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Bio-Inspired Algorithms in Healthcare
Abstract
Exploring hidden patterns in medical data sets is made possible by the huge potential of medical data mining. A clinical diagnosis can be made with the help of these patterns. Research on bio-inspired algorithms is a recent development. Its primary benefit is its ability to weave together social behavior, emergence, and connectionism subfields. In a nutshell, it involves modeling live phenomena using computers while studying life to make better computer applications. This chapter describes the application of five bio-inspired algorithms, including metaheuristics, to classify seven distinct real health-related information sets. While the other two of these methods rely on random population creation to create classification rules, the other two rely on the computation of similarity between the data used for training and testing. The outcomes demonstrated that bio-inspired supervised medical data classification methods were incredibly effective.
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Sudheer Ch, AK Nema, Bijaya K. Panigrahi, SK Sohani, Ravi Kumar, it appears that Anushree is Malik, BR Chahar, and Ramesh C. Dhiman. A malaria transmission forecasting model based on the support vector machine-firefly algorithm. Neurocomputing, 2014; 129:279–289. [2] Information Technologies, Vol. 5 (1), 2014, 651-656, ISSN NO :0975-9646.
W.S. mcculloch, W. Pitts, A logical calculus of ideas immanent in nervous activity, Bull. Math.Biophys. 5 (1943) 115-133.
Kuldipvora, shrutiyagnik, A Survey on Backpropagation Algorithms for Feed Forward Neural Networks, ISSN: 2321-9939.
Bonabeau E, Dorigo M, Theraulaz G. Swarm Intelligence: From Natural to Artificial Systems. Journal of Artificial Societies and SocialSimulation.1999; 4: 320.
Projections for national health spending, 2019a28 Rising spending growth is driven by an anticipated price rebound. 2020's journalCode hlthaff.
Iztok Humar, Yongfeng Qian, Yixue Hao, Wei Li, and Min Chen. A smart healthcare system based on edge cognitive computing. 86:403–411, Future Generation Computing Systems, 2018.
O. Inan, T. Wehbe, D. Mooney, and A. Javaid. A unique architecture supported by physiological features to quickly discern health issues from hardware worm attacks and mistakes in medical equipment. 106–109 at 2017's IEEE International Workshop on the Hardware Based Security and Trust (HOST).
Darlene Storm. Hackers using medical equipment to gain access to hospital networks is known as "medjacking."2015. Accessed: January 8, 2020.
Hisham Al Majed, Nadra Guizani, Irfan Mohiuddin, Ikram is Ud Din, and Ahmad Almogren. Fuzzy-based governance of trust for the internet of medical things, or FTMiomt, is designed to stop Sybil attacks. IEEE Journal of Internet of Things, 2020. 13
. Interoperability of embedded web technologies for the Internet of Things. 2018, 120(6):7321–7331 in the International Archives of Pure и Applied Mathematics. [12] Kailas K. Devadkar and Rashmi V. Deshmukh. Knowing about DDoS attacks and how they affect cloud environments. In 2015, Procedia Computer Science, 49:202-210.
Peter Reiher, Majid Sarrafzadeh, and Vahab Pournaghshband. safeguarding aging mobile medical equipment. Pages 163–172 of the International Conference of Portable Mobile Communication and Healthcare. Springer, 2012. Bracken, Becky
. Healthcare cyberattacks have increased by 45% since November 2021.
Leila Hawkins. Healthcare is unprepared for the rise in cyberattacks. 2021.
Laura Dyrda. The top 5 biggest hacks in the medical field in 2020.
Ahmad Ashiqur Rahman, Mohammad Hossein Manshaei, Alexander J. Byrne, and Alvi Ataur Khalil. Economic reinforcement learning for effective UAV trajectory planning. The preprint arXiv is arXiv:2103.02676, 2021.
S Siva Sathya, S Binitha, et al. An overview of optimization algorithms inspired by biology. International Journal of Engineering and Soft Computing, 2(2), 2012, 137–151.
Nur Imtiazul Haque, Selcuk Uluagac, Md. Hasan Shahriar, Abdul Ataur Khalil, and Muhammad Ashiqur Rahman. A new framework for using machine learning to conduct smart health system threat assessments. The preprint arXiv is arXiv:2103.03472, 2021.
Mohammad Ashiqur Rahman, Amit Kumar Sikder, AKM Newaz, Gul Imtiazul Haque, and A Selcuk Uluagac. hostile assaults on intelligent healthcare systems based on machine learning. preprint arXiv:2010.03671, 2020; arXiv. [21] Mohammad Ashiqur Rahman, Miguel Alonso, Nur Imtiazul Haque, and Md. Hasan Shahriar. system for detecting intrusions. The 44th IEEE Conference on Computers, Software, the Applications (COMPSAC) 2020, pages 376–385. IEEE, 2020.
