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Connecting Dots

Selected Publications:

Internet, Networks & Computation

  • Shalev Y.*, Painsky A., and Ben-Gal I. (2022), Neural Joint Entropy Estimation, Accepted to IEEE Transactions on Neural Network and Learning Systems (TNNLS), 2022, doi: 10.1109/TNNLS.2022.3204919.(Journal) (pdf file)

  • Cohen, D., Naim, O., Toch, E. and Ben Gal I., (2021). Website Categorization via Design Attribute Learning. Computers & Security, 102312‏., (Journal) (pdf file)

  • David, Y.B., Geller, T.; Bistritz, I., Ben-Gal, I., Bambos, N., Khmelnitsky, E. (2021), Wireless Body Area Network Control Policies for Energy-Efficient Health Monitoring, Sensors, 21(12), 4245., (Journal) (pdf file)

  • Lev, T., Ben-Gal, I., & Shmueli, E. (2021). Influence Maximization Through Scheduled Seeding in a Real-World Setting. IEEE Transactions on Computational Social Systems.‏ (Journal) (pdf file)

  • Shalev Y. and Ben-Gal I. (2019), Context-Based Predictive Information, Entropy, 21 (7), 645‏. (Journal)(pdf file) 

  • Goldenberg, D., Sela, A., & Shmueli, E. (2018). Timing matters: Influence maximization in social networks through scheduled seeding. IEEE Transactions on Computational Social Systems, 5(3), 621-638.‏  (Journal) (pdf file)

  • Sela, A., Goldenberg, D., Ben-Gal, I., & Shmueli, E. (2018). “Active viral marketing: Incorporating continuous active seeding efforts into the diffusion model”, Expert Systems with Applications, 107, 45-60.‏ (Journal)(pdf file) 

  • Ben-Ishay, S., Sela, A. & Ben-Gal, I. (2019). “Spread-It”: A Strategic Game of Competitive Diffusion through Social Networks. IEEE Transactions on Games.‏ Vol 11(2), p. 129 – 141.(Journal) (pdf file) 

  • Sela A., Orit Milo-Cohen, Ben-Gal I. Eugene Kagan (2019) “Spreading Groups” in Twitter: The Flow of Rumors about Stock Prices, Online Information Review. (pdf file)

  • Sela, A., Ben-Gal, I., Pentland, A. S., & Shmueli, E. (2015, August). Improving information spread through a scheduled seeding approach. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 (pp. 629-632). ACM. (pdf file)

  • Ben-Gal, I., Shavitt, Y., Weinsberg, E., & Weinsberg, U. (2014). Peer-to-peer information retrieval using shared-content clustering. Knowledge and information systems, 39(2), 383-408. (pdf file)

  • Sela A., Shekhtman L., Havlin S. and Ben-Gal I. (2016), “Comparing the diversity of information by word-of-mouth vs. web spread”, European Physical Society, EPL 114 (2016) 58003, p. 1-6. (pdf file)

  • Postelnicu, Z., Raviv, T., & Ben‐Gal, I. (2016). Improving websites’ quality of service by shortening their browsing expected path length. Quality and Reliability Engineering International, 32(6), 2017-2031. (pdf file)

  • Rosenbaum, I., Ben-Gal, I., Yechiali, U. (2013) “Capacity Reallocation in Open Jackson Networks”, IIE Transactions, 45, 259–272. (pdf file)

AI & ML Algorithms & Applications

  • Ben-Gal I., Bacher M., Amara M., Shmueli E. (2023), A Non-Parametric Subspace Analysis Approach with Application to Anomaly Detection Ensembles, INFORMS Journal on Data Science,(Journal), (pdf file).

  • Shalev Y.*, Painsky A., and Ben-Gal I. (2022), Neural Joint Entropy Estimation, Accepted to IEEE Transactions on Neural Network and Learning Systems (TNNLS), 2022, doi: 10.1109/TNNLS.2022.3204919.( Journal), (pdf file)

  • Benarous M., Toch E., and Ben-Gal I. (2022). Synthesis of Longitudinal Human Location Sequences: Balancing Utility and Privacy, ACM Transactions on Knowledge Discovery from Data, Vol. 16, No. 6, Article 118. (Journal) (pdf file​)  

  • Douek-Pinkovich, Y., Ben-Gal, I., & Raviv, T. (2022). The Stochastic Test Collection Problem: Models, Exact and Heuristic Solution Approaches. European Journal of Operational Research.‏ (Journal) (pdf file)

  • David, Y.B., Geller, T.; Bistritz, I., Ben-Gal, I., Bambos, N., Khmelnitsky, E. (2021), Wireless Body Area Network Control Policies for Energy-Efficient Health Monitoring, Sensors, 21(12), 4245., (Journal) (pdf file)

