A Computer Vision-Based Quality Assessment Technique for the automatic control of consumables for analytical laboratories
References
[1]
Ahmed M.I.B., Alotaibi R.B., Al-Qahtani R.A., Al-Qahtani R.S., Al-Hetela S.S., Al-Matar K.A., et al., Deep learning approach to recyclable products classification: Towards sustainable waste management, Sustainability 15 (2023) 11138.
[2]
Anthony, M., & Holden, S. B. (1998). Cross-validation for binary classification by real-valued functions: theoretical analysis. In Proceedings of the eleventh annual conference on computational learning theory (pp. 218–229).
[3]
Arlot S., Celisse A., A survey of cross-validation procedures for model selection, Statistics Survey 4 (2010) 40–79.
[4]
Azulay A., Weiss Y., Why do deep convolutional networks generalize so poorly to small image transformations?, Journal of Machine Learning Research 20 (2019) 1–25.
[5]
Baranwal S.K., Jaiswal K., Vaibhav K., Kumar A., Srikantaswamy R., Performance analysis of brain tumour image classification using CNN and SVM, in: 2020 second international conference on inventive research in computing applications, IEEE, 2020, pp. 537–542.
[6]
Chaganti S.Y., Nanda I., Pandi K.R., Prudhvith T.G., Kumar N., Image classification using SVM and CNN, in: 2020 international conference on computer science, engineering and applications, IEEE, 2020, pp. 1–5.
[7]
Chan C.W., Huang G.H., Artificial intelligence for management and control of pollution minimization and mitigation processes, Engineering Applications of Artificial Intelligence 16 (2003) 75–90.
[8]
Chen, C. F. R., Fan, Q., & Panda, R. (2021). Crossvit: Cross-attention multi-scale vision transformer for image classification. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 357–366).
[9]
Cheremisinoff P.N., Ferrante L.M., Waste reduction for pollution prevention, Butterworth-Heinemann, 2013.
[10]
Deng J., Dong W., Socher R., Li L.J., Li K., Fei-Fei L., ImageNet: A large-scale hierarchical image database, in: 2009 IEEE conference on computer vision and pattern recognition, IEEE, 2009, pp. 248–255.
[11]
Dietterich T.G., Approximate statistical tests for comparing supervised classification learning algorithms, Neural Computation 10 (1998) 1895–1923.
[12]
Fatorachian H., Kazemi H., Impact of industry 4.0 on supply chain performance, Production Planning and Control 32 (2021) 63–81.
[13]
Ghadge A., Er Kara M., Moradlou H., Goswami M., The impact of industry 4.0 implementation on supply chains, Journal of Manufacturing Technology Management 31 (2020) 669–686.
[14]
Ghiasi A., Shafahi A., Ardekani R., Adaptive weight decay: On the fly weight decay tuning for improving robustness, 2022, arXiv preprint arXiv:2210.00094.
[15]
Ghobakhloo M., Industry 4.0, digitization, and opportunities for sustainability, Journal of Cleaner Production 252 (2020).
[16]
Ghojogh B., Crowley M., The theory behind overfitting, cross validation, regularization, bagging, and boosting: Tutorial, 2019, arXiv preprint arXiv:1905.12787.
[17]
Goodfellow I., Bengio Y., Courville A., Deep learning, MIT Press, 2016.
[18]
Gu S., Pednekar M., Slater R., Improve image classification using data augmentation and neural networks, SMU Data Science Review 2 (2019) 1.
[19]
Guyon I., Saffari A., Dror G., Cawley G., Model selection: Beyond the Bayesian/frequentist divide, Journal of Machine Learning Research 11 (2010) 61–87.
[20]
Hassoun A., Aït-Kaddour A., Abu-Mahfouz A.M., Rathod N.B., Bader F., Barba F.J., et al., The fourth industrial revolution in the food industry—Part I: Industry 4.0 technologies, Critical Reviews in Food Science and Nutrition 63 (2023) 6547–6563.
[21]
He L., Gong X., Zhang S., Wang L., Li F., Efficient attention based deep fusion CNN for smoke detection in fog environment, Neurocomputing 434 (2021) 224–238.
