{"id":11,"date":"2020-05-18T06:20:56","date_gmt":"2020-05-18T06:20:56","guid":{"rendered":"http:\/\/www.eng.biu.ac.il\/singerg\/?page_id=11"},"modified":"2026-04-15T05:56:07","modified_gmt":"2026-04-15T05:56:07","slug":"publications","status":"publish","type":"page","link":"https:\/\/www.eng.biu.ac.il\/singerg\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n<p><strong>Refereed Papers<\/strong><\/p>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Ben-Gal, I., &amp; Singer, G. (2004). Statistical process control via context modeling of finite-state processes: An application to production monitoring. <em>IIE Transactions<\/em>, 36(5): 401-415.<\/li>\n\n\n\n<li>Singer G., &amp; Ben-Gal I. (2007). The funnel experiment: The Markov-based SPC Approach. <em>Quality and Reliability Engineering<\/em>, 23(8): 899-913.<\/li>\n\n\n\n<li>Singer G., &amp; Khmelnitsky E. (2007). Optimal control of a stochastic system: State-costate analysis. <em>The IASTED Int'l. Conf.<\/em> <em>Modelling, Identification, and Control<\/em> (<em>MIC 2007<\/em>). Innsbruck, Austria.<\/li>\n\n\n\n<li>Khmelnitsky E., &amp; Singer G. (2009). A stochastic inventory control problem with reputation-dependent demand. <em>Proc. 13th IFAC Sym. on Information Control Problems in Manufacturing<\/em>. Moscow, Russia., 3-5 June 2009.<\/li>\n\n\n\n<li>Singer G., &amp; Khmelnitsky E. (2010). A finite-horizon, stochastic optimal control policy for a production-inventory system with backlog-dependent lost sales. <em>IIE Transactions, <\/em>42(12): 855-864.<\/li>\n\n\n\n<li>Apartsin Y., Maymon Y., Cohen Y., &amp; Singer G. (2013). Nationality and risk attitude: Testing differences and similarities of investors' behavior in selected financial markets. <em>Global Finance Journal<\/em>, 24(2): 114-118.<\/li>\n\n\n\n<li>Singer G., Golan M., &amp; Cohen, Y. (2014). From product documentation to a 'method prototype' and standard times: A new technique for complex manual assembly. <em>International journal of Production Research, <\/em>52(2): 507-520.<\/li>\n\n\n\n<li>Ben-Gal I., Dana A., Shkolnik N., &amp; Singer G. (2014). Efficient construction of decision trees by the dual information distance method. <em>Quality Technology &amp; Quantitative Management<\/em>, 11(1): 133-147.<\/li>\n\n\n\n<li>Khmelnitsky E., &amp; Singer, G. (2015). An optimal inventory management problem with reputation-dependent demand. <em>Annals of Operations Research<\/em>, 231(1): 305-316.<\/li>\n\n\n\n<li>Ben-Gal H., I., Pessach D., Avrahami D., &amp; Singer G. (2018). Addressing the growing complexity of global work through human resources analytics: Hidden patterns in turnover. <em>The 34th European Group for Organizational Studies (EGOS) Colloquium<\/em>. Tallinn, Estonia.<\/li>\n\n\n\n<li>Cohen Y., Golan M., Singer G., &amp; Maurizio F. (2018). Workstation\u2013Operator interaction in 4.0 Era: WOI 4.0. <em>Proc. 16th IFAC <\/em>(<em>Int'l. Federation of Automatic Control<\/em>)<em> Sym. on Information Control Problems in Manufacturing<\/em>. Bergamo, Italy.<\/li>\n\n\n\n<li>Ben-Gal I., Weinstock S., Singer G., &amp; Bambos N. (2019). Clustering users by their mobility behavioral patterns. <em>ACM Transactions on Knowledge Discovery from Data, <\/em>13(4), 45<\/li>\n\n\n\n<li>Rabin N., Golan M., Singer G., &amp; Kleper D. (2019). Modeling and Analysis of Students\u2019 Performance Trajectories using Diffusion Maps and Kernel Two-Sample Tests. <em>Engineering Applications of Artificial Intelligence<\/em>, 85: 492-503.<\/li>\n\n\n\n<li>Singer, G., &amp; Golan, M. (2019). Identification of subgroups of terror attacks with shared characteristics for the purpose of preventing mass-casualty attacks: a data-mining approach.