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Recent advancement of intelligent-systems in edible birds nest: A review from production to processing

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Abstract

Edible Bird Nest (EBN) constitutes a thriving industry in several Southeast Asian nations where its value chain encompasses several critical processes starting with nest harvesting to processing, and finally sales. However, a detailed review addressing EBN production from an intelligent system perspective is currently missing. Hence, this paper aims to document a comprehensive study of the various parts of the EBN value chain, where machine-intelligence has been incorporated into various solutions. We classified all the EBN processes into three primary segments: farming and production, quality control, and market analysis. In farming and production, two key areas emerge. First, there is process analysis which involves Failure Mode and Effect Analysis (FMEA) as well as profit, cost, and efficiency analysis, while swiftlet house monitoring pinpoints the optimal environment for swiftlets to flourish. On the other hand, works related to the second segment of quality control can be divided two primary approaches: image analysis which involves either auto-grading or automatic impurities inspection, and chemical analysis to ascertain the origin and authenticity of the EBN. The third and final domain of market analysis, covers both business strategy and customer behavior analysis. We have identified the integration of cutting-edge intelligent methods in each area while offering recommendations for future work. Our findings also unveiled intricate patterns, networks, relationships, and trends in the application of machine intelligence within the EBN value chain. These insights highlight many underexplored areas as well as several strategic aspects in this emerging industry.

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References

  1. Norhayati MK, Azman O, Wan Nazaimoon WM (2010) Preliminary study of the nutritional content of Malaysian edible bird’s nest. Malays J Nutr 16:389–396

    Google Scholar 

  2. Ibrahim RM, Nasir NNM, Bakar MZA et al (2021) The authentication and grading of edible bird’s nest by metabolite, nutritional, and mineral profiling. Foods 10(7):1574. https://doi.org/10.3390/foods10071574

    Article  Google Scholar 

  3. Guo CT, Takahashi T, Bukawa W et al (2006) Edible bird’s nest extract inhibits influenza virus infection. Antiviral Res 70:140–146. https://doi.org/10.1016/j.antiviral.2006.02.005

    Article  Google Scholar 

  4. Lee TH, Wani WA, Koay YS et al (2017) Recent advances in the identification and authentication methods of edible bird’s nest. Food Res Int 100:14–27. https://doi.org/10.1016/j.foodres.2017.07.036

    Article  Google Scholar 

  5. Hao Q, Rahman A (2016) Swiftlets and edible bird’s nest industry in Asia. Pertanika J Sch Res Rev 2:32–48

    Google Scholar 

  6. Jordan D (2004) Globalisation and bird’s nest soup. Int Dev Plan Rev 26:97–110. https://doi.org/10.3828/idpr.26.1.6

    Article  Google Scholar 

  7. Jong CH, Tay KM, Lim CP (2013) Application of the fuzzy Failure Mode and Effect Analysis methodology to edible bird nest processing. Comput Electron Agric 96:90–108. https://doi.org/10.1016/j.compag.2013.04.015

    Article  Google Scholar 

  8. Yee CK, Yeo YH, Cheng LH, Yen KS (2020) Impurities detection in edible bird’s nest using optical segmentation and image fusion. Mach Vis Appl 31:1–8. https://doi.org/10.1007/s00138-020-01124-y

    Article  Google Scholar 

  9. Meng GK, Kin LW, Han TP et al (2017) Size characterisation of edible bird nest impurities: a preliminary study. Procedia Comput Sci 112:1072–1081. https://doi.org/10.1016/j.procs.2017.08.123

    Article  Google Scholar 

  10. Hong TK, Chia Fah C, Ong Han AK (2020) Approach to improve edible bird nest quality & establishing better bird nest cleaning process facility through best value approach. J Adv Perform Inf Value 10:38–50. https://doi.org/10.37265/japiv.v10i1.21

    Article  Google Scholar 

  11. Dai Y, Cao J, Wang Y et al (2021) A comprehensive review of edible bird’s nest. Food Res Int 140:109875. https://doi.org/10.1016/j.foodres.2020.109875

