PATTERN RECOGNITION

Darbi, H. A., & Saleh, E. M. (2022). Decision Support System: Analysis And Design Methodology. 2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), 260–266. https://doi.org/10.1109/MI-STA54861.2022.983 7769

Imbar, R. V. (2007). Decision Support System Architecture , Hardware , and Operating System Platforms. Jurnal Sistem Informasi, 2(1), 41–50.

Moreira, M. W. L., Rodrigues, J. J. P. C., Korotaev, V., Al-Muhtadi, J., & Kumar, N. (2019). A Comprehensive Review on Smart Decision Support Systems for Health Care. IEEE Systems Journal, 13(3), 3536–3545. https://doi.org/10.1109/JSYST. 2018.2890121

Power, D. J. (2008). Decision Support Systems: A Historical Overview BT - Handbook on Decision Support Systems 1: Basic Themes (F. Burstein & C. W. Holsapple (eds.); pp. 121–140). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-48713-5_7

Shim, J. P., Warkentin, M., Courtney, J. F., Power, D. J., Sharda, R., & Carlsson, C. (2002). Past, present, and future of decision support technology. Decision Support Systems, 33(2), 111–126. https://doi.org/10.1016/S0167-9236(01)00139-7

Soori, M., Jough, F. K. G., Dastres, R., & Arezoo, B. (2024). AI-Based Decision Support Systems in Industry 4.0, A Review. Journal of Economy and Technology, August. https://doi.org/10. 1016/j.ject.2024.08.005

Adhikari, D., Jiang, W., Zhan, J., Rawat, D. B., & Bhattarai, A. (2024). Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives. Computer Science Review, 54, 100665. https://doi.org/https://doi.org/10.1016 /j.cosrev.2024.100665

Agrawal, S., Gupta, G. K., Gopalakrishna, P. K., Balasubramaniam, V. S., Goel, L., & Mahadik, S. (2024). Hybrid Machine Learning Models: Combining Strengths of Supervised and Unsupervised Learning Approaches. 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), 7, 1056–1061. https://doi.org/10.1109/IC3I61595.2024.10829140

Almuqati, M. T., Sidi, F., Rum, S. N. M., Zolkepli, M., & Ishak, I. (2024). Challenges in Supervised and Unsupervised Learning: A Comprehensive Overview. International Journal on Advanced Science, Engineering and Information Technology, 14(4), 1449–1455. https://doi.org/10.18517/ijaseit.14.4.20191

Alzubi, J., Nayyar, A., & Kumar, A. (2018). Machine Learning from Theory to Algorithms: An Overview. Journal of Physics: Conference Series, 1142(1). https://doi.org/10.1088/1742-6596/1142/1/012012

Ashenden, S. K., Bartosik, A., Agapow, P.-M., & Semenova, E. (2021). Chapter 2 - Introduction to artificial intelligence and machine learning. In S. K. Ashenden (Ed.), The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry (pp. 15–26). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-820045-2.00003-9

Blocho, M. (2020). Chapter 6 - Parallel algorithms for solving rich vehicle routing problems. In J. Nalepa (Ed.), Smart Delivery Systems (pp. 185–201). Elsevier. https://doi.org/https:// doi.org/10.1016/B978-0-12-815715-2.00010-5

Chander, B., & Gopalakrishnan, K. (2023). 10 - Data clustering using unsupervised machine learning. In T. Goswami & G. R. Sinha (Eds.), Statistical Modeling in Machine Learning (pp. 179–204). Academic Press. https://doi.org/https:// doi.org/10.1016/B978-0-323-91776-6.00015-4

Chang, A. C., & Limon, A. (2024). Chapter 1 - Introduction to artificial intelligence for cardiovascular clinicians. In A. C. Chang & A. Limon (Eds.), Intelligence-Based Cardiology and Cardiac Surgery (pp. 3–120). Academic Press. https://doi. org/https://doi.org/10.1016/B978-0-323-90534-3.00010-X

