Browse Items (14421 total)
Sort by:
-
Prevalence of Cardiovascular Diseases in South Asians: Scrutinizing Traditional Risk Factors and Newly Recognized Risk Factors Sarcopenia and Osteopenia/Osteoporosis
One of the primary reasons for complications and death worldwide are cardiovascular diseases (CVDs), with a death toll of approximately 18 million per year. CVDs include cardiomyopathy, hypertension, ischemic heart disease, coronary heart disease, myocardial infarction, heart attack, hearth failure, etc. Over 80% of the CVD mortality is recorded from lower and middle-income countries. Records from the past decade have highlighted the increase of CVDs among the South Asian populations, and the prime purpose of the review is to jot down the reasons for the steep spike in CVDs. Studies analyzing the causative factors for the increase of CVDs in South Asians are still to be verified. Apart from known predisposing and lifestyle factors, other emerging risk factors associated with CVDs, namely the musculoskeletal diseases sarcopenia and osteopenia, should be tracked to tackle research gaps in upcoming analyses. This requires loads of scientific efforts. With proper monitoring, the raising alarm that the CVD burden generates can be reduced. This review discusses the already established signs and recognizes important clues to the emerging etiology of CVDs in the Asian population and prevention measures to keep it at bay. 2023 Elsevier Inc. -
Prevalence of hypertension and determination of its risk factors in korangrapady, udupi district, coastal Karnataka, India
Objective: Hypertension is a global public health problem that estimates about 4.5% of overall disease burden. It is a general health challenge in economically developing and developed countries. High blood pressure prevalence is increased from 11.2% to 28% (p<0.001) and 2342.2% in rural and urban area according to the study done in Delhi for about 20 years. It is one of the important risk factors of cardiovascular disease, which is associated with morbidity and mortality. The aim was to identify the significant correlates of hypertension in a rural village in south India. Methods: Data were collected through a door-to-door survey among the residents of the village. Data collected was related to demographics and anthropometric measures. Blood pressure was measured with the help of the medical supervisor. Data were analyzed using Chi-square test for comparison between attributes. The potential hazard factor of hypertension was found by performing binary logistic regression model. Result: Of 299 participants considered for the study, 50 were hypertensive contributing to the overall prevalence of 16.72% with 95% confidence interval of 0.12920.2137, in which females have the prevalence rate of 17.8% and males with the prevalence rate of 15.5%. The study outcome identified education level, occupation, and family history of hypertension is the predicted risk factors. Conclusion: The high blood pressure prevalence is low and comparable with the studies conducted in other rural regions of India. More studies are, however, required to decide the appropriation and determinants of hypertension in different parts of this region. 2018 The Authors. -
Preventing Data Leakage and Traffic Optimization in Software-Defined Programmable Networks
The first widely used communication infrastructure was the telephone network, often known as a connection-oriented or circuit-switched network. While making a phone call, these networks will first set up a connection, and then tear it down after the call has ended. The connection made during the call would not be used again. Thus, connectionless or packet-switched networks have been introduced, with an aim to send voice signals as data packets. When compared to conventional network architecture, SDN's separation of the data plane and control plane of networking devices makes the management of these devices directly programmable via a centralised controller. It uses a MAS-based distributed architecture to categorise network flows, and it's called the Traffic Classification Module. Each host or server's high-priority application traffic is isolated via Deep Packet Inspection (DPI). The time consumed for a packet to travel from one endpoint to another is referred to as the average packet delay, whereas the controller's reaction time is twice the average packet delay. Few works existed that utilised routing strategies to decrease the typical packet delay in SDN. To reduce the controller's response time, Software-Defined Networks (SDNs) need a routing algorithm that reduces the average packet delay. Each of the proposed modules and the whole combined SDN-MASTE framework were put through their paces in a series of experiments and emulation-based tests to see how well they performed. 2023 IEEE. -
Prevention and Mitigation of Intrusion Using an Efficient Ensemble Classification in Fog Computing
Cloud services in fog network is a platform that inherits software services to a network to handle cloud-specific problems. A significant component of the security paradigm that supports service quality is represented by intrusion detection systems (IDSs). This work develops an optimization environment to mitigate intrusion using RSLO classifier on a cloud-based fog networks. Here, a three-layer approach namely the cloud, end point, and fog layers is used as a trio to carry out all of the processing. In the cloud layer, three layers of processing are required for handling the dataset metrics which are data transformation metrics, feature selection metrics, and classification processes. With log transformation, data is transformed using KS correlation-based filter which is used to choose a feature. The classification using an ensemble methodology of RideNN classifiers which is a Rider Sea Lion Optimization (RSLO), a created classifier, is used to tune the ensemble classifier. Physical work is carried out at another layer called an end point layer. A trained ensemble classifier is used for intrusion detection in the fog layer. A greater precision, recall, and F-measure were obtained with an accuracy approximately 95%, with all benefits of the suggested RSLO-based ensemble strategy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Prevention of Data Breach by Machine Learning Techniques
