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Privacy-preserving federated learning in healthcare: Fundamentals, state of the art and prospective research directions
Recent collaborations in medical diagnostic systems are based on data private collaborative learning using Federated Learning (FL). In this approach, multiple organizations train a machine-learning model at the same time eventually leading to global model generation. This paper reviews the fundamentals of FL and its evolution path in Healthcare. The objective of this review is to scope a wide variety of healthcare applications in FL. Exactly what research direction is moving in interesting for research communities to guide their future course. This review uniquely focuses on examining numerous FL-based healthcare implementations, detailing their core methodologies and performance metrics, which, to our knowledge, have not been previously available. Privacy-preserving collaborative distributed learning through federated learning in healthcare enhances research collaborations, thereby resulting in better-performing models. This comprehensive review will act as a valuable reference for researchers exploring new FL applications in the healthcare domain. 2024 IEEE. -
Privacy Optimization in Sensors Based Networks With Industrial Processes Management
The Internet of Things (IoT) also known as IoT has the potential that is required to revolutionize industries, this has been discussed in this research article. Advancements in technology have made devices affordable, efficient and reliable. Different sectors have already started to incorporate these devices into their operations to boost productivity, to minimize failure and downtime. They also use it to optimize resource utilization which is also an important factor. However, the use of these devices also has some security challenges which need to be handled. This research paper proposes a security model specifically designed for process management in the industries. The goal of this model is to find the vulnerabilities, to minimize the risks and threats. Also ensuring integrity, confidentiality and availability of processes is a part of the goal. This paper gives evidence from its implementation and trial apart from its explanation. During the implementation phase, the sensitive data achieved a 100% encryption rate, for protection. Also, integrity checks were conducted on 99.8% of data to guarantee data integrity. 2023 IEEE. -
Privacy breach perceptions and litigation intentions: Evidence from e-commerce customers
This paper examines the formation of litigation intentions among e-commerce customers under the privacy breach due to the influence of antecedents such as vulnerability, social risk, privacy dispositions, effectiveness privacy policy, awareness of data management and moderators such as privacy control beliefs, efficacy in coping and litigation complexity. A structural equation modelling analysis revealed that reasons for privacy breach perceptions are customer dispositions about privacy, anticipated vulnerability due to privacy breach, and social risk related to personal information disclosure. The control beliefs and coping skills of customers under privacy threat positively moderate litigation intentions. Similarly, the task complexity of litigation significantly reduces litigation intentions. 2021 -
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Priority-driven Unbalanced Transportation Problem (PUTP) to obtain better Initial Feasible Solution
In this paper, we tackle the Priority-driven Unbalanced Transportation Problem (PUTP), a scenario where total demand exceeds total supply. An innovative algorithm, the Penalty-driven Priority-driven Unbalanced Transportation Problem (PPUTP) is introduced to solve this challenge. PPUTP allocates supplies to high-priority demands by computing penalties and sequentially addressing the most penalized demands, thereby ensuring priority demands are met efficiently. A comparative analysis with Vogel's Approximation Method (VAM) across various problem sets ranging from 5x5 to 50x50 dimensions demonstrates the efficiency of our algorithms. PPUTP consistently shows lower percentage increments from the optimal solution, indicating its robustness in providing near-optimal solutions. This study highlights the importance of algorithm selection based on problem set dimensions and complexity in Priority-driven Unbalanced Transportation Problem, with PPUTP emerging as a versatile and robust solution across various scenarios. 2024 IEEE. -
Priority based prediction mechanism for ranking providers in federated cloud architecture
Cloud computing is a growing and excellent technology, as exponentially increasing the interest among users to utilize cloud applications; they need to depend on any one of the particular service provider. Now a days number of service providers also rapidly increasing in wide range, this leads ambiguity and distrust among the users. In this paper, enhanced broker based federated cloud architecture is proposed to resolve the selection of service provider issue using grading techniques and results proved that better performance improvement than single service provider selection. This broker architecture also addresses to selects the appropriate service provider automatically in the federated cloud architecture for the users submitted requests by previous experience with help of Bayesian network model. The former one implemented through concept of grade system. It is constructed for categorizing the providers based on the level of available resources. Grade and grade values distributed by applying the grade distribution algorithm for distinguishes the components. Total grade values computed for every service provider and sorted using quick sort algorithm to grade the cloud service providers. Priority based feedback decision tree technique added with this for separates similar grade cloud service provider in the selected list. Second Bayesian network model also used to rank the cloud service providers according to the previous performance of the providers with customers. Probability of satisfied customers feedback calculated for individual Service Measurements Index of Cloud Service Providers. 2018, Springer Science+Business Media, LLC, part of Springer Nature. -
Prioritizing the Essentials: The MBA Aspirants Dilemma