&nbsp;<em>Crime Science<\/em>,&nbsp;8(1), 14.<\/li>\n\n\n\n<li>Singer G., Golan M., Rabin N., &amp; Kleper D. (2020). Evaluation of the effect of learning disabilities and accommodations on the prediction of the stability of academic behavior of undergraduate engineering students using decision trees. <em>European Journal of Engineering Education<\/em>, 45(4), 614-630.<\/li>\n\n\n\n<li>Golan, M., Cohen, Y., &amp; Singer, G. (2020). A Framework for Operator - Workstation Interaction in Industry 4.0. <em>International Journal of Production Research<\/em>, 58(8), 2421-2432.<\/li>\n\n\n\n<li>Singer, G., Anuar, R., &amp; Ben-Gal, I. (2020). A Weighted Information-Gain Measure for Ordinal Classification Trees.&nbsp;<em>Expert Systems with Application<\/em>s, 152, DOI: <a rel=\"noreferrer noopener\" href=\"https:\/\/doi.org\/10.1016\/j.eswa.2020.113375\" target=\"_blank\">10.1016\/j.eswa.2020.113375<\/a>.<\/li>\n\n\n\n<li>Golan, M., Singer, G., Rabin, N., &amp; Kleper D. (2020). Integrating actual time usage into assessment of examination provided to disabled college engineering students. <em>Assessment &amp; Evaluation in Higher Education<\/em>, DOI: <a href=\"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/02602938.2020.1717434\">10.1080\/02602938.2020.1717434<\/a>.<\/li>\n\n\n\n<li>Pessach, D., Singer, G., Avrahami, D., Chalutz Ben-Gal, H., Shmueli, E., &amp; Ben-Gal I. (2020). Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. <em>Decision Support Systems<\/em>, 134, DOI:&nbsp;<a href=\"https:\/\/doi.org\/10.1016\/j.dss.2020.113290\">10.1016\/j.dss.2020.113290<\/a>.<\/li>\n\n\n\n<li>Singer, G., &amp; Cohen, I. (2020). An Objective-Based Entropy Approach for Interpretable Decision Tree Models in Support of Human Resource Management : The Case of Absenteeism am Work.  <em>Entropy<\/em>, 22(8), 821. DOI: 10.3390\/e22080821.<\/li>\n\n\n\n<li>Singer, G. &amp; Marudi, M. (2020). Ordinal Decision-Tree-Based Ensemble Approaches: The Case of Controlling the Daily Local Growth Rate of the COVID-19 Epidemic.&nbsp;<em>Entropy,<\/em>&nbsp;<em>22<\/em>, 871.<\/li>\n\n\n\n<li>Singer, G., &amp; Khmelnitsky E. (2021). A production-inventory problem with price-sensitive demand. <em>Applied Mathematical Modelling, <\/em>89(1), 688-699. <\/li>\n\n\n\n<li>Singer, G., &amp; Golan, M. (2021). &nbsp;Applying data mining algorithms to encourage mental health disclosure in the workplace. <em>International Journal of Business Information Systems<\/em>, 36(4), 553-571.<\/li>\n\n\n\n<li>Singer, G., Ratnovsky A., &amp; Nafali S. (2021). Classification of severity of trachea stenosis from EEG signals using ordinal decision-tree based algorithms and ensemble-based ordinal and non-ordinal algorithms. <em>Expert Systems with Application<\/em>s. <a href=\"https:\/\/doi.org\/10.1016\/j.eswa.2021.114707\">https:\/\/doi.org\/10.1016\/j.eswa.2021.114707<\/a>.<\/li>\n\n\n\n<li>Cohen, Y., &amp; Singer, G. (2021). A Smart Process Controller Framework for Industry 4.0 Settings. <em>Journal of Intelligent Manufacturing. <\/em><a href=\"https:\/\/doi.org\/10.1007\/s10845-021-01748-5\">https:\/\/doi.org\/10.1007\/s10845-021-01748-5<\/a>.<\/li>\n\n\n\n<li>Singer G., Cohen, Y. (2021). A framework for smart control using machine-learning modeling for processes with closed-loop control in Industry 4.0. <em>Engineering Applications of Artificial Intelligence. <\/em><a rel=\"noreferrer noopener\" href=\"https:\/\/doi.org\/10.1016\/j.engappai.2021.104236\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.engappai.2021.104236<\/a>.<\/li>\n\n\n\n<li>Chalutz Ben-Gal, H., Avrahami, D., Singer G., Pessach D., &amp; Ben-Gal., I. (2021).&nbsp; Examination of Turnover : HR Analytics Perspective.