primary Care Medicine.
Alvi, S. B. (n.d.). A methodology to support online monitoring and control of complex processes in the foundry industry using machine learning [Phdthesis]. Dissertation, Duisburg, Essen, Universität Duisburg-Essen, 2023.
Anandan, R., & Kalaivani, K. (n.d.). Chapter-5 Regression and Classification in Machine Learning. ADVANCES IN ENGINEERING TECHNOLOGY, 69.
Andersson, J. (2017). Aktiemarknadsprediktion med artificiella neurala nätverk.
Andriani, A., & Hartati, S. (2021). Prognosis of Diabetes Mellitus with Transfer Learning-Based Naı̈ve Bayes Method. Journal of Physics: Conference Series, 1898(1), 012019.
Andriani-UBSI, A. (2020). Optimasi Data dengan Regresi Linier pada Klasifikasi Potensi Kenaikan CFR Demam Berdarah. SPEED-Sentra Penelitian Engineering Dan Edukasi, 12(4).
Antariksa, K., WP, Y. S. P., & Ernawati, E. (2019). Klasifikasi Ujaran Kebencian pada Cuitan dalam Bahasa Indonesia. Jurnal Buana Informatika, 10(2), 164–171.
Araújo, M. A. de, Flauzino, R. A., Moraes, L. A. de, Borges, F. A. S., & Spatti, D. H. (2019). Decision Trees Applied to Fault Locations in Distribution Systems with Smart Meters. 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), 1–6.
Arienti, J. H. L. (2020). Time series forecasting applied to an energy management system-A comparison between Deep Learning Models and other Machine Learning Models [Phdthesis].
Arowolo, M. O. (2021). Hybrid Dimensionality Reduction Model for Classification of Ribonucleic Acid Sequencing Malaria Vector Dataset [Phdthesis]. Landmark University.
Arppe, H. (2019). The Relationship Between Legal Technology and the Representations and Warranties Clause.
Ashritha, S., & Padmashree, T. (2020). Machine learning for automation software testing challenges, use cases advantages & disadvantages. International Journal of Innovative Science and Research Technology, 5(9).
Atanassov, A., Al-Barznji, K., & Tomova, F. (2018). System for Sentiment Analysis of Big Text Data. Machines. Technologies. Materials., 12(8), 316–319.
Auer, M., Oßwald, K., Volz, R., & Woidasky, J. (2019). Artificial Intelligence-based Process for Metal Identification. Resource Efficient Scrap Sorting. Industrial Life Cycle Management, 135–144.
Authority, A. P. (n.d.). Convolution Neural Network Fault Identifier In Distribution Network In The Presence of Distribution Generation Units.
Avuçlu, E. (2023). Determining the most accurate machine learning algorithms for medical diagnosis using the monk’problems database and statistical measurements. Journal of Experimental & Theoretical Artificial Intelligence, 1–20.
Bailey, J. D., Baker, J. C., Rzeszutek, M. J., & Lanovaz, M. J. (2021). Machine learning for supplementing behavioral assessment. Perspectives on Behavior Science, 1–15.
Bellegarda, J. R. (2024). Model compression using cycle generative adversarial network knowledge distillation. Google Patents.
Bento, L. V. (2016). No Mere Deodands: Human Responsibilities in the Use of Violent Intelligent Systems Under Public International Law [Phdthesis].
Berni, D. L. de M. (2022). Fundamentos para uma autonomia cientı́fica do direito digital no ordenamento jurı́dico brasileiro.
Binder, J., Post, S. D., Tackin, O., & Gruber, T. R. (2021). Voice trigger for a digital assistant. Google Patents.
Binder, J., Post, S. D., Tackin, O., & Gruber, T. R. (2023). Voice trigger for a digital assistant. Google Patents.
Binder, J., Post, S. D., Tackin, O., & Gruber, T. R. (2024). Voice trigger for a digital assistant. Google Patents.
Bitencourt-Ferreira, G., & de Azevedo, W. F. (2019). Machine learning to predict binding affinity. Docking Screens for Drug Discovery, 251–273.
Blatz, J. L., Aggarwal, A., Bhargava, R., Liu, D., Ramaswamy, P., & So, K. T. P. (2021). Providing relevant data items based on context. Google Patents.
Blatz, J. L., Aggarwal, A., Bhargava, R., Liu, D., Ramaswamy, P., & So, K. T. P. (2022). Providing relevant data items based on context. Google Patents.
Blatz, J. L., Malta, A. W., Jay, M., Ramaswamy, P., & Weinstein, A. (2022). User activity shortcut suggestions. Google Patents.
Blue, J., Condell, J., & Lunney, T. (2019a). It is Probably Me: A Bayesian Approach to Weighting Digital Identity Sources. 2019 International Symposium on Networks, Computers and Communications (ISNCC), 1–6.
Blue, J., Condell, J., & Lunney, T. (2019b). Proving yourself: addressing the refugee identity crisis with Bayesi-chain probability & digital footprints. 2019 International Symposium on Networks, Computers and Communications (ISNCC), 1–6.
Blue, J., Condell, J., Lunney, T., & Furey, E. (2018). Bayesi-chain: Intelligent identity authentication. 2018 29th Irish Signals and Systems Conference (ISSC), 1–6.
Bock, A. (n.d.-a). Themen in Bearbeitung.
Bock, A. (n.d.-b). Theses in process Rootline Navigation.
Bosse, S. (2018). The Unknown World: Model-Free Computing and Machine Learning. Material-Integrated Intelligent Systems-Technology and Applications: Technology and Applications, 329–342.
Bouganim, J., & Olsson, K. (n.d.). Using Deep Learning to Predict Back Orders.
Bouganim, J., & Olsson, K. (2019). Using Deep Learning to Predict Back Orders: A study in the Volvo Group Aftermarket Supply Chain.
Brand, F., Weiß, I. C., & Müller, M. (2018). Chord transition features for style classification of music recordings. Master Thesis.
Brintrup, A., Pak, J., Ratiney, D., Pearce, T., Wichmann, P., Woodall, P., & McFarlane, D. (2020). Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing. International Journal of Production Research, 58(11), 3330–3341.
Brown, A. D., & Marotta, T. R. (2017). A natural language processing-based model to automate MRI brain protocol selection and prioritization. Academic Radiology, 24(2), 160–166.
Brown, R. J., Hewage, C., & Jayal, A. (n.d.). “Breaking and Entering”: Evaluation of the Use of Machine Learning for Code Breaking.
Brown, R., & others. (2017). “Breaking and Entering”: Evaluation of Various Decryption Techniques to Decipher a Polyalphabetic Substitution Cipher. [Phdthesis]. Cardiff Metropolitan University.
Brummer, B. (2023). Crowdfunding Campaigns. Success Prediction Through Natural Language Processing. GRIN Verlag.
Burggräf, P., Adlon, T., Steinberg, F., Salzwedel, J., Nettesheim, P., & Tschauder, H. (2023). Transforming Food Production: Smart Containers for Sustainable and Transparent Food Supply Chains. IFIP International Conference on Advances in Production Management Systems, 489–503.
Calvert, E. I. (2018). Re: Privacy; investigating the accessibility of online legal documents in relation to the privacy paradox.
Campanaro, R., Demartis, P. N., Dı́az, D. J., Dı́az Toledo, S., & Viola, M. B. (2017). Valoración crı́tica del uso de KPI (Key Performance Indicators) basado en su análisis sobre cantidades masivas de datos financieros.
CAMPANARO, R. S., DEMARTIS, P. N., DIAZ, D. J., & VIOLA, M. B. (n.d.). UTILIZACIÓN DE HERRAMIENTAS DE INTELIGENCIA DE NEGOCIOS PARA EVALUAR EL COMPORTAMIENTO DE INDICADORES DE MEDICIÓN DE GESTIÓN ORGANIZACIONAL (KPI) PARA SER APLICADOS A FUENTES MASIVAS DE DATOS FINANCIEROS. REPOSITORIO DIGITAL UNIVERSITARIO (RDU-UNC), 93.
Campanaro, R. S., Dı́az, D. J., Gardenal, L., & Marchese, A. G. (2016). Análisis de estados contables aplicando XBRL y herramientas de inteligencia de negocios. DUTI, 8.
Campbell-Redl, R. (2023). Whats on your Tensors? A Design exploration of applied artifical intelligence to help facilitate digital equity. [Phdthesis]. Open Access Te Herenga Waka-Victoria University of Wellington.
Can, C. (2021). Eğitim alanında makine öğrenimi sınıflandırma algoritmalarının incelenmesi.
Carlsson, O., & Nabhani, D. (2017). User and Entity Behavior Anomaly Detection using Network Traffic.
Carrasquilla, A. (2023). Lithofacies prediction from conventional well logs using geological information, wavelet transform, and decision tree approach in a carbonate reservoir in southeastern Brazil. Journal of South American Earth Sciences, 104431.
Carrillo López, C. D., & Castrillón Calderón, C. A. (2021). Deteccion de la enfermedad de Alzheimer a partir de neuroimagenes mediante el uso de tecnicas de Inteligencia Artificial.
Cha, M., Gwon, Y. L., Kung, H., Sukopp, T., Berger, L., Han, Y., Roh, H., Jaehoon, K., Hwang, S. H., & Avin, S. (n.d.). Journal of AI Humanities.
Chen, M. (n.d.). Intelligent audio mastering.
Cherenet, Z. (2018). Data-driven decision support to reduce.
Cheun, J.-Y., Liew, J.-Y.-L., Tan, Q.-Y., Chong, J.-W., Ooi, J., & Chemmangattuvalappil, N. G. (2023). Design of polymeric membranes for air separation by combining machine learning tools with computer aided molecular design. Processes, 11(7), 2004.
Chinthakunta, M., Ong, P. W., Paek, T. S., Tappana, L. E., & Van Os, M. (2023). Digital assistant integration with telephony. Google Patents.
Chmielecki, P. (2019). Machine Learning Based on Cloud Solutions.
Clarke, D. C., Larsen, E. E., Thulstrup, F. Z., & Rhiger, M. (2018). An investigation of an artificial intelligence trained with Q-learning solving different game levels.
Constâncio, A. S., Tsunoda, D. F., Silva, H. de F. N., Silveira, J. M. da, & Carvalho, D. R. (2023). Deception detection with machine learning: A systematic review and statistical analysis. Plos One, 18(2), e0281323.
Cook, P. (2018). Brain Based Enterprises: Harmonising the Head, Heart and Soul of Business. Routledge.
