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Predicting Football Players Market Value via Machine Learning
Football, arguably the most popular sport in the world, has become much more than just a sport, it is a multibillion-dollar industry with its center in Europe. Every year millions of euros are spent in transfer window to buy and sell players and a common theme that has been seen is players not living up to the price the clubs paid for them. This research aims to predict football players market values using machine learning techniques. Departing from traditional methods that broadly categorize players into positions like Goalkeeper, Defender, Midfielder, and Forward, this study provides a more nuanced approach by classifying players into specific roles such as Center-back, Full-back, Defensive Midfielder, Attacking Midfielder, and Winger. By incorporating performance metrics tailored to each position and weighing the performance indicators based on the relevance to that specific position, the research aims to provide a robust method to predict players market value within a negotiation tolerance range. Using data from the past three seasons, including detailed player performance statistics and contractual details, models were developed to assist clubs in making data-driven transfer decisions. Machine learning algorithms, including Random Forest Regressor and Light GBM, were utilized, with RMSE and R2 Score as evaluation metrics. Both algorithms demonstrated robust performance, with some positional models predicting market values within an acceptable error range of 312million, enabling clubs to negotiate transfer fees with greater precision based on empirical evidence of player performance. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Predicting Financial Market Volatility Using Regression and Machine Learning Techniques
In standard Simple Linear Regression (SLR), one of the major assumptions is that the error terms have constant variance (homoscedasticity). However, this assumption is frequently violated in many real-world datasets, resulting in inefficient estimates and reduced predictive accuracy. To overcome this shortcoming, we propose a hybrid modeling platform that combines SLR with statistical and machine learning methods. The approach starts with SLR to identify the main linear relationship. Whenever residual diagnostics report the presence of heteroskedasticity, an Autoregressive Conditional Heteroskedasticity (ARCH) model is used to estimate time-varying variance. Such estimated variances are utilized in a Weighted Generalized Least Squares (WGLS) model, which stabilizes the error structure. Finally, to capture any remaining nonlinear patterns, an Artificial Neural Network (ANN) is applied on the residuals of the WGLS model. By layering these techniques, the hybrid framework improves both stability and predictive power. Simulation studies and empirical tests on Apple Inc. stock data confirmed that the hybrid framework yields reduced MAE and RMSE values and greater explanatory strength than individual approaches. 2025 IEEE. -
Predicting Financial Distress in India: A Deep Learning Approach
The present study examines the efficacy of deep learning models in predicting financial distress in India. For this purpose, the study employs three distinct architectures: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Conventional Neural Network (CNN) models. Utilizing data from companies that filed for bankruptcy under the Insolvency and Bankruptcy Code 2016 for the period of 20162023, the study adopts a balanced sample approach to categorize them into distressed and non-distressed groups. Nineteen financial variables are utilized to predict financial distress. Python is used as the programming language, and Jupyter Notebook facilitates algorithm development. The findings reveal that the LSTM model, when compared to RNN and CNN, achieved 91% accuracy using parameters such as 8 LSTM units with tanh activation and a dense layer with sigmoid activation function, a batch size of 10, 50 epochs, RMSprop optimizer, and binary cross-entropy loss were used. The study suggests that deep learning presents a novel approach that can enhance performance in financial distress prediction studies. This study is believed to be the first to utilize deep learning models for financial distress prediction in India based on single-year data, offering valuable insights for financial institutions and investors seeking more effective risk management strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Predicting Financial Distress in India: A Deep Learning Approach
The present study examines the efficacy of deep learning models in predicting financial distress in India. For this purpose, the study employs three distinct architectures: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Conventional Neural Network (CNN) models. Utilizing data from companies that filed for bankruptcy under the Insolvency and Bankruptcy Code 2016 for the period of 20162023, the study adopts a balanced sample approach to categorize them into distressed and non-distressed groups. Nineteen financial variables are utilized to predict financial distress. Python is used as the programming language, and Jupyter Notebook facilitates algorithm development. The findings reveal that the LSTM model, when compared to RNN and CNN, achieved 91% accuracy using parameters such as 8 LSTM units with tanh activation and a dense layer with sigmoid activation function, a batch size of 10, 50 epochs, RMSprop optimizer, and binary cross-entropy loss were used. The study suggests that deep learning presents a novel approach that can enhance performance in financial distress prediction studies. This study is believed to be the first to utilize deep learning models for financial distress prediction in India based on single-year data, offering valuable insights for financial institutions and investors seeking more effective risk management strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Predicting financial asset prices with neural network: a comparative study of neural networks effectiveness in financial decision-making
