Current Trends in Incubator Control for Premature Infants with Artificial Intelligence Based on Fuzzy Logic Control: Systematic Literature Review

Nia Maharani Raharja, Iswanto Suwarno, Sugiyarta Sugiyarta

Abstract


Incubator Control for Premature Babies has benefited greatly from the development of creative methods and uses of artificial intelligence. Due to the immaturity of the epidermis, premature infants lose fluid and heat early in life, which causes hyperosmolar dehydration and hypothermia. Water loss through the epidermis. Therefore, in order to maintain the baby's healthy temperature, an incubator is required. As a result, it is anticipated that the baby will maintain the same temperature as in the mother's womb. A temperature regulation system with good measurement and regulation quality is necessary due to the necessity of Incubator Control for Premature Infants with Artificial Intelligence Based on Fuzzy Logic in treating premature infants. The purpose of this research is to assess current trends in artificial intelligence-based fuzzy logic incubator control for preterm infants. The Preferred Reporting Items for Systematic Review (PRISMA) were used in this study's systematic literature review. 188 suitable articles that fit the inclusion requirements were found after the articles were screened and chosen. The outcomes demonstrated that the Incubator Control for Premature Infants offered the best environment for newborns with growth or disease-related issues (premature babies). An incubator is a sealed space free of dust and bacteria with the ability to regulate temperature, humidity, and oxygen to maintain a stable environment.


Keywords


incubator control; premature infant; artificial intelligence; fuzzy logic

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References


J. Liu et al., “Artificial intelligence in the 21st century,” IEEE Access, vol. 6, no. March, pp. 34403–34421, 2018, doi: 10.1109/ACCESS.2018.2819688.

Muhammad Ahmad Baballe, Mukhtar Ibrahim Bello, and Abdulhamid Shariff Mahmoud, “Comparative Study of Gas Alarm Detection System,” JTCIS, vol. 1, no. 3, Oct. 2021.

D. Vernon, G. Metta, and G. Sandini, “A survey of artificial cognitive systems: Implications for the autonomous development of mental capabilities in computational agents,” IEEE Trans. Evol. Comput., vol. 11, no. 2, pp. 151–180, 2007, doi: 10.1109/TEVC.2006.890274.

Nia Maharani Raharja and Ipin Prasojo, “Decision Tree Algorithm Based Portable Automation Device to Reduce Electricity Usage on Television,” JTCIS, vol. 1, no. 3, Oct. 2021.

D. Kirsh, “Thinking with external representations,” AI Soc., vol. 25, no. 4, pp. 441–454, 2010, doi: 10.1007/s00146-010-0272-8.

Rian Ordila, Hendry Fonda, and Hasti Rizki, “Expert system for selection of saving products using the backward chaining method at PT. State saving bank (persero) tbk. Pekanbaru branch office,” JTCIS, vol. 1, no. 3, Dec. 2021.

S. M. Mohammad, “Artificial Intelligence in Information Technology,” SSRN Electron. J., vol. 7, no. 6, pp. 168–175, 2020, doi: 10.2139/ssrn.3625444.

Wiwin A. Oktaviani, Taufik Barlian, Azizul Muttaqin, Israa Al_barazanchi, and Irfan Ahmad, “Particle Swarm Optimization Algorithm Based Optimization of Steam Turbine Generator and Gas Turbine Generator at Pt. Sriwidjaja Fertilizer,” JTCIS, vol. 1, no. 3, Oct. 2021.

R. Cioffi, M. Travaglioni, G. Piscitelli, A. Petrillo, and F. De Felice, “Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions,” Sustain., vol. 12, no. 2, 2020, doi: 10.3390/su12020492.

O. A. Widodo, N. H. Syani Harahap, A. Alamsyah, M. A. Muslim, and Y. Dasril, “Decision Support System Promotion of Structural Position Improvement of Civil Servants Using Fuzzy Umano,” ICOCA, vol. 1, no. 1, pp. 1–8, Dec. 2022.

A. Hayani, E. A. Sari, and Sukiman, “Artificial intelligence librarian as promotion of iain lhokseumawe library in the revolutionary Era 4.0,” J. Robot. Control, vol. 2, no. 2, pp. 88–93, 2021, doi: 10.18196/jrc.2258.

