A rudimentary aspect of human life is the health of an individual,and most commonly the wellbeing is impacted in a colossal manner through the consumption of food. The intake of calories therefore is a crucial aspect ...A rudimentary aspect of human life is the health of an individual,and most commonly the wellbeing is impacted in a colossal manner through the consumption of food. The intake of calories therefore is a crucial aspect that must be meticulously monitored. Various health gremlins can be largely circumvented when there is a substantial balance in the number of calories ingested versus the quantity of calories expended.The food calorie estimation is a popular domain of research in recent times and is meticulously analyzed through various image processing and machine learning techniques. However,the need to scrutinize and evaluate the calorie estimation through various platforms and algorithmic approaches aids in providing a deeper insight on the bottlenecks involved,and in improvising the bariatric health of an individual. This paper pivots on comprehending a juxtaposed approach of food calorie estimation through the use of employing Convolution Neural Network(CNN)incorporated in Internet of Things(IoT),and using the Django framework in Python,along with query rule-based training to analyze the subsequent actions to be followed post the consumption of food calories in the constructed webpage. The comparative analysis of the food calorie estimate implemented in both platforms is analyzed for the swiftness of identification,error rate and classification accuracy to appropriately determine the optimal method of use. The simulation results for IoT are carried out using the Raspberry Pi4B model,while the Anaconda prompt is used to run the server holding the web page.展开更多
Objective:To screen the chitosan producing ability of endolichenic fungi and its antibacterial activity.Methods:Lichen collected from mangroves was screened for endophytes and the chitosan producing ability of endolic...Objective:To screen the chitosan producing ability of endolichenic fungi and its antibacterial activity.Methods:Lichen collected from mangroves was screened for endophytes and the chitosan producing ability of endolichenic fungi by submerged fermentation was also determined.Antibacterial activity was carried out against different pathogens.Results:Totally4 different groups of fungi were isolated from the lichen Roccella montagnei.Among the four genera,Aspergillus niger(A.niger)is potential to produce chitosan(1.3 g/L)on the twelfth day of incubation.Glucose plays an important role in the pnjductivity of chitosan and the yield was maximum at 10%(1.93 g/L).Antibacterial activity revealed that Vibrio cholerae was sensitive to chitosan followed by Escherichia coli.Conclusions:In conclusion,our findings suggest that A.niger is a potential candidate to produce more chitosan than the other strains and glucose plays an important role in the production of chitosan which proves to have a good antibacterial activity.展开更多
文摘A rudimentary aspect of human life is the health of an individual,and most commonly the wellbeing is impacted in a colossal manner through the consumption of food. The intake of calories therefore is a crucial aspect that must be meticulously monitored. Various health gremlins can be largely circumvented when there is a substantial balance in the number of calories ingested versus the quantity of calories expended.The food calorie estimation is a popular domain of research in recent times and is meticulously analyzed through various image processing and machine learning techniques. However,the need to scrutinize and evaluate the calorie estimation through various platforms and algorithmic approaches aids in providing a deeper insight on the bottlenecks involved,and in improvising the bariatric health of an individual. This paper pivots on comprehending a juxtaposed approach of food calorie estimation through the use of employing Convolution Neural Network(CNN)incorporated in Internet of Things(IoT),and using the Django framework in Python,along with query rule-based training to analyze the subsequent actions to be followed post the consumption of food calories in the constructed webpage. The comparative analysis of the food calorie estimate implemented in both platforms is analyzed for the swiftness of identification,error rate and classification accuracy to appropriately determine the optimal method of use. The simulation results for IoT are carried out using the Raspberry Pi4B model,while the Anaconda prompt is used to run the server holding the web page.
基金supported by Ministry of Environment and Forests.Govt.of India(grant No.22-9/2008-CS-1)
文摘Objective:To screen the chitosan producing ability of endolichenic fungi and its antibacterial activity.Methods:Lichen collected from mangroves was screened for endophytes and the chitosan producing ability of endolichenic fungi by submerged fermentation was also determined.Antibacterial activity was carried out against different pathogens.Results:Totally4 different groups of fungi were isolated from the lichen Roccella montagnei.Among the four genera,Aspergillus niger(A.niger)is potential to produce chitosan(1.3 g/L)on the twelfth day of incubation.Glucose plays an important role in the pnjductivity of chitosan and the yield was maximum at 10%(1.93 g/L).Antibacterial activity revealed that Vibrio cholerae was sensitive to chitosan followed by Escherichia coli.Conclusions:In conclusion,our findings suggest that A.niger is a potential candidate to produce more chitosan than the other strains and glucose plays an important role in the production of chitosan which proves to have a good antibacterial activity.