[22]
He C., Zhang C., Bian T., Jiao K., Su W., Wu K.J., et al., A review on artificial intelligence enabled design, synthesis, and process optimization of chemical products for industry 4.0, Processes 11 (2023) 330.
[23]
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).
[24]
Hernández-García A., König P., Further advantages of data augmentation on convolutional neural networks, in: Artificial neural networks and machine learning. Proceedings of ICANN 2018: 27th international conference on artificial neural networks, Springer, 2018, pp. 95–103.
[25]
Huang G., Liu Z., Van Der Maaten L., Weinberger K.Q., Densely connected convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, IEEE, 2017, pp. 4700–4708.
[26]
Hussain M., Bird J.J., Faria D.R., A study on CNN transfer learning for image classification, in: Advances in computational intelligence systems: contributions presented at the 18th UK workshop on computational intelligence, Springer, 2019, pp. 191–202.
[27]
Huynh M.H., Pham-Hoai P.T., Tran A.K., Nguyen T.D., Automated waste sorting using convolutional neural network, in: Proceedings of the 2020 7th NAFOSTED conference on information and computer science, IEEE, 2020, pp. 102–107.
[28]
Iandola F.N., Han S., Moskewicz M.W., Ashraf K., Dally W.J., Keutzer K., SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size, 2016, arXiv preprint arXiv:1602.07360.
[29]
Iqbal R., Maniak T., Doctor F., Karyotis C., Fault detection and isolation in industrial processes using deep learning approaches, IEEE Transactions on Industrial Informatics 15 (2019) 3077–3084.
[30]
Jan Z., Ahamed F., Mayer W., Patel N., Grossmann G., Stumptner M., et al., Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities, Expert Systems with Applications 216 (2023).
[31]
Javaid M., Haleem A., Singh R.P., Rab S., Suman R., Significance of sensors for industry 4.0: Roles, capabilities, and applications, Sensors International 2 (2021).
[32]
Javaid M., Haleem A., Singh R.P., Rab S., Suman R., Exploring impact and features of machine vision for progressive industry 4.0 culture, Sensors International 3 (2022).
[33]
Kagermann H., Lukas W.D., Wahlster W., Industrie 4.0: Mit dem internet der dinge auf dem Weg zur 4. Industriellen revolution, VDI Nachrichten 13 (2011) 2–3.
[34]
Kagermann H., Wahlster W., Helbig J., et al., Recommendations for implementing the strategic initiative INDUSTRIE 4.0, Final Report of the Industrie 4 (2013) 82.
[35]
Kang L., Kumar J., Ye P., Li Y., Doermann D., Convolutional neural networks for document image classification, in: Proceedings of the 2014 22nd international conference on pattern recognition, IEEE, 2014, pp. 3168–3172.
[36]
Kensert A., Harrison P.J., Spjuth O., Transfer learning with deep convolutional neural networks for classifying cellular morphological changes, SLAS Discovery: Advancing Life Sciences R & D 24 (2019) 466–475.
[37]
Khan S., Muhammad K., Mumtaz S., Baik S.W., de Albuquerque V.H.C., Energy-efficient deep CNN for smoke detection in foggy IoT environment, IEEE Internet of Things Journal 6 (2019) 9237–9245.
[38]
Khin S., Kee D.M.H., Factors influencing industry 4.0 adoption, Journal of Manufacturing Technology Management 33 (2022) 448–467.
[39]
Kingma D.P., Ba J., Adam: A method for stochastic optimization, 2014, arXiv preprint arXiv:1412.6980.
[40]
Krizhevsky A., Hinton G., et al., Learning multiple layers of features from tiny images, (MSc thesis) 2009.
[41]
Krizhevsky A., Sutskever I., Hinton G.E., ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems 25 (2012).
[42]
Krogh A., Hertz J., A simple weight decay can improve generalization, Advances in Neural Information Processing Systems 4 (1991).
[43]
LeCun Y., Bengio Y., et al., Convolutional networks for images, speech, and time series, in: The handbook of brain theory and neural networks, Vol. 3361, 1995, 1995.
