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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-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. -
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. -
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. -
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. -
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. -
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. -
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 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 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 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. -
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. -
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. -
Audio Recognition of Animals Using Optimized Deep Learning Techniques for the Conservation of Wildlife
The classification of animal sounds has emerged as a vital tool in contemporary research, offering numerous benefits for animal occurrence records, taxonomic research, and behavioral studies. However, the problem of accurately identifying animal species based on their vocalizations remains a significant challenge, particularly in real-world environments where background noise and variability in sound patterns can hinder classification accuracy. In this paper addressed this challenge by proposing a CNN-optimized approach for classifying animal sounds. In order to enhance the number of sound samples, utilized augmentation techniques to extract animal sounds from the Kaggle animal sounds dataset. The animal sounds totally 600 audio samples are used. To improve performance, this model was developed using feature extractions from the MFCC, ZCR, and Mel-Spectrogram. The seamless deployment of forest department workers is ensured by the interpretability of our model for real-world applications related to wildlife conservation and monitoring. The main goal is to successfully identify animals using auditory properties, such as tiger, leopard, elephant, and otter noises, based on their vocalizations. Additionally, The optimized CNN and LSTM for sound classification. The Optimized CNN outperformed all other models, achieving an outstanding 98.32 % training accuracy rate. 2025 IEEE. -
Audit Tenure, Audit Fee, and Audit Quality: Evidence from India
This paper examined the relationship between the tenure of the auditor and the audit quality of Indian companies, particularly in the wake of two significant regulations in the financial reporting, the implementation of Ind AS (the IFRS compliant accounting standards) and mandatory auditor rotation. Using Discretionary Accruals as a proxy for audit quality, the study took the data of all the companies listed on the NSE for 11 financial years, from 2009 2019 (totaling 8,171 firm-year observations). It deployed panel data regression with a random-effects model. The results showed that audit quality improved up to specific auditors tenure, particularly with the IFRS compliance and Big 4 auditors. The higher audit fee is positively significantly associated with lower earnings quality. The study suggested that mandatory auditor rotation might provide the full benefit only along with other regulations on IFRS, auditor reputation, and audit fee. The study provided an impetus to the regulators, audit fraternity, and companies to improve the relevance of financial statements. This is one of the first longitudinal studies examining the interaction effects of different audit regulations. Robustness checks with other proxies of audit quality provided the same results. 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Augmentation of the energy storage potential by harnessing the defects of charcoal for supercapacitor application
The depletion of fossil fuel reserves coupled with an avalanche in the global energy demand has driven the need for developing facile techniques for energy storage devices to a large extent. Supercapacitors, has emerged as one of the most promising energy storage devices to address the demands of providing high energy density, quick charge discharge cycles and long cyclic stability. Although carbon based materials play an imperative role in the fabrication of electrode material of this device, the inherent defects are known to hinder the performance of the system. Even so, these defects can be engineered in a way to improve its overall functionality. The present work reports the tuning of the inherent defects of wood charcoal by surface functionalisation and doping via thermal annealing in order to incorporate substitutional impurities such as Nitrogen and Sulfur resulting in the improvement of the surface area and porosity of the system. The specific surface area of the system is observed to increase significantly from 4.2 m2/g of the bare material to 411.19 m2/g and 865.36 m2/g with the addition of Nitrogen and Sulfur respectively at a pyrolysis temperature of 900 C. Furthermore, the incorporation of Nitrogen exhibits a remarkable specific capacitance of 567 F/g and 193.24 F/g, and the addition of Sulfur exhibits 644 F/g and 255.1 F/g in the three-electrode and two-electrode systems respectively at a current density of 1 A/g. They also exhibit an energy density of 26.83 Whkg?1 and 17.36 Whkg?1 respectively with a capacitance retention of 88.5 % and 86.1 % for 5000 cycles. 2024 Elsevier Ltd -
Augmented and Virtual Reality in Immersive Healthcare
