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Applying machine learning models on blockchain platform selection

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Abstract

Recently, technology like Blockchain is gaining attention all over the world today, because it provides a secure, decentralized framework for all types of commercial interactions. When choosing the optimal blockchain platform, one needs to consider its usefulness, adaptability, and compatibility with existing software. Because novice software engineers and developers are not experts in every discipline, they should seek advice from outside experts or educate themselves. As the number of decision-makers, choices, and criteria grows, the decision-making process becomes increasingly complicated. The success of Bitcoin has spiked the demand for blockchain-based solutions in different domains in the sector such as health, education, energy, etc. Organizations, researchers, government bodies, etc. are moving towards more secure and accountable technology to build trust and reliability. In this paper, we introduce a model for the prediction of blockchain development platforms (Hyperledger, Ethereum, Corda, Stellar, Bitcoin, etc.). The proposed work utilizes multiple data sets based on blockchain development platforms and applies various traditional Machine Learning classification techniques. The obtained results show that models like Decision Tree and Random Forest have outperformed other traditional classification models concerning multiple data sets with 100% accuracy.

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References

  • Abdulsalam SO, Kayode S, Jimoh RG (2011) Stock trend prediction using regression analysis - a data mining approach

  • Akben SB (2019) Determination of the blood, hormone and obesity value ranges that indicate the breast cancer, using data mining based expert system. Irbm 40(6):355–360

    Article  Google Scholar 

  • Alzubi J, Nayyar A, Kumar A (2018) Machine learning from theory to algorithms: an overview. J Phys Conf Ser 1142:012012. https://doi.org/10.1088/1742-6596/1142/1/012012

    Article  Google Scholar 

  • Anagnostopoulos T, Kyriakopoulos GL, Ntanos S, Gkika E, Asonitou S (2020) Intelligent predictive analytics for sustainable business investment in renewable energy sources. Sustainability 12(7):2817

    Article  Google Scholar 

  • Aswath GI, Vasudevan SK, Sampath N (2020) A frugal and innovative telemedicine approach for rural India-automated doctor machine. Int J Med Eng Inform 12(3):278–290

    Google Scholar 

  • Balasubramanian K, Ananthamoorthy NP (2021) Robust retinal blood vessel segmentation using convolutional neural network and support vector machine. J Amb Intell Human Comput 12:3559–3569

    Article  Google Scholar 

  • Belderrar A, Hazzab A (2021) Real-time estimation of hospital discharge using fuzzy radial basis function network and electronic health record data. Int J Med Eng Inform 13(1):75–83

    Google Scholar 

  • Bheeram VR, Malla RR, Kumari S, Saha A, Mukkamala SB (2019) Cytotoxic effect of photoluminescent re3+ doped ca3 (po4) 2 nanorods on breast cancer cell lines. IRBM 40(5):270–278

    Article  Google Scholar 

  • Biau G, Scornet E (2015) A random forest guided tour. TEST 25:11. https://doi.org/10.1007/s11749-016-0481-7

    Article  MathSciNet  Google Scholar 

  • Boser B E, Guyon I M, Vapnik V N. A training algorithm for optimal margin classifiers. In: proceedings of the 5th annual ACM Workshop on Computational Learning Theory, pp 144–152

  • Brownlee J (2016) Machine learning mastery with Python: understand your data, create accurate models, and work projects end-to-end. Machine Learning Mastery

  • Budak Ü, Güzel AB (2020) Automatic grading system for diagnosis of breast cancer exploiting co-occurrence shearlet transform and histogram features. IRBM 41(2):106–114

    Article  Google Scholar 

  • Buguk C, Wade Brorsen B (2003) Testing weak-form market efficiency: evidence from the Istanbul stock exchange. Int Rev Financ Anal 12(5):579–590

    Article  Google Scholar 

  • Chen X, Ji J, Luo C, Liao W, Li P (2018) When machine learning meets blockchain: a decentralized, privacy-preserving and secure design. pp 1178–1187, https://doi.org/10.1109/BigData.2018.8622598

  • Christidis K, Devetsikiotis M (2016) Blockchains and smart contracts for the internet of things. IEEE Access 4:2292–2303. https://doi.org/10.1109/ACCESS.2016.2566339