[44]
Lee K.B., Cheon S., Kim C.O., A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes, IEEE Transactions on Semiconductor Manufacturing 30 (2017) 135–142.
[45]
Lei, C., Hu, B., Wang, D., Zhang, S., & Chen, Z. (2019). A preliminary study on data augmentation of deep learning for image classification. In Proceedings of the 11th Asia-Pacific symposium on internetware (pp. 1–6).
[46]
Liang S.Y., Hecker R.L., Landers R.G., Machining process monitoring and control: the state-of-the-art, Journal of Manufacturing Science and Engineering 126 (2004) 297–310.
[47]
Liao Y., Ragai I., Huang Z., Kerner S., Manufacturing process monitoring using time-frequency representation and transfer learning of deep neural networks, Journal of Manufacturing Processes 68 (2021) 231–248.
[48]
Liquet B., Moka S., Nazarathy Y., The mathematical engineering of deep learning, CRC Press, 2024.
[49]
Lyu Y., Chen J., Song Z., Image-based process monitoring using deep belief networks, IFAC-PapersOnLine 51 (2018) 115–120.
[50]
Meng, L., Li, H., Chen, B. C., Lan, S., Wu, Z., Jiang, Y. G., et al. (2022). Adavit: Adaptive vision transformers for efficient image recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 12309–12318).
[51]
Mikołajczyk A., Grochowski M., Data augmentation for improving deep learning in image classification problem, in: Proceedings of the 2018 international interdisciplinary PhD workshop, IEEE, 2018, pp. 117–122.
[52]
Mohamed M., Challenges and benefits of industry 4.0: An overview, International Journal of Supply and Operations Management 5 (2018) 256–265.
[53]
Monostori L., Cyber-physical production systems: Roots, expectations and R & D challenges, Procedia Cirp 17 (2014) 9–13.
[54]
Mu K., Luo L., Wang Q., Mao F., Industrial process monitoring and fault diagnosis based on temporal attention augmented deep network, Journal of Information Processing Systems 17 (2021) 242–252.
[55]
Nakamura K., Hong B.W., Adaptive weight decay for deep neural networks, IEEE Access 7 (2019) 118857–118865.
[56]
Narayan Y., DeepWaste: Applying deep learning to waste classification for a sustainable planet, 2021, arXiv preprint arXiv:2101.05960.
[57]
Nasir V., Sassani F., A review on deep learning in machining and tool monitoring: Methods, opportunities, and challenges, International Journal of Advanced Manufacturing Technology 115 (2021) 2683–2709.
[58]
Oyelade O.N., Irunokhai E.A., Wang H., A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification, Scientific Reports 14 (2024) 692.
[59]
Pandiri D.K., Murugan R., Goel T., Smart soil image classification system using lightweight convolutional neural network, Expert Systems with Applications 238 (2024).
[60]
Panwar H., Gupta P., Siddiqui M.K., Morales-Menendez R., Bhardwaj P., Sharma S., et al., AquaVision: Automating the detection of waste in water bodies using deep transfer learning, Case Studies in Chemical and Environmental Engineering 2 (2020).
[61]
Peres R.S., Jia X., Lee J., Sun K., Colombo A.W., Barata J., Industrial artificial intelligence in industry 4.0 - systematic review, challenges and outlook, IEEE Access 8 (2020) 220121–220139.
[62]
Popkova E.G., Ragulina Y.V., Bogoviz A.V., Industry 4.0: industrial revolution of the 21st century, in: Studies in systems, decision and control, vol. 169, Springer, 2019.
[63]
Porokhnya A., Yakimenko I., Role of sensors in the paradigm of industry 4.0, in: AIP conference proceedings, AIP Publishing, 2023.
[64]
Ramsurrun N., Suddul G., Armoogum S., Foogooa R., Recyclable waste classification using computer vision and deep learning, in: Proceedings of the 2021 zooming innovation in consumer technologies conference, IEEE, 2021, pp. 11–15.