A. D. Lestari, D. A. A. Pertiwi, S. R. Hidayah, E. N. Dianti, and O. G. Khoirunnisa, “Logistic Service Quality in Improving the Quality of Logistics Services for Companies Using the Analytical Hierarchy Process (AHP) Method,” ICOCA, vol. 1, no. 1, pp. 9–16, Dec. 2022.

O. A. Nnamdi and Sukidjo, “The future of jobs amidst the rise of artificial intelligence: How ready are Asian undergraduates?,” J. Robot. Control, vol. 1, no. 6, pp. 208–212, 2020, doi: 10.18196/jrc.1639.

T. Lailatul Nikmah, R. M. Syafei, R. Muzayanah, A. Salsabila, and A. A. Nurdin, “Prediction of Used Car Prices Using K-Nearest Neighbour, Random Forest, and Adaptive Boosting Algorithm,” ICOCA, vol. 1, no. 1, pp. 17–22, Dec. 2022.

K. B. Jang, C. H. Baek, and T. H. Woo, “Risk Analysis of Nuclear Power Plant (NPP) Operations by Artificial Intelligence (AI) in Robot,” J. Robot. Control, vol. 3, no. 2, pp. 153–159, 2022, doi: 10.18196/jrc.v3i2.13984.

W. F. Abror, M. A. Muslim, D. A. A. Pertiwi, and J. Jumanto, “Combination of Weak Learner and Strong on Stacking to Increase Bankruptcy Risk Prediction,” ICOCA, vol. 1, no. 1, pp. 23–28, Dec. 2022.

A. Rajkomar, J. Dean, and I. Kohane, “Machine Learning in Medicine,” N. Engl. J. Med., vol. 380, no. 14, pp. 1347–1358, 2019, doi: 10.1056/nejmra1814259.

W. Hastomo, A. S. B. Karno, N. Kamilia, N. Yuningsih, and A. Tunjungsari, “Plant Disease Identification Using EfficienNet,” ICOCA, vol. 1, no. 1, pp. 29–40, Jan. 2023.

H. Shimizu and K. I. Nakayama, “Artificial intelligence in oncology,” Cancer Sci., vol. 111, no. 5, pp. 1452–1460, 2020, doi: 10.1111/cas.14377.

C. W. Park et al., “Artificial Intelligence in Health Care: Current Applications and Issues,” World J. Orthop., vol. 35, no. 42, pp. 1–11, 2020, doi: 10.3346/jkms.2020.35.e379.

D. Isaacs, “Artificial intelligence in health care,” J. Paediatr. Child Health, vol. 56, no. 10, pp. 1493–1495, 2020, doi: 10.1111/jpc.14828.

M. A. Mahdi, S. A. Gittaffa, and A. H. Issa, “Multiple Fault Detection and Smart Monitoring System Based on Machine Learning Classifiers for Infant Incubators Using Raspberry Pi 4,” J. Eur. des Systèmes Autom., vol. 55, no. 6, pp. 771–778, Dec. 2022.

K. M. A.-A. Ghada M.Amer, “Novel Technique to Control The Premature Infant Incubator System Using ANN,” Syst. Anal. Autom. Control, vol. I, 2005.

M. Visscher and V. Narendran, “The Ontogeny of Skin,” Adv. Wound Care, vol. 3, no. 4, pp. 291–303, 2014, doi: 10.1089/wound.2013.0467.

F. J. Aguayo-Canela et al., “Middleware-based multi-agent development environment for building and testing distributed intelligent systems,” Cluster Comput., vol. 24, no. 3, pp. 2313–2325, Sep. 2021.

H. B. D. L. Mathew, Ashish Gupta, “Controlling of Temperature and Humidity for an Infant Incubator Using Microcontroller,” Int. J. Adv. Res. Electr. Electron. Instrum. Eng., vol. 4, no. 6, pp. 4975–4982, 2015, doi: 10.15662/ijareeie.2015.0406012.

M. Caplan, “Premature Infants,” Fetal Neonatal Physiol. 2-Volume Set, no. April 2016, pp. 1652-1657.e2, 2017, doi: 10.1016/B978-0-323-35214-7.00163-3.

H. Nakanishi et al., “Clinical characterization and long-term prognosis of neurological development in preterm infants with late-onset circulatory collapse,” J. Perinatol., vol. 30, no. 11, pp. 751–756, 2010, doi: 10.1038/jp.2010.41.

