Quantum computing and machine learning for transforming precision medicine and drug discovery Lakshmana Kumar Yenduri, Chaithra M. H., S. Shahedhadeennisa, M. Annamalai, L. Rajesh, Shaik Mohammed Imran Modern Superhypersoft Computing Trends in Science and Technology, 2024 Quantum Machine Learning can be considered one of the transformational technologies in precision medicine and drug discovery because quantum computing combines immense processing power with high capability, predictive machine learning. This chapter will discuss the potential for QML to revolutionize complex biological data analysis to rapidly identify disease biomarkers and highly personalized treatment approaches. The chapter describes the two superior performances that can be achieved by quantum algorithms in simulating molecular interactions, and thereby drastically reducing time and cost in drug development. It also discusses key applications that include quantum-enhanced neural networks and support vector machines to diagnose diseases and predict outcomes of treatments. Challenges regarding scalability, noise reduction, and hardware limitations are discussed together with some very promising future directions. When quantum technology has reached full maturity, the opportunities it will give to machine learning will provide unparalleled breakthroughs in medical research.
Evaluating the Effectiveness of Smote for Imbalanced Data Expansion and Its Impact on Classification Accuracy Shaik Mohammed Imran, Angelina Geetha 2024 1st International Conference for Women in Computing Incowoco 2024 Proceedings, 2024 Big data classification technology has emerged together with artificial intelligence, offering valuable support for studies on auxiliary diagnostics in medicine. Medical big data is frequently unbalanced because of the various conditions in the many sample collections. Many popular learning methods have their classification performance hindered by the class-imbalance problem. While the SMOTE algorithm's random sample point generation feature could lead to an increase in the imbalance rate, the blindness of parameter selection and marginalization formation make its deployment problematic. This research seeks to remedy the situation by presenting a normal distribution-based SMOTE algorithm that is superior to its predecessor. The extra sample points will be distributed more fairly, which will prevent larger data portions from being underrepresented. Experiments show that when applied to imbalanced datasets like Pima, WDBC, WPBC, Ionosphere, and Breast Cancer, the new method outperforms the original SMOTE algorithm in terms of classification performance. This was demonstrated in Wisconsin. Our thorough testing also revealed that maintaining the distribution properties of the original data through the selection of appropriate parameters in the suggested method results in the best classification impact.
YARS-IDS: A Novel IDS for Multi-Class Classification T Pruthviraj Singh, G Deepak Kumar, M Mutharasu, K Tejaswaroop Rao, S Mohammed Imran Proceedings 3rd International Conference on Advances in Computing Communication and Applied Informatics Accai 2024, 2024 Intrusion Detection Systems (IDS) guard against many online threats in today’s digital environment. Two innovative Deep Learning (DL) intrusion detection systems (IDSs) for multi-class categorization are presented in this study. Two IDS. The first uses LuNet and Bidirectional LSTM. The second one uses TCN, CNN, and Bi-LSTM. Both models are carefully validated and trained on NSL-KDD and UNSW-NB15. The NSLKDD dataset is prioritized during testing.The results show that the suggested IDSs work better than traditional Machine Learning (ML) based methods and a lot of current Deep Learning (DL) models, showing better classification accuracy and detection rates. The base paper did a great job with CNN using Condensed Nearest Neighbour resampling. This extension builds on that work and improves performance even more by using ensemble methods. When estimates from several different models are put together, especially when CNN + BiLSTM and CNN + LSTM setups are used,accuracy rates rise to an amazing 99%. This study not only pushes the limits of intrusion detection systems (IDS), but it also shows how useful ensemble methods can be in making defense solutions stronger. It opens up exciting new areas to explore and improve in the future.
Federated Learning Methods for Privacy-Preserving Collaborative Machine Learning Y. Nagender, S. Deena, Shaik Mohammed Imran, Navdeep Singh, R Anuradha, Dhiraj Kapila 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical Electronics and Computer Engineering Upcon 2023, 2023 Federated learning is a cutting-edge data solution that protects privacy as well as a fresh machine learning model developed on collaborative data sets. A major concern privacy protection aspect of machine learning has proven quite successful. The PFMLP framework, which is based on federated learning as well as collaborative machine learning, is presented in this paper for multi-party safeguarding privacy machine learning. All learning partners ought to send only homomorphic encrypted gradients, according to the basic idea. According to experiments, the PFMLP-trained model almost always achieves the same accuracy, with a variance of less than 1.%. The upcoming privacy-preserving data technology and a fresh category of distributed learning models, federated learning, were both invented by Google. In this research, we examine distributed ML applied to dispersed data sets and federated learning as an option for privacy-preserving data access. Additionally, a federated learning architecture that protects privacy is presented.
