製造為國家經濟命脈，世界各國也極為重視製造業的發展。在生產作業中，業者透過規劃(P)、執行(D)、監控(C)、評量與行動(A)來提升生產績效。物聯網的普及與人工智慧的發展，使產業進入智慧製造時代，透過即時資料蒐集與分析，掌握生產現況與趨勢以及偵測異常，也提供績效評量與持續改善之依據。然而，傳統中小企業因資金與技術能力不足，仍需仰賴人工的作業。我國產業仍以傳統與中小企業為主，在此工業4.0的時代，如何為人工作業之監控與績效評量提供人工智慧的解決方案，仍是當前業界與學界的課題。
針對中小企業人工作業之智慧化監控與評量之需求，本研究提出一「電腦視覺為基之人工作業績效評量方法」，並開發「人員辨識」、「動作評量」與「品質檢驗」技術，提供一套人工作業監控與評量之人工智慧解決方案。
本研究所提「人員辨識」技術，可解決戴口罩之場域的人員辨識，在1張照片多張人臉的辨識準確率達93%以上，並可實際應用於即時之人員辨識；在動作評量方面，「動作評量」技術能夠區分合格與不合格的動作，準確率高達98%；在產出品質檢驗方面，能夠有效分辨出合格的產出與不合格的產出，辨識準確率達75%以上，在某些測試場景中，甚至高達100%。實驗證實，本研究所提之人工作業績效評量方法與技術之可行性與有效性。

In the manufacturing process, humans still play an indispensable role. For example, in the tasks where machines cannot fulfill the current needs and rely on humans, and in quality inspection through sensory inspection (Vigneau et al., 2018). Therefore, even in the modern era of 2022, although many industries have moved into the realm of Industry 4.0, human still plays a significant role in the production process.
However, human is not cold machine that can operate for long periods of time and whose output status can be understood through parameter settings. Therefore, it is very important to understand the performance of the personnel. The traditional performance measurement method is to calculate the total output, effective output, and ineffective output of the workers mainly by "manual" calculation, which is very unreliable and costly.
In view of this, this study hopes to help traditional SMEs reduce human intervention and cost by using artificial intelligence, data science, and computer vision in the “manual performance evaluation" section, and proposes a manual performance evaluation method, which is subdivided into "personnel identification model", "action evaluation model", and "output quality inspection model" to help SMEs grasp more comprehensive information when making decisions.
In the proposed method, the “personnel identification model” can solve the recognition of personnel in the field of wearing masks, and the recognition accuracy of multiple faces in one photo is over 93%, and can be applied to real-time personnel recognition. In the “action evaluation model”, it is able to distinguish between qualified and unqualified actions with an accuracy rate of 98%. In the “output quality inspection model”, it can effectively identify the qualified output and the unqualified output with an accuracy rate of more than 75% and even up to 100% in some testing scenarios; and the analysis results obtained from the above model can be used for manual performance evaluation to analyze the performance of personnel. The experiments and results show that the method is feasible and effective.

中文文獻：
林晏僖(2010)。中文名詞組的辨識：規則式判別、監督式、半監督式與非監督式學習法的實驗，國立台灣大學資訊網路與多媒體研究所，碩士論文。
李昱緯(2012)。智慧型食品品質控管系統暨自動化製造流程管理改善，國立東華大學電機工程學系，碩士論文。
李俊賢(2020)。基於機器學習之食品供應鏈異常偵測方法與技術研發，國立成功大學製造資訊與系統研究所，碩士論文。
陳建安(2021)。以影響力為基之市場區隔趨勢預測方法與技術研發，國立成功大學製造資訊與系統研究所，碩士論文。
黃郁珊(2021)。具持續改善機制之食品品質監控模型與分析方法研究，國立成功大學製造資訊與系統研究所，碩士論文。
普皓群(2021)。基於深度學習之心智圖自動產生方法與技術研發: 以數位閱讀與寫作能力培養之應用為例，國立成功大學製造資訊與系統研究所，碩士論文。

英文文獻：
Agin, G. J. (1980). Computer vision systems for industrial inspection and assembly. Computer, 13(05), 11-20.