Investing requires deep knowledge of complex financial markets, making it incredibly tedious to predict inflation and deflation. Predictive conventional models like ARIMA and GARCH do not accurately capture the non-linearity and volatility presented in financial datasets. This research examines the different forms of predictive assets, real estate, stocks, commodities, bonds, and cryptocurrency using Long Short-Term Memory (LSTM) Neural networks. The primary focus of this research is to assess the valuable prediction capabilities of LSTM across assets and its integration with financial decision-making. According to the empirical results, deep learning LSTM models give better outcomes with equities and gold, with the R2 indicator reaching over 99% alongside a low RMSE. LSTMs had an over 100% MPE prediction error rate for other assets during the test phase, making it harder to predict intensely volatile assets. The model's verification transfers residual autocorrelation, showing that it can enhance forecasting performance with detailed macroeconomic indicators and sentiment analysis data. Studies show that LSTMs are effective in high-frequency markets with non-linear price changes, but they require special attention to balance interpretability and overfitting. Despite the progress that has been achieved in utilizing neural networks for financial forecasting, hybrid models integrated with XAI are recommended to improve efficiency and real-world applicability. These results contribute to the growing domain of AI-powered finance by offering additional means for many investors, analysts, and decision-makers who wish to utilize data for market speculation. Dr. Aishwarya Nagarathinam et al. -
Predicting energy source diversification in emerging Asia: The role of global supply chain pressure
This study investigates energy diversification trends in six Emerging Asian countries from 1998 to 2021 while exploring the predicting effects of the global supply chain pressure, total investment, innovation, economic growth, and globalisation on energy diversification. This study considers the Kernel-Based Regularized Least Squares (KRLS) estimations and prediction models (Adam and Stochastic Gradient Descent optimisers). The impacts of global supply chain pressure and total investment on energy diversification are positive. Innovation also emerges as crucial factor to enhance energy diversification. Deeper integration into the global economy (globalisation) and economic growth strengthen energy diversification. The study underscores the importance of tailored policies, advocating for investments in innovation, targeted total investment, and inclusive growth strategies to address energy diversification in emerging Asian countries. 2024 Elsevier B.V. -
Predicting Employee Attrition Using Machine Learning Algorithms
Employees are considered the foundation of any organization. Due to their importance, the Human resources department implements various policies to sustain them. Yet the attrition rate in any organization is increasing yearly. The attrition rate signifies the number of employees who leaves a firm without being replaced. It is regarded as a well-known issue that requires the administration to make the best choices to retain highly competent staff. It is interesting to note that artificial intelligence is frequently used as a successful technique for foreseeing such an issue. This review paper aims to study the different machine learning approaches that predict employee attrition and factors influencing an employee to attrite from an organization. A Hybrid model comprising the various ensemble models is proposed to predict attrition at its earliest. The forecasted attrition model aids in not only taking preventive action but also in improving recruiting choices and rewarding top performers who contribute to the company's success. 2022 IEEE. -
Predicting emotional intelligence, creative performance and knowledge management in higher education using multiple regression
Higher education institutions are paramount in emerging nations like India. Post-globalisation, India witnessed the growth of HEIs, especially in the private sector. However, today most of the institutions are struggling for their existence. One of the most vital reasons for such a staggering performance is the absence of creativity. It will not be an exaggeration to say that the present era is the era of creativity and performance and organisations that cant perform are bound to perish. Creativity can be nurtured and yield success only if it is supported by the emotional intelligence (EI) of the employees and knowledge management (KM) processes. The current paper explored the nexus between emotional intelligence, knowledge management processes and creative performance in HEIs in India and implied that though emotional intelligence affects creative performance, the impact gets manifolded in the presence of the knowledge management process. Copyright 2025 Inderscience Enterprises Ltd. -
