Internet of things-inspired healthcare system for urine-based diabetes prediction.

Healthcare industry is the leading domain that has been revolutionized by the incorporation of Internet of Things (IoT) technology resulting in smart medical applications. Conspicuously, this study presents an effective system of home-centric Urine-based Diabetes (UbD) monitoring system. Specifically, the proposed system comprises of 4-layers for predicting and monitoring diabetes-oriented urine infection. The system layers including Diabetic Data Acquisition (DDA) layer, Diabetic Data Classification (DDC) layer, Diabetic-Mining and Extraction (DME) layer, and Diabetic Prediction and Decision Making (DPDM) layer allow an individual not exclusively to track his/her diabetes measure on regular basis but the prediction procedure is also accomplished so that prudent steps can be taken at early stages. Additionally, probabilistic measurement of UbD monitoring in terms of Level of Diabetic Infection (LoDI), which is cumulatively quantified as Diabetes Infection Measure (DIM) has been performed for predictive purposes using Recurrent Neural Network (RNN). Moreover, the existence of UbD is visualized based on the Self-Organized Mapping (SOM) procedure. To validate the proposed system, numerous experimental simulations were performed on datasets of 4 individuals. Based on the experimental simulation, enhanced results in terms of temporal delay, classification efficiency, prediction efficiency, reliability and stability were registered for the proposed system in comparison to state-of-the-art decision-making techniques.

A comprehensive framework for student stress monitoring in fog-cloud IoT environment: m-health perspective.

Over the last few years, Internet of Things (IoT) has opened the doors to innovations that facilitate interactions among things and humans. Focusing on healthcare domain, IoT devices such as medical sensors, visual sensors, cameras, and wireless sensor network are leading this evolutionary trend. In this direction, the paper proposes a novel, IoT-aware student-centric stress monitoring framework to predict student stress index at a particular context. Bayesian Belief Network (BBN) is used to classify the stress event as normal or abnormal using physiological readings collected from medical sensors at fog layer. Abnormal temporal structural data which is time-enriched dataset sequence is analyzed for various stress-related parameters at cloud layer. To compute the student stress index, a two-stage Temporal Dynamic Bayesian Network (TDBN) model is formed. This model computes stress based on four parameters, namely, leaf node evidences, workload, context, and student health trait. After computing the stress index of the student, decisions are taken in the form of alert generation mechanism with the deliverance of time-sensitive information to caretaker or responder. Experiments are conducted both at fog and cloud layer which hold evidence for the utility and accuracy of the BBN classifier and TDBN predictive model in our proposed system. Graphical Abstract Student stress monitoring in IoT-Fog Environment.

Kyasanur Forest Disease Classification Framework Using Novel Extremal Optimization Tuned Neural Network in Fog Computing Environment.

Kyasanur Forest Disease (KFD) is a life-threatening tick-borne viral infectious disease endemic to South Asia and has been taking so many lives every year in the past decade. But recently, this disease has been witnessed in other regions to a large extent and can become an epidemic very soon. In this paper, a new fog computing based e-Healthcare framework has been proposed to monitor the KFD infected patients in an early phase of infection and control the disease outbreak. For ensuring high prediction rate, a novel Extremal Optimization tuned Neural Network (EO-NN) classification algorithm has been developed using hybridization of the extremal optimization with the feed-forward neural network. Additionally, a location based alert system has also been suggested to provide the global positioning system (GPS)-based location information of each KFD infected user and the risk-prone zones as early as possible to prevent the outbreak. Furthermore, a comparative study of proposed EO-NN with state of art classification algorithms has been carried out and it can be concluded that EO-NN outperforms others with an average accuracy of 91.56%, a sensitivity of 91.53% and a specificity of 97.13% respectively in classification and accurate identification of risk-prone areas.

IoT-based cloud framework to control Ebola virus outbreak.

