Human emotions are strongly coupled with physical and mental health of any individual. While emotions exbibit complex physiological and biological phenomenon, yet studies reveal that physiological signals can be used as an indirect measure of emotions. In unprecedented circumstances alike coronavirus (Covid-19) outbreak, a remote IoT enabled solution, coupled with AI can interpret and communicate emotions to serve substantially in healthcare and related fields. This work proposes an integrated IoT framework which enables wireless communication of physiological signals to data processing hub where Long Short-Term Memory (LSTM) based emotion recognition is performed. The proposed framework offers real-time communication and recognition of emotions which enables health monitoring and distance learning support amidst pandemics. In this study, the achieved results are very promising. In proposed IoT protocols (TS-MAC and R-MAC) ultra-low latency of 1 millisecond is achieved. R-MAC also offers improved reliability in comparison to state-of-the-art. In addition, the proposed deep learning scheme offers high performance (f-score) of 95%. The achieved results in communications and AI match the interdependency requirements of deep learning and IoT frameworks, thus ensuring the suitability of proposed work in distance learning, student engagement, healthcare, emotion support and general wellbeing.

PurposeThis study is aimed at examining the mediating effect of meaningful work (MFW) between human resource practices (HRP) i.e. staffing, training, participation, performance-based evaluation, and reward with innovative work behavior (IWB) of Indian small and medium-sized enterprise (SME) employees.Design/methodology/approachThis is a cross-sectional study with data of 199 respondents collected from the Indian SME sector. The mediation path was analyzed using multiple hierarchical regression analysis and processes.FindingsResults of the study indicate that human resource practices, i.e. staffing, training and participatory decision making, are positively related to IWB; MFW mediates the relationships between these human resource practices and IWB. Interestingly, performance-based evaluation and reward are not found to be related positively to IWB in SMEs.Originality/valueThe study adds value to SME literature on how SMEs may promote innovation amongst their employees. In addition, the findings of the present study add to human resource management (HRM) literature regarding practices in Indian SMEs.

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Nowadays, overseas commerce has increased drastically in many countries. Plenty fruits are imported from the other nations such as oranges, apples etc. Manual identification of defected fruit is very time consuming. This work presents a novel defect segmentation of fruits based on color features with K-means clustering unsupervised algorithm. We used color images of fruits for defect segmentation. Defect segmentation is carried out into two stages. At first, the pixels are clustered based on their color and spatial features, where the clustering process is accomplished. Then the clustered blocks are merged to a specific number of regions. Using this two step procedure, it is possible to increase the computational efficiency avoiding feature extraction for every pixel in the image of fruits. Although the color is not commonly used for defect segmentation, it produces a high discriminative power for different regions of image. This approach thus provides a feasible robust solution for defect segmentation of fruits. We have taken apple as a case study and evaluated the proposed approach using defected apples. The experimental results clarify the effectiveness of proposed approach to improve the defect segmentation quality in aspects of precision and computational time. The simulation results reveal that the proposed approach is promising

Vision-based human activity recognition is the process of labelling image sequences with action labels. Accurate systems for this problem are applied in areas such as visual surveillance, human computer interaction and video retrieval. The challenges are due to variations in motion, recording settings and gait differences. Here the authors propose an approach to recognize the human activities through gait. Activity recognition through Gait is the process of identifying an activity by the manner in which they walk. The identification of human activities in a video, such as a person is walking, running, jumping, jogging etc are important activities in video surveillance. The authors contribute the use of Model based approach for activity recognition with the help of movement of legs only. Experimental results suggest that their method are able to recognize the human activities with a good accuracy rate and robust to shadows present in the videos.