Analysis of Mammographic Images for Early Detection of Breast Cancer Using Machine Learning Techniques

Breast cancer is the main reason for death among women. Radiographic images obtained from mammography equipment are one of the most frequently used techniques for helping in early detection of breast cancer. The motivation behind this study is to focus the tumour types of breast cancer images .It is methodology to anticipated a sickness in view of the visual conclusion of breast disease tumour types with precision, particularly when numerous feature are related. Breast Cancer (BC) is one such sample where the phenomenon is very complex furthermore numerous feature of tumour types are included. In the present investigation, various pattern recognition techniques were used for the classification of breast cancer using mammograms image processing techniques .The pattern recognition techniques for tumour image enhancements, segmentation, texture based image feature extraction and subsequent classification of breast cancer mammogram image was successfully performed. When two machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) were used to classify 120 images, it was observed from the results that Artificial Neural Network classifiers demonstrated the h classification rate 91.31% and the SVM with both Radial Basis Function (RBF) and linear kernel classifiers demonstrated the highest classification rate of 92.11% and RBF classification rate is 92.85%

Analysis of Mammographic Images for Early Detection of Breast Cancer Using Machine Learning Techniques

Breast cancer is the main reason for death among women. Radiographic images obtained from mammography equipment are one of the most frequently used techniques for helping in early detection of breast cancer. The motivation behind this study is to focus the tumour types of breast cancer images .It is methodology to anticipated a sickness in view of the visual conclusion of breast disease tumour types with precision, particularly when numerous feature are related. Breast Cancer (BC) is one such sample where the phenomenon is very complex furthermore numerous feature of tumour types are included. In the present investigation, various pattern recognition techniques were used for the classification of breast cancer using mammograms image processing techniques .The pattern recognition techniques for tumour image enhancements, segmentation, texture based image feature extraction and subsequent classification of breast cancer mammogram image was successfully performed. When two machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) were used to classify 120 images, it was observed from the results that Artificial Neural Network classifiers demonstrated the h classification rate 91.31% and the SVM with both Radial Basis Function (RBF) and linear kernel classifiers demonstrated the highest classification rate of 92.11% and RBF classification rate is 92.85%

Science of malaria elimination: using knowledge of bottlenecks and enablers from the Malaria Elimination Demonstration Project in Central India for eliminating malaria in the Asia Pacific region

Malaria poses a major public health challenge in the Asia Pacific. Malaria Elimination Demonstration Project was conducted as a public-private partnership initiative in Mandla between State government, ICMR, and FDEC India. The project employed controls for efficient operational and management decisions. IEC campaigns found crucial in schools and communities. Capacity building of local workers emphasized for better diagnosis and treatment. SOCH mobile app launched for complete digitalization. Better supervision for Indoor Residual Sprays and optimized Long Lasting Insecticidal Nets distribution. Significant malaria cases reduction in Mandla. Insights from MEDP crucial for malaria elimination strategies in other endemic regions of the Asia Pacific

A Pragmatic Plan for the Mental Health Consequences During Covid-19 Pandemic Through Ayurveda

Introduction: Beyond infection, the COVID-19 pandemic has also affected individuals through associated mental illnesses like anxiety and stress and has caused a collateral damage. Ayurveda has described 3 main factors which are responsible for the occurrence of diseases, one of them is Prajnaparadha, which is stated as the main cause for all the mental illness. The threefold treatment principles of Daivavyapashraya, Yuktivyapashraya and Satvavajaya targeting the Ahara, Achara and Chesta is an ideal plan to deal with stress built up in this pandemic. Materials and methods: The Ayurvedic classical textbooks and the peer reviewed articles focusing mental health researches were reviewed. This plan involves the implementation of Daivavyapashraya, Yuktivyapashaya and Satvavajaya based on the exposure and exhibition of symptoms of COVID-19. Daivavyapashraya Chikitsa is employed by Vishnusahasranama recitation/listening, Yuktivyapashraya Chikitsa is employed by the various drugs like Bramhi, Shankapushpi, Ashwagandha etc. and formulations which have psycho-neuro-immune-response, Satvavajaya Chikitsa by the process of counseling. Results and Discussion: The interdependent nature of immunity and psychological state is already well established and it decides the outcome of disorders. An immune response can be largely affected by mental well-being and mental illness can negatively affect its outcome. Conclusion: The three fold treatment plan centering the pshycho-neuro-immune action is a complete health promotive, preventive and curative plan and will certainly help in the revival of mental health in the times and after the COVID-19 pandemic

