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AUDIENCE PERCEPTION OF TELEVISION INFOMERCIALS
Infomercials are one of the most effective tools for communication . Infomercials bring out the importance of content and presentation of various products. Infomercials whether international or Indian / Hindi infomercials try to identify consumers behaviour and attitude towards the product . The study aims to analyse the perception of audience of television infomercials by applying qualitative and quantitative ( survey and in depth interview ) methodology. -
Attributes of virtual influencers: Science mapping and future agenda
Virtual influencers, constructed using advanced computer-generated imagery and artificial intelligence, have emerged as disruptive forces in the realm of influencer marketing. In contrast to their human counterparts, virtual influencers provide brands with enhanced control, adaptability, and scalability, effectively engaging audiences on a global scale across various digital platforms. However, despite their increasing prevalence and commercial uptake, scholarly exploration into their attributes and impacts remains sporadic and underdeveloped. This study conducts a systematic literature review and bibliometric analysis of 172 peer-reviewed articles indexed in Scopus, employing the SPAR-4-SLR methodology to delineate the knowledge framework surrounding virtual influencers. By utilizing Biblioshiny from the bibliometrix package and VOSviewer for analytical and visualization purposes, this research reveals that attributes such as trustworthiness, expertise, attractiveness, similarity, and anthropomorphism dominate the current discourse. Conversely, attributes such as authenticity, source realness, emotional expression, novelty, autonomy, and interactivity have been identified as largely underexplored. The bibliometric analysis illustrates a marked increase in academic output since 2020, primarily concentrated in journals focused on marketing, social sciences, and human?computer interaction. Thematic mapping further highlights key areas of focus, with motor themes identified as anthropomorphism, virtual reality, and consumer engagement, indicating their foundational importance in the current landscape of virtual influencer research. Several research gaps persist, particularly in the areas of cross-cultural consumer responses to virtual influencers, ethical considerations, parasocial interactions, and comparative effectiveness analyses between virtual and human influencers. Overall, our study synthesizes the literature, systematically categorizes the key attributes of virtual influencers, and proposes a future research agenda aimed at advancing theoretical frameworks and managerial effectiveness. The insights gained here will guide researchers in identifying pertinent variables for empirical investigation and assisting practitioners in crafting strategies that enhance consumer trust, engagement, and purchase intention, ultimately navigating the evolving terrain of digital marketing more effectively. 2026, Malque Publishing. All rights reserved. -
Attribute optimization to improve breast cancer prediction using machine learning techniques
Breast cancer (BC) arises when cells grow out of control. It affects women more than men. Seeking cancer treatment can be both costly and time-consuming, with test results spanning from a few hours to several weeks. The duration of these tests depends on the number of attributes within the dataset. This research paper endeavors to optimize the dataset attributes and find the accuracy of the optimized dataset. The primary goal is to reduce features using recursive feature elimination to minimize the time taken for the test result. This work discusses the machine learning technique and the random forest (RF) algorithm, which helps determine the parameter accuracy on the Wisconsin BC diagnostic dataset. The method achieves an accuracy of 96.49% with only eighteen attributes. It has aided the healthcare industry in finding BC in less time and improving the treatment. Copyright (c) 2026 Peddireddy Venkateswara Reddy, Alaguchamy Parivazhagan. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. -
Attitude towards the medical profession among higher secondary students in relation to medical aptitude, parental innfluene, peer influence and perceived social expectations
Attitude towards the medical profession includes concepts, motives or beliefs associated with the profession of medical science. A profession that challenges the intellectual competency of the aspirant to qualify- with soaring expectations from family and society- the demands of the career entail the individual to possess an appropriate attitude and a realistic understanding of it. The current study explores the role of medical aptitude, parental influence, peer influence and perceived social expectations in the formation of attitude towards the medical profession and their differences among higher secondary students based on their gender and type of pre-university college. The study-cum-survey employed a quantitative approach using the paradigm of post-positivism. The participants comprised of 396 second-year pre-university students from colleges of Bangalore South, who were selected by