ref.bib

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@Online{Sha99,
  author = {Chandra Shah and Gerald Burke},
  editor = {Kluwer Academic Publishers},
  title  = {An undergraduate student flow model: Australian higher education},
  year   = {1999},
  url    = {https://s3.amazonaws.com/academia.edu.documents/46873010/a_3A100376522225020160628-24881-76g4vs.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1559310657&Signature=VmZdxJEzPWnXXBX3UJXI15NVkjo%3D&response-content-disposition=inline%3B%20filename%3DAn_undergraduate_student_flow_model_Aust.pdf},
}

@Unpublished{Kaw05,
  author = {Yohei Kawaguchi and Tetsuo Shoji and Weijane Lin and Koh Kakusho and Michihiko Minoh},
  title  = {Face Recognition-based Lecture Attendance System},
  year   = {2005},
  url    = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.560.7003&rep=rep1&type=pdf},
}

@Article{Kotevski2018,
  author    = {Zoran Kotevski and Natasa Blazheska-Tabakovska and Andrijana Bocevska and Tome Dimovski},
  title     = {On the Technologies and Systems for Student Attendance Tracking},
  journal   = {International Journal of Information Technology and Computer Science},
  year      = {2018},
  volume    = {10},
  number    = {10},
  month     = {oct},
  pages     = {44--52},
  doi       = {10.5815/ijitcs.2018.10.06},
  publisher = {{MECS} Publisher},
}

@Online{Pat14,
  author = {Mr. C. S. Patil and Mr. R. R. Karhe and Mr. M. D. Jain},
  editor = {International Journal of Research in Advent Technology},
  title  = {Student Attendance Recording System Using Face Recognition with GSM Based},
  year   = {2014},
  url    = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.640.4151&rep=rep1&type=pdf},
}

@Online{Sid16,
  author = {Muhammad Zulqarnain Siddiqui and Prof. Dr. P. Sellappan},
  editor = {International Journal of Scientific \& Engineering Research},
  title  = {Effective Mechanism for Advancement of monitoring Process of Educational Sectors of Underdeveloped Countries – A Study Based on Educational Sector of Pakistan},
  year   = {2016},
  url    = {https://www.researchgate.net/profile/Muhammad_Siddiqui65/publication/315689570_Effective_Mechanism_for_Advancement_of_Monitoring_Process_of_Educational_Sectors_of_Underdeveloped_Countries_-_A_Study_Based_on_Educational_Sector_of_Pakistan/links/58dbabc392851c611d0091f1/Effective-Mechanism-for-Advancement-of-Monitoring-Process-of-Educational-Sectors-of-Underdeveloped-Countries-A-Study-Based-on-Educational-Sector-of-Pakistan.pdf},
}

@Article{Blerkom1992,
  author    = {Malcolm L. Van Blerkom},
  title     = {Class Attendance in Undergraduate Courses},
  journal   = {The Journal of Psychology},
  year      = {1992},
  volume    = {126},
  number    = {5},
  month     = {sep},
  pages     = {487--494},
  doi       = {10.1080/00223980.1992.10543382},
  publisher = {Informa {UK} Limited},
}

@Article{Fjortoft2005,
  author    = {Nancy Fjortoft},
  title     = {Students{\textquotesingle} Motivations for Class Attendance},
  journal   = {American Journal of Pharmaceutical Education},
  year      = {2005},
  volume    = {69},
  number    = {1},
  month     = {sep},
  pages     = {15},
  doi       = {10.5688/aj690115},
  publisher = {American Journal of Pharmaceutical Education},
}

@Online{Ola19,
  author = {N. K Oladejo and A. Abolarinwa and S.O Salawu and M.O Bamiro and A.F Lukman},
  editor = {International Journal of Mechanical Engineering and Technology},
  title  = {Application of optimization principles in classroom allocation using linear programming},
  year   = {2019},
  url    = {http://eprints.lmu.edu.ng/2145/},
}

@Book{Datta2015,
  author    = {Datta, Asit Kumar and Datta, Madhura and Banerjee, Pradipta Kumar},
  title     = {Face Detection and Recognition},
  year      = {2015},
  date      = {2015-10-28},
  publisher = {Taylor \& Francis},
  pagetotal = {352},
  url       = {https://www.ebook.de/de/product/25204083/asit_kumar_datta_madhura_datta_pradipta_kumar_banerjee_face_detection_and_recognition.html},
  ean       = {9781482226577},
}