[65]
Reis M.S., Gins G., Industrial process monitoring in the big data/industry 4.0 era: From detection, to diagnosis, to prognosis, Processes 5 (2017) 35.
[66]
Ren W., Jin Z., Phase space visibility graph, Chaos, Solitons & Fractals 176 (2023).
[67]
Ren W., Jin N., OuYang L., Phase space graph convolutional network for chaotic time series learning, IEEE Transactions on Industrial Informatics 20 (2024) 7576–7584.
[68]
Reyad M., Sarhan A.M., Arafa M., A modified adam algorithm for deep neural network optimization, Neural Computing and Applications 35 (2023) 17095–17112.
[69]
Romero D., Stahre J., Taisch M., The operator 4.0: Towards socially sustainable factories of the future, Computers & Industrial Engineering 139 (2020).
[70]
Romero, D., Stahre, J., Wuest, T., Noran, O., Bernus, P., & Fast-Berglund, D. (2016). Towards an Operator 4.0 typology: A human-centric perspective on the fourth industrial revolution technologies. In Proceedings of the international conference on computers and industrial engineering (pp. 29–31). Tianjin, China.
[71]
Rumelhart D.E., Hinton G.E., Williams R.J., Learning representations by back-propagating errors, Nature 323 (1986) 533–536.
[72]
Schaffer C., Selecting a classification method by cross-validation, Machine Learning 13 (1993) 135–143.
[73]
Schütze A., Helwig N., Schneider T., Sensors 4.0–smart sensors and measurement technology enable industry 4.0, Journal of Sensors and Sensor systems 7 (2018) 359–371.
[74]
Scott D.M., Industrial process sensors, CRC Press, 2018.
[75]
Segura Á., Diez H.V., Barandiaran I., Arbelaiz A., Álvarez H., Simões B., et al., Visual computing technologies to support the operator 4.0, Computers & Industrial Engineering 139 (2020).
[76]
Shaha M., Pawar M., Transfer learning for image classification, in: Proceedings of the 2018 second international conference on electronics, communication and aerospace technology, IEEE, 2018, pp. 656–660.
[77]
Shorten C., Khoshgoftaar T.M., A survey on image data augmentation for deep learning, Journal of Big Data 6 (2019) 1–48.
[78]
Simonyan K., Vedaldi A., Zisserman A., Deep inside convolutional networks: Visualising image classification models and saliency maps, 2013, arXiv preprint arXiv:1312.6034.
[79]
Simonyan K., Zisserman A., Very deep convolutional networks for large-scale image recognition, 2014, arXiv preprint arXiv:1409.1556.
[80]
Srivastava N., Hinton G., Krizhevsky A., Sutskever I., Salakhutdinov R., Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research 15 (2014) 1929–1958.
[81]
Suleiman Z., Shaikholla S., Dikhanbayeva D., Shehab E., Turkyilmaz A., Industry 4.0: Clustering of concepts and characteristics, Cogent Engineering 9 (2022).
[82]
Sundararajan M., Taly A., Yan Q., Axiomatic attribution for deep networks, in: Proceedings of the international conference on machine learning, PMLR, 2017, pp. 3319–3328.
[83]
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818–2826).
[84]
Tanner M.A., Wong W.H., The calculation of posterior distributions by data augmentation, Journal of the American Statistical Association 82 (1987) 528–540.
[85]
Taylor L., Nitschke G., Improving deep learning with generic data augmentation, in: Proceedings of the 2018 IEEE symposium series on computational intelligence, IEEE, 2018, pp. 1542–1547.
[86]
Torrey L., Shavlik J., Transfer learning, in: Handbook of research on machine learning applications and trends: algorithms, methods, and techniques, IGI Global, 2010, pp. 242–264.
[87]
Tripathi M., Analysis of convolutional neural network based image classification techniques, Journal of Innovative Image Processing (JIIP) 3 (2021) 100–117.
[88]
Tufo G., Zribi M., Pitolli F., Pagliuca P., Advanced computer vision techniques for drug abuse detection, in: Book of abstracts of the 21st IMACS world congress, IMACS, 2023, p. 226.