Ahsani, R., Wardhani, A. A., Nisa, U. F., Syaiin, M., Adhitya, R. Y., Setiawan, E., ... & Soelistijono, R. T. (2017, October). Comparison of Neural Network (NN) and Extreme Learning Machine (ELM) on thickness auto adjustment of instant noodle dough on roll press machine. In 2017 International Symposium on Electronics and Smart Devices (ISESD) (pp. 107-111). IEEE.
Aldalil, M. A New Method for Measuring Leveling Heights Using Computer Vision Techniques. In 2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE) (pp. 1-6). IEEE.
An, Y., Ding, S., Shi, S., & Li, J. (2018). Discrete space reinforcement learning algorithm based on support vector machine classification. Pattern Recognition Letters, 111, 30-35.
Ayoub, M. (2020). A review on machine learning algorithms to predict daylighting inside buildings. Solar Energy, 202, 249-275.
Bai, Y., Li, C., Sun, Z., & Chen, H. (2017, July). Deep neural network for manufacturing quality prediction. In 2017 Prognostics and System Health Management Conference (PHM-Harbin) (pp. 1-5). IEEE.
Badillo, S., Banfai, B., Birzele, F., Davydov, I. I., Hutchinson, L., Kam‐Thong, T., ... & Zhang, J. D. (2020). An introduction to machine learning. Clinical Pharmacology & Therapeutics, 107(4), 871-885.
Benjdira, B., Khursheed, T., Koubaa, A., Ammar, A., & Ouni, K. (2019, February). Car detection using unmanned aerial vehicles: Comparison between faster r-cnn and yolov3. In 2019 1st International Conference on Unmanned Vehicle Systems-Oman (UVS) (pp. 1-6). IEEE.
Bilen, H., Fernando, B., Gavves, E., Vedaldi, A., & Gould, S. (2016). Dynamic image networks for action recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3034-3042).
Bishop, C. M. (2016). Pattern Recognition and Machine Learning. New York, United States: Springer Publishing.
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of computational science, 2(1), 1-8.
Bose, I., & Mahapatra, R. K. (2001). Business data mining—a machine learning perspective. Information & management, 39(3), 211-225.
Bouchefry, Khadija El., & de Souza R. S. (2020). Chapter 12 - Learning in Big Data: Introduction to Machine Learning. Knowledge Discovery in Big Data from Astronomy and Earth Observation, 1, 225-249.
Brännvall, R., Sarkinen, J., Svartholm, J., Gustafsson, J., & Summers, J. (2019, July). Digital twin for tuning of server fan controllers. In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) (Vol. 1, pp. 1425-1428). IEEE.
Buczak, A. L., & Guven, E. (2015). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications surveys & tutorials, 18(2), 1153-1176.
Canny, J. (1983). Finding edges and lines in images (Tech. Rep.). Cambridge, MA: Massachusetts Institute of Technology.
Chang, Y. H., Ho, W. H., Shi, H. Y., & Chou, J. H. (2013, July). Prediction and sensitivity analysis by TS fuzzy neural network for fungal growth in food products. In 2013 International Conference on System Science and Engineering (ICSSE) (pp. 41-44). IEEE.
Chen, C., Seff, A., Kornhauser, A., & Xiao, J. (2015). Deepdriving: Learning affordance for direct perception in autonomous driving. In Proceedings of the IEEE international conference on computer vision (pp. 2722-2730).
Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2017). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4), 834-848.
Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and electronics in agriculture, 151, 61-69.
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
Choi, J., Gao, C., Messou, J. C., & Huang, J. B. (2019). Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition. Advances in Neural Information Processing Systems, 32.
Chua, L. O. (1998). CNN: A paradigm for complexity (Vol. 31). World Scientific.
