Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.

Fraudulence in cosmetic ingredients is becoming increasingly prevalent, alongside the rising demand and utilization of cosmetics within the populace. One of the whitening agents still utilized in cosmetics is mercury, present in forms such as mercury chloramide (HgNH₂Cl₂) and mercury chloride (HgCl₂). Prolonged mercury exposure can have adverse health effects. To address this issue, alternative mercury analysis methods in samples have been developed, including the utilization of silver nanoparticles amalgamated with sweet potato starch as a stabilizing agent. This paper aims to delve into the roles of silver nanoparticle AgNO₃ and sweet potato starch (as a stabilizer) as a sensor for mercury detection, which can be applied in cosmetic products. Detection of mercury utilizing nanoparticles is based on the Surface Plasmon Resonance phenomenon, which endows a high level of selectivity and sensitivity toward the presence of mercury metal ions. When interaction occurs between mercury metal and silver nanoparticles, the liquid undergoes a color change from yellowish-brown to transparent. This phenomenon arises from the oxidation of AgO (yellow) to Ag⁺ ions (transparent) by the mercury metal. Consequently, a silver nanoparticle sensor utilizing sweet potato starch as a stabilizing agent exhibits the potential to detect mercury metal within a substance with high efficacy.

Breast cancer was a prevalent form of cancer worldwide. Thermography, a method for diagnosing breast cancer, involves recording the thermal patterns of the breast. This article explores the use of a convolutional neural network (CNN) algorithm to extract features from a dataset of thermographic images. Initially, the CNN network was used to extract a feature vector from the images. Subsequently, machine learning techniques can be used for image classification. This study utilizes four classification methods, namely Fully connected neural network (FCnet), support vector machine (SVM), classification linear model (CLINEAR), and KNN, to classify breast cancer from thermographic images. The accuracy rates achieved by the FCnet, SVM, CLINEAR, and k-nearest neighbors (KNN) algorithms were 94.2%, 95.0%, 95.0%, and 94.1%, respectively. Furthermore, the reliability parameters for these classifiers were computed as 92.1%, 97.5%, 96.5%, and 91.2%, while their respective sensitivities were calculated as 95.5%, 94.1%, 90.4%, and 93.2%. These findings can assist experts in developing an expert system for breast cancer diagnosis.

Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.

The changes the magnetic flux generated (electric, magnetic and electromagnetic waves) on the surface of earth due to sudden changes is a matter of discussion. These emissions occur along the fault line generated due to geological and tectonic processes. When sudden changes occur in the environment due to seismic and atmospheric variations, these sensing was observed by creatures and human bodies because the animals and trees adopt the abnormal signals and change the behavior. We have analyzed the changing behavior of recorded signal by live sensors (i.e., banyan tree). So we use the deep-rooted and long-aged banyan tree. The root of banyan tree (long-aged) has been working as a live sensor to record the geological and environmental changes. We record the low frequency signals propagated through solar-terrestrial environment which directly affect the root system of the banyan tree and changes that have been observed by live sensors. Then, very low frequency (VLF) signal may propagate to the earth-ionosphere waveguide. We have also analyzed the different parameters of live cells which is inbuilt in latex of the tree, so we record the dielectric parameters of green stem latex and found some parameters i.e., dielectric constant (ε) and dielectric loss (ε’) of various trees to verify these natural hazards and found good correlation. Therefore, we can say by regularly monitoring the bio-potential signal and dielectric properties of banyan tree and we are able to find the precursory signature of seismic hazards and environmental changes.