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Detection of Forest Fire Using Modified LSTM Based Feature Extraction with Waterwheel Plant Optimisation Algorithm Based VAE-GAN Model
A crucial natural resource that directly affects the ecology is forests. Forest fires have become a noteworthy problem recently as a result of both natural and man-made climatic changes. A smart city application that uses a forest fire discovery technology based on artificial intelligence is provided in order to prevent significant catastrophes. A major danger to the environment, animals, and human lives is posed by forest fires. The early detection and suppression of these fires is crucial. This work offers a thorough method for detecting forest fires using advanced deep learning (DL) algorithms. Preprocessing the forest fire dataset is the initial step in order to improve its relevance and quality. Then, to enable the model to capture the dynamic character of forest fire data, long short-term memory (LSTM) networks are used to extract useful feature from the dataset. In this work, weight optimisation in LSTM is performed using a Modified Firefly Algorithm (MFFA), which enhances the model's performance and convergence. The Variational Autoencoder Generative Adversarial Networks (VAEGAN) model is used to classify the retrieved features. Furthermore, every DL model's success depends heavily on hyperparameter optimisation. The hyperparameters of an VAEGAN model are tuned in this research using the Waterwheel Plant Optimisation Algorithm (WWPA), an optimisation technique inspired by nature. WPPA uses the idea of plant growth to properly tune the VAEGAN's parameters, assuring the network's peak fire detection performance. The outstanding accuracy (ACC) of 97.8%, precision (PR) of 97.7%, recall (RC) of 96.26%, F1-score (F1) of 97.3%, and specificity (SPEC) of 97.5% of the suggested model beats all other existing models, which is probably owing to its improved architecture and training techniques. Copyright: 2024 The authors. This piece is published by IIETA and is approved under the CC BY 4.0 license. -
Exploring the Use of the Therapists Self in Therapy: A Systematic Review
Purpose: This systematic qualitative review explored how psychotherapists use their self in therapy within the psychotherapy literature. It sought to examine the key documented ways through which the therapists self is intentionally used in therapy and the process of using the therapists self. Methods: Following PRISMA guidelines, databases including PubMed, ProQuest, APA PsycArticles, and APA PsycINFO were searched. The review question How do therapists use their self in therapy? guided the search using derivative keywords. Of the 149 screened articles, 20 underwent full-text review, and only four studies met inclusion criteria. Findings: All studies that met the inclusion criteria were from the West. Therapeutic self-disclosure (TSD) emerged as the primary way through which therapists used their self in therapynotably, the only way documented in the studies reviewed. Studies discussed the nature, rationale, influencing factors, and effectiveness of TSD. This article elaborates upon the themes from the reviewed studies. It critically examines existing literature, lists avenues for future research, and discusses implications for psychotherapy practice. Conclusions: The review underscores a significant gap in empirical qualitative research regarding therapists use of their self beyond TSD in therapy. There is an urgent need for further exploration in this domain. 2024 The Author(s). -
A comprehensive survey on features and methods for speech emotion detection
Human computer interaction will be natural and effective when the interfaces are sensitive to human emotion or stress. Previous studies were mainly focused on facial emotion recognition but speech emotion detection is gaining importance due its wide range of applications. Speech emotion recognition still remains a challenging task in the field of affective computing as no defined standards exist for emotion classification. Speech signal carries large information related to the emotions conveyed by a person. Speech recognition system fails miserably if robust techniques are not implemented to address the variations in speech due to emotion. Emotion detection from speech has two main steps. They are feature extraction and classification. The goal of this paper is to give an overview on the types of corpus, features and classification techniques that are associated with speech emotion recognition. 2015 IEEE. -
Therapists as Researchers: Navigating Dilemmas From Research on Indian Therapists
Background and Aim: This paper explores the journey of an Indian therapist-researcher as she delves into the complex realm of the therapists' use of self inIndia. It examines the reflection of an experienced Indian therapist-researcher investigating the complex phenomenon of the use of self. Methods: Using qualitative phenomenological interviews, the present study explores the dilemmas that therapists as researchers face during the interview process. This paper draws from a study with eight participants on the topic Use of Self in Therapy: An Exploratory Study Among Indian Counsellors and Therapists using interpretative phenomenological analysis. Results and Discussion: The dynamic interplay between researcher and participant identities underscoresthe necessity of carefully navigating boundaries and tailoring the inquiry process to participant needs and research objectives. This paper illustrates possible pathways of responding to these dilemmas of identity by transforming the interview process into a more participant-led conversation and using therapeutic skills to ethically navigate research interviews. As the researcher grapples with the question of whether the therapist self should be held back or freely expressed, the paper offers insights into the delicate balance between knowledge-gathering as part of research and maintaining participant safety. Finally, this study emphasisesthe importance of reflexivity in understanding the self of the therapist, particularly in the context of India's evolving therapy landscape. Conclusion: This paper highlights the questions and dilemmas that arise in qualitative research and the need for ethical reflexivity, responsiveness and sensitivity while working with people. 2025 British Association for Counselling and Psychotherapy. -
