Electrical Engineering Technical Journal

Renewable energy generation can help countries achieve the Sustainable Development Goals by providing
clean, safe, reliable and affordable energy. Conventional energy production is impractical due to shortages,
high fuel prices and harmful emissions from fossil fuels. Convergence is the best way to address renewable
energy issues, because it combines multiple renewable sources simultaneously. This research proposed a
hybrid renewable energy system that combines energy from two or more locally accessible sources to meet
demand in remote locations. A hybrid solar, wind and biogas grid system has been proposed, evaluated using
the homer Pro software and an artificial intelligence technique called the genetic algorithm. The proposed
strategy provided low cost, optimal volume, reduced emissions, high reliability. Comparing the results of
GA-based optimization and Homer Pro, a clear trend emerged: GA-based optimization showed better
performance stability and lower energy costs, which indicates a more economically efficient system. The
GA-based optimization also proposed smaller sizes for Homer components, which reduced the net current
cost and proved that a system with the same efficiency and reliability could effectively meet the energy
requirements of the site.

This paper investigates the application of Static VAR Compensators (SVCs) with neural network control to enhance
power system grid stability, particularly in multi-source energy systems. SVCs, as Flexible alternating current
Transmission Systems (FACTS) devices, are crucial for reactive power compensation and voltage regulation. The
study models and simulates an SVC controlled by a neural network in MATLAB/Simulink, assessing its performance
under three-phase fault conditions. The fault a 3-phase to-ground short circuit fault is introduced at location in close
proximity to the wind energy. Results demonstrate that the proposed control scheme effectively reduces system
oscillations and improves dynamic response, leading to faster fault recovery and enhanced overall grid stability. The
superior dynamic performance of the SVC-based neural network controller confirms its potential for improving power
system resilience.

The Internet of Things has expanded rapidly in recent years, introducing new capabilities into everyday devices while
simultaneously exposing those systems to increasingly sophisticated Distributed Denial-of-Service attacks. In light of
this dual-edged growth, the present study utilizes the NS-3 simulator for a granular traffic analysis that is capable of
both detecting and mitigating Distributed Denial of Service (DDoS) attacks. Multiple machine-learning classifiers —
Random Forest (RF), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) — were cycled
through the experiment to gauge their speed and precision. The LSTM model achieved 98.5% accuracy in detecting
DDoS attacks in the simulated IoT environment primarily due to its ability to effectively capture temporal patterns
and dependencies in sequential network traffic data. Overall, the strategy outlines a set of security protocols that should
scale effectively across large IoT landscapes

Biomedical sensors and implants are regarded as a significant technology for improving the quality of healthcare, as they enable proactive illness management and ongoing monitoring of the patient vital signs. Like many other devices, they are restricted to a limited amount of energy, and this leads to a challenge in terms of the lifespan of the device. This study aims to address this issue by designing and implementing a wireless energy transmission system specifically designed for such devices. The proposed design is based on utilizing spider-web and the series–parallel configuration to provide sufficient energy transmission for the device. The model has been examined under various conditions, including changes to the transmission distance between coils, the source voltage level, and operating frequency. Examined performance metrics including the output DC voltage and power transfer, as well as the overall efficiency of power transfer, proving that the strategy is feasible. As many biomedical implants such as pacemakers required 5 volts to operate, the study target voltage was 5 V. Two source voltages (10, and 20 V) were demonstrated. The design was examined at six operating frequencies, ranging from 1.78 MHz to 6.78 MHz. The most acceptable results were achieved at 1.78 MHz. Power transfer efficiencies at a 10 mm transmission distance were 91.5% and
91.15% for source voltages of 10 V and 20 V, respectively. The proposed design demonstrates high efficiency which is appropriate for powering BMI wirelessly.

Tongue diagnosis is an important method in both Traditional Chinese Medicine (TCM) and Western Medicine (WM), as the tongue\'s appearance can reflect a person’s overall health. Among the key features observed, tongue shape and color play a major role in identifying certain diseases and tracking their progression. This study focuses on the tongue image analysis method of artificial intelligence (AI) to detect shapes and colors of tongue for fast health screening without any need for human intervention. The proposed system firstly used the You Only Look Once version 10 model (YLOVv10) a deep learning object detection system on 750 tongue images in four tasks. The first task used the YOLOv10 model to detect and isolate the entire tongue region from the input image to ensure that the following tasks focus only on the tongue region. The second task was to accurately classify the tongue into seven shape categories, including normal, geographic, fissured, scalloped, thin, swollen, and deviated tongues. Thirdly the system detected crack types associated with fissured tongue, including side cracks, vertical cracks, deep cracks and irregular cracks. Lastly, the system detected whether the tongue contains ulcers or spots or not. The study also used the machine learning CatBoost model to train 5550 color images captured at different color saturations and under different light conditions and classified into seven classes (red, yellow, green, blue, gray, white, and pink) using several color space models, including (RGB, YcbCr, HSV, LAB, and YIQ) as input features to analyze and extract tongue color. The WebApp was developed using Streamlit to offer an easy-to-use graphical interface and provides an automatic tongue shape and color detection tool and compares results based on both TCM and WM perspectives, thus supporting early screening and medical analysis in a fast and reliable way(https://ai-linguasense-version2025.streamlit.app/).