Open Access
BIO Web Conf.
Volume 86, 2024
International Conference on Recent Trends in Biomedical Sciences (RTBS-2023)
Article Number 01067
Number of page(s) 11
Published online 12 January 2024
  • S. Kim, H. Kim, E. S. Lee, C. Lim, and J. Lee, “Risk score-embedded deep learning for biological age estimation: Development and validation,” Inf Sci (N Y), vol. 586, pp. 628–643, Mar. 2022, doi: 10.1016/j.ins.2021.12.015. [CrossRef] [Google Scholar]
  • P. P. Mondal et al., “Review on machine learning-based bioprocess optimization, monitoring, and control systems,” Bioresour Technol, vol. 370, Feb. 2023, doi: 10.1016/j.biortech.2022.128523. [CrossRef] [PubMed] [Google Scholar]
  • H. M. Elattar, M. A. El-Brawany, H. K. Elminir, D. Adel Ibrahim, and E. A. Ramadan, “Artificial Intelligence-based data-driven prognostics in Industry: A survey,” Comput Ind Eng, p. 109605, Oct. 2023, doi: 10.1016/j.cie.2023.109605. [Google Scholar]
  • Kareem Thajeel, K. Samsudin, S. Jahari Hashim, and F. Hashim, “Dynamic feature selection model for adaptive cross site scripting attack detection using developed multi- agent deep Q learning model,” Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 6, Jun. 2023, doi: 10.1016/j.jksuci.2023.01.012. [Google Scholar]
  • J. K. Basak et al., “Prediction of body composition in growing-finishing pigs using ultrasound based back-fat depth approach and machine learning algorithms,” Comput Electron Agric, vol. 213, Oct. 2023, doi: 10.1016/j.compag.2023.108269. [CrossRef] [Google Scholar]
  • “Deep Learning Algorithms in Industry 5.0: A Comprehensive Experimental Study - Search |” Accessed: Oct. 29, 2023. [Online]. Available: [Google Scholar]
  • M. Gu et al., “Insight from untargeted metabolomics: Revealing the potential marker compounds changes in refrigerated pork based on random forests machine learning algorithm,” Food Chem, vol. 424, Oct. 2023, doi: 10.1016/j.foodchem.2023.136341. [Google Scholar]
  • C. Cui et al., “Physical metallurgy guided deep learning for yield strength of hot-rolled steel based on the small labeled dataset,” Mater Des, vol. 223, Nov. 2022, doi: 10.1016/j.matdes.2022.111269. [Google Scholar]
  • M. Macas, C. Wu, and W. Fuertes, “A survey on deep learning for cybersecurity: Progress, challenges, and opportunities,” Computer Networks, vol. 212, Jul. 2022, doi: 10.1016/j.comnet.2022.109032. [CrossRef] [Google Scholar]
  • C. Zhong and G. Li, “Comprehensive learning Harris hawks-equilibrium optimization with terminal replacement mechanism for constrained optimization problems,” Expert Syst Appl, vol. 192, Apr. 2022, doi: 10.1016/j.eswa.2021.116432. [CrossRef] [Google Scholar]
  • M. A. Ahmed, M. S. Hossain, W. Rahman, A. H. Uddin, and M. T. Islam, “An advanced Bangladeshi local fish classification system based on the combination of deep learning and the internet of things (IoT),” J Agric Food Res, vol. 14, Dec. 2023, doi: 10.1016/j.jafr.2023.100663. [Google Scholar]
  • B. Gladysz, T. anh Tran, D. Romero, T. van Erp, J. Abonyi, and T. Ruppert, “Current development on the Operator 4.0 and transition towards the Operator 5.0: A systematic literature review in light of Industry 5.0,” J Manuf Syst, vol. 70, pp. 160–185, Oct. 2023, doi: 10.1016/j.jmsy.2023.07.008. [CrossRef] [Google Scholar]
  • C. Li, P. Zheng, Y. Yin, B. Wang, and L. Wang, “Deep reinforcement learning in smart manufacturing: A review and prospects,” CIRP J Manuf Sci Technol, vol. 40, pp. 75–101, Feb. 2023, doi: 10.1016/j.cirpj.2022.11.003. [CrossRef] [Google Scholar]
  • K. Shin et al., “Enhancing the performance of premature ventricular contraction detection in unseen datasets through deep learning with denoise and contrast attention module,” Comput Biol Med, vol. 166, Nov. 2023, doi: 10.1016/j.compbiomed.2023.107532. [CrossRef] [PubMed] [Google Scholar]
