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Hybrid Renewable Source Powered Dual Input Single Output Converter With High Voltage Gain for Rural Healthcare Facilities
Rural dwellers need a well-equipped healthcare service for a decent life. Most of the rural areas located in southern parts of India away from the grid connection thereby lack in electricity. Unreliable electric power leads to the limited access or inaccessibility of most essential medical equipment in the clinic. The deficiency has also reduced rural healthcare centers ethics criteria. This research work finds all the available resources in the rural healthcare clinic and proposes hybrid solar PV source and supercapacitor-based approaches to make sure of reliable energy access and uninterrupted power supply. Any healthcare facilities include an emergency room, waiting hall, nursing room, consulting room, delivery room, male and female room, and a testing lab. It may take a daily average energy consumption of 16 kWh with 3 kW peak demand. In the input side, solar PV system with an H-type clamped capacitorbased boost converter is proposed for the reduction of input current ripples and power switch conduction losses. At the load side, a capacitor with a switch (switched capacitor) is considered to reduce voltage stress of the components present in the topology and to attain high gain. This research work adopts interleaved structure-based capacitors for current ripple reduction, and the series structure is considered to attain high gain. The proposed novel converter takes the input voltage of 40 V and produces the output voltage of 280 V. The DC link output is then connected with the voltage source inverter (VSI) to get a desired output. The proposed novel converter is employed to run a 3? induction motor for the AC load with the rating of 400 V, 15 A AC power. MATLAB R2015a software is preferred for the simulation analysis. Copyright 2025 K. M. D. Riyaz Ali et al. Journal of Electrical and Computer Engineering published by John Wiley & Sons Ltd. -
DDoS Intrusions Detection in Low Power SD-IoT Devices Leveraging Effective Machine Learning
Security and privacy are significant concerns in software-defined networking (SDN)-applied Internet of Things (IoT) environments, due to the proliferation of connected devices and the potential for cyberattacks. Hence, robust security mechanisms need to be developed, including authentication, encryption, and distributed denial of service (DDoS) attack detection, tailored to the constraints of low-power IoT devices. Selecting a suitable tiny machine learning (TinyML) algorithm for low-power IoT devices for DDoS attack detection involves considering various factors such as computational complexity, robustness in dealing with heterogeneous data, accuracy, and the specific constraints of the target IoT device. In this paper, we present a two-fold approach for the optimal TinyML algorithm selection leveraging the hybrid analytical network process (HANP). First, we make a comparative analysis (qualitative) of the machine learning algorithm in the context of suitability for TinyML in the domain of SD-IoT devices and generate the weights of suitability for TinyML applications in SD-IoT. Then we evaluate the performance of the machine learning algorithms and validate the results of the model to demonstrate the effectiveness of the proposed method. Finally, we see the effect of dimensionality reduction with respect to features and how it affects the precision, recall, accuracy, and F1 score. The results demonstrate the effectiveness of the scheme. 1975-2011 IEEE. -
DDoS Intrusions Detection in Low Power SD-IoT Devices Leveraging Effective Machine Learning
Security and privacy are significant concerns in software-defined networking (SDN)-applied Internet of Things (IoT) environments, due to the proliferation of connected devices and the potential for cyberattacks. Hence, robust security mechanisms need to be developed, including authentication, encryption, and distributed denial of service (DDoS) attack detection, tailored to the constraints of low-power IoT devices. Selecting a suitable tiny machine learning (TinyML) algorithm for low-power IoT devices for DDoS attack detection involves considering various factors such as computational complexity, robustness in dealing with heterogeneous data, accuracy, and the specific constraints of the target IoT device. In this paper, we present a two-fold approach for the optimal TinyML algorithm selection leveraging the hybrid analytical network process (HANP). First, we make a comparative analysis (qualitative) of the machine learning algorithm in the context of suitability for TinyML in the domain of SD-IoT devices and generate the weights of suitability for TinyML applications in SD-IoT. Then we evaluate the performance of the machine learning algorithms and validate the results of the model to demonstrate the effectiveness of the proposed method. Finally, we see the effect of dimensionality reduction with respect to features and how it affects the precision, recall, accuracy, and F1 score. The results demonstrate the effectiveness of the scheme. 1975-2011 IEEE. -
Machine LearningEnabled NIR Spectroscopy. Part 2: Workflow for Selecting a Subset of Samples from Publicly Accessible Data
