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Secure IBS Scheme for Vehicular Ad Hoc Networks
Vehicular Ad hoc Networks (VANET) havedrastically grown in recent years since they provide a better and more secure driving experience. Due to its characteristics, it is vulnerable to many security attacks. Even though many authentication schemes are proposed, their overheads are high. Hence, this study proposes a new Identity-Based Signature (IBS) for authentication with privacy-preservation. It supports secure communications with additional security features. It requires less overhead since it uses XOR operations and one-way hash functions for the signing and verification process. When the proposed schemes performance is compared to the recent schemes, it is observed that the proposed approach is more efficient in computation and communication. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
CNN-based approach for brain tumor detection and severity prediction
Artificial intelligence is widely used in healthcare, especially in medical imaging. It leads to advanced diagnosis using innovative approaches to analyze complex data more accurately and provide personalized treatments. This helps the clinicians efficiently analyze the imaging data, leading to early detection of diseases like brain tumors, cancer, cardiovascular diseases, etc. The research work focuses on the detection and severity prediction of brain tumors. Magnetic resonance imaging (MRI) scan images are preprocessed in the proposed model using different methods. The convolutional neural network model (CNN) is used to detect and predict brain tumors and can be used in personalized treatments. The proposed method has an accuracy of about 98% in classification and severity prediction. 2025, IGI Global Scientific Publishing. All rights reserved. -
Deep Learning-Based Approach for Automated Cataract Detection
Advancements in deep learning approaches is of profound significance in the early detection of cataracts. Automated cataract detection using deep learning approaches is proposed in this chapter. Initially, two pretrained custom convolutional neural network (CNN) architectures, VGG-19 and MobileNetV2, were implemented to detect cataracts. ODIR-5K dataset is used for training, testing, and validating these models, and it has almost 6,400 fundus images. This preprocessed dataset provides the metadata of the available images and is labeled with diagnostic keywords. Since the dataset is highly imbalanced, class weighting techniques are utilized to avoid the impact of the imbalanced dataset. The performance of the models is evaluated, and results show that the ensemble approach outperforms other pretrained models, demonstrating the efficacy of hybrid CNN architecture in enhancing the accuracy of the diagnosis process. 2026 selection and editorial matter, T. Ananth Kumar, R. Rajmohan, M. Niranjanamurthy and G. Sambasivam. -
A Comparative Analysis of AlexNet and ResNet for Pneumonia Detection
Pneumonia damages the lungs and results in swelling and fluid build-up in the air sacs, a major problem that can be assessed using AI techniques. Rapid detection is very important for timely treatment and effective medical care. Chest X-ray imaging stands out as a forefront diagnostic modality for pneumonia, owing to its non-invasive characteristics, cost-effectiveness, and ubiquitous accessibility across many medical facilities. A large volume of data, which can be used to generate medical intelligence, is being collected daily. In recent years, CNNs have exhibited exceptional efficacy in diverse medical image-processing endeavors; X-ray images of the chest play a pivotal role, particularly in diagnosing pneumonia. In CNN architectures, AlexNet and ResNet emerge as the frequently utilized and effective models for various medical problems and diagnoses. As a result, our research involves a comparative analysis of how well AlexNet and ResNet perform in identifying pneumonia. According to our experimental results, ResNet outperforms AlexNet regarding effective classification parameters. 2025 Scrivener Publishing LLC. -
An Ensemble Approach Using ResNet and DenseNet for Cataract Detection
Cataracts represent a widespread ocular condition that profoundly affects an individuals vision and overall quality of life. Timely detection proves crucial for effective treatment, yet existing methodologies often entail invasive and discomforting procedures. Hence, an innovative approach is proposed for cataract detection utilizing an ensemble framework, which presents numerous significant advantages. It uses an ensemble framework amalgamating ResNet and DenseNet pre-trained learning models for cataract detection. This strategy enhances the precision and dependability of diagnosing cataracts. On the other hand, it diminishes false positives and negatives, consequently ensuring more accurate and timely diagnoses. Beyond mere accuracy, our ensemble framework brings about additional benefits. It bolsters the resilience of cataract detection by mitigating the influence of individual model biases and variances. Furthermore, it enhances the systems adaptability, making it applicable to various patient demographics and ocular conditions. Such adaptability is significant in the global healthcare landscape, facilitating effective deployment across diverse regions and populations. Moreover, our approach alleviates the discomfort and invasiveness associated with conventional cataract detection methods, promoting early diagnosis and reducing patient apprehension. Streamlining the diagnostic process also eases the burden on healthcare providers and improves overall patient care. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing Food E-Commerce Through Immersive Virtual Reality: An Reality: An Extended Technology Acceptance Model Approach for Consumer Adoption in the Post-Pandemic Era
