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A study of consumers' attitude towards online private label brands using the Tri - Component model
Online private label products seem to be a promising and profitable deal for the Indian online retailers. The purpose of this paper was to understand the consumers' attitude and buying behaviour towards online private label brands. For this purpose, we empirically tested a model comprising of variables such as cognitive, affective, behavioural, purchase intention, and actual buying behaviour. Data were gathered by using a schedule. A sample of 400 respondents was gathered, and the hypotheses were tested by performing structural equation modelling. The findings highlighted that the cognitive, affective, and behavioural factors of attitude influenced each other strongly as well as the purchase intention. In addition, the results obtained revealed that purchase intention led to the buying of online private label brands. It is expected that the findings of this study will enable the marketers of online private label brands to be more informed about the consumers' attitude formation process. Furthermore, it will help them to understand the areas related to private label brands, which need their attention. 1964-2018 Associated Management Consultants. -
Sentiment analysis on social media data using intelligent techniques
Social media gives a simple method of communication technology for people to share their opinion, attraction and feeling. The aim of the paper is to extract various sentiment behaviour and will be used to make a strategic decision and also aids to categorize sentiment and affections of people as clear, contradictory or neutral. The data was preprocessed with the help of noise removal for removing the noise. The research work applied various techniques. After the noise removal, the popular classification methods were applied to extract the sentiment. The data were classified with the help of Multi-layer Perceptron (MLP), Convolutional Neural Networks (CNN). These two classification results were checked against the others classified such as Support Vector Machine (SVM), Random Forest, Decision tree, Nae Bayes, etc., based on the sentiment classification from twitter data and consumer affairs website. The proposed work found that Multi-layer Perceptron and Convolutional Neural Networks performs better than another Machine Learning Classifier. International Research Publication House. -
An efficient load balancing in cloud computing using hybrid Harris hawks optimization and cuckoo search algorithm
Cloud computing has rapidly emerged as a burgeoning research field in recent times. However, despite this growth, a comprehensive examination of this domain reveals persistent issues in the application of cloud-based systems concerning workload distribution. The abundance of resources and virtual machines (VMs) within cloud computing underscores the importance of efficient task allocation as a critical process. Within the infrastructure as a service (IaaS) architecture, load balancing (LB) remains a pivotal but challenging task. The occurrence of overloaded or underloaded hosts/servers during cloud access is undesirable, as it leads to operational delays and system performance degradation. To address LB issues effectively, it is imperative to deploy a proficient access scheduling algorithm capable of distributing tasks across the available resources. A novel approach was introduced by combining the Harris hawks optimization and cuckoo search algorithm (HHO-CSA), with a specific focus on critical service level agreement (SLA) parameters, particularly deadlines, to uphold LB in a cloud environment. The primary objective of the hybrid HHO-CSA methodology is to provide task attributes, resource allocation, VMs prioritization, and quality of service (QoS) to clients within cloud computing applications. The outcome analysis reveals that the proposed hybrid HHO-CSA algorithm results in a resource utilization reduction of 52%, with an execution time of 529.84 ms and a makespan of 638.88 ms. These values outperform those of existing SLA-based LB algorithms. Effective task scheduling plays a pivotal role in ensuring the seamless execution of tasks within a cloud system, while LB significantly aligns with the SLAs available to users. Drawing insights from the existing literature, the suggested hybrid HHO-CSA method addresses the research gap by effectively mitigating the challenges. 2023, Accent Social and Welfare Society. All rights reserved. -
Inverse Hilbert Fractal-Metamaterial Rings for Microstrip Antennas and Wideband Applications
A Novel Metamaterial (MTM) property is obtained using a fractal pattern known as Inverse Hilbert. The Mu-negative(MNG) characteristics have been recovered by adopting NRW method. This MTM characteristic is studied for 2.45 GHz using FR4 epoxy as substrate. The dimension of the substrate is 30mm36mm 1.6mm. This fractal metamaterial structure can be amalgamated with an optimized Microstrip antenna (MSA) for improvement in antenna parameters and can be used for RF energy harvesting. 2022 IEEE. -
Progression of Metamaterial for Microstrip Antenna Applications: A Review