[89]
Tummala S., Kadry S., Bukhari S.A.C., Rauf H.T., Classification of brain tumor from magnetic resonance imaging using vision transformers ensembling, Current Oncology 29 (2022) 7498–7511.
[90]
Uraikul V., Chan C.W., Tontiwachwuthikul P., Artificial intelligence for monitoring and supervisory control of process systems, Engineering Applications of Artificial Intelligence 20 (2007) 115–131.
[91]
Vaccari M., Bacci di Capaci R., Brunazzi E., Tognotti L., Pierno P., Vagheggi R., et al., Optimally managing chemical plant operations: An example oriented by industry 4.0 paradigms, Industrial & Engineering Chemistry Research 60 (2021) 7853–7867.
[92]
Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A.N., et al., Attention is all you need, Advances in Neural Information Processing Systems 30 (2017).
[93]
Villalba-Diez J., Schmidt D., Gevers R., Ordieres-Meré J., Buchwitz M., Wellbrock W., Deep learning for industrial computer vision quality control in the printing industry 4.0, Sensors 19 (2019) 3987.
[94]
Vogel-Heuser B., Diedrich C., Pantförder D., Göhner P., Coupling heterogeneous production systems by a multi-agent based cyber–physical production system, in: Proceedings of the 2014 12th IEEE international conference on industrial informatics, IEEE, 2014, pp. 713–719.
[95]
Wang J., Perez L., et al., The effectiveness of data augmentation in image classification using deep learning, in: Convolutional neural networks vis. recognit, Vol. 11, 2017, pp. 1–8.
[96]
Wu X., Goepp V., Siadat A., Concept and engineering development of cyber physical production systems: A systematic literature review, International Journal of Advanced Manufacturing Technology 111 (2020) 243–261.
[97]
Wu H., Zhao J., Self-adaptive deep learning for multimode process monitoring, Computers & Chemical Engineering 141 (2020).
[98]
Yadav D., Singh R., Kumar A., Sarkar B., Reduction of pollution through sustainable and flexible production by controlling by-products, Journal of Environmental Informatics 40 (2022) 106.
[99]
Yang S., Xiao W., Zhang M., Guo S., Zhao J., Shen F., Image data augmentation for deep learning: A survey, 2022, arXiv preprint arXiv:2204.08610.
[100]
Yaqub M., Feng J., Zia M.S., Arshid K., Jia K., Rehman Z.U., et al., State-of-the-art CNN optimizer for brain tumor segmentation in magnetic resonance images, Brain Sciences 10 (2020) 427.
[101]
Yates L.A., Aandahl Z., Richards S.A., Brook B.W., Cross validation for model selection: A review with examples from ecology, Ecological Monographs 93 (2023).
[102]
Ying X., An overview of overfitting and its solutions, in: Journal of physics: conference series, IOP Publishing, 2019.
[103]
Yu J., Yan X., Active features extracted by deep belief network for process monitoring, ISA Transactions 84 (2019) 247–261.
[104]
Zhang Q., Zhang X., Mu X., Wang Z., Tian R., Wang X., et al., Recyclable waste image recognition based on deep learning, Resources, Conservation and Recycling 171 (2021).
[105]
Zhao C., Perspectives on nonstationary process monitoring in the era of industrial artificial intelligence, Journal of Process Control 116 (2022) 255–272.
[106]
Zhao R., Yan R., Chen Z., Mao K., Wang P., Gao R.X., Deep learning and its applications to machine health monitoring, Mechanical Systems and Signal Processing 115 (2019) 213–237.
[107]
Zheng Y., Jiang W., et al., Evaluation of vision transformers for traffic sign classification, Wireless Communications and Mobile Computing 2022 (2022).
[108]
Zhou X., Understanding the convolutional neural networks with gradient descent and backpropagation, Journal of Physics: Conference Series 1004 (2018).
[109]
Zribi M., Pagliuca P., Pitolli F., Convolutional neural networks for the automatic control of consumables for analytical laboratories, in: Book of abstracts of the BUILD-IT2023 worskhop, CNR-INM, 2023, pp. 95–97.
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