  • J. Shah, D. Vaidya, and M. Shah, “A comprehensive review on multiple hybrid deep learning approaches for stock prediction,” Intelligent Systems with Applications, vol. 16, Nov. 2022, doi: 10.1016/j.iswa.2022.200111. [CrossRef] [Google Scholar]
  • M. Schmitt, “Securing the Digital World: Protecting smart infrastructures and digital industries with Artificial Intelligence (AI)-enabled malware and intrusion detection,” J Ind Inf Integr, p. 100520, Dec. 2023, doi: 10.1016/j.jii.2023.100520. [Google Scholar]
  • H. Jia, G. Qiao, and P. Han, “Machine learning algorithms in the environmental corrosion evaluation of reinforced concrete structures - A review,” Cem Concr Compos, vol. 133, Oct. 2022, doi: 10.1016/j.cemconcomp.2022.104725. [Google Scholar]
  • D. Painuli, S. Bhardwaj, and U. köse, “Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review,” Comput Biol Med, vol. 146, Jul. 2022, doi: 10.1016/j.compbiomed.2022.105580. [CrossRef] [PubMed] [Google Scholar]
  • M. Mohsin and F. Jamaani, “A novel deep-learning technique for forecasting oil price volatility using historical prices of five precious metals in context of green financing – A comparison of deep learning, machine learning, and statistical models,” Resources Policy, vol. 86, Oct. 2023, doi: 10.1016/j.resourpol.2023.104216. [Google Scholar]
  • J. Chen, R. Ma, and J. Oyekan, “A deep multi-agent reinforcement learning framework for autonomous aerial navigation to grasping points on loads,” Rob Auton Syst, vol. 167, Sep. 2023, doi: 10.1016/j.robot.2023.104489. [Google Scholar]
  • S. Zheng and J. Zhao, “High-fidelity positive-unlabeled deep learning for semi-supervised fault detection of chemical processes,” Process Safety and Environmental Protection, vol. 165, pp. 191–204, Sep. 2022, doi: 10.1016/j.psep.2022.06.058. [CrossRef] [Google Scholar]
  • Y. Dai, K. Roy, Z. Fang, G. M. Raftery, and J. B. P. Lim, “Web crippling resistance of cold-formed steel built-up box sections through experimental testing, numerical simulation and deep learning,” Thin-Walled Structures, vol. 192, p. 111190, Nov. 2023, doi: 10.1016/j.tws.2023.111190. [CrossRef] [Google Scholar]
  • W. X. Chu, Y. H. Lien, K. R. Huang, and C. C. Wang, “Energy saving of fans in air- cooled server via deep reinforcement learning algorithm,” Energy Reports, vol. 7, pp. 3437–3448, Nov. 2021, doi: 10.1016/j.egyr.2021.06.003. [CrossRef] [Google Scholar]
  • Shruti, S. Rani, and G. Srivastava, “Secure hierarchical fog computing-based architecture for industry 5.0 using an attribute-based encryption scheme,” Expert Syst Appl, vol. 235, Jan. 2024, doi: 10.1016/j.eswa.2023.121180. [CrossRef] [Google Scholar]
  • B. Wang et al., “Human Digital Twin in the context of Industry 5.0,” Robot Comput Integr Manuf, vol. 85, Feb. 2024, doi: 10.1016/j.rcim.2023.102626. [Google Scholar]
  • Y. Kim et al., “Self-supervised representation learning anomaly detection methodology based on boosting algorithms enhanced by data augmentation using StyleGAN for manufacturing imbalanced data,” Comput Ind, vol. 153, Dec. 2023, doi: 10.1016/j.compind.2023.104024. [Google Scholar]
  • R. F. Greaves et al., “Key questions about the future of laboratory medicine in the next decade of the 21st century: A report from the IFCC-Emerging Technologies Division,” Clinica Chimica Acta, vol. 495, pp. 570–589, Aug. 2019, doi: 10.1016/j.cca.2019.05.021. [CrossRef] [Google Scholar]
  • G. Cao, Y. Duan, J. S. Edwards, and Y. K. Dwivedi, “Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision- making,” Technovation, vol. 106, Aug. 2021, doi: 10.1016/j.technovation.2021.102312. [Google Scholar]
  • B. Gajdzik and R. Wolniak, “Smart Production Workers in Terms of Creativity and Innovation: The Implication for Open Innovation,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 8, no. 2, Jun. 2022, doi: 10.3390/joitmc8020068. [Google Scholar]
  • G. Ambrogio, L. Filice, F. Longo, and A. Padovano, “Workforce and supply chain disruption as a digital and technological innovation opportunity for resilient manufacturing systems in the COVID-19 pandemic,” Comput Ind Eng, vol. 169, Jul. 2022, doi: 10.1016/j.cie.2022.108158. [CrossRef] [PubMed] [Google Scholar]
