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International Journal of Applied and Advanced Multidisciplinary Research (IJAAMR)
Published by MULTITECH PUBLISHER
ISSN : -     EISSN : 30262437     DOI : https://doi.org/10.59890/ijaamr.v1i2
Core Subject : Economy, Humanities, Education, Social, Engineering,
Humanities Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Education Social Sciences
International Journal of Applied and Advanced Multidisciplinary Research (IJAAMR) is a multidisciplinary journal that publishes high quality research papers in the areas of Business Management, Agriculture, Information Technology, Engineering, Health & Life Science, Zoology, Humanities, Applied Sciences, Biology, Criminal Justice, History, Public Administration, Political Science, Sociology, Social, English, Science, Mathematics, Human Resource Management, Accounting, Business Administration and Management, Computer Science, Communication, etc. International Journal of Applied and Advanced Multidisciplinary Research (IJAAMR) publishes articles monthly.
Arjuna Subject : Teknik (semua kategori) - Teknik Sipil dan Struktur
Articles 8 Documents
 1 
Search results for , issue "Vol. 2 No. 1 (2024): January, 2024" : 8 Documents clear
AI in Healthcare 5.0: Opportunities and Challenges Soham Date; Meenakshi Thalor
International Journal of Applied and Advanced Multidisciplinary Research Vol. 2 No. 1 (2024): January, 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v2i1.281

Abstract

The advent of Explainable AI (XAI) in healthcare, often referred to as Healthcare 5.0, presents both significant opportunities and challenges. XAI promises to enhance clinical decision-making by providing transparent and interpretable insights into AI-driven diagnoses and treatment recommendations, thereby increasing trust and adoption among healthcare practitioners. This paper explores the evolving landscape of XAI in healthcare, highlighting its potential to improve patient outcomes, reduce errors, and optimize resource allocation. However, it also addresses the challenges of implementing XAI, including data privacy concerns, regulatory hurdles, and the need for robust validation methods. Balancing these opportunities and challenges is critical for realizing the full potential of XAI in revolutionizing healthcare delivery.
Intrusion Detection Systems Using Machine Learning. Rohit Utekar; Anuja Phapale
International Journal of Applied and Advanced Multidisciplinary Research Vol. 2 No. 1 (2024): January, 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v2i1.550

Abstract

The utilization of machine learning to enhance Intrusion Detection Systems (IDS). It encompasses an exploration of diverse IDS categories, fundamental evaluation metrics, and the dynamic landscape of machine learning methodologies. Recent trends underscore a shift towards the adoption of deep learning techniques for improving attack detection capabilities. Challenges arise from heightened model complexity and increased resource requirements. The paper also suggests future directions that encompass the development of updated datasets and the efficient management of resources through cloud integration. Throughout, this study emphasizes the continuous demand for research and innovation in the field of cybersecurity.
Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms Pragati Mahale; Sejal Khopade
International Journal of Applied and Advanced Multidisciplinary Research Vol. 2 No. 1 (2024): January, 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v2i1.664

Abstract

This study discusses fully distributed fault detection via a wireless sensor network. Initially, we suggested using the Convex hull approach to determine a range of extreme points including nearby nodes. As the number of nodes rises, the message's duration is constrained. Secondly, in order to enhance convergence performance and identify node errors, we suggested using a convolution neural network (CNN) and a Naïve Bayes classifier. Lastly, we use real-world datasets to examine CNN, convex hull, and Naïve bayes algorithms to find and classify the defects. Based on performance measures, the results of simulations and experiments demonstrate that the CNN algorithm has better-identified defects than the convex hull technique while maintaining feasibility and economy.
Traffic Flow Prediction on Road using Machine Learning Anuja Phapale; Sushant Shravagi
International Journal of Applied and Advanced Multidisciplinary Research Vol. 2 No. 1 (2024): January, 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v2i1.690