Chua, L. O., & Roska, T. (1993). The CNN paradigm. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 40(3), 147-156.
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B. and Bharath, A.A., 2018. Generative adversarial networks: An overview. IEEE Signal Processing Magazine, 35(1), pp.53-65.
de Vries, S., & Thierens, D. (2021). A reliable ensemble-based approach to semi-supervised learning. Knowledge-Based Systems, 215, 106738.
Deng, L. (2014). A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Transactions on Signal and Information Processing, 3, e2. doi: 10.1017/atsip.2013.9
Do, T. T., Kijak, E., Furon, T., & Amsaleg, L. (2010, October). Deluding image recognition in SIFT-based CBIR systems. In Proceedings of the 2nd ACM Workshop on Multimedia in forensics, Security and Intelligence (pp. 7-12).
Eigen, D., Puhrsch, C., & Fergus, R. (2014). Depth map prediction from a single image using a multi-scale deep network. Advances in neural information processing systems, 27.
Fan, R., Bocus, M. J., Zhu, Y., Jiao, J., Wang, L., Ma, F., ... & Liu, M. (2019, June). Road crack detection using deep convolutional neural network and adaptive thresholding. In 2019 IEEE Intelligent Vehicles Symposium (IV) (pp. 474-479). IEEE.
Feng, Y., Chen, J., Zhang, T., He, S., Xu, E., & Zhou, Z. (2021). Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis. ISA transactions.
Francis, F., & Mohan, M. (2019, June). Arima model based real time trend analysis for predictive maintenance. In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 735-739). IEEE.
Gao, H., Cheng, B., Wang, J., Li, K., Zhao, J., & Li, D. (2018). Object classification using CNN-based fusion of vision and LIDAR in autonomous vehicle environment. IEEE Transactions on Industrial Informatics, 14(9), 4224-4231.
Gartner, W. C. (1994). Image formation process. Journal of travel & tourism marketing, 2(2-3), 191-216.
George, R. V., Harsh, H. O., Ray, P., & Babu, A. K. (2019). Food quality traceability prototype for restaurants using blockchain and food quality data index. Journal of Cleaner Production, 240, 118021.
Gers, F. A. (1999). Learning to Forget: Continual Prediction with LSTM. 9th International Conference on Artificial Neural Networks(ICANN), 99: 850-855.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.
Graves, A., Mohamed, A. R., & Hinton, G. (2013, May). Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 6645-6649).
Han, Y., Cui, S., Geng, Z., Chu, C., Chen, K., & Wang, Y. (2019). Food quality and safety risk assessment using a novel HMM method based on GRA. Food Control, 105, 180-189.
Hao, G., Min, L., & Feng, H. (2013, August). Improved self-adaptive edge detection method based on Canny. In 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics (Vol. 2, pp. 527-530). IEEE.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
He, Z., Tran, K. P., Thomassey, S., Zeng, X., Xu, J., & Yi, C. (2021). Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning. Journal of Manufacturing Systems.
Hochreiter, S. (1998). The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 6(02), 107-116.
Hossain, M. S., Al-Hammadi, M., & Muhammad, G. (2018). Automatic fruit classification using deep learning for industrial applications. IEEE Transactions on Industrial Informatics, 15(2), 1027-1034.
Huang, G. B., Zhou, H., Ding, X., & Zhang, R. (2011). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2), 513-529.
Hyun, S., Kaewprag, P., Cooper, C., Hixon, B., & Moffatt-Bruce, S. (2020). Exploration of critical care data by using unsupervised machine learning. Computer methods and programs in biomedicine, 194, 105507.
Inverso, S. (2002). Ellipse detection using randomized Hough transform. Final Project: introduction to computer vision, 4005-4757.
Ippolito, M., Ferguson, J., & Jenson, F. (2021). Improving facies prediction by combining supervised and unsupervised learning methods. Journal of Petroleum Science and Engineering, 200, 108300.