This article provides an overview of the evolution of metamaterials (MTM) and all the aspects related to metamaterial development for antenna applications. It will be a useful collection of information for antenna researchers working in metamaterials applications. It gives an insight into the various metamaterial structures utilized along with miniature antenna designs. Different types of design parameters studied by the previous researchers are showcased to understand better perception of the metamaterial usage. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Successful turnarounds: the role of appropriate entrepreneurial strategies
Purpose The purpose of this paper is to report on a research study aimed at comparing the causes of organisational decline and turnaround strategies involved in cases of successful and unsuccessful turnarounds, with a view to identifying the differences, if any, between the two groups, which in turn is expected to provide useful information to academics, practitioners and policy makers. Design/methodology/approach Since turnaround is a business phenomenon of general interest, their stories are often published in business periodicals, which are a rich source of data on them. In order to tap this data source, the present paper employed a method of content analysis for the proposed investigation on the cause of organisational decline and turnaround strategies used. In order to quantify the data, a three-point scale was developed, where the presence of a cause/strategy is rated as 3, its ambivalence as 2 and its absence as 1, whose validity was assessed through the inter-rater agreement indices. The data thus generated are amenable to statistical analyses, using which the more commonly prevalent causes of organisational decline and the strategies commonly employed for turnaround by the successful and unsuccessful companies are identified. Findings The findings of the present study have generated a few useful insights. First, the primary causes for organisational decline are the internal weaknesses of the organisation; in fact the external changes can adversely affect the organisation only if it is internally weak. Second, organisational decline caused by multiple factors (which is usually the case) can be managed effectively by adopting a variety of strategies; hence a single-pronged strategy is often found to be ineffective. Third, the more successful turnarounds had a diverse portfolio of strategies including those of institution-building, often employed in a phased manner, consistent with the stage theories of turnaround. Research limitations/implications The limitations of this research arise mainly from the generation of data from published sources and the consequent biases, which can be managed, to a large extent, by using multiple sources for the same case for reducing the publishers biases as well as by having multiple raters for identifying the researchers biases, if any. Originality/value The study has highlighted the need for addressing the internal causes of organisational decline on a priority-basis rather than blaming the external factors, besides pointing to the need for adopting a variety of strategies for dealing with the diversity of causes affecting the organisations health, particularly the need for institutionalising the changes. These findings can be of help especially to turnaround managers and policy-makers in dealing with organisational decline and thus contribute to the creation and enhancement of economic value. 2015, Emerald Group Publishing Limited. -
A Scoping Review on Integration of Electroencephalogram Neurofeedback Training for Alcohol Use Disorder: Clinical and Neurocognitive Outcomes
Background. The conventional treatment for alcohol use disorder (AUD) consists of dual treatment encompassing pharmacotherapy and psychotherapy. Nonetheless, the impact of these treatments on clinical and neurocognitive outcomes is only low to medium efficacy. Research studies substantiate the integration of electroencephalogram neurofeedback training (EEG-NFT) as an add-on tool with significant improvements in clinical and neurocognitive outcomes. Methods. A scoping review of the existing literature on EEG-NFT and AUD, which are open access, including review papers and empirical studies in the English language, and with human subjects are deemed worthy of the scope of this study. The keywords electroencephalogram neurofeedback training, alcohol use disorder, stress, neurocognition, and relapse were used. The primary sources of the literature search were Science Direct, Scopus, PubMed, and Google Scholar. A total of 35 articles have been included in the scoping review. Studies from the last 15 years were considered for the same. Results. This review revealed that EEG-NFT is a promising tool with significant improvements in stress levels, cognitive deficits, and relapse rates for individuals with AUD when used in integration with conventional treatments. Conclusion. Chronic alcohol use affects cognitive functions, escalates relapse rate, and increases stress experienced by the individual. The present study highlights the significance of NFT as a potent add-on treatment modality to improve clinical and cognitive outcomes, thereby facilitating abstinence and reducing relapse rates in individuals with AUD. Copyright: 2023. -
