International Journal of Applied and Advanced Multidisciplinary Research (IJAAMR)
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.
Articles
90 Documents
<![CDATA[The Influence of Animated Film Media on the Independence of Group B Children Aged 5-6 Years at TK Prestige Bilingual School ]]>
<![CDATA[Cindy Syachrani Fauziah]]>;
<![CDATA[Kamtini]]>
<![CDATA[ International Journal of Applied and Advanced Multidisciplinary Research Vol. 1 No. 2 (2023): October 2023 ]]>
Publisher : <![CDATA[MultiTech Publisher ]]>
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DOI: 10.59890/ijaamr.v1i2.680
<![CDATA[This research aims to determine the results of children's independence through the use of animated film media in group B children at TK Prestige Bilingual School. The results of this research were that the average score given the animated film treatment was 10.53 in the very good category, while the average score not treated with the animated film was 9.13 in the very good category. From the score statement, the data shows that learning activities using animated films can increase the independence of group B children aged 5-6 years at TK Prestige Bilingual School. This can be proven by obtaining scores given treatment and no treatment. Based on the results of the hypothesis test, it shows that there is a significant influence from the use of animated film media on the independence of group B children, and from the results of the t-test, it is obtained that tcount < ttable, namely 0.000 < 0.05 at the level α = 0.005 with the results of the t-test it can be concluded that There is a significant influence from the use of animated film media on the independence of group B children aged 5-6 years at TK Prestige Bilingual School.]]>
<![CDATA[AI in Healthcare 5.0: Opportunities and Challenges ]]>
<![CDATA[Soham Date]]>;
<![CDATA[Meenakshi Thalor]]>
<![CDATA[ International Journal of Applied and Advanced Multidisciplinary Research Vol. 2 No. 1 (2024): January, 2024 ]]>
Publisher : <![CDATA[MultiTech Publisher ]]>
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DOI: 10.59890/ijaamr.v2i1.281
<![CDATA[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.]]>
<![CDATA[Customized Education Artificial Intelligence's Role In Tailored E-Learning. ]]>
<![CDATA[Gaurang Kanbur]]>;
<![CDATA[Meenakshi Thalor]]>
<![CDATA[ International Journal of Applied and Advanced Multidisciplinary Research Vol. 1 No. 3 (2023): November 2023 ]]>
Publisher : <![CDATA[MultiTech Publisher ]]>
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DOI: 10.59890/ijaamr.v1i3.469
<![CDATA[This research is driven by the immense potential of personalized e-learning systems to address the challenges of effective online education delivery. It focuses on proposing an efficient architectural framework for personalized e-learning systems, exploring various techniques and challenges and offering innovative solutions. The paper conducts a thorough review of current state-of-the-art methodologies in implementing personalized e-learning systems, along with discussions on the crucial requirements and challenges for successful deployment. Furthermore, it presents an efficient framework for building effective e-learning systems, while also discussing mechanisms, challenges and future research directions that the research community can consider. The subsequent sections of this paper provide a detailed exploration of the research, followed by a proposal for a personalized learning system, and insights into important issues for the community to address. The paper concludes by summarizing its findings and contributions. ]]>
<![CDATA[Schinzophrenia Detection using Machine Learning ]]>
<![CDATA[Kiran Mane]]>;
<![CDATA[Pragati Mahale]]>
<![CDATA[ International Journal of Applied and Advanced Multidisciplinary Research Vol. 1 No. 4 (2023): December 2023 ]]>
Publisher : <![CDATA[MultiTech Publisher ]]>
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DOI: 10.59890/ijaamr.v1i4.524
<![CDATA[Schizophrenia is a complex mental disorder characterized by disruptions in thinking, perception, and emotional regulation. Early diagnosis is crucial for effective treatment and improved patient outcomes. This abstract explores the application of machine learning in schizophrenia detection. By analyzing diverse data sources, including neuroimaging, genetic, and clinical data, machine learning models can aid in identifying patterns and biomarkers associated with schizophrenia. This approach offers the potential for early and accurate diagnosis, enabling timely interventions and personalized treatment plans. The integration of machine learning into the diagnostic process holds promise for enhancing the understanding and management of schizophrenia, ultimately improving the quality of life for affected individuals.]]>
<![CDATA[Intrusion Detection Systems Using Machine Learning. ]]>
<![CDATA[Rohit Utekar]]>;
<![CDATA[Anuja Phapale]]>
<![CDATA[ International Journal of Applied and Advanced Multidisciplinary Research Vol. 2 No. 1 (2024): January, 2024 ]]>
Publisher : <![CDATA[MultiTech Publisher ]]>
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DOI: 10.59890/ijaamr.v2i1.550
<![CDATA[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.]]>
<![CDATA[Psychological Emotion Recognition of Students Based Chatbot ]]>
<![CDATA[Atharva Senapati]]>;
