Abstract
Engineering and manufacturing processes and systems designs involve many challenges, such as dynamism, chaotic behaviours, and complexity. Of late, the arrival of big data, high computational speed, cloud computing and artificial intelligence techniques (like machine learning and deep learning) has reformed how many engineering and manufacturing professionals approach their work. These technologies offer thrilling innovative ways for engineers and manufacturers to tackle real-life challenges. On the other hand, the field of Artificial Intelligence (AI) is extensive. Several diverse theories, algorithms, and methods are available, which presents a challenge and a barrier in choosing the right AI technique for the appropriate engineering process or manufacturing process and environments. Besides, the pertinent literature is disseminated over various journals, conference proceedings, and research communities. Hence, conducting a systematic survey to scrutinise and classify the existing literature is worthwhile. However, it is challenging, but previous review studies have not adequately addressed AI’s use and advancement in engineering and manufacturing (EM). Besides, some concentrated on single AI models, and others focused on a specific area in EM. This paper presents a comprehensive systematic review of studies on AI and its application in EM. To limit the scope of the current study, we conducted a keyword search in official publisher websites and academic databases, such as Springer, Elsevier, Scopus, Science Publication, Taylor & Francis, Directory of Open Access Journals (DOAJ), Association for Computing Machinery (ACM), Wiley online library, Inderscience and Google scholar. The search results (173 articles) were filtered according to a proposed framework, which resulted in ninety-one (91) relevant research articles. We reviewed the articles based on a proposed taxonomy (the year of publication, the AI algorithm and machine learning task adopted, the application area in EM, the train and test split of data, the error, and accuracy metrics used, the potential benefits). Our assessment using the proposed taxonomy gave a helpful insight into the literature’s anatomy on various AI applications in engineering and manufacturing. Also, we identified opportunities for future research in AI application in the field of EM.
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Abbas, A., Schuld, M., & Petruccione, F. (2020). On quantum ensembles of quantum classifiers. Quantum Machine Intelligence, 2(1), 1–8. https://doi.org/10.1007/s42484-020-00018-6.
Aggour, K. S., Gupta, V. K., Ruscitto, D., Ajdelsztajn, L., Bian, X., Brosnan, K. H., Chennimalai Kumar, N., Dheeradhada, V., Hanlon, T., Iyer, N., Karandikar, J., Li, P., Moitra, A., Reimann, J., Robinson, D. M., Santamaria-Pang, A., Shen, C., Soare, M. A., Sun, C., & Vinciquerra, J. (2019). Artificial intelligence/machine learning in manufacturing and inspection: A GE perspective. MRS Bulletin, 44(7), 545–558. https://doi.org/10.1557/mrs.2019.157.
Ahila, R., Sadasivam, V., & Manimala, K. (2015). An integrated PSO for parameter determination and feature selection of ELM and its application in classification of power system disturbances. Applied Soft Computing, 32, 23–37. https://doi.org/10.1016/j.asoc.2015.03.036.
Akshatha, K. R., & Shreedhara, K. S. (2018). Implementation of machine learning algorithms for crop recommendation using precision agriculture. In International Journal of Research in Engineering (Vol. 1, Issue 6). www.ijresm.com.
Akyeramfo-Sam, S., Addo Philip, A., Yeboah, D., Nartey, N. C., & Kofi Nti, I. (2019). A web-based skin disease diagnosis using convolutional neural networks. International Journal of Information Technology and Computer Science, 11(11), 54–60. https://doi.org/10.5815/ijitcs.2019.11.06.
Alipour, M., Harris, D. K., Barnes, L. E., Ozbulut, O. E., & Carroll, J. (2017). Load-capacity rating of bridge populations through machine learning: application of decision trees and random forests. Journal of Bridge Engineering, 22(10), 04017076. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001103.
Allamanis, M., Barr, E. T., Devanbu, P., & Sutton, C. (2018). A survey of machine learning for big code and naturalness. ACM Computing Surveys. https://doi.org/10.1145/3212695.
Appiah, A. Y., Zhang, X., Ayawli, B. B. K., & Kyeremeh, F. (2019). Long short-term memory networks based automatic feature extraction for photovoltaic array fault diagnosis. IEEE Access, 7, 1–1. https://doi.org/10.1109/ACCESS.2019.2902949.
Arcomano, T., Szunyogh, I., Pathak, J., Wikner, A., Hunt, B. R., & Ott, E. (2020). A machine learning-based global atmospheric forecast model. Geophysical Research Letters. https://doi.org/10.1029/2020GL087776.
