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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 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 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 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 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 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 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 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 Sign Language Recognition Through LSTM Model
Sign language recognition is a remarkable task in this project completed through two state-of-the-art methods, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This way, the system is able to quickly process each frame of the webcam with real-time information regarding face, body and posture in order to extract critical values. this research seeks to provide the necessary resources and opportunities for deaf people to be able to communicate effectively, obtain an education and enjoy their lives as much as other human beings This makes it a very important tool for education where the system can convert sign motions into text on-the-fly. The data was collected through a live camera, and key points from face, body, and pose were detected for training the model. Kindergarten used the four categories of vegetables, fruits, colors and animals. There were 40 video sequences of 40 frames with a sign in each. the model tries to fit too much to noisy points of data. However comprehensive the training, after 19 epochs the validation accuracy is an impressive 93%. The oscillations in the truth values of models are indicative of some uncertainty in learning where the accuracy is still to be settled. The graph in general shows that the LSTM based sign language movement classifier has a good capacity to learn and identify sign language movements with high precision. 2025 IEEE. -
Enhancing Security and Resource Optimization in IoT Applications with Blockchain Inclusion
The rapid proliferation of Internet of Things (IoT) devices has ushered in a new era of connectivity and data-driven applications. However, optimizing the allocation of resources within IoT networks is a pressing challenge. This research explores a novel approach to resource optimization, combining blockchain technology with enhanced security measures, while addressing the critical concerns of time and energy consumption. In this study, we propose a resource allocation framework that leverages the transparency and immutability of blockchain to enhance data integrity and security in IoT applications. The blockchain-based method is utilized to identify the malicious users in the IoT applications. The proposed method is implemented in MATLAB and performance is evaluated by performance metrics such as the probability of detection, false alarm probability, average network throughput, and energy efficiency. The proposed method is compared by existing methods such as Friend or Foe and Tidal Trust Algorithm. To further optimize this process, we introduce a Hybrid Artificial Bee Colony-Whale Optimization Algorithm (ABC-WOA), a powerful optimization technique designed to minimize time delays and energy consumption in IoT environments. Our findings demonstrate the effectiveness of the proposed approach in achieving resource efficiency, reducing time and conserving energy within IoT networks. 2023 IEEE. -
Enhancing Satellite Imagery with GAN Based Cloud Removal
Satellite imaging is one of the most common uses for applications agricultural, urban planning and environmental monitoring to mention a few. Unfortunately, even the best-laid plans for aerial photography can be decimated by one thing: cloud cover. The novel way of cloud extraction from the satellite data that is demonstrated in this article, use a Generative Adversarial Network (GAN). For the betterment of cloud removal, ResNet based discriminator and a UNet-based generator are utilized in the suggested approach. To accurately train the networks, a new technique was also developed to introduce noise that resembles natural cloud patterns. The PSNR score, as a qualitative and quantitative index card that uses the PyTorch- based GAN methodology to verify different performances in traditional methods based on EuroSat. 2025 IEEE. -
Enhancing Road Safety and Efficiency Through IoT-Enabled Car-to-Car Communication
Today's vehicles have been revolutionized by integrating Internet of Things (IoT) technology, which facilitates communication between cars, known as car-to-car (C2C) communication. This paper explores the potential of IoT-enabled C2C communication systems to improve security and efficiency by creating dynamic, real-time data exchange between vehicles. Through a comprehensive review of existing literature and technological advances, this study leads to an understanding of how IoT-based C2C communication can reduce incidents, reduce traffic accidents, and create a more peaceful driving environment. It also highlights the potential impact of C2C communications on transportation and policy development. This paper highlights the potential of IoT-enabled C2C communications as a revolutionary technology for the automotive industry, promoting road safety and better vehicle management. The findings highlight the importance of regulatory frameworks, data processing, and stakeholder collaboration for the successful deployment of communications systems of IoT-based C2C networks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing Retailer Auctions and Analyzing the Impact of Coupon Offers on Customer Engagement and Sales Through Machine Learning
Systems that use coupons have been used extensively to boost customer interaction on platforms having a digital component. We use causal machine learning techniques to determine the effect of an advertising intervention, especially a discount offer, on the bids of a shop. Discount shopping coupons are a popular tactic for increasing sales. The largest challenge for dealers is accurately anticipating the wants of their customers, and here is where they always struggle. Machine learning algorithms have been utilized by researchers to address a variety of problems. Selecting the right coupon is a challenging undertaking because every customer's behavior differs depending on the deal. Due to categorical data adjustments being necessary due to the majority of characteristics having missing values, the situation is made more difficult. The dataset is used to classify the dataset, and machine learning algorithms like logistic regression, random forest and SVM model, decision tree and naive bayes models are used to determine the correctness of the classification. 2023 IEEE. -
Enhancing Reliability and Accuracy in Wind Energy Forecast Using CNN-LSTM Hybrid Model
