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Invalidation of emotions and emotional eating: The moderating role of emotion regulation among young adults
Our emotions play a decisive role in shaping our behavior. However, when the expression of these emotions is met with disapproval, it leads to us experiencing emotional invalidation. This experience has a detrimental influence on cognitive abilities, including decision-making, thinking, and reasoning, ultimately impeding ones daily functioning. The present study examined the role of perceived invalidation of emotions on emotional eating tendencies. Further, it explores the moderating role of emotion regulation. The sample consisted of 320 young adults aged between 18 and 25. The data was collected through an online survey and offline questionnaires distributed in higher education institutions. The Perceived Invalidation of Emotions Scale, Emotion Regulation Scale, and Emotional Eating Scale were administered. Multiple linear regression and moderation analysis using Process Macro were performed to interpret the results. The results showed a positive relationship between perceived invalidation of emotions and emotional eating. Further, cognitive reappraisal and emotional suppression were found to moderate the relationship between perceived invalidation of emotions and emotional eating. These results have implications for designing physical health and well-being interventions that address invalidation of emotions. The moderation analysis results extend to interventions targeting emotional eating tendencies, emphasizing enhancing cognitive reappraisal skills. Health psychologists and nutrition experts can identify the antecedents of maladaptive eating patterns and apply this understanding while dealing with clients facing similar issues. 2025 Taylor & Francis Group, LLC. -
Skin cancer prediction using AI: A bibliometric analysis
Skin cancer is a major public health concern globally, with early detection being crucial for successful treatment and management. Artificial intelligence (AI) has emerged as a promising tool for aiding in the early detection of skin cancer [15, 19, 23, 41]. This paper conducts a literature review and bibliometric analysis to explore the current landscape of AI-based skin cancer prediction. This bibliometric analysis systematically examines the landscape of research on skin cancer prediction using AI. The aim of the study is to identify the research trends, keyword contributors, influential authors, and research hotspots [13, 31]. Through this bibliometric analysis, this study offers insights into the evolution of AI-based approaches for skin cancer prediction. By producing and analyzing bibliometric data from relevant scholarly publications, this study provides a comprehensive overview of the current state of research in this domain, informing future directions for advancing skin cancer prediction using AI technologies. 2025 Author(s). -
Ethical Sourcing
In an era where sustainable development is a celebrated concept, the notion of ethical sourcing has been explored comprehensively as companies are making a conscious and sustainable effort to gauge their supply chains and examine the sources of procurement of their material and services. This chapter delves deeper into the importance of ethical sourcing, focusing on its benefits and fundamental needs. It also talks about why ethical sourcing is an essential concept in corporate realms, drawing a distinction between sustainable and ethical sourcing. Today, ethical sourcing can be an important tool in the hands of corporations to draw a competitive edge and there would be serious repercussions of indulging in unethical practices. Further, it discusses key principles that act as a beacon of light for sourcing the material ethically, the three levels of ethical sourcing, and how technology can be leveraged to ensure the same. It is crucial to have partnerships and collaborations for sustainable procurement along with robust mechanisms to measure the impact of ethical sourcing. 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Depression Severity Prediction Among Higher Education Students Using Neural Network Model
Depression significantly affects students' mental health and academic performance, highlighting the need for effective early detection methods. This study investigates machine learning approaches for automated classification of depression severity using responses from the Patient Health Questionnaire-9 (PHQ-9). Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and hybrid models combining structured PHQ-9 scores with descriptive text responses were evaluated. The experimental results show that the LSTM model achieved the highest classification precision (90%), demonstrating its ability to capture sequential relationships between items in PHQ-9. The findings indicate that sequence-based models are well suited to assess the severity of depression in student populations. Integrating such predictive models into digital mental health screening systems may support the early identification of at-risk students and enable timely, data-driven interventions in academic settings. 2026 IEEE. -
Sex without Sexual Attraction?: Perceived Sexual Experiences of Indian Sex-favourable Asexuals