Ardabili, S., Mosavi, A., & Várkonyi-Kóczy, A. R. (2020). Systematic review of deep learning and machine learning models in biofuels research. In Melting threshold and thermal conductivity of CdTe under pulsed laser irradiation (Vol. 101, pp. 29–42). Springer. https://doi.org/10.1007/978-3-030-36841-8_10.
Ayawli, B. B. K., Mei, X., Shen, M., Appiah, A. Y., & Kyeremeh, F. (2019). Mobile robot path planning in dynamic environment using voronoi diagram and computation geometry technique. IEEE Access, 7, 86026–86040. https://doi.org/10.1109/ACCESS.2019.2925623.
Aziz, F., Ul Haq, A., Ahmad, S., Mahmoud, Y., Jalal, M., & Ali, U. (2020). A novel convolutional neural network-based approach for fault classification in photovoltaic arrays. IEEE Access, 8, 41889–41904. https://doi.org/10.1109/ACCESS.2020.2977116.
Badesa, F. J., Morales, R., Garcia-Aracil, N., Sabater, J. M., Casals, A., & Zollo, L. (2014). Auto-adaptive robot-aided therapy using machine learning techniques. Computer Methods and Programs in Biomedicine, 116(2), 123–130. https://doi.org/10.1016/j.cmpb.2013.09.011.
Bagheri, M., Al-jabery, K., Wunsch, D., & Burken, J. G. (2020). Examining plant uptake and translocation of emerging contaminants using machine learning: Implications to food security. Science of the Total Environment, 698, 133999. https://doi.org/10.1016/j.scitotenv.2019.133999.
Bedbrook, C. N., Yang, K. K., Robinson, J. E., Mackey, E. D., Gradinaru, V., & Arnold, F. H. (2019). Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics. Nature Methods, 16(11), 1176–1184. https://doi.org/10.1038/s41592-019-0583-8.
Ben Guedria, N. (2016). Improved accelerated PSO algorithm for mechanical engineering optimisation problems. Applied Soft Computing, 40, 455–467. https://doi.org/10.1016/j.asoc.2015.10.048.
Bi, Z., Yu, L., Gao, H., Zhou, P., & Yao, H. (2020). Improved VGG model-based efficient traffic sign recognition for safe driving in 5G scenarios. International Journal of Machine Learning and Cybernetics. https://doi.org/10.1007/s13042-020-01185-5.
Boateng, O. N., Asante, M., & Nti, I. K. (2017). Implementation of advanced encryption standard algorithm with key length of 256 bits for preventing data loss in an organization. International Journal of Science and Engineering Applications, 6(3), 88–94. https://doi.org/10.7753/IJSEA0603.1004.
Borges Hink, R. C., Beaver, J. M., Buckner, M. A., Morris, T., Adhikari, U., & Pan, S. (2014). Machine learning for power system disturbance and cyber-attack discrimination. 7th International symposium on resilient control systems, ISRCS 2014. https://doi.org/10.1109/ISRCS.2014.6900095
Catal, C., & Diri, B. (2009). A systematic review of software fault prediction studies. Expert Systems with Applications, 36(4), 7346–7354. https://doi.org/10.1016/j.eswa.2008.10.027.
Catalina, A., Torres-Barrán, A., Alaíz, C. M., & Dorronsoro, J. R. (2020). Machine learning nowcasting of PV energy using satellite data. Neural Processing Letters, 52(1), 97–115. https://doi.org/10.1007/s11063-018-09969-1.
Cemernek, D., Cemernek, S., Gursch, H., Pandeshwar, A., Leitner, T., Berger, M., Klösch, G., & Kern, R. (2021). Machine learning in continuous casting of steel: a state-of-the-art survey. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01754-7.
Chen, L., Han, W., Li, H.-T., Xu, Z.-K., Zhang, J.-W., & Cao, X. (2020). Long distance wireless fault diagnosis for photovoltaic modules based on back propagation neural network. The International Journal of Electrical Engineering & Education. https://doi.org/10.1177/0020720920940601.
Chen, S., Leng, Y., & Labi, S. (2020). A deep learning algorithm for simulating autonomous driving considering prior knowledge and temporal information. Computer-Aided Civil and Infrastructure Engineering, 35(4), 305–321. https://doi.org/10.1111/mice.12495.
Chen, T. (2006). A hybrid SOM-BPN approach to lot output time prediction in a wafer fab. Neural Processing Letters, 24(3), 271–288. https://doi.org/10.1007/s11063-006-9027-4.
Chin, K., Hellebrekers, T., & Majidi, C. (2020). Machine learning for soft robotic sensing and control. Advanced Intelligent Systems, 2(6), 1900171. https://doi.org/10.1002/aisy.201900171.
