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In India, we all know that Agriculture is the backbone of the country. This paper predicts the yield of almost all kinds of crops that are planted in India. This script makes novel by the usage of simple parameters like State, district, season, area and the user can predict the yield of the crop in which year he or she wants to. The paper uses advanced regression techniques like Kernel Ridge, Lasso and ENet algorithms to predict the yield and uses the concept of Stacking Regression for enhancing the algorithms to give a better prediction. To be precise and accurate in predicting crop yield and deliver the end user with proper recommendations about required fertilizer ratio based on soil parameters.

A project employing machine learning to track student attendance in schools is the attendance management and student tracking system using facial recognition. Each student's face is captured by a camera as they enter the classroom, and the system compares that image to the student's previously saved information to record their attendance. To develop a distinctive template for each student that is utilised for recognition, the system uses deep learning algorithms to extract information from the faces. Additionally, the system has the ability to recognise many faces at once and may record multiple pupils' attendance in a single frame. The technology tracks student movement throughout the classroom and gives the teacher with real-time data about student behaviour and activities in addition to managing attendance. The system makes use of this information to produce reports and analytics that administrators and teachers may use to assess student performance and make informed decisions. Overall, the facial recognition-based attendance and student tracking system offers a creative approach to streamlining classroom management and raising student achievement.

Liquefied Petroleum Gas (LPG) is one of the most used fuels in both domestic and commercial kitchens. Basically, LPG is a combination or mixture of propane and butane which is a highly flammable, colorless chemical and also odorless so an odorant has been added to it to help in detecting the leakage. Gas leakages causes a threat in our households and the surroundings. In recent days, kitchen-based accidents have been increased in both domestic and commercial premises. The main reason for these accidents is gas leakage as it enhances the risk of explosions. To dodge this problem, there is a requirement for an automatic gas leakage detection system to distinguish the spillage of LPG and give alerts. In the present day, IOT (Internet of Things) is extensively used to automate the household appliances. In this paper we are discussing about an Internet of Things (IOT) based system which enables the initial detection of gas leaks which can prevent house fires in a kitchen by measuring the temperature and humidity using sensors. So, if we can detect these gas leakages onset, we can avert a serious explosion and protect lives.

Humans use their facial expressions to communicate their emotions, which is a strong tool in communication. Facial expression identification is one of the most difficult and powerful challenges in social communication, as facial expressions are crucial in nonverbal communication. Facial Expression Recognition (FER) is just an important study topic in Artificial Intelligence, with numerous recent experiments employing Convolutional Neural Networks (CNNs). The emotions that have grown in the face image have a significant impact on judgments and debates on a variety of topics. Surprise, fear, disgust, anger, happiness, and sorrow are the six basic categories in which a person's emotional states can be categorized according to psychological theory. The automated identification of these emotions from facial photos can be useful in human-computer interaction and a variety of other situations. Deep neural networks, in particular, are capable of learning complicated characteristics and classifying the derived patterns. A deep learning-based framework for human emotion recognition is offered in this system. The proposed framework extracts feature with Gabor filters before classifying them with a Convolutional Neural Network (CNN). The suggested technique improves both the speed of CNN training and the recognition accuracy, according to the results of the experiments. Keywords: convolutional neural network (CNN), Gabor filter

Tuberculosis (TB) is a chronic infection of Mycobacterium tuberculosis. It is spread through the air from person to person and mainly targets the lungs. The tuberculosis epidemic has been complicated by the emergence of drug-resistant TB and HIV-associated TB. In Malaysia, there were an estimated 0.4% of 40,000 TB cases with MDR-TB in 2014. The acquisition of drug resistance in M. tuberculosis is mainly depending on the efflux mechanisms which allow the bacteria to tolerate higher levels of a drug. The evolutionary pathway of drug resistance is influenced by epistasis and bacterial fitness. There are a few risk factors that responsible for the occurrence of drug-resistant TB. Keywords: Tuberculosis, Drug­resistant Tuberculosis, Malaysia.

