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Relevant bibliographies by topics / Optimization feature selections

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Academic literature on the topic 'Optimization feature selections'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Optimization feature selections"

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Sabeena, B., S. Sivakumari, and Dawit Mamru Teressa. "Optimization-Based Ensemble Feature Selection Algorithm and Deep Learning Classifier for Parkinson’s Disease." Journal of Healthcare Engineering 2022 (April 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/1487212.

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PD (Parkinson’s Disease) is a severe malady that is painful and incurable, affecting older human beings. Identifying PD early in a precise manner is critical for the lengthened survival of patients, where DMTs (data mining techniques) and MLTs (machine learning techniques) can be advantageous. Studies have examined DMTs for their accuracy using Parkinson’s dataset and analyzing feature relevance. Recent studies have used FMBOAs for feature selections and relevance analyses, where the selection of features aims to find the optimal subset of features for classification tasks and combine the lear

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Ghada, Rawashdeh, Mamat Rabiei, Binti Abu Bakar Zuriana, and Hafhizah Abd Rahim Noor. "Comparative between optimization feature selection by using classifiers algorithms on spam email." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5479–85. https://doi.org/10.11591/ijece.v9i6.pp5479-5485.

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Spam mail has become a rising phenomenon in a world that has recently witnessed high growth in the volume of emails. This indicates the need to develop an effective spam filter. At the present time, Classification algorithms for text mining are used for the classification of emails. This paper provides a description and evaluation of the effectiveness of three popular classifiers using optimization feature selections, such as Genetic algorithm, Harmony search, practical swarm optimization, and simulating annealing. The research focuses on a comparison of the effect of classifiers using K-neare

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Rawashdeh, Ghada, Rabiei Mamat, Zuriana Binti Abu Bakar, and Noor Hafhizah Abd Rahim. "Comparative between optimization feature selection by using classifiers algorithms on spam email." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5479. http://dx.doi.org/10.11591/ijece.v9i6.pp5479-5485.

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<span lang="EN-US">Spam mail has become a rising phenomenon in a world that has recently witnessed high growth in the volume of emails. This indicates the need to develop an effective spam filter. At the present time, Classification algorithms for text mining are used for the classification of emails. This paper provides a description and evaluation of the effectiveness of three popular classifiers using optimization feature selections, such as Genetic algorithm, Harmony search, practical swarm optimization, and simulating annealing. The research focuses on a comparison of the effect of

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Liu, Ruru, Rencheng Fang, Tao Zeng, et al. "A Novel Adaptive Sand Cat Swarm Optimization Algorithm for Feature Selection and Global Optimization." Biomimetics 9, no. 11 (2024): 701. http://dx.doi.org/10.3390/biomimetics9110701.

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Feature selection (FS) constitutes a critical stage within the realms of machine learning and data mining, with the objective of eliminating irrelevant features while guaranteeing model accuracy. Nevertheless, in datasets featuring a multitude of features, choosing the optimal feature poses a significant challenge. This study presents an enhanced Sand Cat Swarm Optimization algorithm (MSCSO) to improve the feature selection process, augmenting the algorithm’s global search capacity and convergence rate via multiple innovative strategies. Specifically, this study devised logistic chaotic mappin

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Jorgensen, Palle E. T., Myung-Sin Song, and James Tian. "Conditional mean embedding and optimal feature selection via positive definite kernels." Opuscula Mathematica 44, no. 1 (2024): 79–103. http://dx.doi.org/10.7494/opmath.2024.44.1.79.

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Motivated by applications, we consider new operator-theoretic approaches to conditional mean embedding (CME). Our present results combine a spectral analysis-based optimization scheme with the use of kernels, stochastic processes, and constructive learning algorithms. For initially given non-linear data, we consider optimization-based feature selections. This entails the use of convex sets of kernels in a construction o foptimal feature selection via regression algorithms from learning models. Thus, with initial inputs of training data (for a suitable learning algorithm), each choice of a kern

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Masrom, Suraya, Norhayati Baharun, Nor Faezah Mohamad Razi, Rahayu Abdul Rahman, and Abdullah Sani Abd Rahman. "Particle Swarm Optimization in Machine Learning Prediction of Airbnb Hospitality Price Prediction." International Journal of Emerging Technology and Advanced Engineering 12, no. 1 (2022): 146–51. http://dx.doi.org/10.46338/ijetae0122_14.