A Quasi-Experimental Study on the Effectiveness of Integrated Electroencephalogram Neurofeedback Training and Group Psychotherapy for Harmful Alcohol Use: Neurocognitive and Clinical Outcomes
Introduction. This study investigates the efficacy of integrating electroencephalogram (EEG) neurofeedback training and group psychotherapy for individuals with harmful alcohol use (AUDIT-10 scores 1013). Methods. Seventy-six participants were purposively sampled and divided into treatment (EEG neurofeedback training and group psychotherapy) and control groups. Baseline assessments measured alcohol consumption (AUDIT-10), stress (perceived stress scale [PSS]), neurocognition (NIMHANS neuropsychological battery), craving (PACS), and visual analog scale. The treatment group underwent 20 sessions of EEG neurofeedback (Peniston-Kulkosky and Scott-Kaiser modification protocols) and four sessions of group psychotherapy (motivational interviewing [MI], psychoeducation). Result/Discussion. A repeated measures ANOVA showed significant improvement in postcondition scores for the treatment group compared to controls, who exhibited deterioration over time. The study provides evidence supporting the efficacy of integrated EEG neurofeedback training and group psychotherapy in mitigating harmful alcohol use progression. Conclusion. By addressing stress, cognition, and cravings, this intervention offers crucial support to individuals with problematic drinking. 2024. Panicker et al. -
An Empirical Analysis of Turnaround and its Benefits to Stakeholders
International Journal of Applied Management Research, Vol-6 (1(3), pp. 470-473. ISSN-0974-8709 -
Correlation of temperature, velocity and perforation location in a flat unglazed transpired solar collector (Utc) due to air flow
An unglazed transpired solar collector is a system that can leverage the abundant solar energy for various purposes. The solar collector is available in flat or corrugated form and is seen to be installed as an exterior layer of building facades. The cladding thus made absorbs radiation from the sun and heats up air being sucked by fan and flowing through perforations. In this paper, the focus has been to understand the correlation of plate temperature, exit temperature, the velocity distribution in the chamber and perforation location when air flows past a flat unglazed transpired solar collector (UTC). The establishment of correlations was carried out in the dataset of flow variables obtained after solving the problem using Navier-Stokes (NS) equations along with the standard k-? turbulence model and shear stress transport (SST) k-? model. An attempt has also been made to compute Pearsons correlation coefficient of any two flow variables to understand their strong and weak correlations. A linear regression analysis has been done to predict the response variables against the response obtained in CFD solver by using an open source software Rstudio . A strong correlation among cavity vertical velocity, perforation location and temperature has been established. From the study, it is noted that the location of a perforation has a strong correlation with the cavity vertical velocity and a weak correlation exists with temperature and plate temperature. 2020, Pushpa Publishing House. All rights reserved. -
Numerical simulation of flow over a flat unglazed transpired solar collector (UTC) and its performance prediction
An unglazed transpired solar collector is a system that can leverage the abundant solar energy for various purposes. The solar collector is available in flat or corrugated form and is seen to be installed as an exterior layer of building facades. The cladding thus made absorbs radiation from the sun and heats up air being sucked by fan and flowing through perforations. In this research the focus has been to understand the correlation of plate temperature, exit temperature, the velocity distribution in the chamber and perforation location when air flows past an unglazed transpired solar collector (UTC). The establishment of correlations was carried out in the dataset of flow variables obtained after solving the problem using Navier-Stokes (NS) equations along with standard two-equation (k-?) turbulence models and Shear Stress Transport (SST ) k-? models for turbulent flow. The same problem was also solved using NS equation using laminar model. An attempt has also been made to compute Pearsons correlation coefficient of any two variables to understand their strong and weak correlations. A linear regression analysis was done through an open source software Rstudio for a dataset produced during the computational modeling using a commercial CFD solver, Ansys Fluent. At the end a Monte Carlo simulation has been done to predict the likelihood of using the flat UTC for drying as well as to understand the dependency of system efficiency on plate exit temperature, suction velocity and freestream temperature. BEIESP. -
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Plano framework for graph indexing - A statistical analysis