In today's data communication environment, network and system security is vital. Hackers and intruders can gain unauthorized access to networks and online services, resulting in some successful attempts to knock down networks and web services. With the progress of security systems, new threats and countermeasures to these assaults emerge. Intrusion Detection Systems are one of these choices (IDS). An Intrusion Detection System's primary goal is to protect resources from attacks. It analyses and anticipates user behavior before determining if it is an assault or a common occurrence. We use Rough Set Theory (RST) and Gradient Boosting to identify network breaches (using the boost library). When packets are intercepted from the network, RST is used to pre-process the data and reduce the dimensions. A gradient boosting model will be used to learn and evaluate the features chosen by RST. RST-Gradient boost model provides the greatest results and accuracy when compared to other scale-down strategies like regular scaler. 2022 IEEE. -
Price Discovery and Asymmetric Volatility Spillovers in Indian Spot-Futures Gold Markets
International Journal of Economic Sciences and Applied Research, Vol-5 (3), pp. 65-80. ISSN-1791-5120 -
Price Discovery of Currency Futures at NSE
The current study aimed to examine the causal relationship between the NSE currency future rates and currency spot rates in order to identify the price discovery mechanism at NSE market and its integration with foreign exchange market (spot market). To study the causal relationship between the said markets, we have considered daily closing rates for NSE currency futures and currency spot rates for selected pairs of currencies, i.e. USD/INR, GBP/INR, JPY/INR and EURO/INR. The data was obtained from www.nseindia.com and www.investing.com for the period from Jan-2010 to Sep-2017, which makes approximately 1750 observations for each currency pair in each market. It is found that the spot rate for JPY/INR leads the future rate. It is also identified that the spot rate for USD/INR does not cause the changes in futures. It indicates that the market integration between spot and futures at NSE for currency pair USD/INR is strong compared to other selected currency pairs. From the variance decomposition test we found that there is almost no impact of variance in USD/INR spot rate on future rate variance forecast errors. It implies that the causal relationship between for USD/INR spot and future rates is strong and mature compared to the measured causal relationships for the remaining currency pairs. This study concludes that the price discovery process for currency pair USD/INR is better at NSE currency futures among the selected currency pairs. Copyright 2022 by authors, all rights reserved. Authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License -
Price Minds: AI-Driven Insights, Recommendations and Dynamic Pricing
This research aims to enhance e-commerce systems by leveraging customer behavior analysis, dynamic pricing, and personalized recommendations. With the increasing demand for tailored shopping experiences and competitive pricing, businesses require adaptive solutions. The study integrates synthetic and real-time customer data to identify purchasing patterns and segment customers effectively. Dynamic pricing strategies are applied to optimize revenue while maintaining customer satisfaction. A unified framework combines clustering techniques, real-time data streams, and decision-making models to deliver actionable insights for business operations. The proposed system dynamically adjusts pricing and recommends products based on individual customer preferences and behavior. The approach addresses the growing need for intelligent systems that adapt to market trends and consumer demands. Results demonstrate improved operational efficiency, better customer engagement, and enhanced profitability. This work highlights the importance of real-time analytics and intelligent pricing mechanisms in advancing e-commerce and creating competitive advantages in rapidly evolving markets. 2025 IEEE. -
Pricing and content Netflixs dilemma in India
Learning outcomes: The learning outcomes of this study are as follows:1. Analyze the pricing strategy followed by Netflix in India;2. Examine the challenges faced by media companies, including over-the-top (OTT) service providers, in developing content for target consumers in emerging markets; and3. Evaluate the dynamics of the Indian OTT industry and understand the effect of external and internal factors on the growth of Netflix in India. Case overview/synopsis: This case discusses the dilemma faced by Netflix in India regarding pricing and content. Netflix was accused of hurting the religious and political sentiments of Indians by broadcasting bold shows such as Sacred Games and A Suitable Boy. Netflix is caught in a dilemma between its pursuit to achieve its target of achieving 100 million subscribers from India versus continuing its profitable high pricing strategy. Another key dilemma is regarding the streaming of attractive bold content which may occasionally hurt the religious/political sentiments of some Indians or stream only safe content which may be deemed as boring by its young target audience. Complexity academic level: Undergraduate and postgraduate students studying Marketing courses in Commerce and Business Management streams can use this case. Supplementary materials: Teaching notes are available for educators only. Subject code: CSS 8: Marketing. 2022, Emerald Publishing Limited. -
