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Predicting Player Engagement in Online Gaming: A Machine Learning Approach
The aim of this research is to make precise forecasts on player participation in online game using state-of-the-art machine learning algorithms. Player engagement plays a crucial element in determining the success of online games because it affects player retention, satisfaction and monetization. By understanding and predicting engagement levels, game developers and marketers can enhance the gaming experience and develop strategies to keep players invested. This research involves a comprehensive analysis of player behavior data from an online gaming platform. The dataset includes various demographic and behavioral features such as age, gender, location, game genre, playtime hours, in-game purchases, game difficulty, sessions per week, average session duration, player level, achievements unlocked, and engagement level. The data was preprocessed through handling missing values, normalizing numerical features, and encoding categorical variables. Exploratory Data Analysis (EDA) was conducted to understand the distribution and relationships between different features. Multiple machine learning models were evaluated to predict player engagement levels, including Random Forest, Gradient Boosting, XGBoost, and Support Vector Machine (SVM). These models were then compared through the accuracy, precision, recall, and F1-score metrics. In the comparison, XGBoost emerged as the best model. Since it is the best-performing model, we can make the feature importance analysis to identify the best factors for predicting engagement in the next step. The XGBoost model achieved the highest accuracy of 91%, demonstrating superior precision, recall, and F1-scores across all engagement levels (High, Medium, Low). Ensemble methods like XGBoost, Gradient Boosting, and Random Forest outperformed the SVM model, highlighting their effectiveness in handling complex datasets. 2024 IEEE. -
Predicting Price Direction of Bitcoin based on Hybrid Model of LSTM and Dense Neural Network Approach
Bitcoin is a rapidly growing but extremely risky cryptocurrency. It marks a watershed moment in the history of cash. These days, digital currency is preferred to actual money. Bitcoin has decentralized authority and placed it in the hands of its users. Many people are joining the largest and most well-known Bitcoin mining pools as the risk of working alone is too great. In order to enhance their chances of creating the next block in the Bitcoins blockchain and decrease the mining reward volatility, users can band together to form Bitcoin pools. This tendency toward consolidation may also be seen in the rise of large-scale mining farms equipped with powerful mining resources and speedy processing capability. Because of the risk of a 51% assault, this pattern shows that Bitcoin's pure, decentralized protocol is moving toward greater centralization in its distribution network. Not to be overlooked is the resulting centralization of the bitcoin network as a result of cloud wallets making it simple for new users to join. Because of the easily hackable nature of Bitcoin technologies, this could lead to a wide range of security vulnerabilities. The proposed approach uses normalization and filling missing values in preprocessing, PCA for feature Extraction and finally training the model using LSTM-DNN Models. The proposed approach outperforms other two models such as CNN and DNN. 2023 IEEE. -
Predicting Song Popularity Using Data Analysis
In today's music landscape, predicting a song's success is crucial for musicians, record labels, and streaming platforms. This paper introduces a methodology for estimating popularity using Spotify data, termed the 'Proxy Popularity Score.' Three models - Random Forest, LightGBM Regressor, and XGBoost Regressor - are utilized for prediction. Performance metrics including mean absolute error, mean squared error, root mean squared error, and R-squared error are employed to evaluate model accuracy. Correlation values of 99.85%, 99.87%, and 99.84% are achieved for XGBoost, LightGBM, and Random Forest respectively. The study concludes with a ranking of songs based on predicted popularity scores. 2024 IEEE. -
Predicting Stock Market Indexes with Artificial Intelligence
The forecasting of the Share market has been a popular research area, involving the analysis of input and output stock data using computer technology and algorithmic knowledge. This involves building unpredictable relationships among the data and analyzing the stock market trends to provide a reference for investors. The inception of artificial intelligence (AI) technology, blended with the web, immense data, and cloud computing has provided technical support for various industries. AI technology is employed to scrutinize and predict the equity market, exploring curvilinear associations amid stock market information, and furnishing a foundation for investors to formulate investment determinations. Predicting equity prices is a demanding undertaking due to diverse factors like governmental happenings, fiscal circumstances, business resolutions, investor mentality, and overseas currency hazards. The securities exchange is a vastly active and disordered framework, and producing precise projections of the securities exchange is of paramount significance. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Predicting Stock Market Movements Through Multisource Data Fusion Graphs: An Approach Employing Graph Convolutional Neural Network
