BACKGROUND: In recent years, a lot of research work has been done to transmit vital parameters of the patient using IOT (Internet of Things). The information related to Patient is sent by means of WBAN (Wireless Body Area Network) and sensors to the healthcare database. This has been shown a great potential to transmit vital parameter of the patient at a remote location. Remote monitoring of patient(s), including vital signs, sound and video is to be transmitted, particularly when a patient is in transit. By transmitting the imperative parameters of the patient to a specialist diminishes, the ideal opportunity for starting treatment and permits the emergency crew to be better prepared. This will facilitate a doctor to make a diagnostic conclusion.

OBJECTIVES: This study aims to develop an advance system to transmit the vital parameters of the patient to the smart phone of the doctor at a remote location. After seeing essential parameters of the patient, the specialist can convey/endorse the medication from the remote area.

METHODS: To monitor patient’s data in real-time an android application is developed which helps the medical staff to monitor the patient from remote location. All the patient related information is stored in Real Time Clinical Database.

RESULTS: The proposed model will allow the doctors to monitor physiological parameters (HR, NIBP, Temp, ECG, SPO2) of the patient, as well as endorse medication from the remote location. This system will be helpful to provide virtual presence of the doctor inside the ambulance and to provide lifesaving drugs to the patient while the patient is in transit.

CONCLUSION: The present invention provides a fully integrated system for the doctor to view the vital signs of the patient from the remote location in real-time.

INTRODUCTION: Parkinson's disease (PD) occurs due to the deficiency of dopamine that regulates various activities of the human body. Researchers have identified that voice is an underlying symptom of PD. Recently, Machine learning (ML) has helped in solving problems of computer vision, natural language processing, speech recognition etc.

OBJECTIVES: This paper aims to analyse the effect of feature type selection i.e. MFCC and TQWT on the efficiency of voice based PD detection system along with the use an ensemble learning based classifier for this task.

METHODS: Hence, in this work, various machine learning models, including Logistic Regression, Naive Bayes, KNN, Random Forest, Decision Tree, SVM, MLP, and XGBoost, have been employed and explored for PD detection purpose. The task of Feature selection was also done using minimum-Redundancy and Maximum-Relevance (mRMR) and Recursive Feature Elimination (RFE) techniques.

RESULTS: The results of the XGBoost with mRMR feature selection, outperformed all other models with a high accuracy of 95.39% and precision, recall and F1-score of 0.95 each, when both MFCC and TQWT features were selected.

CONCLUSION: The results obtained strongly support the use of XGBoost model for the voice sample based detection of PD along with mRMR feature selection technique.

Capturing the complex correlations among the data on the crude oil price time series is challenging, hence accurate prediction of it is difficult. Contrast to multilayer artificial neural network, Pi-Sigma neural network (PSNN) is characterized with stronger approximation ability, fast learning and higher fault tolerance. Fireworks algorithm (FWA) is a new metaheuristic motivated from the occurrence of fireworks explosion, characterized with fast convergence capability, parallelism and finds global optima. This article intends to achieve the synergetic effect of both on hybridizing them. It uses FWA for optimization of weight and bias of PSNN (FWA-PSNN) and overcomes the limitations of gradient based training. It has single hidden layer structure and trainable weight, hence fast and robust. The FWA-PSNN is evaluated on prediction of crude oil price time series. Extensive simulation results, performance analysis, and statistical significance test suggested the suitability of FWA-PSNN.

INTRODUCTION: There are several online mapping systems like Google Maps, Waze, Here, Apple Maps, Bing Maps, etc. which are developed to visualize real-time traffic conditions which rely on crowdsourced GPS trails; obtained from worldwide smartphone users. Such systems still suffer from some limitations like a) inadequate traffic information in suburban cities and rural zones, b) system failure to infer the proper reasons for slow traffic state, c) difficulties in the extraction of raw traffic data for further development of any customized application. Significant spatio-temporal similarity patterns are observed in city traffic behavior unless there are some exceptional events like any disaster, VIP visit, international cricket match or bad weather condition, etc.

OBJECTIVES: Designing a framework and developing a system which enables collection of raw sensor information from users and to identify a model to generate a speed profile of city roads using historical logs as well as to infer the context of slow traffic based on ambient subjective road features and to provide map visualization.

METHODS: We have used road surface quality, density of vehicles, type of neighborhood and road geometry for developing speed profile for a particular road segment. We have carried out the experiments on different classification algorithms like, K-nearest Neighbor(KNN), Decision Tree(DT), Random Forest(RF) and Gradient Boost(GB) with necessary tuning of parameters.

RESULTS: GB outperforms other classification algorithms in estimating the speed class of road segments among all classifier algorithms with highest F1- Score of 0.8345. A fair driver rating system which can be derived from our results.

CONCLUSION: The results obtained from the proposed novel framework provide a proof of concept that speed profiles may be successfully derived from ambient road features even when sample space is sparse.

INTRODUCTION: A number of promising designs of electric vehicles use separate wheeled motors. In this case, an important task of designing a power supply system is to provide effective control of electric motors and battery charge / discharge modes.

