Assessment of Vaccination Strategies Using Fuzzy Multi-criteria Decision Making
01 Jan 2015-pp 195-208
TL;DR: The objective of this research is to use fuzzy logic based VIKOR method for evaluating H1N1 Influenza vaccination strategies and the alternative of a vaccination strategy includes the combination of “people”, “spatial” and “temporal”.
Abstract: Alternative selection of vaccination strategy has become a challenging task for the public health, and it is considered as a complex decision making problem. Decision makers often use linguistic variables to rate the alternatives. The objective of this research is to use fuzzy logic based VIKOR method for evaluating H1N1 Influenza vaccination strategies. The experimental design of the proposed decision making model is illustrated with a case study in Vellore, Tamil Nadu, India. The alternative of a vaccination strategy considered in this study includes the combination of “people”, “spatial” and “temporal”.
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TL;DR: This paper uses dynamic time warping (DTW) algorithm to compare the various shapes of foot movements collected from the wearable IoT devices to evaluate the effectiveness of the DTW method for Alzheimer disease diagnosis.
Abstract: Alzheimer disease is a significant problem in public health. Alzheimer disease causes severe problems with thinking, memory and activities. Alzheimer disease affected more on the people who are in the age group of 80-year-90. The foot movement monitoring system is used to detect the early stage of Alzheimer disease. internets of things (IoT) devices are used in this paper to monitor the patients’ foot movement in continuous manner. This paper uses dynamic time warping (DTW) algorithm to compare the various shapes of foot movements collected from the wearable IoT devices. The foot movements of the normal individuals and people who are affected by Alzheimer disease are compared with the help of middle level cross identification (MidCross) function. The identified cross levels are used to classify the gait signal for Alzheimer disease diagnosis. Sensitivity and specificity are calculated to evaluate the DTW algorithm based classification model for Alzheimer disease. The classification results generated using the DTW is compared with the various classification algorithms such as inertial navigation algorithm, K-nearest neighbor classifier and support vector machines. The experimental results proved the effectiveness of the DTW method.
237 citations
TL;DR: The proposed MKL with ANFIS based deep learning method follows two-fold approach and has produced high sensitivity, high specificity and less Mean Square Error for the for the KEGG Metabolic Reaction Network dataset.
Abstract: Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System (MKL with ANFIS) based deep learning method is proposed in this paper for heart disease diagnosis. The proposed MKL with ANFIS based deep learning method follows two-fold approach. MKL method is used to divide parameters between heart disease patients and normal individuals. The result obtained from the MKL method is given to the ANFIS classifier to classify the heart disease and healthy patients. Sensitivity, Specificity and Mean Square Error (MSE) are calculated to evaluate the proposed MKL with ANFIS method. The proposed MKL with ANFIS is also compared with various existing deep learning methods such as Least Square with Support Vector Machine (LS with SVM), General Discriminant Analysis and Least Square Support Vector Machine (GDA with LS-SVM), Principal Component Analysis with Adaptive Neuro-Fuzzy Inference System (PCA with ANFIS) and Latent Dirichlet Allocation with Adaptive Neuro-Fuzzy Inference System (LDA with ANFIS). The results from the proposed MKL with ANFIS method has produced high sensitivity (98%), high specificity (99%) and less Mean Square Error (0.01) for the for the KEGG Metabolic Reaction Network dataset.
195 citations
TL;DR: A Bayesian hidden Markov model (HMM) with Gaussian Mixture (GM) Clustering approach is used to model the DNA copy number change across the genome and is compared with various existing approaches such as Pruned Exact Linear Time method, binary segmentation method and segment neighborhood method.
Abstract: The change in the DNA is a form of genetic variation in the human genome. In addition, the DNA copy number change is also linked with the progression of many emerging diseases. Array-based Comparative Genomic Hybridization (CGH) is considered as a major task when measuring the DNA copy number change across the genome. Moreover, DNA copy number change is an essential measure to diagnose the cancer disease. Next generation sequencing is an important method for studying the spread of infectious disease qualitatively and quantitatively. CGH is widely used in continuous monitoring of copy number of thousands of genes throughout the genome. In recent years, the size of the DNA sequence data is very large. Hence, there is a need to use a scalable machine learning approach to overcome the various issues in DNA copy number change detection. In this paper, we use a Bayesian hidden Markov model (HMM) with Gaussian Mixture (GM) Clustering approach to model the DNA copy number change across the genome. The proposed Bayesian HMM with GM Clustering approach is compared with various existing approaches such as Pruned Exact Linear Time method, binary segmentation method and segment neighborhood method. Experimental results demonstrate the effectiveness of our proposed change detection algorithm.
182 citations
TL;DR: SVM model with a weighted kernel function method is significantly identifies the Q wave, R wave and S wave in the input ECG signal to classify the heartbeat level to prove the effectiveness of the proposed Linear Discriminant Analysis (LDA) with an enhanced kernel based Support Vector Machine (SVM) method.
