Spices and herbs play an important role in our day to day life with its application varying from flavouring the food to various medicinal uses. But the major limitations linked with these nature oriented spicesare individualized and restricted information about the identification and quality mapping. With increasing demands, adulteration of spices and herbs become a major problem for all the stakeholders . Artificial intelligence based machine learning and deep learning models have already been implemented in the various ways for the identification of herbal images in real time basis. Evidence from past studies related to identification of plants images strengthens our concept for the implementation of the artificial intelligence in the spice sector for the adulteration identification which can become pioneer step in solving the problem of adulteration. There are various opportunities for advancement in producing a robust model for the identification of spices accurately in real time basis. In this review paper, various reliable and efficient machine learning algorithms for herbs and spice image classification has been reviewed. Techniques involved forpreparation of such model have been discussed in details for the better understanding of readers. With inclusion of various globally available herbal image datasets and review of recent research related to plants image identification through machine learning, this article also explains various machine learning model such as artificial neural network, convolutional neural network etc along with different parameters involved in the authentication of the developed model to devise an artificial intelligence based methodology for quality assessment of herbs and spices.

Background: Diabetes mellitus with coexisting hypertension contributes to increased morbidity and mortality. The study aimed to investigate the impact of the patients’ physical activity status and the type of cooking oil consumed by patients in their daily routine on glycemic profile, lipid profile, the hypertensive profile of the patients, and the length of stay, and overall cost of the treatment. Methods: A prospective observational study. All the patients referred to the medicine department of the three different hospitals located in Moga, City Punjab and those hospitalized due to diabetes mellitus (type-I and type-II) with coexisting hypertension were asked to participate in the study. Results: The patients’ mean age was found to be M= 53.85, SD= 11.54 years. Out of 1914 patients, 914 were male (47.8%); it was observed that the majority of the patients 525 (27.43%) in North India using butter or ghee- clarified butter as edible oil, followed by mustard oil 517 (27.01%) patients. About 345 (18.03%) of the patients consume soybean oil, whereas 226 (11.81%) of the patients like sunflower oil. Discussion: This study explored that cooking oil and physical activity are associated with length of stay in days & overall cost of the treatment, respectively. Our study results revealed that the type of oil compared with the treatment’s overall cost was significant for olive oil, soybean oil, and groundnut oil. Conclusion: The study revealed that moderate and low physical activity increases the length of stay compared to high physical activity. The consumption of olive oil as a regular food habit in daily routine decreases patients’ length of stay with diabetes with coexisting hypertension when doing the high physical activity but increases the overall cost of treatment.

Background: The global cases of Covid-19 increasing day by day. On Nov. 25, 2020, a total of 59,850,910 cases reported globally with a 1,411,216 global death. In India, total cases in the country now stand at 91,77,841 including 86,04,955 recoveries and 4,38,667 active cases as of Nov. 24, 2020, as per data issued by ICMR. A new generation of voice/audio analysis application which can tell whether the person is suffering from COVID-19 or not. Aims: To describe how to establish a new generation of voice/audio analysis applications to identify the suspected covid-19 hidden cases in hotspot areas with the help of an audio sample of the general public. Materials & Methods: The different patents and data available as literature on the internet are evaluated to make a new generation of voice/audio analysis application with the help of an audio sample of the general public. Results: The collection of the audio sample will be done from the already suffered covid-19 patients in (.Wave files) personally or through phone calls. The audio samples like the sound of the cough, the pattern of breathing, respiration rate, and way of speech will be recorded. The parameters will be evaluated for loudness, articulation, tempo, rhythm, melody, and timbre. The analysis and interpretation of the parameters can be made through machine learning and artificial intelligence to detect corona cases with an audio sample. Discussion: The voice/audio application current project can be merged with a mobile App called “Aarogya Setu” by Govt. of India. The project can be implemented in the high-risk area of Covid-19 in the country. Conclusion: This new method of detecting cases will decrease the workload in the covid-19 laboratory.

Introduction: The coexistence of diabetes mellitus (DM) and hypertension (HTN) worsen clinical outcomes and contribute to increased morbidity and mortality. Objective: This study aims to analyze the length of stay and healthcare costs by calculating the direct and indirect costs of diabetes with co-existing hypertension in North India. Methods: A prospective observational study was conducted at the medicine department of the three different hospitals. Results: The patients’ mean age was found to be (M=53.8, SD=11.5) years. Out of 1914 patients, 53.65% were found female. Our study revealed that the median cost of medical supplies and equipment was found to be 21.2 $. The median cost of dialysis was found at 47.5 $; the median cost of hospitalization was found to be 142.6 $. The treatment’s median direct cost was 188.5 $, followed by the overall median cost of 295.6 $. The maximum overall cost of treatment was observed at 603.9 $. It was observed that that maximum LOS was found to be 14 days for patients having BPS between 140 to 159 mmHg and BPD between 110- 119 mmHg, and minimum LOS was found to be 3.5 days. Conclusion: The present study highlighted that diabetes co-existing hypertension poses a high economic burden on patients. This study explored that highly significant result for BPS, BPD, FBS, and HbA1c, whereas the significant results were obtained when RBS is compared with LOS and treatment costs. Our study concluded that a mean difference of 9.24 $ in patients having FBS: 261-290 mg/dl and > 290 mg/dL. The LOS increases 6.57 days for patients with BPS between 140-159 mmHg compared to BPS between 180 -above 209 mmHg, which lower treatment costs by -21.31$. Keywords: Diabetes, Hypertension, length of stay, cost of treatment, direct medical cost, indirect medical cost

A document by Amit Sharma. Click on the document to view its contents.