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Enhancing Small and Medium OEMs' Adoption of IIoT Technologies
Small and Medium Original Equipment Manufacturers (SME OEMs) face challenges of high initial costs, lack of skilled workforce, data security concerns, and limited infrastructure for IIoT implementation. This research explores the crucial factors influencing the successful integration of Industry Internet of Things (IIoT) technologies into products and processes of SME OEMs. The study investigates the impact of IIOT Manufacturers' operational and business support, training effectiveness, and awareness of benefits on SME OEMs' adoption intention of IIoT solutions. A survey was conducted among 263 firms operating in 103 different equipment manufacturing operations across 67 cities, representing 11 industry sectors. The participants were SME OEMs, and data were collected to assess the influence of various factors on their willingness to adopt IIoT technologies. The study revealed significant insights into adopting IIoT solutions among SME OEMs. Training provided by IIoT manufacturers was found to have the most substantial impact on the adoption intention. Moreover, awareness of benefits and business and operational support had an equal and notable influence on the adoption intention of SME OEMs. These findings underline the importance of effective training programs and comprehensive support from IIoT manufacturers in facilitating successful IIoT integration. The study's outcomes emphasize the value of fostering strategic partnerships between Small and Medium Original Equipment Manufacturers and Industry IoT Manufacturers. Such collaborations can be pivotal in enhancing IIoT adoption rates among SME OEMs, enabling them to stay competitive in the fast-paced market. 2024 IEEE. -
Enhancing Smart Home Security Through Dynamic Authentication for Voice-Activated Commands
The proliferation of voice-activated IoT devices in smart environments has introduced significant security challenges, particularly around unauthorized access and command spoofing. In response, this paper proposes a unique multi-factor authentication framework that combines dynamically generated code words with smartphone-based verification to ensure secure voice command execution. Unlike conventional fixed-passphrase systems, our method generates unique session-based code words and requires real-time confirmation via a trusted mobile device, offering increased resistance against replay and impersonation attacks. The proposed framework is designed to integrate seamlessly with existing virtual assistants and IoT ecosystems, and its applicability extends beyond smart homes to include connected vehicles and industrial IoT systems, offering a scalable and secure authentication solution. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Enhancing social cognition in individuals with ADHD: An eastern approach
With the increasing prevalence of ADHD in the global front, it is essential to explore different effective methods for providing support and intervention. The difficulties with social cognition are reflected in their limitations in emotional self-regulation, emotion recognition, and empathy. Though several interventions exist for ADHD, many at times, the effectiveness of eastern approaches are overlooked due to the limited awareness about its nature. Research suggests that systematic and regular practice of yoga helps to improve attention, control emotion, and reduce restlessness among them. Several asanas are found to be especially helpful for managing ADHD symptoms including cobra (bhujangasana) pose, cat-cow pose (bitilasana marjaryasana), downward-facing dog (adho mukha shvanasana), tree pose (vrikshasana), mountain pose (tadasana), among many others. The chapter gives a comprehensive summary on the application of yoga techniques on the improvement of social cognition in individuals with ADHD. 2024, IGI Global. -
Enhancing Software Cost Estimation using COCOMO Cost Driver Features with Battle Royale Optimization and Quantum Ensemble Meta-Regression Technique
This research suggests a unique method for improving software cost estimates by combining Battle Royale Optimisation (BRO) and Quantum Ensemble Meta-Regression Technique (QEMRT) with COCOMO cost driver characteristics. The strengths of these three strategies are combined in the suggested strategy to increase the accuracy of software cost estimation. The COCOMO model is a popular software cost-estimating methodology that considers several cost factors. BRO is a metaheuristic algorithm that mimics the process of the fittest people being selected naturally and was inspired by the Battle Royale video game. The benefits of quantum computing and ensemble learning are combined in the machine learning approach known as QEMRT. Using a correlation-based feature selection technique, we first identified the most important COCOMO cost drivers in our study. To get the best-fit model, we then used BRO to optimize the weights of these cost drivers. To further increase the estimation's accuracy, QEMRT was utilized to meta-regress the optimized model. The suggested method was tested on two datasets for software cost estimating that are available to the public, and the outcomes were compared with other cutting-edge approaches. The experimental findings demonstrated that our suggested strategy beat the other approaches in terms of accuracy, robustness, and stability. In conclusion, the suggested method offers a viable strategy for improving the accuracy of software cost estimation, which might help software development organizations by improving project planning and resource allocation. 2023 IEEE. -