Pricing of liquidity risk in the indian stock market
Empirical literature from developed stock markets identifies liquidity risk to have impacts on the price of a stock. Given this, using one-minute trade and quote data of fifty stocks constituting the NIFTY 50 Index, this study examines the pricing of liquidity risk in the Indian stock market. The study uses thirteen liquidity measures identified from literature that cover the cost, quantity, time and multidimensional aspects of liquidity. The innovations in the liquidity measures are considered as the proxy for liquidity risk. Employing Generalized Methods of Moments estimation, the study proves that Indian investors expect to have a premium for holding securities that are illiquid when the whole market is illiquid. It proves liquidity risk as a priced factor and thus validates the liquidity-adjusted capital asset pricing model in the Indian stock market. It cautions the investors that the liquidity shocks can have significant inferences on portfolio diversification strategies to be adopted. 2020 GEA College – Faculty of Entrepreneurship. All rights reserved. -
Primary mirror active control system simulation of Prototype Segmented Mirror Telescope
The upcoming large astronomical telescopes are trending towards the Segmented Mirror Telescope (SMT) technology, initially developed at the W M Keck Observatory in Hawaii, where two largest SMTs in the world are in use. SMT uses large number of smaller hexagonal mirror segments aligned and positioned by the use of three position/force actuators and six intersegment edge sensors. This positioning needs to be done within nanometer range to make them act like a monolithic primary mirror in the presence of different disturbances like wind, vibration & thermal effects. The primary mirror active control system of SMT does this important task at two levels. First at a global scale, by measuring edge sensor information continuously and commanding actuators to correct for any departure from the reference surface. And second at local actuator level, where all the actuators maintain their position to the reference control inputs. The paper describes our novel approach of primary mirror active control simulation of Prototype Segmented Mirror Telescope (PSMT) under design and development at Indian Institute of Astrophysics (IIA), Bangalore. The PSMT is a 1.5m segmented mirror telescope with seven hexagonal segments, 24 inductive edge sensors, and 21 soft actuators. The state space model of the soft actuator with Multiple-Input Multiple-Output (MIMO) capability is developed to incorporate dynamic wind disturbances. Further, a segment model was developed using three such actuators which accept actuator position commands from the global controller and telescope control system and yields tip-tilt-piston (TTP) of a single segment. A dynamic wind disturbance model is developed and used to disturb the mirror in a more realistic way. A feed forward PID controller is implemented, and gains are appropriately tuned to get the good wind rejection. In the last phase, a global controller is implemented based on SVD algorithm to command all the actuators of seven segments combined to act as a single monolithic mirror telescope. In this paper, we present the progress of PSMT active control system simulation along with the simulation results. 2017 IEEE. -
Primordial Planets with an Admixture of Dark Matter Particles and Baryonic Matter
It has been suggested that primordial planets could have formed in the early universe and the missing baryons in the universe could be explained by primordial free-floating planets of solid hydrogen. Many such planets were recently discovered around the old and metal-poor stars, and such planets could have formed in early epochs. Another possibility for missing baryons in the universe could be that these baryons are admixed with DM particles inside the primordial planets. Here, we discuss the possibility of the admixture of baryons in the DM primordial planets discussed earlier. We consider gravitationally bound DM objects with the DM particles constituting them varying in mass from 20 to100 GeV. Different fractions of DM particles mixed with baryonic matter in forming the primordial planets are discussed. For the different mass range of DM particles forming DM planets, we have estimated the radius and density of these planets with different fractions of DM and baryonic particles. It is found that for heavier-mass DM particles with the admixture of certain fractions of baryonic particles, the mass of the planet increases and can reach or even substantially exceed Jupiter mass. The energy released during the process of merger of such primordial planets is discussed. The energy required for the tidal breakup of such an object in the vicinity of a black hole is also discussed. 2023 by the authors. -
Principles and clinical interventions in social cognition