Predicting electric vehicle performance metrics using a convolution neural network-gated recurrent unit-attention based deep learning architecture
The indicators of electric vehicle performance such as state of charge (SOC), remaining useful life (RUL), and charge demand need to be accurately forecasted to ensure maximum energy control and battery life. The models used are usually not able to capture the spatial and temporal correlation of battery data and be robust to the presence of noisy measurements. In this study, we model a sequential attention-based deep learning structure with convolutional neural networks, gated recurrent units, and an attention mechanism that can ultimately understand the local features, temporal relationships, and dynamic significance of various features in sequential battery data. The hybrid architecture of this model allows it to extract local spatial features, long-term sequential dependencies and dynamically find the importance of the critical time steps. We also develop a hybrid loss that is an accumulation of Huber loss and Mean Squared Error, which is much more resilient to outliers and at the same time has high prediction accuracy. It is experimentally proven that the proposed model has R2 values of 0.9575, 0.9558, and 0.9199 on SOC, RUL, and charge demand, respectively, which are better than the current single-architecture methods. 2026 The Authors -
Predicting customer churn: A systematic literature review
Churn prediction is an active topic for research and machine learning approaches have made significant contributions in this domain. Models built to address customer churn, aim to identify customers who are at a high risk of terminating services offered by a company. Hence, an effective machine learning model indirectly contributes to the revenue growth of an organization, by identifying at risk customers, well in advance. This improves the success rate of retention campaigns and reduces costs associated with churn. The aim of this study is to explore the state-of-the-art machine learning techniques used in churn prediction. A systematic literature review, that is driven by 5 research questions and rigorous quality assessment criteria, is presented. There are 38 primary studies that are selected out of 420 studies published between 2018 and 2021. The review identifies popular machine learning techniques used in churn prediction and provides directions for future research. Firstly, the study finds that churn models lack generalization capability across industry domains. Hence, it identifies a need for researchers to explore techniques that extend beyond model experimentation, to improve efficiency of classifiers across domains. Secondly, it is observed that the traditional approaches to churn prediction depend significantly on demographic, product-usage, and revenue features alone. However, recent papers have integrated social network analysis-related features in churn models and achieved satisfactory results. Furthermore, there is a lack of scientific work that utilizes information-rich content of customer-company-interaction instances via email, chat conversations and other means. This area is the least explored. Thirdly, there is scope to investigate the effect of hybrid sampling strategies on model performance. This has not been extensively evaluated in literature. Lastly, there is no formal guideline on correct evaluation parameters to be used for models applied on imbalanced churn datasets. This is a grey area that requires greater attention. 2022 Taru Publications. -
Predicting cryptocurrency prices model using a stacked sparse autoencoder and Bayesian optimization
In recent years, digital currencies, also known as cybercash, digital money, and electronic money, have gained significant attention from researchers and investors alike. Cryptocurrency has emerged as a result of advancements in financial technology and has presented a unique opening for research in the field. However, predicting the prices of cryptocurrencies is a challenging task due to their dynamic and volatile nature. This study aims to address this challenge by introducing a new prediction model called Bayesian optimization with stacked sparse autoencoder-based cryptocurrency price prediction (BOSSAE-CPP). The main objective of this model is to effectively predict the prices of cryptocurrencies. To achieve this goal, the BOSSAE-CPP model employs a stacked sparse autoencoder (SSAE) for the prediction process and resulting in improved predictive outcomes. The results were compared to other models, and it was found that the BOSSAE-CPP model performed significantly better. 2023, IGI Global. -
Predicting Crude Oil Futures using Feed Forward Neural Networks and Technical Indicators: A Comparative Study on WTI and Brent