The stock market plays an important role in the capital market, and investigating price fluctuations in the stock market has consistently been a prominent subject for researchers. The application of soft computing techniques to predict and categorize stock market movements is a significant research challenge that has gathered considerable attention from researchers. Although several studies highlight the significance of incorporating information from two sources in stock movement prediction, the potential of advanced graphical techniques for modeling and analyzing multi-source data remains an unattended research area. This study aims to address this gap by introducing a novel model that utilizes multi-source data fusion graphs to predict future market movements. The primary challenge involves establishing a model that can effectively gather the relationships among various data sources and employ this understanding to improve prediction performance. Compared to several existing methods relying only on historical data or sentiment data, which show limited predictive power and lack generality, the proposed approach seeks to overcome these limitations. The proposed model integrates various information sources, including historical prices, news data, Twitter data, and technical indicators for predicting future stock market trends. This presented method involves constructing a subgraph map for each data type to capture events from both rising and falling markets. Then, a Gated Recurrent Unit (GRU) is employed to aggregate the subgraph nodes. These aggregated nodes are then integrated with a Graph Convolutional Neural Network (GCNN) to classify the multi-source graph, therefore achieving stock market trend prediction effectively. To further validate its effectiveness, the presented model is applied to Indian stock market data, demonstrating its feasibility in fusing multi-source stock data and establishing its suitability for effectively predicting stock market movements. 2024 Seventh Sense Research Group -
Predicting Stock Market Price Movement Using Machine Learning Technique: Evidence from India
The stock market is uncertain, volatile, and multidimensional. Stock prices have been difficult to predict since they are influenced by a variety of factors. In order to make critical investment and financial decisions, investors and analysts are interested in predicting stock prices. Predicting a stock's price entails developing price pathways that a stock might take in the future. ANN and mathematical Geometric Brownian movement technique were employed in this study to forecast a stock market closing price of Indian companies. The comparative analysis indicates that the Geometric Brownian Method is better than ANN in giving better MAPE and RMSE Values. 2022 IEEE. -
Predicting Stock Market Trends: Machine Learning Approaches of a Possible Uptrend or Downtrend
This paper delves into a statistical analysis of the stock market, emphasizing the significance of accuracy in stock predictions. Large data sets can be handled by machine learning algorithms, which can also forecast outcomes based on past data and spot intricate patterns in financial data. They assist control risks, automate decision-making procedures, and adjust to changing circumstances. Multi-source data can be combined by ML models to provide a comprehensive picture of market circumstances. They can manage intricate, nonlinear interactions, provide impartial analysis, and lessen human bias. Models are able to adjust to shifting market conditions through ongoing learning and retraining. They must, however, exercise caution when deploying models in real-world situations and ensure that they are validated. Although machine learning has advantages for stock market analysis, it must be carefully evaluated for dangers and validated before being used in practical situations. The traditional machine learning model, Logistic Regression has been used in order to predict stock prices. It focuses on binary classification based on the trend of the stock. Through the model training and evaluation and additional analysis done on the results, this research contributes towards obtaining predictions and studying reasons of a possible uptrend or downtrend to further assist companies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Predicting Stock Market Trends: Machine Learning Approaches of a Possible Uptrend or Downtrend
