Volume 13 | Issue 4 |

 Abstract

???? ?????? ??????? ? ????? ?????? ?? ???????????? ???? ???? ??? ??? ??? ??? ?? ??? ???? ???? ???? ??? ?????? ??, ???? ?????? ??????? ???? ???, ??????? ??? ??? ??? ?? ?????? ?? ??????? ????? ???? ??? ??? ??????????? ????-??????? ??? ???????? ??????? ?? ???????????? ????? ??? ?? ??? ?????? ??? ??? ???????? ??????? ?? ??? ?? ??? ?? ?????????? ?? ??? ?? ?? ???? ??? ?????? ?????? ???? ???? ??? ???? ????? ?????? ?????? ?? ?????? ?? ???? ?? ??????? ??? ???? ??? ????????? ???????? ????? ?? ???? ???, ???? ???????? ??????? ?? ??????? ?? ???? ????? ???? ??? ??? 1980 ?? ??? ??? ??????? ??? ?.??.??. ?? ?????? ???????? ???? ?? ???? ??????? ??? ????? ????? ???????????? ?? ?????? ???? ?????? ?????? ?? ?????? ??????? ??????????? ??? ?? ????? ?????? ?????? ????? ???

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

???????? ???????, ????????, ????????????, ??????, ???? ???????

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Tepotinib, Tepmetko, RP-HPLC, Isocratic, Acetonitrile

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Precision Operational Amplifier, Auto-Zero Amplifier, Ping-Pong Architecture, High Precision Analog Design

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Heart disease remains one of the leading causes of mortality worldwide, making early and accurate diagnosis critically important. In this project, we explore the use of machine learning techniques to predict the likelihood of heart disease in patients based on key clinical and demographic features. The system utilizes supervised learning algorithms trained on the Cleveland Heart Disease dataset, which includes variables such as age, gender, chest pain type, resting blood pressure, cholesterol levels, maximum heart rate, and more. Data preprocessing techniques such as normalization, encoding of categorical features, and handling of missing values were applied to ensure the quality and usability of the data. Various machine learning models including Logistic Regression, Random Forest, Support Vector Machines, and XGBoost were implemented and evaluated based on accuracy, precision, recall, F1-score, and ROC-AUC metrics. The experimental results demonstrate that ensemble models like Random Forest and XGBoost offer high predictive accuracy and robustness, making them suitable for medical diagnostic support systems. This work emphasizes the potential of machine learning to assist healthcare professionals in making informed decisions and improving patient outcomes through early diagnosis and intervention.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

A broad category of heart-related conditions, including blocked arteries, irregular heart rhythms, and heart failure, affecting the heart's ability to function properly. o

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The female reproductive tract is a complex and dynamic system responsible for sexual reproduction, menstruation, and childbirth. It comprises the vulva, vagina, cervix, uterus, fallopian tubes, and ovaries, which work in harmony to facilitate gamete transport, fertilization, embryogenesis, and parturition. Hormonal fluctuations regulate the tract's function, with the menstrual cycle preparing the uterus for potential pregnancy. Understanding the female reproductive tract's anatomy, physiology, and pathology is crucial for addressing reproductive health issues, such as infertility, endometriosis, and cervical cancer, and promoting women's overall well-being. Congenital anomalies of the female reproductive tract are developmental defects of mullerian derivates associated with various malformations of the female reproductive system. Congenital anomalies of the female genital tract encompass various structural irregularities that arise during embryonic development. These anomalies may involve the uterus, cervix, fallopian tubes, and vagina, manifesting as deviating from the typical anatomical configuration. These structural irregularities may also contribute to a spectrum of gynecological issues, affecting menstrual health, urinary and bowel function, and overall quality of life for affected individuals. Understanding female genital tract anomalies is paramount in reproductive medicine, gynecology, and obstetrics. These anomalies can profoundly impact reproductive outcomes, influencing fertility, pregnancy, and childbirth.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Embryogenesis, Congenital anomalies, Female genital tract, Fertility.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Health education i? a vital component of promoting health and well-being in both rural and urban communitie?. De?pite differing challenge? and opportunitie?, health education can empower individual? with knowledge, ?kill?, and attitude? to make informed deci?ion? about their health. In rural area?, health education can addre?? unique challenge? like limited re?ource? and healthcare acce??, while in urban area?, it can tackle i??ue? like health di?paritie? and life?tyle-related di?ea?e?. By integrating comprehen?ive health education into ?chool? and communitie?, we can improve health outcome?, enhance quality of life, and reduce healthcare co?t?. Thi? ab?tract highlight? the importance of health education in bridging the gap between rural and urban health di?paritie? and promoting overall well-being.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Education, Skill, Health education, Communitie?, Urban ?chool, Rural ?chool

