Analysis of digital volume pulse (DVP) signal measured by photoplethysmograph (PPG) technique is a low cost non-invasive method of obtaining vital information related to arterial conditions. In this paper, we present a new two-pulse synthesis (TPS) model for deriving arterial parameters, useful for noninvasive assessment of human vascular health. The model is based on the use of Rayleigh function. Relevance of the proposed model is established by applying it on a sample set of 113 PPG signals, obtained form healthy and treated hypertensive subjects. The TPS model compares well with the conventional methods in determining parameters such as pulse transit time or foot-to-foot delay (D), reflection index (RI), stiffness index (SI) and pulse wave velocity (PWV). A new parameter, viz. differential pulse spread (DPS) has also been introduced for DVP signals using the model. The differential pulse spread provides a new dimension to estimate the process of arterial degeneration.

Accurate and real-time product demand forecasting is the need of the hour in the world of supply chain management. Predicting future product demand from historical sales data is a highly non-linear problem, subject to various external and environmental factors. In this work, we propose an optimised forecasting model - an extreme learning machine (ELM) model coupled with the Harris Hawks optimisation (HHO) algorithm to forecast product demand in an e-commerce company. ELM is preferred over traditional neural networks mainly due to its fast computational speed, which allows efficient demand forecasting in real-time. Our ELM-HHO model performed significantly better than ARIMA models that are commonly used in industries to forecast product demand. The performance of the proposed ELM-HHO model was also compared with traditional ELM, ELM auto-tuned using Bayesian Optimisation (ELM-BO), Gated Recurrent Unit (GRU) based recurrent neural network and Long Short Term Memory (LSTM) recurrent neural network models. Different performance metrics, i.e., Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Percentage Error (MPE) were used for the comparison of the selected models. Horizon forecasting at 3 days and 7 days ahead was also performed using the proposed approach. The results revealed that the proposed approach is superior to traditional product demand forecasting models in terms of prediction accuracy and it can be applied in real-time to predict future product demand based on the previous week’s sales data. In particular, considering RMSE of forecasting, the proposed ELM-HHO model performed 62.73% better than the statistical ARIMA(7,1,0) model, 40.73% better than the neural network based GRU model, 34.05% better than the neural network based LSTM model, 27.16% better than the traditional non-optimised ELM model with 100 hidden nodes and 11.63% better than the ELM-BO model in forecasting product demand for future 3 months. The novelty of the proposed approach lies in the way the fast computational speed of ELMs has been combined with the accuracy gained by tuning hyperparameters using HHO. An increased number of hyperparameters has been optimised in our methodology compared to available models. The majority of approaches to improve the accuracy of ELM so far have only focused on tuning the weights and the biases of the hidden layer. In our hybrid model, we tune the number of hidden nodes, the number of input time lags and even the type of activation function used in the hidden layer in addition to tuning the weights and the biases. This has resulted in a significant increase in accuracy over previous methods. Our work presents an original way of performing product demand forecasting in real-time in industry with highly accurate results which are much better than pre-existing demand forecasting models.

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This paper presents a non-invasive, preventive approach to measure arterial parameters by analysis of photo plethysmograph (PPG) signals. A recently proposed Two Pulse Synthesis (TPS) model [Goswami 2010a] has been used to measure pulse wave velocity (PWV) and several new parameters of normal and diseased subjects having hypertension or diabetes or both. Results, presented through tables and figures, indicate the merits of TPS model. A scheme for integration of cardiac care for rural people has been outlined. Longitudinal studies using the TPS model has also been advocated as a part of cardiac care supported by a set of experimental results.