This paper delves into a statistical analysis of the stock market, emphasizing the significance of accuracy in stock predictions. Large data sets can be handled by machine learning algorithms, which can also forecast outcomes based on past data and spot intricate patterns in financial data. They assist control risks, automate decision-making procedures, and adjust to changing circumstances. Multi-source data can be combined by ML models to provide a comprehensive picture of market circumstances. They can manage intricate, nonlinear interactions, provide impartial analysis, and lessen human bias. Models are able to adjust to shifting market conditions through ongoing learning and retraining. They must, however, exercise caution when deploying models in real-world situations and ensure that they are validated. Although machine learning has advantages for stock market analysis, it must be carefully evaluated for dangers and validated before being used in practical situations. The traditional machine learning model, Logistic Regression has been used in order to predict stock prices. It focuses on binary classification based on the trend of the stock. Through the model training and evaluation and additional analysis done on the results, this research contributes towards obtaining predictions and studying reasons of a possible uptrend or downtrend to further assist companies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Predicting sustainable equity indices using deep long short-term memory neural network: Evidence from developed and emerging markets
The present study aims to propose a predictive model to forecast the sustainable stock indices. For this, the Long Short-Term Memory (LSTM) neural network model is applied through Keras and TensorFlow to closing values of six developed and emergingmarkets: the US, the UK, Japan, Brazil, South Africa, and China. Further, the Adam optimiser and mean squared error loss function are used to train the model. To gauge the superiority of the LSTM model, a rolling window Autoregressive Integrated Moving Average (ARIMA) model is also employed. The performance accuracy of both models is evaluated using the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R2). The LSTM model, with two LSTM and two dense layers, yields the best results, achieving the highest precision in predicting the values of sustainable indices. The values of RMSE and MAPE confirmed this, and the accuracy is also verified by the R2 values. LSTM shows superior predictive accuracy and is indicated to be fit for non-linear market patterns than rolling window ARIMA. The study enables policymakers and practitioners to forecast these indices and design policies to motivate related investments. 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Predicting the Cerebral Blood Flow Change Condition during Brain Strokes using Feature Fusion of FMRI Images and Clinical Features
By fusing clinical information with functional magnetic resonance imaging (fFMRI) pictures, this study describes a novel method for predicting changes in cerebral blood flow during brain strokes. The FMRI data and patient-specific variables, such as age, gender, and medical history, are combined via feature fusion in the proposed technique. As a result, the model developed can accurately forecast changes in cerebral blood flow that occur during brain strokes. The efficiency of the suggested strategy is shown by experimental findings. The performance of the model is greatly enhanced when FMRI data and clinical characteristics are combined as opposed to just one data source. The findings of this study have important ramifications for increasing the accuracy of stroke diagnosis and treatment and, eventually, for bettering patient outcomes. The experimental results showed that the proposed method a high level of accuracy in predicting changes in cerebral blood flow after brain strokes. The performance of the model was much enhanced by combining clinical characteristics with FMRI data as opposed to using only one of these data sources. This emphasizes the value of including pertinent clinical information in the diagnosis and management of stroke. 2023 IEEE. -
Predicting the financial behavior of Indian salaried-class individuals
COVID-19 has caused not only unprecedented health crises but also economic crises among individuals across the world. White-collar (salaried-class) employees with a fixed salary face financial insecurity due to job loss, pay cuts and uncertainty in retaining a job. This study examines the financial behavior of Indian white-collar salariedclass investors to their cognitive biases. In addition, the mediating effect of financial self-efficacy on cognitive biases and financial behavior is examined. Respondents were given structured questionnaires (google forms) through emails and WhatsApp for data collection. SPSS and R-PLS are used to analyze the data. Conservatism (r = -.603, p < 0.05) and herding bias (r = -.703, p < 0.05) have a significant negative correlation with financial behavior. Financial self-efficacy has a significant positive correlation (r =.621. p < 0.050). Conservatism and herding predicted 60.5% and 62.2% of the variance, respectively. The direct and indirect paths between conservatism bias, financial self-efficacy, and financial behavior are significant. The paths between herding, financial self-efficacy and financial behavior are also significant. Ankita Mulasi, Jain Mathew, Kavitha Desai, 2022. -
Predicting the Stock Markets Using Neural Network with Auxiliary Input