Cho, K. H., Jo, H. C., Kim, E. S., Park, H. A., & Park, J. H. (2020). Failure diagnosis method of photovoltaic generator using support vector machine. Journal of Electrical Engineering and Technology, 15(4), 1669–1680. https://doi.org/10.1007/s42835-020-00430-9.
Cortés Sáenz, D., Gordillo Castillo, N., Riba Romeva, C., & Lloveras Macià, J. (2015). A fuzzy approach for the selection of non-traditional sheet metal cutting processes. Expert Systems with Applications, 42(15–16), 6147–6154. https://doi.org/10.1016/j.eswa.2015.03.030.
Damisa, U., Gbadegesin, A. O., & Nwulu, N. I. (2019). Optimal power scheduling in a grid-connected prosumer-only DC microgrid. 2019 IEEE 6th international conference on engineering technologies and applied sciences (ICETAS) (pp. 1–5). https://doi.org/10.1109/ICETAS48360.2019.9117335
Dao, T.-K., Pan, T.-S., Nguyen, T.-T., & Pan, J.-S. (2018). Parallel bat algorithm for optimising makespan in job shop scheduling problems. Journal of Intelligent Manufacturing, 29(2), 451–462. https://doi.org/10.1007/s10845-015-1121-x.
Deng, S., & Yeh, T. H. (2011). Using least squares support vector machines for the airframe structures manufacturing cost estimation. International Journal of Production Economics, 131(2), 701–708. https://doi.org/10.1016/j.ijpe.2011.02.019.
Ding, D., Torres, A. J., Pikus, F. G., & Pan, D. Z. (2011). High performance lithographic hotspot detection using hierarchically refined machine learning. Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC. https://doi.org/10.1109/ASPDAC.2011.5722294.
Ding, D., Yu, B., Ghosh, J., & Pan, D. Z. (2012). EPIC: Efficient prediction of IC manufacturing hotspots with a unified meta-classification formulation. Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC. https://doi.org/10.1109/ASPDAC.2012.6164956.
du Preez, A., & Oosthuizen, G. A. (2019). Machine learning in cutting processes as enabler for smart sustainable manufacturing. Procedia Manufacturing, 33, 810–817. https://doi.org/10.1016/j.promfg.2019.04.102.
El Sallab, A., Abdou, M., Perot, E., & Yogamani, S. (2017). Deep reinforcement learning framework for autonomous driving. IS and T International Symposium on Electronic Imaging Science and Technology. https://doi.org/10.2352/ISSN.2470-1173.2017.19.AVM-023.
Elbes, M., Alzubi, S., Kanan, T., Al-Fuqaha, A., & Hawashin, B. (2019). A survey on particle swarm optimisation with emphasis on engineering and network applications. Evolutionary Intelligence, 12(2), 113–129. https://doi.org/10.1007/s12065-019-00210-z.
Eliseeva, O. V., Kirk, T., Samimi, P., Malak, R., Arróyave, R., Elwany, A., & Karaman, I. (2019). Functionally graded materials through robotics-inspired path planning. Materials and Design, 182, 107975. https://doi.org/10.1016/j.matdes.2019.107975.
Fan, Z., & Liu, R. (2017). Investigation of machine learning based network traffic classification. In Proceedings of the international symposium on wireless communication systems, 2017-Augus (pp. 1–6). https://doi.org/10.1109/ISWCS.2017.8108090.
Feldmann, S., Herzig, S. J. I., Kernschmidt, K., Wolfenstetter, T., Kammerl, D., Qamar, A., Lindemann, U., Krcmar, H., Paredis, C. J. J., & Vogel-Heuser, B. (2015). Towards effective management of inconsistencies in model-based engineering of automated production systems. IFAC-PapersOnLine, 28(3), 916–923. https://doi.org/10.1016/j.ifacol.2015.06.200.
Fong, J., Ocampo, R., Gross, D. P., & Tavakoli, M. (2020). Intelligent robotics incorporating machine learning algorithms for improving functional capacity evaluation and occupational rehabilitation. Journal of Occupational Rehabilitation, 30(3), 362–370. https://doi.org/10.1007/s10926-020-09888-w.
Fourati, H., Maaloul, R., & Chaari, L. (2020). A survey of 5G network systems: Challenges and machine learning approaches. International Journal of Machine Learning and Cybernetics. https://doi.org/10.1007/s13042-020-01178-4.
Fuge, M., Peters, B., & Agogino, A. (2014). Machine learning algorithms for recommending design methods. Journal of Mechanical Design, Transactions of the ASME, 136(10), 1–8. https://doi.org/10.1115/1.4028102.