&nbsp; <em>The European Academy of Management Online Conference<\/em>.<\/li>\n\n\n\n<li>Chalutz Ben-Gal, H., Avrahami, D., Singer G., Pessach D., &amp; Ben-Gal., I. (2021). Artificial Intelligence at Work: Examination of Turnover: HR Analytics Prespective. <em>The 37<sup>th<\/sup> EGOS Online Colloquium: European Group for Organizational Studies. <\/em><\/li>\n\n\n\n<li>Chalutz Ben-Gal, H., Avrahami, D., Pessach D. &amp; Singer G. (2021). A Machine Learning Examination of Turnover: Hidden Patterns and New Insights. <em>The Virtual 81<sup>st<\/sup> Annual Meeting of the Academy of Management. <\/em><strong>Best Paper. <\/strong><\/li>\n\n\n\n<li>Chalutz Ben-Gal H., Forma I., &amp; Singer G. (2022). A flexible employee recruitment and compensation model: A bi-level optimization approach. <em>In press: Computers &amp; Industrial Engineering.<\/em><\/li>\n\n\n\n<li>Avrahami, D., Pessach, D., Singer, G., &amp; Chalutz Ben-Gal H. (2022). A human resources analytics and machine-learning examination of turnover: implications for theory and practice.&nbsp;<em>In press: International Journal of Manpower<\/em>.<\/li>\n\n\n\n<li>Singer, G., Golan, M., Shiff, R., &amp; Kleper, D. (2022). Evaluating the Effectiveness of Accommodations Given to Students with Learning Impairments: Ordinal and Interpretable Machine Learning-Based Methodology.&nbsp; <em>In press: <\/em>&nbsp;<em>IEEE Transactions on Learning Technologies.<\/em><\/li>\n\n\n\n<li>Rabin, N., Singer, G., Friedman, S., Hedvat, O., &amp; Ben-Mayor C. (2022). Graph-based Extreme Feature Selection for Multi-class Classification Tasks. <em>INFORMS Workshop on Data Science. <\/em>&nbsp;Indianapolis, Indiana, USA. <\/li>\n\n\n\n<li>Abukasis Shifman, D., Cohen, I., Huang, K., Xiaochen, X., &amp; Singer G. (2023). An adaptive machine learning algorithm for the resource-constrained classification problem. <em>Engineering Applications of Artificial Intelligence.<\/em><\/li>\n\n\n\n<li>Haba, R., Singer, G., Naftali, S., Kramer R., M., &amp; Ratnovsky, A. (2023). A remote and personalised novel approach for monitoring asthma severity levels from EEG signals utilizing classification algorithms. <em>Expert Systems with Application<\/em>s.<\/li>\n\n\n\n<li>Volk, O., Ratnovsky, A., Naftali, S., &amp; Singer, G. (2023). Classification of Tracheal Stenosis with Asymmetric Misclassification Errors from EMG Signals Using an Adaptive Cost-Sensitive Learning Method. <em>Biomedical Signal Processing and Control.<\/em><\/li>\n\n\n\n<li>Khmelnitsky, E., &amp; Singer, G. (2023). Optimal Real Estate Pricing and Offer Acceptance Strategy.&nbsp;<em>IEEE Access<\/em>.<\/li>\n\n\n\n<li>\u200fVolk, O., &amp; Singer, G. (2024). An adaptive cost-sensitive learning approach in neural networks to minimize local training\u2013test class distributions mismatch.&nbsp;<em>Intelligent Systems with Applications<\/em>, 200316.<\/li>\n\n\n\n<li>\u200fRabkin, L., Cohen, I., &amp; Singer, G. (2024). Resource allocation in ordinal classification problems: A prescriptive framework utilizing machine learning and mathematical programming.&nbsp;Engineering Applications of Artificial Intelligence,&nbsp;132, 107914.<\/li>\n\n\n\n<li>\u200fDanino-Levi, M., Goldberg, T., Keter, M., Akselrod, N., Shprach-Buaron, N., Safra, M., Singer, G., &amp; Alon, S. (2024). Computational analysis of super-resolved in situ sequencing data reveals genes modified by immune-tumor contact events.&nbsp;<em>RNA<\/em>, rna-079801.<\/li>\n\n\n\n<li>\u200fLevin, D., &amp; Singer, G. (2024). GB-AFS: graph-based automatic feature selection for multi-class classification via mean simplified silhouette. <em>Journal of Big Data.