  • Singer, Gonen, Roee Anuar, and Irad Ben-Gal. “A Weighted Information-Gain Measure for Ordinal Classification Trees.” Expert Systems with Applications, 134, (2020): 113290.‏ (Journal)  (pdf file)

  • Pessach D., Singer G., Avrahami D., Chalutz Ben-Gal H., Shmueli E. and Ben-Gal I. (2020), “Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming, Accepted to Decision Support Systems, (Journal), (pdf file)

  • Matzliach B., Ben-Gal I., and Kagan E. (2020), Cooperative Detection of Multiple Targets by the Group of  Mobile Agents Entropy 2020, 22, 512; doi:10.3390/e22050512, (Journal)  (pdf file)

  • Y. Douek-Pinkovich, I. Ben-Gal, and T. Raviv, The Generalized Test Collection Problem (2020), TOPJournal, Springer. (Journal) (pdf file)

  • Ben-Gal I., Weinstock S., Singer G., Bambos N. (2019) “Clustering Users by Their Mobility Behavioral Patterns”, Accepted. ACM Transactions on Knowledge Discovery from Data (Journal) (pdf file)

  • Goldenberg, D., Sela, A., & Shmueli, E. (2018). Timing matters: Influence maximization in social networks through scheduled seeding. IEEE Transactions on Computational Social Systems, 5(3), 621-638.‏  (Journal) (pdf file​)

  • Ben-Ishay, S., Sela, A. & Ben-Gal, I. (2019). “Spread-It”: A Strategic Game of Competitive Diffusion through Social Networks. IEEE Transactions on Games.‏ Vol 11(2), p. 129 – 141. (Journal)(pdf file)

  • Shalev Y. and Ben-Gal I. (2019), Context-Based Predictive Information, Entropy, 21 (7), 645‏. (Journal) (pdf file)

  • Ben-Gal, I., Dana, A., Shkolnik, N., & Singer, G. (2014). Efficient construction of decision trees by the dual information distance method. Quality Technology & Quantitative Management, 11(1), 133-147. (pdf file)

  • Gruber, A., & Ben-Gal, I. (2019). “A targeted Bayesian network learning for classification”. Quality Technology & Quantitative Management, 16(3), 243-261.‏ Published online: 26 Oct 2017.(Journal)(pdf file)

  • Ben-Gal I., Bayesian Networks, In Ruggeri F., Faltin & Kenett R. (Eds.), Encyclopedia of Statistics in Quality and Reliability, John Wiley & Sons (2007). (pdf file)

  • Kagan E. and Ben-Gal I., (2013), Probabilistic Search for Tracking Targets: Theory & Modern Applications (330 pages), Wiley & Sons.

  • Ben-Gal I. and Trister (2015) “Parallel Construction of Decision Trees with Consistently Non-Increasing Expected Number of Tests,” Applied Stochastic Models in Business and Industry, Vol. 31(1) 64-78.(Journal) (pdf file)

  • Kagan E. and Ben-Gal I (2014).  “A Group-Testing Algorithm with Online Informational Learning”, IIE Transactions, 46:2, 164-184, (pdf file)

  • Ben-Gal I., Dana A., Shkolnik and Singer G. (2014), Efficient Construction of Decision Trees by the Dual Information Distance Method”, Quality Technology & Quantitative Management (QTQM), 11( 1), 133-147. (pdf file)

  • Rybalov A., Kagan E., Rapoport A., Ben-Gal I. (2014), Fuzzy Implementation of Qubits Operators, Journal of Computer Science & Systems Biology, 7:5 (pdf file)

Robotics & AI Agents

  • Matzliach B.*, Ben-Gal I., and Kagan E. (2022), Detection of Static and Mobile Targets by an Autonomous Agent with Deep Q-Learning Abilities, Entropy 2022, 24, 1168. (Journal),  (pdf file).

  • Matzliach B., Ben-Gal I., and Kagan E. (2020), Cooperative Detection of Multiple Targets by the Group of  Mobile Agents Entropy 2020, 22, 512; doi:10.3390/e22050512, (Journal)  (pdf file)

  • Kagan E., Shvalb N. and Ben-Gal I. (2019), “Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming”, Wiley & Sons. (New Book Link)

  • Kagan E. and Ben-Gal I. (2015), Search and Foraging: Individual Motion and Swarm Dynamics (268 Pages), CRC Press, Taylor and Francis (Amazon)

  • Kagan E. and Ben-Gal I., (2013), Probabilistic Search for Tracking Targets: Theory & Modern Applications (330 pages), Wiley & Sons.