Cordeiro, M. A., Arce, J. E., Guimarães, F. A. R., Bonete, I. P., Silva, A. V. dos S., Abreu, J. C. de, & Binoti, D. H. B. (2022a). Estimativas volumétricas em povoamentos de eucalipto utilizando máquinas de vetores de suporte e redes neurais artificiais. Madera y Bosques, 28(1).
Cordeiro, M. A., Arce, J. E., Guimarães, F. A. R., Bonete, I. P., Silva, A. V. dos S., Abreu, J. C. de, & Binoti, D. H. B. (2022b). Volumetric estimates in eucalyptus stands using support vector machines and artificial neural networks. Madera y Bosques, 28(1).
Correa, O. I. S., & Liesa, R. T. (2015). Multimedia Big Data Computing for Trend Detection [Phdthesis]. Universitat Politècnica de Catalunya. Facultat d’Informàtica de Barcelona ….
Criollo Saavedra, A. C., & Merino Arellano, T. A. (2023). Espectroscopı́a con imágenes hiperespectrales y detección de bandas principales para estimar parámetros de control de calidad en la harina de pescado con modelos de Machine Learning.
Crozier, J. A., & Karlstrom, L. (2022). Very-long-period seismicity over the 2008-2018 eruption of Kilauea Volcano. Authorea Preprints.
Crozier, Josh, & Karlstrom, L. (2021). Wavelet-Based characterization of very-long-period seismicity reveals temporal evolution of shallow magma system over the 2008–2018 eruption of Kı̄lauea volcano. Journal of Geophysical Research: Solid Earth, 126(6), e2020JB020837.
Crozier, Joshua. (2021). Using Spectral Analysis and Fluid Dynamics to Understand Supraglacial Stream Networks on the Greenland Ice Sheet and Seismicity at Kilauea Volcano [Phdthesis]. University of Oregon.
da Silva Carvalho, E. (2023). Constitutive Modelling in Hyperelasticity with Neural Networks.
Dankwa, R. W. (2021). Internet of Things (IoT) Anomaly Detection Using Machine Learning Techniques: Parameter Optimization and Algorithm Comparison [Phdthesis]. Indiana University of Pennsylvania.
Darney, H. (2020). Questions from a Contraceptive Pill Junkie: Applying Human Psychometrics to Investigate Gender Bias in Machine Learning.
Dave, A. H. (2018). Application of machine learning in digital logic circuit design verification and testing. California State University, Fresno.
Davila, M. (2017). Exploring best practices for employee engagement in a retail company located in Ecuador supported by Machine Learning Algorithms. [Phdthesis]. University of Liverpool.
de Rabat, M. V., & Ismaili-Alaoui, A. (n.d.). Methodology for an Augmented Business Process Management in IoT Environment.
Deike, L. (2020). Kontinuierliche Qualitätsoptimierung von Produktionsprozessen durch maschinelle Lernverfahren [Phdthesis]. Dissertation, Duisburg, Essen, Universität Duisburg-Essen, 2020.
Detka, J., Coyle, H., Gomez, M., & Gilbert, G. S. (2023). A Drone-Powered Deep Learning Methodology for High Precision Remote Sensing in California’s Coastal Shrubs. Drones, 7(7), 421.
Dewi, L. A. (n.d.). Klasifikasi Machine Learning Untuk Mendeteksi Penyakit Jantung Dengan Algoritma K-Nn, Decision Tree dan Random Forest [B.S. thesis]. Fakultas Sains dan Teknologi UIN Syarif Hidayatullah Jakarta.
Dharwadkar, N. V., Poojara, S. R., & Kannur, A. K. (2021). Risk Analysis of Diabetic Patient Using Map-Reduce and Machine Learning Algorithm. In Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics (pp. 307–329). IGI Global.
Dias, A. O. M. (2020). Aprendizado de máquina aplicado à predição de falhas em caminhão fora de estrada.
DIAZ, D. J. (n.d.). VALORACIÓN DE LA INCORPORACIÓN EN LA CURRICULA DE ASIGNATURAS DE IT DE NUEVAS TECNOLOGIAS. EL CASO DE DATOS MASIVOS, Y CIENCIA DE DATOS. REPOSITORIO DIGITAL UNIVERSITARIO (RDU-UNC), 51.
DÍAZ, J. G. (2018). UNIDAD DE POSGRADO DE LA FACULTAD DE ADMINISTRACIÓN [Phdthesis]. UNIVERSIDAD NACIONAL DE SAN AGUSTIN.
Dietlmeier, J. (2016). A machine learning approach to the unsupervised segmentation of mitochondria in subcellular electron microscopy data [Phdthesis]. Dublin City University.
Do, D. (2018). Building real time object detection iOS application using machine learning.
DO DESGASTE, M. L. P. A., & DA FORÇA, D. T. (n.d.). JUNIOR MAURICIO STAUDT.
Doktorand, E., & Nolte, M. (n.d.). Brotkrumen-Navigation.
dos Santos, R. M. M. P. (2016). Parallel Computing for Process Mining.
Dounias, G. (2018). Hybrid Computational Intelligence and the Basic Concepts and Recent Advances. Encyclopedia of Information Science and Technology, Fourth Edition, 180–190.
Dunér, F., & Johansson, E. (2023). Solving Problems, One Role at a Time.
Durelli, V. H., Durelli, R. S., Borges, S. S., Endo, A. T., Eler, M. M., Dias, D. R., & Guimarães, M. P. (2019). Machine learning applied to software testing: A systematic mapping study. IEEE Transactions on Reliability, 68(3), 1189–1212.
Earl, J. (2019). Optimaztion of Fantasy Basketball Lineups via Machine Learning.
Ebeed, M., & Hossam-Eldin, A. (2022). Convolution Neural Network Fault Identifier in Distribution Network in the Presence of Distribution Generation Units. 2022 23rd International Middle East Power Systems Conference (MEPCON), 1–6.
Emelyanova, E. (n.d.). Tool-Supported Project Prediction.
ERIHBRA, S. E. (2015). LANGUAGE IDENTIFICATION USING K-NEAREST NEIGHBOUR AND NAÏVE BAYES CLASSIFIERS [Phdthesis]. UNIVERSITY OF LAGOS.
Ernesto, M. M. G., Antonio, A. F. M., & Carlos, P. O. J. (n.d.). Aproximación a la clasificación de la atención basada en optimización por enjambre de partı́culas con pruebas de seguimiento ocular.
Escarrone, A. L. L. (2017). Extração de perfis de usuários com e sem deficiência utilizando agrupamentos.
Estrella Oliva, Á. (2020). Aplicaciones basadas en aprendizaje automático (Machine learning) en plataformas de bajo consumo.
Evermann, G. (2023). System and method for inferring user intent from speech inputs.
Faidi, S. (2018). Finding Anomalous eNodeBs.
Faraji, F. (2021). Gas-condensate Reservoir Performance Modelling [Phdthesis]. Teesside University.
Faraji, F., Santim, C., Chong, P. L., & Hamad, F. (2022). Two-phase flow pressure drop modelling in horizontal pipes with different diameters. Nuclear Engineering and Design, 395, 111863.
Fasan, M., & others. (2021). Nuove tecnologie, diritti e modelli di regolamentazione. L’Intelligenza Artificiale come nuova frontiera per il diritto costituzionale.
Fazel Zarandi, M. H., Sadat Asl, A. A., Sotudian, S., & Castillo, O. (2020). A state of the art review of intelligent scheduling. Artificial Intelligence Review, 53, 501–593.
Febrikagraha, S. R. A. I. (2020). Klasifikasi Lirik Lagu Bahasa Indonesia Berdasarkan Emosi Berbasis Metode Support Vector Machine [Phdthesis]. UNIVERSITAS ATMA JAYA YOGYAKARTA.
Fennis, J. (2019). Machine learning solutions for exception handling. University of Twente.
Frank, P., Heimann, K., Kolbe, V., & Schuster, C. (n.d.). Cleaner and Responsible Consumption.
Frank, U. (n.d.-a). Themen in Bearbeitung.
Frank, U. (n.d.-b). Theses in Process.
Frank, U. (n.d.-c). Unternehmensmodellierung//Themenvorschläge.
Frank, U. (n.d.-d). Verfahren des maschinellen Lernens: Grundlegende Konzepte, Potenziale und Voraussetzungen, Einsatzszenarien in Unternehmen.
Freeman, D., & Barrentine, D. B. (2023). Method and system for operating a multifunction portable electronic device using voice-activation. Google Patents.
Gada, J. V., & Elagha, M. H. (2022). Methods and user interfaces for voice-based control of electronic devices. Google Patents.
Garcia, J. C., McCarthy, P. S., & Piersol, K. (2023). Natural assistant interaction.
Garcı́a, L. G., Rivera, M. F. M., & Flores, M. R. Z. (2018). CLASIFICADOR MEJORADO DE TEXTOS PARA EL CONTEXTO DE MEDIO AMBIENTE USANDO NAIVE BAYES MULTINOMIAL EN MÉXICO. 12.
Gautam, S., Khunteta, A., & Sharma, P. (2022). A Review on Software Testing Using Machine Learning Techniques. ECS Transactions, 107(1), 3393.
Ghazi Alwattar, H. (2021). Redesigning Post-Operative Processes Using Data Mining Classification Techniques. International Journal of Software Engineering and Computer Systems, 7(2), 64–73.
Gilani, S., O’Brien, W., & Gunay, H. B. (2018). Simulating occupants’ impact on building energy performance at different spatial scales. Building and Environment, 132, 327–337.
Godbole, M., Agarwal, A., & Sahay, B. (2021). APPLICATION OF AI/ML/NLP TECHNOLOGY INTO THE BUSINESS PROCESS MODELLING. International Journal of Advanced Research in Engineering and Technology (IJARET), 12(5), 37–50.
Goeschel, K. (2016). Reducing false positives in intrusion detection systems using data-mining techniques utilizing support vector machines, decision trees, and naive Bayes for off-line analysis. SoutheastCon 2016, 1–6.
Golomboš, J. (2022). Klasifikacija i analiza poljoprivrednih zemljišta u Donjem Međimurju pomoću podataka misija Sentinel [Phdthesis]. University of Zagreb. Faculty of Science. Department of Geography.
Gomes, V. F. (2022). Tempo de carregamento de páginas web e fatores associados: aplicação de métodos de aprendizado de máquina supervisionados.
Gong, J., & Laput, G. (2023). User identification using headphones.
Gonzalez Viejo, C., Torrico, D. D., Dunshea, F. R., & Fuentes, S. (2019). Development of artificial neural network models to assess beer acceptability based on sensory properties using a robotic pourer: A comparative model approach to achieve an artificial intelligence system. Beverages, 5(2), 33.