There are a plethora of questions experts are asking surrounding the intersection of clinical intervention practices with social cognition. How do neuro-cognitive processes shape social understanding? What experimental methods illuminate social cognitive complexities? How can social cognition be applied practically in clinical contexts and psycho-social rehabilitation? How does social cognition influence decision-making and cross-cultural perspectives? To find the answers to these concerns, researchers can now look to Principles and Clinical Interventions in Social Cognition, a research book which delves into recent advances, practical applications, and future trajectories within the intricate relationship between social processes and cognitive mechanisms. It adopts a unique structure, each chapter offering a concise introduction to a specific aspect of social cognition. From foundational principles to applications in clinical interventions and individual well-being, it covers neuro-cognitive processes, experiments, and social cognition in various clinical and health conditions. This book stands out for its emphasis on the practical applications of social cognition in interventions and rehabilitation for clinical populations. By addressing the intersection of social psychology and cognition, it provides actionable insights and potential solutions to the complex challenges in the field. The content spans a broad spectrum, including attention and perception, affect, motivation, behavior, learning, memory, language, decision-making, attitudes, prejudice, stereotyping, theory of mind, self, and others. The interdisciplinary nature of this book makes it an authoritative resource for professionals, researchers, and students in psychology, neuropsychology, cognitive psychology, cognitive neuroscience, social work, sociology, management, allied health sciences, and other areas of social science. By offering a blend of historical context, cognitive processes, social behavior, and real-world applications, this book bridges gaps and offers a holistic understanding of social cognition. 2024 by IGI Global. All rights reserved. -
Prior Cardiovascular Disease Detection using Machine Learning Algorithms in Fog Computing
The term latent disease refers to an infection that does not show symptoms but remains forever. In this paper, proposed a novel methodology for addressing latent diseases in machine learning by integrating fog computing techniques. Here there is a link between HIV to heart disease, that is when a person progresses to the next stage of HIV, a plague infection develops, causing cholesterol deposits to form. Plaque development causes the inside of the arteries to constrict over time, which may stimulate the release of numerous heat shock proteins and immune complexes into the bloodstream, potentially leading to heart disease. Heart disease has long been considered as a significant life-threatening illness in humans. Heart disease is driven by a range of factors including unhealthy eating, lack of physical exercise, gaining overweight, tobacco, as well as other hazardous lifestyle choices. Five different classifiers are used to perform the precision; they are Support vector machine, K-nearest neighbor, decision tree, and random forest, after we have used the classifier, the recommended ideal will split disease into groups which is created based on their threat issues. This will be beneficial to doctors assisting doctors in analyzing the risk factors associated with their patients. 2023 IEEE. -
Prioritisation of Challenges in OTT Video Platforms: A Multi-criteria Decision-Making Approach
Over-the-top (OTT) video platforms have emerged as the preferred choice for on-demand entertainment. In this ever-changing landscape, OTT video platforms face many challenges to being relevant in the market. Identifying and prioritizing challenges is pivotal for the sustainable growth of OTT platforms. This paper aims to comprehensively examine and prioritize the challenges of the OTT video platform. The challenges are identified through an extensive literature review and unstructured interviews with six OTT industrial experts. The importance of each challenge is measured based on the analytical hierarchy process (AHP) to develop a hierarchy of those challenges. The AHP analysis results indicated customer retention as the most significant challenge, followed by content, customer experience, infrastructure, and bandwidth. The study is subjective to the experts opinions and available literature regarding the OTT platforms. The insights gleaned from this research are poised to offer substantial value to digital platform operators, media professionals, and managers. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Prioritisation of Challenges in OTT Video Platforms: A Multi-criteria Decision-Making Approach
Over-the-top (OTT) video platforms have emerged as the preferred choice for on-demand entertainment. In this ever-changing landscape, OTT video platforms face many challenges to being relevant in the market. Identifying and prioritizing challenges is pivotal for the sustainable growth of OTT platforms. This paper aims to comprehensively examine and prioritize the challenges of the OTT video platform. The challenges are identified through an extensive literature review and unstructured interviews with six OTT industrial experts. The importance of each challenge is measured based on the analytical hierarchy process (AHP) to develop a hierarchy of those challenges. The AHP analysis results indicated customer retention as the most significant challenge, followed by content, customer experience, infrastructure, and bandwidth. The study is subjective to the experts opinions and available literature regarding the OTT platforms. The insights gleaned from this research are poised to offer substantial value to digital platform operators, media professionals, and managers. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Prioritisation of Human Resource Strategies in the Digital Transformation Process of SMEs
