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An improved web caching system with locally normalized user intervals
Caching is one of the most promising areas in the field of future internet architecture like Information-centric Networking, Software Defined Networking, and IoT. In Web caching, most of the web content is readily available across the network, even if the webserver is not reachable. Several existing traditional caching methods and cache replacement strategies are evaluated based on the metrics like hit ratio and byte hit Ratio. However, these metrics have not been improved over the period because of the traditional caching policies. So, in this paper, we have used an intelligent function like locally normalized intervals of page visit, website duration, users' interest between user groups is proposed. These intervals are combined with multiple distance metrics like Manhattan, squared Euclidean, and 3-,4-,5-norm Minkowski. In order to obtain significant common user navigation patterns, the clustering relation between the users using different intervals and distances is thoroughly analyzed. These patterns are successfully coupled with greedy web cache replacement strategies to improve the efficiency of the proposed web cache system. Particularly for improving the caching metrics more, we used an AI-based intelligent approach like Random Forest classifier to boost the prefetch buffer performance and achieves the maximum hit rate of 0.89, 0.90, and byte hit rate of 0.87, 0.89 for Greedy Dual Size Frequency and Weighted Greedy Dual Size Frequency algorithms, respectively. Our experiments show good hit/byte hit rates than the frequently used algorithms like least recently used and least frequently used. 2013 IEEE. -
Parity labeling in Signed Graphs
Let S = (G; ?) be a signed graph where G = (V;E) is a graph called the underlying graph of S and ?: E(G) ? {+; -}. Let f: V(G) ? {1, 2, ..., |V(G)|} such that ?(uv) = + if f(u) and f(v) are of same parity and ?(uv) = - if f(u) and f(v) are of opposite parity. The bijection f induces a signed graph Gf denoted as S, which is a parity signed graph. In this paper, we initiate the study of parity labeling in signed graphs. We define and find `rna' number denoted as ?-(S) for some classes of signed graphs. We also characterize some signed graphs which are parity signed graphs. Some directions for further research are also suggested. 2021, Journal of Prime Research in Mathematics. All rights reserved. -
Marital Stress and Domestic Violence during the COVID- 19 Pandemic
Marital stress and domestic violence is prevalent in every society around the world. It has become a major concern during the Covid-19 pandemic. Governments have resorted to lockdown measures in order to contain the pandemic. The pandemic has made the weaker and more vulnerable people in a household more exposed to abusive partners. Social isolation and home confinement have detrimental effects on ones mental and physical well-being. Women have been shown to be at a very high risk from violence during The Covid19 pandemic. The research paper aims to understand the factors which compel women to stay in abusive and stressful marriages and the ways in which they can be empowered to lead their life with dignity and self-respect. The cultural contexts of most societies force women to stay in abusive marriages as the woman is often portrayed as the symbol of unity in families. Understanding the cultural bindings of women trapped in abusive households during the COVID-19 pandemic is a very crucial aspect as this can help in understanding the fear and apprehensions of women trapped in destructive marriages. This can be a key factor which can make it easier for support groups while providing counselling and other kinds of support to women trapped in abusive marriages. The paper also discusses the impact of abusive relationships on children and how it negatively shapes their personality and their emotional well- being. 2021 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Image and signal processing in the underwater environment
To handle submerged action recognition, researchers must first understand the fundamental principles of photonic crystals mostly in the liquid phase. Deterioration effects are produced by the mediums physical attributes, which are not present in typical pictures captured in the air because light is increasingly reduced as it passes through water, submarine pictures are characterized by low readability. As a consequence, the sceneries are poorly contrasting and murky. Its vision capability is limited to approximately twenty meters in clear blue water and five meters or less in muddy water due to light dispersion. Absorbing (the removal of incident light) and dispersion are the two factors that produce light degradation. So the actual quality of submersible digital imaging is influenced by the destructive interference processes of light in water. Longitudinal scattered (haphazardly diverted light traveling from objects to the cameras) causes picture details to be blurred. 2021, SciTechnol, All Rights Reserved. -
Intelligent machine learning approach for cidscloud intrusion detection system