Objective decision-making while choosing an appropriate college for a Master in Business Administration (MBA) is only half-done. It is critical that the student be able to find the best placement at the end of the course by acquiring the most critical skills/specializations affecting placements and involves data-driven decision-making based on past placement trends. Viti and Vania have done their preliminary selection, of ABC College for their MBA course, based on the colleges credence quality. However, they are trying to understand the key success factors (KSFs) affecting placements at ABC to focus their next two years on getting most placement-ready. Having been provided with the placement details of the outgoing batch, they are looking to analyze the data to discover the most critical parameters affecting placements. NeilsonJournals Publishing 2023. -
Prioritizing Factors Affecting Customers Satisfaction in the Internet Banking Using Artificial Intelligence
Internet banking has revolutionised the way customers interact with their banks, providing them with convenient access to a wide range of financial services from the comfort of their homes or mobile devices. Customer satisfaction the success of an endeavour is contingent upon a vital component internet banking Service provision, as it pertains directly impacts customer retention and loyalty. This research explores the application of artificial intelligence (AI) techniques, specifically random forest and convolutional neural networks (CNN), to prioritise the factors that affect customer satisfaction in internet banking. The study begins with data collection from a diverse sample of internet banking customers, including demographic information, transaction history, and customer feedback. These may include the ease of navigation, the response time of the platform, and the level of trust in the bank's security measures. Furthermore, convolutional neural networks (CNN) are utilised to analyse unstructured data such as customer feedback and reviews. By applying natural language processing techniques, CNN s extract sentiment and topic information from customer comments. This approach can ultimately lead to improved customer retention and loyalty, ensuring the long-term success and competitiveness of internet banking platforms. In conclusion, this study showcases the power of AI, specifically Random Forest and CNN, in prioritising factors affecting customer satisfaction in internet banking. It highlights the significance of using both quantitative and qualitative investigations in order to attain a comprehensive comprehension of customer sentiments and preferences in the digital banking landscape. 2024 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. -
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. -
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. -
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. -
Prime Influential Factors Determining Prime Influential Factors Determining Employability of Engineering Graduates in Bangalore
The question of employability has risen as a problem worldwide. India produces around 400000 engineers every year. But, according to a study done by Nasscom, only one in four engineering graduates is employable. The remaining lagged in technical skills and know-how, ability to converse in English, make oral presentations or work in teams. According to a recent survey jointly carried out by the Federation of Indian Chambers of Commerce and Industry (FICCI) and the World Bank, 64 percent of surveyed employers are ??somewhat, ??not really or ??not at all satisfied with the quality of the engineering graduates skills. Graduates are found lacking in important skills like entrepreneurship, communication in English and use of modern tools and technologies. It has been argued that if colleges want to improve the employability of their graduates, they have to focus on reducing these important skill gaps through improvements in curriculum and teaching methods. The primary objective of the paper is to identify a set of factors that may have a bearing on the employability of engineering graduates in Bangalore and then find out how they are being rated by the institutions engaged in training these graduates on one hand and their prospective employers on the other and then find out the prime factors or dimensions influencing the kind of response received from each side, i.e. the institution and the industry. Lastly, if differences are found between the decisive factors or principal components of the industry and the institution, the study also intends to propose qualitative suggestions that can help to bridge the gaps thereby accelerating chances of employability of engineering graduates. -
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. -
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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. -
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. -
PRICE DISCOVERY, CAUSALITY, CONDITIONAL VOLATILITY & FORECASTING IN INDIAN FUTURES MARKET: EVIDENCE FROM 2001 - 2011
This work investigates the price discovery, causality, conditional volatility and forecasting in S&P CNX Nifty spot Market returns and S&P CNX Nifty Future Market Returns. The overall data study is classified into In-sample and Out-sample observations and the research work has been carried out by using the daily data from 1st January 2001 to Dec 31st 2011. The Out-sample analysis is carried on from 1st Jan 2011 to 31st Dec 2011. The dataset for the above analysis was retrieved from www.nseindia.com. The research also refines down with long term and short term dynamics of prices between spot and futures market by using various forecasting models such as Root Mean Square Error, Mean Absolute Error, Theil??s Inequality U test etc for Out- sample observation. Apart from this, the In-sample analysis has been carried out by Market Model, Johansen Cointegration test, Granger Causality test, Vector Error Correlation Model and GARCH Model. The results of the above tests indicate futures become the base for building up the spot, the causality tests and response analysis function indicate that future prices tend to discover new information more rapidly than spot, and the hence this will indicate more accurate forecasts of spot prices but not the spot to future and this price discovery function of futures prices has strengthened of homogenous structure of Index over recent years and it is disseminated in price discovery and risk management functions Keywords: Price Discovery, NSE, Volatility, GARCH, Cointegration, VECM JEL Classification: C22, C32, F47, G11 -
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 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