Abstract: An increasingly large dataset of pharmaceuticsdisciplines is frequently challenging to comprehend. Since machine learning needs high-quality data sets, the open-source dataset can be a place to start. This work presents a systematic method to choose representative subsamples from the existing research, along with an extensive set of quality measures and a visualization strategy. The preceding article (Muthudoss et al. in AAPS PharmSciTech 23, 2022) describes a workflow for leveraging near infrared (NIR) spectroscopy to obtain reliable and robustdata on pharmaceutical samples. This study describes the systematic and structured procedure for selecting subsamples from the historical data. We offer a wide range of in-depth quality measures, diagnostic tools, and visualization techniques. A real-world, well-researched NIR dataset was employed to demonstrate this approach. This open-source tablet dataset (http://www.models.life.ku.dk/Tablets) consists of different doses in milligrams, different shapes, and sizes of dosage forms, slots in tablets, three different manufacturing scales (lab, pilot, production), coating differences (coated vs uncoated), etc. This sample is appropriate; that is, the model was developed on one scale (in this research, the lab scale), and it can be great to investigate how well the top models are transferable when tested on new data like pilot-scale or production (full) scale. A literature review indicated that the PLS regression models outperform artificial neural network-multilayer perceptron (ANN-MLP). This work demonstrates the selection of appropriate hyperparameters and their impact on ANN-MLP model performance. The hyperparameter tuning approaches and performance with available references are discussed for the data under investigation. Model extension from lab-scale to pilot-scale/production scale is demonstrated. Highlights: We present a comprehensive quality metrics and visualization strategy in selecting subsamples from the existing studies A comprehensive assessment and workflow are demonstrated using historical real-world near-infrared (NIR) data sets Selection of appropriate hyperparameters and their impact on artificial neural network-multilayer perceptron (ANN-MLP) model performance The choice of hyperparameter tuning approaches and performance with available references are discussed for the data under investigation Model extension from lab-scale to pilot-scale successfully demonstrated Graphical Abstract: [Figure not available: see fulltext.]. 2023, The Author(s). -
Machine Learning-Enabled NIR Spectroscopy. Part 3: Hyperparameter by Design (HyD) Based ANN-MLP Optimization, Model Generalizability, and Model Transferability
Data variations, library changes, and poorly tuned hyperparameters can cause failures in data-driven modelling. In such scenarios, model drift, a gradual shift in model performance, can lead to inaccurate predictions. Monitoring and mitigating drift are vital to maintain model effectiveness. USFDA and ICH regulate pharmaceutical variation with scientific risk-based approaches. In this study, the hyperparameter optimization for the Artificial Neural Network Multilayer Perceptron (ANN-MLP) was investigated using open-source data. The design of experiments (DoE) approach in combination with target drift prediction and statistical process control (SPC) was employed to achieve this objective. First, pre-screening and optimization DoEs were conducted on lab-scale data, serving as internal validation data, to identify the design space and control space. The regression performance metrics were carefully monitored to ensure the right set of hyperparameters was selected, optimizing the modelling time and storage requirements. Before extending the analysis to external validation data, a drift analysis on the target variable was performed. This aimed to determine if the external data fell within the studied range or required retraining of the model. Although a drift was observed, the external data remained well within the range of the internal validation data. Subsequently, trend analysis and process monitoring for the mean absolute error of the active content were conducted. The combined use of DoE, drift analysis, and SPC enabled trend analysis, ensuring that both current and external validation data met acceptance criteria. Out-of-specification and process control limits were determined, providing valuable insights into the models performance and overall reliability. This comprehensive approach allowed for robust hyperparameter optimization and effective management of model lifecycle, crucial in achieving accurate and dependable predictions in various real-world applications. Graphical Abstract: [Figure not available: see fulltext.]. 2023, The Author(s). -
A comparative study of the impact of thermal indices on Indian coral ecosystem
Coral reefs have been the diversified ecosystem in the planet. Advantages are opportunities in tourism, coastal protection and fisheries production. Corals, as key ingredient is sourced got drug manufacturing. Its distribution is evident in locations of where sea water temperature ranges between 16C to 30C. Their presence is >0.2% of ocean area and supports >25% of marine species. India has five reef formations. Globally, last two decades have seen an increase in reporting reef deterioration. The reason significantly attributed to be climate change, apart other challenges such as pollution, sedimentation, oil spillage, etc. Such events lead to widespread mortality of corals. Mortality during bleaching events are inevitable and varied; depends on intensity of such events. The primary reason is due to significant rise in average sea surface temperature (SST). Recovery takes time after such events, and it becomes worse with recurring events. The reefs of Indian seas have reported events of severe bleaching during 1998, 2010 and 2016. IPCC reviews show mass bleaching will be prominent in future due to elevated SST. This work tries to compare the HS values of a few regions. The data collected is from 2001 to 2017. A few significant observations are drawn which could further help us to extend the work to take help from Artificial Intelligence to make predictions for the future. This study uses the indices derived out of SST to look at relative risk faced by Indian reefs. The need for comprehensive and localized actions will be discussed. 2021 Author(s). -
