ROLE OF MACHINE LEARNING DATA MINING AND ANALYTICS
Keywords:
Machine Learning, Information Technology Industry, Data MiningAbstract
The business world has made extensive use of data mining, and because machine learning is capable of doing data analysis and pattern recognition, it is an essential component of the application of data mining. Data mining has been used in a large number of business. The research presented here provides an in-depth examination of the following aspects of machine learning: its definition, model, development stage, classification, and commercial application. In addition, the study throws a strong emphasis on the role that machine learning plays in the processes that are involved in data mining. When selecting the appropriate method for a particular application, it is helpful to have a solid understanding of the various machine learning techniques. As a result, this article provides a summary and analysis of machine learning technology, as well as a discussion of the benefits and drawbacks associated with its application in data mining.
References
B. Zhao, H. Zhou, G. Li, and Y. Huang, “ZenLDA: Large-scale topic model training on distributed data-parallel platform,” Big Data Min. Anal., vol. 1, no. 1, pp. 57–74, 2018, doi: 10.26599/BDMA.2018.9020006.
N. Yu, Z. Li, and Z. Yu, “Survey on encoding schemes for genomic data representation and feature learning-from signal processing to machine learning,” Big Data Min. Anal., vol. 1, no. 3, pp. 191–210, 2018, doi: 10.26599/BDMA.2018.9020018.
Faiz, M., Fatima, N., Sandhu, R., Kaur, M., & Narayan, V. (2023). IMPROVED HOMOMORPHIC ENCRYPTION FOR SECURITY IN CLOUD USING PARTICLE SWARM OPTIMIZATION. Journal of Pharmaceutical Negative Results, 2996-3006.
J. Wang et al., “Relationship between Health Status and Physical Fitness of College Students from South China: An Empirical Study by Data Mining Approach,” IEEE Access, vol. 8, pp. 67466–67473, 2020, doi: 10.1109/ACCESS.2020.2986039.
Y. Luo and Y. Xiang, “Application of data mining methods in internet of things technology for the translation systems in traditional ethnic books,” IEEE Access, vol. 8, pp. 93398–93407, 2020, doi: 10.1109/ACCESS.2020.2994551.
Rajesh, R., Koh, S. C. L., & Ganesh, K. (2012). Modelling Optimization and Computing 2012.
Mall, P. K., Narayan, V., Pramanik, S., Srivastava, S., Faiz, M., Sriramulu, S., & Kumar, M. N. (2023). FuzzyNet-Based Modelling Smart Traffic System in Smart Cities Using Deep Learning Models. In S. Pramanik & K. Sagayam (Eds.), Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities (pp. 76-95). IGI Global. https://doi.org/10.4018/978-1-6684-6408-3.ch005
Choudhary, S., Narayan, V., Faiz, M., & Pramanik, S. (2022). Fuzzy approach-based stable energy-efficient AODV routing protocol in mobile ad hoc networks. In Software Defined Networking for Ad Hoc Networks (pp. 125-139). Cham: Springer International Publishing.
Faiz, M., Daniel, A.K. (2022). Wireless Sensor Network Based Distribution and Prediction of Water Consumption in Residential Houses Using ANN. In: Misra, R., Kesswani, N., Rajarajan, M., Veeravalli, B., Patel, A. (eds) Internet of Things and Connected Technologies. ICIoTCT 2021. Lecture Notes in Networks and Systems, vol 340. Springer, Cham. https://doi.org/10.1007/978-3-030-94507-7_11
S. S. Gill, G. S. Lehal, and R. Malhotra's 2019 article "A Comprehensive Review of Machine Learning for Big Data Analysis"
Manogaran G., Alazab M., Saravanan V., Rawal B. S., Sundarasekar R., Nagarajan S. M., and Montenegro-Marin C. E.. 2020. Machine learning assisted information management scheme in service concentrated IoT. IEEE Transactions onIndustrial Informatics.
Raizada S., Mala S., and Shankar A.. 2020. Vector-borne disease outbreak prediction by machine learning. In 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE). IEEE, 213–218.
Gonaygunta, Hari, Factors Influencing the Adoption of Machine Learning Algorithms to Detect Cyber Threats in the Banking Industry, DAI-A 85/7(E), Dissertation Abstracts International, Ann Arbor, United States, ISBN 9798381387865, 142, 2023.
