Diabetes mellitus is a metabolic disorder characterized by a relative or total insulin deficit and hyperglycemia (Karunasagara et al., 2020). Diabetic patients suffer from numerous disorders, including vascular illness, like atherosclerosis, neuropathic pain, and diabetes neuropathy (Papatheodorou et al., 2018). According to the report of 2019, 463 million individuals between the ages of 20 and 79 are estimated to have diabetes globally (Saeedi et al., 2019). By 2045, the number of affected people with diabetes is expected to increase to 700 million worldwide. Bangladesh has been in the top ten positions in the world for the number of adults (20-79 years) with diabetes (8.4 million) in 2019. The prevalence of diabetes will be increased to 15.0 million by 2045. Bangladesh has also been ranked nine in 2019 among the countries for 4.7 million adults with undiagnosed diabetes (Atlas, 2015;

This research was designed to evaluate the CNS depressant, anxiolytic, and analgesic action of aqueous and ethanol extract of Ganoderma applanatum , a valuable medicinal fungus used in multiple disorders belongs to Ganodermataceae family. Two extracts of G. applanatum were prepared using distilled water and ethanol as solvents and named AEGA and EEGA. Open field method, rotarod method, tail suspension method, and hole cross method were utilized for the CNS depressant action. In contrast, elevated plus-maze test and hole board method were utilized for the anxiolytic action. For determining the analgesic potential, acetic acid-induced writhing test, hot plate method, and tail immersion test were used. Besides, molecular docking has been implemented by using Discovery studio 2020, UCSF Chimera and PyRx autodock vina. At both doses (200 and 400 mg/kg) of AEGA and EEGA showed significant CNS depressant effect ( p < 0.05 to 0.001) against all four tests used for CNS depressant activity. Both doses of AEGA and EEGA exhibited important anxiolytic activity effect ( p < 0.05 to 0.001)against the EPM and hole board test. Both doses of AEGA and EEGA also exhibited a potential analgesic effect ( p < 0.05 to 0.001) against all three tests used for analgesic action. In addition, in the molecular docking the compounds obtained the scores of −5.2 to −12.8 kcal/mol. Ganoapplanin, sphaeropsidin D and cytosporone C showed the best binding affinity to the selected recptors. It can be concluded that AEGA and EEGA have potential CNS depressant, anxiolytic, and analgesic action, which can be used as a natural antidepressant, anxiolytic, and analgesic source.

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Automation in every part of life has become a frequent situation because of the rapid advancement of technology, mostly driven by AI technology, and has helped facilitate improved decision-making. Machine learning and the deep learning subset of AI provide machines with the capacity to make judgments on their own through a continuous learning process from vast amounts of data. To decrease human mistakes while making critical choices and to improve knowledge of the game, AI-based technologies are now being implemented in numerous sports, including cricket, football, basketball, and others. Out of the most globally popular games in the world, cricket has a stronghold on the hearts of its fans. A broad range of technologies are being discovered and employed in cricket by the grace of AI to make fair choices as a method of helping on-field umpires because cricket is an unpredictable game, anything may happen in an instant, and a bad judgment can dramatically shift the game. Hence, a smart system can end the controversy caused just because of this error and create a healthy playing environment. Regarding this problem, our proposed framework successfully provides an automatic no-ball detection with 0.98 accuracy which incorporates data collection, processing, augmentation, enhancement, modeling, and evaluation. This study starts with collecting data and later keeps only the main portion of bowlers’ end by cropping it. Then, image enhancement technique are implied to make the image data more clear and noise free. After applying the image processing technique, we finally trained and tested the optimized CNN. Furthermore, we have increased the accuracy by using several modified pretrained model. Here, in this study, VGG16 and VGG19 achieved 0.98 accuracy and we considered VGG16 as the proposed model as it outperformed considering recall value.

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