Challenging Colonial Hegemony through Khalil Gibrans Beautiful and Rare Sayings
The paper examines how Khalil Gibrans Arabic book (Al-Badai waal-Taraif), translated as Beautiful and Rare Sayings, is a rebellious call for the rebirth of the Arab nation. In this work, Gibran expresses his strong political views and dreams of liberty, urging Arabs to awaken from their slumber, regain their freedom, and build their future independently, without relying on foreign powers. Having witnessed the damaging effects of colonialism on the Arab world, Gibran realized how it threatened the future of his people and sought to reform his nation based on the values of liberty and justice. He criticizes oppressive systems such as the decline of the Ottoman Empire and European imperialism, which had long prevented the unity and peace of the Arab world. Through many insights in the book, Gibran gives voice to the pain of his people while guiding them toward the path of freedom and inspiring them with the broad aspirations they can achieve. This work represents the revolutionary spirit of its time, providing a counter-narrative to those in power who seek to silence the opposite. Additionally, this paper explores how Gibrans use of language and metaphors critiques the social and political conditions of the Arab world, reflecting his vision of unity beyond ethnic divisions. By analyzing Beautiful and Rare Sayings, the paper highlights Gibrans role as a cultural mediator, navigating colonial hegemony and inspiring cultural awakening and identity within the Arab context. 2024 selection and editorial matter, Dr. L. Santhosh Kumar, Ms. Minu A., Dr. Barnashree Khasnobis, Dr. Preetha M. and Dr. Merrin R. S.; individual chapters, the contributors. -
Therapeutic romanticism: Khalil Gibrans journey from rebellion to spiritual healing
This article argues that Khalil Gibrans Romanticism functions not merely as literary expression but as a poetic mechanism for processing trauma, cultivating resilience, and fostering relational awareness, especially in contexts of exile and existential fragmentation. To operationalize this reading, the study employs a literary micro-model: Van der Kolk and Fisleriss Dissociation and the Fragmentary Nature of Traumatic Memories used to map The Madman (1918) as the ruptured phase, where fragmentation, irony, and self-estrangement externalize pain; while Seligmans PERMA model frames The Prophet (1923) as the integrative phase, where meaning, relationships, and transcendence articulate universalist healing. By juxtaposing alienation and flourishing, the analysis highlights Gibrans movement from rebellion to harmony, revealing poetrys potential as a therapeutic medium for personal and collective crises. 2025 National Association for Poetry Therapy. -
The computational model of nanofluid considering heat transfer and entropy generation across a curved and flat surface
The entropy generation analysis for the nanofluid flowing over a stretching/shrinking curved region is performed in the existence of the cross-diffusion effect. The surface is also subjected to second-order velocity slip under the effect of mixed convection. The Joule heating that contributes significantly to the heat transfer properties of nanofluid is incorporated along with the heat source/sink. Furthermore, the flow is assumed to be governed by an exterior magnetic field that aids in gaining control over the flow speed. With these frameworks, the mathematical model that describes the flow with such characteristics and assumptions is framed using partial differential equations (PDEs). The bvp4c solver is used to numerically solve the system of non-linear ordinary differential equations (ODEs) that are created from these equations. The solutions of obtained through this technique are verified with the available articles and the comparison is tabulated. Meanwhile, the interpretation of the results of this study is delivered through graphs. The findings showed that the Bejan number was decreased by increasing Brinkman number values whereas it enhanced the entropy generation. Also, as the curvature parameter goes higher, the speed of the nanofluid flow diminishes. Furthermore, the increase in the Soret and Dufour effects have enhanced the thermal conduction and the mass transfer of the nanofluid. 2023, The Author(s). -
Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security
Recent security applications in mobile technologies and computer systems use face recognition for high-end security. Despite numerous security tech-niques, face recognition is considered a high-security control. Developers fuse and carry out face identification as an access authority into these applications. Still, face identification authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized user. In the existing spoofing detection algorithm, there was some loss in the recreation of images. This research proposes an unobtrusive technique to detect face spoofing attacks that apply a single frame of the sequenced set of frames to overcome the above-said problems. This research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused structure. First, this pro-posedmethodistestedwiththeCross-ethnicityFaceAnti-spoofing (CASIA), Fetal alcohol spectrum disorders (FASD) dataset. This database has three models of attacks: distorted photographs in printed form, photographs with removed eyes portion, and video attacks. The images are taken with three different quality cameras: low, average, and high-quality real and spoofed images. An extensive experimental study was performed with CASIA-FASD, 3 Diagnostic Machine Aid-Digital (DMAD) dataset that proved higher results when compared to existing algorithms. 2023, Tech Science Press. All rights reserved. -