Ebola is a deadly infectious virus that spreads very quickly through human-to-human transmission and sometimes death. The continuous detection and remote monitoring of infected patients are required in order to prevent the spread of Ebola virus disease (EVD). Healthcare services based on Internet of Things (IoT) and cloud computing technologies are emerging as a more effective and proactive solution which provides remote continuous monitoring of patients. A novel architecture based on Radio Frequency Identification Device (RFID), wearable sensor technology, and cloud computing infrastructure is proposed for the detection and monitoring of Ebola infected patients. The aim of this work is to prevent the spreading of the infection at the early stage of the outbreak. The J48 decision tree is used to evaluate the level of infection in a user depending on his symptoms. RFID is used to automatically sense the close proximity interactions (CPIs) between users. Temporal Network Analysis (TNA) is applied to describe and monitor the current state of the outbreak using the CPI data. The performance and accuracy of our proposed model are evaluated on Amazon EC2 cloud using synthetic data of two million users. Our proposed model provided 94 % accuracy for the classification and 92 % of the resource utilization.

SECURE INTERNET OF THINGS-BASED CLOUD FRAMEWORK TO CONTROL ZIKA VIRUS OUTBREAK.

OBJECTIVES: Zika virus (ZikaV) is currently one of the most important emerging viruses in the world which has caused outbreaks and epidemics and has also been associated with severe clinical manifestations and congenital malformations. Traditional approaches to combat the ZikaV outbreak are not effective for detection and control. The aim of this study is to propose a cloud-based system to prevent and control the spread of Zika virus disease using integration of mobile phones and Internet of Things (IoT). METHODS: A Naive Bayesian Network (NBN) is used to diagnose the possibly infected users, and Google Maps Web service is used to provide the geographic positioning system (GPS)-based risk assessment to prevent the outbreak. It is used to represent each ZikaV infected user, mosquito-dense sites, and breeding sites on the Google map that helps the government healthcare authorities to control such risk-prone areas effectively and efficiently. RESULTS: The performance and accuracy of the proposed system are evaluated using dataset for 2 million users. Our system provides high accuracy for initial diagnosis of different users according to their symptoms and appropriate GPS-based risk assessment. CONCLUSIONS: The cloud-based proposed system contributed to the accurate NBN-based classification of infected users and accurate identification of risk-prone areas using Google Maps.

Wearable IoT sensor based healthcare system for identifying and controlling chikungunya virus.

Chikungunya is a vector borne disease that spreads quickly in geographically affected areas. Its outbreak results in acute illness that may lead to chronic phase. Chikungunya virus (CHV) diagnosis solutions are not easily accessible and affordable in developing countries. Also old approaches are very slow in identifying and controlling the spread of CHV outbreak. The sudden development and advancement of wearable internet of things (IoT) sensors, fog computing, mobile technology, cloud computing and better internet coverage have enhanced the quality of remote healthcare services. IoT assisted fog health monitoring system can be used to identify possibly infected users from CHV in an early phase of their illness so that the outbreak of CHV can be controlled. Fog computing provides many benefits such as low latency, minimum response time, high mobility, enhanced service quality, location awareness and notification service itself at the edge of the network. In this paper, IoT and fog based healthcare system is proposed to identify and control the outbreak of CHV. Fuzzy-C means (FCM) is used to diagnose the possibly infected users and immediately generate diagnostic and emergency alerts to users from fog layer. Furthermore on cloud server, social network analysis (SNA) is used to represent the state of CHV outbreak. Outbreak role index is calculated from SNA graph which represents the probability of any user to receive or spread the infection. It also generates warning alerts to government and healthcare agencies to control the outbreak of CHV in risk prone or infected regions. The experimental results highlight the advantages of using both fog computing and cloud computing services together for achieving network bandwidth efficiency, high quality of service and minimum response time in generation of real time notification as compared to a cloud only model.

An Automatic Prediction of Epileptic Seizures Using Cloud Computing and Wireless Sensor Networks.