Goyal, T., Mehta, S. V., Srinivasan, B. V., & Jain, A. (2023). Tagging documents with security policies.
Graham, D. C., Irani, C. D., Piercy, A., & Alsina, T. (2023). Intelligent automated assistant for media exploration. Google Patents.
Gross, D. C., Coffman, P. L., Dellinger, R. R., Gauci, J. J., Haghighi, A. D., Irani, C. D., Jones, B. A., Kapoor, G., Lemay, S. O., Morris, C. C., & others. (2021). Systems and methods for proactively identifying and surfacing relevant content on an electronic device with a touch-sensitive display. Google Patents.
Gruber, Thomas R, Brigham, C. D., Cheyer, A. J., Keen, D., & Kocienda, K. (2022). Systems and methods for integrating third party services with a digital assistant. Google Patents.
Gruber, Thomas R, Sabatelli, A. F., Aybes, A. A., Pitschel, D. W., Voas, E. D., Anzures, F. A., & Marcos, P. D. (2021). Performing actions associated with task items that represent tasks to perform.
Gruber, Thomas R, Saddler, H. J., Bellegarda, J. R., Nyeggen, B. H., & Sabatelli, A. (2023). Multi-command single utterance input method. Google Patents.
Gruber, Thomas Robert, Cheyer, A. J., Kittlaus, D., Guzzoni, D. R., Brigham, C. D., Giuli, R. D., Bastea-Forte, M., & Saddler, H. J. (2022). Task flow identification based on user intent. Google Patents.
Gruenen, J., Bode, C., & Hoehle, H. (2017). Predictive procurement insights: B2B business network contribution to predictive insights in the procurement process following a design science research approach. Designing the Digital Transformation: 12th International Conference, DESRIST 2017, Karlsruhe, Germany, May 30–June 1, 2017, Proceedings 12, 267–281.
Grundberg, M., & Altintas, V. (2021). Generating 3D Scenes From Single RGB Images in Real-Time Using Neural Networks.
Grünen, J. (2021). Digitalization technologies and business trends in procurement [Phdthesis]. Universitaet Mannheim (Germany).
Guarda, T., Balseca, J., Garcı́a, K., González, J., Yagual, F., & Castillo-Beltran, H. (2021). Digital transformation trends and innovation. IOP Conference Series: Materials Science and Engineering, 1099(1), 012062.
Gudivada, V. N., Irfan, M. T., Fathi, E., & Rao, D. L. (2016). Cognitive analytics: Going beyond big data analytics and machine learning. In Handbook of statistics (Vol. 35, pp. 169–205). Elsevier.
Gudivada, V. N., Rao, D. L., & Ding, J. (2018). Evolution and Facets of Data Analytics for Educational Data Mining and Learning Analytics. Responsible Analytics and Data Mining in Education: Global Perspectives on Quality, Support, and Decision Making, 16–42.
Guerrero Ludueña, R. E. (2017). Data Driven Approach to Enhancing Efficiency and Value in Healthcare.
Hagan, R. (2022). Predictive Analytics in an Intensive Care Unit by Processing Streams of Physiological Data in Real-time [Phdthesis]. Queen’s University Belfast.
Hagan, R., Gillan, C. J., & Mallett, F. (2021). Comparison of machine learning methods for the classification of cardiovascular disease. Informatics in Medicine Unlocked, 24, 100606.
Hahto, M. (2023). Supplier Data Analysis and Utilization in Supply Chain Management: Case ABB Smart Power.
Hamadah, S., & Aqel, D. (2020). Cybersecurity becomes smart using artificial intelligent and machine learning approaches: An overview. ICIC Express Letters, Part B: Applications, 11(12), 1115–1123.
Han, Y., Roh, H., Kim, J., & Hwang, S. (2018). Enhancing the impact of color through artificial intelligence for visual narration. Journal of AI Humanities, 2, 145–168.
Han, Y., Roh, H., Whelan, T., Hwang, S., & Kim, M. (2022). Labelling the psychological impact of colour in films for deep learning processing based on the humanities approach: RGB information in colour classification in film. Jahr: Europski Časopis Za Bioetiku, 13(1), 193–210.
Hansen, B., Ghotbi, N., Gui, Y., Huang, X., Phipps, B. S., Ray, E., Shanbhag, M. R., Tecarro, J., & Wattal, S. (2023). Digital assistant hardware abstraction.
Harjula, M. A. D. (2017a). Exploring Best Practices for Employee Engagement in a Retail Company Located in Ecuador Supported by Machine Learning Algorithms [Phdthesis]. The University of Liverpool (United Kingdom).
Harjula, M. A. D. (2017b). Thesis submitted in accordance with the requirements of the University of Liverpool for the degree of Doctor of Business Administration.
Härkönen, E. (2021). Forecasting stock index trend with Support Vector Machine and Long-Short term memory: a case study of models fitted on OMXH25 data.
Harmaala, T. (2018). Gas Turbine Power Plant Benchmarking and Optimization with Machine Learning in Industrial Internet environment.
Hennelly, H., Meadowcroft, M., Solfest, S., & Galligan, T. (2021). Using Machine Learning to Detect and Categorize the Presence of Cancer.
Heo, W., Kwak, E. J., & Grable, J. E. (2022). The role of big data research methodologies in describing investor risk attitudes and predicting stock market performance: deep learning and risk tolerance. In Handbook of Research on New Challenges and Global Outlooks in Financial Risk Management (pp. 293–315). IGI Global.
Hewage, C., Jayal, A., Jenkins, G., & Brown, R. J. (2022). A Learned Polyalphabetic Decryption Cipher.
Hildebrandt, H. (2018). Deep Reinforcement Learning zur Minderung von Verspätungen im ÖPNV.
Hiltunen, A. (2018). Välikerros" Chili" Pepper-robotin ohjelmoinnin helpottamiseksi.
Hincapié Romero, C. A. (2021). Diseño e implementación de un sistema reactivo para análisis de sentimientos con aprendizaje automático.
Hindi, M. M., Dasari, R., & Trungtin, T. (2021). Attention aware virtual assistant dismissal. Google Patents.
Hindi, M. M., Dasari, R., & Trungtin, T. (2022). Attention aware virtual assistant dismissal. Google Patents.
Hinkle, L. B. (2016). Determination of emotional state through physiological measurement.
Hinostroza Hualpa, S. C., & Salazar Rey, B. (2016). Marketing computacional: diseño automático de productos.
Hjalmarsson, M., & Björkman, M. (2017). Bedömning av fakturor med hjälp av maskininlärning.
Hoang, L. P. N. (2022). SERS-based ssDNA composition analysis using chemical enhancement and machine learning methods [Phdthesis]. University of California, San Diego.
Hoover, J. C. (2022). Using machine learning to identify causes of differential item functioning [Phdthesis]. University of Kansas.
Hoover, J. C., & Thompson, W. J. (n.d.). Evaluating the Performance of Person-Fit Detection Methods in Diagnostic Classification Models.
Hoseini, C. (2020). Leveraging machine learning to identify quality issues in the Medicaid claim adjudication process [Phdthesis]. Indiana State University.
Hu, S., & Huang, C. (2023). Machine-Learning Approaches for the Discovery of Electrolyte Materials for Solid-State Lithium Batteries. Batteries, 9(4), 228.
Huhta-Koivisto, T., & others. (2020). Work disability risk prediction with machine learning.
Hunt, R. L. (2022). A Data Processing, Feature Engineering, Variable Selection, and Machine Learning Modeling Framework for Predictive Agriculture. North Carolina State University.
Hunter, N. (2020). Computer Vision Gesture Recognition for Rock Paper Scissors.
Hupperich, T. (2017). On the feasibility and impact of digital fingerprinting for system recognition [Phdthesis]. Ruhr University Bochum, Germany.
Hutauruk, G., Afrianti, D. D., & Siburian, V. (2018). PREDICTING BEST SELLER BOOKS BASED ON VISUAL AND SENTIMENT FEATURES. PROGRAM STUDI SARJANA SISTEM INFORMASI FAKULTAS TEKNIK INFORMATIKA DAN ELEKTRO.
İLYAS, T., & URFALIOĞLU, F. (2018). Anfis ve regresyon analizi ile enflasyon tahmini ve karşılaştırması. Sosyal Bilimler Araştırma Dergisi, 7(3), 120–141.
Iqbal, A. M., Setiadi, I. T., Pratama, A. D., & Imelda, I. (2023). Stock Price Prediction of PT. Kimia Farma, Tbk Using Bayesian Ridge Algorithm. Al Qalam: Jurnal Ilmiah Keagamaan Dan Kemasyarakatan, 17(3), 2218–2229.
Jain, S., & others. (2022). Comprehensive Survey on Data science, Lifecycle, Tools and its Research Issues. 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), 1, 838–842.
Jakubı́k, B. M. (n.d.). Genetické algoritmy pro rešenı́ úloh optimalizace investiˇcnı́ch strategiı́ na finanˇcnı́ch trzı́ch.
Jansson, D., & Sjöbohm, V. (2020). En maskininlärningsanalys av ursprunget till bostadsomr\aadens attraktionskraft: En kvantitativ studie kring Points of Interest inverkan p\aa bostadsomr\aadens attraktionskraft.
Jarmatz, N., Augustin, W., & Scholl, S. (2023). Generation of experimental data for model training to optimize fouling prediction. Heat and Mass Transfer, 1–10.
Jarosz, S. (2023). Artificial Intelligence-an agenda for management sciences. E-Mentor, 99(2), 47–55.
Jarosz, S., & Karyś, Z. (n.d.). AI in management-research trends.
Jeong, H.-D. J., Jeong, G.-S., Kim, W.-J., Kim, J., Song, H., Ryu, M.-U., & Lee, J. R. (2018). A search for computationally efficient supervised learning algorithms of anomalous traffic. Innovative Mobile and Internet Services in Ubiquitous Computing: Proceedings of the 11th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS-2017), 590–600.
Jevremović, A., & others. (2020). Distributed Laser Therapy System. Sinteza 2020-International Scientific Conference on Information Technology and Data Related Research, 120–127.
Jiang, H., Xu, C.-W., Liu, Z.-Y., & Yu, L.-Y. (2017). GPU-accelerated Apriori algorithm. ITM Web of Conferences, 12, 03046.
Jina, V., Hildick-smith, S. J., Kirby, P. M., & SRIDHAR, V. K. R. (2024). Providing message response suggestions. Google Patents.
Jooravan, A. (2023). Automatic detection of melanoma in dermoscopic images of skin lesions [Phdthesis].