Food purchasing differs from other types of internet shopping. With the introduction of the new retail structure, nearly every e-commerce platform has set up fresh food retail one after another. As a result, electronic gadgets have evolved into tools that marketers may use to initiate interactions with customers. Brands may use augmented reality enabled mobile applications to deliver precise information about products and services while also influencing consumer impressions. Perceived usefulness was the only factor that supported perceived ease of use as a mediator. Our findings provide useful information for researchers and industry experts to improve the effectiveness of VR systems by better understanding user adoption. 2025 IEEE. -
Enhancing neurocognitive skills for effective leadership and decision-making
In today's dynamic workplace, human resource development and management (HRDM) professionals face multifaceted challenges requiring advanced cognitive abilities. This book chapter explores the critical interplay between leadership skills, decision-making, and executive functions (EFs) in HRDM. It sheds light on their pivotal role in shaping workplace dynamics and organizational outcomes. Focusing on skills such as emotional intelligence, cognitive flexibility, and continuous learning, the chapter delves into their neurocognitive underpinnings, particularly within the prefrontal cortex. It discusses strategies for enhancing EFs, including reflective practice, empathy training, and mindfulness, and emphasizes the concept of neuroplasticity in fostering continuous learning and adaptation within HRDM. By integrating insights from neuroscience into HR practices, the chapter offers valuable guidance for HR professionals seeking to optimize organizational performance, enhance leadership qualities, and drive effective decision-making processes. 2024 by IGI Global. All rights reserved. -
Psycho-neuroendocrine regulation of autoimmune dysfunction in polycystic ovary syndrome
Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting 8-13% of women of reproductive age, marked by hormonal imbalances, insulin resistance, including various metabolic and reproductive issues. Psychological stress plays a crucial role in PCOS, acting as both a trigger and a maintaining factor. This review focused on outlining how psychological stress may disrupt the hypothalamic-pituitary-adrenal (HPA) axis, leading to dysfunction in endocrine and immunological systems. The review has highlighted how glucocorticoids impair hormonal equilibrium and exacerbate immune dysfunction in PCOS. Additionally, the review examines the association between PCOS and various autoimmune conditions. There exists a complex interaction between psychological stress, neuroendocrine disruption, and immune dysfunction in PCOS. Understanding these relationships is essential for developing more effective management strategies and addressing the broader health implications of the syndrome. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Unveiling the Motivation Drivers in Start-Up Workspace
This article delves into the relationship between workplace happiness and productivity in startup settings. Its primary objective is to dissect the multifaceted factors impacting employee well-being, aiming to enhance overall efficiency by customizing the work environment in myriad ways. For this study, a descriptive causal methodology was employed to investigate the impact of workplace happiness on productivity within start-up companies. A carefully structured questionnaire was administered to 256 employees within well-established organisations in Bangalore, India. Participants were selected through a Judgement sampling process to ensure impartial and unbiased representation. Survey respondents preferred the pre-COVID working conditions, acknowledging their advantages. However, the increased autonomy and flexibility in work arrangements have led to enhanced productivity under the new hybrid model. Notably, when employees are entrusted with greater responsibility, their job satisfaction rises, resulting in increased work output. organizations are now tasked with offering additional incentives to remote employees, thereby elevating the satisfaction and job fulfilment experienced by these individuals. Effectively tackling challenges necessitates the alignment of learning and development objectives with the internal business processes that maximize each employee's abilities and potential. This involves meeting the criteria outlined in the balanced scorecard components. 2024, Iquz Galaxy Publisher. All rights reserved. -
SIGNIFICANT DIFFERENCE, CULTURAL DISTANCE, AND CULTURAL HUMILITY IN CHILDRENS MEDIA RESEARCH
