India is believed to be crowned as the diabetes capital of the world by 2030. Considering the same, the present study was undertaken to elucidate the antidiabetic potential of selected Indian spices, viz., Cinnamomum zeylanicum Blume, Cuminum cyminum L., Laurus nobilis L., Piper nigrum L. and Elettaria cardamomum (L.) Maton. Phytochemical constituents of the spices were qualitatively identified and enzyme inhibitory assays performed. The study used 50% hydroacetone extracts of the above-mentioned five spices to evaluate in vitro α-amylase and α-glucosidase inhibitory activities. The IC 50 for α-amylase inhibitory activity were 706.

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Clinical depression is a type of soft biometric trait that can be used to characterize a person. Because of its importance in a variety of legal situations, this mood illness can be included in forensic psychological evaluations. In recent years, research into the automatic detection of depression based on medical data has yielded a variety of algorithmic approaches and auditory indicators. Machine learning algorithms have recently been used successfully in a variety of applications. Automatic depression recognition - the recognition of expressions linked with sad behavior is one of the most important applications. Modern algorithms for detecting depression usually look at both geographical and temporal data separately. This research introduces a novel machine learning strategy for accurately representing face information associated to depressive behaviors from real-world medical data. Our suggested architecture outperforms state- of-the-art algorithms in automatic depression recognition, according to results from two benchmark datasets. Keywords: Depression recognition, deep learning, deep neural network.

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