Future-proof your medical expertise with this indispensable guide that offers a comprehensive, expert-led exploration of how Augmented and Virtual Reality (AR/VR) are revolutionizing healthcare. Augmented reality (AR) and virtual reality (VR) have profoundly impacted the healthcare sector, introducing ground-breaking applications ranging from surgical simulations and pain management to mental health therapies and patient education. This book delves into the various applications of AR and VR, including deep learning, surgical training, teleconsultation, and patient rehabilitation. By blending theoretical insights with practical case studies, it provides a deeper understanding of how these technologies are being integrated into medical practices to improve outcomes, enhance patient care, and train healthcare professionals. It also explores cutting-edge developments, such as AI-powered AR systems and VR-based mental health treatments, highlighting the future of healthcare innovation. With contributions from leading experts in healthcare, technology, and academia, this book offers a multidimensional perspective on the challenges and opportunities associated with AR and VR in the medical field. Through its accessible language and detailed illustrations, this book is an indispensable guide for anyone interested in the intersection of technology and medicine. Readers will find the volume: Provides a comprehensive exploration of how immersive technology is unlocking new possibilities in healthcare, education, and rehabilitation; Offers insights from top researchers and practitioners in the field, providing authoritative perspectives on the latest technologies, applications, and trends shaping the future of XR; Offers practical guidance and actionable insights to help you harness the power of AR and VR, accelerating innovation and driving real-world impacts; Uncovers the diverse range of applications empowered by the convergence of healthcare and gamification, revolutionizing healthcare delivery and improving patient outcomes. Audience Healthcare professionals, medical educators and students, engineers, developers, researchers, and policymakers looking for cutting-edge technologies to enhance patient care and treatment outcomes. 2026 Scrivener Publishing LLC. -
Augmented intelligent water drops optimisation model for virtual machine placement in cloud environment
Virtual machine placement in cloud computing is to allocate the virtual machines (VMs) (user request) to suitable physical machines (PMs) so that the wastage of resources is reduced. Allocation of appropriate VMs to suitable and effective PMs will lead the service provider to be a better competitor with more available resources for affording a greater number of VMs simultaneously which in turn reflects with the growth in the economy. In this research work, an augmented intelligent water drop (IWD) algorithm is used for effectively placing VMs. The preliminary goal of this proposed work is to reduce the overall resource utilisation by packing the VMs to appropriate PMs effectively. The proposed IWD model is tested under the standard simulation process as it is given in the literature. Performance of IWD is compared with the existing techniques first fit decreasing, least loaded and ant colony optimisation algorithm. Performance analysis shows the significance of the proposed method over existing techniques. The Institution of Engineering and Technology 2020. -
Augmented Realities: Unlocking Consumer Engagement and Brand Advocacy Through Comprehensive AR Strategies
Augmented reality (AR) is an emerging concept having an impact on a wide range of industries, including marketing, business, tourism, gaming, human-computer interface, and manufacturing. Consumer engagement and brand advocacy are critical goals for companies looking to build long-lasting relationships with their target consumers in today's digitally driven environment. In this environment of shifting consumer preferences and technology breakthroughs, AR has become a potent instrument for revolutionizing the way brands communicate with their customers. AR presents exceptional chances to captivate audiences, enhance experiences, and foster brand loyalty like never before by seamlessly integrating virtual aspects with the real world. This study has used extensive review of relevant literature of the past nine years from 2016 to 2024. By creating close connections and evoking intense emotions in customers, AR acts as a catalyst to promote brand advocacy. It is important for scholars and AR managers to keep an eye on the most recent developments in AR. One of the most exciting uses of AR is the capability to virtually test things before making a purchase. This book chapter shall cover an exhaustive list of AR attributes' benefits, values, and their interconnections and significance. Thus, managers can design appropriate AR strategies by using these identified characteristics and benefits. According to past studies, consumer engagement is a critical factor influencing sales, profits, customer satisfaction, and loyalty. 2025 by Gajalakshmi N. S. and Seranmadevi R. All rights reserved. -
Augmented Reality Based Medical Education
The education in medical field requires both theoretical knowledge and practical knowledge. It is important for medical student to acquire effective practical skills. Since the students apply the theoretical knowledge in practical manner in human body. Human body is very volatile, gentle, and difficult system. If a student apply trial in the humans for practical knowledge, there may cause the human error which leads to death of the person. To avoid this, the proposed system 'Augmented Reality Based Medical Education' is useful. Augmented reality makes the learning process more interactive and interesting. It can reproduce specific circumstances that assist students to rehearse with virtual objects that look like the human body and organ. Like traditional learning, it does not require real patients. By this way, augmented reality prevents risk of human life. Medical education with augmented reality extensively provides real time experiences. It has low risks and also affordable. When any human error occurs, there is no human loss. So the human life can be prevented by the system. The proposed system is developed using tools like Unity which is the complete platform for the developing our application, Vuforia-developer portal, a tool to create image target and Blender which is used to create 3D objects. 2023 IEEE.