Asexuality is not a complete aversion to sex or sexual activity; instead, there are a range of ways that some asexual people engage with sexual activities. The current qualitative study explored the sexual experiences (self-pleasure and partnered activities) of sex-favourable asexuals within the Indian cultural context. Semi-structured interviews were conducted with 15 participants and analysed using thematic analysis. Results revealed how sex-favourable asexuals experience sexual arousal without experiencing sexual attraction, leading them to engage in sexual activities. Participants understood their sexuality through feeling different from others, followed by self-exploration, resulting in self-acceptance. Further, participants identified self-pleasure as a tool for self-empowerment and stress management, leading to a preference for masturbation over partner-involved sex. Partner-involved sex was primarily due to extrinsic demands (peer pressure, pressure from partner). This study calls attention towards developing inclusive practices, sex education and community awareness about this understudied section of the queer community and, most importantly, enabling representation of their experiences within the academic literature about sex and sexuality. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Determinants of Adoption of Green Technology in a Corporate Sustainability Setting: A Quantitative Analysis
Selecting a green technological solution that aligns with the enterprise's operational characteristics is a critical step in attaining sustainable green goals. It also plays a key role in the technology's successful commercialization. The obstructions lie in identifying the determinants that may arise during the adoption and transformation of green technology. A survey of 526 respondents from the Indian manufacturing industry was conducted, and the data collected were evaluated using artificial neural networks (ANNs) and a structural equation modeling approach. The study's findings reveal that barriers related to market and cost greatly affected green technology adoption. Regarding drivers, green technology implementation was positively impacted by business, environmental, and economic factors. This study discovered valued understandings for corporate decision-makers seeking to implement green technologies and contribute to enduring environmental and economic sustainability. 2025 ERP Environment and John Wiley & Sons Ltd. -
Fostering employee engagement and motivation in global workforces: Leadership strategies for building a culture of accountability and commitment
Employee engagement and motivation are critical for success in today's global business landscape. This study examines leadership strategies to address challenges in diverse and dispersed workforces, such as linguistic barriers and differing expectations. It explores how transformational, transactional, and servant leadership styles enhance commitment and align employee efforts with organizational goals. Emphasizing cultural awareness, inclusivity, and adaptability, the study highlights strategies tailored to diverse norms and expectations. The role of technology and digital platforms in improving collaboration and connectivity among remote teams is also analyzed. Drawing on case studies and theoretical frameworks, the research offers actionable insights for creating cohesive, accountable, and high-performing teams. By prioritizing employee well- being while fostering alignment with organizational objectives, these strategies provide practical guidance for leaders in multinational organizations, bridging academic research and real- world human resource management applications. 2025, IGI Global Scientific Publishing. All rights reserved. -
Organisational justice, job performance and work engagement: The mediating role of perceived supervisory support
The study establishes a relationship between organisational justice, work engagement, job performance, perceived supervisory support and their sub-dimensions. The major research objectives of the study were to check if perceived supervisory support does mediate between organisational justice, work engagement and job performance of faculty in higher educational institutions. A research model was framed and tested to determine the direct and indirect effect of justice of faculty members on work engagement and job performance in the presence of supervisory support in higher education sector. The present study is based on 912 faculty members of higher educational institutions. Five valid, reliable and standard questionnaires were adopted to collect primary data from the higher educational institutions' faculty members in south India. From the study, it is proved that perceived supervisory support partially mediates organisational justice, work engagement and job performance. 2023 British Educational Research Association. -
Effective Emoticon Based Framework for Sentimental Analysis of Web Data
