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Enhancing Dimensional Geometry Casting using Computer Modeling
Sand casting method is used to produce many useful products for many applications. The aim of the study is to manufacture a product with excellent dimensional geometry is achieved in sand casting process at low cost. We would expect manuscripts to show how design and/or manufacturing problems have been solved using computer modeling, simulation and analysis. In this work, the important mechanical properties of hardness and surface roughness are investigated on Aluminum 6063 cast material with and without incorporating the copper tubes as a vent hole in sand casting process. Since copper has high thermal conductivity when compared to other metals, the heat transfer rate will be varying from existing system. The copper tubes have made different diameters of holes on outer surfaces with selective distance of intervals. The specific number of copper tubes with various diameters are designed by CATIA modeling software and analyzed with Taguchi Design of Experiment. Taguchi L9 orthogonal array is used proficiently in the optimal value of hardness and surface roughness. The results are revealed that the maximum hardness value of 104 BHN is attained for 10mm distance of holes made on copper tube with an angle of 90o degree. The minimum surface roughness of 2.11 micron is achieved for 20mm distance of holes made on copper tube with 45o of angle as a vent hole in sand casting process. 2024 E3S Web of Conferences -
Enhancing Disease Prediction in Healthcare: A Comparative Analysis of PSO and Extreme Learning Approach
The healthcare business generates a tremendous quantity of data, and the goal is to collect it and use it effectively for analysis, prediction, and treatment. The best approach to disease management is disease prevention through early intervention. There are a number of methods that can advise you on how to treat a specific sickness, but much fewer that can tell you with any degree of certainty if you will actually get sick in the first place. Preprocessing, feature selection, feature extraction, and model training are all parts of the proposed method. The suggested layout includes a preprocessing stage that takes care of things like moving average, missing values, and normalization. Feature selection describes the process of selecting the most relevant features from a dataset. After gathering features, the models are trained using PSO-ELM. The proposed strategy is superior to the widely used PSO and ELM. 2023 IEEE. -
Enhancing Early Detection of Alzheimers Disease Through Integrated Deep Learning Models: A Multimodal Diagnostic Approach
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and functional impairment. Early detection is crucial for effective management and intervention. This study explores the effectiveness of an integrated deep learning approach combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to enhance the early detection of Alzheimer's disease using multimodal data. A novel deep learning model was developed and validated, integrating neuroimaging data (MRI and PET scans) with clinical data using a decision-level fusion strategy. The study utilized a dataset comprising 1000 anonymized patient records from the Alzheimers Disease Neuroimaging Initiative (ADNI). Models were assessed based on accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). The integrated model demonstrated superior performance with an accuracy of 95%, precision of 94%, recall of 93%, and an F1-score of 93.5%. The model's AUC was 0.97, indicating excellent diagnostic capability. The proposed deep learning approach significantly improves the early detection of Alzheimers disease by effectively analyzing complex, multimodal data. This model holds considerable potential for clinical applications, providing a robust tool for healthcare professionals to diagnose AD in its early stages. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing Early Detection of Cardiovascular Disease through Feature Optimization Methods
cardiovascular diseases are the most common reason for mortality around the world. Early detection of the ailment can help to reduce the mortality rate considerably. The ever-growing technologies like machine learning algorithms and deep learning models can be used for this purpose. The AI models thus developed can be used for health sector for assisting doctors in assessing the stage of the disease and detection and tracking of the clots in the cardio blood vessels. The proposed work uses two benchmark datasets for analysing the performance of various machine learning algorithms including KNN, Nae Bayes, Decision Tree and Random Forest. The performance was compares based on the AUC %. The method feature reduction were used here to reduce the computational complexity of the model. The results show that Random Forest Algorithm gave the best result when compared to other algorithms in case of UCI dataset and MLP classifier gave best results for Kaggle dataset. 2024 IEEE. -
Enhancing Early Seedling Stage Salinity Tolerance in Rice Through Brassinosteroid Priming