    Article  Google Scholar 

  12. Chua LS, Zukefli SN (2016) A comprehensive review of edible bird nests and swiftlet farming. J Integr Med 14:415–428. https://doi.org/10.1016/S2095-4964(16)60282-0

    Article  Google Scholar 

  13. Koay MY, Loh SXC, Goh KM, Lai WK (2018) Feature selection for automated grading of edible birds nest with ANFIS. In Proc - 10th Int Conf Bioinfo and Biomed Tech 2018:25–32. https://doi.org/10.1145/3232059.3232075

    Article  Google Scholar 

  14. Quek MC, Chin NL, Yusof YA et al (2018) Pattern recognition analysis on nutritional profile and chemical composition of edible bird’s nest for its origin and authentication. Int J Food Prop 21:1680–1696. https://doi.org/10.1080/10942912.2018.1503303

    Article  Google Scholar 

  15. Jamalluddin NH, Tukiran NA, Ahmad Fadzillah N, Fathi S (2019) Overview of edible bird’s nests and their contemporary issues. Food Control 104:247–255. https://doi.org/10.1016/j.foodcont.2019.04.042

    Article  Google Scholar 

  16. Ismail M, Alsalahi A, Aljaberi MA et al (2021) Efficacy of edible bird’s nest on cognitive functions in experimental animal models: a systematic review. Nutrients 13. https://doi.org/10.3390/nu13031028

  17. Lee TH, Wani WA, Lee CH et al (2021) Edible bird’s nest: the functional values of the prized animal-based bioproduct from southeast asia–a review. Front Pharmacol 12. https://doi.org/10.3389/fphar.2021.626233

  18. Yeo B-H, Tang T-K, Wong S-F et al (2021) Potential residual contaminants in edible bird’s nest. Front Pharmacol 12. https://doi.org/10.3389/fphar.2021.631136

  19. Chok KC, Ng MG, Ng KY et al (2021) Edible bird’s nest: recent updates and industry insights based on laboratory findings. Front Pharmacol 12:746656. https://doi.org/10.3389/fphar.2021.746656

    Article  Google Scholar 

  20. Popovic D (2000) CHAPTER 13 - Expert Systems in Process Diagnosis and Control. In: Computing S, Systems I (eds) SINHA NK, GUPTA MM. Academic Press, San Diego, pp 309–335

    Google Scholar 

  21. Doucet MS, Doucet TA (2003) Public Accounting Firms. In: Bidgoli H (ed) Encyclopedia of Information Systems. Elsevier, New York, pp 601–606

    Chapter  Google Scholar 

  22. Saad FSA, Shakaff AYM, Zakaria A et al (2012) Edible bird nest shape quality assessment using machine vision system. In Proc - 3rd Int Conf Intell Syst Model Simul, ISMS 2012:325–329

    Google Scholar 

  23. Saad FSA, Ibrahim MF, Shakaff AYM, Zakaria A (2015) Edible bird nest shape inspection using fourier descriptor (FD) and farthest fourier point signature (FFPS) method. J Teknol 76:17–24. https://doi.org/10.11113/jt.v76.5859

    Article  Google Scholar 

  24. Septiarini A, Maulana F, Hamdani H et al (2022) Classifying the swallow nest quality using support vector machine based on computer vision. In Proc -IEEE Int Conf on Cybernetics and Computational Intelligence, CyberneticsCom 2022:474–478. https://doi.org/10.1109/CyberneticsCom55287.2022.9865498

    Article  Google Scholar 

  25. Gan JE, Lai WK (2019) Automated grading of edible birds nest using hybrid bat algorithm clustering based on K-Means. In Proc - IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019:73–78. https://doi.org/10.1109/I2CACIS.2019.8825077

  26. Lai WK, Gan JE, Koh PM (2020) Artificial Honey Bee Swarm Intelligence for the Autograding of EBN. In: Liu Y, Wang L, Zhao L, Yu Z (eds) Advances in Intelligent Systems and Computing. Springer International Publishing, Cham, pp 472–480

    Google Scholar 

  27. Lee WW, Lai WK (2021) A novel flower pollination algorithm for auto-grading of edible birds nest. In Proc - IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2021:140–145. https://doi.org/10.1109/I2CACIS52118.2021.9495911