  • S. K. Samal et al., “3D-Printed Satellite Brackets: Materials, Manufacturing and Applications,” Crystals (Basel), vol. 12, no. 8, Aug. 2022, doi: 10.3390/CRYST12081148. [Google Scholar]
  • K. Zheng Yang et al., “Application of coolants during tool-based machining – A review,” Ain Shams Engineering Journal, 2022, doi: 10.1016/J.ASEJ.2022.101830. [Google Scholar]
  • S. Subramaniam et al., “Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review,” Sustainability (Switzerland), vol. 14, no. 16, Aug. 2022, doi: 10.3390/SU14169951. [Google Scholar]
  • V. S. Rana et al., “Assortment of latent heat storage materials using multi criterion decision making techniques in Scheffler solar reflector,” International Journal on Interactive Design and Manufacturing, 2023, doi: 10.1007/S12008-023-01456-9. [Google Scholar]
  • S. Bali et al., “A framework to assess the smartphone buying behaviour using DEMATEL method in the Indian context,” Ain Shams Engineering Journal, 2023, doi: 10.1016/J.ASEJ.2023.102129. [Google Scholar]
  • M. Z. ul Haq et al., “Circular Economy Enabler: Enhancing High-Performance Bricks through Geopolymerization of Plastic Waste,” in E3S Web of Conferences, EDP Sciences, 2023, p. 01202. [Google Scholar]
  • M. Z. ul Haq et al., “Eco-Friendly Building Material Innovation: Geopolymer Bricks from Repurposed Plastic Waste,” in E3S Web of Conferences, EDP Sciences, 2023, p. 01201. [Google Scholar]
  • M. Z. ul Haq et al., “Sustainable Infrastructure Solutions: Advancing Geopolymer Bricks via Eco-Polymerization of Plastic Waste,” in E3S Web of Conferences, EDP Sciences, 2023, p. 01203. [Google Scholar]
  • H. Sood, R. Kumar, P. C. Jena, and S. K. Joshi, “Eco-friendly approach to construction: Incorporating waste plastic in geopolymer concrete,” Mater Today Proc, 2023. [Google Scholar]
  • H. Sood, R. Kumar, P. C. Jena, and S. K. Joshi, “Optimizing the strength of geopolymer concrete incorporating waste plastic,” Mater Today Proc, 2023. [Google Scholar]
  • M. Nandal, H. Sood, P. K. Gupta, and M. Z. U. Haq, “Morphological and physical characterization of construction and demolition waste,” Mater Today Proc, 2022. [Google Scholar]
  • Md. Z. ul Haq, H. Sood, and R. Kumar, “Effect of using plastic waste on mechanical properties of fly ash based geopolymer concrete,” Mater Today Proc, 2022. [Google Scholar]
  • .Hao, S.Z., Zhou, D.I., Hussain, F., Liu, W.F., Su, J.Z., Wang, D.W., Wang, Q.P., Qi, Z.M., Singh, C. and Trukhanov, S., 2020. Structure, spectral analysis and microwave dielectric properties of novel x (NaBi) 0.5 MoO4-(1-x) Bi2/3MoO4 (x= 0.2∼ 0.8) ceramics with low sintering temperatures. Journal of the European Ceramic Society, 40(10), pp.3569-3576. [CrossRef] [Google Scholar]
  • 75. Dar, S.A., Sharma, R., Srivastava, V. and Sakalle, U.K., 2019. Investigation on the electronic structure, optical, elastic, mechanical, thermodynamic and thermoelectric properties of wide band gap semiconductor double perovskite Ba 2 InTaO 6. RSC advances, 9(17), pp.9522-9532. [CrossRef] [PubMed] [Google Scholar]
  • 76. Singh, J.I.P., Dhawan, V., Singh, S. and Jangid, K., 2017. Study of effect of surface treatment on mechanical properties of natural fiber reinforced composites. Materials today: proceedings, 4(2), pp.2793-2799. [CrossRef] [Google Scholar]
  • 77. Kaur, T., Kumar, S., Bhat, B.H., Want, B. and Srivastava, A.K., 2015. Effect on dielectric, magnetic, optical and structural properties of Nd–Co substituted barium hexaferrite nanoparticles. Applied Physics A, 119, pp.1531-1540. [CrossRef] [Google Scholar]
  • 78. Patel, S., 2012. Potential of fruit and vegetable wastes as novel biosorbents: summarizing the recent studies. Reviews in Environmental Science and Bio/Technology, 11, pp.365-380. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.