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E-service quality-impact on customer satisfaction
The paper aims to determine the impact of e-service quality on customer satisfaction. The study utilised data from 252 customers of private and public banks in India through questionnaires. It was found that the e-service quality has significant impact on customer satisfaction in public sector banks as well as private sector banks. 2019 SERSC. -
E-shopping orientation, trust and impulse buying in the online context a study based on female members of Generation Z in India
A large number of studies have attempted to understand consumer behaviour in the online context. One construct that has been of particular interest to marketers, retailers and researchers, is impulse buying behaviour. The number of studies attempting to understand the drivers of impulsive purchases has been on a rise. The current pandemic also saw a rise in impulsive purchases and the interest in the construct was renewed. The current study is based on the S-O-R model and evaluates the relationship between e-shopping orientation, trust and impulse buying behaviour. The findings are based on data collected from female members of Generation Z and suggest that frequent visits to e-retail stores and increased patronage can increase the level of trust in the retail partner and influence the number of impulsive purchases. The findings are particularly significant for retailers looking to drive sales through impulsive purchases. In addition, the findings provide empirical support for the application of the S-O-R model to online retail context. Copyright 2024 Inderscience Enterprises Ltd. -
Eagle Strategy Arithmetic Optimisation Algorithm with Optimal Deep Convolutional Forest Based FinTech Application for Hyper-automation
Hyper automation is the group of approaches and software companies utilised to automate manual procedures. Financial Technology (FinTech) was processed as a distinctive classification that highly inspects the financial technology sector from a broader group of functions for enterprises with utilise of Information Technology (IT) application. Financial crisis prediction (FCP) is the most essential FinTech technique, defining institutions financial status. This study proposes an Eagle Strategy Arithmetic Optimisation Algorithm with Optimal Deep Convolutional Forest (ESAOA-ODCF) based FinTech Application for Hyperautomation. The ESAOA-ODCF technique has achieved exceptional performance with maximum accu y of 98.61%, and F score of 98.59%. Extensive experimental research revealed that the ESAOA-ODCF model beat more modern, cutting-edge approaches in terms of overall performance. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
Ear Recognition Using Rank Level Fusion of Classifiers Outputs
An individuals authentication plays a vital role in our daily life. In the last decade, biometric-based authentication has become more prevalent than traditional approaches like passwords and pins. Ear recognition has gained attention in the biometric community in recent years. Researchers defined several features for the identification of a person from ear image. The features play a vital role in the success of classification models. This paper considers an ensemble of features for designing a new classification model. The features are assessed in isolation as well as through feature-level fusion. Subsequently, a rank-level fusion for classification is introduced. The experiments are conducted on both constrained and unconstrained ear datasets. The results are promising and open up new possibilities in machine learning-based ear recognition 2023, International journal of online and biomedical engineering.All Rights Reserved. -
Earliness of SME internationalizationand performance: Analyzing the role of CEO attributes
Purpose: The purpose of this paper is to understand the mediating effects of Chief Executive officer (CEO) attributes on the earliness of internationalization and performance in context of Indian small and medium enterprises (SMEs). Design/methodology/approach: The proposed framework is tested through analysis of a sample of 102 internationalized SMEs of the engineering industry in the Bangalore city region of India. Findings: Results highlight that CEOs age and educational background moderates between early internationalization and performance in the Indian SME context. Practical implications: Overall results facilitate in leveraging the decision-makers capabilities to successfully formulate and strategize their international marketing efforts to achieve higher performance. Originality/value: The study enriches the importance of CEO attributes in influencing the early internationalization and degree of internationalization in the context of an emerging economy where studies are limited. 2019, Emerald Publishing Limited. -
Early bruise detection, classification and prediction in strawberry using Vis-NIR hyperspectral imaging