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Particle Swarm Optimization is a metaheuristics algorithm widely used for optimization problems. This paper presents the research design and implementation of using Particle Swarm Optimization to automate the features selections in the machine learning models for Airbnb price prediction. Today, Airbnb is changing the business models of the hospitality industry globally. While a bigger impact has been given by the Airbnb community to the local economic development of each country, there has been very little effort that investigates on Airbnb pricing issue with machine learning techniques. Focus

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Gbenga, Fadare Oluwaseun, Adetunmbi Adebayo Olusola, and Oyinloye Oghenerukevwe Elohor. "Towards Optimization of Malware Detection using Extra-Tree and Random Forest Feature Selections on Ensemble Classifiers." International Journal of Recent Technology and Engineering 9, no. 6 (2021): 223–32. http://dx.doi.org/10.35940/ijrte.f5545.039621.

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The proliferation of Malware on computer communication systems posed great security challenges to confidential data stored and other valuable substances across the globe. There have been several attempts in curbing the menace using a signature-based approach and in recent times, machine learning techniques have been extensively explored. This paper proposes a framework combining the exploit of both feature selections based on extra tree and random forest and eight ensemble techniques on five base learners- KNN, Naive Bayes, SVM, Decision Trees, and Logistic Regression. K-Nearest Neighbors retu

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Fadare, Oluwaseun Gbenga, Adebayo Olusola Adetunmbi, and Oghenerukevwe Elohor Oyinloye. "Towards Optimization of Malware Detection using Extra-Tree and Random Forest Feature Selections on Ensemble Classifiers." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 6 (2021): 223–32. https://doi.org/10.35940/ijrte.F5545.039621.

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<strong>Abstract:</strong> The proliferation of Malware on computer communication systems posed great security challenges to confidential data stored and other valuable substances across the globe. There have been several attempts in curbing the menace using a signature-based approach and in recent times, machine learning techniques have been extensively explored. This paper proposes a framework combining the exploit of both feature selections based on extra tree and random forest and eight ensemble techniques on five base learners- KNN, Naive Bayes, SVM, Decision Trees, and Logistic Regressio

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C, Sathish Kumar, and Thangaraju P. "OPTIMAL ENSEMBLE FEATURE SELECTION (OEFS) METHOD AND KERNEL WEIGHT CONVOLUTIONAL NEURAL NETWORK (KWCNN) CLASSIFIER FOR MEDICAL DATASETS." ICTACT Journal on Soft Computing 12, no. 3 (2022): 2657–67. http://dx.doi.org/10.21917/ijsc.2022.0380.

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Disease detection software that works automatically in healthcare domain refers to the proactive or reactive use of computerised data systems for diagnosis of diseases. Medical knowledge base, data processing, and data analytics are the three major components of the system. The procedures of data processing and data analytics are crucial. Data mining (DM) techniques were used to process these processes. DM is a tool for finding patterns in massive amounts of data and retrieving knowledge. Clinical and diagnostic evidence has created a slew of reliable timely detection services and other health

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Borowik, Piotr, Leszek Adamowicz, Rafał Tarakowski, Krzysztof Siwek, and Tomasz Grzywacz. "Odor Detection Using an E-Nose With a Reduced Sensor Array." Sensors 20, no. 12 (2020): 3542. http://dx.doi.org/10.3390/s20123542.

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Recent advances in the field of electronic noses (e-noses) have led to new developments in both sensors and feature extraction as well as data processing techniques, providing an increased amount of information. Therefore, feature selection has become essential in the development of e-nose applications. Sophisticated computation techniques can be applied for solving the old problem of sensor number optimization and feature selections. In this way, one can find an optimal application-specific sensor array and reduce the potential cost associated with designing new e-nose devices. In this paper,

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Dissertations / Theses on the topic "Optimization feature selections"

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Huang, Yu'e. "An optimization of feature selection for classification." Thesis, University of Ulster, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.428284.

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Issa, Tina. "Multiobjective optimization and feature selection in deep learning." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG056.

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Les avancées technologiques ont considérablement impacté l'analyse des données, en particulier avec l'essor du Big Data. L'apprentissage profond a émergé comme une solution puissante pour gérer la complexité et le volume des données. Les modèles profonds utilisent plusieurs niveaux d'abstraction pour extraire des motifs complexes. Leur efficacité a été démontrée dans diverses tâches, notamment la reconnaissance d'images.Cependant en génomique, les nouvelles techniques de séquençage produisent des quantités massives de données, où le nombre de variables dépasse largement le nombred'échantillons

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Cosson, Raphaël. "Multi-Objective Landscape Analysis and Feature-based Algorithm Selection." Electronic Thesis or Diss., Université de Lille (2022-....), 2023. http://www.theses.fr/2023ULILB038.