A qualitative study on community perceptions on quality of healthcare services they received in the Malaria Elimination Demonstration Project in district Mandla, India

Abstract Background The utilization and impact of the healthcare services depend on the perceived quality, appropriateness, ease of availability, and cost of the services. This study aimed to understand the community's perception of the quality of healthcare services delivered as part of the Malaria Elimination Demonstration Project (MEDP), Mandla, Madhya Pradesh, India. Methods The study used qualitative techniques to analyze the community perceptions that emerged from the participants’ narratives during the Focus Group Discussions (FGDs) and in-depth Interviews with Key Informants (IKIs) on the promptness and quality of healthcare service delivery, the behaviour of MEDP staff, Information, Education and Communication, and Behavioural Change Communication activities, coordination with community members and other health personnel, and capacity building of healthcare workers and the community. Results 36 FGDs and 63 IKIs with 419 respondents were conducted in nine blocks of district Mandla. Overall, 97% to 100% of beneficiaries associated MEDP with regularity and prompt service delivery, availability of diagnostics and drugs, friendly behaviour, good coordination, and community mobilization to enhance treatment-seeking behaviour. Conclusions The study's findings highlighted the importance of building and maintaining the community's participation and promoting the demand for optimal utilization of healthcare services inside the village to promptly achieve the malaria elimination goal

Proceedings of the International Conference on Frontiers in Desalination, Energy, Environment and Material Sciences for Sustainable Development

This proceeding contains articles on the various ideas of the academic community presented at the International Conference on Frontiers in Desalination, Energy, Environment and Material Sciences for Sustainable Development (FEEMSSD-2023) & Annual Congress of InDA (InDACON-2023) jointly organized by the Madan Mohan Malaviya University of Technology Gorakhpur, KIPM-College of Engineering and Technology Gida Gorakhpur, and Indian Desalination Association, India on 16th-17th March 2023.  FEEMSSD-2023 & InDACON-2023 focuses on addressing issues and concerns related to sustainability in all domains of Energy, Environment, Desalination, and Material Science and attempts to present the research and innovative outputs in a global platform. The conference aims to bring together leading academicians, researchers, technocrats, practitioners, and students to exchange and share their experiences and research outputs in Energy, Environment, Desalination, and Material Science.  Conference Title: International Conference on Frontiers in Desalination, Energy, Environment and Material Sciences for Sustainable Development & Annual Congress of InDAConference Acronyms: FEEMSSD-2023 & InDACON-2023Conference Date: 16th-17th March 2023Conference Location: Madan Mohan Malaviya University of Technology, GorakhpurConference Organizers: Madan Mohan Malaviya University of Technology Gorakhpur, KIPM-College of Engineering and Technology Gida Gorakhpur, and Indian Desalination Association, Indi

Abstracts of National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020

This book presents the abstracts of the papers presented to the Online National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020 (RDMPMC-2020) held on 26th and 27th August 2020 organized by the Department of Metallurgical and Materials Science in Association with the Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, India. Conference Title: National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020Conference Acronym: RDMPMC-2020Conference Date: 26–27 August 2020Conference Location: Online (Virtual Mode)Conference Organizer: Department of Metallurgical and Materials Engineering, National Institute of Technology JamshedpurCo-organizer: Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, IndiaConference Sponsor: TEQIP-