Urinary incontinence (UI) is still a common chronic health problem affecting physical, psychological and social well-being of women in developing countries. UI is a challenge to women's health because of the number affected and lack of access to affordable care that can cure or relieve associated symptoms especially in resource limited settings. This study explored the prevalence of UI and assessed effectiveness of a Video Assisted Teaching Program for Kegel's Exercises (VATPKE) in reducing severity of UI symptoms among community dwelling women. A survey was used to obtain data from a sample of 598 community dwelling Indian women in Coimbatore district in Tamilnadu State. A pre-posttest design was then used to assess effectiveness of the VATPKE in reducing UI symptom severity in affected women. Data were analysed using a paired samples t-test. Of the 598 women, 202 (34%) reported having some level of UI and most participants affected were married (78%), less educated (56%), had high BMI (52%) and lower socio-economic status. Affected women mostly reported the severity level of UI symptoms at pre-intervention as moderate (78%) or mild (22%).The mean post-intervention UI symptoms severity score (M = 21·72, SD = 3·99) was lower than pre-intervention (M = 29·91, SD = 5·12) and paired t-test results showed that the difference was highly statistically significant (p < 0·00). The VATPKE used in this study was effective in reducing the severity of self-reported UI symptoms in community dwelling Indian women.

In today's scenario, e-learning has become a significant part of the academic environment as well as of the corporate training sectors. Advancement in Information and Communication Technologies (ICTS) has brought new intersection of education, teaching, and learning that defines e-learning. E-learning systems deliver information for education at any time and at any place in an efficient manner. E-learning system consists of course content or learning materials in the form of nodes. These nodes are linked such that users can traverse the other nodes in the hypermedia environment. These learning concepts are available synchronously and asynchronously in different ways of representation. This presents learning materials in a disorganized manner to the learners. Due to this, learners may decline to adapt the learning material or may deviate from their goals. This requires a user model to respond to different needs of a learner. To handle the uncertainty of learner's mind while learning the concepts an intuitionistic fuzzy approach is used.

Sustainable energy is a significant power generation resource for a cleaner and CO2 free environment. Out of different renewable energies out there, wind energy is rapidly growing sector and integrated to power grid. However, uncertainty,stochastic and non stationary nature of meteorological features, on which wind power depends, makes it difficult to predict accurately. Efficiency of wind farms and the power grid is directly proportional to efficient wind power predictive analytics. This study describes a hybrid model named PowerNet for improving the predicted accuracy in the field of wind power analytics. The improved hybrid model is a combination of Convolution 1 Dimensional and Bidirectional Long short memory (BiLSTM) models. Firstly, Conv-1D layers extract the spatial features of timestamped data sequentially. Then the output generated by multiple convolution operations at the nested layers is embedded with BiLSTM to work on the temporal characteristics of wind power data. The nesting of spatial and temporal extractor generates a novelarchitecture, Powernet for wind power forecasting from raw data. The effectiveness of powernet has been validated on real time wind power NREL dataset. Also, error and computational analysis has been conducted for short-term wind power forecasting with an ensemble of LSTM based models. The comparative analysis demonstrates that the proposed model powernet achieves better prediction than traditional deep learning standalone and hybrid models. Also, the statistical models are compared to show the raw data needs to be pre-processed when conventional models are applied. However, Powernet does not need the overhead of pre-processing for generating better predictions.

In today's scenario, e-learning has become a significant part of the academic environment as well as of the corporate training sectors. Advancement in Information and Communication Technologies (ICTS) has brought new intersection of education, teaching, and learning that defines e-learning. E-learning systems deliver information for education at any time and at any place in an efficient manner. E-learning system consists of course content or learning materials in the form of nodes. These nodes are linked such that users can traverse the other nodes in the hypermedia environment. These learning concepts are available synchronously and asynchronously in different ways of representation. This presents learning materials in a disorganized manner to the learners. Due to this, learners may decline to adapt the learning material or may deviate from their goals. This requires a user model to respond to different needs of a learner. To handle the uncertainty of learner's mind while learning the concepts an intuitionistic fuzzy approach is used.