Abstract: Electrocardiographic (ECG) signals often consist of unwanted noises and speckles. In order to remove the noises, various image processing filters are used in various studies. In this paper, FIR and IIR filters are initially used to remove the linear and nonlinear delay present in the input ECG signal. In addition, filters are used to remove unwanted frequency components from the input ECG signal. Linear Discriminant Analysis (LDA) is used to reduce the features present in the input ECG signal. Support Vector Machines (SVM) is widely used for pattern recognition. However, traditional SVM method does not applicable to compute different characteristics of the features of data sets. In this paper, we use SVM model with a weighted kernel function method to classify more features from the input ECG signal. SVM model with a weighted kernel function method is significantly identifies the Q wave, R wave and S wave in the input ECG signal to classify the heartbeat level such as Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Premature Ventricular Contraction (PVC) and Premature Atrial Contractions (PACs). The performance of the proposed Linear Discriminant Analysis (LDA) with enhanced kernel based Support Vector Machine (SVM) method is comparatively analyzed with other machine learning approaches such as Linear Discriminant Analysis (LDA) with multilayer perceptron (MLP), Linear Discriminant Analysis (LDA) with Support Vector Machine (SVM), and Principal Component Analysis (PCA) with Support Vector Machine (SVM). The calculated RMSE, MAPE, MAE, R2 and Q2 for the proposed Linear Discriminant Analysis (LDA) with enhanced kernel based Support Vector Machine (SVM) method is low when compared with other approaches such as LDA with MLP, and PCA with SVM and LDA with SVM. Finally, Sensitivity, Specificity and Mean Square Error (MSE) are calculated to prove the effectiveness of the proposed Linear Discriminant Analysis (LDA) with an enhanced kernel based Support Vector Machine (SVM) method.
180 citations
Cites background from "Assessment of Vaccination Strategie..."
...Big Data is defined as a collection of the large volume of data that becomes complex to process by using traditional data processing techniques and platforms [9, 12]....
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01 Jan 2018
TL;DR: The main focus of this work is to secure Authentication and Authorization of all the devices, Identifying and Tracking the devices deployed in the system, Locating and tracking of mobile devices, new things deployment and connection to existing system, Communication among the devices and data transfer between remote healthcare systems.
Abstract: This chapter proposes an efficient centralized secure architecture for end to end integration of IoT based healthcare system deployed in Cloud environment. The proposed platform uses Fog Computing environment to run the framework. In this chapter, health data is collected from sensors and collected sensor data are securely sent to the near edge devices. Finally, devices transfer the data to the cloud for seamless access by healthcare professionals. Security and privacy for patients’ medical data are crucial for the acceptance and ubiquitous use of IoT in healthcare. The main focus of this work is to secure Authentication and Authorization of all the devices, Identifying and Tracking the devices deployed in the system, Locating and tracking of mobile devices, new things deployment and connection to existing system, Communication among the devices and data transfer between remote healthcare systems. The proposed system uses asynchronous communication between the applications and data servers deployed in the cloud environment.
141 citations
References
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Book•
01 Jan 1970TL;DR: A reverse-flow technique is described for the solution of a functional equation arising in connection with a decision process in which the termination time is defined implicitly by the condition that the process stops when the system under control enters a specified set of states in its state space.
Abstract: By decision-making in a fuzzy environment is meant a decision process in which the goals and/or the constraints, but not necessarily the system under control, are fuzzy in nature. This means that the goals and/or the constraints constitute classes of alternatives whose boundaries are not sharply defined.
An example of a fuzzy constraint is: “The cost of A should not be substantially higher than α,” where α is a specified constant. Similarly, an example of a fuzzy goal is: “x should be in the vicinity of x0,” where x0 is a constant. The italicized words are the sources of fuzziness in these examples.
Fuzzy goals and fuzzy constraints can be defined precisely as fuzzy sets in the space of alternatives. A fuzzy decision, then, may be viewed as an intersection of the given goals and constraints. A maximizing decision is defined as a point in the space of alternatives at which the membership function of a fuzzy decision attains its maximum value.
The use of these concepts is illustrated by examples involving multistage decision processes in which the system under control is either deterministic or stochastic. By using dynamic programming, the determination of a maximizing decision is reduced to the solution of a system of functional equations. A reverse-flow technique is described for the solution of a functional equation arising in connection with a decision process in which the termination time is defined implicitly by the condition that the process stops when the system under control enters a specified set of states in its state space.
6,919 citations
TL;DR: The VIKOR method as mentioned in this paper was developed to solve MCDM problems with conflicting and noncommensurable (different units) criteria, assuming that compromising is acceptable for conflict resolution, the decision maker wants a solution that is the closest to the ideal, and the alternatives are evaluated according to all established criteria.
1,303 citations
TL;DR: The Analytical Hierarchy Process (AHP) as mentioned in this paper is a potential decision-making method for use in project management, which is used as an example for the contractor prequalification problem.
981 citations
TL;DR: This paper studies the Sanchez's approach for medical diagnosis and extends this concept with the notion of intuitionistic fuzzy set theory (which is a generalization of fuzzySet theory).
848 citations
Journal Article•
733 citations