Fujiyoshi, H., Hirakawa, T., & Yamashita, T. (2019). Deep learning-based image recognition for autonomous driving. In IATSS research (Vol. 43, Issue 4, pp. 244–252). Elsevier Ltd. https://doi.org/10.1016/j.iatssr.2019.11.008.
George, A. (2012). Anomaly detection based on machine learning dimensionality reduction using PCA and classification using SVM. International Journal of Computer Applications, 47(21), 5–8. https://doi.org/10.5120/7470-0475.
Ghadimi, N. (2015). A new hybrid algorithm based on optimal fuzzy controller in multimachine power system. Complexity, 21(1), 78–93. https://doi.org/10.1002/cplx.21544.
Gu, G. X., & Chen, C. (2018). Materials Horizons machine learning : simulation , additive manufacturing , and experiment. 939–945. https://doi.org/10.1039/c8mh00653a.
Guisiano, J., Chiky, R., Orensanz, J., & Ahvar, S. (2020). Multilayered ensemble learning for short-term forecasting in agro-climatology. In Proceedings of the 2020 6th international conference on computer and technology applications (pp. 7–11). https://doi.org/10.1145/3397125.3397130.
Hegde, J., & Rokseth, B. (2020). Applications of machine learning methods for engineering risk assessment—A review. Safety Science. https://doi.org/10.1016/j.ssci.2019.09.015.
Hoermann, S., Bach, M., & Dietmayer, K. (2018). Dynamic occupancy grid prediction for urban autonomous driving: A deep learning approach with fully automatic labeling. Proceedings of IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/ICRA.2018.8460874.
Hwang, S. H., Ham, D. H., & Kim, J. H. (2012). A new measure for assessing the efficiency of hydrological data-driven forecasting models. Hydrological Sciences Journal, 57(7), 1257–1274. https://doi.org/10.1080/02626667.2012.710335.
Jabbour, C. J. C. (2013). Environmental training in organisations: From a literature review to a framework for future research. Resources, Conservation and Recycling, 74(1), 144–155. https://doi.org/10.1016/j.resconrec.2012.12.017.
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2018.02.016.
Kateris, D., Moshou, D., Pantazi, X. E., Gravalos, I., Sawalhi, N., & Loutridis, S. (2014). A machine learning approach for the condition monitoring of rotating machinery. Journal of Mechanical Science and Technology, 28(1), 61–71. https://doi.org/10.1007/s12206-013-1102-y.
Kilundu, B., Dehombreux, P., & Chiementin, X. (2011). Tool wear monitoring by machine learning techniques and singular spectrum analysis. Mechanical Systems and Signal Processing, 25(1), 400–415. https://doi.org/10.1016/j.ymssp.2010.07.014.
Kim, D., Kang, P., Cho, S., Lee, H. J., & Doh, S. (2012). Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing. Expert Systems with Applications, 39(4), 4075–4083. https://doi.org/10.1016/j.eswa.2011.09.088.
Kim, D.-H., Kim, T. J. Y., Wang, X., Kim, M., Quan, Y.-J., Oh, J. W., Min, S.-H., Kim, H., Bhandari, B., Yang, I., & Ahn, S.-H. (2018). Smart machining process using machine learning: A review and perspective on machining industry. International Journal of Precision Engineering and Manufacturing-Green Technology, 5(4), 555–568. https://doi.org/10.1007/s40684-018-0057-y.
Köksal, G., Batmaz, I., & Testik, M. C. (2011). A review of data mining applications for quality improvement in manufacturing industry. In Expert systems with applications (Vol. 38, Issue 10, pp. 13448–13467). https://doi.org/10.1016/j.eswa.2011.04.063.
Kong, X., Fu, Y., Wang, Q., Ma, H., Wu, X., & Mao, G. (2020). A high generalisable feature extraction method using ensemble learning and deep auto-encoders for operational reliability assessment of bearings. Neural Processing Letters, 51(1), 383–406. https://doi.org/10.1007/s11063-019-10094-w.
Kong, X., Li, C., Wang, C., Zhang, Y., & Zhang, J. (2020). Short-term electrical load forecasting based on error correction using dynamic mode decomposition. Applied Energy. https://doi.org/10.1016/j.apenergy.2019.114368.
Kroll, B., Schaffranek, D., Schriegel, S., & Niggemann, O. (2014). System modeling based on machine learning for anomaly detection and predictive maintenance in industrial plants. In BT 19th IEEE international conference on emerging technologies and factory automation, ETFA 2014, September 16, 2014–September 19, 2014. IEEE Industrial Electronics Society (IES); Technic. https://doi.org/10.1109/ETFA.2014.7005202.