S. M. Kim, E. Y. Lee, J. Chen, and S. A. Ringer, “Improved care and growth outcomes by using hybrid humidified incubators in very preterm infants,” Pediatrics, vol. 125, no. 1, 2010, doi: 10.1542/peds.2008-2997.

T. A. Tisa, Z. A. Nisha, and M. A. Kiber, “Design of an Enhanced Temperature Control System for Neonatal Incubator,” Bangladesh J. Med. Phys., vol. 5, no. 1, pp. 53–61, 2013, doi: 10.3329/bjmp.v5i1.14668.

J. S. Machado, T. S. Ferreira, R. C. G. Lima, V. C. Vieira, and D. S. de Medeiros, “Premature birth: Topics in physiology and pharmacological characteristics,” Rev. Assoc. Med. Bras., vol. 67, no. 1, pp. 150–155, 2021, doi: 10.1590/1806-9282.67.01.20200501.

P. Dodrill, “Feeding Difficulties in Preterm Infants,” ICAN Infant, Child, Adolesc. Nutr., vol. 3, no. 6, pp. 324–331, 2011, doi: 10.1177/1941406411421003.

A. G. Shabeeb, A. J. Al-Askery, and Z. M. Nahi, “Remote monitoring of a premature infants incubator,” Indones. J. Electr. Eng. Comput. Sci., vol. 17, no. 3, pp. 1232–1238, 2019, doi: 10.11591/ijeecs.v17.i3.pp1232-1238.

Q. Hidayati, N. Yanti, N. Jamal, and M. Adisaputra, “Portable Baby Incubator Based On Fuzzy Logic,” Journal of Telematics and Informatics (JTI), vol. 8, no. 1, 2020.

D. Lourençoni, D. C. T. C. De Brito, P. T. L. De Oliveira, S. H. N. Turco, and J. S. Cunha, “FUZZY CONTROLLER APPLIED TO TEMPERATURE ADJUSTMENT IN INCUBATION OF FREE-RANGE EGGS,” Engenharia Agrícola, vol. 42, 2022.

J. A. Freeman, “Fuzzy Systems for Control Applications : The Truck Backer-Upper,” Mathematica, pp. 64–69, 1994.

C. C. Lee, “Fuzzy Logic in Control Systems: Fuzzy Logic Controller, Part II,” IEEE Transactions on Systems, Man and Cybernetics, vol. 20, no. 2. pp. 419–435, 1990. doi: 10.1109/21.52552.

W. Widhiada, T. G. T. Nindhia, I. N. Gantara, I. N. Budarsa, and I. N. Suarndwipa, “Temperature stability and humidity on infant incubator based on fuzzy logic control,” ACM Int. Conf. Proceeding Ser., pp. 155–159, 2019, doi: 10.1145/3330482.3330527.

M. Cevik and C. Senturk, “Cypriot Journal of Educational Multidimensional 21st century skills scale : Validity and reliability study,” Cypriot Journal of Educational Sciences, vol. 14, no. 1, pp. 11–28, 2019.

E. van Laar, A. J. van Deursen, J. A. van Dijk, and J. de Haan, “The Essential Skills of 21 st Century Classroom – President Barak Obama Research,” Sage Open, vol. 10, no. 1, 2020, doi: 10.13140/RG.2.2.36190.59201.

D. Khullar, L. P. Casalino, Y. Qian, Y. Lu, H. M. Krumholz, and S. Aneja, “Perspectives of Patients About Artificial Intelligence in Health Care,” JAMA Network Open, vol. 5, no. 5, pp. 1–4, 2022, doi: 10.1001/jamanetworkopen.2022.10309.

H. Purwaningsih, N. Istiqomah, Y. Widyastuti, and I. Kusuma, “Menstrual Hygiene Management (MHM) Education in Elementary School Students as the Implementation of Personal Hygiene Behavior during Menstruation,” J. Pengabdi. dan Pemberdaya. Masy. Indones., vol. 1, no. 11, pp. 451–458, 2021.

E. P. Klement, Fuzzy Logic in Artificial Intelligence. Springer Berlin, 2005.

A. Dyorita, “Improving the Quality of Life for Bipolar Survivors during the Covid-19 Pandemic in the Bipolar Community of Yogyakarta,” J. Pengabdi. dan Pemberdaya. Masy. Indones., vol. 1, no. 9, pp. 363–370, 2021.

