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All Journal Journal of Management - Small and Medium Enterprises (SME's) International Journal of Engineering, Science and Information Technology
Prabhu, M
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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A STUDY ON LOYALTY AMONG PRIVATE SECTOR EMPLOYEES AND ITS DETERMINANTS Prabhu, M; Madan Mohan, G
Journal of Management Small and Medium Enterprises (SMEs) Vol 18 No 1 (2025): JOURNAL OF MANAGEMENT Small and Medium Enterprises (SME's)
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jom.v18i1.15543

Abstract

Employees with high loyalty or morale are precious to any firm. This descriptive study has attempted to assess the degree of loyalty prevalent among private-sector employees in Iraq and how such loyalty is driven by job satisfaction and stress. Primary data has been collected by administering a structured questionnaire to 127 private employees selected using simple random sampling. The study results reveal that the employees surveyed are loyal to their company. 106 of the 127 employees have displayed good loyalty towards their firm, and only 13 have shown signs of disloyalty. The employees surveyed do not exhibit signs of stress. However, the employees have indicated that they encounter tiredness from their job, which is indicative of exhaustion due to work pressure, which results in them finding it a bit difficult to sleep and developing headaches that merit immediate attention and rectification. Keywords: Employees; Job Satisfaction; Loyalty; Stress
Fake News Detection in Model Integral: A Hybrid CNN-BiLSTM Model Nyayadhish, Renuka; Jadhav, Chaya; Bhupati, Ch; Mabel Rose, R.A.; Prabhu, M
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1058

Abstract

The act of recognizing news that intentionally spreads false information via social media or traditional news sources is known as fake news detection. The characteristics of fake news make it difficult to identify. The spread of fake news and misleading information has increased dramatically due to social media's role as a communication tool and the quick advancement of technology. There is an urgent need for automated and intelligent systems that can differentiate between authentic and fraudulent information due to the fast dissemination of unverified content. The proposed hybrid model efficiently captures regional and worldwide relationships in textual details to address this by combining multiscale residual CNN and BiLSTM layers. The BiLSTM layers manage contextual representations and sequential dependencies, while the CNN layers concentrate on extracting deep local features. The model's capacity to recognize patterns of deception in textual content and comprehend semantic flow is enhanced by this dual architecture. The Edge-IIoT set data and the IoT-23 information from Aposemat were utilized in this study to assess the suggested framework empirically. A concept based on information transfer and sophisticated adaptive systems, we provide an understanding of outliers management paradigm of "generation–spread–identification–refutation" for identifying false information during emergencies. Findings from experiments clearly illustrate the superiority of the BiLSTM approach, demonstrating not only its state-of-the-art efficacy in identifying fake news but also its significant edge over traditional machine learning algorithms. This highlights the BiLSTM approach's critical role in protecting our information ecosystems from the ubiquitous threat of misinformation.
Co-Authors Bhupati, Ch Jadhav, Chaya Mabel Rose, R.A. Madan Mohan, G Nyayadhish, Renuka