  28. Lai WK, Maul T, Liao IY, Goh KM (2021) Artificial Intelligence and Computer Vision – a Match Made in Heaven? J Inst Eng Malaysia 82. https://doi.org/10.54552/v82i1.73

  29. Indrajaya D, Setiawan A, Hartanto D, Hariyanto H (2022) Object Detection to Identify Shapes of Swallow Nests Using a Deep Learning Algorithm. Khazanah Inform J Ilmu Komput dan Inform 8(2). https://doi.org/10.23917/khif.v8i2.16489

  30. Subramaniama Y, Faib YC, Ming ESL (2015) Edible bird nest processing using machine vision and robotic arm. J Teknol 72:85–88. https://doi.org/10.11113/jt.v72.3889

    Article  Google Scholar 

  31. Yeo YH, Yen KS (2021) Impurities Detection in Intensity Inhomogeneous Edible Bird’s Nest (EBN) Using a U-Net Deep Learning Model. Int J Eng Technol Innov 11:135–145. https://doi.org/10.46604/IJETI.2021.6891

    Article  Google Scholar 

  32. Weng W, Zhu X (2021) INet: Convolutional Networks for Biomedical Image Segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) IEEE Access. Springer International Publishing, Cham, pp 16591–16603

    Google Scholar 

  33. Yeo YH, Yen KS (2022) Development of a hybrid autoencoder model for automated edible bird’s nest impurities inspection. J Electron Imaging 31:51603. https://doi.org/10.1117/1.JEI.31.5.051603

    Article  Google Scholar 

  34. Medway L (1962) The relation between the reproductive cycle, moult and changes in the sublingual salivary glands of the swiftlet collocalia maxima kijme. Proc Zool Soc London 138:305–315. https://doi.org/10.1111/j.1469-7998.1962.tb05700.x

    Article  Google Scholar 

  35. Ma F, Liu D (2012) Sketch of the edible bird’s nest and its important bioactivities. Food Res Int 48:559–567. https://doi.org/10.1016/j.foodres.2012.06.001

    Article  Google Scholar 

  36. Azmi NA, Lee TH, Lee CH et al (2021) Differentiation unclean and cleaned edible bird’s nest using multivariate analysis of amino acid composition data. Pertanika J Sci Technol 29:677–691. https://doi.org/10.47836/pjst.29.1.36

    Article  Google Scholar 

  37. Guo L, Wu Y, Liu M et al (2018) Rapid authentication of edible bird’s nest by FTIR spectroscopy combined with chemometrics. J Sci Food Agric 98:3057–3065. https://doi.org/10.1002/jsfa.8805

    Article  Google Scholar 

  38. Guo L, Wu Y, Liu M et al (2014) Authentication of Edible Bird’s nests by TaqMan-based real-time PCR. Food Control 44:220–226. https://doi.org/10.1016/j.foodcont.2014.04.006

    Article  Google Scholar 

  39. Noor AS, Tukiran NA, M. A. NA et al (2020) Detection of edible bird’s nest using Fourier Transform Infrared Spectroscopy (FTIR) combined with Principle Component Analysis (PCA). J Halal Ind Serv 3. https://doi.org/10.36877/jhis.a0000168

  40. Shim EKS, Chandra GF, Pedireddy S, Lee SY (2016) Characterization of swiftlet edible bird nest, a mucin glycoprotein, and its adulterants by Raman microspectroscopy. J Food Sci Technol 53:3602–3608. https://doi.org/10.1007/s13197-016-2344-3

    Article  Google Scholar 

  41. Shi J, Zhang F, Li Z et al (2017) Rapid authentication of Indonesian edible bird’s nests by near-infrared spectroscopy and chemometrics. Anal Methods 9:1297–1306. https://doi.org/10.1039/c6ay03352k

    Article  Google Scholar 

  42. Adenan MNH, Moosa S, Muhammad SA et al (2020) Screening Malaysian edible bird’s nests for structural adulterants and geographical origin using Mid-Infrared – Attenuated Total Reflectance (MIR-ATR) spectroscopy combined with chemometric analysis by Data-Driven – Soft Independent Modelling of Class Ana. Forensic Chem 17:100197. https://doi.org/10.1016/j.forc.2019.100197