Mohammad Ashiqur Rahman, Selcuk Uluagac, Imtiaz Parvez, Javier Franco, and Alvi Ataur Khalil. an assessment of the research on the security and functionality of cyber-physical systems enabled by blockchain. 2021; arXiv preprint arXiv:2107.07916. Sarsij Tripathi, Manu Vardhan, and Shubhra Dwivedi.
. constructing an effective anomaly detection system for intrusion detection utilizing the grasshopper optimization method. Cluster Computing, 2021, pages 1-20. Chanan Singh, Mahmood-Reza Haghifam, and Hamid Falaghi
. A fuzzy multiobjective technique based on ant colony optimization is used to locate sectionalizing switches for distribution networks. 24(1):268–276, IEEE Transactions upon Power Delivery, 2008.
Bingyu Yang, Huaizhong Li, Bos Yang, Dayou Liu, Zhe Yu, who is Wenchang Kang, and Liming Shen. utilizing fruit fly optimization to evolve support vector 14 machines for the classification of medical data. 96:61–75, Knowledge-Based Systems, 2016.
Fei Ye. utilizing a hybrid approach to evolve the SVM model for medical diagnostics, combining a genetic algorithm with swarm optimization approaches. 77(3):3889–3918 in Multimedia Tools as well as Applications, 2018.
Huiling Chen and Mingjing Wang. Support vector machine voor medical diagnosis augmented by a chaotic multi-swarm whale optimizer. 2020
Amaal Al Shorman, Ibrahim Aljarah, and Hossam Faris. Applied Soft Computing. Unsupervised intelligent system for iot botnet identification based on grey wolf optimization and a single class support vector machine. 2020
Tangherloni A, Rundo L, Nobile MS. Proactive particles in swarm optimization: a settings-free algorithm for real-parameter single objective optimization problems. In: Proc. IEEE Congress on Evolutionary Computation (CEC). IEEE, 2017
Nobile MS, Tangherloni A, Rundo L, Spolaor S, Besozzi D, Mauri G, Cazzaniga P. Computational intelligence for parameter estimation of biochemical systems. In: Proc. Congress on Evolutionary Com- putation (CEC). IEEE, 2018
Mastriani M. Quantum image processing? Quantum Inf. Process., 2017. 16(1):27. [33] Yan F, Iliyasu AM, Jiang Z. Quantum computation-based image representation, processing operations and their applications. Entropy, 2014.
Tangherloni A, Rundo L, Spolaor S, Nobile M, Merelli L, Besozzi D, Mauri G, Cazzaniga P. Liò P. High performance computing for haplotyping: models and platforms. In: Mencagli G, et al. (eds.), Proc. 24th International European Conference on Parallel and Distributed Computing (Euro-Par 2018), Workshop on Advances in High-Performance Bioinformatics, Systems Biology (Med-HPC 2018), volume 11339 of LNCS. Springer, 2019. [35] Nobile MS, Cazzaniga P, Tangherloni A, Besozzi D. Graphics processing units in bioinformatics, compu- tational biology and systems biology. Brief. Bioinform., 2016.
Eklund A, Dufort P, Forsberg D, LaConte SM. Medical image processing on the GPU-past, present and future. Med. Image Anal., 2013. [37] Smistad E, Falch TL, Bozorgi M, Elster AC, Lindseth F. Medical image segmentation on GPUs-a comprehensive review. Med. Image Anal., 2015.
Tajbakhsh N, Shin JY, Gurudu SR, et al. Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging, 2016.
Gatenby RA, Grove O. Gillies RJ. Quantitative imaging in cancer evolution and ecology. Radiology. 2013. [39] Evanko D. Two pictures are better than one. Nat. Methods, 2008. 15
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2015.
Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer, 2012. 48(4):441-446.
Lambin P, Leijenaar RT, Deist TM, Peerlings J, de Jong EE, van Timmeren J, Sanduleanu S, Larue RT, Even AJ, Jochems A, et al. Radiomics: the bridge between medical imaging and personalized m edicine. Nat. Rev. Clin. Oncol., 2017.
Rosenstein BS, West CM, Bentzen SM, Alsner J, Andreassen CN, Azria D, Barnett GC, Baumann M, Burnet N, Chang-Claude J, et al. Radiogenomics: radiobiology
DOI: https://doi.org/10.31326/jisa.v7i2.2145
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