Rice (Oryza sativa), astaple food for the major global population, faces substantial productivity challenges due to salinity stress, an increasingly prevalent issue exacerbated by climate change. Salinity impacts rice at critical growth stages especially at seedling stage, root development, shoot elongation and ultimately seedling establishment. This study evaluated the effect of brassinosteroid (BR) priming as astrategy to improve seedling stage salinity tolerance in rice seedlings across 15genotypes subjected to moderate (140?mM NaCl), and severe (200?mM NaCl) salinity stress in comparison with control. BR-primed seeds demonstrated enhanced germination rates, seedling vigor index, shoot length and root length under salinity conditions compared to non-treated seeds. BR priming led to a35% improvement in SVI under control conditions and up to 30% under severe salinity, suggesting that BRs may facilitate osmotic regulation and ion homeostasis, key for maintaining growth under stress. Furthermore, BR priming significantly increased root development, essential for water uptake and nutrient acquisition in saline environments. Our results showed the prospect of BR priming as an effective approach to enhance rice resilience to salinity stress, providing afoundation for further field-based research on BR-mediated stress tolerance mechanisms. This study underscores the relevance of BR priming in improving rice productivity in saline-prone areas, contributing to food security in the face of increasing soil salinization. The author(s), exclusively licensed to Springer-Verlag GmbH Germany, a part of Springer Nature 2025. -
Enhancing Education Policy Estimation: A Novel Ridge Fuzzy Regression Approach for Handling Multicollinearity with Fuzzy Input Data
Multicollinearity often complicates regression analysis, both in classical and fuzzy input setup. This research introduces a new approach that combines ridge regression with fuzzy regression to tackle correlated covariates impact, with a specific focus on improving education policy systems. Our method utilizes the ?-level estimation algorithm and a dataset where Grade Point Average (GPA) serves as a fuzzy input, while input variables remain crisp. We assess our estimators performance using RMSE and MAPE. This applied research showcases the potential of our method in enhancing education policies through more accurate data-driven decision-making. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Enhancing Educational Adaptability: A Review and Analysis of AI-Driven Adaptive Learning Platforms
This study explores the transformative potential of AI-powered adaptive learning platforms (ALPs) in education, specifically focusing on personalized learning paths and their impact on student engagement and outcomes. Through a comprehensive analysis of four prominent ALPs - Carnegie Learning, DreamBox Learning, Smart Sparrow, and Knewton - this study investigates their approaches to content tailoring and feedback delivery. The comparative analysis highlights each platform's strengths and limitations, providing educators with valuable insights for informed selection and implementation. This study also considers the broader landscape of ALPs, acknowledging concerns such as bias, data privacy, and the role of educators in the tech-driven educational environment. The findings contribute to our understanding of how ALPs can empower educators, personalize learning, and address achievement gaps, offering a nuanced perspective on the complex tapestry of AI in education. 2024 IEEE. -
Enhancing Educational Engagement Among Kattunayakan Tribal Children Through Culturally Tailored Educational Activity Centres
The deep forests of the Western Ghats are home to the aboriginal tribes Kattunayakans, Categorized under the Particularly Vulnerable Tribal Groups (PVTGs) in India. Despite various government schemes to improve their economic status, the literacy rates remain very low due to geographical isolation and traditional lifestyle. The main objective is to look into designing educational activity centres that exclusively cater to the needs of the Kattunayakan children, integrating indigenous knowledge and methods with teaching-learning sessions to promote lifelong learning. Technological progress and AI can be game changers for accessing quality education, including visual learning, language translation, and personalized methods while preserving cultural heritage. The research is based on a systematic narrative review, critically examining scholarly articles, policy and government reports. The outcome shows that these programs help improve academic skills while keeping them closer to their culture and identity. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Enhancing Employee Onboarding Through Digital Twin Technology
Digital twins and their transformative technology have simplified corporate processes while establishing secure and data-driven environments for customized expertise enhancement, workflow optimization, and scenario building. This chapter assesses the impact of digital twins on job design and human resource (HR) practices in developing new and more productive human-focused workplaces. Traditional or old-fashioned onboarding practices are not customizable or flexible and do not give real-time feedback, which creates negative workplace experiences and higher turnover levels. Digital transformation (DT) techniques suggest using digital twins for interactive, customized onboarding solutions to engage and retain employees better. Research indicates that digital twins have effects fundamentally altering HR practices by providing adaptive approaches in real time, cost-free scenario testing, and prediction analytics on employee retention. They help in monitoring workload, preventing burn-out, and customize approaches to enhance individual well-being. Most importantly, they predict career paths and determine training needs; hence, they are good at improving talent development. Successful implementation, however, is dependent on resolving challenges such as data privacy, model accuracy, system interoperability, and bias in artificial intelligence (AI). Applications of digital twin technology (DTT) in onboarding are numerous and important in real life concerning the retention, productivity, and attrition levels of employees. Real-time and predictive data can allow proactive improvement in onboarding and the happiness levels of workers as well as overall company performance results. 2026 Channi Sachdeva, Veena Grover, Balamurugan Balusamy, Veer P. Gangwar, and Pardeep Kumar. -