Jooravan, A., Reddy, S., & Pillay, N. (2022). Comparative Study of Binary Classifiers for Reducing False Negative Detection of Melanoma in Skin Lesions. 2022 International Conference on Engineering and Emerging Technologies (ICEET), 1–6.
Ju, Y. J., Hong, T. E., & Shin, J. H. (2016). Pattern Analysis of Traffic Accident data and Prediction of Victim Injury Severity Using Hybrid Model. Smart Media Journal, 5(4), 75–82.
Ju, Yeongji, Kim, B., & Shin, J. (2017). Detection of Malicious Code using Association Rule Mining and Naive Bayes classification. Journal of Korea Multimedia Society, 20(11), 1759–1767.
Ju, YeongJi, Kim, M., & Shin, J. (2020). Detection of malicious code using the direct hashing and pruning and support vector machine. Concurrency and Computation: Practice and Experience, 32(18), e5483.
Jueschke, P., & Fischer, G. (2017). Machine learning using neural networks in digital signal processing for RF transceivers. 2017 IEEE AFRICON, 384–390.
Jüschke, P. (2023). Physically Inspired Predistortion of RF Power Amplifiers with Artificial Neural Networks [Phdthesis]. FAU University Press.
Kabas, O., Kayakus, M., Ünal, İ., & Moiceanu, G. (2023). Deformation energy estimation of cherry tomato based on some engineering parameters using machine-learning algorithms. Applied Sciences, 13(15), 8906.
Kahsay, S. (2020). Prediktiva system för kreditbedömning: En studie om hur prediktiva system p\aaverkar kreditbedömares agerande.
Kaivola, A. (2018). Current and future trends in data driven talent identification in MNCs.
Kaliyugarasan, S. K. (2019). Deep transfer learning in medical imaging. The University of Bergen.
Kang, M. (2022). Potential of Data-driven Approaches for Modeling Heat and Mass Convection Processes [Techreport]. Arizona State University.
Kang, M., Hwang, L. K., & Kwon, B. (2020). Machine learning flow regime classification in three-dimensional printed tubes. Physical Review Fluids, 5(8), 081901.
Karashchuk, P., GALVEZ, T. A. V., & Gruber, T. R. (2022). Intelligent automated assistant in a messaging environment. Google Patents.
Karić, K., Gaborović, A., Blagojević, M., Milošević, D., Mitrović, K., & Plašić, J. (2022). Comparison of regression methods and tools using the example of predicting the success of graduate master’s students in different fields of education.
Karthiban, R., Ambika, M., & Kannammal, K. (2019). A review on machine learning classification technique for bank loan approval. 2019 International Conference on Computer Communication and Informatics (ICCCI), 1–6.
Kassara Guennoun, C. (2020). Estudio y comparación de diferentes métodos de aprendizaje profundo referidos a la detección de heridas en la piel.
Kelley, K., Todd, M., Hopfer, H., & Centinari, M. (2022). Identifying wine consumers interested in environmentally sustainable production practices. International Journal of Wine Business Research, 34(1), 86–111.
Kettler, K. M. (2019). Potential Applications of IntelligentSystems in the Production Lineat Scania: Potential Applications of IntelligentSystems in the Production Lineat Scania.
Khaiter, P. A., & Erechtchoukova, M. G. (2022). Advanced Scientific Methods and Tools in Sustainable Forest Management: A Synergetic Perspective. In Forest Dynamics and Conservation: Science, Innovations and Policies (pp. 279–309). Springer.
Kiliç, H. (n.d.). Improvement of mobile banking adoption with clustering algorithm. Fen Bilimleri Enstitüsü.
Kilicarslan, S., Adem, K., & Celik, M. (2020). Diagnosis and classification of cancer using hybrid model based on ReliefF and convolutional neural network. Medical Hypotheses, 137, 109577.
Kilpijoki, J. (2020). Tekoäly ja robotiikka tilitoimistossa: niiden merkitys asiakassuhteessa pk-yritykseen-Case: Tilitoimisto X Oy.
Kim, Y., Bridle, J., Atkins, J. D., Li, F., & Souden, M. (2022). Detecting a trigger of a digital assistant. Google Patents.
Kimari, A. M., Niyitunga, E. B., & Mohammad, J. (2023). The Effects of Artificial Intelligence on Service Delivery in South African Local Municipalities. African Journal of Development Studies, 13(4).
Kjøller-Hansen, A., & Jensen, S. S. (n.d.). DEFAULT PREDICTION.
Knoll, L. (2020). Nationwide estimation of groundwater nitrate concentrations using machine learning.
Kogut, T., Niemeyer, J., & Bujakiewicz, A. (2016). Neural networks for the generation of sea bed models using airborne lidar bathymetry data. Geodesy and Cartography 65 (2016), Nr. 1, 65(1), 41–53.
Korpela, E. (2018). Keinoälyn hyödyntäminen väylänpidossa.
Korpela, J. (2019). Koneoppimisen hyödyntäminen kaupallisen lentoyhtiön toiminnoissa.
Kovač, L. (2015). Umjetna inteligencija danas [Phdthesis]. University of Rijeka. Faculty of Humanities.
KOVALSKII, V. (2022). Algoritmi de recunoaștere automată a gesturilor mâinii, captate prin contracția mușchilor [Phdthesis]. Universitatea Tehnică a Moldovei.
Koυτ\acuteελακης, Σ. (2018). No\acuteημoνες μ\acuteεθoδoι αν\acuteαλυσης ιατρικ\acuteων δεδoμ\acuteενων εφαρμoγ\acuteες σε πρoβλ\acuteηματα δι\acuteαγνωσης νευρoλoγ\acuteιας.
Kraus, D. (2018). Machine learning and evolutionary computing for gui-based regression testing. ArXiv Preprint ArXiv:1802.03768.
KŘÍŽ, P. (2015). Business Intelligence řešenı́ pro společnost 1188. Brno University of Technology.
Krumpolc, P. (2020). Detekce obličejovỳch bod\uu v prostředı́ automobilu.
Kudurshian, A. D., Jones, B., Cranfill, E. C. F., & Saddler, H. J. (2021). Intelligent digital assistant in a multi-tasking environment. Google Patents.
Kudurshian, A. D., Jones, B., Cranfill, E. C. F., & Saddler, H. J. (2023). Intelligent digital assistant in a multi-tasking environment. Google Patents.
Kurniawan, H., Rosmansyah, Y., & Dabarsyah, B. (2015). Android anomaly detection system using machine learning classification. 2015 International Conference on Electrical Engineering and Informatics (ICEEI), 288–293.
Kwon, B., Ejaz, F., & Hwang, L. K. (2020). Machine learning for heat transfer correlations. International Communications in Heat and Mass Transfer, 116, 104694.
Langen, H. (2016). Ultra-wideband radar simulator for classifying humans and animals based on micro-Doppler signatures. NTNU.
Lauer, Timothy M, Wood, G. P., Farkas, D., Sathish, H. A., Samra, H. S., & Trout, B. L. (2016). Molecular investigation of the mechanism of non-enzymatic hydrolysis of proteins and the predictive algorithm for susceptibility. Biochemistry, 55(23), 3315–3328.
Lauer, Timothy Michael. (2015). In silico tools for the development of biotherapeutics [Phdthesis]. Massachusetts Institute of Technology.
Laufenberg, F. (2021). Ethical assessment of AI systems in healthcare: A use case [Phdthesis]. Goethe University Frankfurt.
Law, M. (2019). Predicting disability progression in secondary progressive multiple sclerosis by machine learning: a comparison of common methods and analysis of data limitations [Phdthesis]. University of British Columbia.
Leal, J. P. S. (2015). Wizdee Discovery-Automatic Analysis and Visualization of Information [Phdthesis]. Universidade de Coimbra (Portugal).
Lei, J., Mu, J., Zeng, L., Han, Q., Hu, L., Chen, X., & Chen, L. (2020). An intellegent vehicle oriented EMC reverse diagnostic model based on SVM. 2020 IEEE Intelligent Vehicles Symposium (IV), 1544–1549.
Lemay, S. O., Bastian, M. R., Holenstein, R., Jeong, M., Maalouf, C., Newendorp, B. J., Nieto, H., Paek, T., Peterson, J., Scully, S., & others. (2021). Raise to speak. Google Patents.
Lemay, S. O., Bastian, M. R., Holenstein, R., Jeong, M., Maalouf, C., Newendorp, B. J., Nieto, H., Paek, T., Peterson, J., Scully, S., & others. (2022). Raise to speak. Google Patents.
Lemay, S. O., Bastian, M. R., Holenstein, R., Jeong, M., Maalouf, C., Newendorp, B. J., Nieto, H., Paek, T., Peterson, J., Scully, S., & others. (2024). Raise to speak. Google Patents.
Lemay, S. O., Newendorp, B. J., & Dascola, J. R. (2021). Virtual assistant activation. Google Patents.
Lemus Cárdenas, L. (2020). Enhancement of vehicular ad hoc networks using machine learning-based prediction methods.
Leone, R. M. (2016). Machine learning multi-stage classification and regression in the search for vector-like quarks and the Neyman construction in signal searches [Phdthesis]. The University of Arizona.
Lima, T., Araújo, F., Vieira, P., Carvalho, N. de S., & Rodrigues, L. (2019). Descriçao e Classificaçao de Nódulos Pulmonares em Imagens de Tomografia Computadorizada. Anais Da VII Escola Regional de Computação Aplicada à Saúde, 270–275.
Lin, G., Liang, Y., Fu, X., Chen, G., & Cai, S. (2019). Design of a daily brief business report generator based on web scraping with KNN algorithm. Journal of Physics: Conference Series, 1345(5), 052064.
Lindahl, A. M. (2022). Electronic devices with voice command and contextual data processing capabilities. Google Patents.
Lindberg, A.-M. (2018). Use of predictive analytics in B2B sales lead generation.
Linke, C. (2021). Digitale Wissensorganisation. Nomos Verlagsgesellschaft mbH & Co. KG.
Litsey, R. (2017). Resources anytime, anywhere: How interlibrary loan becomes resource sharing. Chandos Publishing.
Lobato, R. S. (2019). Parallelization of the DIANA Algorithm in OpenMP. Parallel and Distributed Computing, Applications and Technologies: 19th International Conference, PDCAT 2018, Jeju Island, South Korea, August 20-22, 2018, Revised Selected Papers, 931, 171.
Lokesh, J., Padmasali, A., Mahesha, M., & Kini, S. (2023). Comparison and validation of neural network models to estimate LED spectral power distribution. Lighting Research & Technology, 55(3), 281–299.
