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.

The mechanical, physical and interfacial properties of aluminum alloys are improved by reinforcing the silicon carbide particles (SiC_p). Machinability of such alloys by traditional methods is challenging due to higher tool wear and surface roughness. The objective of research is to investigate the machinability of SiC_p reinforced Al6061 composite by Wire-Electrical Discharge Machining (wire-EDM). The effect of wire-EDM parameters namely current (I), pulse-on time (T_on), wire-speed (W_s), voltage (I_v) and pulse-off time (T_off) on material removal rate (MRR) is investigated and their settings are optimized for achieving the high MRR. The experiments are designed by using Taguchi L₁₆ orthogonal arrays. The MRR obtained at different experiments are analyzed using statistical tools. It is observed that all the chosen process parameters showed significant influence of on the MRR with contribution of 27.39%, 22.08%, 21.32%, 15.76% and 12.94% by I, I_v, T_off, T_on and W_s, respectively. At optimum settings, the Wire-EDM resulted in MRR of 65.21 mg/min and 62.41 mg/min for samples with 4% and 8% SiC_p. The results also indicated reinforcing SiC_p upto 8% showed marginally low influence on MRR. Microstructural investigation of the cut surface revealed the presence of craters with wave pattern on its surface. The top surface of the crater is featured by the recast layers connecting adjacent craters. Further, the statistical model is developed using linear regression to predict the MRR (R²—73.65%) and its predicting accuracy is verified by the confirmation trials. The statistical model is useful for predicting the MRR for different settings of the process parameters. The optimized settings can be used to improve the machining productivity by increasing the MRR while machining of Al6061-SiC_p (upto 8 wt. %) alloy by wire-EDM industries.

The usage of natural fibers is regarded as the most viable solution for controlling the consumption of synthetic materials. However, the low moisture resistance, stiffness and poor adhesion capabilities restrict their use in several advanced composite applications. Hence, different surface treatments are employed to improve the physical characteristics of natural materials. In this study, the effect of chemical treatments (sodium hydroxide, acetic acid, potassium permanganate and bromodecane) on the surface characteristics and the adhesion capabilities of Furcraea foetida (FF) fiber with epoxy material is evaluated. From the study, it is seen that the chemical treatment (CT) eliminates the O-H functional groups and enhances the hydrophobic characteristics in FF fiber. Furthermore, the CT removes the amorphous organic attachments and improves the crystallinity in the fiber. However, no substantial increase in the thermal stability of FF fiber was observed post chemical treatment. The microscopic analysis of chemically treated FF (CTFF) fiber shows the elimination of organic attachments and developed neat uniform surface structure. The NaOH-treated FF fiber exhibited maximum tensile strength (σ_t: 241.75 MPa). Whereas the acetic acid-treated FF fiber showed maximum tensile modulus (σ_m: 6.9 GPa) and interfacial shear strength (IFSS: 0.06 MPa) compared to CTFF and untreated FF fiber (UTFF).

Abrasive particles in the suspension mixture in an abrasive water suspension jet (AWSJ) machining causes acute skin friction effect thereby effectively changing the jet diameter due to wear, which in turn influences jet exit kinetic energy. This results in lowering the life of the jet for effective machining. In consideration of this aspect, the present work examines the effect of inlet pressure on skin friction coefficient and jet exit kinetic energy. It is inferred from the analysis that an increase in inlet pressure causes a significant increase in skin friction coefficient and also results in proportional increase in the exit kinetic energy of the jet. Further, it is revealed from the analysis that an increase volume fraction of abrasive (abrasive concentration) in water results in significant decrease in the skin friction coefficient and jet exit kinetic energy.

In recent days the rising concern over environmental pollution with excessive use of synthetic materials has led to various eco-friendly innovations. Due to the organic nature, abundance and higher strength, natural fibers are gaining a lot of interest among researchers and are also extensively used by various industries to produce ecological products. Natural fibers are widely used in the composite industry as an alternative to synthetic fibers for numerous applications and new sources of fiber are continuously being explored. In this study, a fiber extracted from the Furcraea foetida (FF) plant is characterized for its feasibility as a reinforcement to fabricate polymer composite. The results show that the fiber has a density of 0.903 ± 0.07 g/cm³, tensile strength (σ_t) of 170.47 ± 24.71 MPa and the fiber is thermally stable up to 250 °C. The chemical functional groups and elements present in the FF fiber are evaluated by conducting Fourier transform infrared spectroscopy (FT-IR) and energy dispersive spectroscopy (EDS). The addition of FF fibers in epoxy reduced the density (13.44%) and hardness (10.9%) of the FF/Epoxy (FF/E) composite. However, the void content (V_c < 8%) and water absorption (WA: < 6%) rate increased in the composite. The FF/E composite with 30% volume of FF fibers showed maximum σ_t (32.14 ± 5.54 MPa) and flexural strength (σ_f: 80.23 ± 11.3 MPa).

In the last few decades, natural composite materials have been considered one of the highly sustainable ecological alternatives for reducing the consumption of synthetic materials. Today, research on natural fiber composites is the main thrust for their use in various industrial applications. Further, continuous research works are being carried out to utilize natural composites as an alternative to synthetic materials. However, the inhomogeneity of composites, delamination, fiber pullout, higher surface roughness (SR) and dimensional inaccuracy under traditional machining have led the attention towards non-traditional machining, such as abrasive water jet machining, to achieve high-quality components. Hence, in this study, an experimental analysis based on the design of experiments is conducted on the machinability of a hybrid rice straw/Furcraea foetida composite under abrasive water jet machining (AWJM). Further, the concentration of the rice straw and the AWJ process parameters are varied, and their effects on the quality of machining is evaluated. The experimental trials are designed based on the Taguchi L₂₇ orthogonal array, followed by an analysis of variance (ANOVA). From extensive experimentations, the concentration of rice straw is observed to be the most contributing (93.5%) factor to the SR. The traverse speed (TS) shows the highest percentage contributions of 93.13%, 55.50 and 55.70% to the material removal rate (MRR) and the top (T_KW) and bottom kerf widths (B_KW), respectively. However, the interaction between the fiber concentration and traverse speed gives the maximum contribution (35.04%) to the kerf taper (K_T). A second-order response surface model is generated to study the effects of the process parameters on the SR, MRR, T_KW, B_KW and K_T in any experimental domain. Finally, the microstructural characteristics of the machined surfaces, such as micro-cracks, debonding, and fiber pullout, are discussed.