    Article  Google Scholar 

  • Clincy V, Shahriar H (2019) Blockchain development platform comparison. In 2019 IEEE 43rd annual computer software and applications conference (COMPSAC), volume 1, pp 922–923. IEEE

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Article  Google Scholar 

  • Duarte JJ, Gonzlez SM, Cruz J (2021) Predicting stock price falls using news data: evidence from the Brazilian market. Computat Econom 57(1):311–340

    Article  Google Scholar 

  • Ganiyu IA (2016) Data mining: a prediction for academic performance improvement of science students using classification. Int J Inform Commun Technol Res 6(04):16

    Google Scholar 

  • Gupta V, Mittal M (2019) Qrs complex detection using stft, chaos analysis, and pca in standard and real-time ecg databases. J Instit Eng Ser B 100:489–497

    Article  Google Scholar 

  • Gupta V, Mittal M, Mittal V (2021) Frwt-ppca-based r-peak detection for improved management of healthcare system. IETE J Res 69:1–15

    Google Scholar 

  • Gupta V, Mittal M, Mittal V, Gupta A (2022) An efficient ar modelling-based electrocardiogram signal analysis for health informatics. Int J Med Eng Inform 14(1):74–89

    Google Scholar 

  • Gupta V, Saxena NK, Kanungo A, Kumar P, Diwania S (2022) Pca as an effective tool for the detection of r-peaks in an ecg signal processing. Int J Syst Assur Eng Manag 13(5):2391–2403

    Article  Google Scholar 

  • Gupta V, Mittal M, Mittal V, Saxena N K (2022d) Spectrogram as an emerging tool in ecg signal processing. In Recent Advances in Manufacturing, Automation, Design and Energy Technologies: Proceedings from ICoFT 2020, pages 407–414. Springer

  • Harmouche M, Maasrani M, Verhoye J-P, Corbineau H, Drochon A (2014) Coronary three-vessel disease with occlusion of the right coronary artery: what are the most important factors that determine the right territory perfusion? IRBM 35(3):149–157

    Article  Google Scholar 

  • Helen MMC, Singh D, Deepak KK (2020) Changes in scale-invariance property of electrocardiogram as a predictor of hypertension. Int J Med Eng Inform 12(3):228–236

    Google Scholar 

  • Hijazi A A, Perera S, Al-Ashwal A M, Neves Calheiros R (2019) Enabling a single source of truth through bim and blockchain integration. In Proceedings of the 2019 International Conference on Innovation, Technology, Enterprise and Entrepreneurship (ICITEE 2019), 24-25 November 2019, Kingdom of Bahrain, pp 385–393,

  • Jérôme Velut P-A, Lentz DB, Coatrieux J-L, Toumoulin C (2011) Assessment of qualitative and quantitative features in coronary artery mra. IRBM 32(4):229–242

    Article  Google Scholar 

  • Karayazi Ferhat, Bereketli Ilke (2020) Criteria weighting for blockchain software selection using fuzzy ahp. In: international conference on intelligent and fuzzy systems, pp 608–615. Springer

  • Karthik R, Menaka R, Kathiresan GS, Anirudh M, Nagharjun M (2022) Gaussian dropout based stacked ensemble cnn for classification of breast tumor in ultrasound images. Irbm 43(6):715–733

    Article  Google Scholar 

  • Kaushal C, Bhat S, Koundal D, Singla A (2019) Recent trends in computer assisted diagnosis (cad) system for breast cancer diagnosis using histopathological images. Irbm 40(4):211–227

    Article  Google Scholar 

  • Kim N, Lee Y-W (2016) Machine learning approaches to corn yield estimation using satellite images and climate data: a case of iowa state. J Korean Soc Surv Geod Photogr Cartogr 34(4):383–390

    Google Scholar 

  • Kotsiantis SB (2011) Decision trees: a recent overview. Artif Intell Rev 39:261–283

    Article  Google Scholar 

  • Kuo T-T, Rojas HZ, Ohno-Machado L (2019) Comparison of blockchain platforms: a systematic review and healthcare examples. J Am Med Inform Assoc 26(5):462–478