A. Primanita and F. Muliawan, “Fuzzy Logic Implementation on Enemy Speed Control to Raise Player Engagement,” The 1st International Conference on Computer Science and Engineering, pp. 119–123, 2014.

Y. Rahmawati and N. Rahma, “Training on Identification of Ioded Salt for Members of Nasyiatul ’ Aisyiyah Moyudan and Seyegan Sleman Yogyakarta,” J. Pengabdi. dan Pemberdaya. Masy. Indones., vol. 1, no. 10, pp. 389–394, 2021.

C. Engg, C. Engg, E. Engg, and C. Engg, “A study depicting the advent of artificial intelligence in health care,” European Journal of Molecular Clinical Medicine, vol. 7, no. 11, pp. 131–146, 2020.

S. Anggraini and A. Dona, “Educational Dissemination for the Implementation of the Covid- 19 Health Protocol at Mawar Paud Posts,” J. Pengabdi. dan Pemberdaya. Masy. Indones., vol. 1, no. 9, pp. 349–356, 2021.

F. Herrera, M. Lozano, and J. L. Verdegay, “Tuning fuzzy logic controllers by genetic algorithms,” Int. J. Approx. Reason., vol. 12, no. 3–4, pp. 299–315, 1995, doi: 10.1016/0888-613X(94)00033-Y.

R. Pitriani and R. Andriyani, “Socialization of corner care in newborn babies as an efforts to prevent infections in the corner at PMB Ernita Pekanbaru,” J. Pengabdi. dan Pemberdaya. Masy. Indones., vol. 1, no. 3, pp. 111–118, 2021.

A. Alkhayyat et al., “Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review,” Int. J. Distrib. Sens. Networks, vol. 18, no. 7, 2022, doi: 10.1177/15501329221113508.

L. Rosidaa, I. Mutiara, and F. Padia, “Training and counseling of violence women and children for motivators of motivation in the work area of Community Health Center Kasihan I Bantul Yogyakarta,” J. Pengabdi. dan Pemberdaya. Masy. Indones., vol. 1, no. 5, pp. 178–188, 2021.

H. R. Berenji, “A reinforcement learning—based architecture for fuzzy logic control," International Journal of Approximate Reasoning, vol. 6, no. 2, pp. 267-292, 1992.

Wantonoro, D. Prihatiningsih, and E. Koni, “Comprehensive home based diabetic wounds care program during covid-19 pandemic in Yogyakarta,” J. Pengabdi. dan Pemberdaya. Masy. Indones., vol. 1, no. 6, pp. 235–240, 2021.

A. Tursunbayeva and M. Renkema, “Artificial intelligence in health-care: implications for the job design of healthcare professionals,” Asia Pacific J. Hum. Resour., no. April, 2022, doi: 10.1111/1744-7941.12325.

D. A. Hashimoto, Ã. G. Rosman, D. Rus, O. R. Meireles, and Ã. Facs, “Artificial Intelligence in Surgery : Promises and Perils,” Annals of surgery vol. 268, no. 1, pp. 70-76, 2018, doi: 10.1097/SLA.0000000000002693.

G. Zhang, S. S. Band, S. Ardabili, K. W. Chau, and A. Mosavi, “Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperature,” Eng. Appl. Comput. Fluid Mech., vol. 16, no. 1, pp. 713–723, 2022, doi: 10.1080/19942060.2022.2043187.

T. Tiwari, T. Tiwari, and S. Tiwari, “How Artificial Intelligence, Machine Learning and Deep Learning are Radically Different?,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 8, no. 2, p. 1, 2018, doi: 10.23956/ijarcsse.v8i2.569.

D. Chen, J. P. Esperança, and S. Wang, “The Impact of Artificial Intelligence on Firm Performance: An Application of the Resource-Based View to e-Commerce Firms,” Front. Psychol., vol. 13, no. April, 2022, doi: 10.3389/fpsyg.2022.884830.

D. Derrington, J. The, M. Corporation, and C. D. Mclean, Artificial Intelligence for Health and Health Care, 2017.

M. J. Sousa, F. D. Mas, A. Pesqueira, C. Lemos, J. M. Verde, and L. Cobianchi, “The Potential of AI in Health Higher Education to Increase the Students’ Learning Outcomes,” TEM Journal, vol. 10, no. 2, pp. 488–497, 2021, doi: 10.18421/TEM102.

