As is known, batteries have started to be used increasingly in both power distribution and transmission networks. This study develops a near-optimal approach for ancillary services in power networks from the perspective of the battery owner. We first model the optimization algorithm for the battery owner, then utilize a grey wolf optimization approach, where near-optimal actions are selected daily from available services. We use real data of frequency, voltage magnitude, combined home and Photovoltaic system, and transformer load to perform the simulations. The simulation results show that battery owners may profit from these services and help the system operators solve the issues such as over-voltage, under-voltage, frequency, and similar.

In tomato production, one of the most significant problems is the identification of Tomato Leaf Disease (TLD). Plant leaf disease is the primary factor that influences both the quality and quantity of crop production. India holds the second position in tomato making. However, multiple diseases contribute to the decline in the quality of tomatoes and the decrease in crop yield. Hence, it is important to accurately categorize and diagnose the tomato plant leaf infection. The productions of tomatoes are impacted by many leaf diseases. Early recognition of the diseases helps to reduce the disease infection and improve the yield of crops. Certain diseases are identified and classified using several methods. Therefore, the TLD classification and identification model is developed to solve the above problems. The images related to tomato leaves are aggregated in the initial phase through online sources. Then, the images are forwarded to the pre-processing phase. Further, the pre-processed image is given to the segmentation process, where the Adaptive Fuzzy C-Means (AFCM) technique is utilized. Meanwhile, the parameters of the AFCM algorithm complicate the cluster assignment in the presence of outliers or noise, thus resulting in reduced clustering performance. So, the parameters of AFCM are tuned by utilizing the new improved algorithm named Dingo Optimization Algorithm (DOA) to improve the clustering accuracy. It is done by assuming the AFCM parameters as a population of Dingoes and the maximum classification accuracy as its fitness function. Finally, the segmented images are fed to the classification process, where the Residual Attention Network (RAN) is used to attain the classified outcomes. Therefore, the investigated system shows a more efficient TLD prediction rate compared to traditional techniques in the experimental investigation. The results from the experiments indicate that the suggested models exhibit exceptional classification performance, achieving an accuracy rate of 95.22%. Therefore, the model suggests advancement in predictive capabilities over traditional methods.

Dental diseases have risen in the past few years due to improper hygiene. Early detection and diagnosis can control this rapid growth in dental diseases. Therefore, different traditional techniques are employed for the detection of dental problems. However, these classical techniques such as X-Ray and CT scans are considered to be time-consuming, ineffective, and prone to errors due to human intervention. Hence, AI techniques are used to obtaining precise outcomes for dental-related issues. The conventional ML (Machine Learning) techniques are inefficient for obtaining enhanced outcomes as the efficiency of ML techniques heavily depends on image processing approaches. They are performed and also the quality of the features that have been extracted. Further, ML techniques lack in producing better outcomes while dealing with huge datasets. Therefore, the proposed model employs DL (Deep Learning) techniques due to its capability to learn the features strongly from the data by using a general-purpose learning procedure. So, DL techniques can work efficiently on huge datasets. The proposed DC (De-convolution Component) with RES (Residual Prolonged Bypass) is employed in the present research work as it is responsible to increase the spatial resolution of the feature maps and helps in recovering lost spatial information during the down sampling process. Likewise, the RES model aids in proficiently proliferating both low-level and high-level features to the deep layers, which help in generating better-segmented images. RES model includes prolonged bypass paths that carry feature information across multiple layers. This ensures that features extracted at earlier layers (low-level features) are available at much deeper layers. Implementation of the present research work contributes to enhancing the overall performance and effectiveness in detecting and diagnosing various dental issues and possesses the capability to work on both small and massive datasets effectively. Also, the proposed work contributes to deliver better accuracy, IoU (Intersection Over Union) and Dice coefficient, compared to Multi-Headed CNN and Context Encoder-Net, thereby assisting dental professionals in the detection and diagnosis of various dental issues due to the effectiveness of the proposed model.

Approximate computing is a new technique that promises to speed up computations while preserving a level of precision suitable for error-tolerant systems such as neural networks and graphics. At the edge, a lot of computationally complex methods are now in use. As such, designing quick and low-energy circuits is crucial. This work presents a novel approximate full adder approach that lowers power consumption and delay at the expense of some output mistakes. To achieve these objectives, the proposed full adder architecture makes use of fundamental gate logic reduction techniques. Evaluations based on the Intel FPGA synthesis tool indicate that the suggested adder surpasses state-of-the-art techniques in terms of power, speed, and propagation delay. The design parameters - area, power dissipation, and latent characteristics of proposed adder are verified by simulation using EDA tools. The results demonstrate that our proposed approximate adder runs faster and requires fewer logic components than earlier equivalent systems. The synthesis reports testify to the fact that compared to other adders currently in use, the suggested adder used less logic elements. Furthermore, suggested approximation adders were used to execute image additions. Using image addition, the image quantitative statistics are used to application-level accuracy metrics analysis. Quantitative results confirm the superior functioning of the full adder cell approximation over comparable models.