Graph Mining is becoming one of the most dominant fields of research. There are plenty methods to index, re-index and to search the features throughout the index but still from the literature study there is no specific frame work which can sum up all three so that indexing and updating the index with new feature can be done in consistent intervals according to the arrival of new features. PLANO is the frame work which has the latest algorithms to look into the data and index. In this paper, Time and Memory efficiency of the proposed algorithms in the PLANO framework is tested statistically and compared with the existing algorithms memory and time usage. Research India Publications. -
Large power factors in wide band gap semiconducting rFeO3 materials for high-temperature thermoelectric applications
While most of the thermoelectric materials work well only at low and mid temperatures, high-temperature thermoelectric materials (T > 900 K) are equally important for the operation of deep-spacecraft missions, nuclear reactors, and high-temperature industrial reactors. To accomplish this demand, this work provides insights into wide band gap semiconducting RFeO3 (rare-earth orthoferrites) for high-temperature thermoelectric applications. Using the first-principles density functional theory calculations, we have demonstrated the coexistence of extremely flat and corrugated flat bands near the Fermi region in a wide band gap material. The presence of such features enhances and sustains the thermopower, electrical conductivity, and power factor, which are the crucial factors for the efficiency of thermoelectric materials. Semiclassical Boltzmann formalism was then employed to study the transport properties of four orthorhombic RFeO3 materials (R = Pr, Nd, Sm, and Gd). Our results reveal high Seebeck coefficients (thermopower) along with the large electrical conductivities over the high hole doping carrier concentration and in the high-temperature region (T > 900 K). Furthermore, significantly large power factors are obtained with very low theoretical minimum lattice thermal conductivity in the range 1.41?1.51 W m?1 K?1. These huge power factors directly suggest the maximum power output in RFeO3, which we believe is a more appropriate performance index than the figure of merit, especially for high-temperature thermoelectric applications. We also emphasize that the outcomes of our work would be certainly useful for experimentalists in designing high-temperature thermoelectric materials. 2020 American Chemical Society -
Ultrahigh Power Factors in Ultrawide-Band-Gap GaB3N4and AlB3N4for High-Temperature Thermoelectric Applications
With recent thermoelectric studies concentrating too much on low- and mid-temperature applications, an interesting question is, "are there any materials suitable for high-temperature thermoelectric operations?"To answer this, we have demonstrated in this work the viability of the ternary ultrawide-band-gap materials GaB3N4 and AlB3N4 for high-temperature thermoelectric applications using the first-principles calculation method. Our accurate transport calculations, considering both elastic and inelastic scattering mechanisms, reveal the ultrahigh power factors as high as 1821 ?W m-1 K-2 in GaB3N4 and 1876 ?W m-1 K-2 in AlB3N4 at 2000 K. The power factors are calculated from the Seebeck coefficients and electrical conductivities for both electron and hole carrier concentrations between 1018 and 1021 cm-3. For the figure-of-merit (ZT) calculation, the obtained power factors along with the electronic thermal conductivities determined from the definite Lorenz numbers and the lattice thermal conductivities from the phonon vibrations were used. The calculated ZT values seem to be appreciable for high-temperature applications considering the materials' stability factor and the temperature range within the optimum electron carrier concentration of 1021 cm-3. Although the lattice thermal conductivities are higher, which decrease the values of ZT, considering the ultrahigh power factors instead of the ZT factor could be the right choice for high-temperature thermoelectric applications. -
Effective and Efficient Video Compression by the Deep Learning Techniques
Deep learning has reached many successes in Video Processing. Video has become a growing important part of our daily digital interactions. The advancement of better resolution content and the large volume offers serious challenges to the goal of receiving, distributing, compressing and revealing highquality video content. In this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask, which creatively combines the Deep Learning Techniques on Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). The video compression method involves the layers are divided into different groups for data processing, using CNN to remove the duplicate frames, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using GAN and recorded with Long Short-Term Memory (LSTM). Instead of the complete image, the small changes generated using GAN are substituted, which helps with frame-level compression. Pixel wise comparison is performed using K-nearest Neighbours (KNN) over the frame, clustered with K-means and Singular Value Decomposition (SVD) is applied for every frame in the video for all three colour channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors. Video frames are packed with parameters with the aid of a codec and converted to video format and the results are compared with the original video. Repeated experiments on several videos with different sizes, duration, Frames per second (FPS), and quality results demonstrated a significant resampling rate. On normal, the outcome delivered had around a 10% deviation in quality and over half in size when contrasted, and the original video. 2023 CRL Publishing. All rights reserved. -
