Bharath University

Skin diseases commonly affect different age groups and with different groups of conditions. The severity of skin disease varies according to diverse conditions, with normal concerns such as acne and eczema, and serious diseases like cancer. However, there is a need for large and standardized datasets, image variations due to lighting effects, and high similarities among the diseases have a major impact on overall efficiency in traditional techniques. Thus, an advanced segmentation and classification model is necessary to achieve highly effective dermatological care. Therefore, this work designed an innovative deep learning-enabled skin disease classification model to alleviate the issues in dermatological image analysis. The skin images from publicly available sources like HAM10000 and PH2 Dataset are collected. These collected images are further fed into the developed Full Resolution Residual Networks with Attention Mechanism (FRRNet-AM) to segment abnormal regions from the image. The developed FRRNet-AM identifies accurate details of lesion boundaries, and it captures the subtle variations in the image. Then, various skin disease types are classified using the developed Adaptive Mobilenet (AMNet) framework, where several parameters are optimized by the novel Modified Random Function-based Secretary Bird Optimization Algorithm (MRF-SBOA). The developed AMNet classification module finds different skin disease classes with a high accuracy of 97.33

The present work aims to focus on the synthesis, growth, and characterization of a slow-evaporation-grown 2-amino-6-methylpyridinium hemifumarate dihydrate. X-ray diffraction analysis confirmed the crystalline quality of the compound with lattice parameters of a = 9.7112(10) Å, b = 14.4343(12) Å, c = 7.4723(7) Å and volume = 1040 Å3. Verification of the presence of functional groups was achieved through FTIR and FT-Raman studies. The identification of carbon and hydrogen molecules was confirmed via NMR analysis. UV–vis–NIR spectral studies were employed to determine the optical transmittance range and cut-off wavelength. The luminescent properties of the 2AF crystal were explored, while the dielectric properties of the 2AF crystal were analyzed through dielectric studies. Thermal analysis indicated stability up to 104 °C, with subsequent decomposition stages observed through differential thermal analysis (DTA). The crystal exhibited reverse saturable absorption (RSA) behavior, crucial for applications in optical limiting and laser protection. The microhardness analysis indicated that the crystal is classified as a soft material.

Although integrated optimization and multifunctional validation are still lacking, green production of metal oxide nanoparticles presents a viable substitute for traditional techniques. This work describes an environmentally friendly production of NiO nanoparticles (NiO NPs) utilizing seed extract from Syzygium cumini, which was followed by a thorough physicochemical, photocatalytic, and biological assessment. In order to achieve stable nanostructures, key parameters such as precursor concentration, extract volume, pH, temperature, and reaction time were tuned during the preparation of the nanoparticles using a phytochemical-mediated reduction method. Nanoparticle production was validated by UV-Vis spectroscopy, which showed a distinctive absorption peak at 330 nm. A crystalline cubic NiO phase was discovered by XRD analysis, and well-dispersed spherical particles with an average size of 15–25 nm were observed by HRTEM. TGA showed excellent thermal stability, VSM verified weak ferromagnetic behavior, and FTIR revealed phytochemical-mediated capping. According to pseudo-first-order kinetics, photocatalytic tests employing methylene blue demonstrated 78

The accurate forecasting of mechanical characteristics of natural fiber-reinforced composites were essential for development like structural materials. Advancement of natural fiber–reinforced hybrid composites were frequently impeded expensive, time-consuming, and resource-demanding processes. Although machine learning (ML) presents robust option for expediting prediction, conventional models frequently operate ‘black boxes,’ so constraining reliability and lacking scientific insights essential for genuine material design. This research addresses a significant gap by formulating and validating a comprehensive framework that integrates experimentation, explainable machine learning (ML), and finite element analysis (FEA). Date palm fruit stalk fiber (DPFSF) reinforced epoxy composites incorporating areca nut husk biofiller were experimentally fabricated and evaluated to elucidate and predict their mechanical characteristics. Three supervised ML models Gradient Boosting, k-Nearest Neighbor and Random Forest was trained and constructed using comprehensive experimental dataset. Random Forest model demonstrated greatest prediction, attaining R2 values of 0.90 for Tensile Strength (TS) and 0.95 for Flexural strength (FS) during validation. To tackle “black box” issue of machine learning, SHapley Additive exPlanations (SHAP) analysis was utilized, identifying filler percentage and length as primary determinants influencing strength estimates. This study demonstrates that explainable machine learning is a viable and sustainable method for optimizing hybrid composites based on natural fibers.

Aluminum matrix composites are progressively utilized in automotive, aerospace, and structural applications that necessitate lightweight materials with superior wear resistance and consistent frictional properties under rigorous operating conditions. Enhancing the tribological performance of aluminum alloys by appropriate reinforcement techniques and effective multi-objective optimization is a significant task. This work examined and refined tribological attribute of stir-cast AA8011 aluminum matrix composites augmented with bimodal micro-sized silicon dioxide (SiO₂) particles. A constant reinforcement content of 5 wt