Lage Junior, M., & Godinho Filho, M. (2010). Variations of the kanban system: Literature review and classification. International Journal of Production Economics, 125(1), 13–21. https://doi.org/10.1016/j.ijpe.2010.01.009.
Lechevalier, D., Narayanan, A., & Rachuri, S. (2014). Towards a domain-specific framework for predictive analytics in manufacturing. IEEE International Conference on Big Data (Big Data), 2014, 987–995. https://doi.org/10.1109/BigData.2014.7004332.
Lee, J., Ghaffari, M., & Elmeligy, S. (2011). Self-maintenance and engineering immune systems: Towards smarter machines and manufacturing systems. Annual Reviews in Control, 35(1), 111–122. https://doi.org/10.1016/j.arcontrol.2011.03.007.
Lei, Y., Jia, F., Lin, J., Xing, S., & Ding, S. X. (2016). An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Transactions on Industrial Electronics, 63(5), 3137–3147. https://doi.org/10.1109/TIE.2016.2519325.
Li, C., Ding, Z., Zhao, D., Yi, J., & Zhang, G. (2017). Building energy consumption prediction: An extreme deep learning approach. Energies, 10(10), 1525. https://doi.org/10.3390/en10101525.
Li, Y., Zhang, S., Yin, Y., Zhang, J., & Xiao, W. (2019). A soft sensing scheme of gas utilisation ratio prediction for blast furnace via improved extreme learning machine. Neural Processing Letters, 50(2), 1191–1213. https://doi.org/10.1007/s11063-018-9888-3.
Li, Y., Zou, L., Jiang, L., & Zhou, X. (2019). Fault diagnosis of rotating machinery based on combination of deep belief network and one-dimensional convolutional neural network. IEEE Access, 7, 165710–165723. https://doi.org/10.1109/ACCESS.2019.2953490.
Li, Z., Ma, X., & Xin, H. (2017). Feature engineering of machine-learning chemisorption models for catalyst design. Catalysis Today, 280, 232–238. https://doi.org/10.1016/j.cattod.2016.04.013.
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors (Switzerland), 18(8), 1–29. https://doi.org/10.3390/s18082674.
Liang, J., Li, C., & Zheng, L. (2016). Machine learning application in MOOCs: Dropout prediction. In ICCSE 2016 11th international conference on computer science and education, Iccse (pp. 52–57). https://doi.org/10.1109/ICCSE.2016.7581554.
Liu, H., Fu, Z., Yang, K., Xu, X., & Bauchy, M. (2021). Machine learning for glass science and engineering: A review. Journal of Non-Crystalline Solids, 557(April), 119–419. https://doi.org/10.1016/j.jnoncrysol.2019.04.039.
Lu, W., Liang, B., Cheng, Y., Meng, D., Yang, J., & Zhang, T. (2017). Deep model based domain adaptation for fault diagnosis. IEEE Transactions on Industrial Electronics, 64(3), 2296–2305. https://doi.org/10.1109/TIE.2016.2627020.
Mackeprang, J., Dasari, D. B. R., & Wrachtrup, J. (2020). A reinforcement learning approach for quantum state engineering. Quantum Machine Intelligence, 2(1), 1–14. https://doi.org/10.1007/s42484-020-00016-8.
Malhotra, R. (2015). A systematic review of machine learning techniques for software fault prediction. Applied Soft Computing, 27, 504–518. https://doi.org/10.1016/j.asoc.2014.11.023.
McCoy, J. T., & Auret, L. (2019). Machine learning applications in minerals processing: A review. Minerals Engineering. https://doi.org/10.1016/j.mineng.2018.12.004.
Meidan, Y., Lerner, B., Rabinowitz, G., & Hassoun, M. (2011). Cycle-time key factor identification and prediction in semiconductor manufacturing using machine learning and data mining. IEEE Transactions on Semiconductor Manufacturing, 24(2), 237–248. https://doi.org/10.1109/TSM.2011.2118775.
Milosevic, N., Dehghantanha, A., & Choo, K.-K.R. (2017). Machine learning aided Android malware classification. Computers & Electrical Engineering, 61, 266–274. https://doi.org/10.1016/j.compeleceng.2017.02.013.
Mo, Y., Wu, Q., Li, X., & Huang, B. (2021). Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01750-x.
Moayedi, H., Raftari, M., Sharifi, A., Jusoh, W. A. W., & Rashid, A. S. A. (2020). Optimisation of ANFIS with GA and PSO estimating α ratio in driven piles. Engineering with Computers, 36(1), 227–238. https://doi.org/10.1007/s00366-018-00694-w.
