近年來，深度學習模型 (DLM) 在各個領域得到了廣泛應用。學者在使用DLMs時通常會提供豐富的數據集，以使模型更好地學習目標任務。然而阻礙深度學習模型廣泛被普羅大眾使用的難題主因在於其運算成本過高的問題。本論文提出可解釋性的神經元進行特徵選擇並降低模型成本之研究為克服此難題。本論文提出通過在 DLM 中引入用於特徵選擇和成本降低的可解釋神經元來應對這一挑戰。該研究側重於二個不同的領域：工業應用和時空數據庫，工業應用中，本文提出了輕量化的刀具磨耗預測框架。時空數據庫中則針對房地產售價預測及人流預測的多種問題進行特徵選擇及降低成本的研究。每個部份在論文的不同章節中得到專門地詳細介紹。所有提出的方法都通過實驗進行了徹底的檢查、模擬、分析和驗證。實驗結果證明了所提出的方法在解決與 DLM 相關的高計算成本方面的有效性。

Deep learning models (DLMs) have gained widespread application in various domains in recent years. Researchers typically provide rich datasets to enable DLMs to better learn target tasks. However, the high computational cost of DLMs remains a significant barrier to their widespread adoption. This thesis proposes a research study that addresses this challenge by introducing interpretable neurons for feature selection and cost reduction in DLMs. This study focuses on two distinct domains: industrial applications and spatio-temporal databases. In the domain of industrial applications, a lightweight framework for predicting tool wear is proposed. In the spatio-temporal database domain, research is conducted on various aspects, including feature selection and cost reduction for real estate price prediction and crowd flow prediction. Each of these domains is extensively discussed in dedicated chapters throughout the thesis. All proposed methods are thoroughly examined, simulated, analyzed, and validated through experiments. The experimental results demonstrate the efficacy of the proposed approaches in addressing the high computational costs associated with DLMs.

[1] Afshin Abdi and Faramarz Fekri, “Indirect Stochastic Gradient Quantization and Its Application in Distributed Deep Learning,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 3113–3120, Apr. 2020, doi: 10.1609/aaai.v34i04.5707.
[2] Ahmad Ali, Yanmin Zhu, and Muhammad Zakarya, “A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing,” Multimedia Tools and Applications, pp. 1–33, 2021.
[3] F. J. Alonso and D. R. Salgado, “Analysis of the structure of vibration signals for tool wear detection,” Mechanical Systems and Signal Processing, vol. 22, no. 3, pp. 735–748, Apr. 2008, doi: 10.1016/j.ymssp.2007.09.012.
[4] Mohammadreza Amirian and Friedhelm Schwenker, “Radial basis function networks for convolutional neural networks to learn similarity distance metric and improve interpretability,” IEEE Access, vol. 8, pp. 123087–123097, 2020.
[5] Aayush Ankit, Izzat El Hajj, Sai Rahul Chalamalasetti, Sapan Agarwal, Matthew Marinella, Martin Foltin, John Paul Strachan, Dejan Milojicic, Wen-Mei Hwu, and Kaushik Roy, “PANTHER: A Programmable Architecture for Neural Network Training Harnessing Energy-Efficient ReRAM,” IEEE Transactions on Computers, vol. 69, no. 8, pp. 1128–1142, Aug. 2020, doi: 10.1109/TC.2020.2998456.
[6] Luc Anselin, “Thirty years of spatial econometrics,” Papers in Regional Science, vol. 89, no. 1, pp. 3–25, Mar. 2010, doi: 10.1111/j.1435-5957.2010.00279.x.
[7] P. and Hemalatha M. Aravindkumar S. and Varalakshmi, “Generation of Image Caption Using CNN-LSTM Based Approach,” in Intelligent Systems Design and Applications, A. K. and M. P. and G. N. Abraham Ajith and Cherukuri, Ed., Cham: Springer International Publishing, 2020, pp. 465–474.
[8] Jiandong Bai, Jiawei Zhu, Yujiao Song, Ling Zhao, Zhixiang Hou, Ronghua Du, and Haifeng Li, “A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting,” ISPRS International Journal of Geo-Information, vol. 10, no. 7, 2021, doi: 10.3390/ijgi10070485.
[9] Scott Barber, “How fast does a website need to be.” Citeseer, 2015.
[10] Alejandro Barredo-Arrieta, Ibai Laña, and Javier Del Ser, “What lies beneath: A note on the explainability of black-box machine learning models for road traffic forecasting,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019, pp. 2232–2237.
[11] Archith J. Bency, Swati Rallapalli, Raghu K. Ganti, Mudhakar Srivatsa, and B. S. Manjunath, “Beyond Spatial Auto-Regressive Models: Predicting Housing Prices with Satellite Imagery,” in 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, Mar. 2017, pp. 320–329. doi: 10.1109/WACV.2017.42.
[12] Gerardo Beruvides, Ramón Quiza, Raúl del Toro, and Rodolfo E. Haber, “Sensoring systems and signal analysis to monitor tool wear in microdrilling operations on a sintered tungsten–copper composite material,” Sensors and Actuators A: Physical, vol. 199, pp. 165–175, Sep. 2013, doi: 10.1016/j.sna.2013.05.021.
[13] James C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. Boston, MA: Springer US, 1981. doi: 10.1007/978-1-4757-0450-1.
[14] George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung, Time series analysis: forecasting and control. Canada: John Wiley & Sons, 2015.
[15] Lukas Brausch, Holger Hewener, and Paul Lukowicz, “Towards a wearable low-cost ultrasound device for classification of muscle activity and muscle fatigue,” in Proceedings of the 23rd International Symposium on Wearable Computers, 2019, pp. 20–22.
[16] David S. Broomhead and David Lowe, “Radial basis functions, multi-variable functional interpolation and adaptive networks,” Complex Systems, vol. 2, pp. 321–355, 1988.
[17] Harsha Vardhana Krishna Sai Buddana, Surampudi Sai Kaushik, P. V. S. Manogna, and Shijin Kumar PS, “Word level LSTM and recurrent neural network for automatic text generation,” in 2021 International Conference on Computer Communication and Informatics (ICCCI), 2021, pp. 1–4.
[18] Pei Cao, Shengli Zhang, and Jiong Tang, “Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning,” IEEE Access, vol. 6, pp. 26241–26253, 2018, doi: 10.1109/ACCESS.2018.2837621.
[19] Gabriele Cavallaro, Mauro Dalla Mura, Jon Atli Benediktsson, and Antonio Plaza, “Remote Sensing Image Classification Using Attribute Filters Defined Over the Tree of Shapes,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 7, pp. 3899–3911, Jul. 2016, doi: 10.1109/TGRS.2016.2530690.
[20] Chih-Chung Chang and Chih-Jen Lin, “LIBSVM: A Library for Support Vector Machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, May 2011, doi: 10.1145/1961189.1961199.
[21] Chun-Hao Chang, Ladislav Rampasek, and Anna Goldenberg, “Dropout Feature Ranking for Deep Learning Models,” Dec. 2017, [Online]. Available: http://arxiv.org/abs/1712.08645
[22] Baojia Chen, Xuefeng Chen, Bing Li, Zhengjia He, Hongrui Cao, and Gaigai Cai, “Reliability estimation for cutting tools based on logistic regression model using vibration signals,” Mechanical Systems and Signal Processing, vol. 25, no. 7, pp. 2526–2537, Oct. 2011, doi: 10.1016/j.ymssp.2011.03.001.
[23] Dawei Chen, “Research on Traffic Flow Prediction in the Big Data Environment Based on the Improved RBF Neural Network,” IEEE Transactions on Industrial Informatics, vol. 13, no. 4, pp. 2000–2008, 2017, doi: 10.1109/TII.2017.2682855.