Nadella, G. S. (2023). Validating the Overall Impact of IS on Educators in U.S. High Schools Using IS-Impact Model – A Quantitative PLS-SEM Approach, DAI-A 85/7(E), Dissertation Abstracts International, Ann Arbor, ISBN 9798381388480, 189, 2023.
Hari Gonaygunta (2023) Machine Learning Algorithms for Detection of Cyber Threats using Logistic Regression, 10.47893/ijssan.2023.1229.
Hari Gonaygunta, Pawankumar Sharma, (2021) Role of AI in product management automation and effectiveness, https://doi.org/10.2139/ssrn.4637857.
R Pulimamidi, GP Buddha, Applications of Artificial Intelligence Based Technologies in The Healthcare Industry, Tuijin Jishu/Journal of Propulsion Technology 44 (3), 4513-4519.
R Pulimamidi, GP Buddha, AI-Enabled Health Systems: Transforming Personalized Medicine And Wellness, Tuijin Jishu/Journal of Propulsion Technology 44 (3), 4520-4526.
R Pulimamidi, P Ravichandran, Connected Health: Revolutionizing Patient Care Through Artificial Intelligence Innovations, Tuijin Jishu/Journal of Propulsion Technology 44 (3), 3940-3947.
R Pulimamidi, P Ravichandran, Enhancing Healthcare Delivery: AI Applications In Remote Patient Monitoring, Tuijin Jishu/Journal of Propulsion Technology 44 (3), 3948-3954.
P Kanagala, Effective cyber security system to secure optical data based on deep learning approach for healthcare application, Optik 272, 170315.
P Kanagala, Implementing cryptographic-based DH approach for enterprise network, Optik 272, 170252.
P Kanagala, R Jayaraman, FAA-Cloud approach to minimize computation overhead using fuzzy-based crypto security, Soft Computing, 1-11.
P Kanagala, R Jayaraman, Effective encryption approach to improving the secure cloud framework through fuzzy-based encrypted cryptography, Soft Computing, 1-10.
Ramya Manikyam, J. Todd McDonald, William R. Mahoney, Todd R. Andel, and Samuel H. Russ. 2016.Comparing the effectiveness of commercial obfuscators against MATE attacks. In Proceedings of the 6th Workshop on Software Security, Protection, and Reverse Engineering (SSPREW’16)
R. Manikyam. 2019.Program protection using software based hardware abstraction.Ph.D. Dissertation.University of South Alabama.
B. Nagaraj, A. Kalaivani, S. B. R, S. Akila, H. K. Sachdev, and S. K. N, “The Emerging Role of Artificial intelligence in STEM Higher Education: A Critical review,” International Research Journal of Multidisciplinary Technovation, pp. 1–19, Aug. 2023, doi: 10.54392/irjmt2351.
D. Sivabalaselvamani, K. Nanthini, Bharath Kumar Nagaraj, K. H. Gokul Kannan, K. Hariharan, M. Mallingeshwaran, Healthcare Monitoring and Analysis Using ThingSpeak IoT Platform: Capturing and Analyzing Sensor Data for Enhanced Patient Care, IGI Global eEditorial Discovery, Pages: 25, 2024. DOI: 10.4018/979-8-3693-1694-8.ch008.
Sri Charan Yarlagadda, Role of Artificial Intelligence, Automation, and Machine Learning in Sustainable Plastics Packaging markets: Progress, Trends, and Directions, International Journal on Recent and Innovation Trends in Computing and Communication, Vol:11, Issue 9s, Pages: 818–828, 2023.
Sri Charan Yarlagadda, The Use of Artificial Intelligence and Machine Learning in Creating a Roadmap Towards a Circular Economy for Plastics, International Journal on Recent and Innovation Trends in Computing and Communication, Vol:11, Issue 9s, Pages: 829-836, 2023.
Amol Kulkarni, Amazon Athena Serverless Architecture and Troubleshooting, International Journal of Computer Trends and Technology, Vol, 71, issue, 5, pages 57-61, 2023.
Amazon Redshift Performance Tuning and Optimization, International Journal of Computer Trends and Technology, vol, 71, issue, 2, pages, 40-44, 2023.