    Article  Google Scholar 

  43. Huang X, Li Z, Zou X et al (2019) A low cost smart system to analyze different types of edible Bird’s nest adulteration based on colorimetric sensor array. J Food Drug Anal 27:876–886. https://doi.org/10.1016/j.jfda.2019.06.004

    Article  Google Scholar 

  44. Yong CH, Muhammad SA, Aziz FA et al (2022) Detection of adulteration activities in edible bird’s nest using untargeted 1H-NMR metabolomics with chemometrics. Food Control 132:108542. https://doi.org/10.1016/j.foodcont.2021.108542

    Article  Google Scholar 

  45. Chua YG, Chan SH, Bloodworth BC et al (2015) Identification of edible birds nest with amino acid and monosaccharide analysis. J Agric Food Chem 63:279–289. https://doi.org/10.1021/jf503157n

    Article  Google Scholar 

  46. Shim EKS, Chandra GF, Lee S-Y (2017) Thermal analysis methods for the rapid identification and authentication of swiftlet (Aerodramus fuciphagus) edible bird’s nest - A mucin glycoprotein. Food Res Int 95:9–18. https://doi.org/10.1016/j.foodres.2017.02.018

    Article  Google Scholar 

  47. Quek MC, Chin NL, Yusof YA et al (2015) Preliminary nitrite, nitrate and colour analysis of Malaysian edible bird’s nest. Inf Process Agric 2:1–5. https://doi.org/10.1016/j.inpa.2014.12.002

    Article  Google Scholar 

  48. Hudaya R, Syamsi L, Sutian W et al (2021) Development of spectral sensors for nitrite content in edible bird’s nest. In Proc - 2nd International Seminar of Science and Applied Technology, ISSAT 2021:88–92. https://doi.org/10.2991/aer.k.211106.015

    Article  Google Scholar 

  49. Shi J, Hu X, Zou X et al (2017) A rapid and nondestructive method to determine the distribution map of protein, carbohydrate and sialic acid on Edible bird’s nest by hyper-spectral imaging and chemometrics. Food Chem 229:235–241. https://doi.org/10.1016/j.foodchem.2017.02.075

    Article  Google Scholar 

  50. Zhang M, Hu H, Zeng G et al (2022) Discrimination and quantification of adulterated edible bird’s nest based on their improved cohesion stable isotope ratios. Food Control 140:109111. https://doi.org/10.1016/j.foodcont.2022.109111

    Article  Google Scholar 

  51. Ng JS, Muhammad SA, Yong CH et al (2022) Adulteration Detection of Edible Bird's Nests Using Rapid Spectroscopic Techniques Coupled with Multi-Class Discriminant Analysis. Foods 11

  52. Gan SH, Ong SP, Chin NL, Law CL (2016) Color changes, nitrite content, and rehydration capacity of edible bird’s nest by advanced drying method. Dry Technol 34:1330–1342. https://doi.org/10.1080/07373937.2015.1106552

    Article  Google Scholar 

  53. Ma X, Zhang J, Liang J, Chen Y (2020) Element analysis of house-and cave-ebn (Edible bird’s nest) traceability by inductively coupled plasma-mass spectrometry (ICP-MS) integrated with chemo-metrics. Mater Express 10:1141–1148. https://doi.org/10.1166/mex.2020.1742

    Article  Google Scholar 

  54. Lalung JBI, Seow EK et al (2016) Discrimination between Cave and House-Farmed Edible Bird’s Nest Based on Major Mineral Profiles. Pertanika J. Trop. Agric, Sci

    Google Scholar 

  55. Lee TH, Lee CH, Azmi NA et al (2022) Amino acid determination by HPLC combined with multivariate approach for geographical classification of Malaysian Edible Bird’s Nest. J Food Compos Anal 107:104399. https://doi.org/10.1016/j.jfca.2022.104399

