RVR & JC College of Engineering

Stock market prediction plays an important role in economic decisions and informed investment, but it remains a challenging task due to the market’s nonlinear, dynamic and uncertain nature. Existing statistical and deep learning approaches often struggled with limited generalization, overfitting issues and inadequate feature representation. By motivating these issues, a novel StockGAN + + model is introduced by integrating generative adversarial learning and graph-based modeling for stock price prediction. Here, two different types of datasets were used, such as NASDAQ and the stock ticker dataset. At the initial stage, the input data is normalized by z-score normalization for preprocessing. High-level features are extracted from the preprocessed data using a stacked autoencoder module. Based on the collected features, stock prediction is performed by the StockGAN + + model, which combines a gated graph convolutional network (GGCN) and a temporal convolutional autoencoder (TCAE) as a discriminator and generator. The hyperparameters are dynamically tuned using improved chaotic assisted grasshopper optimization (Imp-CGop). The proposed model obtains lower MSE values of 0.0000364 and a correlation of 0.997 at the National Stock Exchange (NSE) dataset. The proposed model has obtained better performance when compared to the state-of-the-art models.

In the pursuit of lightweight, high-strength materials for automotive and aerospace applications, the improvement of hybrid metal matrix composites (MMCs) has gained significant attention. This work investigates the mechanical and microstructural characteristics of LM26 aluminum alloy reinforced with varying weight percentages (2-8 wt.%) of silicon carbide (SiC) and graphite particles using the stir casting method. The aim is to enhance the performance of conventional aluminum alloys by incorporating the synergistic effects of ceramic (SiC) and solid lubricant (graphite) reinforcements. The mechanical properties, such as hardness, tensile, compressive, and flexural strength, were evaluated. Mechanical testing revealed that the composite with 6 wt.% reinforcement exhibited maximum performance, with tensile strength of approximately 300 MPa, compressive strength around 480 MPa, flexural strength near 310 MPa, and hardness reaching 162 BHN. Unlike prior studies focusing on single reinforcements, this research systematically explores combined SiC-graphite effects on LM26 composites. SEM indicated relatively uniform dispersion of reinforcements with minimal agglomeration, while EDS and XRD confirmed phase and elemental composition without deleterious phases. An artificial neural network (ANN) model was developed to accurately forecast mechanical properties from reinforcement composition, showing strong predictive capability. The findings provide quantitative benchmarks and enhanced understanding crucial for designing advanced LM26/SiC/graphite hybrid composites for structural, automotive, and aerospace applications.

Incorporating industrial by-products into concrete reduces the environmental impact ofcement production. This study evaluates sustainable ternary concrete mixes containing 10% fly ash, varying silica fume levels (0%, 6%, 12%, 18%, 24%), and 100% manufactured sand as fine aggregate to identify the optimal mix for enhanced mechanical and microstructural properties using scanning electron microscopy (SEM),energy-dispersive spectroscopy (EDS), thermogravimetric analysis (TGA), and machine learning (ML) assessment were done to streamline the experimental process. Compressive, split tensile, and flexural strengths, as well as ultrasonic pulse velocity, were measured at 7, 28, and 90 days. The mix with 10% fly ash, 12% silica fume, and 100% manufactured sand demonstrated the highest performance, with compressive strength increases of 18.61%, 16.85%, and 19.83% at each interval. Microstructural analysis revealed a dense C-S-H gel and uniform matrix, indicating improved hydrationand reduced porosity. Machine learning models (LASSO, Random Forest, Gradient Boosting, XGBoost, AdaBoost, and ANN) were applied to predict compressive strengthand to minimise the number of experimental trials. Gradient Boosting achieved the mostaccurate predictions, with an R2 of 0.9929 and minimal error, even with limited data. Both laboratory and machine-learning results confirm that concrete with 10% fly ash, 12% silica fume, and 100% manufactured sand provides a durable, high-performance solution for structural applications.

Buildings consume a large share of the energy used for indoor climate control, which raises greenhouse gas emissions and intensifies the urban heat island effect. Improving the thermal performance of roofing elements without reducing their strength is an important goal for sustainable construction. This study evaluates the heat transfer rate, thermal resistance and compressive strength of M20 grade concrete cool roof slab panels made with glass wool and rock wool as insulating materials, silica fume as a partial cement replacement, and graphene oxide as a nano additive. The insulating materials were used in peeled form and as one inch and two-inch-thick pads. Thirty-six panels of size 200 mm x 200 mm x 100 mm were cast and tested. Glass wool panels showed a lower heat transfer rate and lower thermal conductivity than rock wool panels. The top surface temperature dropped by about 15 ºC for glass wool panels and about 10 ºC for rock wool panels, relative to the control. The combined use of silica fume and graphene oxide gave the highest compressive strength, with glass wool outperforming rock wool. Glass wool with silica fume and graphene oxide suits energy efficient cool roof applications.

Abstract—In many small-scale industries and workshops, paper cutting is still performed manually, which is time-consuming, labor-intensive, and less accurate. This paper presents the design and fabrication of a semi-automated paper cutting machine using a Geneva wheel mechanism. The Geneva mechanism converts continuous rotary motion into precise intermittent motion, enabling step-by-step paper advancement for uniform cutting. The cutting blade, actuated via a slider-crank mechanism, operates only during the Geneva wheel’s dwell phase, ensuring complete synchronization. Experimental results confirm consistent cutting lengths and clean cuts. The system is cost-effective and suitable for small-scale printing units, packaging industries, and educational laboratories. Keywords—Geneva wheel mechanism; intermittent motion; paper cutting machine; slider-crank mechanism; automation.