Abstract

The Intelligent Transportation System (ITS) plays a vital role in numerous smart city applications, particularly in improving transportation and commuting processes. A primary goal of ITS is to tackle traffic-related challenges, especially the issue of traffic congestion. The prediction system for road traffic flow has significant relevance in urban transportation and area management. Many urban centers grapple with the daunting task of effective traffic management. However, the incorporation of predictive modeling that considers environmental and weather conditions, such as rainfall and thunderstorms, has proven to be remarkably effective. In response to this challenge, we have introduced a road traffic flow prediction model specifically designed to forecast traffic conditions at hourly intervals extending up to 24 hours. Although various algorithms have been applied in previous research, there is a notable absence of accessible and user-friendly platforms dedicated to road traffic flow prediction.
A Decade of Research on the Effectiveness of Augmented Reality on Students with Special Disability in Higher Education Anuja Phapale; Aditya Shiledar
International Journal of Applied and Advanced Multidisciplinary Research Vol. 2 No. 1 (2024): January, 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v2i1.709

Abstract

This short article summarizes a decade of research on the effectiveness of augmented reality (AR) in higher education for students with disabilities in particular. The study examines how AR technology has been used to enhance the learning experience of these students. Through a comprehensive review of multiple studies and findings, this study examines the impact of AR on access, engagement, and overall educational outcomes for students with unique disabilities. The findings highlighted significant progress in the field, shedding light on the potential of AR to provide inclusive and effective learning environments for this student population, thereby contributing to a broader discussion of technical and higher education.
3D Motion Gesture Control : Gesture Recognition and Adaptation for Human Computer Interaction Anuja Phapale; Shriya Sawashe
International Journal of Applied and Advanced Multidisciplinary Research Vol. 2 No. 1 (2024): January, 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v2i1.730

Abstract

Advancements in human-computer interaction (HCI) have paved the way for more intuitive and immersive interfaces. The first part of the paper delves into the fundamental principles of 3D gesture recognition, including sensor technologies, machine learning algorithms, and computer vision techniques. It discusses the challenges associated with accurate recognition in various environmental conditions and the ways in which these challenges are being addressed by researchers. The second part focuses on the adaptation aspect of the technology. It highlights how 3D gesture recognition can be integrated into adaptive HCI systems, enabling personalized and context-aware interactions. These adaptations can range from adjusting the interface layout to suit the user's preferences to dynamically changing the system's behavior based on the user's gestures. Additionally, the paper discusses the potential applications of 3D gesture recognition in fields such as gaming, virtual reality, healthcare, and beyond. It emphasizes the need for continued research to improve accuracy, robustness, and user-friendliness, ultimately driving the widespread adoption of 3D gesture recognition in HCI.
Arms Control as a Climate Mitigation Strategy: Examining the Nexus Ahmad, Munir; Muhammad Karim; Zahid Hussain; Alyas Ali Chaichi
International Journal of Applied and Advanced Multidisciplinary Research Vol. 2 No. 1 (2024): January, 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v2i1.971

Abstract

Global ecosystems are seriously threatened by climate change, which calls for creative mitigation strategies. In an effort to fill a knowledge vacuum on the subject, this paper investigates the relationship between arms control and climate change mitigation. Specifically, it looks at how arms control policies may boost climate resilience. The research utilizes a thorough literature assessment, incorporating past viewpoints on arms control and current approaches to mitigating climate change. Approach-wise, a qualitative study is carried out to evaluate the empirical and theoretical relationship between weapons control policies and climate change mitigation. Important conclusions show that some weapons control programs can favorably impact climatic outcomes, offering a little-considered option for tackling environmental and security issues. The policy implications highlight the necessity of a more integrated approach to environmental governance and security, and the creation of focused, interdisciplinary policies is emphasized in the recommendations for future research. This study adds to the current conversation on mitigating climate change by providing a fresh viewpoint that supports the double advantages of successful arms control policies in promoting environmental sustainability on a worldwide scale.
Assessing R&D Investment Policy in the Indonesian Defense Sector: Towards Long-Term Sustainability Sarjito, Aris
International Journal of Applied and Advanced Multidisciplinary Research Vol. 2 No. 1 (2024): January, 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v2i1.1262

Abstract

The research examines the current state of research and development (R&D) investment policies in the Indonesian defense sector, focusing on their sustainability and long-term impact. It uses institutional, implementation, network, and innovation diffusion theories and secondary data analysis. The findings reveal institutional influences, challenges in implementation, bureaucratic coordination, and measures to enhance long-term sustainability. Historical, cultural, and regulatory factors influence R&D policies, while limited resources make implementation difficult. The study emphasizes the importance of collaboration between the public and private sectors and continuous monitoring and evaluation. The findings provide recommendations for fostering innovation, overcoming challenges, and ensuring the sector's resilience.

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