journals

Flood Detection and Safe Path Finding Using Dense Neural Network (DNN)

Author(s): Shailza Thakur, Astha Raghvendra Jha, Dev Ankur Mehta, Lavanya K*
Volume: 5 Issue: 3 Pages: 337-348
DOI: https://doi.org/10.47857/irjms.2024.v05i03.0721


Abstract In contemporary flood management, integrating advanced remote sensing and machine learning marks a significant advancement. This research presents a framework leveraging such tech for flood management. It utilizes the SEN-12 flood dataset for satellite imagery analysis, enabling precise flood mapping via pre-processing. By refining the dataset with bandwidth images, targeted flood detection becomes feasible. […]

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Association Regarding S&P BSE Market and Specific Financial Parameters: A Statistical Analysis

Author(s): Avula Sreenivasulu, Rajesh Mamilla*
Volume: 5 Issue: 3 Pages: 331-336
DOI: https://doi.org/10.47857/irjms.2024.v05i03.0720


Abstract The present investigation investigates the relationship between the S&P BSE market and particular financial characteristics through statistical analysis. The Indian stock market is crucial element of the nation’s financial framework, functions via two primary stocks provides: the Bombay Stock Exchange and National Stock Exchange. It examines money supply, Interest Rate (IR), Consumer Price Index

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Coumarin Chalcone Hybrids: Molecular Hybridization Strategy for Drug Design

Author(s): Sumita Kumari*, Amit Sharma, Sonia Yadav
Volume: 5 Issue: 3 Pages: 321-330
DOI: https://doi.org/10.47857/irjms.2024.v05i03.0719


Abstract Naturally occurring and synthetically produced hybrid compounds are potential reservoir for development of novel drug regimen because of various benefits including effectiveness, minimal side effects, receptor interactions and pharmacokinetic features. Coumarin and chalcone are perfect building blocks for creating hybrid scaffolds that can be used as bioactive agents. These are essential classes of synthetic

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Assessment of Improvement in Prescribing Skills of 2nd Phase MBBS Students after Administering an Educational Intervention on Safe Prescribing Based on WHO Guidelines: A Quasi-experimental Study in a Teaching Hospital of Odisha

Author(s): Sansita Parida*, Smita Das, Manika Bose, Debasis Misra
Volume: 5 Issue: 3 Pages: 313-320
DOI: https://doi.org/10.47857/irjms.2024.v05i03.0718


Abstract National Medical Council has included teaching rational prescribing practices into its 2nd phase medical curriculum to inculcate the habit of writing rational and legible prescription by the health-care professionals to reduce prescription errors. The study aims to assess the improvement in prescribing skill of 2nd phase MBBS students by comparing against the WHO guidelines

Assessment of Improvement in Prescribing Skills of 2nd Phase MBBS Students after Administering an Educational Intervention on Safe Prescribing Based on WHO Guidelines: A Quasi-experimental Study in a Teaching Hospital of Odisha Read More »

Parameters of Online and Offline Shopping Experience of Gen Z Customers with Reference to Fashion Industry in India

Author(s): Deepa A* , Elangovan A
Volume: 5 Issue: 3 Pages: 303-312
DOI: https://doi.org/10.47857/irjms.2024.v05i03.0713


Abstract Sellers are attempting to resurrect offline businesses by implementing retail technologies in response to the decline of physical fashion stores. The need for this study was to ascertain how perceived risk and quality were impacted by aspects of the off the internet mobile connected smart selling familiarity in terms of fulfilment. A study including

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Safeguarding Humans from Attacks Using AI-Enabled (DQN) Wild Animal Identification System

Author(s): Govindaprabhu GB*, Sumathi M
Volume: 5 Issue: 3 Pages: 285-302
DOI: https://doi.org/10.47857/irjms.2024.v05i03.0697


Abstract Without advanced artificial intelligence (AI) technologies, monitoring and identifying wildlife has become increasingly difficult. To examine AI-driven methodologies for wild animal identification, this work uses a diverse dataset of annotated images with human, domestic and wild animal annotations. Convolutional Neural Networks (CNNs), AlexNet, and Deep Q-Learning (DQN) models are developed and compared by combining

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Classical Theories of Motivation and Teachers’ Motivation in Public and Private Schools of Guwahati, Assam

Author(s): Samir Sarkar*, Sushmita Bordoloi, Sukriti Das
Volume: 5 Issue: 3 Pages: 266-284
DOI: https://doi.org/10.47857/irjms.2024.v05i03.0695


Abstract Teachers’ motivation has become a significant concern, given their obligation to impart knowledge and skills to students. This research attempts to investigate the motivational factors and their effect on teacher’s performance in private and public schools in Guwahati. The classical theories of motivation have given a clear view on various factors of motivation. The

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Job Satisfaction and Motivation of Covid Hospital Staffs: A Cross-Sectional Study

Author(s): Rakesh Mohanty, Surjeet Sahoo, Sudhansu Sekhar Lenka, Suvendu Narayan Mishra*
Volume: 5 Issue: 3 Pages: 259-265
DOI: https://doi.org/10.47857/irjms.2024.v05i03.0489


Abstract Healthcare today faces a plethora of compelling challenges, and motivation is essential. It is important to recognize that the workforce in the health sector operates distinctly and specially. Consequently, because health organizations are susceptible to external pressures from patients, their families, various governing bodies, and the government, workforce development is an essential component of

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A Real Time Performance Comparison of Rice Plant Disease Identification System using Deep CNN Models

Author(s): K Mahadevan*, A Punitha, J Suresh
Volume: 5 Issue: 3 Pages: 249-258
DOI: https://doi.org/10.47857/irjms.2024.v05i03.0159


Abstract The world’s second-largest producer and exporter of rice is India. Early disease detection is crucial to ensuring healthy rice production. In order to address the issue of diagnosing illnesses in rice plants, a number of strategies have been put forth; nevertheless, it has been discovered from the literature that these models do not perform

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Robust Remote Sensing Image Classification utilizing Multiverse Optimization Model with Deep Transfer Learning Approach

Author(s): Josephine Anitha A*, Gladis D
Volume: 5 Issue: 3 Pages: 751-759
DOI: https://doi.org/10.47857/irjms.2024.v05i03.0969


Abstract The classifying process of Remote Sensing Image (RSI) has been crucial in diverse applications, involving land cover mapping, environmental monitoring, disaster assessment, and urban planning. Conventionally, image classification in Remote Sensing (RS) relies on Machine Learning (ML) and handcrafted feature extraction approaches. But, lately, Deep Learning (DL) has become an effective and powerful method

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