Choudhury, A. D., Banerjee, R., Kimbahune, S., & Pal, A. (2022). Chapter 3 - Sensor signal analytics. In A. D. Choudhury, R. Banerjee, S. Kimbahune, & A. Pal (Eds.), New Frontiers of Cardiovascular Screening Using Unobtrusive Sensors, AI, and IoT (pp. 61–89). Academic Press. https://doi.org/https: //doi.org/10.1016/B978-0-12-824499-9.00003-9

Das, B., & T‏S‏B‏, S. (2023). Multi-contextual learning in disinformation research: A review of challenges, approaches, and opportunities. Online Social Networks and Media, 34–35, 100247. https://doi.org/https://doi.org/10.1016/j.osnem. 2023.100247

David W. Aha, Dennis Kibler, & Albert, M. K. (1991). Instance-Based Learning Algorithms DAVID. Machine Learning, 6(Springer), 37--66.

Dev, V. A., & Eden, M. R. (2019). Gradient Boosted Decision Trees for Lithology Classification. In S. G. Muñoz, C. D. Laird, & M. J. Realff (Eds.), Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design (Vol. 47, pp. 113–118). Elsevier. https://doi.org/https://doi.org/10. 1016/B978-0-12-818597-1.50019-9

Duan, J., Xiong, J., Li, Y., & Ding, W. (2024). Deep learning based multimodal biomedical data fusion: An overview and comparative review. Information Fusion, 112, 102536. https://doi.org/https://doi.org/10.1016/j.inffus.2024.102536

Gandomi, A. H., Yang, X.-S., Talatahari, S., & Alavi, A. H. (2013). 1 - Metaheuristic Algorithms in Modeling and Optimization. In A. H. Gandomi, X.-S. Yang, S. Talatahari, & A. H. Alavi (Eds.), Metaheuristic Applications in Structures and Infrastructures (pp. 1–24). Elsevier. https://doi.org/https://doi.org/

10.1016/B978-0-12-398364-0.00001-2

Ghorpade, S. N., Chougule, S., Chaudhari, B. S., & Zennaro, M. (2024). Chapter 4 - TinyML: principles and algorithms. In B. S. Chaudhari, S. N. Ghorpade, M. Zennaro, & R. Paškauskas (Eds.), TinyML for Edge Intelligence in IoT and LPWAN Networks (pp. 65–86). Academic Press. https://doi.org/ https://doi.org/10.1016/B978-0-44-322202-3.00009-9

Golilarz, N. A., Hossain, E., Addeh, A., & Rahimi, K. A. (2025). AI Learning Algorithms: Deep Learning, Hybrid Models, and Large-Scale Model Integration. 1–18. http://arxiv.org/abs/2410. 09186

Gudivada, V. N., Irfan, M. T., Fathi, E., & Rao, D. L. (2016). Chapter 5 - Cognitive Analytics: Going Beyond Big Data Analytics and Machine Learning. In V. N. Gudivada, V. V Raghavan, V. Govindaraju, & C. R. Rao (Eds.), Cognitive Computing: Theory and Applications (Vol. 35, pp. 169–205). Elsevier. https://doi.org/https://doi.org/10.1016/bs.host. 2016.07.010

Guo, Z. X., & Wong, W. K. (2013). 2 - Fundamentals of artificial intelligence techniques for apparel management applications. In W. K. Wong, Z. X. Guo, & S. Y. S. Leung (Eds.), Optimizing Decision Making in the Apparel Supply Chain Using Artificial Intelligence (AI) (pp. 13–40). Woodhead Publishing. https://doi.org/https://doi.org/10.1533/9780857097842.13

Hasija, Y. (2023). Chapter 9 - A machine learning approach to bioinformatics. In Y. Hasija (Ed.), All About Bioinformatics (pp. 203–224). Academic Press. https://doi.org/https:// doi.org/10.1016/B978-0-443-15250-4.00010-1

Hayashi, C. (1998). What is Data Science ? Fundamental Concepts and a Heuristic Example BT - Data Science, Classification, and Related Methods (C. Hayashi, K. Yajima, H.-H. Bock, N. Ohsumi, Y. Tanaka, & Y. Baba (eds.); pp. 40–51). Springer Japan.