The study of children, adolescents, and media (CAM) places a special emphasis on the welfare of young audiences and the media that socially, culturally, and historically constructs their identity, knowledge, and understanding of themselves and the world around them. CAM scholars form a legion of worriers and warriors focused on making the world a better place for children to live and learn (Jordan, 2021, p. 147). This legion spans the world, embodying the three traditional realms of media studies (audience, texts, and institutions) as a microcosm of media studies (Lemish, 2015, p. 1) and crosses disciplinary, theoretical, and empirical boundaries. As such, CAM scholarship can sometimes be difficult to find since it is often located in many different disciplinary journals and books as well as in proprietary industry reports. Lemish (2019) spoke of her journey in finding a home for her childrens media research and calls for the need for deeper internationalization of CAM that can account for the variance of childrens lives and the structural forces that shape the market and content of childrens media. This special issue contributes to this vision and highlights CAM research produced outside of a Western, educated, industrialized, rich and democratic (WEIRD) society (Jordan & Prendella, 2019). Moreover, it allows for a space to reflect on CAM scholarship as a whole and future directions for consideration. Lets explore some of the limitations in existing childrens media research and ways in which international collaboration can help to mediate some of these concerns. (2023). All Rights Reserved. -
Intelligent approach to automate a system for simulation of nanomaterials
Nanomaterial composites are generally found to have great thermal properties and hence have witnessed an increasing demand in the recent years for manufacturing of efficient miniature electronic devices. The process of finding the right composites that exhibit the desired properties is a rather tedious task involving a lot of trial and error in the current scenario. This paper proposes a methodology to digitize and automate this entire process by administering certain efficient practices of assessing the properties of nanomaterial like Coarse Grained Molecular Dynamics thus resulting in faster simulations. 2023 Author(s). -
Multimodal Emotion Recognition Using Deep Learning Techniques
Humans have the ability to perceive and depict a wide range of emotions. There are various models that can recognize seven primary emotions from facial expressions (joyful, gloomy, annoyed, dreadful, wonder, antipathy, and impartial). This can be accomplished by observing various activities such as facial muscle movements, speech, hand gestures, and so forth. Automatic emotion recognition is a significant issue that has been a hotly debated research topic in recent years. At the moment, several research people have taken a component in inheriting or extra multimodal for higher understanding. This paper indicates a method for emotion recognition that makes use of 3 modalities: facial images, audio indicators, and text detection from FER and CK+, RAVDESS, and Twitter tweets datasets, respectively. The CNN model achieved 66.67 percent on the FER-2013 dataset of labeled headshots while on the CK+ dataset, 98.4 percent accuracy was obtained. Finally, diverse fusion strategies had been approached, and each of those fusion techniques gave distinctive results. This project is a step towards the sense of interaction between human emotional aspects and the growing technology that is the future of development in today's world. 2022 IEEE. -
A Pipeline for Speech-to-Text Summarization and Question Identification for Enhanced Chatbot Interactions
The rapid advancements in natural language processing provide strong support for the new potential application of integrating Google Speech Recognition API, BART, and BERT to create a full pipeline for speech recognition, text summarization and question answering without breaking human interaction. The research aims to develop such a holistic pipeline involves integrating the Google Speech Recognition API to perform speech-to-text, BART for text summarization, and finally BERT for question answering based on both the summary and original transcript. The system was tested under various criteria such as testing accuracy, real-time processing performance, and stress tests for scalability where the findings include an average of 60% text compression with BART, an 88% accuracy in BERT-based question answering, and scores indicating high user satisfaction (4.3/5). Real-time processing latency can be able to cater to interaction within 2-3 seconds and the capacity of the system has proven without performance loss during simultaneous users. The research done can practically find applications in areas like intelligent virtual assistants, customer service automation and e-learning applications that improve accessibility and user experience. 2025 IEEE. -
Mediating role of resilience on the relationship between meta emotions and emotional regulation among neglected adolescents
Background: Adolescence is a pivotal stage in human development, marked by significant biological, cognitive, and emotional transformations that shape an individual's character and future behaviour. The emotional strategies and behavioural patterns inculcated during this phase become integral aspects of one's personality, influencing how one navigates through various life challenges. For vulnerable adolescents, such as neglected children in institutional care or orphans, negative life experiences can heighten the risk of developing psychological concerns. This heightened vulnerability is often exacerbated by impaired emotional regulation and low resilience, which may contribute to the emergence of internalising disorders. Methods: The present study was conducted to examine the relationship between meta emotions and difficulties