In this new era of information technology world, security in cloud computing has gained more importance because of the flexible nature of the cloud. In order to maintain security in cloud computing, the importance of developing an eminent intrusion detection system also increased. Researchers have already proposed intrusion detection schemes, but most of the traditional IDS are ineffective in detecting attacks. This can be attained by developing a new ML based algorithm for intrusion detection system for cloud. In the proposed methodology, a CIDS is incorporated that uses only selected features for the identification of the attack. The complex dataset will always make the observations difficult. Feature reduction plays a vital role in CIDS through time consumption. The current literature proposes a novel faster intelligent agent for data selection and feature reduction. The data selection agent selects only the data that promotes the attack. The selected data is passed through a feature reduction technique which reduces the features by deploying SVM and LR algorithms. The reduced features which in turn are subjected to the CIDS system. Thus, the overall time will be reduced to train the model. The performance of the system was evaluated with respect to accuracy and detection rate. Then, some existing IDS is analyzed based on these performance metrics, which in turn helps to predict the expected output. For analysis, UNSW-NB15 dataset is used which contains normal and abnormal data. The present work mainly ensures confidentiality and prevents unauthorized access. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021. -
Secure biometric authentication with de-duplication on distributed cloud storage
Cloud computing is one of the evolving fields of technology, which allows storage, access of data, programs, and their execution over the internet with offering a variety of information related services. With cloud information services, it is essential for information to be saved securely and to be distributed safely across numerous users. Cloud information storage has suffered from issues related to information integrity, data security, and information access by unauthenticated users. The distribution and storage of data among several users are highly scalable and cost-efficient but results in data redundancy and security issues. In this article, a biometric authentication scheme is proposed for the requested users to give access permission in a cloud-distributed environment and, at the same time, alleviate data redundancy. To achieve this, a cryptographic technique is used by service providers to generate the bio-key for authentication, which will be accessible only to authenticated users. A Gabor filter with distributed security and encryption using XOR operations is used to generate the proposed bio-key (biometric generated key) and avoid data deduplication in the cloud, ensuring avoidance of data redundancy and security. The proposed method is compared with existing algorithms, such as convergent encryption (CE), leakage resilient (LR), randomized convergent encryption (RCE), secure de-duplication scheme (SDS), to evaluate the de-duplication performance. Our comparative analysis shows that our proposed scheme results in smaller computation and communication costs than existing schemes. 2021 M et al. All Rights Reserved. -
Mine Waste-Based Next Generation Bricks: A Case Study of Iron Ore Tailings, Red Mudand GGBS Utilization in Bricks
Utilization of mine wastes as a building material in the construction industry surmises to environmental and sustainable concepts in civil engineering.The potential environmental threat posed by mining wastes, as well as a growing societal awareness of the need to effectively treat mining wastes, has elevated the subject importance.The present research proposes a method of producing bricks that is both cost effective and environmentally benign. The research is based on the geopolymerization, known to save energy by obviating high-temperature kiln firing and lowering greenhouse gas emissions. The methodology encompasses the mixing of red mud and iron ore tailings in the range of 90% to 50% with a decrement of 10% with GGBS in the range of 10% to 50% with an increment of 10%. The raw materials and the developed composites have been tested as per Indian and ASTM standards.In addition to tests pertaining to the physical and mechanical properties, XRF, XRD, and SEM tests have been performed for examining various related issues. Based on the result analysis, the compressive strength values showed noticeable differences in case of IOT and red mud bricks with IOT-based bricks showing better compressive strengths. 2021 M. Beulah et al. -
Research & development premium in the Indian equity market: An empirical study
This article aims to investigate the research and development (R&D) premium and explore the three most prominent asset pricing models: capital asset pricing and the three-and five-factor models (Fama & French, 1993; 2015). The results show that India's annualized average R&D premium is significantly higher than the existing value, market, profitability, size and investment premiums, implying that the R&D premium is a more significant concern for Indian investors, particularly for high R&D firms. It was also observed that by applying the GRS test and the Fama and MacBeth (1973) two-pass procedure, the R&D risk factor augmented the CAPM, FF3F and FF5F models outperforming the existing CAPM, FF3F and FF5F models, respectively. We can also report that R&D is, unquestionably, a priced ingredient and a critical factor in developing pricing models for developing markets such as India. The paper's conclusions add to the current literature in R&D and asset pricing and assist investment professionals in developing better investment and trading strategies. 2021 AESS Publications. All Rights Reserved. -
