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The Efficacy of Multi-Component Intervention for Adolescents with Problematic Video Gaming in a Community-Based Setting
Video gaming is a popular leisure activity enjoyed by millions globally, helping with socialisation, interaction, and relieving stress. It may also become a maladaptive coping mechanism to evade distress and negative emotions, leading to problematic usage. Research evidence shows that problematic gaming is associated with different psychosocial issues. Video games can be a way of negative coping and escaping reality, and problematic usage can hide other problems of players in real life. Adolescents are vulnerable to problematic use due to their developmental stages, and those with specific vulnerabilities and disabilities are at greater risk. No one psychotherapy has all the answers, and the multi-component intervention technique might have better treatment utility than a solitary behaviour intervention. The research aims to show the effectiveness of the intervention for problematic video game usage in a community-based setting. The study focuses on adolescents in seventh through ninth grade who were identified as problematic video gamers (not addictive users) from a selected group of schools in Kerala. The study employed an experimental design, encompassing both intervention and control groups, to systematically assess the effects of the experimental manipulation and establish a baseline measurement. The paired t-test results showed no significant decrease in the intervention groups Gaming Addiction Scale at the post-test, but it did lower the addiction scores. By conducting the research, we provide psychological care for adolescents and help them identify and prevent problematic gaming experiences. The research underscores the significance of early identification and prevention of problematic video game usage among adolescents, advocating for a holistic approach incorporating diverse components. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
Artificial intelligence based system and method for management, recommendation, mapping of skill /
Patent Number: 202111054501, Applicant: Durgansh Sharma.
Artificial intelligence based system (100) and method for management, recommendation, mapping of skill comprising EmpNet (101), recommender system (102), automated machine learning system (103), skillset dataset (104), optimization system (105), industry interface system (106). The method for management, recommendation, mapping of skill comprising the steps of: a) capturing the required skillset personal data by the panchayat system (701); b) verify the skillset (702). -
Linear and non linear electroconvection in a micropolar fluid
This thesis presents a theoretical study of linear and non-linear analyses of Rayleigh Bard Marangoni/Rayleigh Bard electro newlineconvection in a micropolar fluid. The effects of non-uniform basic temperature gradient, suction injection combination and gravity newlinemodulation have been studied in the presence of electric field. The effect of heat transfer in a micropolar fluid in the presence of electric field is also studied and results are presented graphically and discussed qualitatively. These problems assume greater importance in geophysics, newlineastrophysics, oceanography, and engineering and in space situations with g-jitter connected with gravity stimulation study. newlineKeeping in mind the importance and relevance of externally controlled internal convection in a micropolar liquid. We deal with four newlineproblems, details of which are given below. newline(i) Effect of non uniform basic temperature gradient on the onset of Rayleigh Bard Marangoni electro convection in a micropolar fluid. The non-uniform temperature gradient finds its origin in the transient heating or cooling at the boundaries and as a result the basic temperature profile depends explicitly on position and time. This has to be determined by solving the coupled momentum and energy equations. This coupling also makes the problem very complicated. In the present study, therefore, we adopt a series of temperature profiles based on a newlinesimplification in the form of a quasi-static approximation that consists of freezing the temperature distribution at a given instant of time. In this method, we assume that the perturbation grows much faster than the newlineinitial state and hence freeze the initial state into some spatial distribution. newlineTherefore the effects of these non-uniform basic temperature gradient and electric field are studied on the onset of Rayleigh Bard Marangoni convection in micropolar fluid. -
Development and standardiztion of a tool to assess spirituality in families for family based interventions
The aim of the study was to develop and standardize a tool for family spiritual assessment. The sample consisted of 1502 Indian participants which included members from three religious backgrounds namely: Christianity, Hinduism and Islam. The data collected through face-to-face interview was analyzed using exploratory factor analysis (EFA), t-test and ANOVA. A five-item Likert-type tool developed was named as Family Spiritual Assessment Scale (FSAS) through a process of item development. EFA revealed that the 26-item tool with 5-factor solution had an excellent internal consistency of and#945;= .816. Religious factor, Spiritual factor, Mental health factor I (Positive emotions), Mental health factor II (Forgiveness) and Mental health factor III (Negative emotions) are the five important factors of the scale. Gender differences were found in the Spiritual factor, Mental Health, and Total newlinescore of the Scale, where females had higher scores than males. Post-hoc analysis newline(Bonferroni) revealed that total scores of all the three religions differed significantly. The results provide a sound foundation for the future research on spirituality. Family Spiritual newlineAssessment Scale, being the first in India, can be very beneficial to mental health newlineprofessionals and practitioners. -
