Volume 10 | Issue 8 |

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Perceptions, Teaching Profession, Quality education, Secondary school teachers

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Samshodhaneya Uddesha ,Vyapti,Antara, Vimarshe.

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Abstract- It is evolving healthcare systems from conventional to more personalized healthcare systems through which patients can be diagnosed, treated, and monitored more easily. The current global challenge of the pandemic caused by the novel severe respiratory syndrome corona virus 2 presents the greatest global public health crisis since the pandemic influenza outbreak of 1918. Our goal in this study is to determine the role of IoT-based machine learning technologies in COVID-19 tracking and control and review the state-of-the-art architectures, platforms, applications. As the entire world continues fighting the same enemy, consumers and organizations are embracing new technologies and discovering their perks. IoT sensor technologies have played a major role. One of the best Machine Learning for Healthcare applications is a internet bot system that makes the treatment period much easier. A virtual nurse for patients acts as a voice-controlled healthcare assistant that provides information on many illnesses, health disorders, and medicines. An AI assistant is a very handy thing if the patient needs real-time advice and it might be difficult for him or her to get to the doctor. Data engineers are working on solutions for all medical activities that deal with overall health monitoring as well as helping cure or even prevent disease.

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Machine Learning disease diagnosis

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1. Cashless economy. 2. Corruption. 3. Black money. 4. Digitalised Economy.

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speaking them the scholarship of Kaundinya and other four associate are occasionally met in course of deliberations of the Buddha as the tripitaka referred those five members were in touch of other contemporary eminent mendicants like Sanjaya Velatti putta, Purana Kashyapa, Maskari Goshayia, Niggantha Nathaputta, Keshakambali.(N.Datta-3) Those were known as Agrashr?vakas, foremost hearers to Buddha. They had been expert meditation practitioners and contemporary of Gautama Buddha before the awakened. Among the disciples of Buddha like Sari putra, Maudgalyana who had been the disciple of Sanjaya Velatti Putta became the Mah?shr?vaka by their merit. It suggests that his contemporary eminent teachers were non Buddhist. During the life time of Buddha there was a good back ground in mendicancy and scholarship in various the pre-Buddhist systems. Magadha was then an important resident of yati(s) mendicants of different order with scholarship.

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Mah?shr?vaka shr?vaka Tripitaka Kaundinya Mahanama Pragjyotisa

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Currently the most common form of cancer diagnosed in women all over the world is breast cancer. One of the leading causes of death for women, it arises in breast tissue. If this cancer is discovered in its early stages, it can be treated. There are two types of tumors that can occur in breast cancer patients: malignant and benign. Malignant tumors are deadly because its growth rate is much higher than the benign one. Therefore, early identification of tumor type is essential for adequate treatment of a patient with breast cancer. In this work, the Wisconsin breast cancer data set was used, which was collected from the UCI repository. The goal is to analyze the data set and evaluate the performance of various machine learning Breast cancer prediction algorithms, here support vector Machine, logistic regression, K-nearest neighbors, Gradient Boosting Classifier and Random Forest classifiers have been implemented to classify tumors into benign and malignant. To determine the most appropriate algorithm, the accuracy of each is calculated and compared. Accordingly, Logistic Regression classifier has the highest accuracy of 99.12%. These classifiers can be used to build an automatic diagnostic system for preliminary breast diagnosis cancer.

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Breast Cancer diagnosis, Analysis of WBCD Dataset, Malignant-Benign breast cancer classification, Machine Learning Algorithms.