<\/em><\/li>\n\n\n\n<li>Marudi, M., Ben-Gal, I., &amp; Singer, G. (2024). A Decision-Tree-Based Method for Ordinal Classification Problems. <em>IISE Transactions<\/em>, 56(9), 960-974.<\/li>\n\n\n\n<li>Levin, D., &amp; Singer G., (2025). Optimizing Multi-Sensor Systems: A Case Study of Graph-Based Automatic Feature Selection Method. <em>Industrial Engineering and Applications\u2013Europe: 12th International Conference, ICIEA-EU 2024, <\/em>Munich, Germany.<\/li>\n\n\n\n<li>Shifman, D. A., Margolin, I., Halfi, C., &amp; Singer, G., (2025)<em>. <\/em>Classification Tasks with Local and Global Resource Allocation Constraints<em>.&nbsp;IFAC-PapersOnLine,&nbsp;<\/em>59(1), 61-66.<\/li>\n\n\n\n<li>\u200fLevin, D., &amp; Singer, G. (2025). Graph-Based Feature Selection Method Under Budget Constraint for Multiclass Classification Problems.\u00a0<em>INFORMS Journal on Data Science<\/em>.<\/li>\n\n\n\n<li>\u200fForma, I. A., Singer, I., Singer, G., Bukchin, Y., &amp; Chalutz-BenGal, H. (2026). A bi-level optimization decision support tool for maximizing company profit through employee retention.\u00a0<em>Heliyon<\/em>,\u00a0<em>12<\/em>(6).\u200f<\/li>\n<\/ul>\n\n\n\n<p><strong>Book Chapters<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Khmelnitsky E., &amp; Singer G. (2005). A stochastic optimal control policy for a manufacturing system on a finite time horizon. <em>Optimal Control and Dynamic Games: Applications in Finance, Management Science and Economics. <\/em>Christophe Deissenberg, Richard F. Hartl (Eds.). Part of the Advances in Computational Management Science Book Series (AICM), 7: 275-288. [ISBN: 978-0-387-25804-1]<\/li>\n\n\n\n<li>Cohen Y., Singer G., Golan M., &amp; Goren-Bar D. (2013). Automating the transformation from a prototype to a method of assembly. <em>Innovations and Advances in Computer, Information, Systems Sciences, and Engineering Part I, <\/em>Khaled Elleithy, Tarek Sobh (Eds.). Lecture Notes in Electrical Engineering, 152: 99-106. Springer, New York, NY. [ISBN: 978-1-4614-3534-1]<\/li>\n<\/ul>\n\n\n\n<p><strong>Oral presentation at scientific Conferences<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Singer G., &amp; Ben-Gal I. (2000). An information theoretic approach to statistical process control of autocorrelated data. <em>Industrial Engineering and Management Conf<\/em>. Israel.<\/li>\n\n\n\n<li>Ben-Gal &nbsp;I., &amp; Singer G. (2001). A methodology for integrating engineering process control and statistical process control. <em>The 16th Int'l. Conf. on Production Research<\/em> (<em>ICPR<\/em>-16). Prague, Czech Republic.<\/li>\n\n\n\n<li>Singer G., &amp; Khmelnitsky E. (2006). Time-dependent hedging policies for optimal production control under reputation dependent demand. <em>The Annual ORSIS <\/em>(<em>Operation Research Society of Israel<\/em>) <em>Conf<\/em>. Nahariya, Israel.<\/li>\n\n\n\n<li>Halevi, R. Atzmoni, A. Bar-Moshe S., Cohen I., Ben-Nun M., Kaplan N., Ishbir C. &amp; Singer G. (2007). Using predictive maintenance concepts for structure integrity in the IAF. <em>US Air Force \u2013 The Aircraft Structural Integrity Program <\/em>(<em>USAF ASIP<\/em>)<em> Conf<\/em>. Palm Springs, California, USA.<\/li>\n\n\n\n<li>Khmelnitsky E., &amp; Singer G. (2008). An inventory management model with reputation-dependent demand. <em>INFORMS Annual Meeting<\/em>. Washington D.C., USA.<\/li>\n\n\n\n<li>Singer G., &amp; Ben-Gal I. (2008). The funnel experiment: A Markov-based SPC approach. <em>Industrial Engineering and Management Conf<\/em>. Israel. <\/li>\n\n\n\n<li>Kaplan N., Singer G., &amp; Cohen I. (2008). Using business intelligence system for implementation of the maintenance concept in the Israeli Air Force. <em>Industrial Engineering and Management Conf<\/em>. Israel.