Mosavi, A., Salimi, M., Faizollahzadeh Ardabili, S., Rabczuk, T., Shamshirband, S., & Varkonyi-Koczy, A. (2019). State of the art of machine learning models in energy systems, a systematic review. Energies, 12(7), 1301. https://doi.org/10.3390/en12071301.
Mueller, A., & M. . (2017). Reinforcement Learning : MDP applied to autonomous navigation. Machine Learning and Applications: An International Journal, 4(4), 01–10. https://doi.org/10.5121/mlaij.2017.4401.
Mwedzi, N. A., Nwulu, N. I., & Gbadamosi, S. L. (2019). Machine learning applications for fire detection in a residential building. In 2019 IEEE 6th International conference on engineering technologies and applied sciences (ICETAS) (pp. 1–4). https://doi.org/10.1109/ICETAS48360.2019.9117318.
Naresh, E., Vijaya Kumar, B. P., Ayesha, & Shankar, S. P. (2020). Impact of machine learning in bioinformatics research. In Statistical modelling and machine learning principles for bioinformatics techniques, tools, and applications. algorithms for intelligent systems (pp. 41–62). https://doi.org/10.1007/978-981-15-2445-5_4.
Nosratabadi, S., Imre, F., Szell, K., Ardabili, S., Beszedes, B., & Mosavi, A. (2020). Hybrid machine learning models for crop yield prediction. http://arxiv.org/abs/2005.04155.
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2019a). A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review, 53(4), 3007–3057. https://doi.org/10.1007/s10462-019-09754-z.
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2019b). Random forest based feature selection of macroeconomic variables for stock market prediction. American Journal of Applied Sciences, 16(7), 200–212. https://doi.org/10.3844/ajassp.2019.200.212.
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020b). Predicting stock market price movement using sentiment analysis: Evidence from Ghana. Applied Computer Systems, 25(1), 33–42. https://doi.org/10.2478/acss-2020-0004.
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020c). Efficient stock-market prediction using ensemble support vector machine. Open Computer Science, 10(1), 153–163. https://doi.org/10.1515/comp-2020-0199.
Nti, I. K., Appiah, A. Y., & Nyarko-Boateng, O. (2020). Assessment and prediction of earthing resistance in domestic installation. Engineering Reports. https://doi.org/10.1002/eng2.12090.
Nti, I. K., Eric, G., & Samuel, Y. (2017). Detection of plant leaf disease employing image processing and gaussian smoothing approach. International Journal of Computer Applications, 162(2), 20–25. https://doi.org/10.5120/ijca2017913260.
Nti, I. K., & Quarcoo, J. A. (2019). Self-motivation and academic performance in computer programming language using a hybridised machine learning technique. International Journal of Artificial Intelligence and Expert Systems (IJAE), 8(2), 12–30.
Nti, I. K., Samuel, A., Michael, A., Asafo-Adjei, S., Agyemang, M., Samuel, A., & Michael, A. (2019). Predicting monthly electricity demand using soft-computing technique. International Research Journal of Engineering and Technology (IRJET), 06(06), 1967–1973.
Nti, I. K., Teimeh, M., Adekoya, A. F., & Nyarko-Boateng, O. (2020). Forecasting electricity consumption of residential users based on lifestyle data using artificial neural networks. ICTACT Journal on Soft Computing, 10(3), 2107–2116. https://doi.org/10.21917/ijsc.2020.0300.
Nutkiewicz, A., Yang, Z., & Jain, R. K. (2018). Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow. Applied Energy, 225(January), 1176–1189. https://doi.org/10.1016/j.apenergy.2018.05.023.
Panagiotakis, C., Papadakis, H., & Fragopoulou, P. (2020). Unsupervised and supervised methods for the detection of hurriedly created profiles in recommender systems. International Journal of Machine Learning and Cybernetics, 11(9), 2165–2179. https://doi.org/10.1007/s13042-020-01108-4.
Parfitt, W. A., & Jackman, R. B. (2020). Machine learning for the prediction of stopping powers. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 478(May), 21–33. https://doi.org/10.1016/j.nimb.2020.05.015.
Park, C., Took, C. C., & Seong, J. K. (2018). Machine learning in biomedical engineering. Biomedical Engineering Letters, 8(1), 1–3. https://doi.org/10.1007/s13534-018-0058-3.
Park, J. K., Kwon, B. K., Park, J. H., & Kang, D. J. (2016). Machine learning-based imaging system for surface defect inspection. International Journal of Precision Engineering and Manufacturing Green Technology, 3(3), 303–310. https://doi.org/10.1007/s40684-016-0039-x.