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Introduction: Body image can greatly impact a person's daily life and health. This study investigates the influence of desired body image on the pattern of food consumption among female students in selected tertiary institutions in Ogun state. Methods: A multistage sampling technique was used to select one hundred and eighty-seven (187) students from the selected schools. A pre-tested, structured questionnaire was used to obtain information on socio-economic characteristics; a food frequency questionnaire (FFQ) to obtain the consumption pattern; and anthropometric measurements were taken using standard procedures. Data was analyzed using frequency counts, means, standard deviations, correlation, and Chi-Square. Results: It showed that 79.8% of the students wanted a thinner body, while 52.3% of the obese replied that they had tried dieting and 45.7% were afraid of becoming fat or even gaining a small amount of weight. However, 32.7% did engage in exercise to control weight, and 21.2% induced vomiting after meals. It also showed that 22.4% and 10.6% of the students were overweight and obese, respectively. The correlation revealed a significant relationship between socio-economic characteristics and BMI because age and lifestyle were significantly correlated with being overweight (r = 0.312) and obese (r = 0.201) respectively. Conclusion: According to the findings of this study, there is a significant relationship between body image and the student's lifestyle. Hence, the study recommended that public health advice should not only centre on weight loss but also promote broader health and lifestyle messages for all people, regardless of body size and weight.

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body image, BMI, dieting, lifestyle

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ABSTRACT A study was conducted to determine the antibacterial effects of Ficus sycomorus on Shigella dysenteria, and Staphylococcus aureus, after determining the phytochemical composition. Disc diffusion plate method was employed in the antibacterial property determination. The phytochemical screening revealed the presence of alkanoids, flavonoids, tannins, reducing sugar, saponins and steroids. Both Aqueous and ethanolic extracts had the best activities at 15 mg/ml with 6.1, 6.0, 3.7 and 8.1, 7.0, 4.7+0.42 against S. aureus and S dyenteria respectively. The least effect was by 5 mg/ml treatments of both aqueous and ethnolic extracts with 2.5 and 3.3, respectively. The 10 mg/ml treatments had values close to those of the positive control. The results therefore indicate a positive antibacterial activity of F. sycomorus which lend a support to the usage in traditional herbal medicines, and can be included in further investigation with a view to treat diseases caused by these pathogenic bacteria.

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Key words: Ficus sycomorus; S. aureus; S. dysenteria; Antibacterial

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ABSTRACT Rhizospheres of Watermelon and Spinach, in two suburbs of Sokoto (Kwalkwalawa and Ruggar liman), were studied. Nutrient agar was used in cultures while microscopy and biochemical characterization were carried out using the nutrient agar and saboraud dextrose agar for bacterial and fungal respective identification. Streak plate method was used for inoculation of serially diluted soil samples obtained from the respective sampling farm locations. The samples were collected in triplicates for each point. Forty strains belonging to six species including Pseudomonas syringae, P. fluorescence, Bacillus subtilis, and B. cereus were isolated. B. subtilis had eleven occurrences, P. syringae had eleven, P. fluorescence, B. cereus had nine and seven occurrences each, while E. coli occurred two times, only. The most common bacteria on the fields were B. subtilis, followed by Pseudomonas spp. The least occurring was E. coli. The bacterium was absent in sample sites of kwalkwalawa. However, all bacterial species were recorded in all sampling points of Ruggar Liman. Further research into the beneficial activity, or otherwise will help in enabling the farmers know the appropriate steps to take if control is required.

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KEY WORDS; Bacteria Sokoto Farming Suburbs, , Watermelon, Spinach.

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Exploring an original strategy for building deep networks, based on stacking layers of denoising autoencoders which are trained locally to denoise corrupted versions of their inputs. The resulting algorithm is a straightforward variation on the stacking of ordinary autoencoders. It is however shown on a benchmark of classification problems to yield significantly lower classification error, thus bridging the performance gap with deep belief networks (DBN), and in several cases surpassing it. Higher level representations learnt in this purely unsupervised fashion also help boost the performance of subsequent SVM classifiers. Qualitative experiments show that, contrary to ordinary autoencoders, denoising autoencoders are able to learn Gabor-like edge detectors from natural image patches and larger stroke detectors from digit images. This work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations.

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Deep learning, unsupervised feature learning, deep belief networks, autoencoders.

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