RECENT SCHOLAR PUBLICATIONS
Evaluating the Effectiveness of Smote for Imbalanced Data Expansion and Its Impact on Classification Accuracy SM Imran, A Geetha 2024 First International Conference for Women in Computing (InCoWoCo), 1-7 , 2024 2024 Citations: 1
Federated Learning Methods for Privacy-Preserving Collaborative Machine Learning DK Y. Nagender, S Deena, Shaik Mohammed Imran, Navdeep Singh, R Anuradha 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical … , 2023 2023 Citations: 2
DETECTION OF BREAST CANCER IN DIGITAL MAMMOGRAMS USING DEEP LEARNING ALGORITHM DIHMMGSMMKASMSMIMRRRMKSKMNHDTLMSIMBAPKDRHHKD Bhavani IN Patent App. 202,141,026,197 , 2021 2021
Spotting Parkinson’s disorder with OpenCV, and the Helix/Wave using Random Forest Classifier SM Imran, R Bandi, R PN Materials Today: Proceedings ISSN: 2214-7853 https://doi.org/10.1016/j.matpr … , 2021 2021 Citations: 3
Home Loan Prediction by comparing classifiers on imbalanced data using Machine Learning N Shaik Mohammed Imran#1 , Dr. P.N. Renjith*2 , Dr. K Ramesh#3 Parishodh Journal 9 (3), 12510-12518 , 2020 2020
Survey on Internet of Things Based Healthcare A Bharath, MI Shaik ICICSE 2020 Journal of Innovation in Information Technology 4 (Issue 2 July … , 2020 2020
Digital Forensics-The Potential Evidence MI Shaik, K Anbazhagan Journal of Innovations in Information Technology , http://innovation … , 2017 2017
Web Application Security Vulnerabilities DSSR Mohammed Imran Sk International Conference on Paradigms in Engineering & Technology 1 (ISBN … , 2016 2016
Multi Agent Based Cloud Security Model for Association Rule Mining I Qureshi, SM Imran, GR Murthy International Journal of Applied Engineering Research 10 (24), 44422-44426 , 2015 2015
MOST CITED SCHOLAR PUBLICATIONS
Spotting Parkinson’s disorder with OpenCV, and the Helix/Wave using Random Forest Classifier SM Imran, R Bandi, R PN Materials Today: Proceedings ISSN: 2214-7853 https://doi.org/10.1016/j.matpr … , 2021 2021 Citations: 3
Federated Learning Methods for Privacy-Preserving Collaborative Machine Learning DK Y. Nagender, S Deena, Shaik Mohammed Imran, Navdeep Singh, R Anuradha 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical … , 2023 2023 Citations: 2
Evaluating the Effectiveness of Smote for Imbalanced Data Expansion and Its Impact on Classification Accuracy SM Imran, A Geetha 2024 First International Conference for Women in Computing (InCoWoCo), 1-7 , 2024 2024 Citations: 1
DETECTION OF BREAST CANCER IN DIGITAL MAMMOGRAMS USING DEEP LEARNING ALGORITHM DIHMMGSMMKASMSMIMRRRMKSKMNHDTLMSIMBAPKDRHHKD Bhavani IN Patent App. 202,141,026,197 , 2021 2021
Home Loan Prediction by comparing classifiers on imbalanced data using Machine Learning N Shaik Mohammed Imran#1 , Dr. P.N. Renjith*2 , Dr. K Ramesh#3 Parishodh Journal 9 (3), 12510-12518 , 2020 2020
Survey on Internet of Things Based Healthcare A Bharath, MI Shaik ICICSE 2020 Journal of Innovation in Information Technology 4 (Issue 2 July … , 2020 2020
Digital Forensics-The Potential Evidence MI Shaik, K Anbazhagan Journal of Innovations in Information Technology , http://innovation … , 2017 2017
Web Application Security Vulnerabilities DSSR Mohammed Imran Sk International Conference on Paradigms in Engineering & Technology 1 (ISBN … , 2016 2016
Multi Agent Based Cloud Security Model for Association Rule Mining I Qureshi, SM Imran, GR Murthy International Journal of Applied Engineering Research 10 (24), 44422-44426 , 2015 2015