Development and standardization of a tool to assess spirituality in families for family based interventions /
The aim of the study was to develop and standardize a tool for family spiritual assessment. The sample consisted of 1502 Indian participants which included members from three religious backgrounds namely: Christianity, Hinduism and Islam. The data collected through face-to-face interview was analyzed using exploratory factor analysis (EFA), t-test and ANOVA. A five-item Likert-type tool developed was named as Family Spiritual Assessment Scale (FSAS) through a process of item development. -
AI Avatars in Immersive Environments for Communication Skill Training
In the contemporary landscape, communication skills are essential across different spheres, from professional spaces to personal engagements. AI-generated content is gaining popularity in education, which assists students in their assignments and other activities. With the advent and development of technology, AI avatars incorporated with virtual reality environments present a novel and promising pathway for cultivating communication abilities. VR environments are experimenting with AI technology that can create a more realistic environment and provide users with interactive characters around them. The education sector is widely using AI technology for interactive classrooms and presentations, which can improve students' creative communication skills. It is assumed that the AI sector is expected to grow by 48% in the future. The promising potentials of the technology attract more users into the medium as it allows for customization, dynamic assessment patterns, a hybrid learning mode with meaningful interactions, and blended learning experiences. This chapter delves into the potential of AI avatars in virtual reality environments, especially communication skill training applications that help improve communication skills. AI avatars create an interactive space for the users in VR environments that creates an engaged space for the users to be involved in conversations and activities without the fear of being judged. The present study takes an experimental approach with the social learning theory to test whether these AI avatars help users improve communication skills, mainly interpersonal communication skills and public speaking. A mixed methodology is adopted for the research, where data collected from the training and questionnaire data will undergo triangulation to get the appropriate results. The research also addresses ethical considerations like privacy and inclusivity in AI avatar interactions. Overall, the study highlights the effectiveness of incorporating AI avatars in VR environments for improved communication skill development. 2026 Scrivener Publishing LLC. All rights reserved. -
Enhancing student engagement and learning experience through augmented reality: A study on the integration of assemblr studio into hybrid classrooms
The impact of technology, initiated during the fourth industrial revolution with concepts like AI, AR, and VR, has significantly influenced education. Integrating Augmented Reality (AR) in education enhances student engagement and learning. AR enables students to visualize complex theories through real actions, making learning interactive and fun. This paper explores student experiences with Assemblr Studio, a 3D AR visualization tool, in hybrid learning at Christ University, Bangalore. The study examines Assemblr Studio's educational impact, user experience, and classroom effectiveness using questionnaires and focus group discussions. Results show that AR via Assemblr Studio fosters innovative and effective learning, positively altering traditional educational strategies. 2025, IGI Global Scientific Publishing. All rights reserved. -
User-engaged critical thinking abilities through 360-degree virtual reality documentaries
This chapter investigates how selected 360-degree VR documentaries foster critical thinking abilities among journalism students. A qualitative methodology was employed, and group discussions were conducted for data collection. The analysis was based on the Watson Glaser Critical Thinking Appraisal test, where four components are considered parameters for measuring students' critical thinking abilities. The study measures the analytical abilities, reasoning, interpretation, and logical conclusions that students can draw using the 360-degree VR documentaries. The results indicate a positive reception of virtual reality technology in enhancing critical thinking abilities using 360-degree virtual reality documentaries. VR documentaries engage the audience with their storytelling format, which accelerates their thought process and increases the immersive experience of the medium. The data also reflects how VR can be a powerful medium for addressing sensitive issues with an intensity that can pull authorities into implementing powerful policies. However, this chapter also highlights the constraints associated with the medium, which future researchers and content creators should consider to mitigate the challenges. Additionally, the limited content availability is considered a drawback of the study, which restricts the medium from reaching potential audiences. This chapter marks a pioneering attempt in India to understand how 360-degree VR documentaries enhance critical thinking abilities. This chapter's positive results offer an opportunity for educators to adopt the technology to create hybrid classrooms, thereby increasing students' engagement in the learning process. 2025 Twinkle Sara Joseph, Kannan Subramani and Biju Kunnumpurath. All rights reserved. -
