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.

1. According to the World Health Organization, globally more than 400 million people are suffering from eye diseases such as cataract, glaucoma, etc. (World report on vision, World Health Organizat...

This study presents a robust machine learning framework based on Gaussian process regression (GPR) to predict the tensile strength of polymer nanocomposites reinforced with various nanofillers and processed under diverse techniques. A comprehensive dataset comprising 25 polymer matrices, 22 surface functionalization methods, and 24 processing routes was constructed from the literature. GPR, coupled with Monte Carlo sampling across 2000 randomized iterations, was employed to capture nonlinear dependencies and uncertainty propagation within the dataset. The model achieved a mean coefficient of determination (R²) of 0.96, RMSE of 12.14 MPa, MAE of 7.56 MPa, and MAPE of 31.73% over 2000 Monte Carlo iterations, outperforming conventional models such as support vector machine (SVM), regression tree (RT), and artificial neural network (ANN). Sensitivity analysis revealed the dominant influence of Carbon Nanotubes (CNT) weight fraction, matrix tensile strength, and surface modification methods on predictive accuracy. The findings demonstrate the efficacy of the proposed GPR framework for accurate, reliable prediction of composite mechanical properties under data-scarce conditions, supporting informed material design and optimization.

Wire electric discharge machining (WEDM) is one of the foremost methods which has been utilized for machining hard-to-cut materials like Titanium alloys. However, there is a need to optimize their important operating parameters to achieve maximum material removal rate (MRR). The present paper investigates the effect of control factors like current, pulse on time (T_on), pulse off time (T_off) on MRR of machining of Ti-6Al-4V alloy. The study showed that, increase in current from 2 A to 6 A results in a significant increase in MRR by 93.27% and increase in T_on from 20 μs to 35 μs improved the MRR by 7.98%, beyond which there was no improvement of MRR. The increase in T_off showed a counterproductive effect. Increase in T_off from 10 μs to 30 μs showed an almost linear decrease in MRR by 52.77%. Morphological study of the machined surface showed that cut surface consists of recast layer on which microcracks were present, and revealed the presence of globules, ridge-structured formations of recast layers and voids. In addition, a regression model was developed to predict the MRR with respect to the control factors, which showed a good prediction with an R² value of 99.67%.

This work explores the mechanical, thermal, and tribological characteristics of carbon fabric reinforced epoxy (CF-Ep) composites filled with halloysite nanotubes (HNT). The mechanical properties were evaluated, including hardness, interlaminar shear strength (ILSS), tensile strength, and flexural strength. The enhanced curing and even dispersion of HNTs in the epoxy matrix were validated by DSC, FTIR, and SEM measurements. Thermogravimetric analysis (TGA) and dynamic mechanical analysis (DMA) demonstrated improved thermal stability and damping, especially for the 0.75 wt.% HNT composite. Tribological performance was investigated utilizing a pin-on-disc configuration (60 N, 3 m/s) and silica sand (212 µm, 30 N, 2.38 m/s) under three-body abrasion and dry sliding wear, respectively. Hardness was highest at 1.75 wt.% HNT, and wear resistance and mechanical performance were best at 0.75 wt.% HNT composite. Surface damage, including matrix separation, micro-ploughing, and fragmentation, was lessened in 0.75 wt.% HNT composites, according to scanning electron microscopy worn surfaces. Using Minitab 17 and the Taguchi approach, wear experiments was created using an L16 orthogonal array. There were four levels of variation in three factors: load (10–40 N), abrading distance (250–1000 m), and HNT content (0–2.75 wt.%). The most important variables influencing wear volume loss were found to be load + distance and load + filler interactions using ANOVA and regression analysis. Scanning electron microscopy revealed that H0.75% HNT-filled composites had the best resistance to wear because they showed less surface damage mechanisms, such as fragmentation, micro-ploughing, and matrix detachment, when they were worn-out by dry sliding or abrasion. Overall, by strengthening interfacial bonding, improving load transfer, and creating a protective tribolayer that decreased material loss and surface damage during abrasion, HNTs improved mechanical and wear properties. Specifically, the 0.75 wt.% HNT composite showed outstanding heat stability and wear resistance, which made it a good option for high-performance uses such as power plant chute and automobile liners.