Integrating cyber-physical systems with intelligent transportation: Challenges and opportunities
Cyber-physical systems (CPS) are revolutionizing the transportation sector, wherein physical processes are combined with computational systems to create efficient, reliable, and safe transportation solutions. This chapter discusses the ways in which CPS impact contemporary transportation development. The theoretical and practical aspects of CPS have been considered as they follow with the intelligent traffic management systems and driverless cars within this scope of work. The first half of the chapter is then applied to architectural design in CPS, discussing how elements of the physical worldinteraction with cars and roads, for exampleare coupled with cyber systems, such as cloud computing, IoT, and communication networks. Important technical breakthroughs in these areas highlight the key aspects that make real-time decision-making and optimization of systems possible: 5G, edge computing, and artificial intelligence. The chapter also reviews simulation-based techniques in analyzing vehicle behavior and traffic flow, which encompasses insights into how CPS might improve traffic safety and efficiency. Simulations can study very complex transportation scenarios like collision avoidance and control of traffic without the need for real data. The chapter discusses cybersecurity risks, legal issues, and the need for standardized infrastructure to support intelligent transportation systems. It also focuses on the challenges presented by laws and policies in the field of CPS. The interaction of drivers, passengers, and traffic operators with these devices further helps grasp the human factor as well as the experience of a CPS user. The final section of the chapter discusses future directions of CPS research and development, specifically regarding how blockchain technology and quantum computing might advance transportation networks. This chapter will, therefore, give the reader a holistic understanding of how CPS may change the face of transportation in the future by bringing its non-data-driven components to the fore. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
ARTIFICIAL INTELLIGENCE IN NEUROCOGNITIVE REHABILITATION: AI Applications in Assessment, Monitoring, and Therapy
The global rise in neurocognitive disorders, due to aging populations, traumatic brain injuries, and neurodegenerative diseases, demands innovative rehabilitation strategies. Artificial intelligence (AI) is transforming neurocognitive rehabilitation by enabling early detection, real-time monitoring, and personalized therapy through technologies such as machine learning, natural language processing, neuroimaging, and wearable sensors. This chapter explores how AI-powered tools enhance neuropsychological assessments, support continuous monitoring through multimodal data streams, and enable adaptive, patient-centered therapeutic interventions. Additionally, it evaluates the ethical challenges and implementation barriers associated with AI integration in clinical practice. By examining the interplay between AI and neurorehabilitation, the chapter underscores the transformative potential of interdisciplinary, data-driven approaches in cognitive healthcare. 2026 selection and editorial matter, K. Jayasankara Reddy; individual chapters, the contributors. All rights reserved. -
The role of psychoneuroimmunology in mental health disorders: The intertwined pathways
The chapter provides a comprehensive overview of psychoneuroimmunology (PNI), detailing the interactions between psychological processes, the nervous system, and the immune system in relation to mental health disorders. It discusses how psychological stress can affect immune function, leading to altered neurotransmitter function and contributing to conditions like anxiety, depression, and schizophrenia. The chapter also explores the influence of gut microbiota on the immune system and brain function and emphasizes the therapeutic implications of understanding PNI pathways for developing innovative and potentially life-changing treatments for mental health disorders. Overall, the chapter highlights the interconnected pathways of the nervous, endocrine, and immune systems and advocates for a holistic approach to mental health care. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Factors Associated with Psychological Morbidity Among School Students During COVID-19 Pandemic: Lessons from the Peritraumatic Phase for Future Management