in emotional regulation among neglected adolescents and to analyse the mediating effect of resilience on this relationship. The participants of the study were 667 neglected adolescents: 335 males (50.2%) and 332 females (49.8%) who belong to the age group 13 to 17years (mean age = 14.60, S.D = 1.16) years from various institutional homes across Bangalore. Results: The results of this study confirm that in the presence of increased levels of negative meta emotions in an individual, despite the mediating effects of resilience, difficulties in emotional regulation will be further worsened. This was substantiated by the positive correlation between negative meta emotion and difficulties in emotional regulation. Contrastingly, elevated positive meta emotions in an individual, along with the mediating effects of resilience, reduce the difficulties in emotional regulation. This was also reinforced by the strong relationship between positive meta emotions and difficulties in emotion regulation. Conclusion: The findings of the study highlight the prevalence of increased negative meta emotions and poor emotional regulation among neglected adolescents, which is of utmost importance from a psychological, social and policy-making perspective. This calls for the need for tailored and individual-focused interventional strategies to improve the psychological health of these vulnerable children. Moreover, the critical period of adolescence is a crucial time for implementing effective policies in order to shape the behavioural and cognitive aspects of personality with much-needed care, support, and professional guidance. The Author(s) 2025. -
Preparation, characterization, and evaluation of corrosion inhibition efficiency of sodium lauryl sulfate modified chitosan for mild steel in the acid pickling process
The polar head and a hydrophobic long alkyl chain end of surfactants show effective adsorption on the metal surfaces and metal/solution interfaces. The present study deals with the investigation of corrosion inhibition efficiency of chitosan modified with an anionic surfactant, namely sodium lauryl sulfate. The modified chitosan was characterized using spectral techniques such as ATR- FTIR and NMR, thermal analytical methods that include TGA and DSC. The surface charge and particle size distribution were analyzed using Zeta potential analyzer. The corrosion inhibition efficiency of the water-soluble modified chitosan was evaluated using gravimetric and electrochemical methods. A maximum corrosion inhibition efficiency of 96.44% for 6 h of immersion period at 303 K was obtained. The adsorption process obeyed Langmuir isotherm. The adsorption mechanism involved both physisorption and chemisorption. Tafel and impedance studies showed results in agreement with the gravimetric method. Tafel plot indicates the inhibitor controlled both cathodic hydrogen evolution and anodic metal dissolution reactions. AC impedance study supports the increase in surface coverage of the metal surface by the inhibitor, forming a protective film. Further evidence comes from the surface characterization of the inhibited metal surface by contact angle measurement, SEM, EDAX spectra, and atomic force microscopic studies. DFT and Monte Carlo simulation studies showed a proper alignment with the experiment results. 2020 Elsevier B.V. -
Sunova spirulina Powder as an Effective Environmentally Friendly Corrosion Inhibitor for Mild Steel in Acid Medium
Abstract: Spirulina, blue green algae is a rich source of proteins and vitamins with excellent antioxidant properties. Sunova spirulina powder an effective, green corrosion inhibitor was used to evaluate its inhibition efficiency towards mild steel in 1M HCl medium. Weight loss studies of mild steel showed an inhibition efficiency of 96% for 600ppm concentration of inhibitor solution and 12h of immersion period at 303K. The percentage of inhibition efficiency increased with a step up of 10K raise in temperature from 303 to 333K and thereafter decreased. The results obtained were further validated by inductively coupled plasma optical emission spectrometric (ICP-OES) measurements and electrochemical techniques that included Tafel polarisation, linear polarisation and AC impedance studies. Potentiodynamic polarisation study marked the inhibitor to be a mixed type inhibiting both cathodic and anodic reactions. The adsorption studies proved that the adsorption process was spontaneous and followed Langmuir adsorption isotherm. The thermodynamic activation and adsorption parameters calculated showed that the mechanism of inhibition involved a physisorption process initially and then it slightly shifted towards chemisorption process at higher temperature. The protective layer formed on the metal surface was studied using FTIR and SEM. The complex formation between the Fe2+ and the active constituents of the spirulina extract was verified using UV visible spectra and fluorescence spectra. The effect of inhibitor concentration and temperature on corrosion rate was tested statistically using two-way analysis of variance (ANOVA) technique. Graphic Abstract: [Figure not available: see fulltext.]. 2020, Springer Nature Switzerland AG. -
Experimental and Theoretical Approach of Evaluating Chitosan Ferulic Acid Amide as an Effective Corrosion Inhibitor