In the domains of economic management and energy analysis, forecasting the price of crude oil is increasing popularity. It is essential to the facilitating rapid and cost-effective development with improved quality. Accurate prediction of the crude oil market is essential for steady and fast economic development because of its enormous influence on the global economy and society. Moreover, precise crude oil price prediction aids the traders in making accurate decision to maximize profits. In this work, a machine learning method for forecasting future global price data for crude oil is provided based on past data. The proposed model consists of three phases: primarily, historical data of selected crude oil data are gathered and normalized using data normalization technique. Secondly, technical indicators are derived from the crude oil data. Finally, a Feed Forward Neural Network (FFNN) is designed and trained using these technical indicators to forecast the price of crude oil in the future. Daily, weekly, and monthly data from Brent crude oil and West Texas Intermediate (WTI) are used to evaluate the generated model's prediction ability. To find the most effective FFNN configuration, the model's efficacy is evaluated by adjusting hidden layer number and hidden neurons. Performance of the model is also analyzed by varying number of training and testing samples. The experimental outcomes demonstrates that the designed model exhibits excellent performance for both WTI and Brent data. Notably, the model proves to be effective in predicting crude oil prices, when technical indicators are used as input variables. 2026 IEEE. -
Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis
Crude oil is an imperative energy source for the global economy. The future value of crude oil is challenging to anticipate due to its nonstationarity in nature. The focus of this research is to appraise the explosive behavior of crude oil during 20072022, including the most recent influential crisis COVID-19 pandemic, to forecast its prices. The crude oil price forecasts by the traditional econometric ARIMA model were compared with modern Artificial Intelligence (AI)based Long Short-Term Memory Networks (ALSTM). Root mean square error (RMSE) and mean average percent error (MAPE) values have been used to evaluate the accuracy of such approaches. The results showed that the ALSTM model performs better than the traditional econometric ARIMA forecast model while predicting crude oil opening price on the next working day. Crude oil investors can effectively use this as an intraday trading model and more accurately predict the next working day opening price. 2023 World Scientific Publishing Co. Pte Ltd. All rights reserved. -
Predicting Consumers' Usage Intention Towards User-Generated Content: A Hybrid SEM-ANN Approach
With the change in the communication pattern, end-users are engaging in creating content and referring to the content created by other users while making purchase decisions. This research aims at modelling factors affecting consumers' usage intention (UI) towards user-generated content (UGC) using Need for Cognition (NfC) as a moderator of the proposed relationships. The factors affecting consumers' UI involve perceived usefulness (PU), source credibility (SC), information quality (IQ) and NfC. Further, a novel attempt has been made by using the neural network approach to assess the predictive accuracy of the model. A structured questionnaire was used to collect data from 298 consumers through a survey. Data were analysed using two-stage structural equation modelling (SEM) and artificial neural network (ANN). All the independent variables viz., PU, SC, IQ and NfC significantly affect attitude towards UGC, which in turn affects UI. Results of multi-group analysis and a series of chi-square difference tests reveal that a NfC significantly moderates the relationship between PU and attitude, as well as that between SC and attitude. The root mean square error values from the neural network analysis suggest that the models show good predictive accuracy. This study provides a novel assessment of the usage of a hybrid SEM-ANN approach for understanding of UGC by incorporating NfC as a moderator in shaping consumers' attitudes and intentions to use UGC. 2025 World Scientific Publishing Co. -
Predicting Consumers' Usage Intention Towards User-Generated Content: A Hybrid SEM-ANN Approach
With the change in the communication pattern, end-users are engaging in creating content and refer-ring to the content created by other users while making purchase decisions. This research aims at modelling factors affecting consumers' usage intention (UI) towards user-generated content (UGC) using Need for Cogni-tion (NfC) as a moderator of the proposed relationships. The factors affecting consumers' UI involve perceived usefulness (PU), source credibility (SC), information quality (IQ) and NfC. Further, a novel attempt has been made by using the neural network approach to assess the predictive accuracy of the model. A structured ques-tionnaire was used to collect data from 298 consumers through a survey. Data were analysed using two-stage structural equation modelling (SEM) and artificial neural network (ANN). All the independent variables viz., PU, SC, IQ and NfC significantly affect attitude towards UGC, which in turn affects UI. Results of multi-group anal-ysis and a series of chi-square difference tests reveal that a NfC significantly moderates the relationship be-tween PU and attitude, as well as that between SC and attitude. The root mean square error values from the neural network analysis suggest that the models show good predictive accuracy. This study provides a novel assessment of the usage of a hybrid SEM-ANN approach for understanding of UGC by incorporating NfC as a moderator in shaping consumers' attitudes and intentions to use UGC. 2024 World Scientific Publishing Co. -