Holmes, J. H. (2014). Chapter 7 - Knowledge Discovery in Biomedical Data: Theory and Methods. In I. N. Sarkar (Ed.), Methods in Biomedical Informatics (pp. 179–240). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-401678-1.00007-5

Jimenez-del-Toro, O., Otálora, S., Andersson, M., Eurén, K., Hedlund, M., Rousson, M., Müller, H., & Atzori, M. (2017). Chapter 10 - Analysis of Histopathology Images: From Traditional Machine Learning to Deep Learning. In A. Depeursinge, O. S. Al-Kadi, & J. R. Mitchell (Eds.), Biomedical Texture Analysis (pp. 281–314). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-812133-7.00010-7

Kerner, H., Campbell, J., & Strickland, M. (2022). Chapter 1 - Introduction to machine learning. In J. Helbert, M. D’Amore, M. Aye, & H. Kerner (Eds.), Machine Learning for Planetary Science (pp. 1–24). Elsevier. https://doi.org/ https://doi.org/10.1016/B978-0-12-818721-0.00007-0

Kim, G. S., Park, S., & Kim, J. (2024). Quantum Reinforcement Learning: An Overview. International Conference on ICT Convergence, 1145–1150. https://doi.org/10.1109/ICTC6 2082.2024.10826656

Li, G., Jin, Y., Akram, M. W., Chen, X., & Ji, J. (2018). Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditions – A review. Renewable and Sustainable Energy Reviews, 81, 840–873. https://doi.org/https://doi.org/10.1016/j.rser.2017.08.034

Li, M., Andersen, D. G., Park, J. W., Ahmed, A., Josifovski, V., Long, J., Shekita, E. J., Su, B., Li, M., Andersen, D. G., Park, J. W., Smola, A. J., & Ahmed, A. (2014). Sprejeta Strategija za alternativna goriva. Proceedings of OSDI, 583–598. http://www.mzi.gov.si/si/medijsko_sredisce/novica/article/799/8867/

Mahesh, B. (2020). Machine Learning Algorithms - A Review. International Journal of Science and Research (IJSR), 9(1), 381–386. https://doi.org/10.21275/art20203995

Maleki, F., Ovens, K., Najafian, K., Forghani, B., Reinhold, C., & Forghani, R. (2020). Overview of Machine Learning Part 1: Fundamentals and Classic Approaches. Neuroimaging Clinics of North America, 30(4), e17–e32. https://doi.org/https:// doi.org/10.1016/j.nic.2020.08.007

McKay, B. (2008). Evolutionary Algorithms. In S. E. Jørgensen & B. D. Fath (Eds.), Encyclopedia of Ecology (pp. 1464–1472). Academic Press. https://doi.org/https://doi.org/10. 1016/B978-008045405-4.00157-9

Menzies, T., Kocagüneli, E., Minku, L., Peters, F., & Turhan, B. (2015). Chapter 20 - Ensembles of Learning Machines. In T. Menzies, E. Kocagüneli, L. Minku, F. Peters, & B. Turhan (Eds.), Sharing Data and Models in Software Engineering (pp. 239–265). Morgan Kaufmann. https://doi.org/https:// doi.org/10.1016/B978-0-12-417295-1.00020-5

Mongardi, S., Masseroli, M., & Cascianelli, S. (2025). Semi-Supervised Learning. In S. Ranganathan, M. Cannataro, & A. M. Khan (Eds.), Encyclopedia of Bioinformatics and Computational Biology (Second Edition) (Second Edition, pp. 663–672). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-323-95502-7.00098-1

Montalt-Tordera, J., Muthurangu, V., Hauptmann, A., & Steeden, J. A. (2021). Machine learning in Magnetic Resonance Imaging: Image reconstruction. Physica Medica, 83, 79–87. https://doi.org/https://doi.org/10.1016/j.ejmp.2021.02.020

Mukherjee, J., Kar, M., Chakrabarti, A., & Das, S. (2024). 7 - Automated risk prediction of solitary pulmonary nodules. In J. Mukherjee, M. Kar, A. Chakrabarti, & S. Das (Eds.), Application of Artificial Intelligence in Early Detection of Lung Cancer (pp. 199–221). Academic Press. https://doi.org/ https://doi.org/10.1016/B978-0-323-95245-3.00007-X