License Plate Recognition Model based on Improved YOLOv5 and Convolutional LSTM
An end-to-end deep learning model is proposed in this research, for licence plate recognition (LPR) and identification in natural circumstances, which addresses the accuracy and speed limitations of standard licence plate recognition approaches. By adding a better channel attention mechanism and including position data in the output, the proposed method improves the You Only Look Once (YOLOv5) down sampling process and reduces information loss during sampling for better feature extraction. An optimised the YOLO layer is used for single-class recognition to improve efficiency and accuracy. Additionally, Convolutional Long Short-Term Memory (ConvLSTM) combined with Connectionist Temporal Softmax (CTS) is used for character segmentation-free recognition. The utilization of an optimized YOLO layer for single-class recognition enhances both efficiency and accuracy. The integration of ConvLSTM in conjunction with CTS proves to be a breakthrough, facilitating faster convergence, reduced training time, and increase the precision of the model. This configuration speeds up convergence, lowers training time, and increases identification accuracy. The experimental results demonstrate average recognition precision of 99.24% and also robustness, especially in complex situations, with better performance than conventional algorithms. 2025 IEEE. -
Neurofeedback Therapy Meets Transformers: Rewiring Sleep Disorders Through AI-Driven EEG Modulation
Sleep disorders such as insomnia, sleep Apnea, and hypersomnia significantly impair neurophysiological functioning, yet conventional treatments like Cognitive Behavioral Therapy for Insomnia (CBT-I) remain resource-intensive and difficult to personalize. This study introduces a novel AI-powered neurofeedback simulation framework designed to detect dysregulated EEG frequency band activity across sleep stages and simulate targeted interventions. A Transformer-based model serves as the core component, offering a unique capability to model cross-epoch temporal dynamics and frequency-specific spectral patterns. Unlike traditional architectures that treat EEG epochs in isolation, the Transformer captures how EEG band activity evolves across the night, critical for identifying persistent dysregulation patterns and planning stage-specific interventions. Through its multi-head attention mechanism, the model can simultaneously monitor delta, theta, alpha, beta, and gamma fluctuations while preserving sleep architecture transitions using positional encoding. Dysregulated epochs are classified with 92% accuracy, and intervention simulations-such as beta suppression in N2 or delta enhancement in REM-led to measurable improvements: average WASO decreased by 23%, and Sleep Efficiency improved by 13%. This framework not only demonstrates the efficacy of Transformer-based temporal-spectral modelling in EEG but also lays the foundation for closed-loop, wearable-compatible, personalized neurofeedback systems for remote sleep therapy. 2026 A l A KA d V idh hi V -
Hybrid nanofluid flow over a vertical rotating plate in the presence of hall current, nonlinear convection and heat absorption
An exact analysis has been carried out to study a problem of the nonlinear convective flow of hybrid nanoliquids over a vertical rotating plate with Hall current and heat absorption. Three different fluids namely CuAl2O3H2Ohybrid nanofluid, Al2O3H2O nanofluid and H2O basefluids are considered in the analysis. The simulation of the flow was carried out using the appropriate values of the empirical shape factor for five different particle shapes (i.e., sphere, hexahedron, tetrahedron, column and lamina). The governing PDEs with the corresponding boundary conditions are non-dimensionalised with the appropriate dimensionless variables and solved analytically by using LTM (Laplace transform technique). This investigation discusses the effects of governing parameters on velocity and temperature fields in addition to the rate of heat transfer. The numeric data of the density, thermal conductivity, dynamic viscosity, specific heat, Prandtl number and Nusselt number for twelve different hybrid nanofluids at 300 K is presented. The temperature profile of hybrid nanoliquid is larger than nanoliquid for same volume fraction of nanoparticles. Also, the glycerin-based nanoliquid has a high rate of heat transfer than engine oil, ethylene glycol and water-based nanoliquids in order. 2018 by American Scientific Publishers All rights reserved. -
Prediction of Grandiose Narcissism Using Machine Learning