Predicting the stock market has always been a challenging task and has always had a certain appeal for researchers all around the world. Stock markets are supposed to be quite random and people with experience in the market strongly agree to the fact. Thus, predicting the stock market accurately paves the way for endless money. To date, no such algorithm has been devised that could even predict the stock market with a 90% accuracy rate. The difficulty lies in the randomness of the markets, and the various complexities involved in modeling market dynamics. Nevertheless, there have been algorithms with a decent success rate and researchers around the world have been in a constant attempt to improve over them. Thus, through this paper we attempt at predicting the return of a stock over a period of 10days after a particular news was out regarding the stock using the headlines of the news and certain other features important in determining the direction of a stock. The model was implemented with a sigma score of 0.81. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Predicting the stock price index of yahoo data using elman network /
International Journal of Control Theory and Applications, Vol.10, Issue 10, pp.481-497, ISSN: 0974-5572. -
Predicting the Thyroid Disease Using Machine Learning Techniques
An endocrine gland that is allocated in the front of the neck is called the thyroid, which produces thyroid hormones as its main job. Thyroid hormone may be produced insufficiently or excessively as a result of its potential malfunction. There are various thyroid types including Hyperthyroidism, Hypothyroidism, Thyroid Cancer Thyroiditis, swelling of the thyroid. A goiter is an enlarged thyroid gland. When your thyroid gland produces more thyroid hormones than your body requires, you have hyperthyroidism. When the thyroid gland in our body doesnt provide enough thyroid hormones, then our body has hypothyroidism; when you have euthyroid sick, your thyroid function tests during critical illness taken in an inpatient or intensive care setting show alterations. Hypothyroid, hyperthyroid, and euthyroid conditions are expected from these thyroid conditions. The Three similarly used machine learning algorithms are: Support Vector Machine (SVM), Logistic Regression, and Random Forest methods, were evaluated from among the various machine learning techniques to forecast and evaluate their performance in terms of accuracy. Random forest can perform both regression and classification tasks. Logistic Regression is used to calculate or predict the probability of a binary (yes/no) event occurring. SVM classifiers offers great accuracy and work well with high dimensional space. A thyroid data set from Kaggle is used for this. This study has demonstrated the use of SVM, logistic regression, and random forest as classification tools, as well as the understanding of how to forecast thyroid disease. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Predicting Wind Energy: Machine Learning from Daily Wind Data
This paper offers a comprehensive review of the advancements in the realm of renewable energy, specifically focusing on solid oxide fuel cells and electrolysers for green hydrogen production. The review delves into the significance of wind energy as a pivotal renewable energy source and underscores the importance of precise forecasting for efficient energy management and distribution. The integration of machine learning-based approaches, such as Support Vector Regression and Random Forest Regression, has shown promising results in enhancing the accuracy of wind energy production forecasts. Furthermore, the paper explores the broader landscape of renewable energy generation forecasting, emphasizing the rising prominence of machine learning and deep learning techniques. As the penetration of renewable energy sources into the electricity grid intensifies, the need for accurate forecasting becomes paramount. Traditional methods, while valuable, have encountered limitations, paving the way for advanced algorithms capable of deciphering intricate data relationships. The review also touches upon the inherent challenges and prospective research avenues in the domain, including addressing uncertainties in renewable energy generation, ensuring data availability, and enhancing model interpretability. The overarching goal remains the seamless integration of renewable sources into the grid, propelling us towards a greener future. The Authors, published by EDP Sciences, 2024. -
Predicting Work Environment and Job Environment Among Employees using Transfer Learning Approach
Today's enterprises face numerous challenges as a result of the world's rapid evolution. Maintaining a content workforce is crucial to a company's success and survival in today's fast-paced business environment. The efficacy, productivity, efficiency, and dedication of the company's staff are directly associated with the company's capacity to meet the needs of its employees in the workplace. The focus of this system is to identify the factors that contribute to a satisfying work environment for the participants. Preprocessing, feature selection, and model training are the first three steps in the suggested methodology. Data mining systems should get in the habit of normalizing data as a preliminary processing step. The multiple elements assessing company culture and worker satisfaction were consolidated using Principal Components Analysis (PCA) in the feature selection phase. Once features have been selected, KNN-SVM is utilized for model training. When compared to the two most popular alternatives, SVM and KNN, the proposed technique performs better. 2023 IEEE. -
