Jason Bell - Practitioner, Author and Advisor in Machine Learning, Artificial Intelligence and Startups



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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.
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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., 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.
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.
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.
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.
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.
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