The Gen-Zs have the tendency to exhibit a sense of self-importance and superiority excessively over social media. This study intends to predict Grandiose Narcissism based on Instagram usage and Fear of Missing Out (FoMO) among young adults. The study was conducted on a sample size of 300 young adults, recruited using convenient sampling, residing in the state of Assam, India. This study employed various machine learning models, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Gradient Boosting, K-Nearest Neighbours (KNNs), and Gaussian Naive Bayes, to analyse the predictors of Grandiose Narcissism. Results showed that machine learning algorithms, especially KNN (90.7%) and Random Forest (88.70%) predicted Grandiose Narcissism accurately based onFoMO, Self-Esteem, PAUM. Additionally, Area Under Curve (AUC) in the range of 0.850.91 indicated that the variables in the data set are being discriminated in the context of specificity and sensitivity thoroughly. Significant influence of grandiose narcissism and FoMO on Instagram usage highlighted the role of social validation in enhancing online engagement. Future studies can include these algorithms to deduce patterns and develop real timebots to provide psychologically safe online environment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Championing inclusion: Understanding lgbt diversity and social support in the workplace
Purpose : This study investigated the impact of LGBT diversity management practices on the acceptance of LGBT employees by non-LGBT peers in Indian organizations. Based on classical social support theory and signaling theory, the study focused on how social support from co-workers and supervisors influenced this relationship. Methodology : Data were collected by surveying 546 employees across nine tech parks in the Indian IT/ITES sector. Partial Least Square (PLS) predictions and structural equation modeling (SEM) were conducted using Smart PLS version 4. Mediation and moderation analyses were also performed. Findings : The results exhibited that LGBT diversity management positively affected the acceptance of LGBT peers in the workplace (? = 0.298; t = 6.314; p = 0.00). Supervisor support was a complementary mediator (VAF = 0.33), while co-worker support moderated the association (? = 0.514; t = 15.916; p = 0.00). Practical Implications : The study presented managerial acumen regarding how social support from supervisors and co-workers enriched the efficacy of diversity management approaches. These outcomes were predominantly pertinent for organizations considering adopting an all-encompassing place of work for LGBT employees. Originality : This investigation delivered a distinctive offering by inspecting the role of social support in LGBT diversity management among the Indian IT segment. While based in Bengaluru, the study encouraged further investigation into additional businesses and geographies. 2024, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Time allocation between paid and unpaid work among men and women: An empirical study of indian villages
The paper examines the time allocation between paid work (wage earning or self-em-ployed work generally termed as employment work) and unpaid (domestic chores/care work generally termed as non-employment work) along with wage rates, imputed earnings, and occupational structure among men and women and according to different social groups to establish the extent to which the rural labour market is discriminated by sex and social group. The major objective of the paper is to show the differential in wage income between men and women in farm and non-farm activities. The paper also shows the division of time between employment and non-employ-ment activities by men and women. The paper uses high-frequency data and applies econometric techniques to know the factors behind time allocation among different activities across gender. The study finds that males spend more hours on employment work and work at a higher wage rate than females. As a result, a vast monetary income gap between men and women is observed, even though women worked more hours if employment and non-employment activities are jointly taken into consideration. Time spent on employment work and non-employment (mainly domestic chores) has been found to vary significantly due to social identity, household wealth, land, income, educa-tion, and skill. The segregation of labour market by sex was evident in this study, with men shifting to non-farm occupations with greater monetary returns and continued dependence on womens farm activities. Enhancing the ownership of land and other assets, encouraging womens participation particularly among minorities, and improving health are some of the policy recommendations directed from this study to enhance participation in employment work and shifting towards higher wage income employment. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Perspectives about Illness, Attitudes, and Caregiving Experiences among Siblings of Persons with Schizophrenia: A Qualitative Analysis
Background: Siblings of persons diagnosed with schizophrenia (SPS) are one among the major sources of support for persons with schizophrenia. There is a dearth of psychosocial literature on SPS in India. This qualitative study explored the perspectives about the illness, attitudes, and caregiving experiences of SPS. Materials and Methods: Qualitative audio-recorded interviews were conducted with 15 SPS, purposively selected from a tertiary mental health hospital of Southern India. A general inductive approach was adopted to analyze the qualitative data. Results: Four broad themes were identified from qualitative data analysis. (1) SPS described several explanatory models of mental illness in terms of causal attributions and treatment care. (2) They had expressed emotion toward their ill siblings, such as criticality, hostility, and emotional over-involvement. (3) They experienced objective and subjective burden while caring for their ill sibling. In spite of all these, (4) they were part of their ill siblings' care in terms of ensuring regular follow-ups and drug adherence and supported their livelihood. They coped up with adaptive as well as maladaptive strategies. Conclusion: SPS provide significant support to their affected siblings. However, they do have non-biomedical models of mental illness and negative attitudes toward patients and experience burden. Hence, psychosocial interventions may help SPS while caregiving for their affected siblings. 2019 Indian Psychiatric Society - South Zonal Branch. -
