Notice Board :

Call for Paper
Vol. 5 Issue 10

Submission Start Date:
Oct 01, 2024

Acceptence Notification Start:
Oct 10, 2024

Submission End:
Oct 25, 2024

Final MenuScript Due:
Oct 30, 2024

Publication Date:
Oct 30, 2024
                         Notice Board: Call for PaperVol. 5 Issue 10      Submission Start Date: Oct 01, 2024      Acceptence Notification Start: Oct 10, 2024      Submission End: Oct 25, 2024      Final MenuScript Due: Oct 30, 2024      Publication Date: Oct 30, 2024




Volume IV Issue IX

Author Name
Anjani Kumar, Narendra Sharma
Year Of Publication
2023
Volume and Issue
Volume 4 Issue 9
Abstract
In recent decades, heart disease, also known as cardiovascular disease, has emerged as the leading cause of death worldwide. It encompasses a range of conditions that affect the heart and is influenced by various risk factors. It has become increasingly imperative to develop accurate, dependable, and efficient methods for early diagnosis to facilitate timely disease management. To address this challenge, data mining has emerged as a valuable tool in the healthcare domain. Researchers have employed various data mining and machine learning techniques to analyze vast and complex medical datasets, aiding healthcare professionals in predicting the onset of heart disease. This research paper focuses on exploring different attributes associated with heart disease and building predictive models using supervised learning algorithms such as Naïve Bayes, decision trees, K-nearest neighbor, and the random forest algorithm. To conduct this analysis, an existing dataset from the Cleveland database o
PaperID
2023/IJEASM/4/2023/1868

Author Name
Udit Kumar, Chetan Agrawal, Pramila Lovanshi
Year Of Publication
2023
Volume and Issue
Volume 4 Issue 9
Abstract
In 2020, the emergence of the novel Coronavirus (2019-nCoV) led to the dissemination of false information, resulting in significant societal alarm. Fake news frequently utilizes multimedia content, including text and images, to deceive readers and propagate its impact. An essential challenge in identifying false news using multimodal data is to retrieve both the overall properties and combine the inherent characteristics of fake news, such as discrepancies between images and text and picture manipulation. In the digital era, individuals have the ability to get news online through multiple platforms. However, this also leads to the rapid dissemination of incorrect information at an unprecedented rate. Fake news has harmful consequences since it undermines stability in society and public trust, leading to a growing need for fake news detection (FND). Deep learning (DL) has achieved significant success in numerous areas and has also been used to FND tasks, surpassing traditional machine l
PaperID
2023/IJEASM/4/2023/1868a

Author Name
Pawan Kumar, Ajay Swarup
Year Of Publication
2023
Volume and Issue
Volume 4 Issue 9
Abstract
This study delves into the numerical method for analyzing beams and their vibration response using cross-correlation techniques. Beams, being fundamental structural elements, are vital in various engineering applications, and understanding their dynamic behavior is crucial for ensuring structural integrity and performance. The study employs numerical simulations and cross-correlation analysis to investigate the vibration characteristics of beams subjected to different loading conditions and boundary constraints. The numerical method involves modeling the beams and solving the governing equations of motion using finite element analysis (FEA). Subsequently, the vibration responses at various points along the beams are recorded. Cross-correlation techniques are then applied to analyze these responses, enabling the identification of mode shapes, natural frequencies, and damping ratios. The findings of this study contribute to a deeper understanding of how beams behave under dynamic loading
PaperID
2023/IJEASM/4/2023/1869

Author Name
Rishikesh Singh, Vikas Patidar
Year Of Publication
2023
Volume and Issue
Volume 4 Issue 9
Abstract
Earthquakes represent a significant threat to communities worldwide, often resulting in devastating consequences for both human lives and infrastructure. Shimla, an enchanting city nestled in the Himalayan foothills, is no exception to this seismic risk. Recognizing the urgent need to bolster the earthquake resilience of Shimla's bridges and develop a comprehensive disaster mitigation model, this research initiative aims to address these critical issues. Shimla, renowned for its colonial-era architecture and scenic beauty, has witnessed substantial urbanization and population growth in recent years. This urban expansion has placed additional strain on the city's infrastructure, particularly its network of bridges, which are vital for the city's transportation and commerce. However, these bridges also pose a significant vulnerability in the event of an earthquake. This research initiative acknowledges that addressing the seismic vulnerability of Shimla's bridges is a multifaceted challe
PaperID
2023/IJEASM/4/2023/1870