    Article  Google Scholar 

  56. Ang KM, Seow EK, Fam PS, Cheng LH (2022) Classification of edible bird’s nest samples using a logistic regression model through the mineral ratio approach. Food Control 137:108921. https://doi.org/10.1016/j.foodcont.2022.108921

    Article  Google Scholar 

  57. Wong CF, Chan GKL, Zhang ML et al (2017) Characterization of edible bird’s nest by peptide fingerprinting with principal component analysis. Food Qual Saf 1:83–92. https://doi.org/10.1093/fqsafe/fyx002

    Article  Google Scholar 

  58. Seow EK, Ibrahim B, Muhammad SA et al (2016) Differentiation between house and cave edible bird’s nests by chemometric analysis of amino acid composition data. Lwt 65:428–435. https://doi.org/10.1016/j.lwt.2015.08.047

    Article  Google Scholar 

  59. Tong SR, Lee TH, Cheong SK, Lim YM (2021) Geographical Factor Influences the Metabolite Distribution of House Edible Bird’s Nests in Malaysia. Front Nutr 8. https://doi.org/10.3389/fnut.2021.658634

  60. Chua YG, Bloodworth BC, Leong LP, Li SFY (2014) Metabolite profiling of edible bird’s nest using gas chromatography/mass spectrometry and liquid chromatography/mass spectrometry. Rapid Commun Mass Spectrom 28:1387–1400. https://doi.org/10.1002/rcm.6914

    Article  Google Scholar 

  61. Huang X, Li Z, **aobo Z et al (2020) Geographical origin discrimination of edible bird’s nests using smart handheld device based on colorimetric sensor array. J Food Meas Charact 14:514–526. https://doi.org/10.1007/s11694-019-00251-z

    Article  Google Scholar 

  62. Huang J, You JX, Liu HC, Song MS (2020) Failure mode and effect analysis improvement: A systematic literature review and future research agenda. Reliab Eng Syst Saf 199:106885. https://doi.org/10.1016/j.ress.2020.106885

    Article  Google Scholar 

  63. Jong CH, Tay KM, Lim CP (2014) A single input rule modules connected fuzzy FMEA methodology for edible bird nest processing. Advances in Intelligent Systems and Computing 223:165–176. https://doi.org/10.1007/978-3-319-00930-8_15

    Article  Google Scholar 

  64. Tay KM, Jong CH, Lim CP (2015) A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry. Neural Comput Appl 26:551–560. https://doi.org/10.1007/s00521-014-1647-4

    Article  Google Scholar 

  65. Chang WL, Tay KM, Lim CP (2015) Clustering and visualization of failure modes using an evolving tree. Expert Syst Appl 42:7235–7244. https://doi.org/10.1016/j.eswa.2015.04.036

    Article  Google Scholar 

  66. Chai KC, Jong CH, Tay KM, Lim CP (2016) A perceptual computing-based method to prioritize failure modes in failure mode and effect analysis and its application to edible bird nest farming. Appl Soft Comput J 49:734–747. https://doi.org/10.1016/j.asoc.2016.08.043

    Article  Google Scholar 

  67. Indra E, Angelin AS et al (2022) Implementation of Greedy Algorithm for Profit and Cost Analysis of Swallow’s Nest Processing Dirty to Finished Products. IOP Conf Ser Earth Environ Sci 1083:12058. https://doi.org/10.1088/1755-1315/1083/1/012058

    Article  Google Scholar 

  68. Alpandi RM, Kamarudin F, Wanke P et al (2022) Energy efficiency in production of swiftlet edible bird’s nest. Sustainability 14. https://doi.org/10.3390/su14105870

  69. Farazh F, Isamail MZ, Ramli M et al (2022) The Production Efficiency on Edible Birds’ Nest: The Case Study in Gua Musang and Johor Bahru, Malaysia. Int J Acad Res Bus Soc Sci 12. https://doi.org/10.6007/IJARBSS/v12-i1/11643

  70. Gan SH, Ong SP, Chin NL, Law CL (2017) A comparative quality study and energy saving on intermittent heat pump drying of Malaysian edible bird’s nest. Dry Technol 35:4–14. https://doi.org/10.1080/07373937.2016.1155053