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Cette thèse porte sur l'analyse de paysage de problèmes d'optimisation combinatoires multi-objectifs. La résolution de tels problèmes est une tâche difficile, particulièrement en optimisation multi-objectif en raison de la nature contradictoire des objectifs. Ces situations apparaissent fréquemment dans de nombreux scénarios réels et constituent un véritable défi pour les algorithmes. Les approches de résolution reposent sur la découverte de solutions qui forment des compromis intéressants. Parmi ces approches, les algorithmes évolutionnaires s'avèrent particulièrement adaptés. Cependant, il e

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Lin, Lei. "Optimization methods for inventive design." Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAD012/document.

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La thèse traite des problèmes d'invention où les solutions des méthodes d'optimisation ne satisfont pas aux objectifs des problèmes à résoudre. Les problèmes ainsi définis exploitent, pour leur résolution, un modèle de problème étendant le modèle de la TRIZ classique sous une forme canonique appelée "système de contradictions généralisées". Cette recherche instrumente un processus de résolution basé sur la boucle simulation-optimisation-invention permettant d'utiliser à la fois des méthodes d'optimisation et d'invention. Plus précisément, elle modélise l'extraction des contractions généralisée

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Ruan, Tieming. "Selection and optimization of snap-fit features via web-based software." Columbus, Ohio : Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1133282089.

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Danielsson, Stefan. "Investigation of feature selection optimization for EEG signal analysis for monitoring a driver." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-29852.

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Electroencephalogram (EEG) is a well known, and well used method for studying brain activity, and it's possibilities have lately stretched into the car industry, were it's capabilities of detecting sleepiness in drivers are currently being put to the test. When performing EEG signal analysis on the brain, standardized signal bands exists that are characteristic to specific states of mind, such as when a driver is feeling sleepy. However, EEG as a method for studying the brain has major problems. The signal contains a lot of information that can be redundant or irrelevant, and the result is eas

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Mariello, Andrea. "Learning from noisy data through robust feature selection, ensembles and simulation-based optimization." Doctoral thesis, Università degli studi di Trento, 2019. https://hdl.handle.net/11572/367772.

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The presence of noise and uncertainty in real scenarios makes machine learning a challenging task. Acquisition errors or missing values can lead to models that do not generalize well on new data. Under-fitting and over-fitting can occur because of feature redundancy in high-dimensional problems as well as data scarcity. In these contexts the learning task can show difficulties in extracting relevant and stable information from noisy features or from a limited set of samples with high variance. In some extreme cases, the presence of only aggregated data instead of individual samples prevents th

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Mariello, Andrea. "Learning from noisy data through robust feature selection, ensembles and simulation-based optimization." Doctoral thesis, University of Trento, 2019. http://eprints-phd.biblio.unitn.it/3545/1/tesi_mariello.pdf.

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The presence of noise and uncertainty in real scenarios makes machine learning a challenging task. Acquisition errors or missing values can lead to models that do not generalize well on new data. Under-fitting and over-fitting can occur because of feature redundancy in high-dimensional problems as well as data scarcity. In these contexts the learning task can show difficulties in extracting relevant and stable information from noisy features or from a limited set of samples with high variance. In some extreme cases, the presence of only aggregated data instead of individual samples prevents th

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Liang, Wen. "Integrated feature, neighbourhood, and model optimization for personalised modelling and knowledge discovery." Click here to access this resource online, 2009. http://hdl.handle.net/10292/749.

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“Machine learning is the process of discovering and interpreting meaningful information, such as new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques” (Larose, 2005). From my understanding, machine learning is a process of using different analysis techniques to observe previously unknown, potentially meaningful information, and discover strong patterns and relationships from a large dataset. Professor Kasabov (2007b) classified computational models into

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Hedberg, Karolina. "Optimization of Insert-Tray Matching using Machine Learning." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-452871.

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The manufacturing process of carbide inserts at Sandvik Coromant consists of several operations. During some of these, the inserts are positioned on trays. For some inserts the trays are pre-defined but for others the insert-tray matching is partly improvised. The goal of this thesis project is to examine whether machine learning can be used to predict which tray to use for a given insert. It is also investigated which insert features are determining for the choice of tray. The study is done with insert and tray data from four blasting operations and considers a set of standardized inserts sin

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Books on the topic "Optimization feature selections"

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SLSFS 2005 (2005 Bohinj, Slovenia). Subspace, latent structure and feature selection: Statistical and Optimization Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005 : revised selected papers. Springer, 2006.

