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Human-mobile interaction is aimed at facilitating interaction with the smartphone devices. The conventional way to interact with mobile devices is through manual input where most of the applications are made assuming that the end user has full control over their hand movements. However, this assumption excludes people who are unable to use their hands or have suffered limb damage. In this paper, we proposed a nose and teeth based interaction system, which allows the users to control their mobile devices completely hands free. The proposed system uses the front facing camera of the smartphone to track the position of the nose for cursor control on the smartphone screen. The system detects teeth for performing the touch screen events such as tap, scroll, long press, and drag. Viola-Jones algorithm is used to detect the face and teeth based on the Haar features. After detecting the face, the nose position is calculated and tracked continuously using Lucas Kanade’s method for optical flow estimation. All the touch screen events have been implemented in the system so that the user can execute all the operations of the smartphone. To evaluate the performance and the effect of (smartphone) device type on the execution time, the proposed system was installed in 3 smartphone devices and 7 trials for each device were performed by 3 different able-bodied elderly persons. The result shows a significant success rate for the detection of nose and teeth, and for the execution of the operations. The execution time of each operation slightly varies by 0.72s on average because of the configuration of the smartphones.

The aim of this article is to propose a conceptual framework for a system, using which a severely ill or disabled patient admitted in a hospital cabin can exert some control on the surrounding environment using only his/her thoughts. A brain computer interface (BCI) headset can be used to collect Electroencephalographic (EEG) data from the brain and an artificial neural network can be used to process the EEG data and predict what the patient is thinking. The proposed system will provide immense benefits to the patients admitted in hospital cabins, especially in developing countries, where the doctor to patient and nurse to patient ratio is really low and a patient admitted in a hospital cabin is usually left unattended for a long period of time.

Diabetes is a non-communicable disease and various types of dangerous diseases like heart attack, kidney failure, myopia, and so on are caused by it. The number of people suffering from diabetes is increasing rapidly. Though there has no perpetual cure for diabetes, it can be controlled by proper counseling and medication. For this perception, an early determination is needed. In our analysis, 464 patients data with 23 features were collected from various health-care units and preprocessed. A predictive model was developed with artificial neural network technique. Different learning rate, hidden layers were applied in our analysis. Average-weighted accuracy of all observations was approximately 99.69%.

Software Defined Networking (SDN) decouples the control plane from the data plane, thereby enhancing flexibility in network management. Earlier works on SDN modeling only focused on packet-level arrivals without considering flow-level arrivals. However, a model without considering flow-level arrivals cannot correctly reflect the probability of sending packets to the controller. In this paper, we propose an analytical model of SDN considering flow-level (TCP connection) arrivals and packet-level arrivals simultaneously. We use an analytical method termed as 4D state model, which uses four-dimensional states. We have derived the state transition rates of the model and also the packet delay and packet loss probability. We have conducted numerical analysis and extensive simulations. Our results show good matches, which verify the suitability and correctness of our analysis. Error ratios of the analytical results for 4D state model and M/M/1 model against simulation results are also given. Results show that for data packet delays, 4D state can achieve error ratios of 1.16-3.30%, which is much better than 8.57-35.7% attained by M/M/1 model, which only considers packet-level arrivals.