Four new polyoxygenated steroids (1–4) together with four known ones (5–8) have been isolated from the gorgonian Isis hippuris. The structures of the new compounds have been elucidated by spectroscopic analysis and chemical conversion. All of the new steroids showed moderate cytotoxicity against cultured NBT-T2 cells.

Neural network pruning methods can decrease the parameter counts of trained neural networks along with improving the computational performance of inference without compromising the accuracy. This work has demonstrated the effectiveness of the neural network pruning methodology, which was applied to deep learning-based human activity recognition. A long short-term memory (LSTM) architecture was used to recognize daily human activities from smartphone-based accelerometer data. An android-based application has been developed to collect the accelerometer data from smartphones. Data has been preprocessed before training the LSTM network. The accuracy of the proposed LSTM model was 97.2% while recognizing the human activities. Neural network pruning was applied to the model once the model was trained. After 70% of weight pruning on the trained/initial network, the Pruned network accuracy was similar (97.2%) as the original one. However, the accuracy of the pruned network was better than the initial model, while the network has been pruned by 25% (accuracy 97.26%), 50% (accuracy 97.29%), and 60% (accuracy 97.22%). The initial model started to slightly decline the accuracy (from 97.2% to 96.72%), while the model was pruned by 80%. In terms of neural/unit pruning, slightly declination in the accuracy (97.2% to 97.09%) occurred after 50% of pruning.

Cognitive impairment has a significantly negative impact on global healthcare and the community. Holding a person’s cognition and mental retention among older adults is improbable with aging. Early detection of cognitive impairment will decline the most significant impact of extended disease to permanent mental damage. This paper aims to develop a machine learning model to detect and differentiate cognitive impairment categories like severe, moderate, mild, and normal by analyzing neurophysical and physical data. Keystroke and smartwatch have been used to extract individuals’ neurophysical and physical data, respectively. An advanced ensemble learning algorithm named Gradient Boosting Machine (GBM) is proposed to classify the cognitive severity level (absence, mild, moderate, and severe) based on the Standardised Mini-Mental State Examination (SMMSE) questionnaire scores. The statistical method “Pearson’s correlation” and the wrapper feature selection technique have been used to analyze and select the best features. Then, we have conducted our proposed algorithm GBM on those features. And the result has shown an accuracy of more than 94%. This paper has added a new dimension to the state-of-the-art to predict cognitive impairment by implementing neurophysical data and physical data together.

The Deep Neural Networks (DNN) are computationally intensive in terms of processing, energy and memory which becomes a bottleneck to run these models on edge devices. This research study provides a technique for pruning the neural networks to enhance the performance of deep learning models in IoT devices. The proposed method combines magnitude-based pruning, which merges insignificant weights based on their magnitude, with node pruning, which eliminates insignificant nodes based on their contribution to the network. The hybrid pruning technique is designed to be energy-efficient, reducing the computational overhead of deep learning models while maintaining their accuracy. The experimental results demonstrate that the proposed method can achieve significant reductions in model size and energy consumption with minimal loss in accuracy. The technique has the potential to enable the deployment of deep learning models on resource constrained IoT devices.

Recognition of activities by the use of the sensor is one of the context-conscious studies fascinated by many research teams. This paper explains how different types of physical activities are recognized using cell phone’s motion sensors data. Three types of motion sensors (accelerometer, gravity, linear acceleration) data were used for this work. An effective and efficient deep neural network model was proposed for this work that is able to recognize human physical activities from tri-axial motion sensor data in real-time which was later implemented on android-based smartphones using android studio. The sequential model was implemented for this work where LSTM, flatten and the dense layer was added. The model is able to classify seven types of human activities in the real-time data feed. The model achieves 98.8% of classification accuracy while training and testing. Later on, the model has been converted to a smartphone-compatible model using Tensorflow, as the initial deep learning model isn’t compatible to insert into smartphones. The model was successfully converted and implemented to an android based smartphone.