The Explosive development in the social media domain has created a platform for mass generation of textual and emoticon based web data from micro blogging sites. Sentimental Analysis refers to analysis of sentiments or emotions from such heterogeneous reviews are the present urge of the market. Thus, an effective emoticon based framework is proposed which generates scores of both textual and emoticons into seven layered categories using SentiWordNet and weighs performance of various machine learning techniques like SVM/SMO, K-Nearest Neighbor (IBK), Multilayer Perception (MLP) and Naive Bayes (NB). Using Jsoup crawler input reviews are obtained and processed with initial pre-processing model for emoticons and text data followed by stemming and POS tagger. Projected framework is investigated on college and hospital dataset obtaining upper attainment level by Kappa statistic metrics having 98.4% correctness and lesses bug value. Proposed Framework showcases greater competence score with lesser FP Rate based on weighted average of correctness measures. The investigational outcomes are tested on training data with Ten-Fold cross validation. The outcome reveals that suggested emoticon based framework for the task of Sentimental analysis can be efficaciously applied in online decision job. 2019, Springer Nature Singapore Pte Ltd. -
Feature Based Fuzzy Framework for Sentimental Analysis of Web Data
Social mass media has emerged as a projectile platform for the evolution of web data. The sentimental Analysis where the huge textual online reviews are analyzed to extract the actual sentiment or emotions hidden in the reviews. In this paper an effective approach for sentimental analysis of web data is proposed which deploys the fuzzy based machine learning algorithm to accomplish fine-level sentiment analysis of huge online opinions by assimilating the fuzzy linguistic hedges influence on opinion descriptors. The seven layered categories are designed that uses SentiWordNet which has three stages: Pre-processing phase, Feature Selection Phase and Fuzzy based Sentiment Analysis phase. Various machine learning algorithms like AdaBoost, (IBK) K-Nearest Neighbour, (NB) Nae Bayes and (SVM)/SMO Support Vector Machine are used for classification. Jsoup is implemented for gathering web opinions which are subjected to initial processing task later applied with stemming and tagging. This fuzzy based methodology is investigated for Mobile, Laptops dataset, also compared with state-of-the-art approaches which demonstrate upper indication of 94.37% accurateness through Kappa indicators showcasing lesser error rates. The investigational outcomes are tested on training data using Ten-Fold cross validation which concludes that this approach can be efficaciously used in Sentimental analysis as an aid for online decision. 2019 IEEE. -
Looking at psychological well-being through the lens of identity among adolescent girls: An exploration
Purpose: This research endeavours to delve into the intricate dimensions of adolescent girls' psychological well-being and identity, aiming to shed light on their interplay and identify key predictors of psychological well-being. The study, conducted with a sample of adolescent girls, seeks to enrich our understanding of the multifaceted nature of their developmental experiences. Psychological well-being is attained by achieving a state of balance affected by both challenging and rewarding life events and a stable sense of identity. Approach: The present research is an ex-post facto research falling in the area of quantitative research design. Data has been collected on 348 adolescents, purposely recruited from different schools of Delhi NCR. The age range of the respondents was 15 to 17 years. Findings: The results reveal that psychological well-being is being predicted by identity processes among adolescent females. The different dimensions of identity processes are found to be explaining almost 19% variance in the regression model. Commitment has been found to have a ? value of 0.197 (t= 3.511; p<.01), in-depth exploration has a ?= 0.161 (t= 2.867; p<.01), and reconsideration of commitment has a ?= 0.314 (t= 6.294; p<.01). Value: By addressing the objectives of this research, valuable insights may be received by educators, mental health professionals, and policymakers to better support and enhance the well-being of adolescent girls through having a stable sense of identity. 2024 RESTORATIVE JUSTICE FOR ALL. -
An Innovative Method for Enterprise Resource Planning (ERP) for Business and knowledge Management Based on Tree MLP Model
This strategy highlights the benefits of utilizing cutting-edge IT to back up company goals and genuinely assist in changing internal procedures by implementing an ERP-appropriate solution. Any organization, no matter how big or little, can benefit from an enterprise resource planning (ERP) system, which is an integrated suite of tools designed to streamline and improve internal business operations. Staying true to this approach will ensure that you get the greatest results while training the model, selecting features, and doing preprocessing. In order to use dense vector embedding for preparing the raw system logs, ERP system logs are typically represented by a combination of alphanumeric characters. While selecting features, SIM uses Particle Swarm Optimization (PSO) to create uniform product configurations. Using a Tree-MLP, the model was trained. This new strategy outperforms the old one, including Decision Tree and MLP. A 94.30% improvement in accuracy was achieved after implementing the technique. 2024 IEEE. -
Multimodal Emotion Recognition in HumanComputer Interaction Using MFF-CNN