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Machine Learning for Mass Production and Industrial Engineering. Logos-Verlag Berlin, 2007.

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Shawe-Taylor, John, Steve Gunn, Craig Saunders, and Marko Grobelnik. Subspace, Latent Structure and Feature Selection: Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers. Springer London, Limited, 2006.

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(Editor), Craig Saunders, Marko Grobelnik (Editor), Steve Gunn (Editor), and John Shawe-Taylor (Editor), eds. Subspace, Latent Structure and Feature Selection: Statistical and Optimization Perspectives Workshop, SLSFS 2005Bohinj, Slovenia, February 23-25, 2005, ... Papers (Lecture Notes in Computer Science). Springer, 2006.

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Railsback, Steven F., and Bret C. Harvey. Modeling Populations of Adaptive Individuals. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691195285.001.0001.

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Ecologists now recognize that the dynamics of populations, communities, and ecosystems are strongly affected by adaptive individual behaviors. Yet until now, we have lacked effective and flexible methods for modeling such dynamics. Traditional ecological models become impractical with the inclusion of behavior, and the optimization approaches of behavioral ecology cannot be used when future conditions are unpredictable due to feedbacks from the behavior of other individuals. This book provides a comprehensive introduction to state- and prediction-based theory, or SPT, a powerful new approach t

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Grant, Stuart A., and David B. Auyong. Basic Principles of Ultrasound Guided Nerve Block. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190231804.003.0001.

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This chapter provides a clinical description of ultrasound physics tailored to provide the practitioner a solid background for optimal imaging and needle guidance technique during regional anesthesia. Important ultrasound characteristics are covered, including optimization of ultrasound images, transducer selection, and features found on most point-of-care systems. In-plane and out-of-plane needle guidance techniques and a three-step process for visualizing in-plane needle insertions are presented. Next, common artifacts and errors including attenuation, dropout, and intraneural injection are

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Book chapters on the topic "Optimization feature selections"

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Niu, Gang. "Feature Selection Optimization." In Data-Driven Technology for Engineering Systems Health Management. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2032-2_6.

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Barbiero, Pietro, Giovanni Squillero, and Alberto Tonda. "Predictable Features Elimination: An Unsupervised Approach to Feature Selection." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95467-3_29.

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Lohrmann, Christoph, and Pasi Luukka. "Using Clustering for Supervised Feature Selection to Detect Relevant Features." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37599-7_23.

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Vieira, Susana M., João M. C. Sousa, and Uzay Kaymak. "Ant Feature Selection Using Fuzzy Decision Functions." In Fuzzy Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2_16.

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Salcedo-Sanz, Sancho, Mario Prado-Cumplido, Fernando Pérez-Cruz, and Carlos Bousoño-Calzón. "Feature Selection via Genetic Optimization." In Artificial Neural Networks — ICANN 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_89.

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Hu, Zhiwei, Zhaogong Zhang, Zongchao Huang, Dayuan Zheng, and Ziliang Zhang. "Feature Selection Based on Graph Structure." In Combinatorial Optimization and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36412-0_23.

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Bommert, Andrea, and Jörg Rahnenführer. "Adjusted Measures for Feature Selection Stability for Data Sets with Similar Features." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64583-0_19.

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Zhang, Zhihong, Lu Bai, Yuanheng Liang, and Edwin R. Hancock. "Unsupervised Feature Selection by Graph Optimization." In Image Analysis and Processing — ICIAP 2015. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23231-7_12.

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Rakhshani, Hojjat, Lhassane Idoumghar, Julien Lepagnot, and Mathieu Brévilliers. "From Feature Selection to Continuous Optimization." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45715-0_1.

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Bello, Rafael, Amilkar Puris, Rafael Falcón, and Yudel Gómez. "Feature Selection through Dynamic Mesh Optimization." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85920-8_43.

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Conference papers on the topic "Optimization feature selections"

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Ing, David, Said Jabbour, Lakhdar Sais, and Fabien Delorme. "LAD-based Feature Selection for Optimal Decision Trees and Other Classifiers." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/81.