Patel, R. A., & Bhalja, B. R. (2016). Condition monitoring and fault diagnosis of induction motor using support vector machine. Electric Power Components and Systems, 44(6), 683–692. https://doi.org/10.1080/15325008.2015.1131762.
Picasso, A., Merello, S., Ma, Y., Oneto, L., & Cambria, E. (2019). Technical analysis and sentiment emb e ddings for market trend prediction. Expert Systems With Applications, 135, 60–70. https://doi.org/10.1016/j.eswa.2019.06.014.
Ravikumar, S., Ramachandran, K. I., & Sugumaran, V. (2011). Machine learning approach for automated visual inspection of machine components. Expert Systems with Applications, 38(4), 3260–3266. https://doi.org/10.1016/j.eswa.2010.09.012.
Regis, N., Muriithi, C. M., & Ngoo, L. (2019). Optimal battery sizing of a grid-connected residential photovoltaic system for cost minimisation using PSO algorithm. Engineering, Technology & Applied Science Research, 9(6), 4905–4911. https://doi.org/10.48084/etasr.3094.
Reynen, A., & Audet, P. (2017). Supervised machine learning on a network scale: Application to seismic event classification and detection. Geophysical Journal International, 210(3), 1394–1409. https://doi.org/10.1093/gji/ggx238.
Rost, A., & Schädle, S. (2013). The SLS-generated soft robotic hand—An integrated approach using additive manufacturing and reinforcement learning. In Proceedings of 2013 12th international conference on machine learning and applications, ICMLA 2013, 1 (pp. 215–220). https://doi.org/10.1109/ICMLA.2013.44.
Rudin, C., Waltz, D., Anderson, R., Boulanger, A., Salleb-Aouissi, A., Chow, M., Dutta, H., Gross, P., Huang, B., Ierome, S., Isaac, D. F., Kressner, A., Passonneau, R. J., Radeva, A., & Wu, L. (2012). Machine learning for the New York City power grid. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(2), 328–345. https://doi.org/10.1109/TPAMI.2011.108.
Sacco, C., Baz Radwan, A., Anderson, A., Harik, R., & Gregory, E. (2020). Machine learning in composites manufacturing: A case study of automated fiber placement inspection. Composite Structures, 250, 112514. https://doi.org/10.1016/j.compstruct.2020.112514.
Sharma, A., Zhang, Z., & Rai, R. (2020). Interpretive Model of Manufacturing : A Review of Machine Learning in Manufacturing. https://digitalcommons.wayne.edu/business_frp/2.
Sharma, R., Kamble, S. S., Gunasekaran, A., Kumar, V., & Kumar, A. (2020). A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers and Operations Research, 119, 104926. https://doi.org/10.1016/j.cor.2020.104926.
Shastry, K. A., & Sanjay, H. A. (2020). Machine Learning for Bioinformatics. In Statistical modelling and machine learning principles for bioinformatics techniques, tools, and applications (pp. 25–39). https://doi.org/10.1007/978-981-15-2445-5_3.
Shen, T., Nagai, Y., & Gao, C. (2020). Design of building construction safety prediction model based on optimised BP neural network algorithm. Soft Computing, 24(11), 7839–7850. https://doi.org/10.1007/s00500-019-03917-4.
Shin, S. J., Woo, J., & Rachuri, S. (2014). Predictive analytics model for power consumption in manufacturing. Procedia CIRP, 15, 153–158. https://doi.org/10.1016/j.procir.2014.06.036.
Simeone, O. (2018). A very brief introduction to machine learning with applications to communication systems. IEEE Transactions on Cognitive Communications and Networking, 4(4), 648–664. https://doi.org/10.1109/TCCN.2018.2881442.
Stanisavljevic, D., & Spitzer, M. (2016). A review of related work on machine learning in semiconductor manufacturing and assembly lines. August 2018.
Subrahmanya, N., Shin, Y. C., & Meckl, P. H. (2010). A Bayesian machine learning method for sensor selection and fusion with application to on-board fault diagnostics. Mechanical Systems and Signal Processing, 24(1), 182–192. https://doi.org/10.1016/j.ymssp.2009.06.010.
Sun, J., Li, H., Fujita, H., Fu, B., & Ai, W. (2020). Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting. Information Fusion. https://doi.org/10.1016/j.inffus.2019.07.006.
Syafrudin, M., Alfian, G., Fitriyani, N. L., & Rhee, J. (2018). Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing. Sensors (Switzerland). https://doi.org/10.3390/s18092946.
Tam, J. H., Ong, Z. C., Ismail, Z., Ang, B. C., & Khoo, S. Y. (2019). A new hybrid GA−ACO−PSO algorithm for solving various engineering design problems. International Journal of Computer Mathematics, 96(5), 883–919. https://doi.org/10.1080/00207160.2018.1463438.