The rise of technology in the digital era has amplified the importance of understanding human emotions in enhancing humancomputer interactions. Traditional interfaces, mainly focused on logical tasks, often miss the nuances of human emotion, creating a gap between human users and technology. Addressing this gap, the development of the HumanComputer Interface for emotional intelligence uses advanced algorithms and deep learning models to accurately recognize emotions from various cues like facial expressions, voice, and written text. This paper presented a significant approach for emotion detection in HCI and the challenges faced in capturing genuine emotional responses. Historically, the emphasis in HCI design was on operational tasks, neglecting emotional nuances. However, the tide is changing toward embedding emotional intelligence into these interfaces, leading to enhanced user experiences. This research introduces the MFF-CNN, a neural network model combining both textual and visual data for accurate emotion detection. Through sophisticated algorithms and the integration of advanced machine learning techniques, this paper presents a refined approach to emotion detection in HCI, supported by a comprehensive review of related works and a detailed methodology. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Systematic Study on Unimodal and Multimodal Human Computer Interface for Emotion Recognition
A systematic study for human-computer interface (HCI) for emotion recognition is presented in this paper, with a focus on various methods used to identify and interpret human emotions. It delves into various methods used to identify and interpret human emotions and highlights the limitations of unimodal HCI for emotion recognition systems. The paper emphasizes the benefits of multimodal HCI and how combining different types of data can lead to more accurate results. Additionally, it highlights the importance of using multiple modalities for emotion recognition. The study has significant implications for mental health assessments and interventions as it offers insights into the latest techniques and advancements in emotion recognition. Future research can use these insights to improve the accuracy of emotion recognition systems, ultimately leading to better mental health assessments and interventions. Overall, the paper provides a valuable contribution to the field of HCI and emotion recognition, and it underscores the importance of taking a multimodal approach for this critical area of research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Evidence and Predictors of Resilience among Young Adults Exposed to Traumatic Events of the Armed Conflict in Kashmir
No study to date exists regarding resilience in the context of armed conflict in Kashmir, India. Therefore, this study intended to understand the factors that predict resilience among young adults affected by the violence of the protracted conflict in Kashmir. The data were collected from 656 students, who experienced stress, related to the conflict. Findings showed that more than a quarter of the respondents (35.8%) were exposed, from 7 to 10, less than a quarter (16.6%) of participants reported 26, and almost half of the respondents (47.6%) were exposed to 11 or more stressful events related to the conflict in Kashmir. Multiple hierarchical regression analysis was used to examine the role of conflict exposure, social support, and demographic variables in predicting resilience. The results of the final regression model revealed that exposure to armed conflict, social support, level of education, monthly family income and gender, emerged as significant predictors of resilience. The study recommends the formulation of programs to sensitize people living in the areas affected by the armed conflicts, regarding the importance of social support and resilience, to help them withstand various adverse life experiences. 2021 Taylor & Francis Group, LLC. -
Sustainability and Social Responsibility in Marketing
Social trading companies must adhere to the concept of social responsibility by engaging in activities that provide benefits to society. Social responsibility in marketing is through providing products and services that help the community environmentally, socially, and economically. Several literatures have discussed the role of social responsibility in customer intentions toward social trading channels. Discussing and describing the role of social responsibility in marketing is an important issue that needs to be investigated. This chapter describes the role of social responsibility in marketing activities. Promotions that raise awareness of societal issues and problems, recyclable packaging, and direct portions of profits toward charitable efforts are critical factors that will be described in this chapter as well. The majority of business companies, however, are still unable to fulfill the finest CSR principles in their marketing activities. Therefore, the focus of this chapter is on the requirements for CSR as well as the deficiencies in corporate organizations marketing operations. Considering the important of social media for communicating CSR is address at the point, where corporations use social media effectively for delivering their CSR goals and initiatives. This chapter is followed by two main questions: how can corporate social responsibility be delivered in marketing? And why do we need to study CSR?. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
A Systematic Review on e-Wastage Frameworks