<![CDATA[Anuja Phapale]]>
<![CDATA[ International Journal of Applied and Advanced Multidisciplinary Research Vol. 1 No. 3 (2023): November 2023 ]]>
Publisher : <![CDATA[MultiTech Publisher ]]>
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DOI: 10.59890/ijaamr.v1i3.565
<![CDATA[In the context of modern education, where students' emotional well-being plays a crucial role in academic success, the integration of technology, particularly machine learning-based chatbots, presents a promising avenue to support and recognize the psychological states of students. This paper delves into the development and evaluation of a chatbot system designed to recognize and respond to the emotions expressed by students. Leveraging natural language processing and sentiment analysis, the chatbot engages in conversations with students, allowing for the real-time recognition of emotional states. Our study does not only focus on the technical aspects of emotion recognition but also exploration the implications and ethical considerations of deploying such technology in educational settings. By shedding light on the potential of machine learning-based chatbots to enhance emotional support and understanding within educational environments, this research contributes to the ongoing dialogue on students well-being and the role of technology in education.]]>
<![CDATA[Machine Learning Based Weed Detection System ]]>
<![CDATA[Prathamesh Gajbhiye]]>;
<![CDATA[Meenakshi Thalor]]>
<![CDATA[ International Journal of Applied and Advanced Multidisciplinary Research Vol. 1 No. 4 (2023): December 2023 ]]>
Publisher : <![CDATA[MultiTech Publisher ]]>
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DOI: 10.59890/ijaamr.v1i4.568
<![CDATA[This abstract underscores the importance of weed detection in crop cultivation to prevent plant diseases and minimize crop losses. To address these challenges and promote eco-friendly practices, the authors propose a weed detection program employing K-Nearest Neighbors, Random Forest, Decision Tree algorithms, and the YOLOv5 neural network. The abstract also provides a concise overview of existing research in weed identification using machine learning and deep learning. The authors developed a YOLOv5-based weed detection system and evaluated the performance of the algorithm, showing traditional classifiers achieve accuracies of 83.3%, 87.5%, and 80%, while the neural network scores range from 0.82 to 0.92 for each class. The study demonstrates the effectiveness of this approach in classifying low-resolution weed images.]]>
<![CDATA[Enhancing Data Backup and Recovery in Cloud Computing with Secure Database Monitoring ]]>
<![CDATA[Mehul Pawar]]>;
<![CDATA[Anuja S. Phapale]]>
<![CDATA[ International Journal of Applied and Advanced Multidisciplinary Research Vol. 1 No. 4 (2023): December 2023 ]]>
Publisher : <![CDATA[MultiTech Publisher ]]>
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DOI: 10.59890/ijaamr.v1i4.594
<![CDATA[The company uses unregistered data to improve performance, but this comes at the cost of limited backup options, data loss, and a voluntarily damaging week-long downtime. Even if they have backup systems, they cannot meet the SLA recovery time. To solve these problems, this work presents a secure database monitoring method for cloud computing. This approach adjusts the backup speed according to the data volume; This is important when data grows at least 30% per year. For the some companies, the data doubles every 3-4 years or more and SLAs and recovery targets need to be updated. At the same time, business needs for data recovery continue to grow, highlighting the need for more robust and responsive data management strategies.]]>
<![CDATA[Enabling the Future: a Virtualized Approach to 5G and Edge Computing ]]>
<![CDATA[Ankita Jamdade]]>;
<![CDATA[M.A.Thalor]]>
<![CDATA[ International Journal of Applied and Advanced Multidisciplinary Research Vol. 1 No. 4 (2023): December 2023 ]]>
Publisher : <![CDATA[MultiTech Publisher ]]>
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DOI: 10.59890/ijaamr.v1i4.614
<![CDATA[This abstract examines the evolution of wireless communications technologies beyond 5G (B5G) and the emergence of 6G. It highlights their key role in powering the Internet of Things (IoT) and enabling edge computing. Built on shared resources, this system is exposed to various real-time application scenarios and uses simulated user equipment (UE) and operational Nextcloud instances. Performance metrics are analyzed and the system can automatically scale during high network traffic to ensure high availability. Key concepts include Radio Access Network (RAN), Edge Computing, User Equipment and Virtual Network Functions (VNF). This framework relies on shared resources and undergoes rigorous testing with real-time application scenarios, using simulated User Equipment (UE) and operational Nextcloud instances.]]>
<![CDATA[Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms ]]>
<![CDATA[Pragati Mahale]]>;
<![CDATA[Sejal Khopade]]>
<![CDATA[ International Journal of Applied and Advanced Multidisciplinary Research Vol. 2 No. 1 (2024): January, 2024 ]]>
Publisher : <![CDATA[MultiTech Publisher ]]>
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DOI: 10.59890/ijaamr.v2i1.664
<![CDATA[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.]]>
