SK ALTHAF HUSSAIN BASHA
@a1global.ac.in
Professor and Vice Principal
A1 Global Institute of Engineering and Technology, Markapur , India
EDUCATION
Ph.D (CSE), M.Tech(CSE)
RESEARCH INTERESTS
Data Mining and warehousing , Educational Data Mining,Machine Learning, Big Data, DBMS, Cyber Security, IOT, Computer Networks, Image Processing, Higher Education in Computer Applications
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Kretika Tiwari and Dileep Kumar Singh
Auricle Technologies, Pvt., Ltd.
One of the most actively studied topics in modern medicine is the use of deep learning and patient clinical data to make medication and ADR recommendations. However, the clinical community still has some work to do in order to build a model that hybridises the recommendation system. As a social media learning based deep auto-encoder model for clinical recommendation, this research proposes a hybrid model that combines deep self-decoder with Top n similar co-patient information to produce a joint optimisation function (SAeCR). Implicit clinical information can be extracted using the network representation learning technique. Three experiments were conducted on two real-world social network data sets to assess the efficacy of the SAeCR model. As demonstrated by the experiments, the suggested model outperforms the other classification method on a larger and sparser data set. In addition, social network data can help doctors determine the nature of a patient's relationship with a co-patient. The SAeCR model is more effective since it incorporates insights from network representation learning and social theory.
P. Veeramani, P. Rajasekar, V. Ramkumar, R. Azhagumurugan, Shaik Althaf Hussain Basha, and A. Santham Bharathy
AIP Publishing
Nagaraju Devarakonda, Shaik Subhani, and Shaik Althaf Hussain Basha
Springer International Publishing
Identifying abnormal behavior in the chosen dataset is essential for improving the quality of the given dataset and decreasing the impact of abnormal values/patterns in the knowledge discovery process. Outlier detection may be established in many data mining techniques. In this paper Regression analysis have been used to detect the outliers. Partial Least Square approach is mainly used in regression analysis. Laser dataset has been used to find out the outliers. The main objective is used for constructing predictive models. The Mahalanobis distance, Jackknife distance and T2 distance were calculated for finding the outliers.