The electronic devices that are targeted to the end users have become day to day essential parts. Traditional methodologies have changed drastically resulting in efficient mode of communication and fast information retrieval. As the demand and the production are exponentially growing, patterns of sales, storage and their destruction and then again, their collection have also been changed. This paper analyses many such behaviors of (electronic) waste management and recommends solutions like recycling management, different directives and policies required to be followed. Authors have emphasized on providing substantial information that can be useful to the regulating authorities responsible for waste management or the manufacturers of various electronic products and then the policy makers. With an extensive review of electronic wastages, authors have emphasized three variables (sales, stock and lifespan) for replacing/upgrading the older products with advanced versions. The root causes of electronic wastages are found in industrializing countries like India, China, Vietnam, Pakistan, the Philippines, Ghana and Nigeria whereas industrialized countries also play equally important role for its generation. This paper signifies the importance of e-waste management practice to reduce the emerging electronic waste hazards. Authors focus on todays demand of electronic devices, importance of e-waste management and management practices. The paper recommends key findings based on surveying data regarding the lack of regulation to manage the e-waste. The review concludes that the lack of regulation and improper awareness are the basic factors responsible for e-wastage and requires major focus to manage the e-waste. 2021. All Rights Reserved. -
AI-driven emotion recognition systems for sustainable mental health care: an engineering perspective
Emotion recognition systems are transforming human-computer interaction (HCI) applications by enabling AI-driven, adaptive, and responsive mental health interventions. This study explores AI-based emotion recognition technologies using facial expressions, voice analysis, text-based sentiment processing, and physiological signals to develop scalable, real-time mental health support systems. Utilizing datasets such as FER2013, JAFFE, and CK+, our research examines deep learning models, including EfficientNet-XGBoost, which achieved over 90% accuracy across key evaluation metrics. Unlike traditional mental health interventions, AI-driven systems provide cost-effective, accessible, and sustainable solutions through telemedicine, wearable biosensors, and virtual counselors. The study also highlights critical challenges such as algorithmic bias, ethical AI compliance, and the energy consumption of deep learning models. By integrating machine learning, cloud-based deployment, and edge computing, this research contributes to the development of sustainable, ethical, and user-centric AI solutions for mental health care. Future directions include AI model optimization for energy-efficient deployments and the creation of diverse, inclusive datasets to improve performance across global populations. 2025, Intelektual Pustaka Media Utama. All rights reserved. -
Emotion Recognition Through Facial Expressions: A Machine Learning Perspective in Mobile Multimedia
Facial expression-based emotion detection is very attractive because of the possibilities in security systems, mental health monitoring, and human-computer interaction. Even with the progress in accuracy in real-world settings, issues such as the lack of balanced datasets and the inability to differentiate between faint or superimposed emotions continue to plague it. This study aims to bridge these constraints by developing a CNN-based model that would be able to recognize face emotions reliably and be utilized in real-time situations, such as webcam integration. The Affect Net dataset, which is a comprehensive collection of over a million facial photos labeled with the seven major emotions of anger, disgust, fear, happiness, neutrality, sadness, and surprise, was used to train the proposed model. Other pre-processing data techniques used include grayscale conversion, normalization, scaling, and data supplementation to increase the robustness of the model. Using metrics like accuracy and loss trends for evaluation, the model demonstrated efficiency stability at around the 30th training phase. When the model is compared to existing models, this proposed model can attain the competitive level of accuracy up to approximately 60%. It also has the potential to run in real applications through its webcam integration. While the model can differentiate between various clear-cut emotions, it becomes ineffective at identifying subtle emotions, which include Fear and Neutral majorly because of unbalanced data and the subtleness of these expressions. 2025 River Publishers. -
AN ANALYSIS OF PEOPLE'S PERCEPTION OF TINTIN
Certain comics achieve cult status, a status which ensures they are replicated in other forms to reach out to a wider audience. The Adventures of Tintin is one such cult series which has been loved by generations across the globe. Each reader of Tintin is unique in his/her own way and the research aims to figure out if the educational background of the readers influences the way in which they perceive the series. This perception is being looked at from two the point of view of two groups, one consisting of readers from the Journalism/media background and another consisting of readers from various other backgrounds.