Jiang, T., Gradus, J. L., & Rosellini, A. J. (2020). Supervised machine learning: a brief primer. Behavior Therapy, 51(5), 675-687.
Ji, S., Xu, W., Yang, M., & Yu, K. (2012). 3D convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence, 35(1), 221-231.
Keshavamurthy, Mariyam, S. J., Meghamala, M., & Meghashree, M. (2019, May). Automatized Food Quality Detection and Processing System Using Neural Networks. In 2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) (pp. 1442-1446). IEEE.
Lam, H. M., Remais, J., Fung, M. C., Xu, L., & Sun, S. S. M. (2013). Food supply and food safety issues in China. The Lancet, 381(9882), 2044-2053.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
LeCun, Y., & Bengio, Y. (1995). Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10), 1995.
LeCun, Y. A., Bottou, L., Orr, G. B., & Müller, K. R. (2012). Efficient backprop. In Neural networks: Tricks of the trade (pp. 9-48). Springer, Berlin, Heidelberg.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
LeCun, Y., Kavukcuoglu, K., & Farabet, C. (2010, May). Convolutional networks and applications in vision. In Proceedings of 2010 IEEE international symposium on circuits and systems (pp. 253-256). IEEE.
Lee, D. J., Archibald, J. K., & Xiong, G. (2010). Rapid color grading for fruit quality evaluation using direct color mapping. IEEE transactions on automation science and engineering, 8(2), 292-302.
Li, B., Kong, X., Wang, Z., & Fu, H. (2009, September). SIFT-based image retrieval combining the distance measure of global image and sub-image. In 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (pp. 706-709). IEEE.
Li, C. (2021, October). Design and Implementation of Gesture Recognition Rehabilitation System Based on Deep Learning. In 2021 IEEE International Conference on Data Science and Computer Application (ICDSCA) (pp. 843-847). IEEE.
Li, D., & Wu, Q. (2017, August). Applying principal component analysis and grey relation analysis to analyze the influence factors of quality and safety of dairy products in China. In 2017 International Conference on Grey Systems and Intelligent Services (GSIS) (pp. 58-63). IEEE.
Liu, Z., Wang, R., Japkowicz, N., Tang, D., Zhang, W., & Zhao, J. (2021). Research on unsupervised feature learning for Android malware detection based on Restricted Boltzmann Machines. Future Generation Computer Systems, 120, 91-108.
Li, Y., & Yang, T. (2018). Word embedding for understanding natural language: a survey. In Guide to big data applications (pp. 83-104). Springer, Cham.
Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91-110.
Lu, H., & Yan, J. (2019, October). Window frame obstacle edge detection based on improved Canny operator. In 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE) (pp. 493-496). IEEE.
Luo, H., Huang, M., & Zhou, Z. (2018). Integration of Multi-Gaussian fitting and LSTM neural networks for health monitoring of an automotive suspension component. Journal of Sound and Vibration, 428, 87-103.
Luo, R. C., & Kuo, C. W. (2016). Intelligent seven-DoF robot with dynamic obstacle avoidance and 3-D object recognition for industrial cyber–physical systems in manufacturing automation. Proceedings of the IEEE, 104(5), 1102-1113.
Luvizon, D. C., Picard, D., & Tabia, H. (2018). 2d/3d pose estimation and action recognition using multitask deep learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5137-5146).
Lu, Y. E., Yuping, L. I., Weihong, L., Qidao, S. O. N. G., Yanqun, L. I. U., & Xiaoli, Q. I. N. (2015, June). Vegetable price prediction based on pso-bp neural network. In 2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA) (pp. 1093-1096). IEEE.
Maji, S., Bourdev, L., & Malik, J. (2011, June). Action recognition from a distributed representation of pose and appearance. In CVPR 2011 (pp. 3177-3184). IEEE.
Mao, X., Yang, H., Huang, S., Liu, Y., & Li, R. (2019). Extractive summarization using supervised and unsupervised learning. Expert Systems with Applications, 133, 173-181.
