Browse Items (11810 total)
Sort by:
-
Forecasting NIFTY 50 in Volatile Markets Using RNNLSTM: A Study on the Performance of Neural Network Models During the COVID-19 Pandemic
The COVID-19 pandemic has shown us how the market can be highly uncertain and volatile at certain times. This brings a new level of challenges to all the investors and active traders in the market, as they have not seen such a movement in the past. However, as technology is evolving, highly sophisticated tools and techniques are being used by hedge funds and other investment banks to track down these movements and turn this into an opportunity. In this paper, we try to analyse how recurrent neural network (RNN) with long- and short-term memory architecture performs under volatile market conditions. For this study, we tried to perform a comparative analysis between two models within two successive time periods, where one is trained in a volatile market condition and the other in a relatively low volatile market condition. The results showed that the RNN model is less accurate in predicting the prices in a volatile market compared to a relatively low volatile market. We also compared these two models to a separate model where we trained using the combined data from the two successive time periods. Even though the addition in data points for the neural network produced a better result compared to the model trained under volatile conditions, it did not significantly perform better than the model, which was trained in the low volatile period. 2022 Management Development Institute. -
Tobacco Farming, Addiction, Promotion of Gender Equality, Well-being and Monopoly of the Indian Market
Womens land rights are still suppressed in India because men hold most of the land, and men decide what crops to grow. Tobacco use and farming are both detriments to ones health. It causes cancer, and cancer treatment is unavailable in the majority of Indias remote areas. On the other hand, tobacco is grown in remote regions of India, and cancer hospitals are concentrated in major cities. There are eight states in Indias north-eastern region, but only one cancer treatment facility in Guwahati, Assam. There is a need for new cancer hospitals in the north-eastern part of the country, where there is just one cancer hospital for eight states. Mindfulness training and tobacco harmful effects awareness education should be integrated into the educational curriculum and community centres. The school curriculum should include more mindfulness and psychoeducation about tobaccos detrimental effects. The pandemic situation in India and elsewhere make any community-based response difficult right now. Some parts of India, such as A&I Island, the North-Eastern region of India, and J&K, lack high-speed internet connectivity; therefore, radio, television, audio CDs, audio files, recorded videos, reading materials, and cell phones may be the best ways to reach out. Internetbased outreach is another option. A non-governmental organisation (NGO) or other organisation would be required to create regional language reading material, audio files, and video files. Given the global pandemic crisis, such programmes must be put in place as soon as possible. A team of specialists, regional language experts, local cultural experts, and volunteers would be needed to achieve these objectives. 2022 -
Comprehensive strategies of Lignocellulolytic enzyme production from microbes and their applications in various commercial-scale faculties
Activities of anthropological organisms leads to the production of massive lignocellulosic waste every year and these lignocellulolytic enzymes plays crucial role in developing eco-friendly, sustainable and economical methods for decomposing and pre-treating the biomass to produce biofuels, organic acids, feeds and enzymes. Lignocellulolytic enzymes sustainably hydrolyse the biomass and can be utilized in wide range of applications such as personal care, pharmaceutical, biofuel release, sewage treatment, food and beverage industries. Every year a significant ton of biomass waste is released and insight on these crucial enzymes could establish in all the industries. However, due to the increased demand for compost materials, biomass degradation has resulted in composting processes. Several methods for improving compost amount and quality have been explored, including increasing decomposer inoculums, stimulating microbial activity, and establishing a decomposable environment. All of these prerequisites are met by biotechnological applications. Biotechnological procedures are used to improve the activity of enzymes on biomass. It leads to an adequate supply of compost and base materials for enterprises. In terms of effectiveness and stability during the breakdown process, lignocellulolytic enzymes derived from genetically modified species outperformed naturally derived lignocellulolytic enzymes. It has the potential to increase the quality and output of byproducts. This review discussed the development of lignocellulolytic enzyme families and their widespread applications in a variety of industries such as olive oil extraction, carotenoid extraction, waste management, pollution control, second-generation bio-ethanol production, textile and dyeing, pharmaceuticals, pulp and paper, animal feed, food processing industries, detergent, and agricultural industries. 2022 Visagaa Publishing House. -