Pezoulas, V. C., Exarchos, T. P., & Fotiadis, D. I. (2020). Chapter 7 - Machine learning and data analytics. In V. C. Pezoulas, T. P. Exarchos, & D. I. Fotiadis (Eds.), Medical Data Sharing, Harmonization and Analytics (pp. 227–309). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-816507-2.00007-4

Pezzini, P., Gomis-Bellmunt, O., & Sudrià-Andreu, A. (2011). Optimization techniques to improve energy efficiency in power systems. Renewable and Sustainable Energy Reviews, 15(4), 2028–2041. https://doi.org/https://doi.org/10.1016/j.rser.2011.01.009

Polikar, R. (2006). Ensemble based systems in decision making. IEEE Circuits and Systems Magazine, 6(3), 21–45. https://doi.org/10.1109/MCAS.2006.1688199

Pour Haji Kazem, A. A. (2024). Chapter 2 - Artificial intelligent algorithms, motivation, and terminology. In T. Allahviranloo, W. Pedrycz, & A. Seyyedabbasi (Eds.), Decision-Making Models (pp. 9–17). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-443-16147-6.00030-X

Ressi, D., Romanello, R., Piazza, C., & Rossi, S. (2024). AI-enhanced blockchain technology: A review of advancements and opportunities. Journal of Network and Computer Applications, 225, 103858. https://doi.org/https://doi.org/10.1016/ j.jnca.2024.103858

Ristevski, B., & Savoska, S. (2021). 4 - Healthcare and medical Big Data analytics. In A. Khanna, D. Gupta, & N. Dey (Eds.), Applications of Big Data in Healthcare (pp. 85–112). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-820203-6.00005-9

Roads, B. D., & Love, B. C. (2024). The Dimensions of dimensionality. Trends in Cognitive Sciences, 28(12), 1118–1131. https://doi.org/https://doi.org/10.1016/j.tics.2024.07.005

Ryan, C. (2003). Evolutionary Algorithms and Metaheuristics. In R. A. Meyers (Ed.), Encyclopedia of Physical Science and Technology (Third Edition) (Third Edition, pp. 673–685). Academic Press. https://doi.org/https://doi.org/10.1016/B0-12-227410-5/00847-4

Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x

Sawant, S. S., & Prabukumar, M. (2020). A review on graph-based semi-supervised learning methods for hyperspectral image classification. Egyptian Journal of Remote Sensing and Space Science, 23(2), 243–248. https://doi.org/10.1016/j.ejrs. 2018.11.001

Shah, R., & Subasi, A. (2024). Chapter 11 - Heart muscles inflammation (myocarditis) detection using artificial intelligence. In A. Subasi (Ed.), Applications of Artificial Intelligence in Healthcare and Biomedicine (pp. 325–355). Academic Press. https://doi.org/https://doi.org/10.1016/ B978-0-443-22308-2.00008-1

Shedthi B, S., & Shetty, V. (2024). 1 - Role of machine learning in sentiment analysis: trends, challenges, and future directions. In D. J. Hemanth (Ed.), Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications (pp. 1–21). Morgan Kaufmann. https://doi.org/https:// doi.org/10.1016/B978-0-443-22009-8.00011-2

Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017). Membership Inference Attacks Against Machine Learning Models. 2017 IEEE Symposium on Security and Privacy (SP), 3–18. https://doi.org/10.1109/SP.2017.41

Subasi, A. (2024). Chapter 1 - AI techniques for healthcare and biomedicine∗∗This chapter is adapted from “Subasi, A. (2023). Introduction to artificial intelligence techniques for medical image analysis. In Applications of Artificial Intelligence in Medical Imaging (pp. 1–49). Academic Press.” In A. Subasi (Ed.), Applications of Artificial Intelligence in Healthcare and Biomedicine (pp. 1–35). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-443-22308-2.00013-5

Turabieh, H. (2019). Hybrid Machine Learning Classifiers to Predict Student Performance. 2019 2nd International Conference on New Trends in Computing Sciences (ICTCS), 1–6. https://doi.org/10. 1109/ICTCS.2019.8923093

