Several approaches have been proposed to detect any malicious manipulation caused by electricity fraudsters. Some of the significant approaches are Machine Learning algorithms and data-based methods that have shown advantages compared to the traditional methods, and they are becoming predominant in recent years. In this study, a novel method is introduced to detect the fraudulent NTL loss in the smart grids in a two-stage detection process. In the first stage, the time-series readings are enriched by adding a new set of extracted features from the detection of sudden Jump patterns in the electricity consumption and the Autoregressive Integrated moving average (ARIMA). In the second stage, the distributed random forest (DRF) generates the learned model. The proposed model is applied to the public SGCC dataset, and the approach results have reported 98% accuracy and F1-score. Such results outperform the other recently reported state-of-the-art methods for NTL detection that are applied to the same SGCC dataset.

Retinal imaging is a non-invasive technique used to scan the back of the eye, enabling the extraction of potential biomarkers like the artery and vein ratio (AVR). This ratio is known for its association with various diseases, such as hypertensive retinopathy (HR) or diabetic retinopathy, and is crucial in assessing retinal health. HR refers to the morphological changes in retinal vessels caused by persistent high blood pressure. Timely identification of these alterations is crucial for preventing blindness and reducing the risk of stroke-related fatalities. The main objective of this paper is to propose a new method for assessing one of the morphological changes in the fundus through morphometric analysis of retinal images. The proposed method in this paper introduces a novel approach called the arteriovenous length ratio (AVLR), which has not been utilized in previous studies. Unlike commonly used measures such as the arteriovenous width ratio or tortuosity, AVLR focuses on assessing the relative length of arteries and veins in the retinal vasculature. The initial step involves segmenting the retinal blood vessels and distinguishing between arteries and veins; AVLR is calculated based on artery and vein caliber measurements for both eyes. Nine equations are used, and the length of both arteries and veins is measured in the region of interest (ROI) covering the optic disc for each eye. Using the AV-Classification dataset, the efficiency of the iterative AVLR assessment is evalutaed. The results show that the proposed approach performs better than the existing methods. By introducing AVLR as a diagnostic feature, this paper contributes to advancing retinal imaging analysis. It provides a valuable tool for the timely diagnosis of HR and other eye-related conditions and represents a novel diagnostic-feature-based method that can be integrated to serve as a clinical decision support system.

Retinal vessel segmentation, skeletonization, and the generation of vessel segments are considered significant steps in any automated system for measuring the vessel biomarkers of several disease diagnoses. Most of the current tortuosity quantification methods rely on precise vascular segmentation and skeletonization of the retinal vessels. Additionally, the existence of a reference dataset for accurate vessel segment images is an essential need for implementing deep learning solutions and an automated system for measuring the vessel biomarkers of several disease diagnoses, especially for optimized quantification of vessel tortuosity or accurate measurement of AV-nicking. This study aimed to present an improved method for skeletonizing and extracting the retinal vessel segments from the 504 images in the AV classification dataset. The study utilized the Six Sigma process capability index, sigma level, and yield to measure the vessels’ tortuosity calculation improvement before and after optimizing the extracted vessels. As a result, the study showed that the sigma level for the vessel segment optimization improved from 2.7 to 4.39, the confirming yield improved from 88 percent to 99.77 percent, and the optimized vessel segments of the AV classification dataset retinal images are available in monochrome and colored formats.

The tortuosity of retinal blood vessels is an important phenomenon, and it can act as a biomarker in the diagnosis of several eye diseases. The study of abnormalities in the tortuosity of retinal arteries and veins provides ophthalmologists with important information for disease diagnosis. Our study aims to compare the tortuosity relation between retinal arteries and veins by quantifying the vessels’ tortuosity in the retina using 14 tortuosity measures applied to the AV-classification retinal dataset. Two feature sets are created, one for arteries and the other for veins. The comparison between the tortuosity of arteries and veins is based on a two-sample T-test statistical method, a regression analysis between the quantified tortuosity features, principal component analysis at the dataset level, and the introduction of the arteriovenous length ratios concept to compare the variations in these new ratios to see the tortuosity behavior in each image. The methods’ results have shown that the tortuosity of retinal arteries and veins is similar. The result of the two-sample T-test supports the research hypothesis, as the P-value obtained was greater than 0.05. Furthermore, the regression analysis between arteries and veins features showed a high correlation ($r^2$ = 89.39% and 89.11%) for arteries and veins, respectively. The study concludes that the retinal vessel type has no statistical significance in the tortuosity calculation results.

This work presents an innovative approach to test the Arabic language proficiency assessment via Automatic Speech Recognition (ASR) by enhancing the proficiency of the Whisper model in transcribing Arabic speech. The core of our research involved fine-tuning the Whisper model using a substantial, large-scale Arabic speech corpus, with a specific focus on Modern Standard Arabic. This process used a 2000-h Arabic-labeled speech corpus, the QASR dataset, and improved the model’s Word Error Rate (WER). After optimization, the fine-tuned Whisper model’s WER was reduced from 35% to 7% on the QASR dataset, corresponding to an absolute reduction of 28 percentage points (approximately 80% relative reduction). These results demonstrate the strong generalization ability of the fine-tuned model across multiple Arabic ASR benchmarks. A key component of our methodology was the development of a sophisticated scoring system. This system integrates various similarity metrics, such as cosine similarity, the Jaccard index, and the Levenshtein distance, with a machine learning regression model. This multifaceted system provides a comprehensive assessment of reading proficiency, proposing a practical automated assessment method that contributes to the field of AI language transcription and to its application in the assessment of students’ reading. Our research also introduces the ICONET dataset, an augmented Arabic speech corpus comprising 3160 h of diverse and tailored audio–text pairs designed for fine-tuning ASR models. This study demonstrates the potential of fine-tuning pretrained models for specific linguistic contexts (Arabic), establishing a foundation for future research in ASR and language technology.