Notice Board :





Volume XIII Issue XII

Author Name
Sangeeta Kumari, Ritesh Kumar yadav, Varsha Namdeo
Year Of Publication
2021
Volume and Issue
Volume 13 Issue 12
Abstract
The medical field regularly handles enormous amounts of data. Handling huge data by conventional methods can affect the results. Algorithms for machine learning can be used to find out facts in medical research, in particular for disease prediction. The early recognition of disease is crucial for the analysis of patient medicines and specialists. Machine learning algorithms like Decision trees, Support vector machine, Multilayer perceptron, Bayes classifiers, K-Nearest Neighbors Ensemble classifier techniques etc are used to determine various ailments. Using machine learning algorithms can lead to rapid disease prediction with high accuracy. This research paper analyzes how machine learning techniques are used to predict different diseases and its types. This paper examined research papers focusing mainly on the prediction of chronic kidney disease, machine learning, heart disease, diabetes, and breast cancer. The paper also examines the hybrid approach that increases the performance o
PaperID
2021/EUSRM/12/2021/61246

Author Name
Priyadarshni Kumari, Chinmay Bhatt, Varsha Namdeo
Year Of Publication
2021
Volume and Issue
Volume 13 Issue 12
Abstract
Among the different causes of death, heart disease is highlighted as the most common. Due to medical practitioners' lack of knowledge and expertise about warning indications of heart failure, detecting heart illness might be difficult. In the healthcare industry, there is a vast amount of data. Early identification and prevention of heart-related disorders can be accomplished by employing the most effective data mining approaches. In the medical field, both machine learning (ML) and data mining (DM) techniques have proven to be useful and significant. The current study project's goal is to examine many risk parameters that have been highlighted in the analysis of heart disease, as well as to uncover multiple strategies for the detection and prediction of heart disease, as well as to evaluate the shortcomings of previous work. The article uses DM approaches to synthesize existing heart disease prediction studies, considering a variety of DM techniques to determine the most appropriate a
PaperID
2021/EUSRM/12/2021/61247

Author Name
Rakesh Kumar Sharma, Rafat Afroz
Year Of Publication
2021
Volume and Issue
Volume 13 Issue 12
Abstract
Today human beings live in the so-called civilized and democratic society that is based on the principles of equality and freedom for all. It automatically results into the non-acceptance of gender discrimination in principle. Therefore, various International Human Rights norms are in place that insist on the elimination of all forms of discrimination against women and advocate equal rights for women. Women’s year, women decade etc. are observed that led to the creation of mass awareness and sensitization of people about rights of women. Many steps are taken by the government in the form of various policies and programmes to promote the status of women and to realize women's rights. But despite all the efforts, the basic issue that threatens and endangers the very existence of women is the issue of domestic violence against women. Contrary to this woman who constitute about half of the world's population are the worst victim of violence and exploitation within home. Rather it has becom
PaperID
2021/EUSRM/12/2021/61247a

Author Name
Basheer P, R. Purushotham Naik
Year Of Publication
2021
Volume and Issue
Volume 13 Issue 12
Abstract
To address the escalating demands for wideband communications and network densification, a novel approach involving millimeter-wave (mmWave) enabled integrated access and backhaul (IAB) is imperative for the sixth generation (6G) cellular Internet of Things (IoT) network. However, the introduction of mmWave-enabled IAB technology poses challenges to network capacity, specifically related to the varied backhaul capacities of small cell base stations and the diverse interferences among access links in the 6G cellular IoT network. In response, this paper presents a joint framework for traffic load balancing and interference mitigation with the goal of maximizing the network capacity for 6G cellular IoT services. Additionally, to minimize both backhaul burden and interference, a novel matching utility function, considering backhaul capacity and interference, is designed using the many-to-many matching model. The non-convex power allocation subproblem is transformed into a convex problem us
PaperID
2021/EUSRM/12/2021/61246a