Volume 9 | Issue 6 |

 Abstract

Machine Learning has been exponentially growing in the past with applications in almost every field of science and engineering. It is a very useful and efficient technique to make sensible interpretations of the vast amount of data which is very difficult to carry out but the human mind. There are many fields in science where huge amounts of experimental data are systematically developed over several decades. It can be utilized to make new interpretations and understandings with the help of machine learning, which was otherwise not possible. This article reports advancements made in the fields of genetics, material science, and agriculture with the successful use of machine learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine Learning, Material science, Genetics, Agriculture

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

COVID-19 pandemic is the deadliest crisis the entire world is going through. There are many active cases and deaths that have been brought about by the infection. The main symptoms of the disease include fever, dry cough, sore throat, tiredness and difficulty in breathing. To prevent the spread of disease, infected people should maintain hygiene and to be kept in isolation. Masks, sanitiser, and physical distance are to be maintained to control the spread of disease. This disease mainly affects the respiratory system thereby affecting the oxygen intake which is essential for human survival. In the present paperwork, we estimated the probability of a patient being affected with COVID-19 and estimated whether they need oxygen or should be hospitalized or if home quarantine is enough based on the given patient's details.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Logistic Regression, Django, Python, COVID-19, Symptoms, SPO2, Hospitalization

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Women domestic help, development, socio and economic conditions, exploitation.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Diabetes has grown to be a severe problem now a days. So, we need to take severe precautions to eliminate this. To eradicate, we have to predict the occurrence of Diabetes. We predict the occurrence of diabetes with the usage of Random Forest which is Machine Learning Algorithm. Pima dataset is a data of real patients. Using these records, we are able to build accurate model that can predict the existence of diabetes. The factors which we are considering are pregnancies, glucose, blood pressure, skin thickness, insulin, body mass index, diabetes pedigree and age.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Random Forest, PIMA dataset, Machine Learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

RP-HPLC, Dutasteride (DTS), Benign prostatic hyperplasia, ICH Revised Q2 (R1) guidelines.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Surveillance systems play a crucial role in maintaining security and order in public places. Despite CCTV cameras installed at most of the locations, crimes that are captured in CCTVs not reported in an effective way. In this context, there has been an enhanced attention for increased surveillance in order to monitor crowd behaviour and to nab fugitives, suspects and criminals, especially in bus stations, railway stations and airports and other crowded locations. CCTV, in general, requires human supervision which may lead to missing some important crime events due to human error. The proposed project aims to help automate the process of monitoring the CCTV footage and supports in proactively detecting suspicious behaviour in real-time videos with alerts sent to higher authorities. The alert is generated and forwarded to designated authorities if suspicious events occur, which will help in preventing crimes, assisting in crime investigation, identification of suspects and also supports in automating some of the groundwork that is manual and time-consuming. Towards this, the paper attempts to incorporate two independently working machine learning models into one working model. To get the best results, transfer learning has been used for enhanced performance of each model and give better and faster results. The video feed from the CCTV will be taken as the input in our system and image frames will be extracted and fed into the two sub-systems of the project. For criminal/suspects face recognition, transfer learning on VGG16 model is used, for weapon detection, darknet framework is to be applied with YOLOv4 algorithm to get the desired object detection. These machine learning systems have been designed to work in parallel which in turn ensures faster output.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

CCTV surveillance, machine learning, transfer learning, face recognition, VGG16, YOLOv4.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

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HTML, CSS, Java Script, NetBeans IDE 7.1.2,MYSQL DBMS

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cervical carcinoma, risk factors, genetic polymorphism, SNPs, genetic variants.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

ABSTRCT: The present study has explored about teaching aptitude of in-service teacher trainees in-service teacher trainees in relation to their intelligence, gender and locality. A representative sample of 200 in-service teacher trainees from B.Ed. colleges of Raichur district was randomly selected. Teaching aptitude scale (2002) by L.C. Singh and Dahiya and test of general intelligence (2012) by S.K.Pal & K.S.Mishra were used to measure teaching aptitude and general intelligence of in-service teacher trainees. The study depicts that there is significant difference in teaching aptitude between high intelligent and low intelligent in-service teacher trainees revealing that high intelligent group have high teaching aptitude than low intelligent group. While teaching aptitude has no relevance with the gender of the individual but on the other hand teaching his/her affect aptitude of an individual family background or his/her place of living.

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Creative Commons Attribution 4.0 and The Open Definition

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Key words: Teaching Aptitude, In-service teacher trainees, Intelligence, Gender and Locality

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Key words: Kendriaya Vidyalaya, Teaching effectiveness, teaching aptitude, relationship

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Creative Commons Attribution 4.0 and The Open Definition