McMullen, D. P., Hotson, G., Katyal, K. D., Wester, B. A., Fifer, M. S., McGee, T. G., ... & Crone, N. E. (2013). Demonstration of a semi-autonomous hybrid brain–machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(4), 784-796.
Mehra, M., Saxena, S., Sankaranarayanan, S., Tom, R. J., & Veeramanikandan, M. (2018). IoT based hydroponics system using Deep Neural Networks. Computers and electronics in agriculture, 155, 473-486.
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., ... & Van Leemput, K. (2014). The multimodal brain tumor image segmentation benchmark (BRATS). IEEE transactions on medical imaging, 34(10), 1993-2024.
Mery, D., Saavedra, D., & Prasad, M. (2020). X-ray baggage inspection with computer vision: A survey. IEEE Access, 8, 145620-145633.
Mikolov, T., Karafiát, M., Burget, L., Cernocký, J., & Khudanpur, S. (2010, September). Recurrent neural network based language model. In Interspeech (Vol. 2, No. 3, pp. 1045-1048).
Milczarski, P., Stawska, Z., Hłobaż, A., Zieliński, B., Maślanka, P., & Kosiński, P. (2021, September). Machine Learning Methods in Energy Consumption Optimization Assessment in Food Processing Industry. In 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) (Vol. 2, pp. 835-840). IEEE.
Milletari, F., Navab, N., & Ahmadi, S. A. (2016, October). V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV) (pp. 565-571). IEEE.
Mohanta, B. K., Jena, D., Satapathy, U., & Patnaik, S. (2020). Survey on IoT Security: Challenges and Solution using Machine Learning, Artificial Intelligence and Blockchain Technology. Internet of Things, 100227.
Molchanov, P., Gupta, S., Kim, K., & Kautz, J. (2015). Hand gesture recognition with 3D convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 1-7).
Nabavi-Pelesaraei, A., Rafiee, S., Mohtasebi, S. S., Hosseinzadeh-Bandbafha, H., & Chau, K. W. (2017). Energy consumption enhancement and environmental life cycle assessment in paddy production using optimization techniques. Journal of cleaner production, 162, 571-586.
Naphade, M., Chang, M. C., Sharma, A., Anastasiu, D. C., Jagarlamudi, V., Chakraborty, P., ... & Lyu, S. (2018). The 2018 nvidia ai city challenge. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 53-60).
Nasrabadi, N. M. (2007). Pattern recognition and machine learning. Journal of electronic imaging, 16(4), 049901.
Nayak, R., Jiwani, S. A., & Rajitha, B. (2021). Spam email detection using machine learning algorithm. Materials Today: Proceedings.
Nguyen, T. H., Shirai, K., & Velcin, J. (2015). Sentiment analysis on social media for stock movement prediction. Expert Systems with Applications, 42(24), 9603-9611.
Nguyen, V. (2019, June). Bayesian optimization for accelerating hyper-parameter tuning. In 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) (pp. 302-305). IEEE.
Oliveira, J. J. M., & Cordeiro, R. L. F. (2020). Unsupervised dimensionality reduction for very large datasets: Are we going to the right direction?. Knowledge-Based Systems, 196, 105777.
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66.
Otter, D. W., Medina, J. R., & Kalita, J. K. (2020). A survey of the usages of deep learning for natural language processing. IEEE transactions on neural networks and learning systems, 32(2), 604-624.
Pant, J., Pant, R. P., Singh, M. K., Singh, D. P., & Pant, H. (2021). Analysis of agricultural crop yield prediction using statistical techniques of machine learning. Materials Today: Proceedings.
Parmar, P., & Morris, B. T. (2019). What and how well you performed? a multitask learning approach to action quality assessment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 304-313).
Parmar, P., & Morris, B. (2019, January). Action quality assessment across multiple actions. In 2019 IEEE winter conference on applications of computer vision (WACV) (pp. 1468-1476). IEEE.