Transformation of India as investor of outward fdi: A systematic investigation of literature
Besides the economic transformation and industrial up-gradation, Indian enterprises have steadily intensified their overseas investment venture during recent years. A systematic literature review performed to inspect the strategic motives and Outward FDI (OFDI) impact on emerging economies like India. This paper explores relevant theories, strategic rationale, and economic policies that propel the present OFDI trend from India. The effort taken by the Indian government to promote innovations were Cross border commercial and industrial collaboration. These efforts flagged the way for more Outward FDI possibilities in the future (Welch, 1988). This study comprises the literature works till the year 2019, which includes research journals and reports. The analysis observes that knowledge-based industries drive India's Outward FDI and examine whether knowledge-based industries contribute to sustaining long-term domestic and international growth (Pradhan J.P., 2005; Narayanan, 2016). Indian Institute of Finance. -
Social Media and Steganography: Use, Risks and Current Status
Steganography or data hiding is used to protect the privacy of information in the transit; it has been observed that the information that flows through Online Social Networks (OSN) is very much unsafe. Therefore, people hesitate to communicate their sensitive data on social media.. Most of the information on the online social network is not useful to users and appears to disregard such details. People's actions provided a possibility for digital Steganography through the Internet.. TCPIP covert channels were used for steganography until the last decade. People began to utilize social media as a covert conduit to communicate hidden messages to targeted users as social media grew in popularity. There are numerous Online Social Networks accessible nowadays, ranging from Facebook to the more contemporary Twitter and Instagram. All of them may be utilized as covert channels without the general public noticing. The primary characteristic of steganography is the protection of information privacy; nonetheless, it has been utilized more for illicit message transmission, which is a source of concern. To make matters worse, adversaries are using steganalysis techniques to mess with the concealed data. In this article, we examine the different social media steganography techniques, such as those used on Facebook, WhatsApp, and Twitter, as well as the difficulties that these approaches raise. The positive and negative consequences of social media, as well as its current state, are discussed in this study. This paper discusses how the performances of Steganography methods may be assessed using the Entropy value of the Stego object. A look of the three features of steganography. It has been given with undetectability, robustness, and payload capacity. Finally, the paper's concept's future scope is explored. 2013 IEEE. -
Laguerre polynomial-based operational matrix of integration for solving fractional differential equations with non-singular kernel
The Atangana-Baleanu derivative and the Laguerre polynomial are used in this analysis to define a new computational technique for solving fractional differential equations. To serve this purpose, we have derived the operational matrices of fractional integration and fractional integro-differentiation via Laguerre polynomials. Using the derived operational matrices and collocation points, we reduce the fractional differential equations to a system of linear or nonlinear algebraic equations. For the error of the operational matrix of the fractional integration, an error bound is derived. To illustrate the accuracy and the reliability of the projected algorithm, numerical simulation is presented, and the nature of attained results is captured in diverse order. Finally, the achieved consequences enlighten that the solutions obtained by the proposed scheme give better convergence to the actual solution than the results available in the literature. 2021 The Author(s). -
Post listing IPO returns and performance in India: An empirical investigation
Objectives: (a) To analyse the performance of Indian IPOs in the short term. (b) To determine the significance of abnormal return of the IPOs. (c) To study the impact of over-subscription, profit after tax, promoters' holdings, issue price and market returns on IPO performance. Design/ Methodology/Approach: This research paper is based on empirical analysis. All the 52 IPO's listed in the NSE (National Stock Exchange, India) during the year 2018 to 2020 were considered for the study. The study is based on secondary data. The daily share price and Nifty-50 index value were taken from NSE website (www.nseindia.com) and other relevant data from red-herring prospectus of the respective company. The research / statistical tools used are: Market adjusted short run performance model, Wealth relative model, 't' test and regression analysis. Scope of the study: The scope of the study is limited to the IPO's listed only in the National Stock Exchange (NSE), India. Period of study: The study covers a period from January 2018 to December, 2020. Limitation of the study: The study considers only the influence of the external factors on the performance