OBJECTIVES: The paper considers the problem of determining optimal coefficients of the electric motor proportionalintegral (PI) controller and their influence on the power distribution in the electric vehicle on-board power supply system.

METHODS: It is proposed to implement separate adaptive control of electric motors, taking into account conditions of operating, road surface, and other factors. There are introduced two options for the motor controller implementation: an adaptive PI-controller and an intelligent PI-controller with an adaptive observer based on a neural network.

RESULTS: The simulation results show that the adaptive PI-controller provides a reduction in the transient duration, but insufficient energy efficiency. Intelligent PI controller on the base of neuroregulator provides 2 times reduction of transition time, reduction of energy losses and engine overshoot.

CONCLUSION: The use of the neuroregulator makes it possible to automatically select and adjust PI controller coefficients. In addition, the proposed control method reduces inrush currents and torque spikes, that prolongs the service life of mechanical components. During motor operation, the neural network can continue learning and adjusting PIcontroller coefficients to changes in operating conditions (for example, seasonal) and motor parameters. Assumed outcomes of this solution will be improving electric vehicle characteristics, increasing mileage and battery life time, and prospective transition to an electronic differential.

The viewing of categories of facial emotions is predicted using features of viewer’s scalp potentials, such as event-related potentials (ERPs) measured during the viewing of pictures of facial emotions. All visual stimuli were rated using two-dimensional emotional scales, and the responses for each viewer were converted into sensitivities using item response theory (IRT). This sensitivity to facial emotions can be predicted using discrimination analysis and the extracted features of ERPs recorded during the viewing of the images. The categories of facial emotions viewed were estimated to a certain level of significance using regression analysis, and the sensitivities predicted for the emotional scales were calculated accurately, as performance depended on the reactions to the images of the emotions. The results showed that categories of facial emotions viewed can be predicted to a level of significance using features of scalp potentials.

Proactive password checkers have been widely used to persuade users to select stronger passwords by providing machine-generated strength ratings of passwords. If such ratings do not match human-generated ratings of human users, there can be a loss of trust in PPCs. In order to study the effectiveness of PPCs, it would be useful to investigate how human users perceive such machine- and human-generated ratings in terms of their trust, which has been rarely studied in the literature. To fill this gap, we report a large-scale crowdsourcing study with over 1,000 workers. The participants were asked to choose which of the two ratings they trusted more. The passwords were selected based on a survey of over 100 human password experts. The results revealed that participants exhibited four distinct behavioral patterns when the passwords were hidden, and many changed their behaviors significantly after the passwords were disclosed, suggesting their reported trust was influenced by their own judgments.

Clustering trajectories from GPS data is a crucial task for developing applications in intelligent transportation systems. Most existing approaches perform clustering on raw data consisting of series of GPS positions of moving objects over time. Such approaches are not suitable for classifying moving behaviours of vehicles, e.g., how to distinguish between a trajectory of a taxi and a trajectory of a private car. In this paper, we focus on the problem of clustering trajectories of vehicles having the same moving behaviours. Our approach is based on histogram-based feature extraction to model moving behaviours of objects and utilizes traditional clustering algorithms to group trajectories. We perform experiments on real datasets and obtain better results than existing approaches.

In image compression and video coding, quantization error helps to reduce the amount of information of the high frequency components. However, in temporal prediction the quantization error contributes its value as noise in the total residual information. Therefore, the residual signal of the inter-picture prediction is greater than the expected one and always differs zero value even input video contains only homogeneous frames. In this paper, we reveal negative effects of quantization errors in inter prediction and propose a video encoding scheme which is able to avoid side effects of quantization errors in the stationary parts. We propose to implement a motion detection algorithm as the first stage of video encoding to separate the video into two parts: motion and static. The motion information allows us to force residual data of non-changed part to zero and keep the residual signal of motion regularly. Beside, we design block-based filters which improve motion results and filter those results fit into block encode size well. Fixed residual data of static information permits us to pre-calculate its quantized coefficient and create a bypass encoding path for it. Experimental results with the JPEG compression (MJPEG-DPCM) showed that the proposed method produces lower bitrate than the conventional MJPEG-DPCM at the same quantization parameter and a lower computational complexity.

Under-Five Mortality Rate (U5MR) is an important indicator because it reflects the socio-economic conditions and developments in health sector. U5MR is obtained from Demographic and Health Survey (DHS) where the level of estimation is designed for national and provincial level. The decentralization system makes the importance of U5MR for sub-domain of province such as district/municipality level. Small area estimation (SAE) can be used for estimating U5MR in district/municipality level by using a mixed model. The model that is often used is generalized linear mixed model (GLMM). Direct estimation of U5MR produces a large proportion of zero values (excess zero), so the Poisson model is not suitable for modeling the data. Excess zero is the reason for violating the equidispersion in Poisson model. In this study, quasi Poisson modelproduces better predictions than direct estimation. In addition, the U5MR estimation for municipality makes it possible to produce U5MR maps in municipality level.