Valous, N. A., & Sun, D.-W. (2012). 4 - Image processing techniques for computer vision in the food and beverage industries. In D.-W. Sun (Ed.), Computer Vision Technology in the Food and Beverage Industries (pp. 97–129). Woodhead Publishing. https://doi.org/https://doi.org/10.1533/9780857095770.1.97

van Engelen, J. E., & Hoos, H. H. (2020). A survey on semi-supervised learning. Machine Learning, 109(2), 373–440. https://doi.org/10.1007/s10994-019-05855-6

Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2017). Chapter 11 - Beyond supervised and unsupervised learning. In I. H. Witten, E. Frank, M. A. Hall, & C. J. Pal (Eds.), Data Mining (Fourth Edition) (Fourth Edition, pp. 467–478). Morgan Kaufmann. https://doi.org/https://doi.org/10.1016/B978-0-12-804291-5.00011-8

Wu, J., & Zhao, Y. (2019). Machine learning technology in the application of genome analysis: A systematic review. Gene, 705, 149–156. https://doi.org/https://doi.org/10.1016/ j.gene.2019.04.062

Yang, Y. (2017). Chapter 4 - Ensemble Learning. In Y. Yang (Ed.), Temporal Data Mining Via Unsupervised Ensemble Learning (pp. 35–56). Elsevier. https://doi.org/https://doi.org/10. 1016/B978-0-12-811654-8.00004-X

Yazici, İ., Shayea, I., & Din, J. (2023). A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems. Engineering Science and Technology, an International Journal, 44, 101455. https://doi.org/https:// doi.org/10.1016/j.jestch.2023.101455

Zafar, M. H., Langås, E. F., & Sanfilippo, F. (2024). Exploring the synergies between collaborative robotics, digital twins, augmentation, and industry 5.0 for smart manufacturing: A state-of-the-art review. Robotics and Computer-Integrated Manufacturing, 89, 102769. https://doi.org/https:// doi.org/10.1016/j.rcim.2024.102769

Akter, S., Habib, A., Islam, M. A., Hossen, M. S., Fahim, W. A., Sarkar, P. R., & Ahmed, M. (2021). Comprehensive Performance Assessment of Deep Learning Models in Early Prediction and Risk Identification of Chronic Kidney Disease. IEEE Access, 9(iii), 165184–165206. https://doi.org/10.1109/ACCESS.2021.3129491

Alem, A., & Kumar, S. (2022). Deep Learning Models for Remote Sensed Hyperspectral Image Classification. 2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022, 10(October). https://doi.org/10.1109/ICCCNT54827.2022.9984282

Arias-Duart, A., Mariotti, E., Garcia-Gasulla, D., & Alonso-Moral, J. M. (2023). A Confusion Matrix for Evaluating Feature Attribution Methods. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 3709–3714. https://doi.org/10.1109/CVPRW59228.2023. 00380

Chefira, R., & Rakrak, S. (2021). Accuracy assessment of applied supervised machine learning models on usual data probability distributions. Journal of Physics: Conference Series, 1743(1). https://doi.org/10.1088/1742-6596/1743/1/012 011

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Erbani, J., Portier, P. É., Egyed-Zsigmond, E., & Nurbakova, D. (2024). Confusion Matrices: A Unified Theory. IEEE Access, 12(December). https://doi.org/10.1109/ACCESS.2024.3507199

Fraser, A., & Marcu, D. (2007). Measuring word alignment quality for statistical machine translation. Computational Linguistics, 33(3), 293–303. https://doi.org/10.1162/coli.2007.33.3.293

Jakilim, N. M., Mahamudul Hasan, S., & Hassan, E. (2022). A Benchmark of Machine Learning and Deep Learning Algorithms for Detecting Fake News in Bangla Language. 2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI), 1–6. https://doi.org/10.1109/STI56238. 2022.10103235

Jurado, S., Nebot, A., Mugica, F., & Avellana, N. (2015). Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques. Energy, 86(15), 276–291.