The global shift to sustainable energy is increasing the demand for wind energy. Accurate forecasting becomes crucial for renewable energy systems to function effectively in terms of resource allocation, grid management, and overall reliability. The need for wind energy is growing as a result of the worlds transition to sustainable energy. For renewable energy systems to operate efficiently in terms of resource allocation, grid management, and overall reliability, accurate forecasting becomes essential. It is challenging for current forecasting methods to correctly predict the dynamic nature of wind energy demand. For utilities and grid managers, the inherent variability and unpredictability in wind energy generation pose serious issues. The forecasting models that are now in use are challenged by the variable and sporadic character of wind energy generation. This makes it more difficult to integrate wind energy into the electrical grid effectively and increases the risk of grid instability and inefficient resource utilization. This research addresses these challenges by proposing a hybrid forecasting model that integrates the strengths of Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs). By capturing both spatial and temporal dependencies in wind data, the hybrid model aims to enhance accuracy and reliability in wind energy forecasts. The precise forecasting of wind energy is made more difficult by shifting weather patterns, changing environmental factors, and shifting patterns of energy usage. Improving the forecasting models accuracy and dependability in the renewable energy industry requires addressing these difficulties. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing Regional Language Proficiency in Large Language Models Through Translated Datasets
Although Large Language Models (LLMs) have made significant progress in Natural Language Processing the lack of high-quality training data frequently limits their ability to perform well in regional languages. To improve LLM competency this study methodically translates an English dataset into the low-resource language of Bhojpuri. On this new dataset we apply a structured translation methodology and then refine an LLM that has already been trained. The models capacity to produce contextually relevant and culturally appropriate responses in Bhojpuri has significantly improved according to a comparison of its performance before and after fine-tuning. Our findings show that this translation-centric approach provides a practical and affordable way to enhance the usefulness and inclusivity of LLMs increasing the effectiveness and accessibility of these potent AI tools for underrepresented linguistic groups globally. For linguistic groups that are marginalized globally. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Enhancing red wine quality prediction through Machine Learning approaches with Hyperparameters optimization technique
In light of the intricacy of the winemaking process and the wide variety of elements that could affect the taste and quality of the finished product, predicting red wine quality is difficult. ML methods have been widely used in forecasting red wine quality from its chemical characteristics in recent years. This Paper evaluated the comparison of classification and regression methods to predict the quality of red wine and performed the initial data analysis and exploratory data analysis on the data. This study implemented different Classifiers and Regressors that were trained and tested. Contrasted and Comparative analysis of the accuracies of eight models with hyperparameter tuning optimization, including Logistic Regression, Gradient Boosting, Extra Tree, Ada Boost, Random Forest, Support Vector Classifier, and Decision Tree, Knn and measured the classification report with F1, Accuracy, and Recall Scores. For Imbalance data, SMOTE Classifier was used. This study performed the Cross-validation technique, such as Grid search and with the best hyperparameters tuning. The study's findings demonstrated that the Gradient Boosting technique accurately predicted red wine quality. This research shows the promising results of Gradient Boosting for predicting red wine quality and adds important context to the usage of machine learning classifiers for this challenge. 2023 IEEE. -
Enhancing rainwater harvesting and groundwater recharge efficiency with multi-dimensional LSTM and clonal selection algorithm
Rainwater harvesting stands out as a promising solution to alleviate water scarcity and alleviate pressure on conventional water reservoirs. This work introduces a pioneering strategy to elevate the efficiency of rainwater harvesting systems through the fusion of Multi-Dimensional Long Short-Term Memory (LSTM) networks and the Clonal Selection Algorithm (CSA). The Multi-Dimensional LSTM networks serve to model intricate temporal and spatial rainfall patterns, enabling precise predictions regarding the optimal times and locations for rainwater abundance. This insight is pivotal in refining the design and operation of rainwater harvesting setups. Drawing inspiration from the immune system, the Clonal Selection Algorithm is employed to optimize site selection and resource allocation, ensuring the maximal utilization of harvested rainwater. The adaptability and robustness of CSA prove invaluable in tackling the dynamic nature of rainfall patterns. This research endeavor is dedicated to enhancing groundwater levels and optimizing its sources through the implementation of efficient harvesting techniques. By delving into innovative methodologies, it aims to contribute significantly to sustainable water management practices and ensure a reliable supply of groundwater for various societal needs. The experiments are conducted to study the effectiveness of rainwater harvesting systems, where the proposed method achieves increased efficiency, thereby reducing dependence on conventional water sources and contributing to sustainable water management practices. The proposed CSA-LSTM model demonstrates superior performance compared to ACO-ANN and PSO-BPNN, achieving higher training, testing, and validation accuracies while exhibiting lower training, testing, and validation losses. Additionally, CSA-LSTM showcases excellent site suitability, high resource utilization, and robustness to changes, with a fast response time, emphasizing its potential for efficient and effective applications. 2024 Elsevier B.V. -
Enhancing Quick Commerce Service Experience Through AI Marketing: An Empirical Investigation
This study examines the impact of AI-driven marketing strategies on quick commerce service experience (QCSE). Specifically, it investigates how personalization, smart search, and dynamic pricing influence consumers' perceptions of service experience in ultra-fast delivery platforms. Using partial least squares structural equation modelling (PLS-SEM) on survey data from 427 quick commerce users in India, the study finds that personalization has the strongest positive effect on QCSE, followed by dynamic pricing, while smart search has a weaker yet significant impact. The research validates QCSE as a higher-order formative construct comprising app design, security, fulfilment, and service support dimensions. The findings contribute to signaling theory by demonstrating how AI-driven marketing features serve as signals of platform experience perceptions. For practitioners, the results highlight the importance of AI-powered personalization and pricing strategies in enhancing service experiences. 2025 by IGI Global Scientific Publishing. All rights reserved.