Lopez, C. (2019). Distributed reinforcement learning in emergency response simulation [Phdthesis]. University of British Columbia.
Lowin, M., Kellner, D., Kohl, T., & Mihale-Wilson, C. (2021). From Physical to Virtual: Leveraging Drone Imagery to Automate Photovoltaic System Maintenance.
Lozano San Juan, G., & Colı́n Rivera, R. (2020). Propuesta metodológica y de análisis computacional para identificar el proceso fotográfico en fotografı́as históricas del siglo XIX y XX.
LS, F., & Souza, A. (2019). Uso de Dados Provenientes de Rede Social e Técnica de Mineração de Dados para Classificar Crimes em Belém-PA.
LUDOVICO, S. N., & others. (2020). Previsão de indicadores diários de preços no mercado futuro de commodities agrı́colas utilizando aprendizagem de máquina.
Ludovico, S. N., Salgado, R. M., Beijo, L. A., Miguel, E. C., & Rezende, M. L. (2022). Previsão de preços de commodities agrı́colas via algoritmos de aprendizagem de máquina. Sigmae, 11(2), 45–69.
MACLEOD, N. (2017). On the use of machine learning in morphometric analysis. Biological Shape Analysis: Proceedings of the 4th International Symposium, 134–171.
Maddika, S., ABDELAZIZ, A. S. E. D. H., MANNEMALA, C., Vishnubhotla, S., & Weinberg, G. L. (2023). Multiple state digital assistant for continuous dialog.
Makai, T., & Nyirenda, M. (2024). Smart Approaches to Efficient Text Mining for Categorizing Sexual Reproductive Health Short Messages into Key Themes. Open Journal of Applied Sciences, 14, 511–532.
Malia, B. K. (2021). Integration of Spin Squeezed States Into Free Space Atomic Sensors [Phdthesis]. Stanford University.
Malik, S. A., Maddika, S., & Naik, D. K. (2022). Determining head pose based on room reverberation. Google Patents.
Malmi, J. (2023). Asiakaspoistuman ennustaminen päätöspuuhun perustuvien koneoppimismallien avulla-Case vakuutusyhtiö.
Maluje, S. E. A. (n.d.). Seismic response estimation of timber buildings via advanced data science methods.
Manousakis, N. (2020). > Prometheus Bound<–A Separate Authorial Trace in the Aeschylean Corpus (Vol. 98). Walter de Gruyter GmbH & Co KG.
Manuele, A., Dambal, D., Satpal, J., Lofton, M., Tandel, S., & Sidaras-Tirrito, M. (n.d.). Mouse Movement Authentication for Multiple-Choice Tests.
Manurung, T. H., Lumban Gaol, Y., & Simanjuntak, V. G. (2018). PENERAPAN CONVOLUTIONAL NEURAL NETWORK PADA PENGENALAN TEKS TULISAN TANGAN. PROGRAM STUDI SARJANA SISTEM INFORMASI FAKULTAS TEKNIK INFORMATIKA DAN ELEKTRO.
Manzo, J. P. C. (n.d.). Master Thesis Law and Technology LLM Tilburg Law School.
Marian, M, & Tremmel, S. (2021). Current Trends and Applications of Machine Learning in Tribology–A Review. Lubricants 2021, 9, 86. Machine Learning in Tribology, 165.
Marian, Max, Mursak, J., Bartz, M., Profito, F. J., Rosenkranz, A., & Wartzack, S. (2023). Predicting EHL film thickness parameters by machine learning approaches. Friction, 11(6), 992–1013.
Marian, Max, & Tremmel, S. (2023). Physics-Informed Machine Learning—An Emerging Trend in Tribology. Lubricants, 11(11), 463.
Marijan, R. (2020). Relevantnost informacijskega priklica pri strojnem učenju za binarno besedilno klasifikacijo [Phdthesis]. Univerza v Mariboru (Slovenia).
Markros, A. (2019). Design and analysis of a learning-based testing system for certification of vehicle systems.
Martin, N., Mathieu, N., Pallamin, N., Ragot, M., & Diverrez, J.-M. (2018). Automatic recognition of virtual reality sickness based on physiological signals. IBC.
Martin, R. D. (2020). Determining estuarine seagrass density measures from low altitude multispectral imagery flown by remotely piloted aircraft [Phdthesis]. The University of Waikato.
Martinez, C. M., & Cao, D. (2018). iHorizon-Enabled Energy management for electrified vehicles. Butterworth-Heinemann.
Martinez, R. T., & Alfaro, S. C. A. (2020). Data analysis and modeling techniques of welding processes: The state-of-the-art. Welding-Modern Topics.
Martinez Velasco, R. J. (2022). Sistema domótico para personas con capacidad limitada de movimiento en la extremidad superior derecha utilizando reconocimiento de gestos de la mano y algoritmos de Machine Learning [B.S. thesis]. Universidad Técnica de Ambato. Facultad de Ingenierı́a en Sistemas ….
Martı́n, R. G. (2019). Facultad de Ciencias de la Educación [Phdthesis]. UNIVERSIDAD DE SEVILLA.
Martı́nez, R. T., Bestard, G. A., Silva, A. M. A., & Alfaro, S. C. A. (2021). Analysis of GMAW process with deep learning and machine learning techniques. Journal of Manufacturing Processes, 62, 695–703.
Maryana, S., & Karlitasari, L. (2018). Search Of Favorite Books As A Visitor Recommendation of The Fmipa Library Using CT-Pro Algorithm. Journal of Science Innovare, 1(1), 09–13.
Maschler, B. (2024). Eine Architektur für maschinelles Transfer-Lernen in industriellen Automatisierungssystemen. Shaker.
Mehrabi, M. A. (2022). Hardware implementation of elliptic curve cryptography based on residue number systems [Phdthesis]. Macquarie University.
Meltzer, F. (2018). Using neural networks and support vector machines for default prediction in South Africa [Phdthesis]. University of the Witwatersrand, Faculty of Science, School of Computer ….
Mensah, K., & Akobre, S. (2015). Mining Social Media for Conflict Prevention and Resolution.
Merhi, S. (2022). OPEN GOVERNMENT DATA AND VALUE CREATION [Phdthesis]. University of Ottawa.
Merhi, S. (2023). Open Government Data and Value Creation: Exploring the Roles Canadian Data Intermediaries Play in the Value Creation Process [Phdthesis]. Université d’Ottawa/University of Ottawa.
Meyer, J. E., Champagne, K., DE, J. P. D. A. F., Gusev, A., Kramer, C. B., Li, Y., Weinstein, A., & others. (2023). User configurable task triggers. Google Patents.
Miao, Y., Ruan, Z., Pan, L., Zhang, J., & Xiang, Y. (2018). Comprehensive analysis of network traffic data. Concurrency and Computation: Practice and Experience, 30(5), e4181.
Milden, K. (2023). Voice activated device for use with a voice-based digital assistant. Google Patents.
Milusheva, T. (n.d.). A concept for a modular sensor-based Predictive Maintenance system for Industry 4.0.
Min, W. (2018). Application of Network and Information Security Risk Monitoring and Early Warning Platform in Electric Power Enterprises. 2018 China International Conference on Electricity Distribution (CICED), 2718–2721.
Miñano Sanchez, C. J. (2022). Comparación de técnicas de minerı́a de datos para descubrir información relevante de ventas de una Mype comercial.
MINING, M. L. A. E. D., & DEEP, Y. (n.d.). IX ESCUELA DE VERANO-2020.
Moorthy, S. (2018). Modeling and characterization of mechanical properties in laser powder bed fusion additive manufactured Inconel 718. Colorado School of Mines.
Moosbrugger, M. (2021). Übertriebene Wertversprechen und Erwartungen im Startup-Crowdinvesting. Springer.
Mukherjee, S., Mishra, P., Ali, N., Aljuwayhel, N., Ebrahim, S., & Chaudhuri, P. (2022). Thermo-physical properties and heat transfer potential of novel silica-ethylene glycol mono nanofluid: Experiments and multi-layer perceptron (MLP) modelling. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 648, 129412.
Mulè, G., Burlet, C., & Vanbrabant, Y. (2017). Automated curve fitting and unsupervised clustering of manganese oxide Raman responses. Journal of Raman Spectroscopy, 48(11), 1665–1675.
Müller, A. (n.d.). Methodik zur datenbasierten Typisierung von Quartieren anhand baulicher Strukturen.
MURAINA, I. O. (2022). Exploration of Data Science Techniques in Predicting StudentsT Relevant Courses of Study on Getting to Higher Institutions.
Mussgnug, A. M. (2022). The predictive reframing of machine learning applications: good predictions and bad measurements. European Journal for Philosophy of Science, 12(3), 55.
Nainggolan, A. T., & Saragih, A. (2018). DIAGNOSIS PENYAKIT MENGGUNAKAN ALGORITMA MATCHMAKING DAN MACHINE LEARNING. Program Sarjana Program Studi Teknik Informatika Fakultas Teknik Informatika ….
Nasuha, R. A. H. (2020). SENTIMEN ANALISIS TWITTER PADA PEMILIHAN PRESIDEN DAN WAKIL PRESIDEN REPUBLIK INDONESIA TAHUN 2019 MENGGUNAKAN SUPPORT VECTOR MACHINE DENGAN LSA DAN TF-IDF [Phdthesis]. Program Studi Informatika S1 Fakultas Teknik Universitas Widyatama.
Nathwani, C. L., Wilkinson, J. J., Fry, G., Armstrong, R. N., Smith, D. J., & Ihlenfeld, C. (2022). Machine learning for geochemical exploration: classifying metallogenic fertility in arc magmas and insights into porphyry copper deposit formation. Mineralium Deposita, 57(7), 1143–1166.
Navya, K., Prasad, K., & Singh, B. M. K. (2021). Classification of blood cells into white blood cells and red blood cells from blood smear images using machine learning techniques. 2021 2nd Global Conference for Advancement in Technology (GCAT), 1–4.
Naylor, A. (2023). A RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE.
Nazari, Z., & Kang, D. (2015). Density based support vector machines for classification. International Journal of Advanced Research in Artificial Intelligence (IJARAI), 1(4), 69–76.
Newendorp, B. J., & Peterson, J. S. (2022). Maintaining privacy of personal information. Google Patents.
Newendorp, B. J., & Peterson, J. S. (2023). Maintaining privacy of personal information. Google Patents.
Ngan, C.-K. (2019). A Family Review of Parameter-Learning Models and Algorithms for Making Actionable Decisions. In Advanced Methodologies and Technologies in Business Operations and Management (pp. 680–693). IGI Global.