convenience sampling, with Physics, Chemistry and Biology as their core subjects. The findings reveal peer influence to be essential in the development of professionalism and a sense of service mindedness. Parental influence and the higher secondary adolescent s perceived social expectations act as significant predictors in the formation of favourable attitude. However, gender and the type of pre-university colleges bear no significant difference with respect to their attitude towards the profession. The study has set a base on what needs to be focussed in our country on the next generation of medical professionals. It is hoped that educational and medical institutions, policy and curriculum drafters, parents and teachers realise that a combination of sound medical aptitude, parental influence, peer influence and perceived social expectations resonates in the formation of a favourable attitude towards medical science. Assessing those higher secondary students qualifying into medical science would enable to analyse differences that existed, if any, in their entry-level and post-qualifying attitude towards the medical profession. -
Attitude towards the medical profession among higher secondary students in relation to medical aptitude, parental influence, peer influence and perceived social expectations
Attitude towards the Medical Profession includes concepts, motives or beliefs associated with the profession of medical science. Medical science is a profession that challenges the intellectual competency of the aspirant owing to the strenuous demands of the career. It is essential that possession of an appropriate attitude and a realistic understanding of the demands of medical science be a pre-requisite, especially if the higher secondary adolescent aspires this as his or her vocation for life. As theformation of attitude is based on several intrinsic and extrinsic factors, a profession like medical science faces stiff competition among candidates to qualify with high expectations from family and society. The current study throws light on the attitude of higher secondary students towards the profession of medical science in relation to medical aptitude, parental influence, peer influence and perceived social expectations, as the independent variables. It aimed to explore the role played by medical aptitude, parental influence, peer influence and perceived social expectations in the formation of attitude towards the medical profession and the differences in the formation of this attitude among higher secondary students based on their gender and type of pre-university college that they belonged to. The study adopted was a descriptive study-cum-survey that employed a quantitative approach using the paradigm of post-positivism. The participants for the study comprised of 396 second-year higher secondary students from pre-university colleges of Bangalore South, who were
selected by convenience sampling method. -
Attitude toward inter-religious marriage: interplay of generational shifts with religious affiliations and educational attainments
This quantitative research examined the interaction of generational shifts, religious affiliations, and educational attainments in shaping attitudes toward inter-religious marriage. Data were collected from 1231 Indian respondents from iGen/Gen Z, Xennials & Millennials, and Baby Boomers through a demographic response sheet and the Attitude Scale developed by Parker et al. where lower ratings signified positive attitudes and higher ratings indicated negative attitudes. The result revealed that generational shifts were significantly associated with religion (?2 = 96.6, p=<.001) and education (?2 = 279, p=<.001). Significant interaction effects were found between generational shifts and religious affiliations (F = 5.36, p <.001, ?2 p =.017) and generational shifts and educational attainments (F = 6.79, p <.001, ?2 p =.027) concerning attitudes toward inter-religious marriage. This study uncovered the interaction of the demographic variables in shaping the attitude toward inter-religious marriage. 2026 Taylor & Francis Group, LLC. -
Attitude of public towards higher education: Conceptual analysis /
Scholedge International Journal Of Multidisciplinary And Allied Studies, Vol.2, Issue 12, pp.19-28, ISSN No: 2394-336X. -
Attitude of Parents Towards Various Behavior Management Techniques Utilized in Pediatric Dental Treatments
Dental experts are trusted to apply the knowledge and abilities they have acquired during their dental education to the diagnosis and effective treatment of any dental illness. When it comes to pediatric patients, however, the dentist's responsibility is different. However, without the right behavior management method (BMT), therapy outcomes would not be effective. Sometimes young children behave disruptively during dental visits, which makes it easier or harder for the dentist to perform dental work. Nonetheless, before being applied to children, behavior management strategies need the parents' acceptance and consent. This review's objective is to evaluate the dentists' use of effective behavior modification techniques (BMT) as well as the parents' attitudes regarding these techniques. RJPT All right reserved. -
Attitude of generations: Does it matter online?