@Article{Wang2014,
  author    = {Yi-Qing Wang},
  title     = {An Analysis of the Viola-Jones Face Detection Algorithm},
  journal   = {Image Processing On Line},
  year      = {2014},
  volume    = {4},
  month     = {jun},
  pages     = {128--148},
  doi       = {10.5201/ipol.2014.104},
  publisher = {Image Processing On Line},
}

@Article{Viola2004,
  author    = {Paul Viola and Michael J. Jones},
  title     = {Robust Real-Time Face Detection},
  journal   = {International Journal of Computer Vision},
  year      = {2004},
  volume    = {57},
  number    = {2},
  month     = {may},
  pages     = {137--154},
  doi       = {10.1023/b:visi.0000013087.49260.fb},
  publisher = {Springer Nature},
}

@InCollection{Schapire2013,
  author    = {Robert E. Schapire},
  title     = {Explaining {AdaBoost}},
  booktitle = {Empirical Inference},
  year      = {2013},
  publisher = {Springer Berlin Heidelberg},
  pages     = {37--52},
  doi       = {10.1007/978-3-642-41136-6_5},
}

@Online{Smola2008,
  author = {Alex Smola and S. V. N. Vishwanathan},
  editor = {Cambridge University Press},
  title  = {Introduction to Machine Learning},
  year   = {2008},
  url    = {http://alex.smola.org/drafts/thebook.pdf},
}

@InProceedings{campbell2008introduction,
  author    = {Campbell, Dan and Campbell, Sherlock},
  title     = {Introduction to regression and data analysis},
  booktitle = {StatLab Workshop Series},
  year      = {2008},
  pages     = {1--15},
}

@Book{bishop2006pattern,
  author    = {Christopher M. Bishop},
  title     = {Pattern recognition and machine learning},
  year      = {2006},
  publisher = {springer},
}

@Book{Keck2014,
  author    = {Christian Keck},
  title     = {Models for correspondence finding and probabilistic representative learning},
  year      = {2014},
  publisher = {epubli GmbH},
  isbn      = {978-3-8442-9787-4},
  url       = {https://www.amazon.com/correspondence-finding-probabilistic-representative-learning/dp/3844297871?SubscriptionId=AKIAIOBINVZYXZQZ2U3A&tag=chimbori05-20&linkCode=xm2&camp=2025&creative=165953&creativeASIN=3844297871},
}

@Online{Mazur2015,
  author = {Matt Mazur},
  title  = {A Step by Step Backpropagation Example},
  year   = {2015},
  url    = {https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/},
}

@Article{Nwankpa2018,
  author      = {Chigozie Nwankpa and Winifred Ijomah and Anthony Gachagan and Stephen Marshall},
  title       = {Activation Functions: Comparison of trends in Practice and Research for Deep Learning},
  year        = {2018},
  date        = {2018-11-08},
  eprint      = {http://arxiv.org/abs/1811.03378v1},
  eprintclass = {cs.LG},
  eprinttype  = {arXiv},
  abstract    = {Deep neural networks have been successfully used in diverse emerging domains to solve real world complex problems with may more deep learning(DL) architectures, being developed to date. To achieve these state-of-the-art performances, the DL architectures use activation functions (AFs), to perform diverse computations between the hidden layers and the output layers of any given DL architecture. This paper presents a survey on the existing AFs used in deep learning applications and highlights the recent trends in the use of the activation functions for deep learning applications. The novelty of this paper is that it compiles majority of the AFs used in DL and outlines the current trends in the applications and usage of these functions in practical deep learning deployments against the state-of-the-art research results. This compilation will aid in making effective decisions in the choice of the most suitable and appropriate activation function for any given application, ready for deployment. This paper is timely because most research papers on AF highlights similar works and results while this paper will be the first, to compile the trends in AF applications in practice against the research results from literature, found in deep learning research to date.},
  file        = {:http\://arxiv.org/pdf/1811.03378v1:PDF},
  keywords    = {cs.LG, cs.CV},
}

@Book{schrijver1998theory,
  author    = {Schrijver, Alexander},
  title     = {Theory of linear and integer programming},
  year      = {1998},
  publisher = {John Wiley \& Sons},
}