Tomin, N., Zhukov, A., Sidorov, D., Kurbatsky, V., Panasetsky, D., & Spiryaev, V. (2015). Random forest based model for preventing large-scale emergencies in power systems. International Journal of Artificial Intelligence, 13(1), 211–228.
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375.
Treml, M., Arjona-medina, J., Unterthiner, T., Durgesh, R., Friedmann, F., Schuberth, P., Mayr, A., Heusel, M., Hofmarcher, M., Widrich, M., Nessler, B., & Hochreiter, S. (2016). Speeding up semantic segmentation for autonomous driving. NIPS 2016 Workshop MLITS, Nips, 1–7. https://openreview.net/pdf?id=S1uHiFyyg%0Ahttps://openreview.net/forum?id=S1uHiFyyg.
Varshney, K. R. (2016). Engineering safety in machine learning. Information Theory and Applications Workshop (ITA), 2016, 1–5. https://doi.org/10.1109/ITA.2016.7888195.
Veloso de Melo, V., & Banzhaf, W. (2018). Automatic feature engineering for regression models with machine learning: An evolutionary computation and statistics hybrid. Information Sciences, 430–431, 287–313. https://doi.org/10.1016/j.ins.2017.11.041.
Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing : Methods and applications. Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.2018.01.003.
Wang, Z., Ma, G., Gong, D., Sun, J., & Zhang, D. (2019). Application of mind evolutionary algorithm and artificial neural networks for prediction of profile and flatness in hot strip rolling process. Neural Processing Letters, 50(3), 2455–2479. https://doi.org/10.1007/s11063-019-10021-z.
Ward, L., O’Keeffe, S. C., Stevick, J., Jelbert, G. R., Aykol, M., & Wolverton, C. (2018). A machine learning approach for engineering bulk metallic glass alloys. Acta Materialia, 159, 102–111. https://doi.org/10.1016/j.actamat.2018.08.002.
Wei, L., Luo, W., Weng, J., Zhong, Y., Zhang, X., & Yan, Z. (2017). Machine learning-based malicious application detection of android. IEEE Access, 5, 25591–25601. https://doi.org/10.1109/ACCESS.2017.2771470.
Wen, J., Li, S., Lin, Z., Hu, Y., & Huang, C. (2012). Systematic literature review of machine learning based software development effort estimation models. Information and Software Technology, 54(1), 41–59. https://doi.org/10.1016/j.infsof.2011.09.002.
Wuest, T., Irgens, C., & Thoben, K. D. (2014). An approach to monitoring quality in manufacturing using supervised machine learning on product state data. Journal of Intelligent Manufacturing, 25(5), 1167–1180. https://doi.org/10.1007/s10845-013-0761-y.
Wuest, T., Weimer, D., Irgens, C., & Thoben, K. (2016). Machine learning in manufacturing: Advantages, challenges, and applications. Production & Manufacturing Research, 4(1), 23–45. https://doi.org/10.1080/21693277.2016.1192517.
Xing, F. Z., Cambria, E., & Welsch, R. E. (2018). Natural language based financial forecasting: A survey. Artificial Intelligence Review, 50(1), 49–73. https://doi.org/10.1007/s10462-017-9588-9.
Zhang, J., Liu, Y., Li, Y., Ding, K., Feng, L., Chen, X., Chen, X., & Wu, J. (2020). A reinforcement learning based approach for online adaptive parameter extraction of photovoltaic array models. Energy Conversion and Management, 214(January), 112875. https://doi.org/10.1016/j.enconman.2020.112875.
Zhang, W., Mehta, A., Desai, P. S., & Iii, C. F. H. (2017). Machine learning enabled powder spreading process map for metal additive manufacturing (AM). 1235–1249.
Zhang, X., Chen, W., Wang, B., & Chen, X. (2015). Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimisation. Neurocomputing, 167, 260–279. https://doi.org/10.1016/j.neucom.2015.04.069.
Zhao, Z., Pozas-Kerstjens, A., Rebentrost, P., & Wittek, P. (2019). Bayesian deep learning on a quantum computer. Quantum Machine Intelligence, 1(1–2), 41–51. https://doi.org/10.1007/s42484-019-00004-7.
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Nti, I.K., Adekoya, A.F., Weyori, B.A. et al. Applications of artificial intelligence in engineering and manufacturing: a systematic review. J Intell Manuf 33, 1581–1601 (2022). https://doi.org/10.1007/s10845-021-01771-6
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DOI: https://doi.org/10.1007/s10845-021-01771-6