A. T. D. A and R. K. B, “The potential for artificial intelligence in healthcare,” Future healthcare journal, vol. 6, no. 2, pp. 94–98, 2019.

W. H. O. Guidance, Ethics and governance of artificial intelligence for health, World Health Organization, 2021.

V. Dhore, “Artificial Intelligence & Machine Learning Unit -I Introduction to AI & ML Course Objective ACQUAINT with fundamentals of artificial intelligence and machine learning. Course Outcomes,” 2022, doi: 10.13140/RG.2.2.21191.98726.

A. Tursunbayeva and M. Renkema, “Artificial intelligence in health-care : implications for the job design of healthcare professionals,” Asia Pacific Journal of Human Resources, 2022, doi: 10.1111/1744-7941.12325.

E. J. Topol, “High-performance medicine: the convergence of human and artificial intelligence,” Nat. Med., vol. 25, no. 1, pp. 44–56, 2019, doi: 10.1038/s41591-018-0300-7.

S. Castagno and M. Khalifa, “Perceptions of Artificial Intelligence Among Healthcare Staff: A Qualitative Survey Study,” Front. Artif. Intell., vol. 3, no. October, pp. 1–7, 2020, doi: 10.3389/frai.2020.578983.

J. M. Kwon et al., “Artificial intelligence algorithm for predicting mortality of patients with acute heart failure,” PLoS One, vol. 14, no. 7, pp. 1–14, 2019, doi: 10.1371/journal.pone.0219302.

S. Reddy, J. Fox, and M. P. Purohit, “Artificial intelligence-enabled healthcare delivery,” J. R. Soc. Med., vol. 112, no. 1, pp. 22–28, 2019, doi: 10.1177/0141076818815510.

R. Manne and S. C. Kantheti, “Application of Artificial Intelligence in Healthcare: Chances and Challenges,” Curr. J. Appl. Sci. Technol., vol. 40, no. 6, pp. 78–89, 2021, doi: 10.9734/cjast/2021/v40i631320.

A. Bohr and K. Memarzadeh, Artificial Intelligence in Healthcare. Academic Press, 2020. doi: 10.1016/B978-0-12-818438-7.00013-7.

S. Quazi, R. P. Saha, and M. K. Singh, “Applications of Artificial Intelligence in Healthcare,” J. Exp. Biol. Agric. Sci., vol. 10, no. 1, pp. 211–226, 2022, doi: 10.18006/2022.10(1).211.226.

M. Chen and M. Decary, “Artificial intelligence in healthcare: An essential guide for health leaders,” Healthc. Manag. Forum, vol. 33, no. 1, pp. 10–18, 2020, doi: 10.1177/0840470419873123.

D. D. Miller and E. W. Brown, “Artificial Intelligence in Medical Practice: The Question to the Answer?,” Am. J. Med., vol. 131, no. 2, pp. 129–133, 2018, doi: 10.1016/j.amjmed.2017.10.035.

K. Denecke and C. R. Baudoin, “A Review of Artificial Intelligence and Robotics in Transformed Health Ecosystems,” Front. Med., vol. 9, no. July, pp. 1–13, 2022, doi: 10.3389/fmed.2022.795957.

K.-H. Hu, F.-H. Chen, M.-F. Hsu, S. Yao, and M.-C. Hung, “Identification of the Critical Factors for Global Supply Chain Management under the COVID-19 Outbreak via a Fusion Intelligent Decision Support System,” Axioms, vol. 10, no. 2, p. 61, Apr. 2021.

Y.-S. Lin, C.-W. Chai, and T.-W. Chao, “Case Study on the Safety and Disaster Prevention System of Factory Intelligent Warehouse,” in 2022 IEEE 5th Eurasian Conference on Educational Innovation (ECEI), 2022, pp. 391–395.

S. Guha, “Public perspectives on Healthcare and Artificial Intelligence (AI),” Int. J. Innov. Educ. Res., vol. 9, no. 7, pp. 1–8, 2021, doi: 10.31686/ijier.vol9.iss7.3207.

O. Asan, A. E. Bayrak, and A. Choudhury, “Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians,” J. Med. Internet Res., vol. 22, no. 6, pp. 1–7, 2020, doi: 10.2196/15154.

P. Esmaeilzadeh, “Use of AI-based tools for healthcare purposes: A survey study from consumers’ perspectives,” BMC Med. Inform. Decis. Mak., vol. 20, no. 1, pp. 1–19, 2020, doi: 10.1186/s12911-020-01191-1.