Enhancing empowerment: Exploring the influence of tourism social entrepreneurship on community engagement
Social entrepreneurship is an evolving force in solving societal challenges, integrating financial viability with social effect. This study delves into the intersection of social entrepreneurship and tourism, exploring how Tourism Social Entrepreneurship fosters community engagement to boost empowerment within locations. Within the tourism sector, Tourism Social Entrepreneurship attempts to stimulate socioeconomic growth while focusing on the welfare of host communities. Despite its potential, a gap persists in knowing how Tourism Social Entrepreneurship efficiently includes and engages local people. This research aims to fill the void by investigating how social entrepreneurs within the tourist sector engage with and empower local communities. By analyzing current literature on social entrepreneurship and conceptualizing Tourism Social Entrepreneurship, this study provides a complete understanding of the dynamics of community engagement in tourism-driven social entrepreneurship endeavors. 2025, IGI Global Scientific Publishing. All rights reserved. -
Enhancing English Learning Through Digital Storytelling in Indian Schools
This study examines the effectiveness of the Digital Storytelling (DST) teaching approach in improving English learning among ninth graders in four schools in Bengaluru, India. Using a sequential mixed-methods design, the quantitative phase included a non-randomized, post-test-only quasi-experimental design with 200 students divided into a DST-based experimental group and a traditional control group of 100 students each. Quantitative data were collected using a 12-item survey questionnaire, while qualitative data included self-reflection logs from 100 and interviews with 20 students from the experimental group. The results show that DST significantly improves language development and student satisfaction. This is evidenced by higher and more consistent post-test scores in the experimental group, with statistical significance confirmed by the Wilcoxon test. Increased engagement, understanding, and motivation reported by students are consistent with the quantitative improvements. 2025 IGI Global. All rights reserved. -
Enhancing environmental sound classification with weighted attention-based spectrogram fusion and overlapping pre-patching
Environmental Sound Classification (ESC) remains challenging due to the diverse and overlapping acoustic characteristics of real-world environments. Traditional models relying on single-feature representations such as Mel spectrograms often fail to capture the full range of spectral and temporal details. This paper introduces a novel algorithm Weighted Attention-based Spectrogram Fusion (WASF) that adaptively integrates Mel spectrograms, Cochleograms, and Correlograms using a hierarchical attention mechanism across channel, temporal, and frequency dimensions. Compared to traditional fusion techniques, WASF uses a learnable attention mechanism to dynamically weight each feature's importance over time and frequency, improving the model's capacity to focus on important acoustic cues. In addition, an overlapping pre-patching strategy is proposed to preserve local temporal continuity, enhancing transformer-based modeling. Proposed model demonstrates superior performance with 95.71 % accuracy on UrbanSound8K, 93.97 % on ESC-50, and 94.91 % on ESC-10 datasets. Extensive ablation studies and interpretability analysis validate the effectiveness of each component, demonstrating robustness across diverse acoustic environments and noise conditions. The computational efficiency and interpretable attention patterns make our approach suitable for real-time deployment in smart city applications, surveillance systems, and assistive technologies. 2025 Elsevier B.V. -
ENHANCING EXECUTIVE FUNCTIONS THROUGH COGNITIVE-BASED INTERVENTION IN INDIVIDUALS WITH SUICIDAL IDEATION AND ATTEMPTS: A Mixed-Method Pilot Study
One of the primary causes of death around the world can be attributed to suicidality. Almost 1 million people across the globe commit suicide annually. Neurocognition has an impact on suicidal ideation, and deficits in cognitive markers influence the progression of suicide-related thoughts to behaviours. The present study aims to determine the efficacy of cognitive-based intervention on executive functions implicated in suicidal ideation and suicide attempters. A mixed-method approach was followed, which involved intervention and a quantitative and qualitative analysis. A group of 22 participants aged between 18 and 25 years with suicidal ideation and behaviour was chosen. Ten participants reported having suicidal ideation and no history of suicide attempt or self-harm, whereas 12 participants reported having suicidal ideation and at least one attempt at self-harm or suicidal behaviour. All the participants were assessed on planning, verbal fluency, and response inhibition tests. The participants then receive eight sessions of cognitive-behavioural intervention focusing on suicidal behaviour and thoughts. Post-therapy, the participants underwent a reassessment of their executive functions. The results suggested that cognitive behaviour-based therapy significantly improved planning, verbal fluency, and response inhibition. The feeling of entrapment and the level of depression were qualitatively found to be influencing suicidal ideation and suicide attempts. The study paves the way for further exploration of factors that predict suicide and determines the cause-and-effect relationship between the factors. 2026 selection and editorial matter, K. Jayasankara Reddy; individual chapters, the contributors. All rights reserved. -