[24] Fan Chen, Linghao Song, and Yiran Chen, “ReGAN: A pipelined ReRAM-based accelerator for generative adversarial networks,” in 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC), IEEE, Jan. 2018, pp. 178–183. doi: 10.1109/ASPDAC.2018.8297302.
[25] Tao Cheng, Jiaqiu Wang, James Haworth, Benjamin Heydecker, and Andy Chow, “A Dynamic Spatial Weight Matrix and Localized Space-Time Autoregressive Integrated Moving Average for Network Modeling,” Geographical Analysis, vol. 46, no. 1, pp. 75–97, Jan. 2014, doi: 10.1111/gean.12026.
[26] Hsiang-Yuan Chen, “Using Youbike Data to Identify the Impact of Covid-19 on Activities in Downtown Taipei,” Master Thesis, National Yunlin University of Science and Technology, 2022. [Online]. Available: https://hdl.handle.net/11296/mfc3sn
[27] Huimin Chen, “A multiple model prediction algorithm for CNC machinewear PHM,” International Journal of Prognostics and Health Management, vol. 2, May 2011.
[28] Jie Chen, Tao Pei, Shih-Lung Shaw, Feng Lu, Mingxiao Li, Shifen Cheng, Xiliang Liu, and Hengcai Zhang, “Fine-grained prediction of urban population using mobile phone location data,” International Journal of Geographical Information Science, vol. 32, no. 9, pp. 1770–1786, 2018.
[29] J. Y. Chen and H. C. Yang, “Predicting Tool Residual Useful Life based on Hybrid Dynamic Neural Network,” Master Thesis, National Kaohsiung First University of Science and Technology, 2016.
[30] Shaohan Chen, Chuanhou Gao, and Ping Zhang, “Incorporation of Data-Mined Knowledge into Black-Box SVM for Interpretability,” ACM Transactions on Intelligent Systems and Technology, vol. 14, no. 1, Nov. 2022, doi: 10.1145/3548775.
[31] Xiaoxuan Chen, Xia Wan, Fan Ding, Qing Li, Charlie McCarthy, Yang Cheng, and Bin Ran, “Data-driven prediction system of dynamic people-flow in large urban network using cellular probe data,” Journal of Advanced Transportation, vol. 2019, 2019.
[32] Yi-Chung Chen, Tzu-Yin Chang, Heng-Yi Chow, Siang-Lan Li, and Chin-Yu Ou, “Using Convolutional Neural Networks to Build a Lightweight Flood Height Prediction Model with Grad-Cam for the Selection of Key Grid Cells in Radar Echo Maps,” Water, vol. 14, no. 2, p. 155, 2022.
[33] Yi-Chung Chen and Dong-Chi Li, “Selection of key features for PM2. 5 prediction using a wavelet model and RBF-LSTM,” Applied Intelligence, vol. 51, no. 4, pp. 2534–2555, 2021.
[34] Yi-Chung Chen, Shao-Chen Liu, Bo-Xiang Chen, Chee-Hoe Loh, and Josh Jia-Ching Ying, “Ensembling-mRBF-LSTM Framework for Prediction of Abnormal Traffic Flows,” in 2020 International Conference on Pervasive Artificial Intelligence (ICPAI), 2020, pp. 206–213.
[35] Tai-Lin Chin, Kuan-Yu Chen, Da-Yi Chen, and De-En Lin, “Intelligent Real-Time Earthquake Detection by Recurrent Neural Networks,” IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 8, pp. 5440–5449, Aug. 2020, doi: 10.1109/TGRS.2020.2966012.
[36] Sheng-Min Chiu, Yi-Chung Chen, Cheng-Ju Kuo, Li-Chun Hung, Min-Hsiung Hung, Chao-Chun Chen, and Chiang Lee, “Development of Lightweight RBF-DRNN and Automated Framework for CNC Tool-Wear Prediction,” IEEE Transactions on Instrumentation and Measurement, vol. 71, 2022, [Online]. Available: http://dx.doi.org/10.1109/TIM.2022.3164063
[37] Sheng-Min Chiu, Yi-Chung Chen, and Chiang Lee, “Estate price prediction system based on temporal and spatial features and lightweight deep learning model,” Applied Intelligence, vol. 52, no. 1, pp. 808–834, 2022.
[38] Sheng-Min Chiu, Yow-Shin Liou, Yi-Chung Chen, Chiang Lee, Rong-Kang Shang, and Tzu-Yin Chang, “Identifying key grid cells for crowd flow predictions based on CNN-based models with the Grad-CAM kit,” Applied Intelligence, pp. 1–29, 2022.
[39] Sheng-Min Chiu, Yow-Shin Liou, Yi-Chung Chen, Chiang Lee, Rong-Kang Shang, Tzu-Yin Chang, and Roger Zimmermann, “Universal Transfer Framework for Urban Spatio-Temporal Knowledge Based on Radial Basis Function,” unpublished, 2022.
[40] Sheng-Min Chiu, Yow-Shin Liou, Yi-Chung Chen, Chiang Lee, Rong-Kang Shang, Tzu-Yin Chang, and Roger Zimmermann, “CRBF-Net: Convolutional Radial Basis Function Network for Key Grid Cells Selection in Spatial-Temporal Flow Prediction,” unpublished, 2023.
[41] Alexander Chudik and M. Hashem Pesaran, “Infinite-dimensional VARs and factor models,” Journal of Econometrics, vol. 163, no. 1, pp. 4–22, Jul. 2011, doi: 10.1016/j.jeconom.2010.11.002.
[42] Stephen Clark, “Traffic prediction using multivariate nonparametric regression,” Journal of transportation engineering, vol. 129, no. 2, pp. 161–168, 2003.
[43] Alfréd Csikós, Zsolt János Viharos, Krisztián Balázs Kis, Tamás Tettamanti, and István Varga, “Traffic speed prediction method for urban networks¡Xan ANN approach,” in 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2015, pp. 102–108.
[44] Zhiyong Cui, Kristian Henrickson, Ruimin Ke, and Yinhai Wang, “Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 11, pp. 4883–4894, Nov. 2020, doi: 10.1109/TITS.2019.2950416.
[45] Othmane Daanouni, Bouchaib Cherradi, and Amal Tmiri, “Automatic Detection of Diabetic Retinopathy Using Custom CNN and Grad-CAM,” Advances in Intelligent Systems and Computing, vol. 1188, pp. 15–26, 2021.
[46] Fei Dai, Penggui Huang, Qi Mo, Xiaolong Xu, Muhammad Bilal, and Houbing Song, “ST-InNet: Deep Spatio-Temporal Inception Networks for Traffic Flow Prediction in Smart Cities,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 19782–19794, 2022, doi: 10.1109/TITS.2022.3179789.
[47] Sonali Das, Rangan Gupta, and Alain Kabundi, “Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models,” Journal of Forecasting, 2010, doi: 10.1002/for.1182.
[48] Sreerupa Das, Richard Hall, Stefan Herzog, Gregory Harrison, Michael Bodkin, and Lockheed Martin, “Essential steps in prognostic health management,” in 2011 IEEE Conference on Prognostics and Health Management, IEEE, Jun. 2011, pp. 1–9. doi: 10.1109/ICPHM.2011.6024332.
[49] Thomas Dimopoulos and Charalambos Yiorkas, “Implementing GIS in real estate price prediction and mass valuation: the case study of Nicosia District,” in Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017), G. Papadavid, D. G. Hadjimitsis, S. Michaelides, V. Ambrosia, K. Themistocleous, and G. Schreier, Eds., SPIE, Sep. 2017, p. 72. doi: 10.1117/12.2280255.
[50] Wenqian Dong, Jie Liang, and Song Xiao, “Saliency Analysis and Gaussian Mixture Model-Based Detail Extraction Algorithm for Hyperspectral Pansharpening,” IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 8, pp. 5462–5476, Aug. 2020, doi: 10.1109/TGRS.2020.2966550.