Improving the Security of Video Embedding Using the CFP-SPE Method
With the amount of data being transferred on a daily basis, it is becoming increasingly dangerous to save data on the Internet in the face of intruders or hackers. This study paper is one of the most effective ways to transmit information in a secure and confidential manner. The authors previously disclosed a way for embedding a secret video inside a cover video in their prior work. The writers have implemented a number of techniques to incorporate the secret video. The current work improves on the existing approach by including encryption and decryption concepts into the video embedding process. The secret data for either a large or little amount of information is put on the cover video utilising the embedding technique. Our proposed method combines compression, encryption, decryption, and secret information embedding to provide a more secure data transfer. 2022 Karthick Panneerselvam et al. -
Robotics: challenges and opportunities in healthcare
Today, healthcare services and systems are becoming very complex and include a large number of entities characterized by shared, distributed and heterogeneous devices, sensors, and information and communication technologies. Various artificial intelligence techniques have been implemented in various sectors like smart cities, energy, IT sectors, banking, agriculture, retails, and many more, but it has been always challenging to demonstrate this technique effectively in healthcare sector due to its sophisticated procedure and its handling. Data analytics research on healthcare data has grown significantly over the past 10-12 years, and the execution of data analytics algorithms and systems in healthcare has been progressing more quickly. The data analytics service section has gained considerable attention with the development of technology, especially artificial intelligence robots, in the healthcare sector. Robots can help people with cognitive, sensory, and motor disabilities, help the sick or injured, support caregivers, and assist the clinical workforce. The purpose of this study is to provide historical evolution of robotics in healthcare with an overview of the influence of robots in healthcare like clinical support, patient transfer in hospitals, to handle heavy surgical instruments, to transport medical waste, for drug delivery, patient management etc. Furthermore, this chapter also covered the challenges and opportunities in healthcare and also offers a comprehensive aspect at how robots are incorporate in various healthcare applications. 2025 Elsevier Inc. All rights reserved. -
Youth and Media Literacy in the Age of Social Media
Living in the age of information means information is all pervasive, uncensored, unreliable, and with the potential to influence. The unfettered access to information and communication through social media is a double-edged sword in the hands of youth. The impact of this was explored from sociocultural and mental health perspectives. Specifically, the role of media literacy in combating the challenges posed by usage of social media was explored in this chapter. Various theories, frameworks, models, and components of media literacy were analysed. Impact of the various media literacy interventions on the youth, case studies of specific information literacy programs across regions, and other relevant critiques were reviewed and consolidated. Further to this, recommendations have been presented on creating robust in-school, and outside-school media literacy programs for the youth. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
A MULTI-OBJECTIVE HUNTER-PREY OPTIMIZATION FOR OPTIMAL INTEGRATION OF CAPACITOR BANKS AND PHOTOVOLTAIC DISTRIBUTION GENERATION UNITS IN RADIAL DISTRIBUTION SYSTEMS
This article put forward the determination of the optimal siting and sizing of capacitor banks and PV-DG (Photo-Voltaic Distribution Generation) units in a radial distribution system. A modern population-based optimization algorithm, Hunter-Prey Optimization (HPO), is applied to determine the optimal capacitor bank and PV-DG placement. This algorithm, HPO, got its motivation from the trapping behaviour of the carnivore (predator/hunter) like lions and wolves towards their target animal like deer. The typical IEEE-33 & 69 test bus systems are scrutinized for validating the effectiveness of the suggested algorithm using MATLAB software R2021b version. The acquired results are collated with the existing heuristic algorithms for the active power loss criterion. The nominal or base values for system losses and voltage profile were considered for the comparison, with the results from HPO. The HPO application has an efficient performance in figuring out the most favourable location and capacity of the capacitor banks and PV DGs compared with the other techniques. 2023 by authors and Galileo Institute of Technology and Education of the Amazon (ITEGAM).