Community-based educational intervention on emotion regulation, self-esteem, and behavioural problems among school children
Recently, there has been a trend where higher education institutions are designing and implementing community-based educational interventions for underprivileged children in the community. It is important to understand whether these interventions are useful to the children in improving their psychosocial development. In this chapter, the author discusses the learnings from an explanatory sequential mixed methods study which aimed at assessing the impact of community educational intervention provided by a higher educational institution on self-esteem, emotional regulation and bbehavioralproblems among adolescents in rural Karnataka. The study included 250 adolescents who were beneficiaries of community educational intervention and another 250 who were non-beneficiaries. Besides this, the chapter also highlights the qualitative results grounded in the focus group discussions to understand the stakeholder's perspective on community educational interventions. Finally, the author demonstrates the processes and mechanisms of change and presents a critical discussion from the quantitative and qualitative data analytic lens. The author anticipates that community educational interventions provided by higher educational institutions are extremely impactful. Several critical factors of stakeholders, institutional, and rural communities might bring change and sustainability in benefits among rural adolescents. 2024 Nova Science Publishers, Inc. -
Short term effects of brief need based psychoeducation on knowledge, self-stigma, and burden among siblings of persons with schizophrenia: A prospective controlled trial
Siblings of persons with schizophrenia are important in providing long-term social support to the patients. Interventions addressing their needs are very sparse. Hence, this study aimed at testing the short-term effects of brief need based psychoeducation on knowledge, self-stigma, and burden among siblings of persons with schizophrenia. In this prospective controlled open label trial, 80 siblings of persons with schizophrenia were allocated in equal numbers to the brief need based psychoeducation group and the treatment-as-usual group. The outcomes were measured at baseline, and after the first and third month post-intervention. RM-ANCOVA was conducted to test the effect of the brief psychoeducation on outcome scores. The groups were similar with respect to socio-demographic, clinical, and outcome scores at the baseline. There was a significant group time interaction effect on knowledge (F = 8.71; p < 0.01; ?p 2 = 0.14) and self-stigma scores (F = 14.47; p < 0.001; ?p 2 = 0.21), wherein the brief psychoeducation group showed a significant increase in knowledge and reduction in self-stigma with medium effect size through baseline to the third month follow-up as compared to the treatment as usual group. We also observed a significant main effect of time; irrespective of the group allocation, there was a significant increase in the knowledge through baseline to third month follow-up (F = 5.69; p = 0.02; ?p 2 = 0.09). No main or interaction effects of group and time were observed on burden. The findings suggest that brief need based psychoeducation may increase knowledge about the illness and reduce self-stigma. Further systematic studies are warranted to test this intervention for long-term effects. 2017 Elsevier B.V. -
Short term effects of brief need based psychoeducation on knowledge, self-stigma, and burden among siblings of persons with schizophrenia: A prospective controlled trial /
Asian Journal of Psychiatry, Vol.7, pp.59-66, ISSN: 2211-3797. -
Personality and Psychological Predictors of Instagram Personalized Ad Avoidance
The purpose of this paper is to apply the meta-theoretical model of motivation and personality (3M) of Mowen to study consumers ad avoidance in the context of online personalized advertisements on Instagram. The current study developed a theoretical framework that links personality traits with reactance arousal and ad avoidance behaviours. Based on the data analysis, it was found that consumers with higher general self-efficacy tend to have more reactance arousal (situational level trait) compared to ad irritation, ad skepticism (surface traits), and ad avoidance behaviours towards personalized advertising on Instagram. The findings will help advertisers and marketers in segmenting the market better based on young users efficacy levels, navigational habits, personality traits, functional motives, and demographic variables to effectively reach the targeted consumers. 2023 IGI Global. All rights reserved. -
Revolutionizing the financial landscape: A review on human-centric AI thinking in emerging markets