Ngan, C.-K., & Li, L. (2016). A patient-centric decision guidance system for detecting glycemia of diabetes patients. International Journal of Applied Decision Sciences, 9(4), 366–399.
Nguyen, P. H., Hong, B., Rubin, S., & Fainman, Y. (2020). Machine learning for composition analysis of ssDNA using chemical enhancement in SERS. Biomedical Optics Express, 11(9), 5092–5121.
Niemi, S.-T. (2023). Kotiin vietävien teknologiapalveluiden konseptointi: ICMT-palvelut kotihoidossa.
Nigro, L., & Cicirelli, F. (2023). Improving Clustering Accuracy of K-Means and Random Swap by an Evolutionary Technique Based on Careful Seeding. Algorithms, 16(12), 572.
Nordström, J. (2018). Automated classification of bibliographic data using SVM and Naive Bayes.
Nugraha, R. E. (n.d.). Implementasi metode vader-lstm dalam pengujian pengaruh sentimen investor terhadap prediksi harga saham [B.S. thesis]. Fakultas Sains dan Teknologi UIN Syarif HIdayatullah Jakarta.
Nuredeen Elfegi, E., & Roohbakhsh, M. (2021). AI I REKRYTERINGSPROCESSEN: Med fokus p\aa hur AI kan hantera förekommandet av bias.
Nyholm, A. (2022). MediaPipe-liitännäisen implementointi ja käyttö pelimoottorissa.
Nyland, R. (2018). A review of tools and techniques for data-enabled formative assessment. Journal of Educational Technology Systems, 46(4), 505–526.
Nyman, M., & Ulug, C. N. (2020). Exploring the potential for machine learning techniques to aid in categorizing electron trajectories during magnetic reconnection.
Omar Ali, N. (2020). A Comparative study of cancer detection models using deep learning. Malmö universitet/Teknik och samhälle.
Omoifo, D. (2018). Obstacle detection in autonomous vehicles using deep learning.
Orr, R. M., Nell, G. R., & Brumbaugh, B. L. (2022). Intelligent assistant for home automation. Google Patents.
Orr, R. M., Nell, G. R., & Brumbaugh, B. L. (2023). Intelligent assistant for home automation. Google Patents.
Örs, F. B. (2020). Veri madenciliğinde veri dönüştürme yöntemlerinin sınıflandırma algoritmalarının performanslarına olan etkisi. Trakya Üniversitesi, Sağlık Bilimleri Enstitüsü.
Osório, D. F. de N. (2022). Novel Approaches to Pervasive and Remote Sensing in Cardiovascular Disease Assessment.
Ovcharenko, S., Chetvergov, V., & Minakov, V. (2021). Application of Machine Learning Methods for Estimating the Fuel Consumption of Locomotives for Switching Service. Transportation Research Procedia, 54, 802–807.
Oya, J. K., & Hoelz, B. W. (2016). Classificação de Fragmentos de Arquivos com Técnica de Aprendizagem de Máquina baseada em Árvores de Decisão. Anais Do XVI Simpósio Brasileiro Em Segurança Da Informação e de Sistemas Computacionais, 86–99.
Özhan, E. (2016). Yapay Zeka ve Veri Madenciliği Uygulamalarında Yüksek Başarımlı Hesaplama Yazılımlarının Kullanımı ve Örnek Tasarım ile Performans Analizleri.
ÖZTORNACI, R. O., CO, E., COŞGUN, E., & TAŞDELEN, B. (2020). Genom-boyu İlişki Çalı Yöntemleri ve Derin Ö Genişliklerinde Performansların.
Paiva, E. B. M. (2023). Desenvolvimento de método para avaliação de falhas em linhas de transmissão utilizando árvores de decisão e modelos de Markov [Phdthesis]. Universidade de São Paulo.
Paschek, D., Luminosu, C. T., & Draghici, A. (2017). Automated business process management–in times of digital transformation using machine learning or artificial intelligence. MATEC Web of Conferences, 121, 04007.
Pasha, S., Ritz, C., Stirling, D., Zulli, P., Pinson, D., & Chew, S. (2018). A deep learning approach to the acoustic condition monitoring of a sintering plant. 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 1803–1809.
Paulik, M., Mason, H. G., & Skinder, J. A. (2023). User-specific acoustic models. Google Patents.
Pavlova, K., & Makarenkova, V. (2022). DATA LEAKAGE PROBLEM IN MACHINE LEARNING. PROSPECTS AND KEY TENDENCIES OF SCIENCE IN CONTEMPORARY WORLD, 85.
Peng, Y. (2019). Policy Direct Search for Effective Reinforcement Learning.
Peterson, C. J., Jessica, P., Biswas, A., & Simmonds, H. (2023). Voice identification in digital assistant systems. Google Patents.
Phala, K., Doorsamy, W., & Paul, B. (2019). A study into intelligent Neutral Section fault monitoring system on the Coal line using wireless sensor networks.
Phan, T. C., Pranata, A., Farragher, J. B., Bryant, A. L., Nguyen, H. T., & Chai, R. (2023). Machine Learning Derived Lifting Technique in People without Low Back Pain. 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 1–4.
Phan, T. C., Pranata, A., Farragher, J., Bryant, A., Nguyen, H. T., & Chai, R. (2022). Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain. Sensors, 22(17), 6694.
Phipps, B. S., Frazzingaro, G., & Schramm, K. F. (2022). Synchronization and task delegation of a digital assistant. Google Patents.
Piernot, P. P., & Binder, J. (2021). Reducing the need for manual start/end-pointing and trigger phrases. Google Patents.
Piernot, P. P., & Weinberg, G. L. (2024). Multi-modal inputs for voice commands. Google Patents.
Piersol, K. (2022). Variable latency device coordination. Google Patents.
Piggott, J. (2015). Identification of business travelers through clustering algorithms. University of Twente.
Pizarro Castro, K. G. (2022). Distribución de camas hospitalarias en un centro de salud mediante la técnica de Machine Learning.
PO, C., & CO, P. (1959). SEMESTER-III. Year of Implementati on of CBCS/ECS, 9.
POLAT, Y. B. (n.d.). TILBURG LAW SCHOOL.
Ponce Romero, J. M., Hallett, S. H., & Jude, S. (2017). Leveraging big data tools and technologies: addressing the challenges of the water quality sector. Sustainability, 9(12), 2160.
Porcello, J. C. (2017). Designing and implementing Machine Learning Algorithms for advanced communications using FPGAs. 2017 IEEE Aerospace Conference, 1–10.
Pranay, Y. S., Tabjula, J., & Kanakambaran, S. (2022). Classification Studies on Vibrational Patterns of Distributed Fiber Sensors using Machine Learning. 2022 IEEE Bombay Section Signature Conference (IBSSC), 1–5.
PREDA, Ștefan. (2018). Using SVM in Classification. Database Systems Journal BOARD, 29.
Preda, S., Oprea, S.-V., Bâra, A., & Belciu, A. (2018). PV forecasting using support vector machine learning in a big data analytics context. Symmetry, 10(12), 748.
Quelopana, A., & Navarra, A. (2021). Integration of strategic open-pit mine planning into hierarchical artificial intelligence. Journal of the Southern African Institute of Mining and Metallurgy, 121(12), 643–652.
Ragot, M., Martin, N., Em, S., Pallamin, N., & Diverrez, J.-M. (2018). Emotion recognition using physiological signals: laboratory vs. wearable sensors. Advances in Human Factors in Wearable Technologies and Game Design: Proceedings of the AHFE 2017 International Conference on Advances in Human Factors and Wearable Technologies, July 17-21, 2017, The Westin Bonaventure Hotel, Los Angeles, California, USA 8, 15–22.
Raja, M. A. Z., Haider, A., Nisar, K. S., & Shoaib, M. (2024). Intelligent computing knacks for infected media and time delay impacts on dynamical behaviors and control measures of rumor-spreading model. AIMS Biophysics, 11(1), 1–17.
Ramjan, S., & Sunkpho, J. (2023). Principles and Theories of Data Mining with RapidMiner. IGI Global.
Rantanen, A., Salminen, J., Ginter, F., & Jansen, B. J. (2020). Classifying online corporate reputation with machine learning: a study in the banking domain. Internet Research, 30(1), 45–66.
Raviya, K., & Vennila, M. (2021). An Implementation of Hybrid Enhanced Sentiment Analysis System using Spark ML Pipeline: A Big Data Analytics Framework. International Journal of Advanced Computer Science and Applications, 12(5).
Ravnik, J., Jovanovac, J., Trupej, A., Vištica, N., & Hriberšek, M. (2021). A sigmoid regression and artificial neural network models for day-ahead natural gas usage forecasting. Cleaner and Responsible Consumption, 3, 100040.
Reddy, S. (2018). Use of artificial intelligence in healthcare delivery. In eHealth-making health care smarter. IntechOpen.
Regis, M. A. J. B., Khan, C. L., Samaniego, J. M., Jamias, S. B., & Mariano, V. Y. (2015). Development of a Building Detection System from an Aerial Image Based in Watershed Transformation and Linear Support Vector Machine. Journal of Society and Technology, 5(1), 55–63.
Rekha, K., & Gowda, N. C. (2020). A framework for sentiment analysis in customer product reviews using machine learning. 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), 267–271.
REVELO, J. D. T. (n.d.). DESARROLLO DE MODELOS DE APRENDIZAJE PROFUNDO PARA LA DETECCIÓN DE INDICADORES DE DEFORESTACIÓN EN COLOMBIA USANDO IMÁGENES SATELITALES E INFORMACIÓN PÚBLICA.
Ribeiro, H., Spolon, R., Manacero, A., & Lobato, R. S. (2019). Parallelization of the DIANA Algorithm in OpenMP. Parallel and Distributed Computing, Applications and Technologies: 19th International Conference, PDCAT 2018, Jeju Island, South Korea, August 20-22, 2018, Revised Selected Papers 19, 171–176.
Ribeiro, H., Spolon, R., Manacero Jr, A., & Lobato, R. S. (2018). Paralelizaçao do algoritmo DIANA em OpenMP. Anais Da IX Escola Regional de Alto Desempenho de São Paulo, 57–60.
Riberg, J., & Selin, L. J. (2021). TIP OF THE SPEAR: CAN SPECIAL FORCES LEAD THE WAY FOR MILITARY APPLICATIONS OF AI? [Phdthesis]. Monterey, CA; Naval Postgraduate School.
Richárd, M. (2015). Data science technikák alkalmazása a futópálya-hatékonyság növelésében. Repüléstudományi Közlemények, 27(3), 159–170.