of IPOs. Findings: The average IPO return on the first trading day is 13.52%, ranging from -23.15% to 82.16% with standard deviation of 26.72%. The average IPO return on the third trading day was the highest and is found to be14.52%, ranging from -19.22% to 117.55% with standard deviation of 18.57%. The analysis reveals that the over subscription impacts the IPO performance and the other factors namely, issue price, Profit after Tax, market returns and promoters holdings do not influence IPO returns. Originality / Value: This is an original work that analyses the listing gain or loss and the post listing performance of IPO's in India and other factors that might influence the listing gain or loss. Copyright 2021. T. Ramesh Chandra Babu and Aaron Ethan Charles Dsouza. Distributed under Creative Commons Attribution 4.0 International CC-BY 4.0 -
Exchange rate, stock price and trade volume in US-China trade war during COVID-19: An empirical study
This article aims to examine the influence of international trade wars on the majority of stock market operations, both directly and indirectly affected. The impact of the trade war on the exchange rates of the participating countries was similarly negative. This article seeks to trace the conversion standards' footprints in the United States, China, and India using several indexes such as the Shanghai Composite Index, Dow Jones index, and Nifty 50. The cost of closing down various indices on a daily basis, as well as the conversion standard upsides of the participating currencies, are all examined in this study. Furthermore, utilizing the OLS and GARCH models, this work provides insights into measuring the uncertainties about the impact of exchanging scale on financial exchange. According to the findings of OLS, changes in the swapping scale have had a minor impact on the daily closing costs of stock records in the individual countries. The conversion standard, on the other hand, has a major impact on trade volumes in all three stock markets. When compared to the SSE and DJI equities, the GARCH model predicts that the contingent shift will be less shocking, resulting in a smaller impact on Nifty trade volume. To replicate the impact of trade wars during the Covid-19 crisis, the final results imply that data from domestic and international financial transactions must include securities market transactions. Author This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). -
Physical Co-location: an intersection of problem-solving and vicarious learning
Scholars have examined Revans' problem-solving praxeology in many contexts but have not fully explored the concept in the case of physical co-location. Hence, we focussed on investigating Revans' conceptualisation in a co-located context by paying particular attention to the different forms of learning' that emerged from it. The research setting for this study involved two coworking spaces in Bangalore, India, whose constituents were co-located start-ups and established enterprises. Held from January to March 2020, the study involved conducting exploratory, semi-structured interviews with twelve firms. The findings suggested that in a co-located environment, a) firms learnt vicariously' from a rich, external knowledge base during the enquiry-led Alpha phase b) firms learnt experientially', through learning by doing and reflecting in the implementation-focussed Beta phase c) firms learnt through the process of emergence that resulted from personal reflection and team interaction, in the revelatory Gamma phase. This study lends a novel direction in acknowledging that vicarious learning, that is, learning through the experience of others, serves as a starting point for problem-solving in a co-located context. We demonstrate that firms gain familiarity with the problem through vicarious sources, that is, from those experienced co-located firms who had journeyed on a similar path. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Optimising QoS with load balancing in cloud computing applying dual fuzzy technique
Cloud computing has become a necessity when the internet usage has increased drastically. This research paper objective is to optimise quality of service in cloud computing using dual fuzzy technique. With the competition to provide the best quality service at cloud data centre, we are analysing the parameters of average response time, average completion time, average CPU utilisation and job success. Cloud-sim simulator along with the mathematical model is used to provide reliable and valid result. To achieve the best result, the load in data centre needs to be efficiently distributed, so that it is managed to process maximum service requests with the best service response time and very few failures. In this paper, we applied dual fuzzy technique for the load balancing in the cloud data centre and the findings were extensive and support the proposed technique. With this technique, cloud computing service provider can provide better quality service. Copyright 2021 Inderscience Enterprises Ltd. -
Addiction treatment in India: Legal, ethical and professional concerns reported in the media