Understanding the winding configurations of electric machines is essential for students of electrical engineering, yet it remains one of the most complex and abstract areas of study. At the beginning of this paper, a brief overview of existing software solutions for electric machine winding design is provided, along with a summary of previously developed educational tools created at the Faculty of Technical Sciences Cacak. These earlier software tools, focused on both DC and AC machine windings, laid the foundation for the creation of more advanced and interactive learning resources. Building on this foundation, and leveraging the experience gained through the development of more than 50 GeoGebra applications at the Faculty, this paper presents a new interactive GeoGebra-based application specifically developed to support the teaching and learning of electric machine windings, with a focus on DC machine armatures. The developed application enables users to define winding parameters, calculate winding steps, generate winding tables, visualize developed winding diagrams, and simulate commutator and brush placement across eight different winding types. These features are designed with a strong emphasis on educational value, offering an intuitive and didactic workflow that serves as an effective teaching aid in mastering topics related to electrical machines. The new tool introduces interactivity, parameter validation, and animation-providing students with a hands-on experience that enhances both comprehension and retention, while also improving the effectiveness of both classroom and distance learning in electrical machines education.

Wireless Multimedia Sensor Networks (WMSNs) are critical for various applications requiring reliable and secure data transmission. Enhancing routing protocols in WMSNs is essential to improve performance and security. Existing routing techniques, such as LEACH, Directed Diffusion, and AODV, often suffer from high energy consumption, limited throughput, and vulnerability to security breaches. These limitations hinder the overall efficiency and reliability of WMSNs. Conventional methods struggle to maintain low latency and high data integrity under increasing network loads, leading to performance degradation. This study proposes the Enhanced Minimum Distance Secure Routing Algorithm (EMDSRA), designed to optimize energy efficiency, increase throughput, and enhance security in WMSNs. The dataset comprises simulations with node densities of 100, 200, 300, 400, 500, and 600 nodes, evaluating metrics such as energy consumption, data throughput, latency, and security. Experimental results show that E-MDSRA reduces energy consumption, increases throughput and significantly improves security metrics compared to existing techniques. Specifically, E-MDSRA shows an improvement in data integrity and reduction in unauthorized access incidents. In comparison, Directed Diffusion and AODV also show improvements, but EMDSRA outperforms them across all evaluated metrics. In conclusion, E-MDSRA demonstrates substantial improvements in network efficiency and security, making it a robust solution for future WMSN deployments.

Effective road maintenance is crucial for ensuring safe and efficient transportation but is often compromised by the widespread occurrence of potholes. This study introduces a novel approach using an EfficientDet-based model for sophisticated pothole monitoring. Potholes pose a significant hazard that requires proactive detection and timely resolution. Traditional detection methods frequently fall short in terms of accuracy and real-time capability. Addressing these limitations, our research leverages the EfficientDet architecture, known for its optimal balance of accuracy and computational efficiency, to enhance the detection and monitoring of potholes. We utilized a carefully curated dataset from Kaggle, which includes 1,500 training images, 1,000 validation images, and 500 test images, encompassing a variety of real-world pothole scenarios. This diversity enables the model to generalize effectively across different conditions. Our experimental evaluations demonstrate that the EfficientDet-based model achieves an impressive average precision of 0.90 and a robust recall of 0.92, highlighting its capacity for accurate and swift pothole detection-an essential component for improving road maintenance. Moreover, we provide a comparative analysis with five contemporary pothole detection algorithms: YOLOv5, RetinaNet, CenterNet, SSD, and Faster R-CNN, among which EfficientDet consistently shows superior performance in terms of precision, recall, F1-Score, and average precision. These findings highlight the significant advancements in road safety, infrastructure management, and resource optimization. By adopting sophisticated deep learning techniques like EfficientDet, we promote a transformative improvement in road maintenance practices, paving the way for more resilient, safe, and disruptionminimized transportation networks.

This study employs the particle swarm optimization (PSO) approach using Stand Still Frequency Responses Testing (SSFR) to identify the time constants (poles and zeros) of the operational inductances along the d and q axes, as well as the parameters of the equivalent circuits for the SSFR1, SSFR2, and SSFR3 synchronous machine models. The difference between the frequency responses of the identified and simulated models at a standstill is minimized using a quadratic criterion in this method. The SSFR3 model accurately represents the synchronous machine, and simulation results show that the PSO approach is effective in terms of convergence rate and offers ideal solutions.

The study presents an Ultra-Wideband Slot-loaded Antipodal Vivaldi Antenna (SL-AVA) designed for through-the-wall radar imaging (TWRI) applications. The antenna incorporates rectangular slots of varying lengths and widths, effectively extending its electrical length and suppressing surface waves. These variable-length slots play a crucial role in enhancing overall performance by improving bandwidth, impedance matching, and radiation characteristics. Fabricated on a Rogers 5880 substrate with dimensions of 60.50 ? 66.10 mm?, the SL-AVA operates efficiently across a wide frequency range of 3 GHz to 10 GHz. It achieves a peak gain of 11 dBi. Experimental fabrication and testing validate the SL-AVA antenna?s characteristics, including compact size, high gain, ultra-wide bandwidth, and directional radiation, making it an excellent choice for TWRI applications.

Traffic monitoring is a critical issue in networking in general, especially in SDN due to its layered architecture in which the control plane represents a single point of failure. Therefore, this paper is tailored to mitigate the control and mitigate the effect od the DDoS attacks in SDN networks. It presents a complete machine learning (ML) workflow that begins with data ingestion and end with a trained model that is capable of analyzing packets in a production network. Three ML pipelines are part of this workflow, where the training process is carried out on a distributed framework, i.e., Spark, to accomplish a near real time analysis for each flow of packets. To evaluate the performance of the suggested workflow, the LRHR DDoS 2024 dataset is employed. The decision tree model outperforms the remaining models with 99% of accuracy and 4 min 33 s of training time.