Comparison between Expert Systems, Machine Learning, and Big Data: An Overview
DOI:
https://doi.org/10.24203/ajas.v10i1.6930Keywords:
Artificial Intelligence, Expert Systems, Machine learning, Big Data, COVID-19Abstract
Today, the science of artificial intelligence has become one of the most important sciences in creating intelligent computer programs that simulate the human mind. The goal of artificial intelligence in the medical field is to assist doctors and health care workers in diagnosing diseases and clinical treatment, reducing the rate of medical error, and saving lives of citizens. The main and widely used technologies are expert systems, machine learning and big data. In the article, a brief overview of the three mentioned techniques will be provided to make it easier for readers to understand these techniques and their importance.
References
Thiebes S., Lins S., and Sunyaev A., “Trustworthy artificial intelligence,”Electronic Markets, vol.31, pp:447–464, October 2020. https://doi.org/10.1007/s12525-020-00441-4
Gunasekeran D. V., Tseng R. M. W., Tham Y., and Wong T. Y., “Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies,”npj Digital Medicine, vol. 4, Article number: 40, pp:1-6, February 2021. https://doi.org/10.1038/s41746-021-00412-9
Pan Y. and Zhang L., “Roles of artificial intelligence in construction engineering and management: A critical review and future trends,”Automation in Construction, vol. 122, pp:103517, February 2021. https://doi.org/10.1016/j.autcon.2020.103517
Mallikarjuna A., “Impact of Artificial Intelligence (AI) Applications on Academic Libraries,”International Journal of Information, Library & Society, vol.9, no.1, pp:12-16, 2020.
Tang A., Tam R., Cadrin-Chênevert A., Guest W., , Chong J., Barfett J., et al., “Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology,”Canadian Association of Radiologists Journal, vol.69, pp:120-135, May 2018. https://doi.org/10.1016/j.carj.2018.02.002
Patel V. L., Shortliffe E. H., Stefanelli M., Szolovits P., Berthold M. R., Bellazzi R., and Abu-Hanna A., “The coming of age of artificial intelligence in medicine,”Artificial Intelligence in Medicine, vol.46, no.1, pp:5-17, May 2009. https://doi.org/10.1016/j.artmed.2008.07.017
Aggarwal K., Mijwil M. M., Al-Mistarehi AH., Alomari S., Gök M., Alaabdin A. M., and Abdulrhman S. H., “Has the Future Started? The Current Growth of Artificial Intelligence, Machine Learning, and Deep Learning,” Iraqi Journal for Computer Science and Mathematics, vol.3, no.1, pp:115-123, January 2022.
Mijwil M. M., Salem I. E, and Abttan R. A. “Utilisation of Machine Learning Techniques in Testing and Training of Different Medical Datasets,” Asian Journal of Computer and Information Systems, vol.9, no.5, pp:29-34, November 2021. https://doi.org/10.24203/ajcis.v9i4.6765
van der Maas H. L. J., Snoek L., and Stevenson C. E., “How much intelligence is there in artificial intelligence? A 2020 update,”Intelligence, vol. 87, pp:101548, August 2021. https://doi.org/10.1016/j.intell.2021.101548
Zeadally S., Adi E., Baig Z., and Khan I. A., “Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity,”IEEE Access, vol.8, pp:23817 - 23837, January 2020.https://doi.org/10.1109/ACCESS.2020.2968045
Leyer M. and Schneider S., “Decision augmentation and automation with artificial intelligence: Threat or opportunity for managers?,”Business Horizons, vol.64, no. 5, pp:711-724, October 2021. https://doi.org/10.1016/j.bushor.2021.02.026
Mutar D. S. M., “Computer Network Attack Detection Using Enhanced Clustering Technologies,”Asian Journal of Applied Sciences, vol.9, no.6, pp:392-396, December 2021. https://doi.org/10.24203/ajas.v9i6.6839
Bennett M. T. and Maruyama Y., “Philosophical Specification of Empathetic Ethical Artificial Intelligence,” IEEE Transactions on Cognitive and Developmental Systems, pp:1-1, July 2021. https://doi.org/10.1109/TCDS.2021.3099945
Arslan A., Cooper C., Khan Z., Golgeci I., and Ali I., “Artificial intelligence and human workers interaction at team level: a conceptual assessment of the challenges and potential HRM strategies,”International Journal of Manpower, July 2021.