<\/li>\n\n\n\n<li>Kedem D., Singer G., &amp; Sabbah G. (2008). Using the SPC method to define and monitor the cycle time in an IAF factory for improvement of aircraft availability.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <em>Industrial Engineering and Management Conf.<\/em> Israel.<\/li>\n\n\n\n<li>Almozlinos B., Khmelnitsky E., &amp; Singer G. (2009). Optimal stopping problem: Dynamic pricing of an asset. <em>The Annual ORSIS<\/em> (<em>Operation Research Society of Israel<\/em>) <em>Conf. <\/em>Herzliya.<\/li>\n\n\n\n<li>Ben-Gal I., &amp;, Singer G. Classification of defectives products via a pattern-based model. <em>INFORMS Annual Meeting<\/em>. San Diego, California, USA.<\/li>\n\n\n\n<li>Khmelnitsky E., &amp; Singer G. (2010). A stochastic optimal control policy for a production-inventory system with lost sales. <em>The 8th Int'l. Conf. on Optimization: Techniques and Applications.<\/em> Shanghai, China, December 2010<\/li>\n\n\n\n<li>Kedem D., Ben-Gal I., &amp; Singer G. (2010). Automatic fault identification in the presence of noise via rough set theory and error correcting codes. <em>Industrial Engineering and Management Conf. <\/em>Israel.<\/li>\n\n\n\n<li>Singer G, Ben-Gal I., &amp; Anuar R. (2010). Automatic fault identification via variable-order Bayesian networks <em>Industrial Engineering and Management Conf. <\/em>Israel. <\/li>\n\n\n\n<li>Ben-Gal I., Kedem D., &amp; Singer G. (2011). Sensoring design via rough set theory and error correcting codes. <em>European Network for Business and Industrial Statistics and DEsign of INDustrial Experiments<\/em>. Torino, Italy.<\/li>\n\n\n\n<li>Ben-Gal I., &amp; Singer G. (2011). Demand sensing via C-B4 pattern analysis and SAS\u00ae forecast server. <em>Proc. SAS Global Forum. <\/em>Las Vegas, Nevada, USA.<\/li>\n\n\n\n<li>Cohen Y., Singer G., Golan M., &amp; Goren-Bar D. (2011). Automating the transformation from a prototype to a method of assembly. <em>Int'l. Conf. on Industrial Electronics, Technology and Automation <\/em>(<em>IETA 11<\/em>).<\/li>\n\n\n\n<li>Khmelnitsky E., &amp; Singer G. (2012). Dynamic pricing of an asset in an unstable market. <em>Industrial Engineering and Management Conf<\/em>.&nbsp; Israel. <\/li>\n\n\n\n<li>Golan M. Singer G., &amp; Goren-Bar D. (2012). Using manual assembly instructions to generate standard times. <em>Industrial Engineering and Management Conf. <\/em>Israel. <\/li>\n\n\n\n<li>Maymon Y., Singer G., &amp; Apartsin Y. (2012). Nationality and attitude toward risk: A quantitative study using machine learning techniques and historical stock prices. <em>Industrial Engineering and Management Conf. <\/em>Israel.<\/li>\n\n\n\n<li>Cohen Y., Singer G., Golan M., &amp;&nbsp; Goren-Bar D. (2012). Using assembly instructions for generating standard times and automation requirements. Int'l. Conf. on Flexible Automation and Manufacturing (FAIM). Stockholm, Sweden.<\/li>\n\n\n\n<li>Singer G., Golan M., Kleper D., Kedar S., &amp;&nbsp; Rabin N. (2014). Using data mining algorithms to evaluate the effectiveness of adjustments given to engineering and science students with learning disabilities. <em>Industrial Engineering and Management Conf. <\/em>Israel.<\/li>\n\n\n\n<li>Chalutz H., &amp; Singer G. (2014). Some thoughts on human resources analytics. <em>INFORMS Annual Meeting<\/em>.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; San Francisco, USA.<\/li>\n\n\n\n<li>Golan M., Rabin N., Singer G, &amp; Kleper D. (2015). The effectiveness of extended time in tests provided to students with learning disabilities.&nbsp;&nbsp;&nbsp;&nbsp; <em>The 14th European Congress of Psychology. <\/em>Milan, Italy.