There has been a major and rising interest in India for increasing vaccination rate among peoples to make the nation healthier and safer. In this paper, a new hybrid deep learning model is proposed to predict and target vaccination rates in the less immunized regions. The Rank-Based Multi-Layer Perceptron (R-MLP) hybrid deep learning framework uses the data collected from the recently updated District Level Household Survey-4 (DLHS). R-MLP model predicts and categorizes the percentage of partly immunized vaccination rates as extreme, low and medium ranges. This predicted findings are cross-verified by Deep Soft Cosine Semantic and Ranking SVM based model (DSS-RSM). DSS-RSM model uses the data obtained from the medical practitioners through a location-based social network. The proposed model predicts and extracts patterns with high similarity frequency for identifying vulnerable low immunization regions. It classifies the predicted patterns into two classes such as Class 1 is denoted as high ranked regions and Class 2 is denoted as low ranked regions based on the percentage of pattern matches. Finally, the results from R-MLP and DSS-RSM models are cross-linked together using ensemble model. This model finds the loss values to identify the target regions were health care program need to be conducted for increasing the level of immunization among children’s. The proposed hybrid deep learning models trains and validates using python-based Keras and TensorFlow deep learning libraries. The performance of the proposed hybrid deep learning model is compared with other variant machine learning techniques such as Decision Tree C5.0, Naive Bayes and Linear Regression. This comparative results are evaluated using evaluation measures such as Precision, Recall, Accuracy and F1-Measure. Our results show that the hybrid deep learning system is clearly superior to any other alternative approach.
For large data, data mining methods were used on a Hadoop-based distributed infrastructure, using map reduction paradigm approaches for rapid data processing. Though data mining approaches are established methodologies, the Apriori algorithm provides a specific strategy for increasing data processing performance in big data analytics by applying map reduction. Apriori property is used to increase the efficiency of level-wise creation of frequent itemsets by minimizing the search area. A frequent itemset’s subsets must also be frequent (Apriori property). If an itemset is rarely, then all of its supersets are infrequent as well. We refined the apriori approach by varying the degree of order in locating frequent item sets in large clusters using map reduction programming. Fixed Pass Combined Counting (FPC) and Dynamic Pass Combined Counting (DPC) is a classical algorithm which are used for data processing from the huge datasets but their accuracy is not up to the mark. In this article, updated Apriori algorithms such as multiplied-fixed-pass combined counting (MFPC) and average time-based dynamic combined counting (ATDFC) are used to successfully achieve data processing speed. The proposed approaches are based on traditional Apriori core notions in data mining and will be used in the map-reduce multi-pass phase by ignoring pruning in some passes. The optimized-MFPC and optimized-ATDFC map-reduce framework model algorithms were also presented. The results of the experiments reveal that MFPC and ATDFC are more efficient in terms of execution time than previously outmoded approaches such as Fixed Pass Combined Counting (FPC) and Dynamic Pass Combined Counting (DPC). In a Hadoop multi-node cluster, this paradigm accelerates data processing on big data sets. Previous techniques were stated in terms of reducing execution time by 60–80% through the use of several passes. Because of the omitted trimming operation in data pre-processing, our proposed new approaches will save up to 84–90% of that time.
This paper explores the use of deep learning architectures to identify and categorize infrared spectral data with the objective of classifying drugs and toxins with a high level of accuracy. The model proposed uses a custom convolutional neural network to learn the spectrum of 192 drugs and 207 toxins. Variations in the architecture and number of blocks were iterated to find the best possible fit. A real-time implementation of such a model faces a lot of issues such as noise from different sources, spectral magnitude off-setting, and wavelength rotation. This paper aims to tackle some of these problems. Another common issue is the use of extensive pre-processing which makes it difficult to automate the entire process. We have aimed to side-step this issue with the architecture proposed. The focus is on 2 applications - detection of drugs and toxins. The data sets used are from different sources, each with its own noise factor and sampling rate. Some of the traditional models like Principal Component Analysis (PCA) and Support Vector Machines (SVM) were also tested on the datasets. The model works with minimal input data of two spectra (and three augmentations of the same) to learn the features and classifies the data from a source independent of the input. The proposed model showed a significant improvement in accuracy when compared to the other models currently in use, achieving an overall accuracy of 96.55\%. The model proposed performs extremely well with a minimal sampling rate and shows no loss in accuracy of classification even with an increase in the number of classes. The research conducted has the scope of being extended to the identification of counterfeit drugs which is a growing cause for concern. Another application could be in the detection of the presence of harmful toxins.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.