Rasheed, F., & Wahid, A. (2021). Learning style detection in E-learning systems using machine learning techniques. Expert Systems with Applications, 174, 114774.
Rasmus, A., Valpola, H., Honkala, M., Berglund, M., & Raiko, T. (2015). Semi-supervised learning with ladder networks. arXiv preprint arXiv:1507.02672.
Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021). Using machine learning approaches for multi-omics data analysis: A review. Biotechnology Advances, 107739.
Ribeiro, A. H., Gedon, D., Teixeira, D. M., Ribeiro, M. H., Ribeiro, A. L. P., Schön, T. B., & Meira, W. (2020, September). Automatic 12-lead ECG classification using a convolutional network ensemble. In 2020 Computing in Cardiology (pp. 1-4). IEEE.
Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 386.
Rozsa, Z., & Sziranyi, T. (2018). Obstacle prediction for automated guided vehicles based on point clouds measured by a tilted LIDAR sensor. IEEE Transactions on Intelligent Transportation Systems, 19(8), 2708-27
Sathyakala, G., Kirthika, V., & Aishwarya, B. (2018, April). Computer vision based fire detection with a video alert system. In 2018 International Conference on Communication and Signal Processing (ICCSP) (pp. 0725-0727). IEEE.
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 & Operations Research, 119, 104926.
Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306.
Sherstinsky, A. (2018, December). Deriving the Recurrent Neural Network Definition and RNN Unrolling Using Signal Processing. In Critiquing and Correcting Trends in Machine Learning Workshop at Neural Information Processing Systems (Vol. 31).
Shetty, D. K., Acharya, U. D., Malarout, N., Gopakumar, R., & Prajual, P. J. (2019, April). A review of application of computer-vision for quality grading of food products. In 2019 International Conference on Automation, Computational and Technology Management (ICACTM) (pp. 297-303). IEEE.
Shin, H. C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., ... & Summers, R. M. (2016). Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging, 35(5), 1285-1298.
Smith, C. J. (2020). Food Safety and Health—Past Problems and Future Solutions. Engineering, 6(4), 384-388.
Stone, E. E., & Skubic, M. (2014). Fall detection in homes of older adults using the Microsoft Kinect. IEEE journal of biomedical and health informatics, 19(1), 290-301.
Sonthited, P., Koolpiruk, D., & Songkasiri, W. (2019, July). Prediction of Production Performance for Tapioca Industry using LSTM neural network. In 2019 16th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (pp. 147-150). IEEE.
Torras, C. (Ed.). (2012). Computer vision: theory and industrial applications. Springer Science & Business Media.
Tsugawa, S. (1994). Vision-based vehicles in Japan: Machine vision systems and driving control systems. IEEE Transactions on industrial electronics, 41(4), 398-405.
Tuda, M., & Luna-Maldonado, A. I. (2020). Image-based insect species and gender classification by trained supervised machine learning algorithms. Ecological Informatics, 60, 101135.
Tu, Z., & Bai, X. (2009). Auto-context and its application to high-level vision tasks and 3d brain image segmentation. IEEE transactions on pattern analysis and machine intelligence, 32(10), 1744-1757.
Vamsikrishna, K. M., Dogra, D. P., & Desarkar, M. S. (2015). Computer-vision-assisted palm rehabilitation with supervised learning. IEEE Transactions on Biomedical Engineering, 63(5), 991-1001.
Velmurugan, P., Kannagi, A., & Varsha, M. (2021). Superior fuzzy enumeration crop prediction algorithm for big data agriculture applications. Materials Today: Proceedings.
Vigneau, E., Courcoux, P., Symoneaux, R., Guérin, L., & Villière, A. (2018). Random forests: A machine learning methodology to highlight the volatile organic compounds involved in olfactory perception. Food Quality and Preference, 68, 135-145.
Wang, J., Yue, H., & Zhou, Z. (2017). An improved traceability system for food quality assurance and evaluation based on fuzzy classification and neural network. Food Control, 79, 363-370.