Enhancing stochastic optimization: investigating fixed points of chaotic maps for global optimization
Chaotic maps, despite their deterministic nature, can introduce controlled randomness into optimization algorithms. This chaotic map behaviour helps overcome the lack of mathematical validation in traditional stochastic methods. The chaotic optimization algorithm (COA) uses chaotic maps that help it achieve faster convergence and escape local optima. The effective use of these maps to find the global optimum would be possible only with a complete understanding of them, especially their fixed points. In chaotic maps, fixed points repeat indefinitely, disrupting the map's characteristic unpredictability. While using chaotic maps for global optimization, it is crucial to avoid starting the search at fixed points and implement corrective measures if they arise in between the sequence. This paper outlines strategies for addressing fixed points and provides a numerical evaluation (using Newton's method) of the fixed points for 20 widely used chaotic maps. By appropriately handling fixed points, researchers and practitioners across diverse fields can avoid costly failures, improve accuracy, and enhance the reliability of their systems. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Enhancing Stock Market Forecasting with LLMs, Sentiment Analysis and Technical Indicators
Forecasting stock market trends remains a complex and demanding endeavor due to the intricate and dynamic nature of financial markets. This study explores the combination of sentiment analysis with technical indicators to improve the accuracy of stock price predictions. The research incorporates stock market and news data spanning from January 2015 to June 2023, ensuring a well-aligned and comprehensive dataset. Data was sourced using the Google News API and the Bombay Stock Exchange (BSE), followed by rigorous preprocessing, which involved handling missing values and standardizing sentiment scores for accuracy and consistency. To analyze sentiment, tools like VADER, TextBlob, and the Gemini-1.5-Flash API were employed, with sentiment scores aggregated at the stock level. Simultaneously, key technical indicators including the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Bollinger Bands, and Exponential Moving Averages were derived from stock price patterns. These diverse data points were integrated to predict 14-day closing stock prices, leveraging the Gemini1.5-Flash model for forecasting. The model s performance was assessed using various error metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE). The results indicated strong predictive accuracy for stable stocks while pointing out challenges in forecasting highly volatile stocks. Ultimately, the findings suggest that combining sentiment analysis with technical indicators strengthens stock market trend predictions, offering a solid foundation for future advancements in real-time financial analytics. 2025 IEEE. -
Enhancing Stock Market Price Prediction with Advanced Machine Learning Techniques: A Comparative Study
The non-linearity and intrinsic volatility of financial markets make accurate stock price prediction an important but challenging undertaking. This research proposes a Gated Recurrent Unit (GRU)-based model to forecast the stock prices of Tata Consultancy Services (TCS) using 18 years of historical data sourced from Yahoo Finance, comprising features such as Date, Open, High, Low, Close, Adjusted Close, and Volume. The methodology includes data preprocessing steps such as feature selection using Recursive Feature Elimination (RFE), normalization with standard scaling, and data splitting into 70% training and 30% testing sets. The proposed GRU model was evaluated and benchmarked against existing models including Long Short-Term Memory (LSTM), Linear Regression (LR), and Decision Tree (DT), using performance metrics such as Root Mean Squared Error (RMSE) and R2 score. Experimental outcomes revealed that the GRU model achieved the best performance with an RMSE of 0.045, outperforming LSTM (38.19), LR (8.66), and DT (5.22). The study's findings have important implications for algorithmic trading and well-informed investment choices, since the GRU model effectively captures temporal trends in stock data while minimizing prediction mistakes. 2025 IEEE. -
Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data