Implementing Ensemble Machine Learning Techniques for Fraud Detection in Blockchain Ecosystem
A new era of digital innovation, notably in the area of financial transactions, has been conducted in by the rise pertaining to block-chain technology. Although the decentralized nature of blockchain technology renders it prone to fraud, it has been praised for its capacity to offer a safe and transparent platform for financial transactions. The integrity of the entire blockchain network may be compromised by fraudulent transactions, which may also damage user and stakeholder trust. This study aims to assess machine learning's efficacy in detecting fraudulent transactions within blockchain networks and identifying the most effective model. To achieve its objectives, this study used a combination of data collection, data preprocessing, and machine learning techniques. The data used in this study was dataset of blockchain transactions and pre-processed using techniques such as feature engineering and normalization. Then trained and evaluated using several machine learning models, including Logistic Regression (LR), Naive Bayes (NB), SVM, XGboost, LightGBM, Random Forest(RF), and Stacking, in order to determine their effectiveness in detecting fraudulent transactions. XGBoost demonstrated the highest accuracy of 0.944 in the stacking model, establishing it as the top-performing model, closely followed by Light GBM. The study's discoveries offer significant practical implications for advancing fraud detection methods in blockchain networks. By pinpointing the most efficient machine learning model and crucial predictive fraud features, this research provides vital insights for refining precise detection algorithms, enhancing blockchain network security, and broadening their reliability across various applications. 2023 IEEE. -
Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems
The Internet of Things (IoT) technologies has gained significant interest in the design of smart grids (SGs). The increasing amount of distributed generations, maturity of existing grid infrastructures, and demand network transformation have received maximum attention. An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling. The dynamic electrical energy stored model using Electric Vehicles (EVs) is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids. This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder (HBFOA-SAE) model for IoT Enabled energy systems. The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge (SOC) values in the IoT based energy system. To accomplish this, the SAE technique was executed to proper determination of the SOC values in the energy systems. Next, for improving the performance of the SOC estimation process, the HBFOA is employed. In addition, the HBFOA technique is derived by the integration of the hill climbing (HC) concepts with the BFOA to improve the overall efficiency. For ensuring better outcomes for the HBFOA-SAE model, a comprehensive set of simulations were performed and the outcomes are inspected under several aspects. The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches. 2023 CRL Publishing. All rights reserved. -
Nifty index: Integrating deep learning models for future predictions and investments
The Indian stock market, led by the NSE and BSE, has witnessed remarkable growth, exemplified by the NIFTY 50 index surpassing INR 176 trillion in market capitalization. Post the transformative New Economic Policy reforms in 1991, the market underwent significant expansion due to increased accessibility. This chapter focuses on predicting Nifty index prices for the upcoming 10-day period, aiming to provide valuable insights for investment decisions. Despite the markets inherent complexity, exacerbated by various factors like economic conditions and investor sentiment, the objective of the research study is clear: to boost profitability, mitigate risk, and safeguard traders capital. Leveraging Long Short-Term Memory (LSTM) and Vector Autoregression (VAR) models, the research study rigorously evaluates prediction accuracy using the Root Mean Square Error (RMSE) metric. The study underscores the potential of deep learning techniques in achieving reasonable accuracy, especially for short-term forecasts, while acknowledging the markets inherent unpredictability. Notably, the findings demonstrate that the LSTM model excels in predicting Nifty Bank prices, with an impressive RMSE score of 242.55 compared to VAR models. Furthermore, optimal data splitting, at an 8:2 ratio, significantly enhances prediction accuracy across all models, emphasizing the critical role of high-quality data in training. In conclusion, this study unequivocally recommends LSTM as the preferred model for Nifty index price prediction, providing practitioners with a robust tool to navigate the complexities of the Indian stock market with enhanced precision and confidence. 2025 selection and editorial matter, Vivek S. Sharma, Shubham Mahajan, Anand Nayyar and Amit Kant Pandit; individual chapters, the contributors. -
Enhancing Mobile Application Security Through Android Threat Classification