As per the Magnitude of Substance Use in India 2019 survey report, over 57 million of the Indian population is in need of professional help for alcohol use disorders and around 7.7 million for opioid use disorders. The increasing demand for addiction treatment services in India calls for professionalising every aspect of the field. Frequent human rights violations and various unethical practices in Indian addiction treatment facilities have been reported in the mass media. This study is a content analysis of newspaper reports from January 1, 2016 to December 31, 2019 looking into legal, ethical and professional concerns regarding the treatment of substance use disorders in India. The content analysis revealed various human rights violations, the use of improper treatment modalities, the lack of basic facilities at treatment settings, and the presence of unqualified professionals in practice. Indian Journal of Medical Ethics 2021. -
Inventory model for deteriorating items with ramp type demand under permissible delay in payment
Permissible delay in payment is a common method of payment often used by the suppliers and it generally leads to higher sales and ultimately higher revenue. This method is significant in the case of deteriorating products. In this paper, an inventory model for the deteriorating items with price and time-dependent ramp type demand is presented with shortages allowed and partially backlogged. The solution procedure is illustrated by numerical examples. The concavity of the profit function with respect to the decision variable is discussed analytically. Numerical analysis shows that the profit per unit time increases with the delay payment facility. Copyright 2021 Inderscience Enterprises Ltd. -
Forecasting intraday stock price using ANFIS and bio-inspired algorithms
The main focus of this study is to explore the predictability of stock price with variants of adaptive neuro-fuzzy inference system (ANFIS) and suggests a hybrid model to enhance the prediction accuracy. Two variants of ANFIS model are designed which includes genetic algorithm-ANFIS (GA-ANFIS) and particle swarm optimisation-ANFIS (PSO-ANFIS) to forecast stock price more accurately. The standard ANFIS is tuned employing GA and PSO algorithm. The experimental data used in this investigation are stocks traded per minute price of four companies from NSE. Sixteen technical indicators are calculated from the historical prices and used as inputs to the developed models. Prediction ability of the developed models is analysed by varying number of input samples. Numerical results obtained from the simulation confirmed that the PSO-ANFIS model has the potential to predict the future stock price more precisely than GA-ANFIS as well as other earlier methods. Copyright 2021 Inderscience Enterprises Ltd. -
Passenger flow prediction from AFC data using station memorizing LSTM for metro rail systems
Metro rail systems are increasingly becoming relevant and inevitable in the context of rising demand for sustainable transportation methods. Metros are therefore going to have a consistently expanding user-base and hence user satisfaction will require meticulous planning. Usage forecast is clearly an integral component of metro planning as it enables forward looking and efficient allocation of resources leading to greater commuter satisfaction. An observation from studying the usage of Kochi Metro Rail Ltd. is that there is a consistently occurring temporal pattern in usage for every station. But the patterns differ from station to station. This hinders the search for a global model representing all stations. We propose a way to overcome this by using station memorizing Long Short-Term Memory (LSTM) which takes in stations in encoded form as input along with usage sequence of stations. This is observed to significantly improve the performance of the model. The proposed architecture with station parameter is compared with algorithms like SVR (support vector regression) and neural network implementation with the best architecture to testify the claim. The proposed model can predict the future flow with an error rate of 0.00127 MSE (mean squared error), which is better than the other models tested. CTU FTS 2021. -
Deep Learning for Stock Market Index Price Movement Forecasting Using Improved Technical Analysis
Equity market forecasting is difficult due to the high explosive nature of stock data and its impact on investor's stock investment and finance. The stock market serves as an indicator for forecasting the growth of the economy. Because of the nonlinear nature, it becomes a difficult job to predict the equity market. But the use of different methods of deep learning has become a vital source of prediction. These approaches employ time-series stock data for deep learning algorithm training and help to predict their future behavior. In this research, deep learning methods are evaluated on the India NIFTY 50 index, a benchmark Indian equity market, by performing a technical data augmentation approach. This paper presents a Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and the three variants of Gated Recurrent Unit (GRU) to analyze the model results. The proposed three GRU variants technique is evaluated on two sets of technical indicator datasets of the NIFTY 50 index (namely TA1 and TA2) and compared to the RNN and LSTM models. The experimental outcomes show that the GRU variant1 (GRU1) with TA1 provided the lowest value of Mean Square Error (MSE=0.023) and Root Mean Square Error (RMSE= 0.152) compared with existing methods. In conclusion, the NIFTY 50 index experiments with technical indicator datasetTA1 were more efficient by GRU. Hence, TA1 can be used to construct a robust predictive model in forecasting the stock index movements. 2021. All Rights Reserved.