Cardiovascular diseases, which are currently the major causes of death globally, can be largely ameliorated through early detection and categorization. Electrocardiogram (ECG) tests have emerged as widely employed, low-cost and non-invasive procedures for evaluating electrical activities of the heart and diagnosing cardiovascular ailments. In this research, by using deep learning techniques to detect specific cardiac disorders like cardiac myocardial infarction(MI), arrhythmia, past history of myocardial infarction(PMI) and normal ECG patterns on a dataset containing patients with heart disease. We propose ECGConVT framework that combines Convolutional Neural Network (CNN) module for extracting local features, and Vision Transformer (ViT) module for capturing global features. The final classification is achieved by combining the two using Multilayer Perceptron (MLP) module. The experimental results indicate promise of ECGConVT in ECG image classification where it outperforms other approaches showing an average accuracy of 98.5%, F1-score: 98.7%, Recall: 98.8% and Precision: 98.5%. In order to meet the practical needs of clinical applications, we implemented a lightweight post-processing step to reduce the size of the model.

The rapid growth in wireless communication demands has led to a surge in research on technologies capable of enhancing communication reliability, coverage, and energy efficiency. Among these, uncrewed aerial vehicles (UAV) and reconfigurable intelligent surfaces (RIS) have emerged as promising solutions. Prior research on using deep reinforcement learning (DRL) to integrate RIS with UAV concentrated on enhancing signal quality and coverage, but it ignored the challenges caused by electromagnetic interference (EMI). This article introduces a novel framework addressing the challenges posed by EMI from Gallium nitride (GaN) power amplifiers in RIS-assisted UAV communication systems. By integrating DRL with quadrature phase shift keying (QPSK) modulation, the proposed system dynamically optimizes UAV deployment and RIS configurations in real-time, mitigating EMI effects, improving signal-to-interference-plus-noise ratio (SINR), and enhancing energy efficiency. The framework demonstrates superior performance, with an SINR improvement of up to 6.5 dB in interference-prone environments, while achieving a 38% increase in energy efficiency compared to baseline models. Additionally, the system significantly reduces EMI impact, with a mitigation rate of over 70%, and extends coverage area by 35%. The integration of QPSK and DRL allows for real-time decision-making that balances communication quality and energy consumption. These results show the system’s potential to outperform traditional methods, particularly in dynamic and challenging environments such as urban, disaster recovery, and remote settings.

In this paper, we proposed a fusion feature extraction method for content based image retrieval. The feature is extracted by focusing on the texture and shape features of the visual image by using the Local Binary Pattern (LBP – texture feature) and Edge Histogram Descriptor (EHD – shape feature). The SVD is used for decreasing the number of the feature vector of images. The Kd-tree is used for reducing the retrieval time. The input to this system is a query image and Database (the reference images) and the output is the top n most similar images for the query image. The proposed system is evaluated by using (precision and recall) to measure the retrieval effectiveness. The values of the recall are between [43% –93%] and the average recall is 64.3%. The values of precision are between [30%-100%] and the average is 72.86% for the entire system and for both databases.

This paper introduces a holistic and scalable framework for optimizing Large Language Models (LLMs) in distributed environments, addressing three critical challenges: computational efficiency, ethical fairness, and governance. As LLMs scale, issues, such as excessive resource consumption, fairness violations, and limited transparency, hinder their broader deployment in real-world applications. We propose a novel three-tier architecture that integrates topology-aware parallelism, communication-efficient gradient aggregation, and memory-aware rematerialization. Our implementation reduces training time by 38% and memory usage by 42% on a 512-GPU A100 cluster, without compromising accuracy. To promote fairness, we incorporate a real-time adversarial debiasing module that reduces demographic AUC gaps by over 60% across gender, ethnicity, and religion. For model interpretability, we introduce a symbolic explainability engine that converts attention weights into transparent rule-based explanations, achieving 89.2% user satisfaction and outperforming Grad-CAM and vanilla attention. Furthermore, a lightweight governance layer aligned with ISO/IEC 27001 and ISO/IEC 23894 standards ensures traceability, audit logging, and policy enforcement throughout the model lifecycle. We validate our framework across diverse datasets, including C4, WikiText-103, RealNews, and BookCorpus, demonstrating low-latency drift and consistent fairness across domains. Comparative benchmarks against DeepSpeed, FairScale, and Megatron-LM show superior throughput, energy efficiency, and transparency. This work advances the foundation for ethical, efficient, and regulation-compliant LLM deployment at scale.

In this paper, we describe the construction of important landmarks of roads in the GIS environment. The system uses the corners between more than two roads as an important landmarks. In this corner points will be saving a number of images, each one represents the movement direction between two segment roads. The objective of our work is to build the geo-database repository depend on the GIS (vector data) and multimedia (raster data) information. This paper considered as a preprocessing step for a roadmap discovery of video film when the input to the system is a dolly video film of roads with absence the GPS locations of frames video, and the output is the pathway (route) on the map inside the GIS environment. This work considered as a starting point for multimedia query inside the GIS environment. That’s mean when query the GIS system about a particular image or video, the GIS system must be able to know and determine the location of this image or video file on the map. Thus, the first step for doing this process needs to building and constructing an appropriate database for matching process later.

In this paper, we present a new idea for a trajectory extraction of a camera sensor based on a vision in GPS-denied environments. The input to the system is video film taken from a camera placed on the vehicle as forward facing camera. The output of the system is a chain code that represents a trajectory of camera movement. The proposed system consists of many main steps, the first one extracts the phase correlation of two consecutive frames of video. The next step is to find a novel chain code method called a dynamic chain code based on the value x-shift of phase correlation. The third step is to determine the direction change to (forward, left, right) according to the threshold and extracted the actual chain code. The next step is to save the chain code into the buffer. Finally, step is to draw the normalized trajectory of camera motion. The proposed system is faster than traditional methods that depend on spatial features and works well in noisy averments and system have done without any external information of camera calibration.