Predicting Consumer's Brand Switching Behaviour for Cell Phones
The IUP Journal of Marketing Management, ICFAI, Vol. XV, Issue 4, ISSN No. 0972-6845 -
Predicting Coal Prices: A Machine Learning Approach for Informed Decision-Making
This research addresses the critical need for accurate coal price prediction in the dynamic global market, crucial for informing strategic decisions and investment choices. With coal playing a vital role in the world energy mix, its price fluctuations impact industries and economies worldwide. The study employs advanced machine learning models, including Linear Regression, Random Forest, SVM, Adaboost, and ARIMA, to enhance prediction precision. Key features such as S&P 500, Crude Oil Price, CPI, Exchange Rates, and Total Electricity Consumption are identified through feature importance analysis. The Random Forest model emerges as the most effective, emphasizing the significance of key variables. Leveraging explainable AI techniques, the study provides transparent insights into model decision-making, offering valuable information for risk management and strategic decision-making in the volatile coal market 2024 IEEE. -
Predicting and Optimizing Synergistic Drug Combinations for Breast Cancer Treatment Using Machine Learning
Objectives: The study aims to identify highly synergistic drug combinations for breast cancer treatment using machine learning models. The primary objective is to predict drug synergy scores accurately and rank combinations with the highest potential for therapeutic efficacy. Methods: Machine learning models, including XGBoost, Random Forest (RF), and CatBoost (CB), were employed to analyze breast cancer drug combination data. Four synergy metricsZIP, Bliss, Loewe, and HSAwere used to quantify drug interaction effects. The models were trained to predict these synergy scores, and their performance was evaluated using normalized root mean squared error (NRMSE) and Pearson correlation coefficient. Predicted top-ranking drug combinations were further validated by comparing observed versus expected dose-response curves and calculating the area under the curve (AUC) for synergy assessment. Results: XGBoost (XGB_5235) outperformed other models, achieving an NRMSE of 0.074 and a Pearson correlation of 0.90 for the Bliss synergy model. Based on average synergy scores, the top 20 drug combinations were identified, with Ixabepilone+Cladribine, SN 38 Lactone+Pazopanib, and Decitabine+Tretinoin emerging as the most promising. These combinations showed high synergy and were supported by biological insights into their mechanisms of action. Conclusions: The study demonstrates the effectiveness of machine learning in predicting synergistic drug combinations for breast cancer. By accelerating the screening process and reducing experimental burden, the approach offers a promising tool for guiding future in vitro and in vivo validation of combination therapies. Copyright 2025 Wolters Kluwer Health, Inc. All rights reserved. -
Predicting and improvising the performance of rocket nozzle throat using machine learning algorithms
This paper is a study of one dimensional heat conduction with thermo physical properties like K, row, Cp of a material varying with temperature. The physical problem is characterized by a cylinder of infinite length and thickness L, imposed with a net heat flux at x= 0, with the other end being insulated. The temperatures at the insulate end are measured by placing thermocouples. As the temperatures at the other end are very high, it is not possible to measure temperatures by keeping thermocouples which will burn away. So the problem is initialized with known sensor values near insulated end. By proper predicting values by ARIMA Model, the temperature distribution in Rocket Nozzle throat system (RNT) is calculated. The outcome of the work is processed with Machine Learning algorithm like Genetic algorithm in identifying the optimal location of sensor position which helps in improvising the performance of RNT. 2020, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Predicting and Analyzing Early Onset of Stroke Using Advanced Machine Learning Classification Technique
Around the world, stroke is the leading cause of death. When blood vessels in the brain rupture, they cause damage. Alternatively, blockage in a blood vessel that supplies oxygen and other nutrients may also lead to this disease. This study uses various machine learning models to predict whether someone will have a stroke or not. Different physiological features were taken into account by this study while using Logistic Regression; Decision Tree Classification; Random Forest Classification; K-Nearest Neighbors (KNN); Support Vector Machine (SVM); Nae Bayes classifier algorithm; and XGBoost classification algorithm - these were used for six different models to ensure accurate predictions are made. We will accomplish the finest exactness with Bayes cv look which may be a hyper-tuning classifier with 92.87%. This consideration can be utilized for future work by doing the increase and include designing on the dataset. It is constrained to literary information, so it might not continuously be right for foreseeing stroke. so utilize the datasets that contain pictures and work on those datasets. 2024 IEEE.