journals

Enhancing Text Classification with Cyberbullying and Machine Learning Algorithms

Author(s): Denis R, Anita Jones Mary Pushpa T, Glory Sangeetha R, Kalyan Devappa Bamane*, Sathya S, Subramanian Selvakumar
Volume: 5 Issue: 4 Pages: 519-530
DOI: https://doi.org/10.47857/irjms.2024.v05i04.01131


Abstract With the exponential rise of social media platforms, cyber bullying has become a significant issue, requiring sophisticated techniques for effective detection and prevention. Existing machine learning approaches, while foundational, often fall short in addressing the complex and nuanced linguistic patterns inherent in cyber bullying. This paper presents a novel framework that combines Recurrent Neural […]

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Unveiling The Enigma: Women’s Representation in After Dark by Haruki Murakami

Author(s): Midhat Tasneem, Laxmi Dhar Dwivedi*
Volume: 5 Issue: 4 Pages: 603-614
DOI: https://doi.org/10.47857/irjms.2024.v05i04.01163


Abstract This paper examines the evolution of societal perceptions and role of women in Japan, particularly in the context of patriarchal ideologies, social and cultural constructs. Drawing on various scholarly perspectives this endeavour explores significant shifts in perceptions and treatment of women in Murakami’s novel After Dark. Through his novel, Murakami aims to explore the

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Revolutionizing Data Transmission Efficiency in IoT-Enabled Smart Cities: A Novel Optimization-Centric Approach

Author(s): Sudhagar D, Swapna Saturi, Mukesh Choudhary, Pranav Senthilkumaran, Eric Howard, Mrutyunjaya S Yalawar, RG Vidhya*
Volume: 5 Issue: 4 Pages: 592-602
DOI: https://doi.org/10.47857/irjms.2024.v05i04.01113


Abstract In the modern era, IoT-based smart cities play a crucial role in enhancing the development and quality of life in advanced countries. As digital technologies and advanced metering systems become increasingly integrated with IoT devices in smart city applications, efficient data transmission strategies are essential. This paper introduces a novel approach, the Deep Belief-based

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Public Awareness on General Environmental and Sustainability Issues

Author(s): Donald Sambuaga*, Tinneke Evie Meggy Sumual, Orbanus Naharia, Victory NJ Rotty
Volume: 5 Issue: 4 Pages: 579-591
DOI: https://doi.org/10.47857/irjms.2024.v05i04.01809


Abstract Indonesia is situated within the ‘ring of fire’, a region characterised by a high risk of natural disasters, which encompasses a number of Indonesian cities, including Manado City. While numerous studies have examined postdisaster mitigation strategies, there is a paucity of research evaluating disaster prevention efforts, particularly in Indonesia and Manado City. This research

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Optimization of Physical Parameters of Fermentation Medium for Biosynthesis of Alginate by A. vinelandii Using Molasses as ‘C’ Source

Author(s): Zarina Shaikh1*, Mohd Shakir2
Volume: 5 Issue: 4 Pages: 1495-1503
DOI: https://doi.org/10.47857/irjms.2024.v05i04.01854


Abstract Biopolymer have varied industrial applications. Alginate is one such polymer conventionally extracted from brown algae of marine waters having versatile applications. Due to its increased market demand alternative options are being investigated for its synthesis from microbes. The present study focuses on the optimization of various physical parameters at flask level. Fermentation medium consisted

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Time Ahead Inclinations in Sustainable Supply Chain Management using Artificial Intelligence: A Bibliometric Approach

Author(s): Tushar Ranjan Sahoo1, Shivani Guru1*, Bishwajit Rout2, Bhanu Prasad Behera3
Volume: 5 Issue: 4 Pages: 1482-1494
DOI: https://doi.org/10.47857/irjms.2024.v05i04.01849


Abstract Sustainable Supply Chain Management [SSCM] focuses on integrating environmentally sustainable practices into supply chains to balance economic growth with environmental care and social responsibility. The adoption of Artificial Intelligence [AI] in SSCM is revolutionizing supply chain operations by offering new opportunities for efficiency, transparency, and sustainability. The research paper is basically done to find

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Maternal Outcome of Pregnancy with New onset Stage 1 Hypertension (ACC) after 20 Weeks of Gestation

Author(s): Prerna Chhabra*, Tapan Pattnaik, Rachita Pravalina, Ritu Priya Chaudhury
Volume: 5 Issue: 4 Pages: 1476-1481
DOI: https://doi.org/10.47857/irjms.2024.v05i04.01847


Abstract In 2017, “The American College of Cardiology and the American Heart Association” introduced a new, lower threshold for diagnosing hypertension, which did not include pregnant women. This study examines the outcomes for normotensive pregnant women in comparison to those with new-onset hypertension, defined as systolic blood pressure (SBP) between 130–139 mm Hg and diastolic

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Moringa oleifera Leaves: A Proficient Biosynthetic Source of Myriad Nanoparticles Pertaining Biomedical Importance

Author(s): Tahsin Bennur*, Nutan Bankar#, Mohammed Saif#, Insha Suleman
Volume: 5 Issue: 4 Pages: 1462-1475
DOI: https://doi.org/10.47857/irjms.2024.v05i04.01846


Abstract The advent of nanotechnology has blurred the boundaries between various fields. Development of newer, efficient, and sustainable approaches for nanomaterial synthesis has become the primary focus of research in recent years. The utilization of biological molecules as matrices for nanoparticle synthesis is increasing, with bacteria, fungi, plants, agrowaste, being common choices. Among them plant

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Identifying Learning Difficulties at an Early Stage in Education with the Help of Artificial Intelligence Models and Predictive Analytics

Author(s): Mahisha Babu J*, Anita Virgin B, Meshach RS Edwin, Gokula Priya P Kathiravan Ravichandran
Volume: 5 Issue: 4 Pages: 1455-1461
DOI: https://doi.org/10.47857/irjms.2024.v05i04.01821


Abstract This empirical study explores the transformative potential of integrating artificial intelligence and predictive analysis to proactively identify learning challenges in the dynamic field of education. Through the utilization of diverse data sources, advanced machine learning algorithms, and an iterative methodology to construct predictive models, the study follows a rigorous technical approach. The study underscores

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Detection of Metabolic Gene of Interest (MGOI) in WGS of Microbacterium barkeri (LMA4)

Author(s): Krishna Kumar Das, Smaranika Pattnaik*
Volume: 5 Issue: 4 Pages: 1439-1454
DOI: https://doi.org/10.47857/irjms.2024.v05i04.01815


Abstract This communication reports about detection of metabolically active genes of interest cell free extract of actinomycetal isolates from Pond soil. The strain LMA4 previously identified as Microbacterium barkeri, based on biotyping as well as 16 s r RNA sequencing, was observed with moderate bioactivity against test strains of Staphylococcus aureus and Escherichia coli. The

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