R. Varatharajan

Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm

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.

Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis.

Geospatial data analytical model is developed in this paper to model the spatial suitability of malaria outbreak in Vellore, Tamil Nadu, India. In general, Disease control strategies are only the spatial information like landscape, weather and climate, but also spatially explicit information like socioeconomic variable, population density, behavior and natural habits of the people. The spatial multi-criteria decision analysis approach combines the multi-criteria decision analysis and geographic information system (GIS) to model the spatially explicit and implicit information and to make a practical decision under different scenarios and different environment. Malaria is one of the emerging diseases worldwide; the cause of malaria is weather & climate condition of the study area. The climate condition is often called as spatially implicit information, traditional decision-making models do not use the spatially implicit information it most often uses spatially explicit information such as socio-economic, natural habits of the people. There is need to develop an integrated approach that consists of spatially implicit and explicit information. The proposed approach is used to identity an effective control strategy that prevents and control of malaria. Inverse Distance Weighting (IDW) is a type of deterministic method used in this paper to assign the weight values based on the neighborhood locations. ArcGIS software is used to develop the geospatial habitat suitability model.

A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system

Abstract Wearable medical devices with sensor continuously generate enormous data which is often called as big data mixed with structured and unstructured data. Due to the complexity of the data, it is difficult to process and analyze the big data for finding valuable information that can be useful in decision-making. On the other hand, data security is a key requirement in healthcare big data system. In order to overcome this issue, this paper proposes a new architecture for the implementation of IoT to store and process scalable sensor data (big data) for health care applications. The Proposed architecture consists of two main sub architectures, namely, Meta Fog-Redirection (MF-R) and Grouping and Choosing (GC) architecture. MF-R architecture uses big data technologies such as Apache Pig and Apache HBase for collection and storage of the sensor data (big data) generated from different sensor devices. The proposed GC architecture is used for securing integration of fog computing with cloud computing. This architecture also uses key management service and data categorization function (Sensitive, Critical and Normal) for providing security services. The framework also uses MapReduce based prediction model to predict the heart diseases. Performance evaluation parameters such as throughput, sensitivity, accuracy, and f-measure are calculated to prove the efficiency of the proposed architecture as well as the prediction model.

A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing

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.

Hybrid Recommendation System for Heart Disease Diagnosis based on Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System

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.

Machine Learning Based Big Data Processing Framework for Cancer Diagnosis Using Hidden Markov Model and GM Clustering

AbstractThe 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.

Intelligent face recognition and navigation system using neural learning for smart security in Internet of Things

Most of the advancements are now carried out by interconnecting physical devices with computers; this is what known as Internet of Things (IoT). The major problems facing by blind people fall in the category of navigating through indoor and outdoor environments consisting of various obstacles and recognition of person in front of them. Identification of objects or person only with perceptive and audio information is difficult. An intelligent, portable, less expensive, self-contained navigation and face recognition system is highly demanded for blind people. This helps blind people to navigate with the help of a Smartphone, global positioning system (GPS) and a system equipped with ultrasonic sensors. Face recognition can be done using neural learning techniques with feature extraction and training modules. The images of friends, relatives are stored in the database of user Smartphone. Whenever a person comes in front of the blind user, the application with the help of neural network gives the voice aid to the user. Thus this system can replace the regular imprecise use of guide dogs as well as white sticks to help the navigation and face recognition process for people with impaired vision.In this paper, we have proposed a novel image recognition and navigation system which provides precise and quick messages in the form of audio to visually challenged people so that they can navigate easily. The performance of the proposed method is comparatively analyzed with the help of ROC analysis.

HIoTPOT: Surveillance on IoT Devices against Recent Threats

Honeypot Internet of Things (IoT) (HIoTPOT) keep a secret eye on IoT devices and analyzes the various recent threats which are dangerous to IoT devices. In this paper, implementation of a research honeypot is presented which is used to learn the recent tactics and ethics used by black hat community to attack on IoT devices. As IoT is open and easy for accessing, all the intruders are highly attracted towards IoT. Recently Telnet based attacks are very famous on IoT devices to get easy access and attack on other devices. To reduce these kinds of threats, it is necessary to know in details about intruder, therefore the aim of this research work is to implement novel based secret eye server known as HIoTPOT which will make the IoT environment more safe and secure.

Secure Disintegration Protocol for Privacy Preserving Cloud Storage

Cloud service providers offer infrastructure, network services, and software applications in the cloud. The cloud services are hosted in a data center that can be used by users with the help of network connectivity. Hence, there is a need for providing security and integrity in cloud resources. Most security instruments have a finite rate of failure, and the intrusion comes with more complex and sophisticated techniques; the security failure rates are skyrocketing. In this paper, we have proposed a secure disintegration protocol (SDP) for the protection of privacy on-site and in the cloud. The architecture presented in this paper is used for cloud storage, and it is used in conjunction with our unique data compression and encoding technique. Probabilistic analysis is used for calculating the intrusion tolerance abilities for the SDP.

Score level based latent fingerprint enhancement and matching using SIFT feature

Latent fingerprint identification is such a difficult task to law enforcement agencies and border security in identifying suspects. It is a too complicate due to poor quality images with non-linear distortion and complex background noise. Hence, the image quality is required for matching those latent fingerprints. The current researchers have been working based on minutiae points for fingerprint matching because of their accuracy are acceptable. In an effort to extend technology for fingerprint matching, our model is to propose the enhancementand matching for latent fingerprints using Scale Invariant Feature Transformation (SIFT). It has involved in two phases (i) Latent fingerprint contrast enhancement using intuitionistic type-2 fuzzy set (ii) Extract the SIFTfeature points from the latent fingerprints. Then thematching algorithm is performedwith n- number of images and scoresare calculated by Euclidean distance. We tested our algorithm for matching, usinga public domain fingerprint database such as FVC-2004 and IIIT-latent fingerprint. The experimental consequences indicatethe matching result is obtained satisfactory compare than minutiae points.