[51] Yunyun Dong, Tengfei Long, Weili Jiao, Guojin He, and Zhaoming Zhang, “A Novel Image Registration Method Based on Phase Correlation Using Low-Rank Matrix Factorization with Mixture of Gaussian,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 1, pp. 446–460, Jan. 2018, doi: 10.1109/TGRS.2017.2749436.
[52] Jian Duan, Jie Duan, Hongdi Zhou, Xiaobin Zhan, Tianxiang Li, and Tielin Shi, “Multi-frequency-band deep CNN model for tool wear prediction,” Measurement Science and Technology, vol. 32, no. 6, p. 065009, Jun. 2021, doi: 10.1088/1361-6501/abb7a0.
[53] Peibo Duan, Guoqiang Mao, Wenwei Yue, and Shangbo Wang, “A unified STARIMA based model for short-term traffic flow prediction,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2018, pp. 1652–1657.
[54] Bowen Du, Hao Peng, Senzhang Wang, Md Zakirul Alam Bhuiyan, Lihong Wang, Qiran Gong, Lin Liu, and Jing Li, “Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 972–985, 2020.
[55] Feng-Lei Fan, Jinjun Xiong, Mengzhou Li, and Ge Wang, “On interpretability of artificial neural networks: A survey,” IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 5, no. 6, pp. 741–760, 2021.
[56] Hongxiao Fei and Fengyun Tan, “Bidirectional grid long short-term memory (bigridlstm): A method to address context-sensitivity and vanishing,” Algorithms, vol. 11, no. 11, p. 172, 2018.
[57] Runhai Feng, Stefan M. Luthi, Dries Gisolf, and Erika Angerer, “Reservoir Lithology Determination by Hidden Markov Random Fields Based on a Gaussian Mixture Model,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 11, pp. 6663–6673, Nov. 2018, doi: 10.1109/TGRS.2018.2841059.
[58] Sandro Fiore, Donatello Elia, Carlos Eduardo Pires, Demetrio Gomes Mestre, Cinzia Cappiello, Monica Vitali, Nazareno Andrade, Tarciso Braz, Daniele Lezzi, Regina Moraes, and others, “An integrated big and fast data analytics platform for smart urban transportation management,” IEEE Access, vol. 7, pp. 117652–117677, 2019.
[59] Tushar Gadhiya and Anil K. Roy, “Superpixel-Driven Optimized Wishart Network for Fast PolSAR Image Classification Using Global ${k}$ -Means Algorithm,” IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 1, pp. 97–109, Jan. 2020, doi: 10.1109/TGRS.2019.2933483.
[60] Amin Ganjavi, Edward Christopher, C. Mark Johnson, and Jon Clare, “A study on probability of distribution loads based on expectation maximization algorithm,” in 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2017, pp. 1–5. doi: 10.1109/ISGT.2017.8086037.
[61] Nan Gao, Hao Xue, Wei Shao, Sichen Zhao, Kyle Kai Qin, Arian Prabowo, Mohammad Saiedur Rahaman, and Flora D. Salim, “Generative Adversarial Networks for Spatio-Temporal Data: A Survey,” ACM Transactions on Intelligent Systems and Technology, vol. 13, no. 2, Feb. 2022, doi: 10.1145/3474838.
[62] Hadi Ratham Al Ghayab, Yan Li, Siuly Siuly, and Shahab Abdulla, “Epileptic EEG signal classification using optimum allocation based power spectral density estimation,” IET Signal Processing, vol. 12, no. 6, pp. 738–747, Aug. 2018, doi: 10.1049/iet-spr.2017.0140.
[63] A. Gouarir, G. Martínez-Arellano, G. Terrazas, P. Benardos, and S. Ratchev, “In-process Tool Wear Prediction System Based on Machine Learning Techniques and Force Analysis,” Procedia CIRP, vol. 77, pp. 501–504, 2018, doi: 10.1016/j.procir.2018.08.253.
[64] Deyong Guan, Lei Huang, and Qiankun Qu, “A predicting method of urban traffic network volume based on STARIMA model,” in 17th COTA International Conference of Transportation Professionals, Shanghai, China, 2018, pp. 3600–3606.
[65] Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan, “Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 922–929, Jul. 2019, doi: 10.1609/aaai.v33i01.3301922.
[66] Rangan Gupta and Sonali Das, “Predicting Downturns in the US Housing Market: A Bayesian Approach,” The Journal of Real Estate Finance and Economics, vol. 41, no. 3, pp. 294–319, Oct. 2010, doi: 10.1007/s11146-008-9163-x.
[67] Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, and William J. Dally, “EIE: efficient inference engine on compressed deep neural network,” ACM SIGARCH Computer Architecture News, vol. 44, no. 3, pp. 243–254, Oct. 2016, doi: 10.1145/3007787.3001163.
[68] Song Han, Huizi Mao, and William J. Dally, “Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding,” in 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings, Y. Bengio and Y. LeCun, Eds., 2016. [Online]. Available: http://arxiv.org/abs/1510.00149
[69] Song Han, Jeff Pool, John Tran, and William J. Dally, “Learning Both Weights and Connections for Efficient Neural Networks,” in Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, in NIPS’15. Cambridge, MA, USA: MIT Press, 2015, pp. 1135–1143.
[70] Yong Han, Tongxin Peng, Cheng Wang, Zhihao Zhang, and Ge Chen, “A Hybrid GLM Model for Predicting Citywide Spatio-Temporal Metro Passenger Flow,” ISPRS International Journal of Geo-Information, vol. 10, no. 4, p. 222, 2021.
[71] Shurong Hao, Mingming Zhang, and Anping Hou, “Short-term Traffic Flow Forecast Based on DE-RBF Fussion Model,” Journal of Physics: Conference Series, vol. 1910, no. 1, p. 12035, May 2021, doi: 10.1088/1742-6596/1910/1/012035.
[72] Babak Hassibi and David Stork, “Second order derivatives for network pruning: Optimal Brain Surgeon,” in Advances in Neural Information Processing Systems, S. Hanson, J. Cowan, and C. Giles, Eds., Morgan-Kaufmann, 1992. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/1992/file/303ed4c69846ab36c2904d3ba8573050-Paper.pdf
[73] Scott Havens, Hans-Peter Marshall, Christine Pielmeier, and Kelly Elder, “Automatic Grain Type Classification of Snow Micro Penetrometer Signals with Random Forests,” IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 6, pp. 3328–3335, Jun. 2013, doi: 10.1109/TGRS.2012.2220549.
[74] Steven C. Hayes, Rhonda M. Merwin, Louise McHugh, Emily K. Sandoz, Jacqueline G. L. A-Tjak, Francisco J. Ruiz, Dermot Barnes-Holmes, Jonathan B. Bricker, Joseph Ciarrochi, Mark R. Dixon, and others, “Report of the ACBS task force on the strategies and tactics of contextual behavioral science research,” Journal of Contextual Behavioral Science, vol. 20, pp. 172–183, 2021.
[75] Tomislav Hengl, Madlene Nussbaum, Marvin N. Wright, Gerard B. M. Heuvelink, and Benedikt Gräler, “Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables,” PeerJ, vol. 6, p. e5518, Aug. 2018, doi: 10.7717/peerj.5518.
[76] Rui He, Yunpeng Xiao, Xingyu Lu, Song Zhang, and Yanbing Liu, “ST-3DGMR: Spatio-temporal 3D grouped multiscale ResNet network for region-based urban traffic flow prediction,” Information Sciences, vol. 624, pp. 68–93, 2023, doi: https://doi.org/10.1016/j.ins.2022.12.066.
[77] Daniel Frank Hesser and Bernd Markert, “Tool wear monitoring of a retrofitted CNC milling machine using artificial neural networks,” Manufacturing Letters, vol. 19, pp. 1–4, Jan. 2019, doi: 10.1016/j.mfglet.2018.11.001.