Wangming, Xu, Jin, Wu, Xinhai, Liu, Lei, Zhu, & Gang, Shi. (2008, December). Application of Image SIFT Features to the Context of CBIR. In 2008 International Conference on Computer Science and Software Engineering (Vol. 4, pp. 552-555). IEEE.
Wang, S. C. (2003). Artificial neural network. In Interdisciplinary computing in java programming (pp. 81-100). Springer, Boston, MA.
Wang, X., He, Y., Xu, Y., Zhang, X., & Zhu, Q. (2019, November). Comprehensive Evaluation Modeling and Analysis Based on ELM Integrated AHP and PCA: Application to Food Safety. In 2019 Chinese Automation Congress (CAC) (pp. 4092-4097). IEEE.
Wu, Y. (2020). Bank Risk Control based on FPGA and Machine Learning. Microprocessors and Microsystems, 103472.
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., ... & Wang, C. (2018). Machine learning and deep learning methods for cybersecurity. Ieee access, 6, 35365-35381.
Xu, L., Oja, E., & Kultanen, P. (1990). A new curve detection method: randomized Hough transform (RHT). Pattern recognition letters, 11(5), 331-338.
Xu, X., Cao, D., Zhou, Y., & Gao, J. (2020). Application of neural network algorithm in fault diagnosis of mechanical intelligence. Mechanical Systems and Signal Processing, 141, 106625.
Xu, Z., Tian, Y., Hu, X., & Pu, F. (2015, September). Dangerous human event understanding using human-object interaction model. In 2015 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) (pp. 1-5). IEEE.
Ye, T. (2017, April). Stock forecasting method based on wavelet analysis and ARIMA-SVR model. In 2017 3rd International Conference on Information Management (ICIM) (pp. 102-106). IEEE.
Zhang, L., Yang, F., Zhang, Y. D., & Zhu, Y. J. (2016, September). Road crack detection using deep convolutional neural network. In 2016 IEEE international conference on image processing (ICIP) (pp. 3708-3712). IEEE.
Zhang, S. J., Pan, J. H., Gao, J., & Zheng, W. S. (2022). Semi-Supervised Action Quality Assessment with Self-Supervised Segment Feature Recovery. IEEE Transactions on Circuits and Systems for Video Technology.
Zhang, W., Cheng, B., & Lin, Y. (2012). Driver drowsiness recognition based on computer vision technology. Tsinghua Science and Technology, 17(3), 354-362.
Zhang, W., Liu, T., Brown, A., Ueland, M., Forbes, S. L., & Su, S. W. (2022). The Use of Electronic Nose for the Classification of Blended and Single Malt Scotch Whisky. IEEE Sensors Journal, 22(7), 7015-7021.
IEEE.
Zhao, F., Chen, L., Xia, T., Ye, Z., & Zheng, Y. (2019). Gas Turbine Exhaust System Health Management Based on Recurrent Neural Networks. Procedia CIRP, 83, 630-635.
Zhou, Y., Han, M., Liu, L., He, J. S., & Wang, Y. (2018, April). Deep learning approach for cyberattack detection. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 262-267).
Zhu, L., & Spachos, P. (2020, July). Food grading system using support vector machine and yolov3 methods. In 2020 IEEE Symposium on Computers and Communications (ISCC) (pp. 1-6). IEEE.
Zhu, Y., Lan, Z., Newsam, S., & Hauptmann, A. (2018, December). Hidden two-stream convolutional networks for action recognition. In Asian conference on computer vision (pp. 363-378). Springer, Cham.
Zhang, Z., Zhang, K., & Khelifi, A. (2018). Multivariate time series analysis in climate and environmental research. Cham: Springer International Publishing.
Zubaľ, M., Lojka, T., & Zolotová, I. (2016, January). IoT gateway and industrial safety with computer vision. In 2016 IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 183-186). IEEE.