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

YouTube is a video-sharing platform that was created in 2005 by three former PayPal employees: Chad Hurley, Steve Chen, and Jawed Karim. It is one of the most popular websites in the world, with over 2 billion monthly active users. On YouTube, users can upload, share, and view videos. The platform features a wide variety of content, including music videos, movie trailers, short films, educational videos, vlogs, and more. Users can also interact with each other by liking, commenting on, and sharing videos. YouTube has become an important tool for content creators and businesses to reach a global audience. Many creators have built successful careers on the platform, with some becoming household names. Additionally, businesses use YouTube to advertise their products and services, often through collaborations with popular YouTubers or by creating their own branded contentYouTube is changing the outlook of the integral world. This video sharing site receives billions of viewers daily, the latest reports show that more than half the viewers are students. "Teens constitute the dominant demographic on YouTube in terms of their viewership, comments, and rating and other feedback mechanisms". The exploration of YouTube videos in recent days are elaborated in review of literature.As such, the platform offers a wide variety of user-generated and corporate media videos. Available content includes video clips, TV show clips, music videos, short and documentary films, audio recordings, movie trailers, live streams, and other content such as video blogging, short original videos, and educational videos.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

A STUDY ON USER BEHAVIOR AND IMPACT OF YOUTUBE VIDEOS WITH REFERENCE TO COIMBATORE CITY

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project presents a novel hybrid framework integrating Multi- Constrained Joint Non-Negative Matrix Factorization (MC-JNMF) with Convolutional Neural Networks (CNNs) to study lung metastasis in soft tissue sarcomas using imaging and genomic data. By jointly analyzing high-dimensional data from medical images (such as CT or MRI scans) and corresponding genomic information, the proposed method seeks to identify critical latent features that contribute to the detection and understanding of lung metastasis. The MCJNMF frameworks integrate multiple constraints to better reflect the biological relationships between the two modalities, while CNNs automatically extract imaging features to improve prediction accuracy. This hybrid approach aims to improve clinical decisionmaking by providing more interpretable models and more accurate predictions regarding lung metastasis development in sarcoma patients. Preliminary results indicate that the integration of imaging and genomic data enhances the model's performance compared to traditional methods.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

CNN,MCJNMf,STS,U-Net,MRI,CT,Cancer

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The process of filing tax return of an assessee through digital or electronic mode is called e-filling. This method aims to substitute and overcome the traditional tax return procedure which create hindrances like stand in long queue, lots of manual paper works, time consuming process etc. The advancement in the technology with high-speed internet connectivity, minimizes the tax compliances in very short period of time with more degree of accuracy compared to previous years. Emerging software advancement in the present era has required huge awareness of its use. The present study concentrates the Facilities, awareness and satisfaction level of the taxpayers regarding e-filling of income tax return. A field survey was conducted at Keonjhar district of Odisha comprising various category of tax payer including men and women. For the above purpose data was collected through structured questionnaire and 280 no. of respondent's response had been received and data are analyzed with the help of various statistical tools like Crosstab technique, Chi-square test etc. The software technology like SPSS and MS Excel also used. The study made a conclusive finding that Gender wise similar response have been received regarding satisfaction on filing IT returns online and variations have been observed on the satisfaction of accessibility & easiness of return filing. Exiting e-filling user satisfied with the modern procedures and new users having lack of awareness regarding this facility.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

E-filing, Satisfaction level. Assessee, Return filing

  License

Creative Commons Attribution 4.0 and The Open Definition