[78] Tao He, Jixiang Guo, Nan Chen, Xiuyuan Xu, Zihuai Wang, Kaiyu Fu, Lunxu Liu, and Zhang Yi, “MediMLP: using grad-CAM to extract crucial variables for lung cancer postoperative complication prediction,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 6, pp. 1762–1771, 2019.
[79] Yu-Lin He, Xi-Zhao Wang, and Joshua Zhexue Huang, “Fuzzy nonlinear regression analysis using a random weight network,” Information Sciences, vol. 364–365, pp. 222–240, 2016, doi: https://doi.org/10.1016/j.ins.2016.01.037.
[80] Hidetomo Ichihashi, Katsuhiro Honda, Akira Notsu, and Eri Miyamoto, “FCM classifier for high-dimensional data,” in 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), IEEE, Jun. 2008, pp. 200–206. doi: 10.1109/FUZZY.2008.4630366.
[81] Sepp Hochreiter and Jürgen Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.
[82] John Hoxmeier, Chris Dicesare, and Manager, “System Response Time and User Satisfaction: An Experimental Study of Browser-based Applications,” Proceedings of the Association of Information Systems Americas Conference, May 2000.
[83] Yu-Liang Hsu and Jeen-Shing Wang, “A Wiener-type recurrent neural network and its control strategy for nonlinear dynamic applications,” Journal of Process Control, vol. 19, no. 6, pp. 942–953, Jun. 2009, doi: 10.1016/j.jprocont.2008.12.002.
[84] Chiou-Jye Huang and Ping-Huan Kuo, “A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities,” Sensors, vol. 18, no. 7, p. 2220, Jul. 2018, doi: 10.3390/s18072220.
[85] Sen-Yu Huang, “Exploring the Life Habits of Different Divisions in the City by Analyzing the Data of Public Rental Bicycle-An Example of Taipei City,” Master Thesis, National Yunlin University of Science and Technology, 2021. [Online]. Available: https://hdl.handle.net/11296/y8qptu
[86] Sitao Huang, Aayush Ankit, Plinio Silveira, Rodrigo Antunes, Sai Rahul Chalamalasetti, Izzat El Hajj, Dong Eun Kim, Glaucimar Aguiar, Pedro Bruel, Sergey Serebryakov, Cong Xu, Can Li, Paolo Faraboschi, John Paul Strachan, Deming Chen, Kaushik Roy, Wen-mei Hwu, and Dejan Milojicic, “Mixed Precision Quantization for ReRAM-based DNN Inference Accelerators,” in Proceedings of the 26th Asia and South Pacific Design Automation Conference, New York, NY, USA: ACM, Jan. 2021, pp. 372–377. doi: 10.1145/3394885.3431554.
[87] Xiaocheng Huang, Yifang Yin, Simon Lim, Guanfeng Wang, Bo Hu, Jagannadan Varadarajan, Shaolin Zheng, Ajay Bulusu, and Roger Zimmermann, “Grab-Posisi: An Extensive Real-Life GPS Trajectory Dataset in Southeast Asia,” in Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, in PredictGIS’19. New York, NY, USA: Association for Computing Machinery, 2019, pp. 1–10. doi: 10.1145/3356995.3364536.
[88] Amit Kumar Jain and Bhupesh Lad, “Data driven models for prognostics of high speed milling cutters,” International Journal of Performability Engineering, vol. 12, pp. 3–12, May 2016.
[89] Mansi Jain, Himani Rajput, Neha Garg, and Pronika Chawla, “Prediction of House Pricing using Machine Learning with Python,” in 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), IEEE, Jul. 2020, pp. 570–574. doi: 10.1109/ICESC48915.2020.9155839.
[90] Yiannis Kamarianakis and Poulicos Prastacos, “Spatial Time-Series Modeling: A review of the proposed methodologies,” Working Papers 0604, University of Crete, 2006. [Online]. Available: https://ideas.repec.org/p/crt/wpaper/0604.html
[91] Yan Kang, Bing Yang, Hao Li, Tie Chen, and Yachuan Zhang, “Deep spatio-temporal modified-inception with dilated convolution networks for citywide crowd flows prediction,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 34, no. 08, p. 2052003, 2020.
[92] Nikola K. Kasabov, “Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems,” Fuzzy Sets and Systems, vol. 82, no. 2, pp. 135–149, Sep. 1996, doi: 10.1016/0165-0114(95)00300-2.
[93] Chungsong Kim, Jinwei Sun, Dan Liu, Qisong Wang, and Sunggyun Paek, “An effective feature extraction method by power spectral density of EEG signal for 2-class motor imagery-based BCI,” Medical & Biological Engineering & Computing, vol. 56, no. 9, pp. 1645–1658, Sep. 2018, doi: 10.1007/s11517-017-1761-4.
[94] Thomas N. Kipf and Max Welling, “Semi-Supervised Classification with Graph Convolutional Networks,” in International Conference on Learning Representations, 2017. [Online]. Available: https://openreview.net/forum?id=SJU4ayYgl
[95] Piotr Kluska and Maciej Zięba, “Post-training Quantization Methods for Deep Learning Models,” in Intelligent Information and Database Systems, 2020, pp. 467–479. doi: 10.1007/978-3-030-41964-6_40.
[96] Dongdong Kong, Yongjie Chen, and Ning Li, “Gaussian process regression for tool wear prediction,” Mechanical Systems and Signal Processing, vol. 104, pp. 556–574, May 2018, doi: 10.1016/j.ymssp.2017.11.021.
[97] Dongdong Kong, Yongjie Chen, Ning Li, Chaoqun Duan, Lixin Lu, and Dongxing Chen, “Tool Wear Estimation in End Milling of Titanium Alloy Using NPE and a Novel WOA-SVM Model,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 7, pp. 5219–5232, Jul. 2020, doi: 10.1109/TIM.2019.2952476.
[98] Xiangyuan Kong, Jian Zhang, Xiang Wei, Weiwei Xing, and Wei Lu, “Adaptive spatial-temporal graph attention networks for traffic flow forecasting,” Applied Intelligence, vol. 52, no. 4, pp. 4300–4316, 2022.
[99] P. Krishnakumar, K. Rameshkumar, and K. I. Ramachandran, “Feature level fusion of vibration and acoustic emission signals in tool condition monitoring using machine learning classifiers,” International Journal of Prognostics and Health Management, vol. 9, no. 1, Nov. 2020, doi: 10.36001/ijphm.2018.v9i1.2694.
[100] S. Vasantha Kumar and Lelitha Vanajakshi, “Short-term traffic flow prediction using seasonal ARIMA model with limited input data,” European Transport Research Review, vol. 7, no. 3, pp. 1–9, 2015.
[101] Jaroslaw Kurek, Grzegorz Wieczorek, Bartosz Swiderski Michal Kruk, Albina Jegorowa, and Stanislaw Osowski, “Transfer learning in recognition of drill wear using convolutional neural network,” in 2017 18th International Conference on Computational Problems of Electrical Engineering (CPEE), IEEE, Sep. 2017, pp. 1–4. doi: 10.1109/CPEE.2017.8093087.
[102] Donald J. Lacombe and Nyakundi M. Michieka, “Forecasting China’s industrial output using a spatial Bayesian vector autoregressive model,” Growth and Change, vol. 49, no. 4, pp. 712–742, Dec. 2018, doi: 10.1111/grow.12251.
[103] Haitao Lang, Yuyang Xi, and Xi Zhang, “Ship Detection in High-Resolution SAR Images by Clustering Spatially Enhanced Pixel Descriptor,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 8, pp. 5407–5423, Aug. 2019, doi: 10.1109/TGRS.2019.2899337.
[104] Gang Leng, Thomas Martin McGinnity, and Girijesh Prasad, “An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network,” Fuzzy Sets and Systems, vol. 150, no. 2, pp. 211–243, Mar. 2005, doi: 10.1016/j.fss.2004.03.001.