AN ECONOMIC RELIABILITY TEST PLAN BASED ON TRUNCATED LIFE TESTS FOR MARSHALL-OLKIN POWER LOMAX DISTRIBUTION WITH APPLICATIONS
In every competitive enterprise, there has been a resurgence of interest in increasing the quality of products. In this paper, we create new acceptance sampling plans based on truncated life tests for the Marshall-Olkin power Lomax distribution. The minimum sample sizes needed to declare the specified mean life with respect to the newly developed sampling plans are obtained for different values of the model parameters. Besides, the operating characteristic function values, minimum ratios of the true value and the required value of the parameter with a given producer risk are discussed. Moreover, the results are illustrated using numerical examples, and a real data set is considered to illustrate the functioning of the recommended acceptance sampling plans. The result shows that the proposed plan is more adequate compared with other acceptance sampling plans available in the open literature. So, it can be used for industry applications. 2010 Mathematics Subject Classification. 60E05, 62E15, 62F10. 2022, Asia Pacific Academic. All rights reserved. -
Two inventory models for growing items under different payment policies with deterioration
Industries of growing items show an upward trend in the production as well as in consumption. Poultry and livestock are good examples of growing items which are both deteriorating and ameliorating in nature. In this study apart from these specific features of growing items, one of the real-world business policies, permission of delay in payment is also considered. Present paper proposed two inventory models, one with the permission of delay in payment and another without it. Concavity of the profit functions with respect to decision variables are discussed analytically for both the models. Solution procedure and numerical examples are provided in order to get the managerial insights. The numerical analysis growth in weight is approximated by Richard's growth function. The numerical analysis predicts that net profit and the initial purchase quantity both increases under the permissible delay payment policy compared to without it. Sensitivity analysis provides important managerial insights. Copyright 2022 Inderscience Enterprises Ltd. -
Energy-Aware Multilevel Clustering Scheme for Underwater Wireless Sensor Networks
The expansion of wireless sensor networks in the underwater environment resulted in underwater wireless sensor networks. It has dramatically impacted the research arena because of its widespread and real-time applications. But successful implementation of underwater wireless sensor networks faces many issues. The primary concern in the underwater sensor network is sensor nodes' energy depletion problem. In this paper, to improve the lifetime of the underwater wireless sensor network, an Energy-Aware Multi-level Clustering Scheme is proposed. The underwater network region is considered 3D concentric cylinders with multiple levels. Further, each level is divided into various blocks, representing one cluster. The proposed algorithm follows vertical communication mode from the sea bed to the surface area in a bottom-up fashion. Multiple levels with varying heights overcome the communication issues due to high water pressure towards the sea bed. Simulations are carried out to show the efficiency of the proposed algorithm, which performs better in terms of a prolonged network lifetime and average residual energy. The simulation result shows significant improvement in the network lifetime compared with current algorithms. 2013 IEEE. -
Effect of Computer Learning on performance in early Architecture Education
A mixed cohort of students with different experience backgrounds join the architecture degree. While some are well familiar with the user interface of computer and 3-D digital tools, others are not. The effect of such prior knowledge and their corresponding digital and analog performance in a designed experiment was evaluated with a sample of 38 first-year students. This was done to understand the performance effects of previous computer learning in students. Computer learning of the sample was studied in terms of years of computer exposure, the number of software known, and knowledge of 3D software or SketchUp. The results suggest that none of the factors contributed to the digital performance of students. This provided suggestions regarding the computer teaching emphasis which should be placed on students having less computer learning. 2022, Rajarambapu Institute Of Technology. All rights reserved. -
Evaluation of hydroxyapatite filler loading on dynamic mechanical properties of combined silk and basalt fabric reinforced epoxy nanocomposites
