salim G shaikh
@aiktc.ac.in
Assistant Professor, Department of Computer Engineering.
AIKTC, Computer Engineering
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Computer Engineering, Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Salim Gulab Shaikh, Billakurthi Suresh Kumar, Geetika Narang, and Nishant Nilkanth Pachpor
Frontier Scientific Publishing Pte Ltd
<p>Vector-borne diseases (VBD) are a class of infectious illnesses that are transmitted to humans and animals through the bites of arthropod vectors, such as mosquitoes, ticks, and fleas. These diseases are caused by a variety of pathogens, including bacteria, viruses, and parasites, and are a significant global public health concern. Vector-borne diseases are prevalent in many parts of the world, particularly in tropical and subtropical regions, where the vectors thrive. This research has contributed by constructing a hybrid machine learning based prediction model, which helps to discover patients who are infected by vector-borne disease at an earlier stage and also helps with the categorization and diagnosis of severe vector-borne disease. The model that has been proposed is made up of units: data conversion, data preprocessing, normalization, extraction of feature, splitting of dataset, and classification and prediction unit. The fact that the suggested prediction model is capable of identifying vector-borne disease in its early phases as well as categorizing the kind of disease using the medical report of a sufferer is one of the innovative aspects of the model. The 7 distinct conventional machine learning and single hybrid machine learning (HML) are applied for classification and Recurrent Neural Network (RNN) based reinforcement learning are utilized for recommendation. In order to evaluate the effectiveness of the system that’s been proposed, a number of tests were carried out. A dataset consisting of 1539 different cases of a disease transmitted by vectors has been collected. The 11 common vector-borne diseases namely malaria, dengue, Japanese encephalitis, kala-azar and chikungunya were taken for experimental evaluation. The performance accuracy of the proposed prediction model has been measured at 98.76%, which assists the healthcare team in making decisions on a timely basis and ultimately helps to save the patient’s lives. The final phase system provides the recommendation for those classifiers resulting in four different classes such as normal, mild, moderate and severe respectively. The recommendation is also demonstrating future direction for cure of vector borne disease.</p>
Salim G. Shaikh, B. Suresh Kumar, Geetika Narang, and N.N. Pachpor
IEEE
Mosquitos influence dengue fever, and the dengue virus is a universal community health issue worldwide. An analysis and prediction are required to resolve the effects of the dengue virus in communities. The main motive of this article is to recognize the classification or recommendation methods based on machine learning (ML) and deep learning (DL) for predicting and detecting dengue fever. The classification methods such as SVM, KNN, DT, and naïve bayes are used to perform experimental results. In this article, a comparison of these methods is executed, and SVM achieves a better accuracy rate. This method is highest accurate and suitable for predicting the dengue virus. The naïve bays is an effective method for better performance with less time-consuming. This method takes 0.01 seconds and reduces the probability of errors. The techniques like DT, KNN, and naïve Bayes provide 55.5%, 96%, and 72% accuracy, respectively. The SVM, DT, and naïve bayes consumed the time of 0.16sec, 0.05sec, and 0.01sec, respectively.
Salim G. Shaikh, B. Suresh Kumar, and Geetika Narang
Springer Nature Singapore
Nishant N. Pachpor, B. Suresh Kumar, Prakash S. Parsad, and Salim G. Shaikh
Springer Nature Singapore
Salim G. Shaikh, B. Suresh Kumar, and Geetika Narang
Informa UK Limited
Abstract Recommender systems use different techniques of machine learning (ML) to suggest users and recommend service or entity in various field of application such as in health care recommender system (HRS). Due to the vast count of algorithms shown in the literature, HRS and various application sectors are now utilizing ML algorithms from the area of artificial intelligence. However, selecting an appropriate ML algorithm in the case of a health recommender system seems to be a time-consuming task. However the development of recommender system in different service domain faces problems of algorithms selection for better accuracy. This article examined the usage of ML techniques in recommender systems for health applications through a survey of the literature. The objectives of this article are (i) recognize the literature review finding of recommender system in health applications using ML and deep learning algorithms. (ii) Assist new researchers with the help of gap in previous research. The results of this study is to proposed new recommender system in health application of mosquito borne disease by using hybrid approach of ML technique.