Riesener, M., Doelle, C., Mendl-Heinisch, M., & Klumpen, N. (2020). Identification of evaluation criteria for algorithms used within the context of product development. Procedia CIRP, 91, 508–515.
Riis, S. (n.d.). 2. semester-for\aar 2020.
Risenmay, M. A. (2023). Application of Supervised Machine Learning to Search for Nuclear Fallout Analytes that Identify Explosive Type. North Carolina State University.
ROCHA, M. L., PRATA, D. N., de OLIVEIRA, J. C. P., & FACCIONI, M. A. F. (2022). MOROSIDADE DO JUDICIÁRIO: PROPOSTAS DE UTILIZAÇÃO DA INTELIGÊNCIA ARTIFICIAL PARA CONTRIBUIR NA CELERIDADE DA RETIFICAÇÃO DA AUTUAÇÃO PROCESSUAL. Revista Juridica, 2(69), 315–338.
Rodriguez, Y. B. (2018). Integración de la red neuronal convolucional con el algoritmo de función de frontera de objeto para reconocimiento de piezas y detección de defectos. Grado Académico de Maestro En Ciencia y Tecnologı́a En Manufactura Avanzada, Corporación Mexicana de Investigación En Materiales, Saltillo, Coahuila, México.
Rodrı́guez Reséndiz, P. O. (2020). Inteligencia artificial y datos masivos en archivos digitales sonoros y audiovisuales.
Roessle, M., & Kuebler, R. (n.d.). ATINER’s Conference Paper Series COM2017-2272.
Romero, A. S., & del Pozo Quintero, A. (n.d.). Estrategia de navegación para robots móviles mediante redes neuronales.
Romero Villasis, M. E. (2017). DESARROLLO DE COMPETENCIAS LABORALES Y LA CALIDAD DE SERVICIOS ADMINISTRATIVOS EN LA MUNICIPALIDAD DISTRITAL DE AMARILIS–2017.
SAATÇIOĞLU, D., & ÖZÇAKAR, D. N. (n.d.). ARALIKLI TALEP YAPISINA SAHİP ÜRÜNLERİN TALEP TAHMİNİNDE MAKİNE ÖĞRENME YÖNTEMLERİNİN UYGULANMASI.
Sáez Martı́nez, J. (2021). Finding Trends in the B2B Textile Sector using Machine Learning.
Sáez Martı́nez, J., & others. (2021). Aplicación de herramientas de ML a la predicción de tendencias en función del análisis de históricos de ventas B2B.
Şahinarslan, F. V. (2019). Makine öğrenmesi algoritmaları ile nüfus tahmini: Türkiye örneği [Phdthesis]. Sosyal Bilimler Enstitüsü.
Sako, Z., Adibi, S., & Wickramasinghe, N. (2020). Addressing data accuracy and information integrity in mHealth solutions using machine learning algorithms. Delivering Superior Health and Wellness Management with IoT and Analytics, 345–359.
Sako, Z. Z., Karpathiou, V., Adibi, S., & Wickramasinghe, N. (2020). Data accuracy considerations with mHealth. In Cognitive Analytics: Concepts, Methodologies, Tools, and Applications (pp. 1623–1638). IGI Global.
Sako, Z. Z., Karpathiou, V., & Wickramasinghe, N. (2016). Data accuracy in mHealth. Contemporary Consumer Health Informatics, 379–397.
Salminen, J., Mustak, M., Sufyan, M., & Jansen, B. J. (2023). How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation. Journal of Marketing Analytics, 11(4), 677–692.
ŠAMÁNEK, P. J. (n.d.). DETEKCE VÝSKYTU OBJEKT\UU VE VIDEOZÁZNAMU.
Şanlı, E. (2018). Yapay Sinir Ağı Kontrollü Otonom RC Araç Uygulaması. İstanbul Gelişim Üniversitesi Fen Bilimleri Enstitüsü.
Santhiranayagam, B. K. (2016). Machine-Learning Applications to Gait Biomechanics using Inertial Sensor Signals [Phdthesis]. Victoria University.
Santos, C. A. L. dos. (2016). Sesame: clustering with semantic similarity based on multiple ontologies [Phdthesis].
Saputra, A. (2019). Klasifikasi Pengenalan Buah Menggunakan Algoritma Naive Baiyes. Jurnal RESISTOR (Rekayasa Sistem Komputer), 2(2), 83–88.
Sariev, E., & Germano, G. (2020). Bayesian regularized artificial neural networks for the estimation of the probability of default. Quantitative Finance, 20(2), 311–328.
Sariyar, M. (2016). Maschinelle Lernverfahren für nieder-und hochdimensionale Probleme: Zusammenführung und Analyse biomedizinischer Daten.
Sayed, H. A., William, A., & Said, A. M. (2023). Smart Electricity Meter Load Prediction in Dubai Using MLR, ANN, RF, and ARIMA. Electronics, 12(2), 389.
Schmidt, C. (n.d.). Addressing data acumen with an introduction to data science.
Schmidt, C. (2023). Spezielles Kapitel: Vorgehensmodelle. In Graphentheorie und Netzwerkanalyse: Eine kompakte Einführung mit Beispielen, Übungen und Lösungsvorschlägen (pp. 171–179). Springer.
Schramm, K. F., Binder, J., Phipps, B. S., & Sung, P. K. (2021). Voice interaction at a primary device to access call functionality of a companion device. Google Patents.
Schreiber, M. (2017). Mit Maximum-Entropie das Parsing natürlicher Sprache erlernen.
Schröder, A. M. (2021). Unboxing The Algorithm: Understandability And Algorithmic Experience In Intelligent Music Recommendation Systems.
Segumpan, R. G., & McAlaney, J. (n.d.). CHALLENGES AND REFORMS IN GULF HIGHER EDUCATION.
Shafi, M. A., Rusiman, M. S., Ismail, S., & Kamardan, M. G. (2019). A hybrid of multiple linear regression clustering model with support vector machine for colorectal cancer tumor size prediction. International Journal of Advanced Computer Science and Applications, 10(4).
Shaukat, M. R., & Sami, A. (n.d.). Pandemic Upsurge: Insights from Higher Education Reforms in the Gulf. In Challenges and Reforms in Gulf Higher Education (pp. 26–40). Routledge.
Shaukat, M. R., & Sami, A. (2023). 2Pandemic Upsurge. Challenges and Reforms in Gulf Higher Education: Confronting the COVID-19 Pandemic and Assessing Future Implications.
Shipway, N., Huthwaite, P., Lowe, M., & Barden, T. (2019). Performance based modifications of random forest to perform automated defect detection for fluorescent penetrant inspection. Journal of Nondestructive Evaluation, 38(2), 37.
Siahaan, S. T. M., Manik, I. V., & Situmorang, S. (2018). Prediksi Turnover Karyawan Menggunakan Algoritma KNN dan Algoritma. PROGRAM DIPLOMA 3 FAKULTAS TEKNIK INFORMATIKA DAN ELEKTRO PROGRAM STUDI ….
Siltala, M., & others. (2020). Simulating data center cooling systems: data-driven and physical modeling methods.
SILVA, D. N. A. da, & others. (2021). Métodos de classificação por teoria da decisão para mensuração de dados do ENEM.
Sinesio, A. J., Coffman, P. L., Kline III, F.-R., KUFELDT, S. E., Macrae, R., PARISA, K. K., & Goyal, A. (2023). Reducing description length based on confidence.
Singh, A. K., Chourasia, B., Raghuwanshi, N., & Raju, K. (2021). Green plant leaf disease detection using K-means segmentation, color and texture features with support vector machine and random forest classifier. J. Green Eng, 11, 3157–3180.
Singh, M. (2019). The product manager in the artificial intelligence world. Product: Management and Development, 17(1), 79–84.
Sklet, V. (2018). Exploring the capabilities of machine learning (ML) for 1D blood flow: Application to coronary flow. NTNU.
Solı́s-Villalta, O. M. (2018). Sistema de predicción del error de modelado para una tarea de manipulación de objetos de la vida diaria para un robot humanoide, utilizando técnicas de aprendizaje de máquina.
Soto, D., & Soto, W. (2022). Evolutionary Algorithm for Solving Supervised Classification Problems: An Experimental Study. 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 24–29.
Stasior, W. F., Carson, D. A., Dasari, R., & Kim, Y. (2021). Zero latency digital assistant. Google Patents.
Stasior, W. F., Carson, D. A., Dasari, R., & Kim, Y. (2023). Zero latency digital assistant. Google Patents.
Steiner, E. (2017). Machine learning concepts in predictive analytics-A case study on wind turbine data.
Stopner, J., & Willberg, C.-\AAke. (2022). Maskininlärningsklassificering av fordonsstatus för minskade reparationskostnader och avbrott inom kollektivtrafiken: Applicering av Random Forest-klassificering p\aa fordonssignaler.
Student, E. P. D., & Nolte, M. (n.d.). Rootline Navigation.
Sulca Correa, O. I. (2015). Multimedia big data computing for trend detection. Universitat Politècnica de Catalunya.
Syed, M. (2018). Machine Learning in Healthcare: Identifying Pneumonia with Artificial Intelligence.
Tadavarthi, Y., Vey, B., Krupinski, E., Prater, A., Gichoya, J., Safdar, N., & Trivedi, H. (2020). The state of radiology AI: considerations for purchase decisions and current market offerings. Radiology: Artificial Intelligence, 2(6), e200004.
Taffese, W. Z., & Sistonen, E. (2017). Significance of chloride penetration controlling parameters in concrete: Ensemble methods. Construction and Building Materials, 139, 9–23.
Taffese, W. Z., Sistonen, E., & Puttonen, J. (2015). CaPrM: Carbonation prediction model for reinforced concrete using machine learning methods. Construction and Building Materials, 100, 70–82.
Tang, J., Henderson, A., & Gardner, P. (2021). Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets. Analyst, 146(19), 5880–5891.
TAŞ, C. (2023). Comparison of Machine Learning and Standard Credit Risk Models’ Performances in Credit Risk Scoring of Buy Now Pay Later Customers. Middle East Technical University.
Tazhiyeva, A. (2018). Challenges and opportunities of introducing Internet of Things and Artificial Intelligence applications into Supply Chain Management.
Tech, M. (2019). ACADEMIC REGULATIONS COURSE STRUCTURE AND DETAILED SYLLABUS. Production and Operations Management, 3, 0–3.