Enhancing Experiences: The Integration of AI in Augmented and Virtual Reality
This chapter examines the integration of Artificial Intelligence (AI) in Augmented Reality (AR) and Virtual Reality (VR), highlighting how AI-driven innovations enhance interactivity, personalization, and real-t ime adaptation in immersive experiences. Through technologies like computer vision, machine learning, and natural language processing, AI enables AR and VR applications to better understand, respond to, and anticipate user needs. Applications of AI-augmented AR and VR are explored across various sectors, including healthcare, education, and industry, where AI-driven systems offer personalized training, virtual assistance, and adaptive simulations. As Mixed Reality (MR) evolves, edge computing plays a key role in improving performance, minimizing latency, and enabling seamless real-time interactions. The chapter also addresses ethical and technical challenges, such as privacy, bias, and processing limitations, emphasizing AIs transformative role in the future of immersive technologies. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Enhancing Experimental Efficiency in Uncertain Data: A Comparative Analysis of Neutrosophic and Classical Latin Square Designs
This research investigates the relative efficiency between Neutrosophic Latin Square Design (NLSD) and Classical Latin Square Design (CLSD), with a particular focus on their use in situations where data is uncertain and ambiguous. Although CLSD is a classic experiment designed for systematic error control, its utility is limited in fields like agriculture and behavioral sciences due to its performance bottleneck regarding data imprecision. The NLSD can relatively easily be extended to incorporate neutrosophic logic to address these challenges, making it a more powerful tool for modeling uncertainty. In this paper, a systematic efficiency evaluation of NLSD against CLSD is performed for inconsistent data. It is found that the NLSD enables significant improvements in experimental efficiency while providing clearer inferences regarding treatment effects and supporting more reliable conclusions. Despite these limitations, these benefits establish NLSD as a promising candidate for overcoming environmental uncertainties, and these observations hold significant potential to further the advancement of experimental designs. The results demonstrate that NLSD conveys a 55 % chance to enhance efficiency relative to LSD, which is especially important in processes that must attain maximum resource utilization and high experimental efficiency. 2025, Ayandegan Institute of Higher Education. All rights reserved. -
Enhancing fabric quality with AI-based defect detection systems
In summary, there is a necessity to use AI-based defect detection systems in fabric quality improvement especially in the process of textile production. These sophisticated solutions eliminate the requirement for time-consuming and error-prone traditional manual procedures, and thus not only speed up the inspection but guarantee a higher quality of the products. -
ENHANCING FAKE NEWS DETECTION ON SOCIAL MEDIA THROUGH ADVANCED MACHINE LEARNING AND USER PROFILE ANALYSIS
Social media news consumption is growing in popularity. Users find social media appealing because it's inexpensive, easy to use, and information spreads quickly. Social media does, however, also contribute to the spread of false information. The detection of fake news has gained more attention due to the negative effects it has on society. However, since fake news is created to seem like real news, the detection performance when relying solely on news contents is typically unsatisfactory. Therefore, a thorough understanding of the connection between fake news and social media user profiles is required. In order to detect fake news, this research paper investigates the use of machine learning techniques, covering important topics like feature integration, user profiles, and dataset analysis. To generate extensive feature sets, the study integrates User Profile Features (UPF), Linguistic Inquiry and Word Count (LIWC) features, and Rhetorical Structure Theory (RST) features. Principal Component Analysis (PCA) is used to reduce dimensionality and lessen the difficulties presented by high-dimensional datasets. The study entails a comprehensive assessment of multiple machine learning models using datasets from "Politifact" and "Gossipofact," which cover a range of data processing methods. The evaluation of the XGBoost classification model is further enhanced by the analysis of Receiver Operating Characteristic (ROC) curves. The results demonstrate the effectiveness of particular combinations of features and models, with XGBoost outperforming other models on the suggested unified feature set (ALL). 2023 Little Lion Scientific. -