The emergence of Industry 4.0 has transformed the financial landscape by integrating unconventional technologies and artificial intelligence (AI) into consumer interactions. This chapter explores the evolving paradigm of human-centric AI-thinking in the context of emerging customer interactions in making financial decisions. The review analyses the opportunities and the challenges that arise from the integration of AI tools and human-centric approaches in addressing the diverse needs and behaviours of consumers within emerging financial markets. More specifically, the review critically examines the utilization of AI-driven technologies, such as predictive analytics, natural language processing (NLP), and machine learning algorithms, in customising the financial services to cater the emerging-market consumers. Moreover, the current study explicates how AI enables personalized customer interactions, risk assessments, and ethical decision making and financial inclusion strategies while considering the socioeconomic and cultural landscapes. The study has focussed on addressing the concerns related to data privacy, risk assessment, and transparency towards AI-powered financial solutions with ethical standards. Through an exhaustive analysis of current trends, and empirical evidence from the existing literature, this review highlights the inevitability of human-centric AI-thinking approach towards financial services decision making. It emphasizes the importance of congruent AIdriven financial solutions in the context of banking where the determinants such as empathy, financial literacy, ethical considerations, and human values plays a significant role in finding the financial services in emerging markets. This research explores the challenges and prospects and has made commendations to all the major stakeholders such as industry stakeholders, policymakers, practitioners, customers, and service providers to create a dynamic financial landscape of Industry 4.0 in AI technologies that embrace a human-centric ethos to meet the evolving needs of consumers within emerging financial ecosystems. 2024, IGI Global. All rights reserved. -
Towards a Framework for Supply Chain Financing for Order-Level Risk Prediction: An Innovative Stacked A-GRU Based Technique
Order financing is changing the game in the banking and financial supply chain industry. It's great for SMEs and opens up new revenue streams for logistics and finance companies. But in order to find the weak spots offered by banks and other financial institutions, companies need to undertake thorough risk assessments right now. Careful timing is crucial for training the model, extracting features, and preprocessing. Outlier identification and missing value handling are the first steps in preprocessing, which also includes normalization and standardization to improve data integrity and reduce unit discrepancies. Principal component analysis makes use of multivariate statistics to aid in feature extraction, guaranteeing effective data representation. Careful consideration of every detail is required during the training of a Stacked-A-GRU model, which follows attribute selection. Impressively outperforming state-of-the-art algorithms SAFE and GRU, the suggested solution achieves a remarkable correctness rating of 97.34%, indicating notable progress in predicting accuracy. 2024 IEEE. -
Leaf Disease Detection in Crops based on Single-Hidden Layer Feed-Forward Neural Network and Hierarchal Temporary Memory
Insects, mites, and fungi are common causes in plant disease, which can significantly reduce yields if not addressed promptly. Farmers are losing money as a result of crop illnesses. As the average under cultivation increases, it becomes more of a burden for farmers to keep an eye on everything. In this study, the median filter is used as a preprocessing step to transform the input image into a grayscale representation which used YCbCr color space. Otsu's segmentation is used to divide photographs that contain bright items on a dark background. Feature extraction using Grey Level Co-occurrence Matrix (GLCM). The proposed technique, known as ELM-HTM combines a simple yet adaptable extreme learning machine (ELM) with a Hierarchical Temporal Memory (HTM). This approach outperforms the ELM and HTM model with an accuracy of about 98.8%. 2023 IEEE. -
Role of Dufour's gland and mandibular gland secretion in Ant Colony organisation and defense mechanisms of Camponotus compressus and Oecophylla smaragdina
Ants are eusocial insects and observed in 3 castes secreting a wide variety of pheromones for surviving in the colony. They can be sex pheromone, trail pheromone and alarm pheromone. These pheromones help in the recruitment of workers, mating, foraging food. These pheromones contain a single compound from a single gland or from two glands. This study analyses the knowledge of these pheromones and their chemical structure. The mandibular gland and Dufours gland of Oecophylla smaragdina and Camponotus compressus were extensively studied to provide a valuable resource in chemical ecology research. Limited research has been done on pheromones released by Oecophylla smaragdina and Camponotus compressus. The Dufour's gland is one of the most well-developed glands, playing vital roles in defense, foraging, information exchange, and reproduction. The chemical components were analyzed using gas chromatography-mass spectrometry. The secretions from the Dufours gland and mandibular gland contained high concentration of n-undecane, which serves as an alarm pheromone, and compositions varied among different castes. This highlights a research gap and the need to investigate the differences in the chemical composition between these two ant species, we analyse the diverse chemicals released from the Dufours gland and the mandibular gland. Authors CC4-NC-ND, ScienceIN. -
Adoption Laws in India : A critical Analysis through a Sociological Lens
Golden Research Thoughts Vol.2, Issue 6, pp.1-7 ISSN No. 2231-5063