Epilepsy is one of the most common neurological disorders which is characterized by the spontaneous and unforeseeable occurrence of seizures. An automatic prediction of seizure can protect the patients from accidents and save their life. In this article, we proposed a mobile-based framework that automatically predict seizures using the information contained in electroencephalography (EEG) signals. The wireless sensor technology is used to capture the EEG signals of patients. The cloud-based services are used to collect and analyze the EEG data from the patient's mobile phone. The features from the EEG signal are extracted using the fast Walsh-Hadamard transform (FWHT). The Higher Order Spectral Analysis (HOSA) is applied to FWHT coefficients in order to select the features set relevant to normal, preictal and ictal states of seizure. We subsequently exploit the selected features as input to a k-means classifier to detect epileptic seizure states in a reasonable time. The performance of the proposed model is tested on Amazon EC2 cloud and compared in terms of execution time and accuracy. The findings show that with selected HOS based features, we were able to achieve a classification accuracy of 94.6 %.

Temporal Informative Analysis in Smart-ICU Monitoring: M-HealthCare Perspective.

The rapid introduction of Internet of Things (IoT) Technology has boosted the service deliverance aspects of health sector in terms of m-health, and remote patient monitoring. IoT Technology is not only capable of sensing the acute details of sensitive events from wider perspectives, but it also provides a means to deliver services in time sensitive and efficient manner. Henceforth, IoT Technology has been efficiently adopted in different fields of the healthcare domain. In this paper, a framework for IoT based patient monitoring in Intensive Care Unit (ICU) is presented to enhance the deliverance of curative services. Though ICUs remained a center of attraction for high quality care among researchers, still number of studies have depicted the vulnerability to a patient's life during ICU stay. The work presented in this study addresses such concerns in terms of efficient monitoring of various events (and anomalies) with temporal associations, followed by time sensitive alert generation procedure. In order to validate the system, it was deployed in 3 ICU room facilities for 30 days in which nearly 81 patients were monitored during their ICU stay. The results obtained after implementation depicts that IoT equipped ICUs are more efficient in monitoring sensitive events as compared to manual monitoring and traditional Tele-ICU monitoring. Moreover, the adopted methodology for alert generation with information presentation further enhances the utility of the system.

An intelligent system for predicting and preventing MERS-CoV infection outbreak.

MERS-CoV is an airborne disease which spreads easily and has high death rate. To predict and prevent MERS-CoV, real-time analysis of user's health data and his/her geographic location are fundamental. Development of healthcare systems using cloud computing is emerging as an effective solution having benefits of better quality of service, reduced cost, scalability, and flexibility. In this paper, an effective cloud computing system is proposed which predicts MERS-CoV-infected patients using Bayesian belief network and provides geographic-based risk assessment to control its outbreak. The proposed system is tested on synthetic data generated for 0.2 million users. System provided high accuracy for classification and appropriate geographic-based risk assessment. The key point of this paper is the use of geographic positioning system to represent each MERS-CoV users on Google maps so that possibly infected users can be quarantined as early as possible. It will help uninfected citizens to avoid regional exposure and the government agencies to manage the problem more effectively.

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing.

H1N1 is an infectious virus which, when spread affects a large volume of the population. It is an airborne disease that spreads easily and has a high death rate. Development of healthcare support systems using cloud computing is emerging as an effective solution with the benefits of better quality of service, reduced costs and flexibility. In this paper, an effective cloud computing architecture is proposed which predicts H1N1 infected patients and provides preventions to control infection rate. It consists of four processing components along with secure cloud storage medical database. The random decision tree is used to initially assess the infection in any patient depending on his/her symptoms. Social Network Analysis (SNA) is used to present the state of the outbreak. The proposed architecture is tested on synthetic data generated for two million users. The system provided 94% accuracy for the classification and around 81% of the resource utilization on Amazon EC2 cloud. The key point of the paper is the use of SNA graphs to calculate role of an infected user in spreading the outbreak known as Outbreak Role Index (ORI). It will help government agencies and healthcare departments to present, analyze and prevent outbreak effectively.