Enhancing food crop classification in agriculture through dipper throat optimization and deep learning with remote sensing
Remote sensing images (RSIs), a keystone of modern agricultural technology, refer to spectral or visual data captured from drones, satellites, or aircraft without direct physical contact with the Earth's surface. These images provide a wide-ranging view of agricultural landscapes, providing valuable insights into land use, crop health, and environmental conditions. Agricultural food crop classification, a vital application within precision agriculture, includes the detection and classification of different crops cultivated in a certain region. Traditionally reliant on manual techniques, the development of technologies, particularly the incorporation of RSIs, has revolutionized this process. Agricultural food crop classification has become more sophisticated and automated by harnessing the wealth of data received from RS, which facilitates precise management and monitoring of crops on a large scale. Deep learning (DL), a branch of artificial intelligence, plays a more effective role in these synergies. The incorporation of DL into the RSI analysis enables high-precision and efficient detection of various crop types, assisting more informed decision-making in agriculture. This study proposes a new Dipper Throat Optimization Algorithm with Deep Learning based Food Crop Classification (DTOADL-FCC) algorithm using Remote Sensing Imaging for Agricultural Resource Management. The DTOADL-FCC method aims to apply DL algorithms for the classification of different crop types. In the DTOADL-FCC method, fully convolutional network (FCN) based segmentation process is performed. Next, the DTOADL-FCC method exploits the SE-ResNet model for learning intrinsic and complex features. The DTOADL-FCC method makes use of DTOA for the hyperparameter tuning process. Lastly, the classification of crop types takes place using the extreme learning machine (ELM) model. The study utilizes mathematical formulations including activation functions, loss functions, fitness calculations, and iterative update processes. A brief set of simulations showcases that the DTOADL-FCC method achieves remarkable performance over other techniques with much improved results. 2024 The Author(s) -
Enhancing Food E-Commerce Through Immersive Virtual Reality: An Reality: An Extended Technology Acceptance Model Approach for Consumer Adoption in the Post-Pandemic Era
Food purchasing differs from other types of internet shopping. With the introduction of the new retail structure, nearly every e-commerce platform has set up fresh food retail one after another. As a result, electronic gadgets have evolved into tools that marketers may use to initiate interactions with customers. Brands may use augmented reality enabled mobile applications to deliver precise information about products and services while also influencing consumer impressions. Perceived usefulness was the only factor that supported perceived ease of use as a mediator. Our findings provide useful information for researchers and industry experts to improve the effectiveness of VR systems by better understanding user adoption. 2025 IEEE. -
ENHANCING FOREST ECOSYSTEM RESILIENCE TO CLIMATE CHANGE WITH VANET AND INTEGRATED NATURAL RESOURCES MODELLING
Forest ecosystems are immediately threatened by rising global temperatures and changing climatic patterns. Periodic assessments also contribute to a reduction in the frequency of monitor-ing, which could cause environmental changes to go unnoticed. This work develops a novel real-time monitoring and early warning system to meet this difficulty. By integrating Vehicular Ad Hoc Networks (VANET) with sophisticated natural resources modelling, the proposed method aims to revolutionise the way forest ecosystems are managed. This study strives to design and implement a comprehensive system that harnesses the power of VANET to collect real-time data from sensors deployed on vehicles, and integrates advanced modelling to predict, assess, and mitigate risks to forest ecosystems. The proposed method involves deploying a network of vehicles equipped with environmental sensors within VANET. These sensors continuously collect data on crucial environmental parameters, such as temperature, humidity, air quality, and spatial information. The data are transmitted through a secure VANET communication protocol to a centralised processing unit, where it is integrated with climate models and ecosystem dynamics models. Resilience metrics and thresholds are defined to trigger a tiered early warning system. Preliminary testing of the system demonstrates promising accuracy and responsiveness. The integrated approach allows for dynamic risk assessment, enabling the identification of potential threats such as extreme weather events, invasive species, or disease outbreaks. Early warnings prompt adaptive management strategies, showcasing the systems potential to significantly enhance forest ecosystem resilience. This research presents a pioneering solution to the escalating challenges faced by forest ecosystems in the time of climate change. The real-time monitoring, early warning system, amalgamating VANET and integrated modelling, stand as a robust tool for forest managers, policymakers, and communities to proactively address environmental changes. The findings underscore the systems potential to transform forest management practices, marking a critical step toward sustainable and resilient ecosystems. 2024, Scibulcom Ltd. All rights reserved.