K. Khotimah, M. I. Sudrajat, and S. Wahyu Hidayat, “Infant Incubator Temperature Controlling and Monitoring System by Mobile Phone Based on Arduino,” in 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 2019, pp. 494–498.

C.-H. Kuo, F. Zal, and S.-L. Wu, “Development of Fuzzy Logic Controllers for Controlling Bipedal Robot Locomotion on Uneven Terrains with IMU Feedbacks,” Indian J. Sci. Technol., vol. 9, no. 28, 2016, doi: 10.17485/ijst/2016/v9i28/98449.

B. Raafi’u, “Design and Development of Fuzzy-PID Controller for Four-Wheeled Mobile Robotic Stability: A Case Study on the Uphill Road,” IPTEK J. Eng., vol. 6, no. 2, p. 6, 2020, doi: 10.12962/j23378557.v6i2.a7245.

S. Mohamed and W. Mohamed, “Developing of Fuzzy Logic Controller for Air Condition System,” Int. J. Comput. Appl., vol. 126, no. 15, pp. 1–8, 2015, doi: 10.5120/ijca2015906083.

J. R. Mahmood, R. S. Ali, H. Migdadi, R. A. Abd-Alhameed, and E. M. Ibrahim, “Development of educational Fuzzy control laboratory using PLC and HMI,” 2015 Internet Technol. Appl. ITA 2015 - Proc. 6th Int. Conf., pp. 383–387, 2015, doi: 10.1109/ITechA.2015.7317432.

D. Sharma, “Designing and Modeling Fuzzy Control Systems,” Int. J. Comput. Appl., vol. 16, no. 1, pp. 46–53, 2011, doi: 10.5120/1973-2644.

C. Urrea, J. Kern, and J. Alvarado, “Design and evaluation of a new fuzzy control algorithm applied to a manipulator robot,” Appl. Sci., vol. 10, no. 21, pp. 1–21, 2020, doi: 10.3390/app10217482.

O. P. Lim, T. C. Ling, and K. K. Phang, “Development of fuzzy logic control system,” Malaysian J. Comput. Sci., vol. 11, no. 2, pp. 8–14, 1998.

I. S. Shaw, Practical Fuzzy Controller Development. Springer, 1998.

R. S. Hernandez-Mesa, F. E. Moreno-García, S. A. Castro-Casadiego, and B. Medina-Delgado, “Experimental Development of Fuzzy Controllers for Thermal and Pneumatic Processes,” Ing. y Cienc., vol. 17, no. 33, pp. 97–120, 2021, doi: 10.17230/ingciencia.17.33.5.

Said LEGHMIZI and Sheng LIU, “A Survey Of Fuzzy Control For Stabilized Platforms,” Int. J. Comput. Sci. Eng. Surv., vol. 2, no. 3, pp. 48–57, 2011, doi: 10.5121/ijcses.2011.2304.

M. Aria, “Fuzzy Logic System for Coordinated Traffic Signal Control with Dynamic Phase Selection,” Telekontran: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan, vol. 5, no. 1, 2017.

F. Dernoncourt, “Fuzzy logic : between human reasoning and artificial intelligence Supervisor : Jean Baratgin,” Report, Ecole Normale Supperieure, Paris, 2011.

Z. Sun and G. Finnie, “A fuzzy logic approach to experience-based reasoning,” Int. J. Intell. Syst., vol. 22, no. 8, pp. 867–889, 2007, doi: 10.1002/int.20220.

N. C. Basjaruddin, Kuspriyanto, D. Saefudin, and I. K. Nugraha, “Developing adaptive cruise control based on fuzzy logic using hardware simulation,” Int. J. Electr. Comput. Eng., vol. 4, no. 6, pp. 944–951, 2014, doi: 10.11591/ijece.v4i6.6734.

A. L. Adewale, A. F. Jumoke, M. Adegboye, and A. Ismail, “An Embedded Fuzzy Logic Based Application for Density Traffic Control System,” Int. J. Artif. Intell. Res., vol. 2, no. 1, p. 6, 2018, doi: 10.29099/ijair.v2i1.44.