[105] James P. LeSage and Anna Krivelyova, “A Spatial Prior for Bayesian Vector Autoregressive Models,” Journal of Regional Science, vol. 39, no. 2, pp. 297–317, May 1999, doi: 10.1111/1467-9787.00135.
[106] James P. LeSage and Zheng Pan, “Using Spatial Contiguity as Bayesian Prior Information in Regional Forecasting Models,” International Regional Science Review, vol. 18, no. 1, pp. 33–53, Jan. 1995, doi: 10.1177/016001769501800102.
[107] Shidong Liang, Minghui Ma, Shengxue He, and Hu Zhang, “Short-term passenger flow prediction in urban public transport: Kalman filtering combined k-nearest neighbor approach,” IEEE Access, vol. 7, pp. 120937–120949, 2019.
[108] Cong Li and Pei Xu, “Application on traffic flow prediction of machine learning in intelligent transportation,” Neural Computing and Applications, vol. 33, no. 2, pp. 613–624, 2021.
[109] Genghui Li, Qingfu Zhang, Qiuzhen Lin, and Weifeng Gao, “A Three-Level Radial Basis Function Method for Expensive Optimization,” IEEE Transactions on Cybernetics, vol. PP, pp. 1–12, May 2021, doi: 10.1109/TCYB.2021.3061420.
[110] Guopeng Li, Victor L. Knoop, and Hans van Lint, “Multistep traffic forecasting by dynamic graph convolution: Interpretations of real-time spatial correlations,” Transportation Research Part C: Emerging Technologies, vol. 128, p. 103185, 2021.
[111] Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf, “Pruning Filters for Efficient ConvNets,” in International Conference on Learning Representations, 2017. [Online]. Available: https://openreview.net/forum?id=rJqFGTslg
[112] He Li, Xuejiao Li, Liangcai Su, Duo Jin, Jianbin Huang, and Deshuang Huang, “Deep Spatio-Temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction,” ACM Transactions on Intelligent Systems and Technology, vol. 13, no. 2, Jan. 2022, doi: 10.1145/3510829.
[113] Keqing Li, Changyong Liang, Wenxing Lu, Chu Li, Shuping Zhao, and Binyou Wang, “Forecasting of short-term daily tourist flow based on seasonal clustering method and PSO-LSSVM,” ISPRS International Journal of Geo-Information, vol. 9, no. 11, p. 676, 2020.
[114] Kui-Lin Li, Chun-Jie Zhai, and Jian-Min Xu, “Short-term traffic flow prediction using a methodology based on ARIMA and RBF-ANN,” in 2017 Chinese Automation Congress (CAC), 2017, pp. 2804–2807. doi: 10.1109/CAC.2017.8243253.
[115] Wan Teng Lim, Lipo Wang, Yaoli Wang, and Qing Chang, “Housing price prediction using neural networks,” in 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), IEEE, Aug. 2016, pp. 518–522. doi: 10.1109/FSKD.2016.7603227.
[116] Shaofu Lin and Hengyu Tian, “Short-term metro passenger flow prediction based on random forest and LSTM,” in 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2020, pp. 2520–2526.
[117] Ziqian Lin, Jie Feng, Ziyang Lu, Yong Li, and Depeng Jin, “Deepstn+: Context-aware spatial-temporal neural network for crowd flow prediction in metropolis,” in Proceedings of the AAAI conference on artificial intelligence, 2019, pp. 1020–1027.
[118] Taoying Li, Miao Hua, and Xu Wu, “A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5),” IEEE Access, vol. 8, pp. 26933–26940, 2020, doi: 10.1109/ACCESS.2020.2971348.
[119] Lishan Liu, Ning Jia, Lei Lin, and Zhengbing He, “A Cohesion-Based Heuristic Feature Selection for Short-Term Traffic Forecasting,” IEEE Access, vol. 7, pp. 3383–3389, 2019, doi: 10.1109/ACCESS.2018.2889814.
[120] Mingzhe Liu, Tongyu Zhu, Junchen Ye, Qingxin Meng, Leilei Sun, and Bowen Du, “Spatio-Temporal AutoEncoder for Traffic Flow Prediction,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–11, 2023, doi: 10.1109/TITS.2023.3243913.
[121] Minjie Liu, Mingming Zhou, Tao Zhang, and Naixue Xiong, “Semi-supervised learning quantization algorithm with deep features for motor imagery EEG Recognition in smart healthcare application,” Applied Soft Computing, vol. 89, p. 106071, Apr. 2020, doi: 10.1016/j.asoc.2020.106071.
[122] Shu Ying Liu, Shuo Liu, Ye Tian, Quan Long Sun, and Yu Yang Tang, “Research on forecast of rail traffic flow based on ARIMA model,” in Journal of Physics: Conference Series, 2021, p. 12065.
[123] Zhidan Liu, Pengfei Zhou, Zhenjiang Li, and Mo Li, “Think Like A Graph: Real-Time Traffic Estimation at City-Scale,” IEEE Transactions on Mobile Computing, vol. 18, no. 10, pp. 2446–2459, Oct. 2019, doi: 10.1109/TMC.2018.2873642.
[124] Bin Lu, Xiaoying Gan, Haiming Jin, Luoyi Fu, Xinbing Wang, and Haisong Zhang, “Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network,” ACM Transactions on Intelligent Systems and Technology, vol. 13, no. 2, Jan. 2022, doi: 10.1145/3488902.
[125] Scott M. Lundberg and Su-In Lee, “A unified approach to interpreting model predictions,” in Advances in neural information processing systems, 2017.
[126] Jian-Hao Luo, Jianxin Wu, and Weiyao Lin, “ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression,” in 2017 IEEE International Conference on Computer Vision (ICCV), IEEE, Oct. 2017, pp. 5068–5076. doi: 10.1109/ICCV.2017.541.
[127] Pengyu Lu, “Human emotion recognition based on multi-channel EEG signals using LSTM neural network,” in 2022 Prognostics and Health Management Conference (PHM-2022 London), 2022, pp. 303–308.
[128] Emmanuel Maggiori, Guillaume Charpiat, Yuliya Tarabalka, and Pierre Alliez, “Recurrent Neural Networks to Correct Satellite Image Classification Maps,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 9, pp. 4962–4971, Sep. 2017, doi: 10.1109/TGRS.2017.2697453.
[129] Lei Ma, Tengyu Fu, Thomas Blaschke, Manchun Li, Dirk Tiede, Zhenjin Zhou, Xiaoxue Ma, and Deliang Chen, “Evaluation of feature selection methods for object-based land cover mapping of unmanned aerial vehicle imagery using random forest and support vector machine classifiers,” ISPRS International Journal of Geo-Information, vol. 6, no. 2, p. 51, 2017.
[130] Gregory Calegari Marchesan, Everton Alceu Carara, Marcelo Serrano Zanetti, and Leonardo Londero de Oliveira, “Exploring the Training and Execution Acceleration of a Neural Network in a Reconfigurable General-purpose Processor for Embedded Systems,” in 2019 17th IEEE International New Circuits and Systems Conference (NEWCAS), IEEE, Jun. 2019, pp. 1–4. doi: 10.1109/NEWCAS44328.2019.8961311.
[131] Lisa A. Marsch, “Digital health data-driven approaches to understand human behavior,” Neuropsychopharmacology, vol. 46, no. 1, pp. 191–196, 2021.
[132] Mathieu Marsot, Jiangqiang Mei, Xiaocai Shan, Liyong Ye, Peng Feng, Xuejun Yan, Chenfan Li, and Yifan Zhao, “An adaptive pig face recognition approach using Convolutional Neural Networks,” computers and Electronics in Agriculture, vol. 173, p. 105386, 2020.
[133] Cesar H. R. Martins, Paulo R. Aguiar, Arminio Frech, and Eduardo Carlos Bianchi, “Tool Condition Monitoring of Single-Point Dresser Using Acoustic Emission and Neural Networks Models,” IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 3, pp. 667–679, Mar. 2014, doi: 10.1109/TIM.2013.2281576.
