The durability and reliability of battery management systems in electric vehicles to forecast the state of charge (SoC) is a tedious task. As the process of battery degradation is usually non-linear, it is extremely cumbersome work to predict SoC estimation with substantially less degradation. This paper presents the SoC estimation of lithium-ion battery systems using six machine learning algorithms for electric vehicles application. The employed algorithms are artificial neural network (ANN), support vector machine (SVM), linear regression (LR), Gaussian process regression (GPR), ensemble bagging (EBa), and ensemble boosting (EBo). Error analysis of the model is carried out to optimize the battery’s performance parameter. Finally, all six algorithms are compared using performance indices. ANN and GPR are found to be the best methods based on MSE and RMSE of (0.0004, 0.00170) and (0.023, 0.04118), respectively.

Traditional screening of cervical cancer type classification majorly depends on the pathologist’s experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years. However, due to the increase in workload, vision screening causes misdiagnosis and low diagnostic efficiency. Medical image processing using the convolutional neural network (CNN) model shows its superiority for the classification of cervical cancer type in the field of deep learning. This paper proposes two deep learning CNN architectures to detect cervical cancer using the colposcopy images; one is the VGG19 (TL) model, and the other is CYENET. In the CNN architecture, VGG19 is adopted as a transfer learning for the studies. A new model is developed and termed as the Colposcopy Ensemble Network (CYENET) to classify cervical cancers from colposcopy images automatically. The accuracy, specificity, and sensitivity are estimated for the developed model. The classification accuracy for VGG19 was 73.3%. Relatively satisfied results are obtained for VGG19 (TL). From the kappa score of the VGG19 model, we can interpret that it comes under the category of moderate classification. The experimental results show that the proposed CYENET exhibited high sensitivity, specificity, and kappa scores of 92.4%, 96.2%, and 88%, respectively. The classification accuracy of the CYENET model is improved as 92.3%, which is 19% higher than the VGG19 (TL) model.

In this work, the experimental investigations were piloted to study the influence of hybrid nanoparticles containing SiO₂ and CeO₂ nanoparticles on thermo-physical characteristics of the paraffin-based phase change material (PCM). Initially, the hybrid nanoparticles were prepared by blending equal mass of SiO₂ and CeO₂ nanoparticles. The hybrid-nano/paraffin (HnP) samples were prepared by cautiously dispersing 0, 0.5, 1.0, and 2.0 percentage mass of hybrid nanoparticles inside the paraffin, respectively. The synthesized samples were examined under different instruments such as field emission scanning electron microscope (FESEM), Fourier transform infrared spectrometer (FTIR), differential scanning calorimetry (DSC), thermogravimetric analyzer (TGA), and thermal properties analyzer to ascertain the influence of hybrid nanoparticles on thermo-physical characteristics of the prepared samples. The obtained experimental results proved that the hybrid nanoparticles were uniformly diffused in the paraffin matrix without affecting the chemical arrangement of paraffin molecules. Prominently, the relative thermal stability and relative thermal conductivity of the paraffin were synergistically enriched up to 115.49% and 165.56%, respectively, when dispersing hybrid nanoparticles within paraffin. Furthermore, the hybrid nanoparticles appropriately amended the melting and crystallization point of the paraffin to reduce its supercooling, and the maximum reduction in supercooling was ascertained as 35.81%. The comprehensive studies indicated that the paraffin diffused with SiO₂ and CeO₂ hybrid nanoparticles at 1.0 mass percentage would yield a better outcome compared to the next higher mass fractions without much diminishing the latent heat of paraffin. Hence, it is recommended to utilize the hybrid-nano/paraffin with 1.0 mass fraction of the aforementioned hybrid nanoparticles for effectively augmenting the thermal energy capacity of low-temperature solar thermal systems.