Diabetes Mellitus is a metabolic disease which affects various organs of the body like heart, kidney, and peripheral nerves. Myocardial dysfunction in diabetic patients in absence of ischemic, valvular or hypertensive heart disease is identified as diabetic cardiomyopathy. It is an early sign of diabetic heart muscle involvement which usually precedes systolic dysfunction. There are very few Studies are found to correlate the relation between the glycemic control and left ventricular diastolic dysfunction. Hence this study is an attempt to determine the proportion of diastolic dysfunction in patients with type 2 DM and to correlate the relation of LVDD withHbA1c levels and duration of DM. A total of 100 diabetic patients with minimum 5 years duration of diabetes were selected from Rajarajeswari medical college and hospital. Patients with minimum history of 5 years of type 2 diabetes were evaluated for Doppler echocardiography and HbA1c levels. Results: In the present study, 58 out of 100 patients had left ventricular diastolic dysfunction and was higher prevalence in HbA1c more than 8%. Patients who had diastolic dysfunction had diabetes duration of diabetes was more than 8 years. Conclusion: LVDD is strongly correlated with HbA1c levels and duration of diabetes mellitus. There was significant positive correlation of age, fasting blood sugars, cholesterol levels and triglyceride levels with LVDD.

The article studies the dynamics of chronic poverty in rural India. The article attempts to analyse the factors influencing the incidence and mobility of poverty and the changes in the influence of these factors over time. This article uses the household panel survey data collected by NCAER. It examines whether there has been change in the influence of factors such as village level infrastructure, household size and composition, and economic growth on poverty dynamics in different periods of time. The impact of a number of factors changes over time implying that the strategies for poverty reduction would have to take into account the changing economic environment. The article further presents an analysis of growth rate of per capita expenditure for the same set households to analyse the extent of consumption growth, which is also an indicator of poverty reduction for rural India.

Introduction: The COVID-19 pandemic has disrupted routine cancer care and training globally. Breast units adopted modified national guidelines in the UK, and significant changes were implemented to ensure the safety of patients and staff. The national breast screening services were temporarily suspended from March 2020. Patients underwent surgery in COVID-19 free zones. Complex oncoplastic procedures and immediate reconstructions were not offered. Adjuvant treatments were modified to reduce the risk of complications and hospital readmission. The objective of our study is to assess the impact of COVID-19 on breast cancer management and surgical training. Methods: The resource reallocation was implemented for 100 days, commencing from the 16th of March,2020. Patients diagnosed with breast cancer during this period were identified from the cancer database, and a comparison was made with patients diagnosed last year within the same time frame. We assessed the time taken from the decision to treatment and modifications made to cancer management due to the pandemic. The impact on resident training was evaluated by comparing the number of cases performed or assisted during this period. Results: During the pandemic period, out of 1064 patients seen in the Breast one-stop clinic, 64 patients (6.0%) were diagnosed with breast cancer. During the same time frame in 2019, out of 1881 new symptomatic patients, 90 (4.8%) were diagnosed with cancer. In 2019, sixty-three patients were treated for screen-detected cancer, whereas only 23 patients entered the screening pathway before the services were suspended. Majority of patients underwent surgery in 2019 as compared to 2020 (80% versus 36%). Fifty-six percent of patients received endocrine treatment as primary or bridging therapy; whereas, in 2019, only 12% received primary endocrine therapy. In 2020, time from decision to surgical treatment has decreased by half as compared to 2019 (8.6 versus 19.1 days). One patient who underwent surgery developed COVID-19 infection after two weeks, and no postoperative mortality was reported. On average, each trainee was involved in 35 procedures during 2020; whereas in 2019, 54 procedures were assisted or performed by a trainee. Conclusion: Our study shows that COVID-19 has made a significant impact on patients’ management and surgical training. Majority of the patients were commenced on neoadjuvant endocrine therapy instead of surgery. The conversion rate to cancers in one-stop clinic improved possibly due to a smaller number of benign referrals during the pandemic. The impact on surgical training is due to the reduction in the number of patients operated during this period, and constraints of performing complex oncoplastic procedures and breast reconstruction. 2019 (16.03.2019 to 22.06.2019)2020COVID period (16.03.2020 to 22.06.2020)Impact on Clinic: Total number of patients seen in OPD18811064Impact on Breast cancer Diagnosis1.Total153862.Screening63(41.17%)22(34.37%) (who were already in pathway)3.Symptomatic90(58.83%)64(65.63%)Impact on Breast cancer Treatment: 1st treatment1.Started on Neo-adjuvant chemotherapy13(8.49%)7(8.13%)2.Started on endocrine therapy (Both primary and neo-adjuvant)18(11.76%)48(55.81%)3.Surgery122(79.73%)31(36.04%)Mean days from decision to initiation of treatment1.Neo-adjuvant chemotherapy13.321.72.Endocrine therapy0.40.13.Surgery19.18.6Total number of surgeries performed Including those diagnosed before the pandemic.160102Breast surgical training Number of surgeries performed/ assisted by a trainee5435 Citation Format: Manoj Gowda S, Kirti Kabeer, Fatima Mansour, Ammara Hasan, Eisa Nael, Vallipuram Gopalan, Daljit Gahir, Apurna Jegannathen, Sekhar Marla, Soni Soumian, Sankaran Narayanan, Sadaf JafferBhoy. The impact of corona virus disease-2019 on breast services at university hospitals of North Midlands, United Kingdom [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS1-13.