The most frequent kind of damage to strawberries is bruising. However, most of the bruises are so barely perceptible at an early stage on the surface, that detection of them with the human eye is quite challenging. This study proposes a method for accurately detecting and classifying the damage using reflectance imaging spectroscopy. In order to carry out the study, an experiment was devised to artificially induce bruises and a dataset was generated at different bruise intervals. A model for detecting and classifying bruises at their latent stage was developed using machine learning classifiers, including support vector machines (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), to investigate the changes over time after bruise occurrence on the detection performance. Regression models for the prediction of bruising time were developed using partial least square regression (PLSR), RF, gradient boosting (GB), support vector regression (SVR), and DT. Among the compared models, both SVM and LDA could achieve 99.99 % classification accuracy. RF was regarded as being the most advisable for detection and prediction jobs due to its high performance. It achieved MSE of 0.052 and R2 of 0.989 for prediction. 2024 Elsevier Ltd -
Early diagnosis of COVID-19 patients using deep learning-based deep forest model
Coronavirus disease-19 (COVID-19) has rapidly spread all over the world. It is found that the low sensitivity of reverse transcription-polymerase chain reaction (RT-PCR) examinations during the early stage of COVID-19 disease. Thus, efficient models are desirable for early-stage testing of COVID-19 infected patients. Chest X-ray (CXR) images of COVID-19 infected patients have shown some bilateral changes. In this paper, deep transfer learning and a deep forest-based model are proposed to diagnose COVID-19 infection from CXR images. Initially, features of X-ray images are extracted using the well-known deep transfer learning model (i.e., ResNet101), which does not require tuning many parameters compared to the deep convolutional neural network (CNN). After that, the deep forest model is utilised to predict COVID-19 infected patients. The deep forest is based upon ensemble learning and requires a small number of hyper-parameters. Additionally, the proposed model is trained on a multi-class dataset that contains four different classes as COVID-19 (+), pneumonia, tuberculosis, and healthy patients. The comparisons are drawn among the proposed deep transfer learning and deep forest-based models, the competitive models. The obtained results show that the proposed model effectively diagnoses COVID-19 infection with an accuracy of 99.4%. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Early Identification and Detection of Driver Drowsiness by Hybrid Machine Learning
Drunkenness or exhaustion is a leading cause of car accidents, with severe implications for road safety. More fatal accidents could be avoided if fatigued drivers were warned ahead of time. Several drowsiness detection technologies to monitor for signs of inattention while driving and notifying the driver can be adopted. Sensors in self-driving cars must detect if a driver is sleepy, angry, or experiencing extreme changes in their emotions, such as anger. These sensors must constantly monitor the driver's facial expressions and detect facial landmarks in order to extract the driver's state of expression presentation and determine whether they are driving safely. As soon as the system detects such changes, it takes control of the vehicle, immediately slows it down, and alerts the driver by sounding an alarm to make them aware of the situation. The proposed system will be integrated with the vehicle's electronics, tracking the vehicle's statistics and providing more accurate results. In this paper, we have implemented real-time image segmentation and drowsiness using machine learning methodologies. In the proposed work, an emotion detection method based on Support Vector Machines (SVM) has been implemented using facial expressions. The algorithm was tested under variable luminance conditions and outperformed current research in terms of accuracy. We have achieved 83.25 % to detect the facial expression change. 2013 IEEE. -
Early prediction of lungs cancer by deep learning algorithms from the CT images with LBP features
The early prediction of the any type of cancer can save the lives of many especially if it is lung cancer which is one of the deadly diseases in the world. Thus the early prediction is implemented we can increase life expectancy and bring the mortality level low. Although there are various methods to detect the lung cancer cells by X-ray and CT scans, however the CT images are more preferred. The 2D images like CT scans are used to get medical results more accurate. The proposed method here will discuss how the LBP features are used to analyze the CT images with the support of Deep Learning methods. In this research work we will discuss how the image manipulation can be done to achieve better results from the CT images through various image processing methods. LBP features helps in estimating the distribution of local binary pattern of an image. A final result with 93% is achieved after the training of the processed images by LBP features. 2020 SERSC. -
Earthquake and flood resilience through spatial Planning in the complex urban system
Urban Communities are exposed to different disaster risks. The paper aims at understanding the interrelation of spatial planning and the resilience of the urban communities for earthquakes and floods. Various spatial planning components were used to evaluate the community resilience to earthquake and flood in the city of Pune of Maharashtra state in India. It has been identified that spatial planning contributes to a greater extent in determining community resilience. Spatial planning results in differential resilience among communities. In the study area, economically weaker households are found to be more vulnerable to disaster risk due to their spatial locations and limited accessibility to share the resources. These factors are found to be contributing to reduced resilience in the city. 2022 The Authors -