  • Goldstain O., Ben-Gal I., Bukchin Y., Evaluation of Remote Interface Component alternatives for Teaching Tele-Robotic Operation, to appear in: Azad A., Auer E. and Harward (Editors), Internet Accessible Remote Laboratories: Scalable E-Learning Tools for Engineering and Science Disciplines, The IGI Global Publishing (2012). (pdf file)

  • Kagan E. and Ben-Gal I., Navigation of Quantum-Controlled Mobile in “Mobile Robots”, ISBN:  978-953-307-909-7. InTechPublishing House. (pdf file)

  • Kagan E. and Ben-Gal I. (2013), “Moving Target Search Algorithm with Informational Distance Measures”, TheOpen Applied Informatics Journal. 6, 1-10 (pdf file)

  • Goldstain O., Ben-Gal I., Bukchin Y., Evaluation of Tele-Robotic Interface Components for Teaching Robot Operation, IEEE transactions on Learning Technologies, vol. 99.  1-13 (pdf file)

​Anomaly Detection & Statistical Process Control

  • Ben-Gal I., Bacher M., Amara M., Shmueli E. (2023), A Non-Parametric Subspace Analysis Approach with Application to Anomaly Detection Ensembles, INFORMS Journal on Data Science,(Journal), (pdf file).

  • Naim O., Cohen D. Ben-Gal I., (2023) Malicious Website Identification Using Design Attribute Learning, Accepted to International Journal of Information Security (Journal) (pdf file)
  • Bacher M., Ben-Gal I., Shmueli E. (2022), A Non-Parametric Subspace Analysis Approach with Application to Anomaly Detection Ensembles, Accepted to INFORMS Journal on Data Science,  https://arxiv.org/abs/2101.04932, https://doi.org/10.48550/arXiv.2101.04932 (pdf file).
  • Ben-Gal I., Weinstock S., Singer G., Bambos N. (2019) “Clustering Users by Their Mobility Behavioral Patterns”, Accepted. ACM Transactions on Knowledge Discovery from Data (Journal) (pdf file)

  • Toch, E., Lerner, B., Ben-Zion, E. and Ben-Gal, I. (2019). “Analyzing Large-Scale Human Mobility Data: a Survey of Machine Learning Methods and Applications”, Knowledge and Information Systems, 58(3), 501-523.‏ (pdf file)

  • Bacher M. and Ben-Gal I. (2017), “Ensemble-Bayesian SPC: A Semi-Supervised Multi-Mode Process Monitoring”, IISE Transactions 49(11):1-17.  (Journal)(pdf file)

  • Ben-Gal I., Outlier detection, In Maimon O. and Rockach (Editors.) Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, Kluwer Academic Publishers (2005). (pdf file)

  • Shmilovici A. and Ben-Gal I. (2012) “Predicting Stock Returns Using a Variable Order Markov Tree Model,” Studies in Nonlinear Dynamics & Econometric, Vol. 16, No. 5,  1-35 (pdf file)

  • Ruschin N., Ben-Gal I., Maimon O., (2013) “Fractal Geometry Based Statistical Process Control for Non-Linear Auto-correlated Processes”, IIE Transactions, 45, 373-391. Selected to be featured at INFORMS 2013. (Journal)(pdf file)

Industrial & Service Applications, Smart Cities

 

  • Eden B., Bistritz I., Bambos N., Ben-Gal I., Khmelnitsky E., (2022), Personalized and Energy-Efficient Health Monitoring: A Reinforcement Learning Approach, Accepted, IEEE Control Systems Letters  (Journal) (pdf file)

  • Matzliach B.*, Ben-Gal I., and Kagan E. (2022), Detection of Static and Mobile Targets by an Autonomous Agent with Deep Q-Learning Abilities, Entropy 2022, 24, 1168. (Journal),  (pdf file​).

  • Benarous M., Toch E., and Ben-Gal I. (2022). Synthesis of Longitudinal Human Location Sequences: Balancing Utility and Privacy, ACM Transactions on Knowledge Discovery from Data, Vol. 16, No. 6, Article 118. (Journal) (pdf file) 

  • Douek-Pinkovich, Y., Ben-Gal, I., & Raviv, T. (2022). The Stochastic Test Collection Problem: Models, Exact and Heuristic Solution Approaches. European Journal of Operational Research.‏ (Journal) (pdf file​)

  • Guan G., Dery Y., Yechezkel M., Ben-Gal I., Yamin D., and Brandeau M.L. (2021), Early Detection of COVID-19 Outbreaks Using Human Mobility Data (2021), Accepted, PLOS ONE (medrxiv) (pdf file) (PlosOne)

  • Pessach D., Singer G., Avrahami D., Chalutz Ben-Gal H., Shmueli E. and Ben-Gal I. (2020), “Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming, Accepted to Decision Support Systems, (Journal), (pdf file)

  • Matzliach B., Ben-Gal I., and Kagan E. (2020), Cooperative Detection of Multiple Targets by the Group of  Mobile Agents Entropy 2020, 22, 512; doi:10.3390/e22050512, (Journal)  (pdf file)