Prediction and analysis of financial crises using machine learning
This study presents a comparative analysis of various machine learning algorithms for credit risk assessment. The algorithms were tested on two credit datasets: German Credit Dataset and Australian Credit Dataset. The performance of the algorithms was evaluated based on several metrics, including sensitivity, specificity, accuracy, F-score, and Kappa. The results showed that the FCPFS-QDNN algorithm outperformed other algorithms in both datasets, achieving high accuracy, sensitivity, specificity, and F-score. On the other hand, the ACO Algorithm and Multilayer Perceptron algorithms were found to perform poorly in both datasets. The findings of this study have significant implications for credit risk assessment in banking and financial institutions. The study recommends the use of the FCPFS-QDNN algorithm for credit risk assessment due to its superior performance compared to other algorithms. 2023, IGI Global. All rights reserved. -
Prediction and modeling of mechanical properties of concrete modified with ceramic waste using artificial neural network and regression model
Over two centuries, concrete has been crucial to building. Thus, eco-friendly concrete is being developed. Emulating these tangible traits has recently gained popularity. Ceramic waste concretes mechanical properties were modeled in this study. Ceramic waste percentages ranged from 5 to 20%. Compressive and tensile concrete strengths were modeled. To predict concrete hardness, regression modeling and artificial neural network (ANN) were used. Model performance was evaluated using prediction coefficients and root-mean-square error (RMSE). ANN models outperformed linear prediction with a coefficient for determination (R2) of 0.97. ANN models achieved root-mean-square errors (RMSEs) of 1.22MPa, 1.21MPa, and 1.022MPa after 7, 14, and 28days of retraining, respectively. Linear regression model showed RMSE values of 1.21, 1.32, and 1.27MPa at 7, 14, and 28days, respectively. In determining the compressive and tensile strength, the R2 was 0.70, meanwhile the ANN model achieved 0.87. Given its accuracy in predicting the strength qualities of ceramics cement and structural stiffness, the ANN model presents a promising tool for representing various types of concrete. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. -
Prediction of Answer Keywords using Char-RNN
Generating sequences of characters using a Recurrent Neural Network (RNN) is a tried and tested method for creating unique and context aware words, and is fundamental in Natural Language Processing tasks. These type of Neural Networks can also be used a question-answering system. The main drawback of most of these systems is that they work from a factoid database of information, and when queried about new and current information, the responses are usually bleak. In this paper, the author proposes a novel approach to finding answer keywords from a given body of news text or headline, based on the query that was asked, where the query would be of the nature of current affairs or recent news, with the use of Gated Recurrent Unit (GRU) variant of RNNs. Thus, this ensures that the answers provided are relevant to the content of query that was put forth. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Prediction of Campus Energy Consumption Patterns Using Machine Learning Techniques
The exponential increase in campus energy consumption results from the rise in population density, leading to urbanisation and the use of higher energy-intensive devices within the environment. This study explored high-performance data analytics techniques to visualise energy consumption across buildings using datasets obtained from a load audit of the entire distribution network within the Federal University of Technology, Owerri (FUTO). Advanced time series models were used to predict and forecast the consumption patterns for a year. Visualisations for this research provided detailed insights into the energy profile across all the clusters, while the SARIMA, ARIMA, and Prophet models predicted the energy demands. The heatmap for the correlation matrix reveals a constant energy scale throughout the week (weekend average energy usage is at least 40% of the weekday). A comparative performance was done to analyse the scalability and predictive abilities of the individual models. Results from the study indicate that SARIMA has the lowest mean square error (4.4896) and the highest R2 score (0.8362). The study concludes that the adoption of machine learning models for energy forecasting and prediction is vital for modern-day energy management in the University. 2025 IEEE.