Determining the trend of the stock market is a complex task influenced by numerous factors like fundamental variables, company performance, investor behavior, sentiments expressed in social media, etc. Although machine learning models support predicting stock market trends using historical or social media data, reliance on a single data source poses a serious challenge. This study introduces a novel Explainable artificial intelligence (XAI) to address a binary classification problem wherein the objective is to predict the trend of the stock market, utilizing an integration of multiple data sources. The dataset includes trading data, news and Twitter sentiment, and technical indicators. Sentiment analysis and the Natural Language Toolkit are utilized to extract the qualitative information from social media data. Technical indicators, or quantitative characteristics, are therefore generated from trade data. The technical indicators are fused with the stock sentiment features to predict the future stock market trend. Finally, a machine learning model is employed for upward or downward stock trend predictions. The proposed model in this study incorporates XAI to interpret the results. The presented model is evaluated using five bank stocks, and the results are promising, outperforming other models by reporting a mean accuracy of 90.14%. Additionally, the proposed model is explainable, exposing the rationale behind the classifier and furnishing a complete set of interpretations for the attained outcomes. 2024, American Scientific Publishing Group (ASPG). All rights reserved. -
Enhancing Stock Price Prediction: A Multi-Model Framework Integrating Technical Indicators and Sentiment Analysis
This paper proposes a multi-model strategy that would improve the predictive power of stock prices by combining time-series analytics with external market indicators. The system allows five different base prediction methods; Long Short-Term Memory (LSTM), Enhanced Bidirectional LSTM (XLSTM), Support Vector Machine (SVM) which may use radial basis function (rbf), linear or polynomial (poly) kernels, Autoregressive integrated moving average (ARIMA), and Seasonal Autoregressive integrated moving average (SARIMA). A stacking procedure which uses linear regression as a meta-model together with a voting ensemble method is then employed to link these base models. The feature engineering is thorough, as it provides for general price and volume data, a battery of technical indicators (SMA10, SMA20, EMA 12, EMA 26, MACD elements, and RSI14) and a general sentiment indicator (summarised financial news). Sentiment analysis is performed by a pipeline that is trained using RoBERTa and yields discrete numerical values (0 negative, 1 neutral, 2 positive). The model's capability is very rigorously gauged by the conventional metrics Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Directional Accuracy (DA). The real-world results demonstrate that the ensemble method is very efficient where the stacking arrangement leads to the lowest total MAPE of 0.6027 % MSFT and the highest directional Accuracy of 75.86 % GOOGL, thus, providing a strong evidence for the effectiveness of the thorough integration of heterogeneous machine-learning, statistical, and sentiment- analysis methods to produce the most accurate financial forecasts. 2026 IEEE. -
Enhancing Stroke Prediction: Leveraging Ensemble Learning for Improved Healthcare
Stroke, a potentially deadly medical disorder, requires excellent prediction and prevention measures to minimize its impact on individuals and healthcare systems. In this study, ensemble learning techniques are employed to enhance the accuracy of stroke prediction. The method combines four different machine learning algorithms, Adaboost, CatBoost, XGBoost, and LightGBM, to produce a strong predictive model. The data was composed of a rich set of demographic, medical, and lifestyle information. The data was preprocessed and features were engineered to maximize predictive performance. Results showed that the stacked ensemble model, which is composed of Adaboost, CatBoost, XGB, LightGBM, and Logistic Regression, meta-model, outperformed other models. The model has the potential to be used as a decision support tool in an early stroke risk assessment system, enhancing clinician decision-making and improving healthcare outcomes. 2024 IEEE. -
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. -
Enhancing student engagement in blended learning through personalization strategies on EdTech platforms: An MCDM-based approach
The rapid growth of educational technology highlights the essential need for personalized learning in blended environments. This study uses Multi-Criteria Decision Making (MCDM) methodologies, including the Analytic Hierarchy Process (AHP), Fuzzy AHP, and Analytic Network Process (ANP), to evaluate and prioritize personalization strategies in EdTech platforms. The research identifies data-driven adaptive learning as the most critical strategy (36.46%), followed by AI-powered content recommendations (15.46%) and personalized learning paths (15.11%). It reveals that personalization strategies are interconnected, creating dynamic feedback loops that reinforce one another, enabling continuous learning optimization. The study provides a holistic framework for educational technologists, policymakers, and designers. This approach bridges technological innovation with pedagogy, emphasizing adaptive, datainformed systems that respond dynamically to learner needs, ensuring the balance between innovation and educational quality. 2025, IGI Global Scientific Publishing. -
Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network
Integrating wind energy forecasting into urban city energy management systems offers significant potential for optimizing energy usage, reducing the carbon footprint, and improving overall energy efficiency. This article focuses on developing a wind power forecasting model using cutting-edge technologies to enhance urban city energy management systems. To effectively manage wind energy availability, a strategy is proposed to curtail energy consumption during periods of low wind energy availability and boost consumption during periods of high wind energy availability. For this purpose, an LSTM-based model is employed to forecast short-term wind power, leveraging a publicly available dataset. The LSTM model is trained with 27,310 instances and 10 wind energy system attributes, which were selected using the Pearson correlation feature selection method to identify crucial features. The evaluation of the LSTM-based forecasting model yields an impressive R2 score of 0.9107. The models performance metrics attest to its high accuracy, explaining a substantial proportion of the variance in the test data. This study not only contributes to advancing wind power forecasting, but also holds promise for sustainable urban energy management, enabling cities to make informed decisions in optimizing energy consumption and promoting a greener, more resilient future. 2023 by the authors. -
Enhancing Teacher-Student Engagement: The Role of Intellectual Humility
The book chapter explores the significant role of intellectual humility in cultivating strong teacher-student engagement within the landscape of education. It proposes that teachers modelling intellectual humility by admitting their mistakes and uncertainties signal students to take intellectual risks by asking questions or expressing their perspectives. Furthermore, the chapter also highlights that intellectually humble students are more open towards diverse viewpoints, are eager to learn from new information and expand their cognitive capacity, which are pivotal for active participation. Lastly, the chapter suggests various strategies for fostering intellectual humility in both teachers and students as well as for enhancing the advancements in the educational environments. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Enhancing the biodegradability and environmental impact of microplastics utilizing Eisenia fetida earthworms with treated low-density polyethylene for sustainable plastic management
Low-density polyethylene (LDPE) is widely used in food packaging and agricultural mulching, but its disposal generates macro, meso and microplastics that infiltrate the food chain and carry harmful substances. The present study aimed to improve remediation strategies for soils contaminated with LDPE and enhance the survivability of Eisenia fetida. The study dissolved LDPE in trichloroethylene and treated it with starch, hydrogen peroxide, nitric acid and acetic acid, initiating thermo-oxidative reactions. The treatment decreased LDPE's crystallinity index from 48.48% to 44.06% (single treatment), 44.06% to 40.02% (double treat-ment) and 40.02% to 32.98% (triple treatment), achieving a 15.5% reduction in crystallinity. LDPE microplastics with 40.02% crystallinity showed lower mortality rates in Eisenia fetida earthworms compared to those with 44.06% and 32.98% crystallinity and untreated LDPE. When introduced to E. fetida, microbiota in the earthworm casts included unidentified species from Pseu-domonas and Zoopagomycota, known polyethylene degraders. Microbial analysis of treated LDPE microplastics showed changes in gut microbiota, including potential degraders from Aeromonas and Malassezia restricta. XRD (X-ray diffraction techniques analyses) and FTIR(Fourier Transform Infrared Spectroscopy) analyses provided insights into distinct LDPE degradation patterns, identifying hydroxyl and carboxylic groups as functional groups. The study also investigated the ability of altered mi-croflora with treated microplastics to degrade LDPE, favouring decreased earthworm mortality rates. The crystallinity index of treated polyethylene further reduced from 40.02% to 23.58% after 21 days of exposure to E. fetida. This research advances the understanding of oxidised plastics' ecological impacts and will help to develop environmentally sustainable and biodegradable LDPE. Author (s). -
Enhancing the confidentiality of text embedding using image steganography in spatial domain