Generational examinations are turning out to be necessary with the characteristics they exhibit. This research work aimed at establishing the interceding relationship of disposition of three distinctive generations-Generation X, Generation Y, and Generation Z. In complete, 1200 responses were acquired from both male and female respondents of each generational class dependent on online purchase data collected by employing Google Forms. For the investigation, the model utilized the SOR framework. The results indicated that attitude does not play a vital role in the purchase intention of Generation X followed by the partial mediation of attitude for Generation Y and full mediation effect for Generation Z. This steady increment of attitudinal change underpins the examination by setting up proof that every age shifts in their mentality and purchasing conduct. Online retailers must concentrate on showcasing systems and create online visual merchandising cues which outwardly advance and make a feeling of stimulating attitude for generations. The current study also added value to the existing literature by classifying the customer base not merely on age, but also on their technological perspective of distinguishing web atmospheric cues and catering to their needs from a generational outlook. The study also took into account the importance of the organism's role played by attitude in the S-O-R framework. In this manner, the study helps marketers to design methodologies and plan online visual marketing space for better generational reaction and benefit. 2021, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Attitude and perception of tourists in Karnataka towards Climate Change
Climate change has a strong relationship with the tourism industry. According to the United Nation's World Tourism Organization???s Davos Declaration (2007) tourism industry contributes on an average about 5% of the global Carbon di Oxide emission in terms of radioactive forcing. Studies pertaining to the state of Karnataka, India indicate that most parts of the state could experience 1.5oC to 2oC warming relative to the level during the pre-industrial period of the 1880s by as early as 2030s under the likely high emission scenario (Kelkar et al, 2004; Dwarakish et al, 2009'; Kumar et al, 2015). Travel decisions to a large extent are influenced by the destination???s images of Sun, Sand, Sea, availability of snow and perceptions of other such climatic variables such as temperature, precipitation and humidity (De Freitas, 2001). Therefore, studying tourist???s perceptions of these environmental changes are crucial for the survival of the tourism industry especially in these climate-sensitive regions (Gossling & Hall, 2006a). Besides, understanding the attitudes and perceptions of different segments of the tourists towards their role in climate change adaptation and mitigation is a fundamental step in designing educational and communication campaigns which could be successfully implemented in this industry (Becken, 2010; Gossling & Hall, 2006; Saarinen et al, 2006; Scott et al, 2008; Martin & Lopez, 2014). Therefore, this study aims to boost our understanding of the relationship between tourism and climate change issues among tourists in Karnataka, India. The study consists of five chapters, namely Introduction, Review of Literature, Report on present investigation, Results and Discussion, Summary and Conclusion. The first chapter Introduces the relationship between tourism and climate change issues and brings out the significance of the role of travel decisions the environment. The second chapter analyses earlier studies conducted on the area of climate change, environment and tourism to gain a better understanding of the existing knowledge and to identify research gaps. The third chapter focuses on the research design of this study. It covers sampling plans, questionnaire design, constructs measurement, plan of analysis and Pilot study. The fourth chapter deals with the analysis of data which were collected for the study followed by the discussion of results. The fifth chapter summarizes the entire study and explains the findings and limitations of the study, Suggestions and conclusion. -
Attitude and intention to adopt FinTech services by Indian rural households
FinTech has been a game changer for many business players. Due to financial technology, there is a paradigm shift in how finance-oriented companies operate today. The study aims to identify the factors driving FinTech adoption amongst rural households. A questionnaire with five points Likert scale has been used for data collection. The technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT) are used for this study. The study found that factors such as perceived trust, perceived usefulness and perceived risk have a major say in adopting FinTech services. The study is a breakthrough for FinTech companies in identifying factors that induce rural users to adopt FinTech. The study helps to improve the existing FinTech apps to attract and tap the rural segments by focusing on these aspects. Copyright 2026 Inderscience Enterprises Ltd. -
AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach
Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and nae Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work. The Author(s) 2024. -
