Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for
The mathematical construction of an ecological model with a prey-predator relationship was done. It presumed that the prey consisted of a stage structure of juveniles and adults. While the adult prey species had the power to fight off the predator, the predator, and juvenile prey worked together to hunt them. Additionally, the effect of the harvest was considered on the prey. All the solution’s properties were discussed. All potential equilibrium points' local stability was tested. The prerequisites for persistence were established. Global stability was investigated using Lyapunov methods. It was found that the system underwent a saddle-node bifurcation near the coexistence equilibrium point while exhibiting a transcritical bifurcation
... Show MoreSignificant advancements in nanoscale material efficiency optimization have made it feasible to substantially adjust the thermoelectric transport characteristics of materials. Motivated by the prediction and enhanced understanding of the behavior of two-dimensional (2D) bilayers (BL) of zirconium diselenide (ZrSe2), hafnium diselenide (HfSe2), molybdenum diselenide (MoSe2), and tungsten diselenide (WSe2), we investigated the thermoelectric transport properties using information generated from experimental measurements to provide inputs to work with the functions of these materials and to determine the
Electrical resistivity tomography (ERT) methods have been increasingly used in various shallow depth archaeological prospections in the last few decades. These non‐invasive techniques can save time, costs, and efforts in archaeological prospection and yield detailed images of subsurface anomalies. We present the results of quasi‐three‐dimensional (3D) ERT measurements in an area of a presumed Roman construction, using a dense electrode network of parallel and orthogonal profiles in dipole–dipole configuration. A roll‐along technique has been utilized to cover a large part of the archaeological site with a 25 cm electrode and profile spacing, respectively. We have designed a new field proce