@Book{walsh1985introduction,
  author    = {Walsh, Gordon Raymond},
  title     = {An introduction to linear programming},
  year      = {1985},
  publisher = {Wiley New York},
}

@Book{zeleny2012linear,
  author    = {Zeleny, Milan},
  title     = {Linear multiobjective programming},
  year      = {2012},
  volume    = {95},
  publisher = {Springer Science \& Business Media},
}

@Book{1990,
  title     = {Introduction to Linear Programming: Applications and Extensions (Chapman \& Hall/CRC Pure and Applied Mathematics)},
  year      = {1990},
  publisher = {CRC Press},
  isbn      = {978-0824783839},
  url       = {https://www.amazon.com/Introduction-Linear-Programming-Applications-Mathematics/dp/0824783832?SubscriptionId=AKIAIOBINVZYXZQZ2U3A&tag=chimbori05-20&linkCode=xm2&camp=2025&creative=165953&creativeASIN=0824783832},
}

@Book{Figueira2006,
  author    = {Figueira, JosÉ and Ehrogott, Matthias and Greco, Salvatore},
  title     = {Multiple Criteria Decision Analysis: State of the Art Surveys},
  year      = {2006},
  date      = {2006-01-20},
  publisher = {Springer-Verlag GmbH},
  url       = {https://www.ebook.de/de/product/11429977/jose_figueira_matthias_ehrogott_salvatore_greco_multiple_criteria_decision_analysis_state_of_the_art_surveys.html},
  ean       = {9780387230818},
}

@Book{2013,
  title     = {Advances in Multicriteria Analysis},
  year      = {2013},
  date      = {2013-03-14},
  publisher = {Springer Us},
  url       = {https://www.ebook.de/de/product/25185326/advances_in_multicriteria_analysis.html},
  ean       = {9781475723830},
}

@Article{Gosselin1986,
  author    = {Karl Gosselin and Michel Truchon},
  title     = {Allocation of Classrooms by Linear Programming},
  journal   = {Journal of the Operational Research Society},
  year      = {1986},
  volume    = {37},
  number    = {6},
  month     = {jun},
  pages     = {561--569},
  doi       = {10.1057/jors.1986.98},
  publisher = {Informa {UK} Limited},
}

@Article{KanjanaThongsanit2014,
  author    = {{Kanjana Thongsanit}},
  title     = {Solving the Course - Classroom Assignment Problem for a University},
  journal   = {Silpakorn University Science and Technology Journal-1},
  year      = {2014},
  volume    = {8},
  doi       = {10.14456/sustj.2014.3},
  keywords  = {Classroom timetable, Integer linear programming},
  publisher = {Silpakorn University Research and Development Institute},
}

@Article{Carter1992,
  author    = {Michael W. Carter and Craig A. Tovey},
  title     = {When Is the Classroom Assignment Problem Hard?},
  journal   = {Operations Research},
  year      = {1992},
  volume    = {40},
  number    = {1-supplement-1},
  month     = {feb},
  pages     = {S28--S39},
  doi       = {10.1287/opre.40.1.s28},
  publisher = {Institute for Operations Research and the Management Sciences ({INFORMS})},
}

@Article{Phillips2015,
  author    = {Antony E. Phillips and Hamish Waterer and Matthias Ehrgott and David M. Ryan},
  title     = {Integer programming methods for large-scale practical classroom assignment problems},
  journal   = {Computers {\&} Operations Research},
  year      = {2015},
  volume    = {53},
  month     = {jan},
  pages     = {42--53},
  doi       = {10.1016/j.cor.2014.07.012},
  publisher = {Elsevier {BV}},
}