  • Y. Douek-Pinkovich, I. Ben-Gal, and T. Raviv, The Generalized Test Collection Problem (2020), TOPJournal, Springer. (Journal) (pdf file)

  • Toch, E., Lerner, B., Ben-Zion, E. and Ben-Gal, I. (2019). “Analyzing Large-Scale Human Mobility Data: a Survey of Machine Learning Methods and Applications”, Knowledge and Information Systems, 58(3), 501-523.‏ (pdf file)

  • Chalutz Ben-Gal H., Ben-Gal I. (2017), “Digital Living 2030: A Note on Quality of Life Perspective” Research in Medical & Engineering Sciences, Vol 2(2), 20-22, Nov 2017. (pdf file)

  • Ben-Gal I., Weinstock S., Singer G., Bambos N. (2019) “Clustering Users by Their Mobility Behavioral Patterns”, Accepted. ACM Transactions on Knowledge Discovery from Data (Journal) (pdf file)

  • Gruber, A., Yanovski, S., & Ben-Gal, I. (2013). Condition-based maintenance via simulation and A targeted bayesian network metamodel. Quality Engineering, 25(4), 370-384. (pdf)

  • Gruber A., Yanovski S., Ben-Gal I. (2013) “Predictive Maintenance Framework through Condition-based Maintenance Bayesian Network and Monte Carlo Simulation”, Quality Engineering, 25(4), 370-384. (pdf file)

  • Gruber A. and Ben-Gal I., (2012) “Efficient Bayesian Network Learning for System Optimization in Reliability Engineering,” Quality Technology & Quantitative Management, Vol. 9, No. 1, pp. 97-114. (pdf file)

Cyber & Homeland Security

 

  • Naim O., Cohen D. Ben-Gal I., (2023) Malicious Website Identification Using Design Attribute Learning, Accepted to International Journal of Information Security (Journal) (pdf file)

  • Cohen, D., Naim, O., Toch, E. and Ben Gal I., (2021). Website Categorization via Design Attribute Learning. Computers & Security, 102312‏., (Journal) (pdf file)

  • Cohen D., Toch E., and Ben-Gal I., “Identification of Malicious Websites by Learning the Websites’ Design Attributes”, under review.

  • Harel, Y., Gal, I. B., & Elovici, Y. (2017). Cyber security and the role of intelligent systems in addressing its challenges. ACM Transactions on Intelligent Systems and Technology (TIST), 8(4), 49. (pdf file)

  • Gruber*, Ben-Gal I.  “Using targeted Bayesian network learning for suspect identification in a communication network, International  Journal Information Security (2017). doi:10.1007/s10207-017-0362-4 (Journal)(pdf file)

  • Ben-Neria M., Yacovzada N., Ben-Gal I.,  (2016) A Risk-Scoring Feedback Model for Webpages and Web Users based on Browsing Behavior. Accepted, ACM Transactions on Intelligent Systems and Technology. (pdf file)

People & Health Analytics

​

  • Chalutz-Ben Gal, H. (2023). Human Resources-Based Organizational Data Mining (HRODM): Themes, Trends, Focus, Future. In Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook (pp. 833-866). Cham: Springer International Publishing.(Journal)(pdf file)

  • Chalutz-Ben Gal, H. (2023). Person–skill fit: Why a new form of employee fit is required. Academy of Management Perspectives, 37(2), 117-137.(pdf file)(Article)(Video)

  • Chalutz - Ben Gal H (2023). , “Artificial intelligence (AI) acceptance in primary care during the coronavirus pandemic: What is the role of patient’s gender, age, and health awareness? A two-phase pilot study" Front. Public Health 10:931225. doi: 10.3389/fpubh.2022.931225 (pdf file)(Article)

  • Eden B., Bistritz I., Bambos N., Ben-Gal I., Khmelnitsky E., (2022), Personalized and Energy-Efficient Health Monitoring: A Reinforcement Learning Approach, Accepted, IEEE Control Systems Letters  (Journal) (pdf file)

  • David, Y.B., Geller, T.; Bistritz, I., Ben-Gal, I., Bambos, N., Khmelnitsky, E. (2021), Wireless Body Area Network Control Policies for Energy-Efficient Health Monitoring, Sensors, 21(12), 4245., (Journal) (pdf file)

  • Guan G., Dery Y., Yechezkel M., Ben-Gal I., Yamin D., and Brandeau M.L. (2021), Early Detection of COVID-19 Outbreaks Using Human Mobility Data (2021), Accepted, PLOS ONE (medrxiv) (pdf file) (PlosOne)

  • Lev, T., Ben-Gal, I., & Shmueli, E. (2021). Influence Maximization Through Scheduled Seeding in a Real-World Setting. IEEE Transactions on Computational Social Systems. (Journal​)‏ (pdf file) ‏

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