[134] Suraya Masrom, Thuraiya Mohd, Nur Syafiqah Jamil, Abdullah Sani Abd. Rahman, and Norhayati Baharun, “Automated Machine Learning based on Genetic Programming: a case study on a real house pricing dataset,” in 2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS), IEEE, Sep. 2019, pp. 48–52. doi: 10.1109/AiDAS47888.2019.8970916.
[135] Wanli Min and Laura Wynter, “Real-time road traffic prediction with spatio-temporal correlations,” Transportation Research Part C: Emerging Technologies, vol. 19, no. 4, pp. 606–616, Aug. 2011, doi: 10.1016/j.trc.2010.10.002.
[136] Yasser Mohammad, Kazunori Matsumoto, and Keiichiro Hoashi, “Deep feature learning and selection for activity recognition,” in Proceedings of the 33rd Annual ACM Symposium on Applied Computing, New York, NY, USA: ACM, Apr. 2018, pp. 930–939. doi: 10.1145/3167132.3167234.
[137] Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz, “Pruning Convolutional Neural Networks for Resource Efficient Inference,” in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, OpenReview.net, 2017. [Online]. Available: https://openreview.net/forum?id=SJGCiw5gl
[138] Nithish B. Moudhgalya, Sharan Sundar S, Siddharth Divi, P. Mirunalini, Chandrabose Aravindan, and S. M. Jaisakthi, “Convolutional Long Short-Term Memory Neural Networks for Hierarchical Species Prediction,” in Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum, Avignon, France, September 10-14, 2018, L. Cappellato, N. Ferro, J.-Y. Nie, and L. Soulier, Eds., in CEUR Workshop Proceedings, vol. 2125. CEUR-WS.org, 2018. [Online]. Available: http://ceur-ws.org/Vol-2125/paper_184.pdf
[139] Fiona Nah, “A study on tolerable waiting time: how long are Web users willing to wait?,” Behaviour & IT, vol. 23, May 2004.
[140] Seol-Hyun Noh, “Analysis of gradient vanishing of RNNs and performance comparison,” Information, vol. 12, no. 11, p. 442, 2021.
[141] Angshuman Parashar, Minsoo Rhu, Anurag Mukkara, Antonio Puglielli, Rangharajan Venkatesan, Brucek Khailany, Joel Emer, Stephen W. Keckler, and William J. Dally, “SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks,” in Proceedings of the 44th Annual International Symposium on Computer Architecture, New York, NY, USA: ACM, Jun. 2017, pp. 27–40. doi: 10.1145/3079856.3080254.
[142] Amir Bahador Parsa, Ali Movahedi, Homa Taghipour, Sybil Derrible, and Abolfazl Kouros Mohammadian, “Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis,” Accident Analysis & Prevention, vol. 136, p. 105405, 2020.
[143] Peibo Duan, Guoqiang Mao, Changsheng Zhang, and Shangbo Wang, “STARIMA-based traffic prediction with time-varying lags,” in 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), IEEE, Nov. 2016, pp. 1610–1615. doi: 10.1109/ITSC.2016.7795773.
[144] Jin Peng, “Study on Dynamic Relation between Share Price Index and Housing Price: Co-integration Analysis and Application in Share Price Index Prediction,” in The Sixth International Symposium on Neural Networks (ISNN 2009), 2009, pp. 837–846. doi: 10.1007/978-3-642-01216-7_89.
[145] Xiaoyu Qi, Gang Mei, Jingzhi Tu, Ning Xi, and Francesco Piccialli, “A Deep Learning Approach for Long-Term Traffic Flow Prediction With Multifactor Fusion Using Spatiotemporal Graph Convolutional Network,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–14, 2022, doi: 10.1109/TITS.2022.3201879.
[146] Benjamin Quost and Thierry Denœux, “Clustering and classification of fuzzy data using the fuzzy EM algorithm,” Fuzzy Sets and Systems, vol. 286, pp. 134–156, Mar. 2016, doi: 10.1016/j.fss.2015.04.012.
[147] Sourjya Roy, Priyadarshini Panda, Gopalakrishnan Srinivasan, and Anand Raghunathan, “Pruning Filters while Training for Efficiently Optimizing Deep Learning Networks,” in 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, Jul. 2020, pp. 1–7. doi: 10.1109/IJCNN48605.2020.9207588.
[148] Devendra Singh Sachan, Manzil Zaheer, and Ruslan Salakhutdinov, “Revisiting lstm networks for semi-supervised text classification via mixed objective function,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2019, pp. 6940–6948.
[149] Athanasios Salamanis, Polykarpos Meladianos, Dionysios Kehagias, and Dimitrios Tzovaras, “Evaluating the effect of time series segmentation on STARIMA-based traffic prediction model,” in 2015 IEEE 18th International Conference on Intelligent Transportation Systems, 2015, pp. 2225–2230.
[150] Sadiq Sani, Nirmalie Wiratunga, and Stewart Massie, “Learning deep features for kNN-based human activity recognition,” in 25th International conference on case-based reasoning (ICCBR 2017), 2017, pp. 95–103.
[151] Biswajit Sarkar and Animesh Biswas, “Pythagorean fuzzy AHP-TOPSIS integrated approach for transportation management through a new distance measure,” Soft Computing, vol. 25, no. 5, pp. 4073–4089, 2021.
[152] Divya Saxena and Jiannong Cao, “Multimodal Spatio-Temporal Prediction with Stochastic Adversarial Networks,” ACM Transactions on Intelligent Systems and Technology, vol. 13, no. 2, Jan. 2022, doi: 10.1145/3458025.
[153] Simone Scardapane, Danilo Comminiello, Amir Hussain, and Aurelio Uncini, “Group sparse regularization for deep neural networks,” Neurocomputing, vol. 241, pp. 81–89, Jun. 2017, doi: 10.1016/j.neucom.2017.02.029.
[154] Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 618–626.
[155] Mohamad Shahbazi and Hamid Aghajan, “A GENERALIZABLE MODEL FOR SEIZURE PREDICTION BASED ON DEEP LEARNING USING CNN-LSTM ARCHITECTURE,” in 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), IEEE, Nov. 2018, pp. 469–473. doi: 10.1109/GlobalSIP.2018.8646505.
[156] J. J. Shann and H. C. Fu, “A fuzzy neural network for rule acquiring on fuzzy control systems,” Fuzzy Sets and Systems, vol. 71, no. 3, pp. 345–357, May 1995, doi: 10.1016/0165-0114(94)00277-E.
[157] Guoliang Shen, Mufan Li, Jiale Lin, Jie Bao, and Tao He, “An Empirical Study for Adopting Machine Learning Approaches for Gas Pipeline Flow Prediction,” Mathematical Problems in Engineering, vol. 2020, 2020.
[158] Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, and Wang-chun Woo, “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting,” in Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, in NIPS’15. Cambridge, MA, USA: MIT Press, 2015, pp. 802–810.
[159] M. Simard, S. S. Saatchi, and G. De Grandi, “The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest,” IEEE Transactions on Geoscience and Remote Sensing, vol. 38, no. 5, pp. 2310–2321, 2000, doi: 10.1109/36.868888.
[160] Linghao Song, Xuehai Qian, Hai Li, and Yiran Chen, “PipeLayer: A Pipelined ReRAM-Based Accelerator for Deep Learning,” in 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA), IEEE, Feb. 2017, pp. 541–552. doi: 10.1109/HPCA.2017.55.
[161] Chuang Sun, Meng Ma, Zhibin Zhao, Shaohua Tian, Ruqiang Yan, and Xuefeng Chen, “Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing,” IEEE Transactions on Industrial Informatics, vol. 15, no. 4, pp. 2416–2425, Apr. 2019, doi: 10.1109/TII.2018.2881543.