The boosting of electrical and microelectronic goods causes the continuous increase in the amount of power per unit volume of these gadgets, leading to unavoidable overheating problems that diminish their functional performance as well as life span. One of the primary aims of materials science is the creation of high-performance materials that are made from renewable resources. Multi-phase composites were recognised as an effective route to achieve a new portfolio of advanced materials with superior performance. Some of the functional fillers in polymer-based composite materials exhibit outstanding electrical insulation, chemical resistance, mechanical and processing properties, and therefore are considered to be the most promising candidates to solve the heat dissipation problem. In this research article, the thermal behavior, namely Fourier Transform Infrared Spectroscopy (FTIR), X-Ray Diffraction Analysis (XRD), and Dynamic mechanical analysis (DMA) of hybrid nano-sized hydroxyapatite filled hybrid fiber reinforced epoxy nanocomposites are investigated. Three important parameters were examined at 1 Hz frequency: storage modulus, loss modulus, and damping, all from room temperature to 160 C. The influence of the nHAP loading on the dynamic mechanical properties are discussed and explained with the highly relevant works from the available literature. In particular, the dependence of the hybrid composite damping on the nHAP loading was explained with regard to damping due to particleparticle and polymer-fiber interaction. The presence of nHAP is confirmed by a wider XRD curve, and the incorporation of nHAP results in the removal of the hydroxy group from the fibers as shown by FTIR. The inclusion of nHAP in a hybrid (S + B/Ep) composite enhances the synergetic effect, which raises the storage and loss modulus, decreases the damping factor, and increases the Tg of the nHAP filled hybrid nanocomposites. 2021 -
The asymmetric relationship between foreign direct investment, oil prices and carbon emissions: evidence from Gulf Cooperative Council economies
We investigate the asymmetric nonlinear link between foreign direct investment, oil prices, and CO2 emissions for the Gulf Cooperation Council nations, using foreign direct investment and oil price data. As foreign direct investment is positively associated with carbon emissions in the long run and oil prices have positive, significant effects on CO2 emissions, our findings support the pollution-haven hypothesis. Furthermore, these variables have an asymmetric nonlinear relationship, which corresponds to the theoretical expectations of the pollution-haven hypothesis. We also find that negative changes in foreign direct investment have positive, significant impacts on carbon emissions in the short run, implying that foreign enterprises utilize green technologies in their manufacturing processes in the short run. In the long run, however, negative changes in oil prices are positively associated with carbon emissions. These findings should help Gulf Cooperation Council economies focus on policies that encourage foreign direct investment in green rather than dirty industries in order to ensure environmental sustainability. 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment
Recently, big data becomes evitable due to massive increase in the generation of data in real time application. Presently, object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation, augmented reality, surveillance, etc. This paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN (AIA-IFRCNN) model in big data environment. The AIA-IFRCNN model annotates the images by Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR), named DCF-CSRT model. The AIA-IFRCNN technique employs Faster RCNN for object detection and tracking, which comprises region proposal network (RPN) and Fast R-CNN. In addition, inception v2 model is applied as a shared convolution neural network (CNN) to generate the feature map. Lastly, softmax layer is applied to perform classification task. The effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%. 2022 Tech Science Press. All rights reserved. -
Significance of aggregation of nanoparticles, activation energy, and Hall current to enhance the heat transfer phenomena in a nanofluid: a sensitivity analysis
The mechanisms involved in the heat transport enhancement due to suspended nanoparticles are still unclear. Many studies have shown that nanoparticle aggregation is a key aspect of increasing nanofluid thermal conductivity. Nevertheless, the fractal dimension of nanoparticle aggregation will have a substantial impact on the nanofluids thermal conductivity. Therefore, the present study examines the influence of nanoparticle aggregation and Hall current on the nanoliquid flow past a spinning disk. The importance of Arrhenius activation energy is also investigated. A revised correlation for the aggregation mechanism is attained using the modified Krieger-Dougherty model (KD-model) and the Maxwell-Bruggeman model (MB-model). A similarity technique and finite difference method are used to construct the numerical solutions for the boundary value problem. The 2D plots and 3D surface plots are shown to investigate how different key parameters impact the velocity, temperature, and concentration fields. The study highlights that the Hall current has a beneficial effect on the fluid flow field. Higher activation energy leads to a productive chemical reaction which, improves the concentration layer. The thermal boundary for NPs aggregationis superior than to that withoutNPs aggregation, and the suspension of nanoparticles will have a favorable impact on the thermal layer. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Molecular level investigation on the impact of geometric isomers as fluorinated ligands in SIFSIX MOF for natural gas sweetening