@Article{Botchkarev2018,
  author       = {Alexei Botchkarev},
  title        = {Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology},
  journaltitle = {Interdisciplinary Journal of Information, Knowledge, and Management, 2019, 14, 45-79},
  date         = {2018-09-09},
  doi          = {10.28945/4184},
  eprint       = {http://arxiv.org/abs/1809.03006v1},
  eprintclass  = {stat.ME},
  eprinttype   = {arXiv},
  abstract     = {Performance metrics (error measures) are vital components of the evaluation frameworks in various fields. The intention of this study was to overview of a variety of performance metrics and approaches to their classification. The main goal of the study was to develop a typology that will help to improve our knowledge and understanding of metrics and facilitate their selection in machine learning regression, forecasting and prognostics. Based on the analysis of the structure of numerous performance metrics, we propose a framework of metrics which includes four (4) categories: primary metrics, extended metrics, composite metrics, and hybrid sets of metrics. The paper identified three (3) key components (dimensions) that determine the structure and properties of primary metrics: method of determining point distance, method of normalization, method of aggregation of point distances over a data set.},
  file         = {:http\://arxiv.org/pdf/1809.03006v1:PDF},
  keywords     = {stat.ME, cs.LG, stat.ML},
}

@TechReport{RePEc:pra:mprapa:84722,
  author      = {Colignatus, Thomas},
  title       = {An overview of the elementary statistics of correlation, R-squared, cosine, sine, and regression through the origin, with application to votes and seats for Parliament},
  institution = {University Library of Munich, Germany},
  year        = {2018},
  type        = {MPRA Paper},
  url         = {https://EconPapers.repec.org/RePEc:pra:mprapa:84722},
  abstract    = {The correlation between two vectors is the cosine of the angle between the centered data. While the cosine is a measure of association, the literature has spent little attention to the use of the sine as a measure of distance. A key application of the sine is a new “sine-diagonal inequality / disproportionality” (SDID) measure for votes and their assigned seats for parties for Parliament. This application has nonnegative data and uses regression through the origin (RTO) with non-centered data. Textbooks are advised to discuss this case because the geometry will improve the understanding of both regression and the distinction between descriptive statistics and statistical decision theory. Regression may better be introduced and explained by looking at the angles relevant for a vector and its estimate rather than looking at the Euclidean distance and the sum of squared errors. The paper provides an overview of the issues involved. Also a new relation between the sine and the Euclidean distance is derived.},
  keywords    = {General Economics; Social Choice; Social Welfare; Election; Parliament; Party System; Representation; Sine Diagonal Inequality / Disproportionality; SDID; Proportion; District; Voting; Seat; Euclid; Distance; Cosine; Sine; Gallagher; Loosemore-Hanby; Sainte-Laguë; Webster; Jefferson; Hamilton; Largest Remainder; Correlation; Diagonal regression; Regression through the origin; Apportionment; Disproportionality; Equity; Inequality},
}

@Article{Marler2009,
  author    = {R. Timothy Marler and Jasbir S. Arora},
  title     = {The weighted sum method for multi-objective optimization: new insights},
  journal   = {Structural and Multidisciplinary Optimization},
  year      = {2009},
  volume    = {41},
  number    = {6},
  month     = {dec},
  pages     = {853--862},
  doi       = {10.1007/s00158-009-0460-7},
  publisher = {Springer Science and Business Media {LLC}},
}

@Online{Pettinger2017,
  author = {Tejvan Pettinger},
  title  = {Pareto efficiency},
  year   = {2017},
  date   = {2017-07-28},
  url    = {https://www.economicshelp.org/blog/glossary/pareto-efficiency/},
}

@Online{John2018,
  author = {Robert John},
  title  = {Linear Regression with TensorFlow Canned Estimators},
  year   = {2018},
  date   = {2018-06-30},
  url    = {https://medium.com/coinmonks/linear-regression-with-tensorflow-canned-estimators-6cc4ffddd14f},
}

@Online{gk_2017,
  author = {gk},
  title  = {Deep Learning in 7 lines of code},
  year   = {2017},
  date   = {2017-04-07},
  url    = {https://chatbotslife.com/deep-learning-in-7-lines-of-code-7879a8ef8cfb},
}

@Online{Dillinger2018,
  author = {Tom Dillinger},
  title  = {Machine Learning and Embedded FPGA IP},
  year   = {2018},
  date   = {2018-07-18},
  url    = {https://semiwiki.com/semiconductor-ip/flex-logix/7598-machine-learning-and-embedded-fpga-ip/},
}

@Online{opencv,
  author = {Opencv},
  title  = {The opencv logo},
  url    = {https://opencv.org/about/},
}

@Online{keras,
  author = {Keras},
  title  = {Keras logo},
  url    = {https://keras.io/},
}

@Comment{jabref-meta: databaseType:biblatex;}