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The curse of dimensionality presents a significant challenge in data mining, pattern recognition, computer vision, and machine learning applications. Feature selection is a primary approach to address this challenge. It aims to eliminate irrelevant and redundant features while preserving the relevant ones to reduce computation time, improve prediction performance, and enhance the understanding of data. In this paper, we introduce a new feature selection (FS) technique based on the Logical Analysis of Data (LAD), a pattern learning framework that combines optimization, Boolean functions, and co

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Zhang, Ping, Guanglei Wang, and Lingshu Kong. "Feature Selection Based on Streaming Labels Considering the Division of the Selected Feature Set." In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, 2024. http://dx.doi.org/10.1109/docs63458.2024.10704435.

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Pan, Jeng-Shyang, Longkang Yue, and Shu-Chuan Chu. "Binary Gannet Optimization Algorithm for Feature Selection Problem." In 2024 11th International Conference on Machine Intelligence Theory and Applications (MiTA). IEEE, 2024. http://dx.doi.org/10.1109/mita60795.2024.10751686.

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Putcha, Dr Poorna Priya, Dr P. Pavitra, Lakshmi Amulya Chellapilla, Vasantha Dhuli, Uma Malleswari Boddeti, and Sandhya Atmakuri. "Feature Selection and Optimization for Cardiovascular Disease Prediction." In First International Conference on Computer, Computation and Communication (IC3C-2025). River Publishers, 2025. https://doi.org/10.13052/rp-9788743808268a068.

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Rafi, Mohammad Abdus Shahid, Volkan Senyurek, John E. Ball, and Ali C. Gurbuz. "Comparative analysis of feature selection techniques to identify a set of optimal features for crop yield estimation using UAS-based multisensor data." In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X, edited by Christoph Bauer and J. Alex Thomasson. SPIE, 2025. https://doi.org/10.1117/12.3054110.

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Güllü, Merve, Nurefşan A K, and Reyhan Dede. "Enhanced Churn Prediction in Telecom with PSO-Based Feature Selection." In 2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA). IEEE, 2025. https://doi.org/10.1109/ichora65333.2025.11017185.

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Korkmaz, Mehmet, Ozgur Koray Sahingoz, and Banu Diri. "Feature Selections for the Classification of Webpages to Detect Phishing Attacks: A Survey." In 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, 2020. http://dx.doi.org/10.1109/hora49412.2020.9152934.

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Ming, Di, and Chris Ding. "Robust Flexible Feature Selection via Exclusive L21 Regularization." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/438.

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Recently, exclusive lasso has demonstrated its promising results in selecting discriminative features for each class. The sparsity is enforced on each feature across all the classes via L12-norm. However, the exclusive sparsity of L12-norm could not screen out a large amount of irrelevant and redundant noise features in high-dimensional data space, since each feature belongs to at least one class. Thus, in this paper, we introduce a novel regularization called "exclusive L21", which is short for "L21 with exclusive lasso", towards robust flexible feature selection. The exclusive L21 regulariza

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Corneo, Andrea, Mauro Suardi, Lorenzo Lancia, Francesco Cannarile, and Alessandra Fidanzi. "A Business-Oriented Feature Selection Method for Enhancing Machine Learning Based Digital Tools Adoption in the Energy Industry." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211601-ms.

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Abstract Objectives/Scope To ensure that predictive models based on Machine Learning (ML) techniques can be fully exploited by energy plant operators, these should be trained on "actionable" features, i.e., a set of features that are explainable and on which operators can directly act to take mitigative and/or preventive actions. To this aim, we have developed a business-oriented feature selection approach which identifies actionable features while optimizing the model predictive capability at the same time. Methods, Procedures, Process We have developed a solution which frames the feature sel

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Zhang, Li, Xiangru Meng, Weijia Wu, and Hua Zhou. "Network Fault Feature Selection Based on Adaptive Immune Clonal Selection Algorithm." In 2009 International Joint Conference on Computational Sciences and Optimization, CSO. IEEE, 2009. http://dx.doi.org/10.1109/cso.2009.342.

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Reports on the topic "Optimization feature selections"

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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k

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Tsur, Yacov, David Zilberman, Uri Shani, Amos Zemel, and David Sunding. Dynamic intraseasonal irrigation management under water scarcity, water quality, irrigation technology and environmental constraints. United States Department of Agriculture, 2007. http://dx.doi.org/10.32747/2007.7696507.bard.

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In this project we studied optimal use and adoption of sophisticated irrigation technologies. The stated objectives in the original proposal were to develop a conceptual framework for analyzing intra-season timing of water application rates with implications for crop and irrigation technology selection. We proposed to base the analysis on an intra-seasonal, dynamic, agro-economic model of plants' water demand, paying special attention to contamination of groundwater and soil in intensively cultivated areas that increasingly rely on water of lesser quality. The framework developed in the projec

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