N. F. Hidayati, Endro Yulianto, and Abd. Kholiq, “Baby Incubator Based on PID Control With Kangaroo Mode (Kangaroo Mode and Humidity),” J. Electron. Electromed. Eng. Med. Informatics, vol. 1, no. 2, pp. 13–17, 2019, doi: 10.35882/jeeemi.v1i2.3.

C. H. Chiang and G. D. J. Su, “Improved light uniformity from light-emitting diodes by heterogeneous microlenses and 3-D printed mold,” IEEE J. Sel. Top. Quantum Electron., vol. 21, no. 4, pp. 436–443, 2015, doi: 10.1109/JSTQE.2014.2382972.

M. Jiménez-Palomares, M. Fernández-Rejano, E. M. Garrido-Ardila, J. Montanero-Fernández, P. Oliva-Ruiz, and J. Rodríguez-Mansilla, “The impact of a preterm baby arrival in a family: A descriptive cross-sectional pilot study,” J. Clin. Med., vol. 10, no. 19, 2021, doi: 10.3390/jcm10194494.

H. A. D. Sukma and S. Tiwari, “Risk Factors for Premature Birth in Indonesia,” J. Biometrika dan Kependud., vol. 10, no. 1, p. 61, 2021, doi: 10.20473/jbk.v10i1.2021.61-67.

M. Veronez, N. A. B. Borghesan, D. A. M. Corrêa, and I. H. Higarashi, “Experience of mothers of premature babies from birth to discharge: notes of field journals. TT - Vivência de mães de bebês prematuros do nascimento a alta: notas de diários de campo.,” Rev Gauch. Enferm, vol. 38, no. 2, pp. e60911–e60911, 2017, [Online]. Available: http://www.scielo.br/scielo.php?script=sci_arttext&nrm=iso&lng=pt&tlng=pt&pid=S1983-14472017000200419

S. Gutierrez, G. Contreras, H. Ponce, M. Cardona, H. Amadi, and J. Enriquez-Zarate, “Development of Hen Eggs Smart Incubator for Hatching System Based on Internet of Things,” in 2019 IEEE 39th Central America and Panama Convention (CONCAPAN XXXIX), 2019, pp. 1–5.

A. G. Shabeeb, A. J. Al-askery, and Z. M. Nahi, “Remote monitoring of a premature infants incubator,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 17, no. 3, pp. 1232–1238, 2020, doi: 10.11591/ijeecs.v17.i3.pp1232-1238.

Stephy Retnam, Pratheesh H, and Aswin R. B, “Development of Fuzzy Logic Controller for Cement Mill,” Int. J. Eng. Res., vol. 5, no. 7, pp. 17–20, 2016, doi: 10.17577/ijertv5is070001.

A. Alimuddin, R. Arafiyah, I. Saraswati, R. Alfanz, P. Hasudungan, and T. Taufik, “Development and performance study of temperature and humidity regulator in baby incubator using fuzzy-pid hybrid controller,” Energies, vol. 14, no. 20, 2021, doi: 10.3390/en14206505.

I. Adam, H. F. Rozi, S. Khan, Z. Zaharuddin, K. A. Kadir, and A. N. Nurdin, “The development of the fuzzy-based infant incubator,” AIP Conf. Proc., vol. 2129, no. July 2021, 2019, doi: 10.1063/1.5118109.

M. Mizanur Rahman and M. Saiful Islam, “Design of a Fuzzy Based Pid Algorithm for Temperature Control of An Incubator,” J. Phys. Conf. Ser., vol. 1969, no. 1, 2021, doi: 10.1088/1742-6596/1969/1/012055.

Yuda Irawan, Achmad Deddy Kurniawan, Refni Wahyuni, Naima Belarbi, and Mbunwe Muncho Josephine, “Android Based Light Control System Using Arduino,” JTCIS, vol. 1, no. 2, Oct. 2021.

P. Ele, J. B. Mbede, and E. Ondoua, “Parameters Modelling and Fuzzy Control System of Neonatal Incubators,” 5th International Conference: Sciences of Electronic, Technologies of Information and Telecommunications, pp. 6–11, 2009.

Muhammad Ahmad Baballe and Mukhtar Ibrahim Bello, “Analysis of the Temperature Detection System: Impact and Challenges,” JTCIS, vol. 1, no. 2, Feb. 2023.