[162] Jie Sun, Liping Di, Ziheng Sun, Yonglin Shen, and Zulong Lai, “County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model,” Sensors, vol. 19, no. 20, p. 4363, Oct. 2019, doi: 10.3390/s19204363.
[163] Shiliang Sun and Changshui Zhang, “The selective random subspace predictor for traffic flow forecasting,” IEEE Transactions on intelligent transportation systems, vol. 8, no. 2, pp. 367–373, 2007.
[164] Shiliang Sun, Changshui Zhang, and Yi Zhang, “Traffic flow forecasting using a spatio-temporal bayesian network predictor,” Lecture Notes in Computer Science, vol. 3697, pp. 273–278, 2005.
[165] Jinjun Tang, Lexiao Li, Zheng Hu, and Fang Liu, “Short-Term Traffic Flow Prediction Considering Spatio-Temporal Correlation: A Hybrid Model Combing Type-2 Fuzzy C-Means and Artificial Neural Network,” IEEE Access, vol. 7, pp. 101009–101018, 2019, doi: 10.1109/ACCESS.2019.2931920.
[166] Chujie Tian, Xinning Zhu, Zheng Hu, and Jian Ma, “Deep spatial-temporal networks for crowd flows prediction by dilated convolutions and region-shifting attention mechanism,” Applied Intelligence, vol. 50, no. 10, pp. 3057–3070, 2020.
[167] Chujie Tian, Xinning Zhu, Zheng Hu, and Jian Ma, “A transfer approach with attention reptile method and long-term generation mechanism for few-shot traffic prediction,” Neurocomputing, vol. 452, pp. 15–27, 2021, doi: https://doi.org/10.1016/j.neucom.2021.03.068.
[168] Teng Tian, Xi Jin, Letian Zhao, Xiaotian Wang, Jie Wang, and Wei Wu, “Exploration of Memory Access Optimization for FPGA-based 3D CNN Accelerator,” in 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), IEEE, Mar. 2020, pp. 1650–1655. doi: 10.23919/DATE48585.2020.9116376.
[169] Manuel Titos, Angel Bueno, Luz Garcia, M. Carmen Benitez, and Jesus Ibanez, “Detection and Classification of Continuous Volcano-Seismic Signals With Recurrent Neural Networks,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 4, pp. 1936–1948, Apr. 2019, doi: 10.1109/TGRS.2018.2870202.
[170] Frederick Tung and Greg Mori, “CLIP-Q: Deep Network Compression Learning by In-parallel Pruning-Quantization,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Jun. 2018, pp. 7873–7882. doi: 10.1109/CVPR.2018.00821.
[171] Mehmet Ozgur Turkoglu, Stefano D’Aronco, Jan Dirk Wegner, and Konrad Schindler, “Gating revisited: Deep multi-layer RNNs that can be trained,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 8, pp. 4081–4092, 2021.
[172] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others, “SciPy 1.0: fundamental algorithms for scientific computing in Python,” Nature methods, vol. 17, no. 3, pp. 261–272, 2020.
[173] Danil E. van de Vlag and Alfred Stein, “Incorporating Uncertainty via Hierarchical Classification Using Fuzzy Decision Trees,” IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 1, pp. 237–245, Jan. 2007, doi: 10.1109/TGRS.2006.885403.
[174] au Wang, au Zhang, au Zhang, and au Lin, “Distance-aware influence maximization in geo-social network,” in 2016 IEEE 32nd International Conference on Data Engineering (ICDE), IEEE, May 2016, pp. 1–12. doi: 10.1109/ICDE.2016.7498224.
[175] Jinjiang Wang, Peng Wang, and Robert X. Gao, “Enhanced particle filter for tool wear prediction,” Journal of Manufacturing Systems, vol. 36, pp. 35–45, Jul. 2015, doi: 10.1016/j.jmsy.2015.03.005.
[176] Donghai and Li Bohan Wang Kai and Guan, “Deep Group Residual Convolutional CTC Networks for Speech Recognition,” in Advanced Data Mining and Applications, 2018, pp. 318–328.
[177] Leye Wang, Xu Geng, Xiaojuan Ma, Feng Liu, and Qiang Yang, “Cross-City Transfer Learning for Deep Spatio-Temporal Prediction,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, International Joint Conferences on Artificial Intelligence Organization, May 2019, pp. 1893–1899. doi: 10.24963/ijcai.2019/262.
[178] Senzhang Wang, Jiannong Cao, Hao Chen, Hao Peng, and Zhiqiu Huang, “SeqST-GAN: Seq2Seq Generative Adversarial Nets for Multi-step Urban Crowd Flow Prediction,” ACM Transactions on Spatial Algorithms and Systems, vol. 6, no. 4, 2020.
[179] Senzhang Wang, Hao Miao, Hao Chen, and Zhiqiu Huang, “Multi-task adversarial spatial-temporal networks for crowd flow prediction,” in Proceedings of the 29th ACM international conference on information & knowledge management, 2020, pp. 1555–1564.
[180] Senzhang Wang, Hao Miao, Jiyue Li, and Jiannong Cao, “Spatio-Temporal Knowledge Transfer for Urban Crowd Flow Prediction via Deep Attentive Adaptation Networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 5, pp. 4695–4705, 2022, doi: 10.1109/TITS.2021.3055207.
[181] Senzhang Wang, Meiyue Zhang, Hao Miao, Zhaohui Peng, and Philip S. Yu, “Multivariate Correlation-Aware Spatio-Temporal Graph Convolutional Networks for Multi-Scale Traffic Prediction,” ACM Transactions on Intelligent Systems and Technology, vol. 13, no. 3, Jan. 2022, doi: 10.1145/3469087.
[182] Yuan Wang and Chao Luo, “An intelligent quantitative trading system based on intuitionistic-GRU fuzzy neural networks,” Applied Soft Computing, vol. 108, p. 107471, Sep. 2021, doi: 10.1016/j.asoc.2021.107471.
[183] Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li, “Learning Structured Sparsity in Deep Neural Networks,” in Proceedings of the 30th International Conference on Neural Information Processing Systems, in NIPS’16. Red Hook, NY, USA: Curran Associates Inc., 2016, pp. 2082–2090.
[184] Matthias Wess, Sai Manoj Pudukotai Dinakarrao, and Axel Jantsch, “Weighted Quantization-Regularization in DNNs for Weight Memory Minimization Toward HW Implementation,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 37, no. 11, pp. 2929–2939, Nov. 2018, doi: 10.1109/TCAD.2018.2857080.
[185] Billy M. Williams, “Multivariate vehicular traffic flow prediction: evaluation of ARIMAX modeling,” Transportation Research Record, vol. 1776, no. 1, pp. 194–200, 2001.
[186] Dihua Wu, Shuaichao Lv, Mei Jiang, and Huaibo Song, “Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments,” Computers and Electronics in Agriculture, vol. 178, p. 105742, Nov. 2020, doi: 10.1016/j.compag.2020.105742.
[187] Junshi Xia, Pedram Ghamisi, Naoto Yokoya, and Akira Iwasaki, “Random Forest Ensembles and Extended Multiextinction Profiles for Hyperspectral Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 1, pp. 202–216, Jan. 2018, doi: 10.1109/TGRS.2017.2744662.
[188] Zhibin Xiao, Yang Wang, Kun Fu, and Fan Wu, “Identifying different transportation modes from trajectory data using tree-based ensemble classifiers,” ISPRS International Journal of Geo-Information, vol. 6, no. 2, p. 57, 2017.
[189] Zhenyi Xu, Yu Kang, and Yang Cao, “High-Resolution Urban Flows Forecasting with Coarse-Grained Spatiotemporal Data,” IEEE Transactions on Artificial Intelligence, 2022.
[190] Dingcheng Yang, Wenjian Yu, Haoyuan Mu, and Gary Yao, “Dynamic Programming Assisted Quantization Approaches for Compressing Normal and Robust DNN Models,” in Proceedings of the 26th Asia and South Pacific Design Automation Conference, New York, NY, USA: ACM, Jan. 2021, pp. 351–357. doi: 10.1145/3394885.3431538.