Phenolic acid grafted chitosan has widespread drug delivery applications, as bio adsorbent, packing material, etc., due to its excellent antioxidant and antimicrobial properties. However, for the first time, the anticorrosive efficiency of ferulic acid modified chitosan has been investigated. The prepared chitosan derivative is characterized using spectral methods, thermal analytical methods, surface charge, and particle size analysis. The evaluation of corrosion inhibition potential showed a highest value of 95.96% at 303K. Thermodynamic activation and adsorption parameters endorse a mixed adsorption process involving an initial electrostatic interaction followed by chemisorption. Electrochemical studies gave results which agreed well with the gravimetric studies. Surface morphological studies were performed using contact angle measurements, FESEM, EDAX, AFM, optical profilometric and UV spectral techniques. Computational studies involving quantum chemical calculations, Monte Carlo and molecular dynamic simulation studies, and radial distribution function analysis are further done to validate the experimental results. Graphical Abstract: [Figure not available: see fulltext.] 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG. -
Corrosion mitigation performance of disodium EDTA functionalized chitosan biomacromolecule - Experimental and theoretical approach
Disodium ethylenediaminetetraacetate salt is known for its excellent coordinating properties with the metal ions. The present study deals with the investigation of the prepared Disodium EDTA functionalized chitosan in corrosion inhibition for mild steel in 1 M HCl. The modified chitosan was characterized by spectral studies, thermal analysis, and Zeta potential studies. The corrosion inhibition efficiency (%) was evaluated using the gravimetric method and electrochemical studies. The electrochemical studies included potentiodynamic polarization, linear polarization resistance, and electrochemical impedance methods. The modified chitosan polymer showed an inhibition efficiency of 96.63% for 500 ppm at 303 K. Adsorption process obeyed Langmuir isotherm. Experimental results and theoretical calculations endorsed initial physisorption followed by a chemisorption process. Surface characterization studies supported the formation of a protective film that enabled the inhibition process. Density functional theory, Monte Carlo studies, and molecular dynamics simulation studies show a good agreement with the experimental results. Two-way Analysis of Variance was performed to test the influence of immersion period and inhibitor concentration on the corrosion rate using the statistical software IBM SPSS 20.0. A quartic model was generated as the best fit with the highest R2 value of 0.973. Design Expert software was employed for statistical modeling fit. 2021 -
Exploring various nanomaterials in enhancing the performance of chiral nematic liquid crystal for blue phase display
This study aims to develop composite liquid crystal (LC) materials for energy-efficient blue phase (BP) display applications with enhanced luminescent and dielectric properties. Chiral nematic liquid crystal (CNLC) was systematically doped with nanomaterials, including nickel zinc ferrite (NZFO), single-walled carbon nanotubes (SWCNT), gold nanoparticles (GNPs), and strontium titanate (SrTiO3). Optimal doping concentrations (0.05 wt% for NZFO and SWCNT; 0.1 wt% for GNPs) enhanced photoluminescence, while SrTiO3 served as a luminescence quencher. Dielectric studies revealed a substantial reduction in the Freedericksz transition threshold voltage, particularly with NZFO (0.05 wt%), which halved the voltage. Optical texture and structural analysis confirmed that the CNLC structure remain intact while maintaining the BP temperature window (12 C). The reduced splay elastic constant in all doped CNLC revealed that the optimum quantity of nanomaterials is occupied in the disclination site of BP, resulting in a reduction of volume and associated free energy around the disclinations to reduce threshold voltage. These findings highlight the potential of nanomaterial-doped CNLCs, especially magnetic NZFO NPs, in enabling high-performance, low-power BP-based LC displays for advanced applications. 2025 Elsevier B.V. -
Exploring various nanomaterials in enhancing the performance of chiral nematic liquid crystal for blue phase display
This study aims to develop composite liquid crystal (LC) materials for energy-efficient blue phase (BP) display applications with enhanced luminescent and dielectric properties. Chiral nematic liquid crystal (CNLC) was systematically doped with nanomaterials, including nickel zinc ferrite (NZFO), single-walled carbon nanotubes (SWCNT), gold nanoparticles (GNPs), and strontium titanate (SrTiO3). Optimal doping concentrations (0.05 wt% for NZFO and SWCNT; 0.1 wt% for GNPs) enhanced photoluminescence, while SrTiO3 served as a luminescence quencher. Dielectric studies revealed a substantial reduction in the Freedericksz transition threshold voltage, particularly with NZFO (0.05 wt%), which halved the voltage. Optical texture and structural analysis confirmed that the CNLC structure remain intact while maintaining the BP temperature window (12 C). The reduced splay elastic constant in all doped CNLC revealed that the optimum quantity of nanomaterials is occupied in the disclination site of BP, resulting in a reduction of volume and associated free energy around the disclinations to reduce threshold voltage. These findings highlight the potential of nanomaterial-doped CNLCs, especially magnetic NZFO NPs, in enabling high-performance, low-power BP-based LC displays for advanced applications. 2025 Elsevier B.V.