Attenuation properties of epoxy-Ta2O5 and epoxy-Ta2O5-Bi2O3 composites at ?-ray energies 59.54 and 662 keV
Epoxy resin filled with suitable high Z elements can be a potential shield for X-rays and ?-rays. In this work, we present the ?-ray attenuation properties of epoxy composites filled with (030 wt%) Tantalum pentoxide (Ta2O5) and Ta2O5-Bi2O3, which were prepared by open mold cast technique. X-ray diffraction patterns showed crystalline peaks of Ta2O5 and bismuth oxide (Bi2O3) in the prepared epoxy-Ta2O5 and epoxy-Ta2O5-Bi2O3 composites. Homogeneity of the samples at higher filler wt% was revealed by SEM images. Mechanical characterization showed the enhanced mechanical strength of epoxy-Ta2O5-Bi2O3 composites compared to epoxy-Ta2O5. Higher storage modulus and glass transition temperature of the epoxy-Ta2O5-Bi2O3 composites showed enhanced stiffness and thermal stability when compared to neat and epoxy-Ta2O5. Decrease in the value of tan(?) at higher content of filler loadings indicated the good adhesion between filler and matrix. Mass attenuation coefficients of epoxy-Ta2O5 (30 wt%) composites at ?-ray energies 59.54 and 662 keV were found to be 0.876 cm2 g1 and 0.084 cm2 g1, while that of epoxy-Ta2O5-Bi2O3 (30 wt% Bi2O3) composite were 1.271 cm2 g1 and 0.088 cm2 g1, respectively. The epoxy-5% Ta2O5-30% Bi2O3 composites with higher ?/? value and tensile strength may be a potential ?-ray shield in various radiation environments. 2020 Wiley Periodicals, Inc. -
Attenuation parameters of polyvinyl alcohol-tungsten oxide composites at the photon energies 5.895, 6.490, 59.54 and 662 keV
The growing demand for lightweight, non-toxic and effective X-A nd ?-ray shielding materials in various fields has led to the exploration of various polymer composites for shielding applications. In this study, tungsten filled polyvinyl alcohol (PVA) composites of varying WO3 concentrations (0-50 wt%) were prepared by solution cast technique. The structural, morphological, and thermal properties of the prepared composite films were studied using X-ray diffraction technique (XRD), Scanning electron microscopy (SEM) and Thermogravimetric analysis (TGA). The AC conductivity studies showed the low conductivity property of the composites. The X-ray (5.895 and 6.490 keV) and ?-ray (59.54 and 662 keV) attenuation studies performed using CdTe and NaI(Tl) detector spectrometers revealed a noticeable increase in shielding efficiency with increase in filler wt%. The effective atomic number (Zeff) calculated by the direct method agreed with the values obtained using Auto-Zeff software. The % heaviness showed that tungsten filled polyvinyl alcohol composites are lighter than traditional shielding materials. 2020 M V Muthamma et al., published by Sciendo 2020. -
Attentional Deep Learning with Inverse Transform Sampling for Robust Respiratory Sound Classification
The necessity for efficient breathing sound classification systems originates from respiratory diseases, which impair oxygen-carbon dioxide exchange and impact lung function. Feature extraction and pattern categorization are general components of such systems. Because of their effectiveness with big datasets, deep neural networks have acquired popularity recently in the category of breathing sounds. Enhancing medical care requires cooperation amongst researchers, medical professionals, and patients. An attentional deep learning model with inverse transform sampling is presented in this study to classify respiratory diseases from audio data. Robust models were developed to classify and detect respiratory elements using the Respiratory Sound dataset. The primary objectives include effectively determining lung sounds and determining respiratory illnesses. The architectures of CNN, VGG16, and ResNet50 were developed to extract features and categorize data. Also, the pre-trained models ResNet50 and VGG16 identify critical characteristics in spectrum pictures more accurately. Inverse transfer sampling is used to rectify class imbalance in respiratory datasets. The models achieved 98% accuracy with the CNN model, 83% accuracy with VGG16, and 95% accuracy with ResNet50. Moreover, LSTM and CRNN models offer more information on how respiratory illnesses are classified. 2026, Hemanth K S, Harisha Naik T, N Kartik, N Nanda kumar, S Senthilkumar and Ramya R. -
Attention-Powered Deep Learning for Employee Analytics: A Multi-Model Approach
In the ever-evolving field of human resources analytics, there is the integration of the latest techniques of machine learning that can strongly enhance decision-making. This paper introduces a revolutionary architecture for multi-model neural networks that integrate disparate networks in analyzing the background, development, performance, and engagement of an employee for all key elements of this employee. Each of the processes with attention fine-tunes the importance of features and therefore largely improves the concentration and interpretability of results. These networks are thus ensured of thorough analysis in the form of in-depth evaluation, which enables classification to be discrete and into clear performance categories. Preparation of raw data was also done with much care; we used the Employee/HR Dataset from Kaggle in order to process this raw data before its use in deep learning application. Our proposed architecture outperformed by accurately classifying the employee performance categories, with result showing a high classification accuracy of 86.49% on the test set. This study, therefore, establishes that customized neural network architectures are applicable in supporting organizations in realizing their data driven culture and in making human resource operations more efficient. 2026, Springer Science and Business Media Deutschland GmbH. All rights reserved. -