In natural gas (NG), significant amounts of hydrogen sulfide (H2S) and carbon dioxide (CO2) are the most menacing contaminants that cause degradation of the purity of fuel. We considered fluorine-functionalized MOFs and employed cheaper and faster computational simulation techniques to understand the adsorption process. Hence, this includes structural optimization of newly designed fluorine-functionalized MOF with Density Functional Theory (DFT) and further Grand Canonical Monte Carlo (GCMC) simulation at room temperature on those MOFs for understanding in detail the adsorptive separation process on sour gases. However, the main emphasis has been made on the adsorptive separation of H2S gas from sour gas. Eventually, the fluorination of organic ligand in [(SiF6)Ni(1,2-di(pyridin-4-yl)ethyne)2] MOF has resulted in an excellent H2S/CO2 separation performance from NG because of the different geometrical isomers. The cis isomer of 1,2-di(pyridin-4-yl)ethyne as ligand in MOF, i.e., SIFSIX-Ni-dpe-3-cis, shows a high CO2 affinity than H2S; on the contrary, the trans isomer of 1,2-di(pyridin-4-yl)ethyne as ligand in MOF, i.e., SIFSIX-Ni-dpe-3-trans, has H2S selective over CO2 and CH4. So, the resulting affinity variation indicates that structural variation by the stereochemistry of ligands in MOF plays a significant role in NG purification, which is further validated through detailed molecular simulation analysis. 2022 Taylor & Francis Group, LLC. -
On ion transport during the electrochemical reaction on plane and GLAD deposited WO3 thin films
Tungsten oxide thin films were deposited on FTO and Corning glass substrates on Plane and GLAD (75) using DC magnetron sputtering and characterized using SEM, XRD, UVVis spectrophotometer, and Electrochemical analyzer systematically. Further, a comparative analysis was carried out in which it was observed that the result of surface morphology for plane showed the denser and GLAD showed nanopillars deposition. The amorphous nature of the sample was evident from XRD analysis. Optical transmittance was between 87% and 81% for both plane and GLAD. The Electrochemical studies showed the diffusion coefficient of H+ ions are more compared to Li+ ions for both plane and GLAD and Coloration efficiency was calculated at the scan rates of 10, 30, and 50 mV/s at the wavelength of 500 to 600 nm. 2021 -
Optimal procurement and pricing policy for deteriorating items with price and time dependent seasonal demand and permissible delay in payment
In practice, items like food, nursery plants, medicines, etc. are seasonal and deteriorating in nature. For this type of products, permissible delay in payment is a common business policy, which is used to increase in the sell volume and to develop trust in buyer-seller relationship. In this paper, we developed an inventory model for time dependent deteriorating seasonal items with the permission of delay in payment. Shortages are permitted and partially back ordered. Our aim is to find optimal selling price and ordering quantity simultaneously. Concavity of profit function with respect to decision variables has been discussed analytically. A solution procedure followed by a numerical example and sensitivity analysis along with managerial insights are provided. Numerical analysis predicts that delay in payment profit policy is a better decision in order to maximise the profit or in order to get more profit. 2022 Inderscience Enterprises Ltd. -
Cat Swarm Optimization Algorithm Tuned Multilayer Perceptron for Stock Price Prediction
Due to the nonlinear and dynamic nature of stock data, prediction is one of the most challenging tasks in the financial market. Nowadays, soft and bio-inspired computing algorithms are used to forecast the stock price. This article assesses the efficiency of the hybrid stock prediction model using the multilayer perceptron (MLP) and cat swarm optimization (CSO) algorithm. The CSO algorithm is a bio-inspired algorithm inspired by the behavior traits of cats. CSO is employed to find the appropriate value of MLP parameters. Technical indicators calculated from historical data are used as input variables for the proposed model. The model's performance is validated using historical data not used for training. The model's prediction efficiency is evaluated in terms of MSE, MAPE, RMSE and MAE. The model's results are compared with other models optimized by various bio-inspired algorithms presented in the literature to prove its efficiency. The empirical findings confirm that the proposed CSO-MLP prediction model provides the best performance compared to other models taken for analysis. 2022 Polish Academy of Sciences. All rights reserved. -
A Component Selection Framework of Cohesion and Coupling Metrics
