MUHIB ANWAR LAMBAY
@aiktc.ac.in
Assistant Professor
Anjuman-I-Islam's, Kalsekar Technical Campus
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Computer Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Muhib Anwar Lambay and S. Pakkir Mohideen
Springer Science and Business Media LLC
Muhib Anwar Lambay and S. Pakkir Mohideen
River Publishers
Recommendations are useful suggestions used by people from all walks of life. However, the usage of recommender systems plays a vital role in modern applications. They are found in different domains such as E-commerce. Concerning the health care industry, recommendations play a very crucial role. This industry has significance as it is linked to the lives of people and their well-being. Human health depends on the diet followed. Keeping this fact in mind, in this paper, we investigated healthy diet recommendations. The recommender systems that are existing in healthcare focused a little in this area. From the literature, it is understood that most of the frameworks on health recommendations are theoretical in nature. As food decides health, it is to be given paramount importance. In this paper, we proposed a hybrid mechanism based on Artificial Intelligence (AI) for big data analytics. Particularly we used Machine Learning (ML) for generating healthy diet recommendations. The proposed system is known as Hybrid Recommender System (HRS). It involves a hybrid approach with Natural Language Processing (NLP) and machine learning. An algorithm named Intelligent Recommender for Healthy Diet (IR-HD) is proposed to analyze data and provide healthy diet recommendations. IR-HD could generate recommendations on a healthy diet and outperform existing models. Python data science platform is used to implement the recommender system. The results of experiments showed that the system is capable of providing quality recommendations and it has performance improvement over the state of the art.
Muhib Anwar Lambay, S. Pakkir Mohideen, and B. S. Abdur Rahman
IEEE
The emergence of Artificial Intelligence (AI) based methods pertaining to machine learning (ML) and deep learning has paved the way for solving many real-world problems. In the healthcare industry, the Quality of Service (QoS) is greatly enhanced with automated approaches in health and diet recommendations, disease prediction and drug recommendations. There is a need for drug reaction prediction as well with AI-enabled approaches. The existing models in this regard suffer from a lack of coordinated Natural Language Processing (NLP) and AI approaches. In this paper, we fill the gap with the proposed framework known as AI-Enabled Drug Reaction Prediction (AIE-DRP). An algorithm known as Multi-Model based ADR Prediction (MM-ADRP). It makes use of multiple ML models such as Logistic Regression, Light Gradient Boosting Machine (LGBM) and MultinomialNB. It also uses deep learning models such as Convolutional Neural Network (CNN) and multiple variants of Long Short Term Memory (LSTM). The framework analyses contextual content and determines whether it has associated adverse drug reaction or not besides categorizing it into either drug or dosage. The experimental results are compared among many models. The CNN model with dropout is found to have the highest prediction performance with 87.35% F1-score. This research can trigger further insights in the healthcare industry to automate adverse drug reaction prediction.
Muhib Anwar Lambay and S. Pakkir Mohideen
IEEE
Healthcare industry is an indispensable entity in the real world where large volumes of data is accumulated from time to time. Such data assumes characteristics of big data and it is desirable to analyze it and bring about latent relationships among variables in the healthcare data. Data in healthcare industry is rich in useful information. However, a comprehensive big data approach is essential to mine the data and acquire business intelligence. There are many use cases of big data analytics. However, in healthcare industry it is imperative to have knowledge-driven recommendations that help all stakeholders. With the emergence of cloud computing, big data analytics has become a reality. Distributed programming frameworks like Hadoop and Spark, to mention few, are available with associated Distributed File System (DFS) to manage big data. Many researchers contributed towards developing algorithms based on machine learning which is part of Artificial Intelligence (AI). Since healthcare industry is one of the sources of big data, it needs distributed environments for processing. Big data analytics is essential to analyze healthcare data in a comprehensive manner. The cloud computing and big data ecosystem is playing favorable role in realizing big data analytics for healthcare recommendations. A typical recommender system in healthcare industry is supposed to produce recommendations in various aspects of the domain. This paper throws light into different recommenders in healthcare domain that use big data analytics to generate recommendations. It not only provides useful insights but also discussed research gaps that can be used to investigate further to improve the state of the art.