<\/li>\n\n\n\n<li>Singer G. (2016). Predictive maintenance using machine learning techniques. <em>5th PHM Israel Conf. <\/em>Tel-Aviv, Israel.<\/li>\n\n\n\n<li>Pessach D., Singer G., Avrahami D. Ben-Gal I., &amp; Ben-Gal H. (2017). A two-step method for a granular job placement optimization. <em>Industrial Engineering and Management Conf. <\/em>Israel.<\/li>\n\n\n\n<li>Pesach D., Singer G., Avrahami D., Ben-Gal H., Shmueli E., &amp; Ben-Gal I. (2019). Using Machine Learning for Employees\u2019 Recruitment &amp; Placement. <em>Industrial Engineering and Management Conf. <\/em>Israel.<\/li>\n\n\n\n<li>Cohen Y., &amp; Singer G., (2020). Industry 4.0 Methodological Framework for Process Control and Maintenance with Embedded AI. <em>The 9th Israeli Industrial Engineering and Management Research Conf<\/em>., Israel.<\/li>\n\n\n\n<li>Marudi M., &amp; Singer G., (2020). Affecting factors on local Cov19 pandemic growth rates via new ordinal decision-tree based algorithms. <em>INFORMS Annual Meeting<\/em>. Digital meeting via ZOOM.<\/li>\n\n\n\n<li>Singer G., &amp; Golan M., (2020). Evaluation of the effect of including parameters related to learning disabilities and extended exam time on the prediction of the exam performance of undergraduate engineering students: Ordinal decision-tree based algorithms. <em>International Conference on Educational Data Mining<\/em>. Digital meeting via ZOOM.<\/li>\n\n\n\n<li>Golan M., &amp; Singer G., (2020). Multi-dimensional evaluation of academic performance using Ensemble learning approach based on ordinal algorithms. <em>International Conference on Educational Data Mining<\/em>. Digital meeting via ZOOM.<\/li>\n\n\n\n<li>Khmelnitsky E., &amp; Singer G., (2021). An optimal policy for a production control problem with uncertain price-sensitive demand. In&nbsp;<em>the POMS 31st Annual Conference<\/em>.<\/li>\n\n\n\n<li>Rabkin L., Cohen Reuven I., &amp; Singer G., (2021). An analytic approach for classification problems with resource constraints via cost-sensitive learning and mathematical programming. <em>Industrial Engineering and Management Conf<\/em>. Israel.<\/li>\n\n\n\n<li>Forma I., Chalutz Ben-Gal H., &amp; Singer G., (2021). An employee recruitment and compensation model for the post COVID-19: A bi-level optimization approach.  <em>Industrial Engineering and Management Conf<\/em>. Israel.<\/li>\n\n\n\n<li>Haba R., Ratnovsky A. Naftali S., &amp; Singer G., (2021). A non-invasive and remote personalized monitoring approach for asthma disease using EEG and ordinal algorithms. <em>Industrial Engineering and Management Conf<\/em>. Israel. <\/li>\n\n\n\n<li>Haba R., Ratnovsky A, Naftali S., Kremer M., &amp; Singer G., (2021). A remote and personalized approach for monitoring Asthma severity level from EEG signals utilizing novel classification algorithms. <em>Israeli Association for Medical Informatics. <\/em>Israel.<\/li>\n\n\n\n<li>Forma I., Chalutz Ben-Gal H., &amp; Singer G., (2022). Flexible employee recruitment and retention models via flexible work plans: A bi-level optimization approach. 32nd European Conference on Operational Research (EURO 2022). Espoo, Finland.<\/li>\n\n\n\n<li>Ratnovsky A., Haba R., Singer G., Kramer M.,&nbsp; &amp; Naftali S., (2022). Asthma severity levels monitoring based on EEG signals using novel classification algorithms. 27<sup>th<\/sup> Congress of the European Society of Biomechanics. Porto, Portugal.<\/li>\n\n\n\n<li>Singer G., &amp; Volk O., (2022). Adaptive Cost-Sensitive Approach in Neural Networks for Medical Applications. <em>INFORMS Annual Meeting<\/em>. Indianapolis, Indiana, USA.