    Article  Google Scholar 

  • Li S, Nunes JC, Toumoulin C, Luo L (2018) 3d coronary artery reconstruction by 2d motion compensation based on mutual information. IRBM 39(1):69–82

    Article  Google Scholar 

  • Liu T, Huang J, Liao T, Pu R, Liu S, Peng Y (2022) A hybrid deep learning model for predicting molecular subtypes of human breast cancer using multimodal data. Irbm 43(1):62–74

    Article  Google Scholar 

  • Liu T, Huang J, Liao T, Pu R, Liu S, Peng Y (2022) A hybrid deep learning model for predicting molecular subtypes of human breast cancer using multimodal data. Irbm 43(1):62–74

    Article  Google Scholar 

  • Lu S-Y, Wang S-H, Zhang Y-D (2023) Bcdnet: an optimized deep network for ultrasound breast cancer detection. IRBM 44(4):100774

    Article  Google Scholar 

  • Mabrouk S, Oueslati C, Ghorbel F (2017) Multiscale graph cuts based method for coronary artery segmentation in angiograms. Irbm 38(3):167–175

    Article  Google Scholar 

  • Mahdi M, Babak M, Amirhossein P, Ali M (2021) Off-chain management and state-tracking of smart programs on blockchain for secure and efficient decentralized computation. Int J Comput Appl 44(9):822–829

    Google Scholar 

  • Mokeddem F, Meziani F, Debbal SM (2020) Study of murmurs and their impact on the heart variability. Int J Med Eng Inform 12(3):291–301

    Google Scholar 

  • Morkunas VJ, Paschen J, Boon E (2019) How blockchain technologies impact your business model. Busin Horiz 62(3):295–306

    Article  Google Scholar 

  • Muhammad KD, Nawaz M (2011) An integration of k-means and decision tree (id3) towards a more efficient data mining algorithm. J Comput 3(12):76–82

    Google Scholar 

  • Nakamoto S (2009) Bitcoin: a peer-to-peer electronic cash system. URL http://www.bitcoin.org/bitcoin.pdf

  • Nikam A, Bhandari S, Mhaske A, Mantri S (2020) Cardiovascular disease prediction using machine learning models. pp 22–27 . https://doi.org/10.1109/PuneCon50868.2020.9362367

  • Niranjanamurthy M, Nithya BN, Jagannatha SJCC (2019) Analysis of blockchain technology: pros, cons and swot. Clust Comput 22(6):14743–14757

    Article  Google Scholar 

  • Niranjanamurthy M, Nithya BN, Jagannatha SJCC (2019) Analysis of blockchain technology: pros, cons and swot. Clust Comput 22(6):14743–14757

    Article  Google Scholar 

  • Nongyao N, Rungruttikarn M (2015) Comparison of classifiers for the risk of diabetes prediction. Proced Comput Sci 69:132–142. https://doi.org/10.1016/j.procs.2015.10.014

    Article  Google Scholar 

  • Ntanos Stamatios, Asonitou Sofia, Karydas Dimitrios, Kyriakopoulos Grigorios (2020) Blockchain technology: A case study from greek accountants. In: strategic innovative marketing and tourism: 8th ICSIMAT, Northern Aegean, Greece, 2019, pp 727–735. Springer

  • Pudaruth S (2014) Predicting the price of used cars using machine learning techniques. Int J Inf Comput Technol 4(7):753–764

    Google Scholar 

  • Rahman MM, Ghasemi Y, Suley E, Zhou Y, Wang S, Rogers J (2021) Machine learning based computer aided diagnosis of breast cancer utilizing anthropometric and clinical features. Irbm 42(4):215–226

    Article  Google Scholar 

  • Ramachandran SK, Manikandan P (2021) An efficient alo-based ensemble classification algorithm for medical big data processing. Int J Med Eng Inform 13(1):54–63

    Google Scholar 

  • Rish I (2001) An empirical study of the naïve bayes classifier. IJCAI 2001 Work Empir Methods Artif Intell, 3

  • Rong-Ho L (2009) An intelligent model for liver disease diagnosis. Artif Intell Med 47(1):53–62. https://doi.org/10.1016/j.artmed.2009.05.005