EASM: An efficient AttnSleep model for sleep Apnea detection from EEG signals
This paper addresses the crucial task of automatic sleep stage classification to assist sleep experts in diagnosing sleep disorders such as sleep apnea and insomnia. The proposed solution presents a novel attention-based deep learning model called, Efficient Attention-sleep Model (EASM), designed specifically for sleep apnea detection using EEG signals. EASM incorporates a streamlined architecture that includes a modified Muti-Resolution Convolutional Neural Network (MRCNN), Adaptive Feature Recalibration (AFR), and a simplified Temporal Context Encoder (TCE) module to reduce complexity. To mitigate overfitting, ridge regression is utilized, which incorporates a penalty term to enhance model generalization. Furthermore, the proposed EASM utilizes a class-balanced focal loss function to address data imbalance issues. The effectiveness of EASM is evaluated on two publicly available datasets, SLEEP EDF-20 and SLEEP EDF-78. Comparative analysis of EASM against state-of-the-art models demonstrates its superior performance in terms of accuracy, training time, and model complexity. Notably, the proposed model achieves a 50% reduction in training time and a 55.7% decrease in complexity compared to the Attnsleep model. The EASM achieves a classification accuracy of 85.8% with minimum loss when compared to the Attnsleep model. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Ebcqi: Enhanced bcqi downlink scheduling algorithm for voip in mobile networks
Long Term Evolution (LTE) is a currently growing technology. It gives high speed data with several useful applications. Voice over Internet Protocol (VoIP) is one of the top most applications in that LTE. Scheduling is the main issue in LTE. This paper proposing an updating version of Best Channel Quality Indicator (BCQI) downlink scheduling algorithm. The proposed algorithm assigns the highest priority to VoIP users followed by video traffic and then other remaining traffics in next priority order. The simulation reports give the better results of increased average throughput in all users, as well as the spectral efficiency development is also increased. Here, in the proposed algorithm, the percentage of packet loss is also consistent with the existing BCQI algorithm. And, it totally emits positive results in both rural and urban area environments with different mobility. Number of user access is also high when compared with BCQI algorithm. IJSTR 2019. -
EC(H) onarrating stories: Ecological thought and metanarrativity in folktales
This paper studies the ecological discourse constructed in folktales, looking at the relationship between folktales and the human-nature-culture paradigm. By closely examining select folktales collected by folklorist A. K. Ramanujan, this paper looks at the metanarrativity of tales and argues from a narratological perspective that folktales deploy nature metaphors to establish a close relationship between nature, women and culture. This, it is argued, is made possible only in the conservation of stories for, in conserving a story, the message of conserving cultures and their artefacts (an ecological metaphor) is spread. The story is conserved; however, not in hoarding it but quite contrarily in transmitting and letting it go. The paper also critically examines how female subjects, through the use of nature metaphors and symbols of fertility and femininity speak of their consciousness in these 'woman-centred tales' in a space characterized by the absence of the Phallic Other but inevitably speak the patriarchal language of feminine inscriptions. Using folkloric research of A. K. Ramanujan as well as ecocritical frameworks, this paper looks at the narratological dimensions of folklore to understand metanarration as a crucial aspect of folklore and ecological conservation. Therefore the lessons of conservation lie not only in the content of the folktales but also in their very telling. The ecological aspects in and of the tale must necessarily be echoed multiple times to enable the tale's transmission, and in effect, their conservation. 2014 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (Dharmaram Vidya Kshetram, Bangalore). -
Eccentric completion of a graph
The eccentric graph Ge of a graph G is a derived graph with the vertex set same as that of G and two vertices in Ge are adjacent if one of them is the eccentric vertex of the other. In this paper, the concepts of iterated eccentric graphs and eccentric completion of a graph are introduced and discussed. 2022 The authors. -
Eccentricity splitting graph of a graph
Let G = (V, E) be any connected graph with (Figure presented.) for all uj, uk ? Si if e(uj) = e(uk)(1 ? i ? t) with each | Si |? 2 and (Figure presented.). The eccentricity splitting graph of a graph denoted by ES(G) is obtained by taking a copy of G and adding vertices w 1, w 2, , wt such that wi is adjacent only to the vertices of Si for 1 ? i ? t. We initiate the study on eccentricity splitting graph ES(G) and examine its structural properties. We also analyze diameter, girth and chromatic number of eccentricity splitting graphs of certain classes of graphs. 2021 Taru Publications. -
Echoes of Conflict: Unveiling the Interconnected Tapestry of Russia-Ukraine Warfare, Oil Price Ballet, and the Asian Stock Symphony