The coronavirus disease 2019 (COVID-19) has increased the prevalence and burden of psychological morbidity in school students. This study attempted to assess the factors associated with psychological morbidity in school students during the peritraumatic phase of the COVID-19 pandemic. We collected data from 16,738 school students in India using a cross-sectional online-based survey tool. We carried out a binomial logistic regression to estimate the odds of the relationship that psychological morbidity had with independent variables. Results indicated that 4 in 10 school students had psychological morbidity. Those students in grades 1112 (OR = 1.3, 95% CI = 1.21.4), 1718 years of age (OR = 1.4, 95% CI = 1.31.6), from a lower socio-economic status (family income of ?20,00130,000 per month) (OR = 1.0, 95% CI = 0.81.0) and a student (OR = 2.5, 95% CI = 1.93.4) or a family member of a student (OR = 1.6, 95% CI = 1.41.8) with COVID-19 infection were associated with higher odds of psychological morbidity. The relationship psychological morbidity had with gender, mental well-being and resilient coping was revelatory. Targeted psychosocial interventions are required for high-risk school students to reduce age, grade and socio-economic disparities in COVID-19-related psychological morbidity. These findings have implications for mental health professionals, counsellors, psychologists, social workers and academicians associated with school students. 2025 SAGE Publications. -
Psychosocial factors and immune function: Phenomena affecting susceptibility to disease in humans
The interplay between psychosocial factors and immune function represents a critical area of research in understanding human susceptibility to disease. This chapter elucidates how psychological and social variables influence immune responses, affecting disease outcomes. This chapter provides a comprehensive overview of how stress, sleep, personality traits, social support, family dynamics, and socioeconomic status modulate immune function by integrating psychoneuroimmunology, epidemiology, and clinical psychology insights. The goal is to offer a nuanced understanding of these phenomena, highlighting the direct and indirect pathways through which psychosocial factors impact health and well-being. Furthermore, the chapter proposes specific psychosocial interventions backed by empirical evidence that aim to improve immune function. It also discusses potential future directions and research implications. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Educational Achievement of Socially and Economically Disadvantaged Children from Urban Slums of Bengaluru City
In the Indian context, marginalized and oppressed individuals often reside in slums newlineand on the streets, facing poor living conditions and inadequate facilities. While newlineurban areas boast elite lifestyles characterized by high levels of educational newlineattainment, access to the latest technologies, and substantial incomes, marginalized groups experience a significant lack of basic living standards and encounter limited access to essential services such as education, healthcare, and employment newlineopportunities. Education, in particular, poses one of the greatest challenges in slum newlineareas. Various factors, including socio-economic background, family characteristics, newlineand educational opportunities, can influence the academic performance of slum children. Additionally, teachers perceptions and classroom practices play crucial roles. The current study aims to explore how family characteristics, socio-economic background, educational opportunities, and teachers perceptions impact the educational achievements of slum children in Bengaluru city. To investigate educators perspectives on socially and economically disadvantaged children, a questionnaire was administered to teachers. The study utilized a mixed-method newlineresearch design to address its research questions. Quantitative data were collected newlinefrom 100 slum children and 100 non-slum children aged 6 to 14 years. During semistructured interviews, the researcher used an open-ended questionnaire to gather newlineresponses from principals and teachers. Thirty-six teachers working with various newlineschool boards in the Byrasandra and Siddapura areas were included in this study. newlineAdditionally, class observations were conducted to assess classroom interactions, the rapport between teachers and students, and levels of student involvement. A newlinepurposive random sampling technique was employed to select participants from the newlinestudy population. Data were meticulously collected and analyzed. -
Endurance and Evolution: Exploring Levels of Resilience Among Indian Breast Cancer Survivors
Resilience for Indian women with breast cancer involves maintaining positivity and adaptability amid the complex challenges affecting their physical, emotional, and social well-being. However, research focused on resilience amongst this population in Indian settings is limited. Therefore, the aim of the study is to explore the experience of levels, patterns, and processes of resilience in Indian women living with breast cancer. A qualitative phenomenological approach was used to study resilience. Thirty-three participants from two hospitals underwent semistructured interviews, including survivors, women in cancer therapy, and family members. Data collected via audio recordings were analyzed using reflective thematic analysis techniques. The finding describes four themes of experience of resilience in women living with breast cancer. (a) Cancer diagnosis is a life-changing experience. Breast cancer diagnosis and therapy cause existential crisis, psychological distress, and social stigma. (b) Restoring healthy perception in an adverse event. Navigating challenges and achieving a balance between internal and external factors. (c) Types of supportthe pathway to resilience. Enhanced their resilience through internal support including attributes, past experiences, sociodemographic factors, and brain fitness. External support includes family, friends, religious or spiritual advisors, medical care, role models, other cancer survivors, and comfortable environments. (d) Learning and growing from the experience. Gained a better perspective on life, ultimately resulting in a new normal and finding meaning in the experience. Data show breast cancer survivors experience dynamic resilience, highlighting the need for culturally tailored interventions and supportive avenues within cancer care by healthcare providers and policymakers. The Author(s) 2024. -
