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The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of these techniques in financial instrument price prediction, market trend analysis, establishing investment opportunities, portfolio optimization, etc. Investors and traders are using machine learning and deep learning models for forecasting financial instrument movements. With the widespread adoption of AI in finance, it is imperative to summarize the recent machine learning and deep learning models, which motivated us to present this comprehensive review of the practical applications of machine learning in the financial industry. This article examines algorithms such as supervised and unsupervised machine learning algorithms, ensemble algorithms, time series analysis algorithms, and deep learning algorithms for stock price prediction and solving classification problems. The contributions of this review article are as follows: (a) it provides a description of machine learning and deep learning models used in the financial sector; (b) it provides a generic framework for stock price prediction and classification; and (c) it implements an ensemble model—“Random Forest + XG-Boost + LSTM”—for forecasting TAINIWALCHM and AGROPHOS stock prices and performs a comparative analysis with popular machine learning and deep learning models.

This paper presents a non-linear finite element model (FEM) to predict the load-carrying capacity of three different configurations of elliptical concrete-filled steel tubular (CFST) short columns: double steel tubes with sandwich concrete (CFDST), double steel tubes with sandwich concrete and concrete inside the inner steel tube, and a single outer steel tube with sandwich concrete. Then, a parametric and analytical study was performed to explore the influence of geometric and material parameters on the load-carrying capacity of elliptical CFST short columns. Furthermore, the current study investigates the effectiveness of machine learning (ML) techniques in predicting the load-carrying capacity of elliptical CFST short columns. These techniques include Support Vector Regressor (SVR), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), XGBoost Regressor (XGBR), MLP Regressor (MLPR), K-nearest Neighbours Regressor (KNNR), and Naive Bayes Regressor (NBR). ML models accuracy is assessed by comparing their predictions with FE results. Among the models, GBR and XGBR exhibited outstanding results with high test R 2 scores of 0.9888 and 0.9885, respectively. The study provided insights into the contributions of individual features to predictions using the SHapley Additive exPlanations (SHAP) approach. The results from SHAP indicate that the eccentric loading ratio (e/2a) has the most significant effect on the load-carrying capacity of elliptical CFST short columns, followed by the yield strength of the outer steel tube ( ) and the inner width of the inner steel tube ( ). Additionally, a user interface platform has been developed to streamline the practical application of the proposed ML.

Crowd management becomes a global concern due to increased population in urban areas. Better management of pedestrians leads to improved use of public places. Behavior of pedestrian’s is a major factor of crowd management in public places. There are multiple applications available in this area but the challenge is open due to complexity of crowd and depends on the environment. In this paper, we have proposed a new method for pedestrian’s behavior detection. Kalman filter has been used to detect pedestrian’s using movement based approach. Next, we have performed occlusion detection and removal using region shrinking method to isolate occluded humans. Human verification is performed on each human silhouette and wavelet analysis and particle gradient motion are extracted for each silhouettes. Gray Wolf Optimizer (GWO) has been utilized to optimize feature set and then behavior classification has been performed using the Extreme Gradient (XG) Boost classifier. Performance has been evaluated using pedestrian’s data from avenue and UBI-Fight datasets, where both have different environment. The mean achieved accuracies are 91.3% and 85.14% over the Avenue and UBI-Fight datasets, respectively. These results are more accurate as compared to other existing methods.

Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been widely used to improve early detection and treatment, several studies have shown low classification accuracies due to overfitting, underfitting, and data noise. This research employs parallel and sequential ensemble ML approaches paired with feature selection techniques to boost classification accuracy. The Pima India Diabetes Data from the UCI ML Repository served as the dataset. Data preprocessing included cleaning the dataset by replacing missing values with column means and selecting highly correlated features using forward and backward selection methods. The dataset was split into two parts: training (70%), and testing (30%). Python was used for classification in Jupyter Notebook, and there were two design phases. The first phase utilized J48, Classification and Regression Tree (CART), and Decision Stump (DS) to create a random forest model. The second phase employed the same algorithms alongside sequential ensemble methods—XG Boost, AdaBoostM1, and Gradient Boosting—using an average voting algorithm for binary classification. Evaluation revealed that XG Boost, AdaBoostM1, and Gradient Boosting achieved classification accuracies of 100%, with performance metrics including F1 score, MCC, Precision, Recall, AUC-ROC, and AUC-PR all equal to 1.00, indicating reliable predictions of diabetes presence. Researchers and practitioners can leverage the predictive model developed in this work to make quick predictions of diabetes mellitus, which could save many lives.

Colletotrichum kahawae (Coffee Berry Disease) spreads through spores that can be carried by wind, rain, and insects affecting coffee plantations, and causes 80% yield losses and poor-quality coffee beans. The deadly disease is hard to control because wind, rain, and insects carry spores. Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93% accuracy using a random forest method. If the dataset is too small and noisy, the algorithm may not learn data patterns and generate accurate predictions. To overcome the existing challenge, early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes, prompt recognition, and accurate classifications. The proposed methodology selects CBD image datasets through four different stages for training and testing. XGBoost to train a model on datasets of coffee berries, with each image labeled as healthy or diseased. Once the model is trained, SHAP algorithm to figure out which features were essential for making predictions with the proposed model. Some of these characteristics were the cherry’s colour, whether it had spots or other damage, and how big the Lesions were. Virtual inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of disease. To evaluate the model’s performance and mitigate excess fitting, a 10-fold cross-validation approach is employed. This involves partitioning the dataset into ten subsets, training the model on each subset, and evaluating its performance. In comparison to other contemporary methodologies, the model put forth achieved an accuracy of 98.56%.