K. F. R. Liu, J. Y. Kuo, K. Yeh, C. W. Chen, H. H. Liang, and Y. H. Sun, “Using fuzzy logic to generate conditional probabilities in Bayesian belief networks: a case study of ecological assessment,” Int. J. Environ. Sci. Technol., vol. 12, no. 3, pp. 871–884, 2015, doi: 10.1007/s13762-013-0459-x.

Sudarno Sudarno, Martono Martono, and Sholih Mauladin, “Arduino Based CNC Router Machine Control,” JTCIS, vol. 1, no. 2, Feb. 2023.

E. Kim, H. Park, K. Kim, Y. Yoon, C. Lim, and J. Kim, “Evaluation of radiation dose to organs of neonatal patients during portable X-ray examination in incubators: A Monte Carlo simulation study,” J. Xray. Sci. Technol., vol. 30, no. 2, pp. 333–342, Mar. 2022.

Ahmed majid jaafar, Al-Hussein Mathar Abbas, and Zain El Abidine Mohamed Hadi, “Survey on Controlling an 8*8 led matrix with Bluetooth mobile and Arduino,” JTCIS, vol. 1, no. 2, Oct. 2021.

N. Y. D. Setyaningsih and A. C. Murti, “Control Temperature on Plant Baby Incubator With Fuzzy Logic,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 7, no. 1, p. 273, 2016, doi: 10.24176/simet.v7i1.514.

H. Mittal, L. Mathew, and A. Gupta, “Design and Development of an Infant Incubator for Controlling Multiple Parameters,” Int. J. Emerg. Trends Electr. Electron., vol. 11, no. 5, pp. 2320–9569, 2015.

K. S. Reddy, “Real Time Monitoring and Control of Substation Parameters using IOT,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 9, no. VI, pp. 1942–1945, 2021, doi: 10.22214/ijraset.2021.35466.

A. Latif, H. A. Widodo, R. A. Atmoko, T. N. Phong, and E. T. Helmy, “Temperature and Humidity Controlling System for Baby Incubator,” Journal of Robotics and Control (JRC), vol. 2, no. 3, pp. 190–193, 2021, doi: 10.18196/jrc.2376.

A. Seyfang, S. Miksch, W. Horn, M. S. Urschitz, C. Popow, and C. F. Poets, “Using time-oriented data abstraction methods to optimize oxygen supply for neonates,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 2101, pp. 217–226, 2001, doi: 10.1007/3-540-48229-6_31.

N. Elqeblawy, A. Mohammed, and H. A. Hefny, “a Proposedfuzzy Logic Approach for Conserving the Energy of Data Transmission in the Temperature Monitoring Systems of the Internet of Things,” Int. J. Comput. Networks Commun., vol. 14, no. 2, pp. 97–114, 2022, doi: 10.5121/ijcnc.2022.14206.

L. Davidson and M. R. Boland, “Towards deep phenotyping pregnancy: A systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes,” Brief. Bioinform., vol. 22, no. 5, pp. 1–29, 2021, doi: 10.1093/bib/bbaa369.

P. Dutta and N. Anjum, “Optimization of Temperature and Relative Humidity in an Automatic Egg Incubator Using Mamdani Fuzzy Inference System,” Int. Conf. Robot. Electr. Signal Process. Tech., pp. 12–16, 2021, doi: 10.1109/ICREST51555.2021.9331155.

M. Shaib, M. Rashid, L. Hamawy, M. Arnout, I. El Majzoub, and A. J. Zaylaa, “Advanced portable preterm baby incubator,” Int. Conf. Adv. Biomed. Eng. ICABME, vol. 2017, no. October, 2017, doi: 10.1109/ICABME.2017.8167522.

L. Lamidi, A. Kholiq, and M. Ali, “A Low Cost Baby Incubator Design Equipped with Vital Sign Parameters,” Indones. J. Electron. Electromed. Eng. Med. informatics, vol. 3, no. 2, pp. 53–58, 2021, doi: 10.35882/ijeeemi.v3i2.3.

T. Restin, M. Gaspar, D. Bassler, V. Kurtcuoglu, F. Scholkmann, and F. B. Haslbeck, “Newborn incubators do not protect from high noise levels in the neonatal intensive care unit and are relevant noise sources by themselves,” Children, vol. 8, no. 8, 2021, doi: 10.3390/children8080704.




DOI: https://doi.org/10.18196/jrc.v3i6.13341

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Journal of Robotics and Control (JRC)

P-ISSN: 2715-5056 || E-ISSN: 2715-5072
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