[191] Linxuan Yang, “Risk prediction algorithm of social security fund operation based on RBF neural network,” International Journal of Antennas and Propagation, vol. 2021, pp. 1–8, 2021.
[192] Wentao Yang, Min Deng, Feng Xu, and Hang Wang, “Prediction of hourly PM2.5 using a space-time support vector regression model,” Atmospheric Environment, vol. 181, pp. 12–19, May 2018, doi: 10.1016/j.atmosenv.2018.03.015.
[193] Huaxiu Yao, Yiding Liu, Ying Wei, Xianfeng Tang, and Zhenhui Li, “Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction,” in The World Wide Web Conference, in WWW ’19. New York, NY, USA: Association for Computing Machinery, 2019, pp. 2181–2191. doi: 10.1145/3308558.3313577.
[194] Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, and Zhenhui Li, “Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction,” in Proceedings of the AAAI conference on artificial intelligence, 2019, pp. 5668–5675.
[195] Jiachen Yao, Baochun Lu, and Junli Zhang, “Multi-Step-Ahead Tool State Monitoring Using Clustering Feature-Based Recurrent Fuzzy Neural Networks,” IEEE Access, vol. 9, pp. 113443–113453, 2021, doi: 10.1109/ACCESS.2021.3104668.
[196] Shuochao Yao, Yiran Zhao, Huajie Shao, ShengZhong Liu, Dongxin Liu, Lu Su, and Tarek Abdelzaher, “FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices,” in Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems, New York, NY, USA: ACM, Nov. 2018, pp. 278–291. doi: 10.1145/3274783.3274840.
[197] Zhixiu Yao, Shichao Xia, Yun Li, Guangfu Wu, and Linli Zuo, “Transfer Learning With Spatial–Temporal Graph Convolutional Network for Traffic Prediction,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–14, 2023, doi: 10.1109/TITS.2023.3250424.
[198] Yang Yue and Anthony Gar-On Yeh, “Spatiotemporal traffic-flow dependency and short-term traffic forecasting,” Environment and Planning B: Planning and Design, vol. 35, no. 5, pp. 762–771, 2008, doi: 10.1068/b33090.
[199] Jinsong Yu, Shuang Liang, Diyin Tang, and Hao Liu, “A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction,” The International Journal of Advanced Manufacturing Technology, vol. 91, no. 1–4, pp. 201–211, Jul. 2017, doi: 10.1007/s00170-016-9711-0.
[200] Siti Mariyam and Hasan Shafaatunnur Zainudin Zanariah and Shamsuddin, “Convolutional Neural Network Long Short-Term Memory (CNN+LSTM) for Histopathology Cancer Image Classification,” in Machine Intelligence and Signal Processing, 2020, pp. 235–245.
[201] Aoming Zhang, Haiyan Xie, and Qinwen Cao, “The Study of Safety of Ships’ Setting Sail Assessment Based on RBF Neural Network,” in 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE), IEEE, Sep. 2020, pp. 607–611. doi: 10.1109/CACRE50138.2020.9230020.
[202] Duona Zhang, Wenrui Ding, Baochang Zhang, Chunyu Xie, Hongguang Li, Chunhui Liu, and Jungong Han, “Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles,” Sensors, vol. 18, no. 3, p. 924, Mar. 2018, doi: 10.3390/s18030924.
[203] Jianjing Zhang, Peng Wang, Ruqiang Yan, and Robert X. Gao, “Deep Learning for Improved System Remaining Life Prediction,” Procedia CIRP, vol. 72, pp. 1033–1038, 2018, doi: 10.1016/j.procir.2018.03.262.
[204] Junbo Zhang, Yu Zheng, and Dekang Qi, “Deep spatio-temporal residual networks for citywide crowd flows prediction,” in Thirty-first AAAI conference on artificial intelligence, 2017.
[205] Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, and Xiuwen Yi, “DNN-based prediction model for spatio-temporal data,” in Proceedings of the 24th ACM SIGSPATIAL international conference on advances in geographic information systems, 2016, pp. 1–4.
[206] Shiguang Zhang, Jian Cao, Quan Zhang, Qi Zhang, Ying Zhang, and Yuan Wang, “An FPGA-Based Reconfigurable CNN Accelerator for YOLO,” in 2020 IEEE 3rd International Conference on Electronics Technology (ICET), IEEE, May 2020, pp. 74–78. doi: 10.1109/ICET49382.2020.9119500.
[207] Shijin Zhang, Zidong Du, Lei Zhang, Huiying Lan, Shaoli Liu, Ling Li, Qi Guo, Tianshi Chen, and Yunji Chen, “Cambricon-X: An accelerator for sparse neural networks,” in 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), IEEE, Oct. 2016, pp. 1–12. doi: 10.1109/MICRO.2016.7783723.
[208] Xiangrong Zhang, Jingyan Zhang, Chen Li, Cai Cheng, Licheng Jiao, and Huiyu Zhou, “Hybrid Unmixing Based on Adaptive Region Segmentation for Hyperspectral Imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 7, pp. 3861–3875, Jul. 2018, doi: 10.1109/TGRS.2018.2815044.
[209] Yang Zhang, Tao Cheng, and Yibin Ren, “A graph deep learning method for short‐term traffic forecasting on large road networks,” Computer-Aided Civil and Infrastructure Engineering, vol. 34, no. 10, pp. 877–896, Oct. 2019, doi: 10.1111/mice.12450.
[210] Yu Zhan, Yuzhou Luo, Xunfei Deng, Huajin Chen, Michael L. Grieneisen, Xueyou Shen, Lizhong Zhu, and Minghua Zhang, “Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm,” Atmospheric Environment, vol. 155, pp. 129–139, Apr. 2017, doi: 10.1016/j.atmosenv.2017.02.023.
[211] Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, and Haifeng Li, “T-gcn: A temporal graph convolutional network for traffic prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 9, pp. 3848–3858, 2019.
[212] Yanfei Zhong, Qiqi Zhu, and Liangpei Zhang, “Scene Classification Based on the Multifeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 11, pp. 6207–6222, Nov. 2015, doi: 10.1109/TGRS.2015.2435801.
[213] Jun-Hong Zhou, Chee Khiang Pang, Zhao-Wei Zhong, and Frank L. Lewis, “Tool Wear Monitoring Using Acoustic Emissions by Dominant-Feature Identification,” IEEE Transactions on Instrumentation and Measurement, vol. 60, no. 2, pp. 547–559, Feb. 2011, doi: 10.1109/TIM.2010.2050974.
[214] Jiawei Zhu, Xing Han, Hanhan Deng, Chao Tao, Ling Zhao, Pu Wang, Tao Lin, and Haifeng Li, “KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, pp. 15055–15065, 2022, doi: 10.1109/TITS.2021.3136287.
[215] Kunpeng Zhu and Tongshun Liu, “Online Tool Wear Monitoring Via Hidden Semi-Markov Model With Dependent Durations,” IEEE Transactions on Industrial Informatics, vol. 14, no. 1, pp. 69–78, Jan. 2018, doi: 10.1109/TII.2017.2723943.
[216] “2010 phm society conference data challenge.” https://www.phmsociety.org/competition/phm/10 (accessed May 15, 2023).
[217] “Dept of Land Administration M. O. I.” https://lvr.land.moi.gov.tw/ (accessed May 12, 2023).
[218] “Department of commerce, moea, commerce industrial service portal.” https://gcis.nat.gov.tw/mainNew/ (accessed May 12, 2023).
[219] “DATA Building Open data.” https://dbuild.cpami.gov.tw/ (accessed May 12, 2023).
[220] “Urban Development Bureau of Taichung City Government.” https://www.ud.taichung.gov.tw/ (accessed May 12, 2023).