In this paper, we mainly present a machine learning based approach to detect real-time phishing websites by taking into account URL and hyperlink based hybrid features to achieve high accuracy without relying on any third-party systems. In phishing, the attackers typically try to deceive internet users by masking a webpage as an official genuine webpage to steal sensitive information such as usernames, passwords, social security numbers, credit card information, etc. Anti-phishing solutions like blacklist or whitelist, heuristic, and visual similarity based methods cannot detect zero-hour phishing attacks or brand-new websites. Moreover, earlier approaches are complex and unsuitable for real-time environments due to the dependency on third-party sources, such as a search engine. Hence, detecting recently developed phishing websites in a real-time environment is a great challenge in the domain of cybersecurity. To overcome these problems, this paper proposes a hybrid feature based anti-phishing strategy that extracts features from URL and hyperlink information of client-side only. We also develop a new dataset for the purpose of conducting experiments using popular machine learning classification techniques. Our experimental result shows that the proposed phishing detection approach is more effective having higher detection accuracy of 99.17% with the XG Boost technique than traditional approaches.

The two most frequently heard terms in the mining industry are safety and production. These two terms put a lot of pressure on blasting engineers and crew to give more while consuming less. The key to achieving the optimum blasting results is sophisticated bench analysis, which must be combined with design blast parameters for good fragmentation and safe ground vibration. Thus, a unique solution for forecasting both optimum fragmentation and reduced ground vibration using rock mass joint angle and blast design parameters will aid the blasting operations in terms of cost savings. To arrive at a proper understanding and a solution, 152 blasts were carried out in various mines by adjusting blast design parameters concerning the measured joint angle. The XG Boost, K-Nearest Neighbor, and Random Forest algorithms were evaluated, and the XG Boost outputs were shown to be superior in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Co-efficient of determination (R2) values. Using XG Boost, the decision-tree-based ensemble Machine Learning algorithm that uses a gradient-boosting framework and a simultaneous formula was developed to predict both fragmentation and ground vibration using joint angle and the same set of parameters.

Renewable Energy Communities (RECs) are crucial in advancing decentralised and sustainable energy systems. Accurate forecasting of short-term electricity demand is essential to support REC operational planning, improve energy efficiency and reduce dependence on external supply. This study presents a practical and effective forecasting framework for hourly electric load in a residential REC in Loureiro, Portugal. The approach is based exclusively on exogenous variables like weather conditions and calendar-based features. Two tree-based machine learning models, Extreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost), were applied to predict REC electric energy demand. The performance of the models was evaluated using standard regression metrics, including the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R2). Both models achieved high accuracy on unseen data, with CatBoost yielding significantly better results: MAE, RMSE, and R2 are equal to 1.21 kWh, 2.31 kWh, and 0.9718, respectively. An intraday 24-hour comparison confirmed the model’s ability to capture intraday consumption dynamics. These findings highlight the potential of exogenous feature-driven forecasting approaches to support efficient energy management in communityscale systems.

This study investigates the role of aluminum oxide nano-additives in enhancing the performance and reducing the environmental footprint of a B30 castor biodiesel blend in a compression ignition (CI) engine, emphasizing principles of green and sustainable chemistry. With global concerns over emissions and the depletion of fossil fuels, there is an urgent need for cleaner and more efficient alternative fuels. Biodiesel derived from non-edible sources, such as castor oil, presents a sustainable solution, but improvements in its combustion efficiency and emissions profile are crucial for widespread adoption. In this research, aluminum oxide nanoparticles were incorporated into a B30 biodiesel blend to enhance combustion properties, reduce ignition delay, and significantly mitigate harmful emissions, including carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NOx). The biodiesel was synthesized through the transesterification of castor oil, which is rich in ricinoleic acid, and tested on a Kirloskar diesel engine operating at a constant speed of 1500 rpm under varying load conditions. Results demonstrated that the nano-additive-infused biodiesel blend outperformed conventional diesel in terms of fuel economy, atomization, vaporization, and overall combustion efficiency. Additionally, the use of aluminum oxide nanoparticles reduced brake-specific fuel consumption (BSFC) and pollutant emissions. To further optimize engine performance and minimize emissions, a machine learning framework was applied, comparing algorithms such as Random Forest and XGBoost. The analysis identified XGBoost as the most accurate predictive tool, offering valuable insights for optimizing engine parameters. This work highlights the application of green and sustainable chemistry through the integration of nano-additives and advanced data analytics to develop cleaner, more efficient biodiesel fuels, contributing to the global shift toward environmentally friendly energy solutions.

From the generation of defective capital contributions to the transfer of transactions, a series of civil disputes and liability responsibilities still need to be explored for a better solution. In this paper, based on the current research status of the defective capital contribution assessment model of enterprise shareholders, we combine the XG Boost model with good classification ability and the logistic regression model with good interpretability and construct a discrete regression (XG Boost-Logistic) model for the evaluation of the defective capital contribution of enterprises. Combined with the data of 123 shareholders’ capital contributions from enterprises’ financial audit reports, the XG Boost discrete model, the Logistic regression model, and the XG Boost-Logistic evaluation combination model were used to conduct empirical analysis and comparative experimental analysis with the evaluation indexes of the model. The research results show that the accuracy rate based on the XG Boost-Logistic evaluation combination model is 87.39%; the efficiency of liability assessment is improved by 7.35% compared with the XG Boost model and 12.38% compared with the Logistic regression model. XG Boost-Logistic evaluation combination model can effectively improve the liability prediction of capital contribution defects classification accuracy and provide a good explanation of shareholder liability classification at the same time and can help companies to avoid financial risks plays a key role.