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  • Robust Length of Stay Prediction Model for Indoor Patients
  • Abstract Due to unforeseen climate change, complicated chronic diseases, and mutation of viruses’ hospital administration’s top challenge is to know about the Length of stay (LOS) of different diseased patients in the hospitals. Hospital management does not exactly know when the existing patient leaves the hospital; this information could be crucial for hospital management. It could allow them to take more patients for admission. As a result, hospitals face many problems managing available resources and new patients in getting entries for their prompt treatment. Therefore, a robust model needs to be designed to help hospital administration predict patients’ LOS to resolve…
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  • Estimating Fuel-Efficient Air Plane Trajectories Using Machine Learning
  • Abstract Airline industry has witnessed a tremendous growth in the recent past. Percentage of people choosing air travel as first choice to commute is continuously increasing. Highly demanding and congested air routes are resulting in inadvertent delays, additional fuel consumption and high emission of greenhouse gases. Trajectory planning involves creation identification of cost-effective flight plans for optimal utilization of fuel and time. This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required. In this paper, an algorithm for dynamic planning of optimized flight trajectories has been proposed. The proposed algorithm…
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  • Ultra-wideband Frequency Selective Surface for Communication Applications
  • Abstract A low-profile ultra-wideband (UWB) band-stop frequency selective surface (FSS) is designed for S-, C-, X- and Ku-bands communication applications. The FSS is constructed by using square and circular loop elements printed on the top and bottom sides of the RO3210 substrate. The FSS has been designed to reduce the electromagnetic interference (EMI) as well as to mitigate the harmful effects of electromagnetic radiation on the human body caused by different radio devices. The dimension and size of the UWB FSS have been reduced to 0.12 λ × 0.12 λ and 90%, respectively, as compared to the reported literature. The other…
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  • Estimating Usable-Security Through Hesitant Fuzzy Linguistic Term Sets Based Technique
  • Abstract The apparent contradiction between usability and security has been discussed in the literature for several years. This continuous trade-off requires be acknowledging and handling whenever security solutions are introduced. However, some progressive analysts point out that present security solutions are usually very difficult for several users, and they have expressed a willingness to simplify the security product user experience. Usable security is still mostly unexplored territory in computer science. Which we are all aware with security and usability on many levels, usable security has received little operational attention. Companies have recently focused primarily on usable security. As consumers prefer to…
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  • An Auction-Based Recommender System for Over-The-Top Platform
  • Abstract In this era of digital domination, it is fit to say that individuals are more inclined towards viewership on online platforms due to the wide variety and the scope of individual preferences it provides. In the past few years, there has been a massive growth in the popularity of Over-The-Top platforms, with an increasing number of consumers adapting to them. The Covid-19 pandemic has also caused the proliferation of these services as people are restricted to their homes. Consumers are often in a dilemma about which subscription plan to choose, and this is where a recommendation system makes their task…
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  • Leveraging Active Decremental TTL Measuring for Flexible and Efficient NAT identification
  • Abstract Malicious attacks can be launched by misusing the network address translation technique as a camouflage. To mitigate such threats, network address translation identification is investigated to identify network address translation devices and detect abnormal behaviors. However, existing methods in this field are mainly developed for relatively small-scale networks and work in an offline manner, which cannot adapt to the real-time inference requirements in high-speed network scenarios. In this paper, we propose a flexible and efficient network address translation identification scheme based on actively measuring the distance of a round trip to a target with decremental time-to-live values. The basic intuition…
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  • Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science
  • Abstract In present digital era, data science techniques exploit artificial intelligence (AI) techniques who start and run small and medium-sized enterprises (SMEs) to have an impact and develop their businesses. Data science integrates the conventions of econometrics with the technological elements of data science. It make use of machine learning (ML), predictive and prescriptive analytics to effectively understand financial data and solve related problems. Smart technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient operations. At the same time, it is needed to develop an effective tool which can assist small to medium sized…
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  • Primary User-Awareness-Based Energy-Efficient Duty-Cycle Scheme in Cognitive Radio Networks
  • Abstract

    Cognitive radio devices can utilize the licensed channels in an opportunistic manner to solve the spectrum scarcity issue occurring in the unlicensed spectrum. However, these cognitive radio devices (secondary users) are greatly affected by the original users (primary users) of licensed channels. Cognitive users have to adjust operation parameters frequently to adapt to the dynamic network environment, which causes extra energy consumption. Energy consumption can be reduced by predicting the future activity of primary users. However, the traditional prediction-based algorithms require large historical data to achieve a satisfying precision accuracy which will consume a lot of time and memory space.…

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  • Improved Software Implementation for Montgomery Elliptic Curve Cryptosystem
  • Abstract The last decade witnessed rapid increase in multimedia and other applications that require transmitting and protecting huge amount of data streams simultaneously. For such applications, a high-performance cryptosystem is compulsory to provide necessary security services. Elliptic curve cryptosystem (ECC) has been introduced as a considerable option. However, the usual sequential implementation of ECC and the standard elliptic curve (EC) form cannot achieve required performance level. Moreover, the widely used Hardware implementation of ECC is costly option and may be not affordable. This research aims to develop a high-performance parallel software implementation for ECC. To achieve this, many experiments were performed…
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  • SutteARIMA: A Novel Method for Forecasting the Infant Mortality Rate in Indonesia
  • Abstract This study focuses on the novel forecasting method (SutteARIMA) and its application in predicting Infant Mortality Rate data in Indonesia. It undertakes a comparison of the most popular and widely used four forecasting methods: ARIMA, Neural Networks Time Series (NNAR), Holt-Winters, and SutteARIMA. The data used were obtained from the website of the World Bank. The data consisted of the annual infant mortality rate (per 1000 live births) from 1991 to 2019. To determine a suitable and best method for predicting Infant Mortality rate, the forecasting results of these four methods were compared based on the mean absolute percentage error…
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  •   Views:62       Downloads:45        Download PDF
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