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All Journal International Journal of Electrical and Computer Engineering Public Health of Indonesia Indonesian Journal of Electrical Engineering and Computer Science
Geetha Mani, Geetha
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Computer Science & IT Electrical & Electronics Engineering Public Health

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DISASTER-RESILIENT HOSPITALS: THE NOAH'S ARK Mani, Geetha; Danasekaran, Raja; Annadurai, Kalaivani
Public Health of Indonesia Vol. 2 No. 4 (2016): October - December
Publisher : YCAB Publisher & IAKMI SULTRA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (151.016 KB) | DOI: 10.36685/phi.v2i4.95

Abstract

Health services are important lifelines of a community any time and this role is more pronounced during times of disasters. Evidence from various parts of the world presents examples of disaster-induced damage to hospitals and failure of health services at times of need. The impact of disasters-induced damage to health care is three-dimensional: health, social and economic. Damage to health care facilities apart from delaying and complicating relief measures also compromise the achievement of planned national and global health and related goals. The indirect and long-term costs of damage to health sector are greater than direct and immediate costs, compounding the disastrous consequences on the economy. The increasing invasion of nature spaces, climate change and urbanisation are bound to aggravate more natural hazards in future. So a resilient health care system is an immediate necessity for all global states. This paper discusses the international and national endeavours towards a resilient health-care system and analyses the strategies to promote safe hospitals in future.
MASS GATHERINGS: PUBLIC HEALTH IMPLICATIONS AND OPPORTUNITIES Mani, Geetha
Public Health of Indonesia Vol. 3 No. 1 (2017): January - March
Publisher : YCAB Publisher & IAKMI SULTRA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (140.693 KB) | DOI: 10.36685/phi.v3i1.106

Abstract

NA
A comparative study of long short-term memory based long-term electrical load forecasting techniques with hyperparameter optimization Mani, Geetha; Seetharaman, Suresh; Kandasamy, Jothinathan; Ladha, Lekshmy Premachandran; Mohandas, Anish John Paul; Sivasubramoniam, Swamy; Renugadevi, Valarmathi Iyappan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7080-7089

Abstract

Long-term load forecasting (LTLF) is crucial for reliable electricity supply, infrastructure planning, and informed energy policies, ensuring grid stability and efficient resource allocation. Traditional methods, like statistical models and expert judgment, rely on historical data but may struggle with dynamic changes in technology, regulations, and consumer behavior. Addressing challenges such as economic uncertainties, seasonal variations, data quality, and integrating renewable energy requires advanced forecasting models and adaptive strategies. This research aims to develop an efficient LTLF model for the Coimbatore region in Tamil Nadu, India, using long short-term memory (LSTM) networks. While LSTM has limitations in capturing long- term dependencies and requires high data quality and complex management, optimizing hyperparameters, including through the opposition-based hunter- prey optimization (OHPO) technique, is explored to enhance its predictive performance. The results show that the proposed OHPO-configured LSTM model for LTLF achieves superior performance compared to other techniques, with a mean square error (MSE) of 0.25, root mean square error (RMSE) of 0.5 and mean absolute percentage error (MAPE) of 0.27. This research underscores the significance of improving LTLF precision for informed decision-making in infrastructure planning and energy policy formulation.
Machine learning based strategies for managing employee retention: determining factors in hospitality industry Kaja Mytheen, Basari Kodi; Jeyakumar, Murugachandravel; Ramasamy, Kannan; Mani, Geetha; Jayamurugan, Prabhu; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1652-1660

Abstract

In order to boost performance and remain competitive, the Indian hospitality industry must recruit and retain employees if it wants to succeed in the long run. In order to do this, it will need to use a number of staff retention initiatives. It is suggested that effective employee retention tactics be analyzed using machine learning (ML) approaches for prediction. The results show that the hotel industry uses tactics to keep its employees, such as competitive compensation and benefits, opportunities for growth and recognition, safe and healthy workplaces, adaptable schedules, employment stability, and ongoing education and development. There is a noticeable disparity between the hotel industry’s demographics and retention tactics. In the hotel industry, there is a modestly negative correlation between employee desire to depart and employee retention methods. Pay and benefits, recognition and gratitude, a safe and healthy workplace, opportunities for professional growth, and development all play a role in how satisfied hospitality workers are with their jobs. The hotel sector has to implement strong welfare initiatives if it wants its workers to have a healthy work-life balance. The hotel business should promote the development of professional connections among its employees.
Co-Authors Ananthan, Bhuvanesh Jayamurugan, Prabhu Jeyakumar, Murugachandravel Kaja Mytheen, Basari Kodi Kalaivani Annadurai, Kalaivani Kandasamy, Jothinathan Ladha, Lekshmy Premachandran Mohandas, Anish John Paul Raja Danasekaran, Raja Ramasamy, Kannan Renugadevi, Valarmathi Iyappan Seetharaman, Suresh Sivasubramoniam, Swamy