Mijwil M. M., Al-Mistarehi AH., and Aggarwal K., “The Effectiveness of Utilising Modern Artificial Intelligence Techniques and Initiatives to Combat COVID-19 in South Korea: A Narrative Review,” Asian Journal of Applied Sciences, vol.9, no.5, pp:343-352, November 2021. https://doi.org/10.24203/ajas.v9i5.6753
Muhammad L. J. and Algehyne E. A., “Fuzzy based expert system for diagnosis of coronary artery disease in nigeria,”Health and Technology, vol.11, pp:319–329, February 2021. https://doi.org/10.1007/s12553-021-00531-z
Salman F. M. and Abu-Naser S. S., “Expert System for Castor Diseases and Diagnosis,” International Journal of Engineering and Information Systems (IJEAIS), vol.3, no.3, pp:1-10, March 2019.
Mahmud T., Sikder J., Salma U., Naher S. R., Fardoush J., Sharmen N., and Tripura S., “An Optimal Learning Model for Training Expert System to Detect Uterine Cancer,” Procedia Computer Science, vol. 184 pp: 356-363, 2021. https://doi.org/10.1016/j.procs.2021.03.045
Mijwil M. M., and Abttan R. A., “Artificial Intelligence: A Survey on Evolution and Future Trends,” Asian Journal of Applied Sciences, vol.9, no.2, pp:87-93, April 2021, https://doi.org/10.24203/ajas.v9i2.6589
Kartika D., Gema R. L., and Pratiwi M., “Identifying Severe Malnutrition in Children with an Expert System,”Journal of Computer Science and Information Technology, vol.7, no.2, pp:15–20. https://doi.org/10.35134/jcsitech.v7i2.3
Pawan E., Thamrin R. M. H., Widodo W., Bei S. H. Y., Luanmasa J. J., “Implementation of Forward Chaining Method in Expert System to Detect Diseases in Corn Plants in Muara Tami District,”International Journal of Computer and Information System, vol.3, no.1, pp:27-33, February 2022.
Mufadhol M., Hartono B., Sulartopo S., Dewi M. U., Danang D., Aryotejo G., “The calculation of point quantity for lighting based on android OS using ionic framework and rule based expert system,” Bulletin of Electrical Engineering and Informatics, vol.10, no.6, pp:3444-3451, December 2021. https://doi.org/10.11591/eei.v10i6.3183
Straub J., Machine learning performance validation and training using a ‘perfect’ expert system, MethodsX, vol.8, pp:101477, 2021. https://doi.org/10.1016/j.mex.2021.101477
Lareyre F., Adam C., Carrier M., and Raffort J., “Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning,”Journal of Clinical Medicine, vol.10, no.15, July 2021. https://doi.org/10.3390/jcm10153347
Gupta S., Bhardwaj A., Mahawar A., and Gupta S., “A Case Study of Artificial Intelligence is being used to Reshape Business,” International Journal of Electrical, Electronics and Computers, vol.6, no.3, June 2021.
Yu G., Chen Z., Wu J., and Tan Y., “Medical decision support system for cancer treatment in precision medicine in developing countries,” Expert Systems with Applications, vol.186, pp:115725, December 2021. https://doi.org/10.1016/j.eswa.2021.115725
Azad M. M., Ganapathy A., Vadlamudi S., Paruchuri H., “Medical Diagnosis using Deep Learning Techniques: A Research Survey,”Annals of the Romanian Society for Cell Biology, vol.25, no.6, pp:5591-5600, May 2021.