    Article  Google Scholar 

  71. Ito Y, Matsumoto K, Usup A, Yamamoto Y (2021) A sustainable way of agricultural livelihood: edible bird’s nests in Indonesia. Ecosyst Heal Sustain 7:1960200. https://doi.org/10.1080/20964129.2021.1960200

    Article  Google Scholar 

  72. Shim EK-S, Lee S-Y (2020) Calcite Deposits Differentiate Cave from House-Farmed Edible Bird’s Nest as shown by SEM-EDX ATR-FTIR and Raman Microspectroscopy. Chem - An Asian J 15:2487–2492. https://doi.org/10.1002/asia.202000520

    Article  Google Scholar 

  73. Idrees MO, Pradhan B (2016) Hybrid Taguchi-Objective Function optimization approach for automatic cave bird detection from terrestrial laser scanning intensity image. Int J Speleol 45:289. https://doi.org/10.5038/1827-806X.45.3.1988

    Article  Google Scholar 

  74. Mcfarlane D, Roberts W, Buchroithner M et al (2015) Terrestrial LiDAR-based automated counting of swiftlet nests in the caves of Gomantong, Sabah, Borneo. Int J Speleol 44:191–195. https://doi.org/10.5038/1827-806X.44.2.8

    Article  Google Scholar 

  75. Rahman MA, Ghazali PL, Chong JL (2018) Environmental parameters in successful edible bird nest swiftlet houses in Terengganu. J Sustain Sci Manag 13:127–131

    Google Scholar 

  76. Noverta R, Wahab NHA, Ahsan MA (2022) Hybrid system on temperature and humidity for shallow nest farm via mobile application in Internet of Things. In Proc - 9th International Graduate Conference on Engineering, Science, and Humanities, IGCESH 2022:93–97

  77. Mamduh SM, Shakaff AY, Saad SM et al (2012) Odour and hazardous gas monitoring system for swiftlet farming using wireless sensor network (WSN). Chem Eng Trans 30:331–336

    Google Scholar 

  78. Nematollahi MA, Al-Haddad SAR, Ramli AR et al (2017) Frequency domain processing for artificial synthesis of swiftlet’s sound Waves. J Telecommun Electron Comput Eng 9:89–93

    Google Scholar 

  79. Mahfurdz AS (2015) Piezoelectric power harvesting from chirps and mating swiftlets attraction sound. World J Eng 12:407–412. https://doi.org/10.1260/1708-5284.12.4.407

    Article  Google Scholar 

  80. Tristanto D, Uranus HP (2011) Microcontroller based environmental control for swiftlet nesting with SMS notification. Proc 2011 Int Conf Elect Eng Inform, ICEEI 2011:1–5

    Google Scholar 

  81. Usmanto B, Dewi NAK (2022) Prototype of Monitoring System and Automation Regulator Sound, Temperature, Humidity, Lighting, Window at the Swiftlet House (RBW Smart System) Based on Webserver. J Electron Comput Netw Appl Math ISSN 2799–1156(2):54–71

    Google Scholar 

  82. Ibrahim AR, Ibrahim NHN, Harun AN et al (2018) Bird Counting and Climate Monitoring using LoRaWAN in Swiftlet Farming for IR4.0 Applications. In: 2018 2nd International Conference on Smart Sensors and Application, ICSSA 2018. 33–37

  83. Ibrahim AR, Nik Ibrahim NH, Harun AN et al (2018) Automated Monitoring and LoRaWAN Control Mechanism for Swiftlet Bird House. Int Conf Intel Adv Syst, ICIAS 2018:1–5

    Google Scholar 

  84. Barry G, Bakar L (2021) Swiftlet House Cooling System Powered by Solar Panel. Prog Eng Appl Technol 2:396–404

    Google Scholar 

  85. Sharifuddin J, Ramalingam L, Mohamed Z, Rezai G (2014) Factors Affecting Intention to Purchase Edible Bird’s Nest Products: The Case of Malaysian Consumers. J Food Prod Mark 20:75–84. https://doi.org/10.1080/10454446.2014.946169