<\/li>\n\n\n\n<li>Forma I., Singer I., Bukchin Y., &amp; Singer G., (2023). Hila Chalutz Ben-Gal A flexible work arrangement for employee: A bi-level optimization approach. <em>Industrial Engineering<\/em><em> and Management Conf<\/em>. Israel.<\/li>\n\n\n\n<li>Volk O, &amp; Singer G., (2023). Adaptive cost-sensitive learning approach in neural networks to minimize local training-test class distribution mismatch. <em>Industrial Engineering<\/em><em> and Management Conf<\/em>. Israel.<\/li>\n\n\n\n<li>Abukasis Shifman D., Cohen, I., Huang, K., Xian, X., &amp; Singer G., (2023). An adaptive machine learning algorithm for the resource-constrained classification problem. <em>Industrial Engineering<\/em><em> and Management Conf<\/em>. Israel.<\/li>\n\n\n\n<li>Abukasis Shifman D., Ben-Mayor C., Margolin I., &amp; Singer G., (2024). A-Two-Phase Classification &amp; Optimization Model with Limited Human Resource Allocation. <em>INFORMS Conference on Quality, Statistics and Reliability (ICQSR). <\/em>Milano, Italy.<\/li>\n\n\n\n<li>Singer G., Abukasis Shifman D., Ben-Mayor C., &amp; Margolin I., (2024). A Hybrid Cost-Sensitive Machine Learning and Optimization Models for the Resource-Constrained Classification Problem. <em>INFORMS Annual Meeting<\/em>. Seattle, Washington, USA.<\/li>\n\n\n\n<li>Naftali, S., Volk, O., Ratnovsky, A., &amp; Singer, G.,<strong> <\/strong>(2024). An Approach for classifying Tracheal Stenosis based on EMG signals utilizing an Adaptive Cost-Sensitive Learning method with Asymmetric Misclassification Errors. <em>INFORMS Annual Meeting<\/em>. Seattle, Washington, USA.<\/li>\n<\/ul>\n\n\n\n<p><strong>Patents<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Ben-Gal I., Shmilovici A., Morag G, &amp; Singer G. Stochastic modeling of spatial distributed sequences. Int'l. Publication No. WO 02\/067075 A3, Int'l. Publication Date: 29 August 2002<\/li>\n\n\n\n<li>Ben-Gal I. Shmilovici A., Morag G., &amp; Singer G. Stochastic modeling of time distributed sequences.&nbsp; Publication No. US 2003\/0061015 A1, Publication Date: 27 March 2003.<\/li>\n\n\n\n<li>Anuar A., Singer G., &amp; Cohen N.L. System, method and computer program product for data analysis Publication No. WO\/2017\/168410, Publication Date: 5 October 2017<\/li>\n\n\n\n<li>Singer G., &amp; Anuar R. A risk-based weighted information-gain measure for ordinal classification decision-trees&nbsp; US Provisional Patent Application No.62\/784,856, 26 December 2018.<\/li>\n\n\n\n<li>Singer G. Adaptive cost-sensitive learning in neural networks for misclassification cost problems US Provisional Patent Application No.63\/157,829, 8 March 2021.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Refereed Papers Book Chapters Oral presentation at scientific Conferences Patents<\/p>\n","protected":false},"author":90,"featured_media":0,"parent":0,"menu_order":2,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-11","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.eng.biu.ac.il\/singerg\/wp-json\/wp\/v2\/pages\/11"}],"collection":[{"href":"https:\/\/www.eng.biu.ac.il\/singerg\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.eng.biu.ac.il\/singerg\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.eng.biu.ac.il\/singerg\/wp-json\/wp\/v2\/users\/90"}],"replies":[{"embeddable":true,"href":"https:\/\/www.eng.biu.ac.il\/singerg\/wp-json\/wp\/v2\/comments?post=11"}],"version-history":[{"count":54,"href":"https:\/\/www.eng.biu.ac.il\/singerg\/wp-json\/wp\/v2\/pages\/11\/revisions"}],"predecessor-version":[{"id":311,"href":"https:\/\/www.eng.biu.ac.il\/singerg\/wp-json\/wp\/v2\/pages\/11\/revisions\/311"}],"wp:attachment":[{"href":"https:\/\/www.eng.biu.ac.il\/singerg\/wp-json\/wp\/v2\/media?parent=11"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}