Attention-Enhanced Vision Transformer Model for Precise Skin Cancer Detection
Skin cancer is one of the most prevalent and potentially fatal diseases, requiring early and accurate detection for effective treatment. Recent advances in deep learning have significantly improved automated skin lesion classification, but traditional Convolutional Neural Networks (CNNs) struggle with capturing long-range dependencies in dermoscopic images. To address this limitation, we propose a Preprocessing-Optimized Vision Transformer (ViT) Model that enhances lesion detection using attention-based feature fusion. Our methodology includes contrast enhancement (CLAHE), hair removal (DullRazor), lesion segmentation (K-Means + Otsus Thresholding), and data augmentation, ensuring robust model training. The proposed Attention-Enhanced ViT Model effectively learns global contextual features from dermoscopic images through self-attention mechanisms. The proposed model is evaluated our model on the ISIC Skin Cancer Dataset, achieving an accuracy of 94.6%, precision of 92.8%, recall of 93.5%, and an AUC-ROC score of 0.97, outperforming traditional CNN-based models such as ResNet50 (92.1% accuracy) and EfficientNet-B0 (93.3% accuracy). Our results demonstrate that integrating preprocessing techniques with Vision Transformers significantly enhances classification performance, making this approach a viable solution for real-world computer-aided dermatology. 2025 IEEE. -
Attention-based CNN for Adversarial File Fragment Detection Against Padding and Bit-Flip Attacks
File fragment classification represents a critical task within digital forensics and cybersecurity that aims to recover fragmented files when their metadata is not available. Even though cutting-edge deep learning models achieve 77-79% accuracy on clean fragments, none of the existing file fragment classification systems currently include detection mechanisms against adversarial attacks, thus remaining defenseless against attackers using byte-level perturbations. This paper addresses this gap by proposing the first adversarial detection framework for file fragment classification. This paper presents an attention-based CNN that combines byte embeddings with both spatial and channel attention mechanisms to detect byte-level perturbations before actual classification. Evaluated over 30.72 million fragments across 75 file types, the detector reaches an accuracy of 91.44% against five attack strategies: null-byte padding, random-byte padding, cross-file padding, random bit-flipping, and header-targeted bit-flipping, at 91.34% recall, 95.46% specificity, and 0.9819 AUC-ROC. With 1.31 M parameters and 1 ms inference time per fragment, the detector enables practical deployment as a preprocessing filter within two-stage forensic pipelines screening suspicious fragments before reaching standard classifiers. This foundational work sets up the first comprehensive benchmark for adversarial robustness evaluation specifically in file fragment classification. 2025 IEEE. -
Attention to Economic Factors and Its Response to Foreign Portfolio Investment: An Evidence from Indian Capital Market
Stock market consists of a variety of investors. Among these, Foreign Portfolio Investors (FPIs) is a key investment influx. These investments can change or fluctuate due to several macroeconomic factors which can cause a shift in the dynamics of the markets in India. This paper examines the factors influencing for foreign portfolio investment in long run as well as short run. The sample comprises of 120 monthly observations on Foreign Portfolio Investment (FPIs) and Macro economic variables such as Oil prices (OP), Gross Domestic Product (GDP), Interest Rate (IR), Exchange rate of Indian Rupee with USD (ER), Inflation (CPI), Nifty Index (NSEI), 10year Bond Prices (BP) and Index of Industrial production (IIP) over a period of 10years, spanning from January 2013 to November 2022. The study employed Autoregressive Distributed Lag model (ARDL) to establish the long run association with error correction models. The result indicates that there is long run association between the Foreign Portfolio Investment and macro-economic variables. Among this, NSEI, IIP and ER played a significant role to determine FPI investments in the long run, whereas in the short run, FPI was impacted by ER and NSEI significantly. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Attention based sentence classification using term frequency-inverse document frequency and n-gram /
Patent Number: 202241048840, Applicant: Nagendra N.
Attention Based Sentence Classification Using Term Frequency-Inverse Document Frequency and N-Gram On an e-commerce platform, products sold have critical and varying reviews from buyers which are difficult to analyze considering the hugenumber of reviews. The proposed model aspect extraction helps in categorizing sentence accurately.