Component-based software engineering is concerned with the development of software that can satisfy the customer prerequisites through reuse or independent development. Coupling and cohesion measurements are primarily used to analyse the better software design quality, increase the reliability and reduced system software complexity. The complexity measurement of cohesion and coupling component to analyze the relationship between the component module. In this paper, proposed the component selection framework of Hexa-oval optimization algorithm for selecting the suitable components from the repository. It measures the interface density modules of coupling and cohesion in a modular software system. This cohesion measurement has been taken into two parameters for analyzing the result of complexity, with the help of low cohesion and high cohesion. In coupling measures between the component of inside parameters and outside parameters. The final process of coupling and cohesion, the measured values were used for the average calculation of components parameter. This paper measures the complexity of direct and indirect interaction among the component as well as the proposed algorithm selecting the optimal component for the repository. The better result is observed for high cohesion and low coupling in component-based software engineering. 2022 CRL Publishing. All rights reserved. -
Capturing non-financial information in integrated reporting
In the contemporary business scenario, integrated reporting is a transformational form of corporate reporting. Integrated reporting provides material and substantial information about an entitys prospects, governance, strategy and actions that serve as a reflection of social and commercial viability within the holistic environment in which it operates (IIRC, 2013). Thus, in integrated reports, along with financial information the critical non-financial aspects that affect the reputation, performance and sustainability of the firm are also required to be reported by companies. While regulations are instituted for compulsory divulgence of non-financial information as part of annual reports, there is a lot of ambiguity regarding the non- financial items to be included and the manner of reporting. This paper delineates the non-financial capital components for disclosure in integrated reports. It also discusses the current practices of integrated reporting world over, which will help organisations gain clarity in presenting the non-financial items under different heads of non-financial capitals. Copyright 2022 Inderscience Enterprises Ltd. -
m-quasi-?-Einstein contact metric manifolds
The goal of this article is to introduce and study the characterstics of m-quasi-?-Einstein metric on contact Riemannian manifolds. First, we prove that if a Sasakian manifold admits a gradient m-quasi-?-Einstein metric, then M is ?-Einstein and f is constant. Next, we show that in a Sasakian manifold if g represents an m-quasi-?-Einstein metric with a conformal vector field V, then V is Killing and M is ?-Einstein. Finally, we prove that if a non-Sasakian (?, )-contact manifold admits a gradient m-quasi-?-Einstein metric, then it is N(?)-contact metric manifold or a ?-Einstein. Kumara H.A., Venkatesha V., Naik D.M., 2022. -
Social Inclusion, Equality, Leadership, and Diversity to Attain Sustainable Development Goal 5 in the Indian Banking Industry
The UN SDG 5 aspires to end all kinds of bigotry and abuse of women, although gender bias still exists in India. Most bank employees are men; few women hold senior positions in Indias banking industry because of the countrys early history of limiting chances for women to enter the profession. The solution to this is to hire women in leadership positions from international locations if the banking sector opens. The development of the banking industry in India relies on the best talent. The banking sector must open its position for multinational expatriates to maintain diversity and bring forth the inclusivity of a multi-talented global workforce. The concept of liberalization, privatization, and globalization in the Indian context is limited. Privatization and globalization can only be anticipated if they have a multicultural workforce within the country and globally. 2022 -
A Novel Edge-Based Trust Management System for the Smart City Environment Using Eigenvector Analysis
The proposed Edge-based Trust Management System (E-TMS) uses an Eigenvector-based approach for eliminating the security threats present in the Internet of Things (IoT) enabled smart city environment. In most existing trust management systems, the trust aggregation process completely depends on the direct trust ratings obtained from both legitimate and malicious neighboring IoT devices. E-TMS possesses an edge-assisted two-level trust computation approach for ensuring the malicious free trust evaluation of IoT devices. The E-TMS aims at removing the false contribution on aggregated trust data. It utilizes the properties of the Eigenvector for identifying compromised IoT devices. The Eigenvector Analysis also helps to avoid false detection. The analysis involves a comparison of all the contributed trust data about every single connected device. A spectral matrix will be generated corresponding to the contributions and the received trust will be scaled based on the obtained spectral values. The absolute sum of obtained values will contain only true contributions. The accurate identification of false data will remove the effect of malicious contributions from the final trust value of a connected IoT device. Since the final trust value calculated by the edge node contains only the trustworthy data, the prediction about the malicious nodes will be accurate. Eventually, the performance of E-TMS has been validated. Throughput and network resilience are higher than the existing system. 2022 G. Nagarajan et al.