The purpose of this research is to look into the impact of the Russia-Ukraine war on the relationship between oil prices and the Asian stock market. While earlier studies have investigated the impact of oil prices on stock markets, there has been little research into the impact of crude oil prices on the Asian stock market in the context of the Russia-Ukraine war. For this purpose, the data is collected from NSE and Bloomberg database the study's findings imply that the Russia-Ukraine war has had a major impact on the relationship between crude oil prices and stock market indices in numerous Asia-Pacific countries. The study suffers from a few limitations such as it only examines the relationship between crude oil prices and stock market indices but there are other macroeconomic factors, such as interest rates, inflation, and political instabil ity which also affect the market. 2024, ASERS Publishing House. All rights reserved. -
Eco Friendly Nitration of Toluene using Modified Zirconia
Bulletin of Chemical Reaction Engineering & Catalysis Vol. 7, No.3, pp.205-214 ISSN No. 1978-2993 -
Eco friendly nitration of toluene using modified zirconia
Nitration of toluene has been studied in the liquid phase over a series of modified zirconia catalysts. Zirconia, zirconia- ceria (Zr0.98Ce0.02)O2, sulfated zirconia and sulfated zirconia- ceria were synthesised by co precipitation method and were characterised by X-ray diffraction, BET surface area, Infra red spectroscopy analysis (FTIR), Thermogravimetric analysis (TGA), Scanning Electron Microscopy (SEM), and Energy Dispersive X ray analysis (EDAX). The acidity of the prepared catalysts was determined by FTIR pyridine adsorption study. X-ray diffraction studies reveal that the catalysts prepared mainly consist of tetragonal phase with the crystallite size in the nano range and the tetragonal phase of zirconia is stabilized by the addition of ceria. The modified zirconia samples have higher surface area and exhibits uniform pore size distribution aggregated by zirconia nanoparticles. The onset of sulfate decomposition was observed around 723 K for sulfated samples. The catalytic performance was determined for the liquid phase nitration of toluene to ortho-, meta- and para- nitro toluene. The effect of reaction temperature, concentration of nitric acid, catalyst reusability and reaction time was also investigated. 2013 BCREC UNDIP. -
Eco-conscious photocatalytic degradation of organic textile dyes using green synthesized silver nanoparticles: a safe and green approach toward sustainability
Green synthesized nanoparticles from Strobilanthes barbatus leaf extracts are environmentally safe and feasible for enduring wastewater treatment, especially for organic textile dye degradation. The synthesized Strobilanthes barbatusmediated silver/silver-oxide nanoparticles (SB-Ag/AgO NPs) showed maximum absorbance at 428nm. The SB-Ag/AgO NPs were generally spherical with an average diameter of 37.59nm (FESEM and TEM analysis). The importance of functional groups in the production of SB-Ag/AgO NPs was recorded by FTIR investigations. In the degradation and rate of degradation for textile dyes, after 320min, SB-Ag/AgO NPs displayed 96.60% (5.31 10?1 L mg?1min?1) and 87.50% (1.179 10?1 L mg?1min?1) degradation of Reactive Blue 220 (RB-220) and Reactive Blue 222A (RB-222A), respectively. When compared to dye effluents, SB-Ag/AgO NPs-treated dye solutions revealed a considerable decrease in inhibitory efficiency during phytotoxicity evaluation on test organisms, Vigna radiata and Artemia salina. The biosynthesized SB-Ag/AgO NPs could serve as a feasible photocatalyst for the treatment of organic textile dyes in organic substancepolluted water ecosystems. SB-Ag/AgO NPs can serve as efficient, cost-effective and environmentally friendly sources for dye degradation. The current research offers a safe and environmentally friendly strategy for sustaining the environment. 2024, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Eco-Conscious Silver Nanoparticles via Quassia indica: Characterization and Multifaceted Applications
This research work explores the green synthesis of silver nanoparticles using Quassia indica (QI-Ag NPs), a natural plant extract, as a stabilizing and reducing agent. The synthesized QI-Ag NPs were characterized using various analytical techniques, including UV-Visible spectroscopy, X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDX), Transmission Electron Microscopy (HR-TEM) and Selected Area Electron Diffraction (SAED). The UV-Visible analysis revealed a characteristic peak at 430 nm, indicating the successful formation of AgNPs. XRD analysis unveiled the crystalline nature of the nanoparticles, with four distinctive peaks corresponding to the silver crystallographic planes. SEM and EDX provided insights into the morphology and chemical composition of the QI-AgNPs. Moreover, TEM and SAED elucidated the structural attributes and crystallinity of the nanoparticles. The Ag NPs exhibited a spherical structure and crystalline nature, as supported by both SAED and XRD findings. The zeta potential of QI-Ag NPs exhibited a value of-24.2 mV. The synthesized QI-Ag NPs were evaluated for their photocatalytic potential, demonstrating a remarkable 97% degradation of Crystal Violet dye. Furthermore, comprehensive studies encompassing antioxidant, antimicrobial and cytotoxicity assessments were conducted, showcasing the multifaceted applications of these nanoparticles. This research underscores the promising potential of Q. indica-mediated silver nanoparticles as environmentally benign and versatile nanomaterials. 2024 World Scientific Publishing Company.