Frames of Isolation: A Reading Through HIV/AIDS Documentaries
The question is: how can a documentary create social impact on its audience and, in turn, on society? Film critics and social scientists have considered this question since the inception of documentary filmmaking. Moreover, in the context of disseminating knowledge about infectious diseases, particularly during the HIV/AIDS epidemic, documentaries played a significant role in educating the public about the disease. Following the epidemic, documentaries were used to understand the disease and to witness the lives of people living with the virus. This article further extends the discourse of documentary studies by critically analysing two specific HIV/AIDS documentaries, 5B (2018) and Desert Migration (2015). This analysis provides insight into how the frames of the moving image capture the isolated spaces occupied by people with HIV/AIDS. For this study, Edward Branigans concept of frames is adopted to explore the essence of isolation. This is achieved by examining frames captured by the filmmakers through the camera lens, with a focus on the immediate surroundings of the person being interviewed. The article terms these frames Frames of Isolation, as the images reflect the spatial and emotional isolation associated with the virus. 2025 House of the Book of Science. All rights reserved. -
Effectiveness of emotion recognition tranining on socail and emotional skills in young children with autism spectrum disorder
The rising prevalence of Autism Spectrum Disorders necessitates the determination of newlinenovel intervention methods for its management. Since deficits in social skills are one of the most prominent features in ASD, efficient interventions for improving social skills become necessary. Several studies suggest a strong relationship between newlineemotional skills and the acquisition of social skills. Objectives: The objective of this study was to find out the effectiveness of emotion recognition training on the social newlineand emotional skills of children with ASD by obtaining quantitative results from the newlineparticipants after emotion recognition training and then following up in-depth through a qualitative thematic analysis after interview with selected parents of the participants. Method: In the quantitative phase, a sample of ten children within three to six years of age who are diagnosed with ASD were selected for the study. The emotion newlinerecognition training followed the modified and adapted version of the hands-on newlineactivities from the Let s Face It curriculum which was validated after a pilot study. Each child was given 20 to 30 sessions of training. The participants were assessed for their social skills using VABS-3 and emotional skills were assessed using CDDC, newlinebefore, during, and after the training. The qualitative phase involved an interview with newlinethe parent using a semi-structured guide. Results: The quantitative and qualitative newlineresults indicated that there is a significant difference in the social skills and emotional newlineskills of the children after the training. The results also showed a sufficient newlinegeneralization of the skills achieved. Incidental finding revealed reduction of problem behaviours. Conclusions: The study clearly shows that emotion recognition training is effective in improving social and emotional skills in children with ASD. -
Effectiveness of emotion recognition training on social and emotional skills in young children with autism spectrum disorder
The rising prevalence of Autism Spectrum Disorders necessitates the determination of novel intervention methods for its management. Since deficits in social skills are one of the most prominent features in ASD, efficient interventions for improving social skills become necessary. Several studies suggest a strong relationship between emotional skills and the acquisition of social skills. Objectives: The objective of this study was to find out the effectiveness of emotion recognition training on the social and emotional skills of children with ASD by obtaining quantitative results from the participants after emotion recognition training and then following up in-depth through
a qualitative thematic analysis after interview with selected parents of the participants. Method: In the quantitative phase, a sample of ten children within three to six years of age who are diagnosed with ASD were selected for the study. The emotion recognition training followed the modified and adapted version of the hands-on activities from the ‘Let’s Face It’ curriculum which was validated after a pilot study. Each child was given 20 to 30 sessions of training. The participants were assessed for their social skills using VABS-3 and emotional skills were assessed using CDDC, before, during, and after the training. The qualitative phase involved an interview with the parent using a semi-structured guide. Results: The quantitative and qualitative results indicated that there is a significant difference in the social skills and emotional skills of the children after the training. The results also showed a sufficient generalization of the skills achieved. Incidental finding revealed reduction of problem behaviours. Conclusions: The study clearly shows that emotion recognition training is effective in improving social and emotional skills in children with ASD.