    Article  Google Scholar 

  • Samudaya N, Rodrigo MNN, Srinath P, Weerasuriya Geeganage T, Hijazi Amer A (2021) A methodology for selection of a blockchain platform to develop an enterprise system. J Indu Inform Integr 23:100215

    Google Scholar 

  • Sannasi Chakravarthy SR, Bharanidharan N, Rajaguru H (2023) Deep learning-based metaheuristic weighted k-nearest neighbor algorithm for the severity classification of breast cancer. IRBM 44(3):100749

    Article  Google Scholar 

  • Smith KA, Willis RJ, Brooks M (2000) An analysis of customer retention and insurance claim patterns using data mining: a case study. J Operat Res Soc 51(5):532–541

    Article  Google Scholar 

  • Srinath P, Samudaya N, Rodrigo MNN, Sepani S, Ralf W (2020) Blockchain technology: Is it hype or real in the construction industry? J Indu Inform Integr 17:100125

    Google Scholar 

  • Sunny AD, Kulshreshtha S, Singh S, Srinabh BM, Sarojadevi DRH (2018) Disease diagnosis system by exploring machine learning algorithms

  • Swan M (2015) Blockchain: Blueprint for a new economy. " O’Reilly Media, Inc."

  • Tanwar Sudeep, Bhatia Qasim, Patel Pruthvi, Aparna Kumari Dr., Singh Pradeep, Hong Wei-Chiang (03 2020) Machine learning adoption in blockchain-based smart applications: the challenges, and a way forward. IEEE Access, 2020: 474. https://doi.org/10.1109/ACCESS.2019.2961372

  • Tyagi Ankita, Mehra Ritika (03 2019) Interactive thyroid disease prediction system using machine learning technique. https://doi.org/10.1109/PDGC.2018.8745910

  • Varun G, Monika M, Vikas M (2022) A novel frwt based arrhythmia detection in ecg signal using ywara and pca. Wirel Pers Commun 1:1–18

    Google Scholar 

  • Varun G, Monika M, Vikas M, Yatender C (2022) Detection of r-peaks using fractional fourier transform and principal component analysis. J Amb Intell Human Comput 1:1–12

    Google Scholar 

  • Vladimir N (1995) Vapnik. The nature of statistical learning theory. Springer-Verlag, New York Inc, pp 387–945

    Google Scholar 

  • Webb GI, Keogh E, Miikkulainen R (2010) Naïve bayes. Encyclop Mach Learn 15:713–714

    Google Scholar 

  • Witten I H, Frank E, Trigg L E, Hall M A, Holmes G, Cunningham S J (1999) Weka: Practical machine learning tools and techniques with java implementations

  • Xu X, Huang L, Wu R, Zhang W, Ding G, Liu L, Chi M, Xie J (2022) Multi-feature fusion method for identifying carotid artery vulnerable plaque. IRBM 43(4):272–278

    Article  Google Scholar 

  • Zeng W, Miwa T, Morikawa T (2017) Application of the support vector machine and heuristic k-shortest path algorithm to determine the most eco-friendly path with a travel time constraint. Transp Res Part D Trans Environ 57:458–473

    Article  Google Scholar 

  • Zhang X (2022) The use of ethereum blockchain using internet of things technology in information and fund management of financial poverty alleviation system. Int J Syst Assur Eng Manag 13(3):1205–1215

    Article  Google Scholar 

  • Zhang L, Cui H, Liu B, Zhang C, Horn BKP (2021) Backpropagation neural network for processing of missing data in breast cancer detection. IRBM 42(6):435–441

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, United College of Engineering and Research, Naini, Prayagraj, U.P, 211010, India

    Chhaya Dubey, Dharmendra Kumar, Ashutosh Kumar Singh & Vijay Kumar Dwivedi

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  1. Chhaya Dubey
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  2. Dharmendra Kumar
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  3. Ashutosh Kumar Singh
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  4. Vijay Kumar Dwivedi
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Correspondence to Chhaya Dubey.

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Dubey, C., Kumar, D., Singh, A.K. et al. Applying machine learning models on blockchain platform selection. Int J Syst Assur Eng Manag (2024). https://doi.org/10.1007/s13198-024-02363-2

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