Faculty publications @ CTUAP

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Browsing Faculty publications @ CTUAP by Author "Mandala, Gangu Naidu"

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    A study of sleep disorders, mental distress, and depression among students during COVID pandemic
    (2023) Mandala, Gangu Naidu
    The present study has attempted to study the effects of depression, mental distress, and sleep disorders among students during the COVID-19 pandemic. Stress, mental distress, depression, sleep disorders, headaches, loneliness, screen fatigue, and high distress levels are common symptoms observed across the population. The study focused on the students of higher education who have been attending online classes since the inception of COVID-19 virus. The detailed questionnaire was circulated online to 450 students, out of which 323 responded. After filtering the incomplete responses, 286 sample sizes were taken into consideration. The data were analysed using SPSS software, and hypotheses and model testing were performed using the AMOS software. A signifi cant relationship was found between depression, distress, sleep disorders, and student behaviour. Loneliness, lack of physical interaction, and overexposure to screens were found to be major trigger elements affecting students’ mental health. To dilute the effect on students’ behaviour and enhance their mental health, the authors recommend taking precautionary measures by the concerned stakeholders.
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    Innovations in distributed computing for enhanced risk management in finance
    (2024) Mandala, Gangu Naidu
    This chapter explores the use of distributed computing technology for enhancing risk management in finance. The performance of traditional risk management systems is compared to that of distributed computing-based risk management systems, and the objectives of the chapter are established. The theoretical framework of distributed computing is discussed, including an overview of its types and technologies for finance risk management. A comparison of distributed computing approaches for finance risk management is presented, and the innovations in distributed computing for enhanced risk management in finance are highlighted. These include advancements in distributed computing architectures and frameworks for finance risk management, new techniques, and emerging trends in distributed computing for finance risk management. Challenges and limitations of distributed computing for finance risk management are identified, including technical challenges in implementing distributed computing and limitations of the technology. Finally, best practices and recommendations for implementing distributed computing in finance risk management are presented. This includes a framework for selecting the right distributed computing approach for finance risk management as well as guidelines for implementation. This chapter provides a comprehensive overview of the use of distributed computing technology in finance risk management, highlighting its benefits, limitations, and best practices. © 2024 John Wiley & Sons Ltd. All rights reserved.
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    Optimization of Dynamic Pricing in E-Commerce Platform with Demand Side Management using Fuzzy Logic System
    (2023) Mandala, Gangu Naidu
    In e-commerce platforms, dynamic pricing has drawn a lot of attention as a potent tactic to boost sales and improve consumer happiness. But monitoring and comprehending client demand is crucial to the success of dynamic pricing. This study suggests an optimization framework for demand-side management that incorporates fuzzy logic into dynamic pricing in e-commerce platforms. To represent and capture the uncertainty and imprecision present in client demand, the suggested framework makes use of fuzzy logic. Fuzzy logic makes it possible to represent and work with linguistic variables, which makes decision-making more adaptable and natural. The approach takes into account the influence of customer behavior and preferences on pricing decisions by incorporating demand-side management. There are two primary steps in the optimization process. First, a fuzzy demand model is created to estimate consumer demand based on a variety of inputs, including pricing, product qualities, and customer traits. This model offers a quantitative knowledge of consumer behavior under various pricing conditions. Second, a pricing plan that maximizes platform profit while accounting for customer happiness and demand changes is determined using an optimization algorithm. By providing personalized pricing based on consumer preferences, the optimized pricing strategy increases revenue while also enhancing customer happiness. Demand-side management and fuzzy logic are combined to improve decision-making and help e-commerce platforms adjust to shifting consumer preferences and market conditions. © 2023 IEEE.