Rapid growth in technological development, the use of the internet has grown many folds. Along with it, the sharing of privacy information in networks creates ownership issues. In order to create a high level of security for sharing private information, the concept of steganography is introduced along with encryption based invisible watermarking techniques. The proposed system hides the encrypted private messages by using onetime pad which follows the concept LSB algorithm in spatial domain. The system combines steganography and encryption for enhancing the confidentiality of the intended messages. At first, the private information of the user is encrypted by using the onetime pad algorithm. Then the encrypted text is hidden the Least Significant Bit (LSB) of the different components of the color image in such a way that as to minimize the perceived loss of quality of the cover image. The beneficiary of the message is able to retrieve the hidden back and from the stego-image and extract the cipher text and find the plaintext from using the onetime pad algorithm. The proposed algorithm will be tested and analysed against three different hiding positions of color image components. 2021 American Institute of Physics Inc.. All rights reserved. -
Enhancing the digital consumer experience: The role of artificial intelligence
In the era of rapid technological advancement, the integration of artificial intelligence (AI) and digital consumerism has created new trends in business. Consumers are able to communicate with brands in new ways owing to developments in digital media. AI-powered recommendation systems, chatbots and virtual assistants are the main drivers of this change. It allows businesses to offer product recommendations, customer support, and personalized content to increase user engagement and satisfaction. This chapter provides real-world examples of AI applications across industries, highlighting success stories of companies using AI to create better value and customer satisfaction. Finally, the integration of artificial intelligence and digital customer experience has the potential to transform the future of e-commerce, marketing, and customer service, opening new horizons for both businesses and consumers. 2024, IGI Global. All rights reserved. -
Enhancing the efficiency of parallel genetic algorithms for medical image processing with Hadoop /
International Journal of Computer Applications, Vol.108, Issue 17, pp.92-97, ISSN No: 0975-8887. -
Enhancing the electrochemical performance of rGO-based ternary composite for next generation supercapacitors
This work explores the rational design and synthesis of a high-performance ternary nanocomposite rGO/CeO2/PPy, by incorporating cerium oxide and polypyrrole into the rGO matrix, through a hybrid approach of combining hydrothermal synthesis with in situ oxidative polymerization. Comprehensive structural characterization of the rGO/CeO2/PPy composite confirms the successful integration of components, revealing a hierarchically porous architecture that optimizes both charge transport and ion diffusion kinetics. The ternary composite exhibits exceptional interfacial interactions, including ?-? conjugation between rGO and PPy, coupled with electrostatic stabilization from CeO2, resulting in enhanced mechanical integrity and improved electrolyte accessibility. Electrochemical characterization reveals remarkable performance metrics, with a specific capacitance of 874 F g?1 and outstanding cyclic durability of 94% capacity retention after 5000 charge-discharge cycles at 1 A g?1. The configured rGO/CeO2/PPy//AC system exhibits exceptional energy storage performance, yielding an energy density of 39.6 Wh kg?1 while sustaining a power density of 2859 W kg?1. These outstanding characteristics underscore the material's suitability as a cutting-edge electrode for sophisticated energy storage systems, showcasing the benefits of strategic component integration in hybrid nanocomposite design. 2025 The Royal Society of Chemistry. -
Enhancing the energy efficiency for prolonging the network life time in multi-conditional multi-sensor based wireless sensor network
A wireless sensor network is one of the networks that is highly demanding by various real-time networking applications nowadays. A huge amount of sensor nodes is deployed in the network randomly and distributed. Most of the applications using wireless sensor network (WSN) are surveillance monitoring applications like a forest, home, healthcare, environment, and remote monitoring systems. Based on the application usage, the type of sensor, a number of sensor nodes are deployed in such a manner where the sensors can be used effectively. But the sensor nodes are restricted in the battery and sensing region. Thus, the battery of the sensor nodes is decreased based on the nodes function. The energy level of the sensor nodes highly affects the network lifetime. Improving the energy efficiency in WSN is one of the most important challenging tasks. Most of the earlier research works have proposed various methods, techniques, and routing protocols, but they are application dependent and as a common method. So, this paper is motivated to propose a Multi-Conditional Network Analysis (MCNA) framework for saving the energy level of the sensor nodes by reducing energy consumption. The MCNA framework involves two different clustering processes with cluster head selection, choosing the best nodes based on the signal strength, and the best route for data transmission. The data transmission is done by cluster based on source-destination based. The simulation results proved that the proposed MCNA framework outperforms the other existing methods. 2022 Northeastern University, China.