    Article  Google Scholar 

  86. Shukri NNHM, Mohd Nawi N, Abdullah AM, Man N (2018) Modeling purchase intention towards edible bird’s nest products among Malaysians. Int Food Res J 25:S165–S171

    Google Scholar 

  87. Rahman N, Zandi GR, Yuan L (2018) The Repurchase Intention Development: the Case of Birds Nest Market Consumers in China. Int J Eng Technol 7:56–59

    Google Scholar 

  88. Mohamad Shukri NNH, Mohd Nawi N, Abdullah AM, Man N (2019) Actual Purchase Behavior of Edible Bird’s Nest Products in Malaysia Using Cluster Analysis. J Food Prod Mark 25:849–860. https://doi.org/10.1080/10454446.2019.1691105

    Article  Google Scholar 

  89. Shukri NNHM, Nawi NM, Abdullah AM, Man N (2019) Segmenting consumers purchase intention towards Edible bird’s nest products using the decision tree techniques. Int J Supply Chain Manag 8:554–559

    Google Scholar 

  90. Sjofjan O, Adli DN (2022) Using fuzzy time series with and without markov chain: to forecast of edible bird nest exported from Indonesia. E3S Web Conf 335:00016. https://doi.org/10.1051/e3sconf/202233500016

    Article  Google Scholar 

  91. Maulana H, Mulyantika U (2020) The prediction of export product prices with holt’s double exponential smoothing method. In Proc - International Conference on Computer and Informatics Engineering, IC2IE 2020:372–375. https://doi.org/10.1109/IC2IE50715.2020.9274679

  92. Nazri NAM, Adi Maimun NH, Ibrahim NL et al (2022) A Spatial Hedonic Analysis of The Effects of Swiftlet Farm on House Prices. J Adv Geospatial Sci Technol 2:154–162

    Google Scholar 

  93. Chok KC, Ng MG, Ng KY, et al (2021) Edible bird’s nest: recent updates and industry insights based on laboratory findings. Front Pharmacol 12:. https://doi.org/10.3389/fphar.2021.746656

  94. Tangjitmanngamkul J (2019) A Comparative Analysis of Thai Bird’s Nest Export to Chinese Market. Eur J Bus Manag. https://doi.org/10.7176/ejbm/11-13-08

    Article  Google Scholar 

  95. Zhao H, Shi J, Qi X et al (2017) Pyramid scene parsing network. In Proc -IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017:6230–6239. https://doi.org/10.1109/CVPR.2017.660

    Article  Google Scholar 

  96. Chen L-C, Papandreou G, Kokkinos I et al (2017) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40:834–848. https://doi.org/10.1109/tpami.2017.2699184

    Article  Google Scholar 

  97. Zhang S, Ma Z, Zhang G, et al (2020) Semantic Image Segmentation with Deep Convolutional Neural Networks and Quick Shift. Symmetry (Basel) 12:. https://doi.org/10.3390/sym12030427

  98. van der Spoel E, Rozing MP, Houwing-Duistermaat JJ et al (2015) Siamese neural networks for one-shot image recognition. In Proc -International Conference on MachineLearning - Deep Learning Workshop, ICML 2015:956–963

    Google Scholar 

  99. Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. In Proc - 31st Conference on Neural Information Processing Systems, NIPS 2017: 4080-4090

  100. Yalcin AS, Kilic HS, Delen D (2022) The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review. Technol Forecast Soc Change 174:121193. https://doi.org/10.1016/j.techfore.2021.121193

    Article  Google Scholar 

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Goh Kam Meng drafted the manuscript, Lim Li Li and Lai Weng Kin collected the literature works, Santhi Krishnamoorthy assisted with the preparation of the manuscript, Tomas Maul and Chaw Jun Kit assisted with reviewed, proof-read and refined the manuscript.

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Correspondence to Kam Meng Goh.

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Goh, K.M., Lim, L.L., Krishnamoorthy, S. et al. Recent advancement of intelligent-systems in edible birds nest: A review from production to processing. Multimed Tools Appl 83, 51159–51209 (2024). https://doi.org/10.1007/s11042-023-17490-4

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  • DOI: https://doi.org/10.1007/s11042-023-17490-4

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