Exposure visit to Bidar District Cooperative Central Bank to study the PACs - SHG Model. The case illustrates how the Primary Agricultural Societies through SHGs have helped in uplifting the socio-economic standards of the disadvantaged people. Trend setter for the entire cooperative in the country. Promoted large number of shg's.

Care delivery in neonatology is dependent on an interprofessional team. Collaborative learning and education amongst professionals can lead to successful management of critically ill patients. This focused BEME review synthesized the components, outcomes, and impact of such interprofessional education (IPE) programs in neonatal medicine. The authors systematically searched four online databases and hand-searched MedEdPublish up to 10 September 2020. Two authors independently screened titles, abstracts, full-texts, performed data extraction and risk of bias assessment related to study methodology and reporting. Discrepancies were resolved by a third author. We reported our findings based on BEME guidance and the STORIES (STructured apprOach to the Reporting in health education of Evidence Synthesis) statement. We included 17 studies on IPE in neonatal medicine. Most studies were from North America with varying learners, objectives, instruction, and observed outcomes. Learners represented nurses, respiratory therapists, neonatal nurse practitioners, patient care technicians, parents, early interventionists, physicians, and medical trainees amongst others. Risk of bias assessment in reporting revealed poor reporting of resources and instructor training. Bias assessment for study methodology noted moderate quality evidence with validity evidence as the weakest domain. IPE instruction strategies included simulation with debriefing, didactics, and online instruction. Most studies reported level 1 Kirkpatrick outcomes (76%) and few reported level 3 or 4 outcomes (23%). Challenges include buy-in from leadership and the negative influence of hierarchy amongst learners. This review highlights IPE program components within neonatal medicine and exemplary practices including a multimodal instructional approach, asynchronous instruction, an emphasis on teamwork, and elimination of hierarchy amongst learners. We identified a lack of reporting on program development and instructor training. Future work should address long term knowledge and skill retention and impact on patient outcomes and organizations.

Maintaining attendance system of students of various classes and accessing them is a difficult task. Calling names of each student in each hour and keeping a record of the attendance is time consuming as well. Other issues would be false sign, name missing out in sheets, manually entering data to computers and there is also a chance of proxy attendance. The following system marks attendance based on the concept of face detection and face recognition. The daily attendance of all the students is noted subject wise and is stored by the administrator. Hence, accessing as well as storing the attendance system is easier and more efficient. It makes effective and perfect application of man power and resources.

The coronavirus disease (COVID-19) pandemic in 2020 has brought about complex challenges in healthcare delivery. With the new rules of lockdown and social distancing and with resources diverted to the management of COVID-19, there are difficulties in continuing usual cancer care. Patients are at risk of contracting COVID-19 with a high chance of patient to healthcare transmission and vice versa. Hospital visits, investigations and all modalities of treatment have potential complications that put patients at risk, some more than others. In this situation, there is a need to change our approach in the management of breast cancer to deliver it safely. We present modified guidelines based on the available consensus statements and evidence.