The Android application market has grown significantly, offering customers an ever-growing range of features to suit a variety of purposes. Users are exchanging more and more sensitive data thanks to the widespread usage of mobile applications, therefore safeguarding personal information is crucial. But this boom has also opened the door for a corresponding rise in cybersecurity risks, especially for malware and adware that target mobile devices. It is imperative to categorise mobile applications into distinct groups such as malware, adware, and benign in order to fortify the mobile ecosystem. This project's primary objective is to create and apply cutting-edge machine learning algorithms that can precisely categorise mobile apps into groups including adware, malware, and benign apps. This will necessitate investigating various machine learning strategies and ensemble methods to improve classification accuracy and robustness. Multiple machine learning models were developed based on feature importance, utilizing various machine learning techniques. The evaluation metrics showcase the effectiveness of the final model, especially the Tuned XGBoost model. While achieving a high overall accuracy of 92.51%, the findings highlight the importance of considering diverse features beyond traditional flow-based ones, providing a more robust and complete perspective on mobile network security. 2025 The Authors. Published by Elsevier B.V. -
Advancements in battery and energy storage materials: Paving the way for sustainable energy solutions
Advanced materials are key to battery and energy storage technology improvements, which are a cornerstone of sustainable energy for the future and are the topic of this chapter. It explores advances in solid- state electrolytes, lithium- sulfuric and sodium- ion batteries, nanomaterials and organic compounds, which all have the potential to enhance energy density, cycle life and environmental sustainability. These materials hold great promise, as they may overcome current limitations in battery performance, safety, and cost, the authors say. The chapter also explores the economic and environmental implications of these innovations, spotlighting their role in the global transition to renewable energy. Given ongoing research efforts and favourable policies, next- generation energy storage systems will play an essential role in advancing clean energy technologies in areas from electric vehicles to electric grid storage. 2025, IGI Global Scientific Publishing. All rights reserved. -
Data Analytics and Automation for a Broadband Franchise
Its challenging to envision a world without the internet. From acquiring knowledge to ordering food, our lives have become incredibly convenient thanks to it. As technology advances, internet access is becoming easier and more affordable, with India being renowned for having the lowest internet costs. Internet service providers (ISPs) aim to offer better speeds, fewer disruptions, and professional service. They charge fees for allowing customers to shop online, browse the web, stay connected with loved ones, and conduct business. Due to a lack of data comprehension, the company struggles to leverage reports in daily operations. Consequently, BSNL is finding it hard to outshine competitors and become profitable. Key highlights from the project include understanding customer mentality and addressing issues faced by franchise owners. This research aimed to enhance organizational operations by reducing manual interventions and automating customer communication. User sentiments toward the brand and its competitors were analyzed, and exploratory data analysis was conducted to assess the organizations position. Data visualization with Tableau and Python programming were utilized to derive insights from the data. 2025 selection and editorial matter, Shruti Sharma, Ashutosh Sharma, and Trinh Van Chien. -
Brain Tumor Localization Using Deep Ensemble Classification and Fast Marching Segmentation
A brain tumor is an unusual and excessive growth of brain cells, which can be cancerous (malignant) or noncancerous (benign). These growths can be risky as they press on healthy brain tissue or expand in the brain. Detecting brain tumors early is tough for radiologists. A typical brain tumor can double in size in just 25days, and without the right treatment, patients often have limited chances of survival, about six months. Initial symptoms can be confused with other illnesses, and brain cancer is difficult to diagnose because of the complex nature of the brain and tumor locations. In this study, we propose a strategy where we first sort medical images based on the presence of a brain tumor. Then, we pinpoint the part of the image containing the tumor through segmentation. We use a combined model of MobileNet-V3 and EfficientNetV2 for image classification. To segment the tumor in the image, we use a fast marching method. The combined model's classification accuracy is 98%, and the segmentation accuracy is 99.6%. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Implementation of Integration of AI and IOT Along with Metaverse Technology in the Field of Healthcare Industry
In the evolving panorama of healthcare, the appearance of Metaverse technology emerges as a transformative pressure, redefining traditional paradigms of healthcare shipping and education. This systematic assessment delves into the multifaceted impact of Metaverse technology, encapsulating their role in revolutionizing healthcare through modern-day academic frameworks, patient care interventions, and groundbreaking enhancements in medical imaging. Through an in-depth assessment of present-day literature, this observe illuminates the Metaverse's potential to facilitate immersive mastering tales, allow far flung interventions, and enhance the pleasant of scientific diagnostics and treatment making plans with its 3 -dimensional virtual environments. The findings underscore a burgeoning growth in Metaverse packages inner healthcare, highlighting its capability to noticeably beautify healthcare outcomes, affected person engagement, and expert abilities. Consequently, this evaluate advocates for the prolonged integration of Metaverse generation in healthcare, urging stakeholders to embody the ones enhancements and adapt to the following digital transformation in healthcare services and education. 2024 IEEE. -