This chapter focuses on the importance of human resource (HR) management strategies in the digital business strategy for small- and medium-sized enterprises (SMEs). With the increasing influence of digital transformation that alters organisational structures and implements new technologies, SMEs have no other choice but to evolve. However, due to the scarcity of resources, it becomes very important for SMEs to allocate its HR appropriately and effectively. To support decision-making on strategies like employee reskilling, recruitment of digital talent, leadership development, and promoting a digital culture, this chapter presents a multi-criteria decision analysis (MCDA) framework. The prioritisation is crucial because not all the strategies can be executed at once, and SMEs should target the most effective ones in the long run, affordable, and relevant to their digital transformation agenda. This chapter illustrates methods for how SMEs can use the proposed prioritisation framework effectively. The hypothetical case study demonstrates the real challenges faced by SMEs during digital transformation and how MCDA assists leaders in selecting the most beneficial HR strategies. The case highlights the necessity of fitting strategies to organisational challenges to allow the customisation of training and leadership to align with business demands and maximise effectiveness while minimising costs. Upon use, this framework enables SMEs to comprehend and direct their digital transformation path more effectively. 2026 Tu? ?im?ek and Ahmet Bahad?r ?im?ek -
Prioritization of Challenges in EdTech Platform to Enhance User Continuance Intention: A Multi-criteria Decision Making Approach
In the rapidly evolving digital education landscape, EdTech platforms face significant challenges that impact user continuance intention. This study employs a fuzzy logic approach within the Multi-criteria Decision Making (MCDM) framework to identify and prioritize these challenges, ensuring the long-term sustainability of EdTech solutions. Key challenges were identified through an extensive literature review and unstructured interviews with eight industry experts. The fuzzy AHP technique was used to rank these challenges, providing a structured approach for EdTech companies to enhance user continuance intention and platform effectiveness. Results reveal Personalization (32.90%) as the most critical factor, followed by Data Privacy and security (20.86%) and User Interface (12.02%). Addressing these prioritized challenges can significantly improve user engagement and contribute to the development of inclusive and accessible educational technologies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Prioritized QoS Enforcement in Smart Healthcare IoT Using Adaptive Deep Q-Network-Based Traffic Decision System
Healthcare IoT systems have been plagued with significant challenges with regard to maintaining an optimum QoS due to the dynamic conditions of the network, diverse device capabilities, and stringent real-time constraints imposed by patient monitoring-type applications. Traditional QoS mechanisms are basically static; they do not take into account changes within the network. Hence, service delivery experiences degradation, with attendant risk to patients' safety. As a solution, this research proposes an adaptive QoS approach employing Deep Q-Network (DQN) reinforcement learning algorithms to dynamically control resource allocation and traffic prioritization in healthcare IoT networks. This system involves multi-agent reinforcement learning architecture where continuous state-action space mapping is utilized for adjusting bandwidth allocation, latency management, and packet prioritization automatically based on network conditions and the criticality levels of applications in real-time. Experimentally, the solution has attained an accuracy of 94.7 percent in QoS prediction, an 87.3 percent reduction in average latency to critical healthcare applications, 91.2 percent improvement in network throughput utilization, and an 89.6 percent success rate in adhering to service level agreements in peak traffic conditions. Through reinforcement learning-based decision making, the adaptive QoS mechanism dynamically accommodates the requirements of healthcare IoT, ensuring reliable service delivery while optimizing the usage of network resources. 2025 IEEE. -
Prioritizing evaluation criteria of IoT-driven warehousing startups: asilver lining to the unorganized sector in food supply chain
Purpose: This research is designed to meet two research objectives: firstly, to weigh up the criteria of Internet of Things (IoT) adoption in warehousing startups; secondly, to rank warehousing startups on the basis of benefits they derive from IoT adoption catering to an unorganized sector in the food supply chain. Design/methodology/approach: A blend of analytic hierarchy process (AHP) and complex proportional assessment (COPRAS) methods of multi-criteria decision-making techniques were applied. AHP determined the weights of various criteria using pairwise comparison, and COPRAS technique ranked the 10 warehousing startups on account of performance indicators. The study has been conducted at the warehousing startups of Bangalore, a hub of food warehousing startups. Findings: The critical findings of the study revealed that these food warehouse startups attain improved productivity in terms of enhancing efficiency when implemented with IoT adoption. When evaluated using both AHP and COPRAS techniques, the combined results show WH5 as the best performing and WH10 as the least performing warehouse startups. Practical implications: Warehouses that are embarking on their business opportunity in food storage can strategize to leverage the benefits of IoT in terms of food safety and security, capacity planning, layout design, space utilization and resilience. Originality/value: Despite the numerous research works on food supply chain, the research on IoT in warehousing startups is limited. The rankings for the 10 food warehousing startups integrated with IoT using AHP-COPRAS approaches are the novelty of this work. 2024, Emerald Publishing Limited.