In recent years, deep learning methods have dramatically improved medical image analysis, though earlier models faced difficulties in capturing intricate spatial and contextual details. These challenges highlighted the necessity for more powerful and flexible models. In this study, we introduce an innovative method for the multi-classification of breast cancer histopathological images utilizing Bidirectional Recurrent Neural Networks (BRNN). The BRNN structure consists of four unique elements: the backbone branch for transfer learning, the Gated Recurrent Unit (GRU), the residual collaborative branch, and the feature fusion module. Specifically, the transfer learning aspect exploits a Convolutional Neural Network (CNN) based on the ResNet50 architecture to draw image features from the ImageNet dataset, enhanced with an attention mechanism for improved feature representation. Additionally, the residual branch identifies specific pathological features using Floating Point Operations (FLOP). This cooperative method ensures thorough extraction of breast cancer classification features. The BRNN model was tested on the BreaKHis breast cancer dataset, comprising 7,909 microscopic images across 8 various classes from 82 patients. Our model achieved an average classification accuracy of 97.25 percent, exceeding current leading techniques. The BRNN model, refined using the Adagrad optimization algorithm, efficiently integrates the learned features from both branches. This tool provides oncologists with a substantial enhancement in diagnosing and planning treatment for breast cancer.

In modern industrial manufacturing processes, induction motors are broadly utilized as industrial drives. Online condition monitoring and diagnosis of faults that occur inside and/or outside of the Induction Motor Drive (IMD) system make the motor highly reliable, helping to avoid unscheduled downtimes, which cause more revenue loss and disruption of production. This can be achieved only when the irregularities produced because of the faults are sensed at the moment they occur and diagnosed quickly so that suitable actions to protect the equipment can be taken. This requires intelligent control with a high-performance scheme. Hence, a Field Programmable Gate Array (FPGA) based on neuro-genetic implementation with a Back Propagation Neural network (BPN) is suggested in this article to diagnose the fault more efficiently and almost instantly. It is reported that the classification of the neural network will provide the output within 2 µs although the clone procedure with microcontroller requires 7 ms. This intelligent control with a high-performance technique is applied to the IMD fed by a Voltage Source Inverter (VSI) to diagnose the fault. The proposed approach was simulated and experimentally validated.

The aging of PV cells reduces their electrical performance i.e., the parasitic parameters are introduced in the solar panel. The shunt resistance (R_Sh), series resistance (R_S), photo current (I_Ph), diode current (I_d), and diffusion constant (a₁) are known as parasitic or extraction parameters. Cracks and hotspots reduce the performance of PV cells and result in poor V–I characteristics. Certain tests are carried out over a long period of time to determine the quality of solar cells; for example, 1000 h of testing is comparable to 20 years of operation. The extraction of solar parameters is important for PV modules. The Tabu Search Optimization (TSO) algorithm is a robust meta-heuristic algorithm that was employed in this study for the extraction of parasitic parameters. Particle Swarm Optimization (PSO) and a Genetic lgorithm (GA), as well as other well-known optimization methods, were used to test the proposed method’s correctness. The other approaches included the lightning search algorithm (LSA), gravitational search algorithm (GSA), and pattern search (PS). It can be concluded that the TSO approach extracts all six parameters in a reasonably short period of time. The work presented in this paper was developed and analyzed using a MATLAB-Simulink software environment.

This paper discusses the design and evaluation of a four-element Multiple-Input, Multiple-Output (MIMO) antenna array with Circular Polarization (CP), tailored explicitly for sub-6 GHz 5G wireless systems. The proposed antenna utilizes an elliptical ring slot with a variable width in the ground plane and an asymmetric feed line layout to achieve orthogonal CP radiation in both the forward and backward directions. Three interconnected strip lines are incorporated into the ground plane for enhanced performance, increasing isolation and ensuring effective linkage between the antenna components and their respective grounds. The antenna exhibits strong isolation, a low Envelope Correlation Coefficient (ECC), and minimal Channel Capacity Loss (CCL), making it ideal for MIMO applications. The analysis confirms the antenna's stable CP gain and operational efficiency throughout the sub-6 GHz band, meeting the rigorous standards of contemporary 5G networks.

In this article, we proposed an integrated application of automatic moisture and Irrigation control using Real-Time Clock (RTC) and Light-Dependent Resistor (LDR). The main idea of this paper is predicated on using as little energy as possible. In this work, we use the DS1307 real-time clock module to automatically switch on or off motor dependent on the time of day. The sensors are used for monitoring the soil conditions in agriculture field. Programming controls the timing of when the device is active. The most significant benefit of the proposed design is the reduction of risk associated with potential crashes and save water. Motors on the farm are often switched on at dry time and left OFF on wet conditions till necessity. Their operation is entirely automatic. Because of the protection of the farmers from the electrical shock during the rainy season, this proposed design intends to automate the operation of soil monitoring and irrigation in order to reduce water, power consumption and advance technological progress in agriculture farms and support in smart India. A significant amount of electricity is lost in the typical lighting system; however, this may be prevented with automatic control employing LDR.