Bowling M., Fürnkranz J., Graepel T., and Musick R., “Machine learning and games,” Machine Learning, vol. 63, pp:211–215, May 2006. https://doi.org/10.1007/s10994-006-8919-x
Mijwil M. M., “Implementation of Machine Learning Techniques for the Classification of Lung X-Ray Images Used to Detect COVID-19 in Humans,” Iraqi Journal of Science, vol.62, no.6., pp: 2099-2109, July 2021. https://doi.org/10.24996/ijs.2021.62.6.35.
Iqbal M J, Javed Z, Sadia H, Qureshi I. A., Irshad A, Ahmed R, et al., “Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future,” Cancer Cell International, vol.21, Article number: 270, pp:1-11, May 2021. https://doi.org/10.1186/s12935-021-01981-1
Mijwil M. M., “Malware Detection in Android OS Using Machine Learning Techniques,” Data Science and Applications, vol.3, no.2, pp:5-9, December 2020.
Mohan S., Thirumalai C., and Srivastava G., “Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques,”IEEE Access, vol.7, pp:81542 - 81554, June 2019. https://doi.org/10.1109/ACCESS.2019.2923707
Mijwil M. M. and Al-Zubaidi E. A., “Medical Image Classification for Coronavirus Disease (COVID-19) Using Convolutional Neural Networks,” Iraqi Journal of Science, vol.62, no.8, pp: 2740-2747, August 2021. https://doi.org/10.24996/ijs.2021.62.8.27.
Mijwil M. M. and Salem I. E., “Credit Card Fraud Detection in Payment Using Machine Learning Classifiers,” Asian Journal of Computer and Information Systems, vol.8, no.4, pp:50-53, December 2020, https://doi.org/10.24203/ajcis.v8i4.6449
Shah D., Patel S., and Bharti S. K., “Heart Disease Prediction using Machine Learning Techniques,”SN Computer Science, vol. 1, Article number: 345, October 2020. https://doi.org/10.1007/s42979-020-00365-y
Xu C. and Jackson S. A., “Machine learning and complex biological data,”Genome Biology, vol. 20, Article number: 76, April 2019. https://doi.org/10.1186/s13059-019-1689-0
Engelen J. E. and Hoos H. H., “A survey on semi-supervised learning,”Machine Learning, vol.109, pp:373–440, November 2019. https://doi.org/10.1007/s10994-019-05855-6
Al-Zubaidi E. A., Mijwil M. M., and Alsaadi A. S., “Two-Dimensional Optical Character Recognition of Mouse Drawn in Turkish Capital Letters Using Multi-Layer Perceptron Classification,” Journal of Southwest Jiaotong University, vol.54, no.4, pp.1-6, Augusts 2019, https://doi.org/10.35741/issn.0258-2724.54.4.4.
Chen C., Juan H., Tsai M., and Lu H. H., Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning, Scientific Reports, vol. 8, Article number: 557, January 2018. https://doi.org/10.1038/s41598-017-18931-5
Sinaga K. P. and Yang M., “Unsupervised K-Means Clustering Algorithm,”IEEE Access, vol.8, pp: 80716 - 80727, April 2020. https://doi.org/10.1109/ACCESS.2020.2988796
Anh T. T., Luong N. C., Niyato D., Kim D. I., and Wang L., “Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach,”IEEE Wireless Communications Letters, vol.8, no.5, pp:1345 - 1348, october 2019. https://doi.org/10.1109/LWC.2019.2917133
Mijwil M. M. and Abttan R. A., “Utilizing the Genetic Algorithm to Pruning the C4.5 Decision Tree Algorithm,” Asian Journal of Applied Sciences, vol.9, no.1, pp:45-52, February 2021, https://doi.org/10.24203/ajas.v9i1.6503
Kersting K. and Meyer U., “From Big Data to Big Artificial Intelligence?,”KI - KünstlicheIntelligenz, vol. 32, pp:3-8, January 2018. https://doi.org/10.1007/s13218-017-0523-7
Aggarwal K., Bhamrah M.S. and Ryait H.S., “Detection of cirrhosis through ultrasound imaging by intensity difference technique”, Eurasip Journal on Image and Video Processing, vol 80, issue-1, 1-10, 2019. https://doi.org/10.1186/s13640-019-0482-z
Aggarwal K., Bhamrah M.S. and Ryait H.S.,“The Identification of Liver Cirrhosis with Modified LBP Grayscaling and Otsu Binarisation”, Springer Plus, vol. 5, issue-1, pp.1-15, 2016.