Terrier, R., & Martin, N. (2020). A Machine Learning Tool to Match 2D Drawings and 3D Objects’ Category for Populating Mockups in VR. Virtual Reality and Augmented Reality: 17th EuroVR International Conference, EuroVR 2020, Valencia, Spain, November 25–27, 2020, Proceedings 17, 240–246.
Thongyoo, T. (2018). Improvement on Automated Thai Assignment Scoring by Using a Thesaurus. Creative Science, 10(1), 87–95.
Thongyoo, T., Saelee, S., & Krootjohn, S. (2016). Automated thai online assignment scoring. 2016 Fifth ICT International Student Project Conference (ICT-ISPC), 33–36.
Tittarelli, G. (2023). Primeras experiencias en la identificación de personas con riesgo de diabetes en la población argentina usando técnicas de aprendizaje automático [Phdthesis]. Universidad Nacional de La Plata.
Todorov, M. P. (n.d.). Komparace distribucı́ frameworku Apache Hadoop.
Torres Revelo, I., & others. (2020). Interpretabilidad de un modelo basado en aprendizaje profundo para el diagnóstico de retinopatı́a diabética.
TPC, I. Y. B. T. C. (n.d.). TEXT BOOKS. COMPUTER SCIENCE & SYSTEMS ENGINEERING, 2(1), 5.
Tran, N. (2018). Application of Data Analytics to Prediction of Initial Production in Tight Oil Reservoir [Phdthesis]. University of Houston.
Trevisam, B. A. (2023). Aplicação de ferramentas inteligentes de classificação de dados não estruturados como suporte a gestão de ativos em sistemas elétricos de potência [Phdthesis]. Universidade de São Paulo.
Tütüncü, T. E. (2022). Makine öğrenmesi Algoritmaları ile Kredi Temerrüt Riskini Tahmin Etme [Phdthesis]. Bursa Uludag University (Turkey).
Tütüncü, T. E., & Gürsakal, S. (2023). Kredi Temerrüt Riskini Tahmin Etmede Makine Öğrenme Algoritmalarının Karşılaştırılması. Avrupa Bilim ve Teknoloji Dergisi, 50, 14–22.
Tσιλιγι\acuteαννη, E. (2015). Aλγóριθμoι Classification σε Big Data.
Uglow, H., Johns, E., & Deisenroth, M. (2019). CuRL: Curriculum Reinforcement Learning for Goal-Oriented Robot Control.
Ünlü, N., UÇAR, Ö., & Özhan, E. (n.d.). Determination of Fundamental Attributes of Phishing Attacks.
Uyulan, Ç., Mayor, D., Steffert, T., Watson, T., & Banks, D. (2023). Classification of the Central Effects of Transcutaneous Electroacupuncture Stimulation (TEAS) at Different Frequencies: A Deep Learning Approach Using Wavelet Packet Decomposition with an Entropy Estimator. Applied Sciences, 13(4), 2703.
Uzel, E. (2018). Makine öğrenmesi ile FOREX piyasalarında alım satım kararları uygulaması. Sosyal Bilimler Enstitüsü.
Vähäkainu, P., & Neittaanmäki, P. (2018). Tekoäly terveydenhuollossa. Informaatioteknologian Tiedekunnan Julkaisuja/Jyväskylän Yliopisto, 2018, 45.
Van Os, M., Novick, G. B., & Herz, S. M. (2022). System and method for processing voicemail. Google Patents.
Van Os, M., Saddler, H. J., Napolitano, L. T., Russell, J. H., Lister, P. M., & Dasari, R. (2022). Intelligent automated assistant for TV user interactions. Google Patents.
Vega Garcı́a, J. F. (2019). Modelo de pronóstico de rendimiento académico de alumnos en los cursos del programa de estudios básicos de la Universidad Ricardo Palma usando algoritmos de Machine Learning.
Vescovi, M. R., Circlaeys, E. M., Warren, R., Bernstein, J. T., & Krenn, M. (2024). Intelligent automated assistant for delivering content from user experiences. Google Patents.
Vescovi, M. R., GALVEZ, T. A. V., Karashchuk, P., Gruber, T. R., & Guzzoni, D. R. (2021). Unconventional virtual assistant interactions. Google Patents.
Vescovi, M. R., GALVEZ, T. A. V., Karashchuk, P., Gruber, T. R., & Guzzoni, D. R. (2024). Unconventional virtual assistant interactions. Google Patents.
Vierschilling, S. P. (2022). Automatisierung der Standortauswahl in der Fabrikplanung [Phdthesis]. Dissertation, RWTH Aachen University, 2022.
Vinı́cius, L., Rodrigues, L., Torquato, M., & Silva, F. A. (2022). Docker platform aging: a systematic performance evaluation and prediction of resource consumption. The Journal of Supercomputing, 78(10), 12898–12928.
Vrbančič, G. (2015). Razvoj spletne aplikacije za analitiko podatkov v realnem času s Spring XD [Phdthesis]. Univerza v Mariboru, Fakulteta za elektrotehniko, računalništvo in informatiko.
Walker, I. R. A., NEWENDORP, B. J., DASARI, R., GIULI, R. D., Gruber, T. R., RADEBAUGH, C. E., Garg, A., Khosla, V., RUSSELL, J. H., PETERSON, C., & others. (2021). Application integration with a digital assistant. Google Patents.
Walker, I. R. A., NEWENDORP, B. J., DASARI, R., GIULI, R. D., Gruber, T. R., RADEBAUGH, C. E., Garg, A., Khosla, V., RUSSELL, J. H., PETERSON, C., & others. (2023). Application integration with a digital assistant.
Walter, M., Vasyutynskyy, V., Trinh, D. A., & Leyh, C. (2019). Machine Learning goes Measure Management: Leveraging Anomaly Detection and Parts Search to Improve Product-Cost Optimization.
Wamala, R. C. (2020). An Equipment Scheduling System using AI-Based Authentication [Phdthesis].
Wang, L., Ye, W., Zhu, Y., Yang, F., & Zhou, Y. (2023). Optimal parameters selection of back propagation algorithm in the feedforward neural network. Engineering Analysis with Boundary Elements, 151, 575–596.
Wardani, S. K., & Ruldeviyani, Y. (2021). Sentiment Analysis of Visitor Reviews on Hotel in West Sumatera. 2021 6th International Workshop on Big Data and Information Security (IWBIS), 1–8.
Wei, X. (2022). Social Support Seeking Behaviors and Provision in Online Communities: Associations between Social Support Seeking Behaviors and Social Support Received [Phdthesis]. The University of Wisconsin-Madison.
Wellander, M., & Sintorn, V. (2022). Machine learning for identifying how much women and men talk in meetings.
Wu, X.-W., Cao, Y., & Dankwa, R. (2022). Accuracy vs Efficiency: Machine Learning Enabled Anomaly Detection on the Internet of Things. 2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), 245–251.
Yalçin, M., & Kalkan, S. B. (2022). Determining the best estimation model with tree-based machine learning methods: implementation on customer spendings for e-commerce websites. Advances and Applications in Statistics, 75, 91–109.
Yan, B., Zhang, J., Cheng, R., & Liu, C. (2020). Modelling of A Flow Meter through Machine learning. 2020 IEEE SENSORS, 1–4.
Yang, C., Liu, J., Zeng, Y., & Saeed, M. (2016). Uncertainty Assessment of Component Degradation Model Based on Support Vector Regression. International Conference on Nuclear Engineering, 50015, V001T01A012.
Yıldırım, M. (2019). Derin öğrenme teknikleri kullanılarak yüz tanıma tabanlı müşteri doğrulama ile bankamatiklerde sahtekârlık tespiti. Konya Teknik Üniversitesi.
York, W. M., GUPTA, G. A., Huang, X., NIETO, H., Phipps, B. S., Piersol, K., & others. (2023). Spoken notifications.
Youngsue, H., Roh, H., Jaehoon, K., & Hwang, S. H. (n.d.). Enhancing the power of color through artificial intelligence in visual storytelling.
Zanca, J. P. P., de Azevedo, L. M. B., & da Silva, A. F. (n.d.). APPLICATION OF MACHINE LEARNING TECHNIQUES IN THE PREDICTION OF CHEMICAL COMPOSITION OF METALLIC CHARGE RECIPES.
Zeitlin, N. (2022). Distributed personal assistant. Google Patents.
Zhang, X. (2020). Deep learning driven tool wear identification and remaining useful life prediction [Phdthesis]. Coventry University.
Zhong, H., Xiao, J., & others. (2017). Enhancing health risk prediction with deep learning on big data and revised fusion node paradigm. Scientific Programming, 2017.
Zhou, W., Yang, X., & Chen, Y. (2023). Adaptive sinh transformation Gaussian quadrature for 2D potential problems using deep learning. Engineering Analysis with Boundary Elements, 155, 197–211.
Zhou, Z., & Su, M. (2021). A Recommendation Algorithm of Insurance’s Productss Based on Optimal Collaborative Filtering. ICMLCA 2021; 2nd International Conference on Machine Learning and Computer Application, 1–4.
Zimmerling, C., Poppe, C., & Kärger, L. (2019). Virtual product development using simulation methods and ai. Lightweight Design Worldwide, 12(6), 12–19.
Zúñiga-Cisneros, J. A. (2019). Análisis de datos y tendencias sociales de las donaciones de sangre en Guadalajara.
Гавриленко, О. В. (2024). Аналіз даних в інформаційно-управляючих системах. Курс лекцій.
Макаренкова, В. М., Павлова, К. А., Макаренкова, В., & Павлова, К. (n.d.). DATA LEAKAGE PROBLEM IN MACHINE LEARNING. Target, 91(86), 89.
Першина, Е. Л. (2017). ББК 22.152 П27 Рецензенты: д-р техн. наук, проф. АЛ Ахтулов (ОмГУПС); канд. техн. наук, доц. ВЮ Кобенко (ОмГТУ).
Першина, Е. Л., & Чуканов, С. Н. (2017). Машинное обучение.
Сергеєв-Горчинський, О. О., & Іщенко, Г. В. (2018). Інтелектуальний аналіз даних: Комп’ютерний практикум.
오지현. (2017). 의료용 인공지능의 허가에 대한 비교제도론적 고찰: 미국· 유럽· 중국· 일본을 중심으로 [Phdthesis]. 연세대학교 보건대학원.
주영지, & others. (2017). 연관규칙 탐사기법과 SVM 을 이용한 악성코드 탐지방법 [Phdthesis]. 조선대학교 산업기술융합대학원.
주영지, 홍택은, & 신주현. (2016). 교통사고 데이터의 패턴 분석과 Hybrid Model 을 이용한 피해자 상해 심각도 예측. 스마트미디어저널, 5(4), 75–82.