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Mei, K., Liu, J., Zhang, X., Rajatheva, N., & Wei, J. (2021). Performance Analysis on Machine Learning-Based Channel Estimation. IEEE Transactions on Communications, 69(8), 5183–5193. https://doi.org/10.1109/TCOMM.2021.308 3597

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Moreira, M. W. L., Rodrigues, J. J. P. C., Korotaev, V., Al-Muhtadi, J., & Kumar, N. (2019). A Comprehensive Review on Smart Decision Support Systems for Health Care. IEEE Systems Journal, 13(3), 3536–3545. https://doi.org/10.1109/ JSYST.2018.2890121

Murphy, A., & Moore, C. (2019). Confusion matrix. Radiopaedia.Org, October. https://doi.org/10.53347/rid-68081

Powers, D. M. W. (2020). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. 37–63. http://arxiv.org/abs/2010.16061

Qi, J., Du, J., Siniscalchi, S. M., Ma, X., & Lee, C. H. (2020). On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression. IEEE Signal Processing Letters, 27, 1485–1489. https://doi.org/10.1109/LSP.2020.3016837

Salmon, B. P., Kleynhans, W., Schwegmann, C. P., & Olivier, J. C. (2015). Proper comparison among methods using a confusion matrix. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 3057–3060. https://doi.org/10.1109/IGARSS.2015.7326461

Steurer, M., Hill, R. J., & Pfeifer, N. (2021). Metrics for evaluating the performance of machine learning based automated valuation models. Journal of Property Research, 38(2), 99–129. https://doi.org/10.1080/09599916.2020.1858937

Tatachar, A. V. (2021). Comparative assessment of regression models based On model evaluation metrics. International Research Journal of Engineering and Technology, 8(9), 853–860. https://d1wqtxts1xzle7.cloudfront.net/73250877/IRJET_V8I9127-libre.pdf

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Voyant, C., Notton, G., Kalogirou, S., Niveta, M. L., Paoli, C., Motte, F., & Fouilloy, A. (2017). Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105, 569–582.

Widyanto, N., Kim, J. W., & Nursikuwagus, A. (2024). Best Classifier Over Feature Selection and Delta Error in Time Series Dataset. Journal of Engineering Science and Technology, 19(2), 390–404.

Yacim, J. A., & D. G. B., B. (2018). Impact of Artificial Neural Networks Training Algorithms on Accurate Prediction of Property Valuese. Journal of Real Estate Research, 40(3), 375–418.

Adhikari, D., Jiang, W., Zhan, J., Rawat, D. B., & Bhattarai, A. (2024). Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives. Computer Science Review, 54, 100665. https://doi.org/https://doi.org/10. 1016/j.cosrev.2024.100665

Benhar, H., Idri, A., & Fernández-Alemán, J. L. (2020). Data preprocessing for heart disease classification: A systematic literature review. Computer Methods and Programs in Biomedicine, 195, 105635. https://doi.org/https://doi.org/10.1016/ j.cmpb.2020.105635

Chen, Z. (2024). 4 - Vehicle motion recognition method based on vehicle road fusion information. In Z. Chen (Ed.), Construction Methods for an Autonomous Driving Map in an Intelligent Network Environment (pp. 75–102). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-443-27316-2.00004-1

Jimenez-del-Toro, O., Otálora, S., Andersson, M., Eurén, K., Hedlund, M., Rousson, M., Müller, H., & Atzori, M. (2017). Chapter 10 - Analysis of Histopathology Images: From Traditional Machine Learning to Deep Learning. In A. Depeursinge, O. S. Al-Kadi, & J. R. Mitchell (Eds.), Biomedical Texture Analysis (pp. 281–314). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-812133-7.00010-7

Kalita, J. K., Bhattacharyya, D. K., & Roy, S. (2024). 8 - Feature selection. In J. K. Kalita, D. K. Bhattacharyya, & S. Roy (Eds.), Fundamentals of Data Science (pp. 161–179). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-32-391778-0.00015-6

Lee, T.-W. (1998). Independent Component Analysis BT - Independent Component Analysis: Theory and Applications (T.-W. Lee (ed.); pp. 27–66). Springer US. https://doi.org/10.1007/978-1-4757-2851-4_2

Maldonado, J., Riff, M. C., & Neveu, B. (2022). A review of recent approaches on wrapper feature selection for intrusion detection. Expert Systems with Applications, 198, 116822. https://doi.org/https://doi.org/10.1016/j.eswa.2022.116822

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