Greaves R. F., Bernardini S., Ferrari M., Fortina P., Gouget B., Gruson D., 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, August 2019. https://doi.org/10.1016/j.cca.2019.05.021
Fritea K., Banica F., Costea T. O., Moldovan L., Dobjanschi L., Muresan M., and Cavalu S., “Metal Nanoparticles and Carbon-Based Nanomaterials for Improved Performances of Electrochemical (Bio)Sensors with Biomedical Applications,” Materials, vol.14, no.21, pp:1-37, October 2021. https://doi.org/10.3390/ma14216319
Mijwil M. M., Alsaadi A. S, and Aggarwal K., “Differences and Similarities Between Coronaviruses: A Comparative Review,” Asian Journal of Pharmacy, Nursing and Medical Sciences, vol.9, no.4, pp:49-61, September 2021. https://doi.org/10.24203/ajpnms.v9i4.6696
Sekeroglu B. and Ozsahin I., “Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks,” SLAS Technology, vol.25, no.6, pp:553–565, September 2020. https://doi.org/10.1177/2472630320958376
Polat Ç., Karaman O., Karaman C., Korkmaz G., Balcı M. C., and Kelek S. E., “COVID-19 diagnosis from chest X-ray images using transfer learning: Enhanced performance by debiasing dataloader,” Journal of X-Ray Science and Technology, vol.29, no.1, pp:19-36, February 2021. https://doi.org/10.3233/XST-200757
Ali A. H., Abdullah M. Z., Abdul-wahab S. N., and Alsajri M., “A Brief Review of Big Data Analytics Based on Machine Learning,” Iraqi Journal For Computer Science and Mathematics, vol. 1, no. 2, pp. 13-15, Jul. 2020.
Ali A. H., Hussain Z. F., and Abd S. N., “Big Data Classification Efficiency Based on Linear Discriminant Analysis,” Iraqi Journal For Computer Science and Mathematics, vol. 1, no. 1, pp. 7-12, Jan. 2020.
Haleem A., Javaid M., Khan I. H., and Vaishya R., Significant Applications of Big Data in COVID-19 Pandemic, Indian Journal of Orthopaedics, vol. 54, pp:526-528, May 2020. https://doi.org/10.1007/s43465-020-00129-z
Wang C. J., Ng C. N., and Brook R. H., “Response to COVID-19 in Taiwan Big Data Analytics, New Technology, and Proactive Testing,” JAMA, vol. 323, no.14, pp:1341-1342. https://doi.org/10.1001/jama.2020.3151
Hussain A. A., Bouachir O., Al-Turjman F., and Aloqaily M., “Notice of Retraction: AI Techniques for COVID-19,” IEEE Access, vol. 8, pp:128776 -128795, July 2020. https://doi.org/10.1109/ACCESS.2020.3007939
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Maad M. Mijwil, Dhamyaa Salim Mutar, Youssef Filali, Karan Aggarwal, Humam Al-Shahwani
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
- Papers must be submitted on the understanding that they have not been published elsewhere (except in the form of an abstract or as part of a published lecture, review, or thesis) and are not currently under consideration by another journal published by any other publisher.
- It is also the authors responsibility to ensure that the articles emanating from a particular source are submitted with the necessary approval.
- The authors warrant that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required.
- The authors ensure that all the references carefully and they are accurate in the text as well as in the list of references (and vice versa).
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Attribution-NonCommercial 4.0 International that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
- The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.