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Sign language recognition is challenged by problems, such as accurate tracking of hand gestures, occlusion of hands, and high computational cost. Recently, it has benefited from advancements in deep learning techniques. However, these larger complex approaches cannot manage long-term sequential data and they are characterized by poor information processing and learning efficiency in capturing useful information. To overcome these challenges, we propose an integrated MediaPipe-optimized gated recurrent unit (MOPGRU) model for Indian sign language recognition. Specifically, we improved the update gate of the standard GRU cell by multiplying it by the reset gate to discard the redundant information from the past in one screening. By obtaining feedback from the resultant of the reset gate, additional attention is shown to the present input. Additionally, we replace the hyperbolic tangent activation in standard GRUs with exponential linear unit activation and SoftMax with Softsign activation in the output layer of the GRU cell. Thus, our proposed MOPGRU model achieved better prediction accuracy, high learning efficiency, information processing capability, and faster convergence than other sequential models.

Emotion recognition (ER) in healthcare has drawn substantial attention owing to recent advancements in machine-learning (ML) and deep-learning (DL) techniques. The ER system, along with a digital twin of a person in real time, will facilitate the monitoring, understanding, and improvement of the physical entity's capabilities, as well as provide constant input to improve quality of life and well-being for personalized healthcare. However, building such ER systems in real time involves technical challenges, such as limited datasets, occlusion and lighting issues, identifying important features, false classification of emotions, and high implementation costs. To resolve this issue, we built a simple, efficient, and adaptable ER system by acquiring and processing images in real time using a web camera. In addition, we propose an end-to-end framework that combines an ER system with a digital twin setup, in which the predicted result can be analyzed and tested prior to providing the best possible personal healthcare treatment before it leads to any life-threatening disease. Our proposed ER system achieved promising results in less training time without compromising the accuracy. Thus, in real time, it will be helpful in healthcare centers to monitor a patient's health condition, early diagnosis of life-threatening diseases, and to obtain the best and most effective treatment for patients during emergencies.

Extracting useful features at multiple scales is a crucial task in computer vision. The emergence of deep-learning techniques and the advancements in convolutional neural networks (CNNs) have facilitated effective multiscale feature extraction that results in stable performance improvements in numerous real-life applications. However, currently available state-of-the-art methods primarily rely on a parallel multiscale feature extraction approach, and despite exhibiting competitive accuracy, the models lead to poor results in efficient computation and low generalization on small-scale images. Moreover, efficient and lightweight networks cannot appropriately learn useful features, and this causes underfitting when training with small-scale images or datasets with a limited number of samples. To address these problems, we propose a novel image classification system based on elaborate data preprocessing steps and a carefully designed CNN model architecture. Specifically, we present a consecutive multiscale feature-learning network (CMSFL-Net) that employs a consecutive feature-learning approach based on the usage of various feature maps with different receptive fields to achieve faster training/inference and higher accuracy. In the conducted experiments using six real-life image classification datasets, including small-scale, large-scale, and limited data, the CMSFL-Net exhibits an accuracy comparable with those of existing state-of-the-art efficient networks. Moreover, the proposed system outperforms them in terms of efficiency and speed and achieves the best results in accuracy-efficiency trade-off.

Solar energy forecasting accuracy is essential for increasing the quantity of renewable energy that can be integrated into the existing electrical grid control systems. The availability of data at unprecedented levels of granularity allows for the development of data-driven algorithms to improve the estimation of solar energy generation and production. In this paper, we develop a prediction of solar potential across large photovoltaic panels from the roof tops using a machine learning method. The Restricted Boltzmann Machine (RBM) is the machine learning method used in the study to predict or forecast the solar potential in rooftops. The machine learning model is supplied with training dataset to get trained with the dataset for conversion into the model and then tested with the test dataset for validating the model. The results of simulation are conducted on R-package over various libraries to predict the rooftop solar potential. The results of simulation shows that the proposed method achieves higher rate of prediction accuracy than the other methods. The results of the simulation show that the proposed method achieves a higher rate of prediction accuracy of 99% than the other methods.

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Extracting useful features at multiple scales is one of the most crucial tasks in computer vision. Emergence of deep learning(DL) techniques and progress of convolutional neural networks (CNNs) allow powerful multi-scale feature extraction that result in stable performance increase in numerous real-life applications. However, currently available state-of-the-art powerful methods primarily rely on parallel multi-scale feature extraction approach and despite providing competitive accuracy, the models lead to poor results in efficient computation and lack at generalizing on small-scale images. On the other hand, efficient and lightweight networks cannot learn useful features properly and result in underfitting problem in case of training with small-scale images or datasets with limited number of samples. Considering these problems, we proposed a novel image classification system based on elaborate data pre-processing step and carefully designed CNN model architecture. Specifically, consecutive multiscale feature learning network (CMSFL-Net) employs consecutive feature learning approach based on usage of various feature maps with different receptive fields to obtain faster training/inference and higher accuracy. In the conducted experiments using 6 real-life image classification datasets, including small-scale, large-scale, and limited data, the proposed system obtained comparable accuracy when compared with the existing state-of-the-art powerful and efficient networks by outperforming them in terms of efficiency and speed. Moreover, the proposed system achieved the best results in accuracy-efficiency tradeoff in comparison with the currently available models.

Lungs are one of the most vital organs which are responsible for gas exchange during respiration .Lung functions can be compromised in working environment especially at building construction work. The aim of the study was to assess the lung functions of the construction workers. Material and Methods: Adult volunteers who had given consent with a field work experience of minimum 2 years were involved in the study. Validated questionnaire was given to them and the responses were collected .Peak flow meter measurement were also analysed. Results: (65.30%) smokers had a peak flow meter reading of less than 400L and plumbers were the group affected by decreased pulmonary function test followed by mason-helpers. Conclusion: Morbidity among the construction workers varied with type of work at the construction site and comorbid factors such as smoking.