GLANCEGuided Language Through Autoregression Establishing Natural and Classifier-Free Editing
In this study, researchers aimed to simplify text conversion into images using the latest text-to-image generation methods. While these methods have improved the quality and relevance of generated images, certain crucial questions remained unanswered, limiting their practicality and overall quality. To address these issues, the researchers introduced a novel text-to-image method. This method allows for better control of the scene depicted in the image through text, enhances the tokenization process by incorporating specific knowledge about key image regions such as faces and important objects, and provides guidance to the transformer model without needing a classifier. The outcome of this work was a model that achieved state-of-the-art results in terms of image quality and human evaluation, enabling the generation of high-fidelity 512?512-pixel images. Moreover, this method introduced new capabilities, including scene editing, text editing with reference scenes, handling out-of-distribution text prompts, and generating story illustrations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
EFFECTIVENESS OF COGNITIVE BEHAVIOURAL THERAPY FOR ADULTS WITH DEPRESSION AND ANXIETY DURING COVID-19: A Systematic Review of Randomised Controlled Trials
Introduction: The COVID-19 pandemic has forced the administration of Cognitive Behavioural Therapy (CBT) either face-to-face or online. This systematic review aims to assess the effectiveness of CBT and Internet-Delivered CBT (iCBT) in treating depression and anxiety disorders during the COVID-19 outbreak. Methods: Three independent reviewers searched the Web of Science, PubMed, Cochrane Library, and Clinical Trial Databases using specific search phrases. PubMed searches included Cognitive Behavioural Therapy/Intervention and COVID-19 and 2019 Coronavirus Disease or 2019-nCoV, internet-administered/internet-based cognitive behavioural therapy, CBT, cognitive behavioural treatment. Two independent reviewers evaluated the risk of bias at the study level, with disagreements settled through discussion with other research team members. The study findings were reported as per the PRISMA guidelines. Results: Thirty-one studies met the inclusion criteria, and 17 were randomised controlled trials. The studies demonstrated that CBT and iCBT effectively treated depression and anxiety disorders during the COVID-19 pandemic. However, a hybrid CBT modality was more beneficial from a long-term perspective. Conclusion: The findings suggest that CBT and iCBT effectively treat depression and anxiety disorders during the COVID-19 pandemic. However, further research is needed to establish these interventions long-term effectiveness and identify the optimal mode of delivery for different populations. 2024 selection and editorial matter, Dr Rajesh Verma, Dr Uzaina, Dr Tushar Singh, Dr Gyanesh Kumar Tiwari, and Prof Leister Sam Sudheer Manickam. -
Unpacking the burden of hypertension and diabetes in Karnataka: implications for policy and practice based on NFHS-5 findings
Objective: To investigate the prevalence, risk factors, and healthcare-seeking patterns of hypertension and diabetes in Karnataka, India, and to offer knowledge that might guide public health initiatives intended to lessen the burden of these illnesses. Methods: In order to examine the prevalence, risk factors, and healthcare-seeking behaviour of hypertension and diabetes in Karnataka, India, a cross-sectional study is carried out using the information gathered from 26,574 households on 30,455 women and 4516 men (who were in their reproductive period) from the National Family Health Survey (201920). The information was summarised using descriptive statistics, which included frequencies and percentages. The association between different risk variables and the likelihood of getting diabetes and hypertension was examined using the chi-squared test and a logistic regression model. Data were analysed using STATA software version 16. Results: The study found that age, gender, education level, religion, and BMI are all significantly associated with hypertension and diabetes (p < 0.001). Tobacco use and alcohol consumption were not significantly associated with hypertension, but tobacco use was significantly associated with diabetes (p < 0.001). However, alcohol consumption was not found to be significantly associated with diabetes whereas the older age groups, males, underweight, overweight and obese, and tobacco use were all associated with increased odds of diabetes. On the other hand, females, secondary education or higher, and alcohol consumption were associated with decreased odds of diabetes. Conclusion: In conclusion, the study found a high prevalence of hypertension and diabetes in Karnataka, India, and identified several risk factors associated with these diseases. The study also highlighted the need for improved healthcare-seeking behaviour among people with hypertension and diabetes. The findings can inform public health interventions aimed at reducing the burden of these diseases in Karnataka and similar settings. The Author(s), under exclusive licence to Research Society for Study of Diabetes in India 2023.