University of Dubai

Color harmonization adjusts the colors of an inserted object so that it perceptually matches the surrounding image, resulting in a seamless composite. The harmonization problem naturally arises in augmented reality (AR), yet harmonization algorithms are not currently integrated into AR pipelines because real-time solutions are scarce. In this work, we address color harmonization for AR by proposing a lightweight approach that supports on-device inference. For this, we leverage classical optimal transport theory by training a compact encoder to predict the Monge-Kantorovich transport map. We benchmark our MKL-Harmonizer algorithm against state-of-the-art methods and demonstrate that for real composite AR images our method achieves the best aggregated score. We release our dedicated AR dataset of composite images with pixel-accurate masks and data-gathering toolkit to support further data acquisition by researchers.

Abstract National incentive plans, labor policies and DEI (Diversity, Equity and Inclusion) regulations actively encourage Emirati women’s participation in the UAE employment sector. Women are the face of the future workforce in emerging economies and Emirati females are leading the charge in equalizing gender balance within companies operating in the UAE . Young Emirati women are a particularly salient force in the UAE labor market. Sociocultural, economic, gender-related and national expectations of local women intersect to inform their experiences at a workplace, thus deciding on possibilities to enter and sustain their careers. In relation to this, this article explores young Emirati women’s employment challenges and opportunities as perceived by the research participants themselves. In addition, the article examines the current UAE employment laws, regulations and government programs that impact Emirati women’s workplace interactions and experiences. Finally, the article aims at offering practical solutions for observed challenges inspired by research findings and successful global practices adapted to the sociocultural framework of the UAE . Identifying effective strategies that cater to young Emirati women’s needs, struggles and the cultural context can help companies overcome challenges in attracting and retaining local female talent reserves.

The pangenome initiative marks a major shift from reliance on a single human reference that undersamples global diversity. Building phased, diploid assemblies from specific regions reveals population haplotypes and structural variants. This review outlines core pangenome concepts and surveys initiatives across Middle Eastern countries, long underrepresented in genomic resources. We summarize advances in long-read sequencing and graph-based bioinformatics that now enable high-quality diploid assemblies and reference graphs. National genome programs exist in six countries, and some have launched pangenome efforts, revealing sequence and variant content absent from GRCh38 and CHM13. We examine how founder effects, consanguinity, and tribe-based endogamy shape the region's genetic architecture, producing runs of homozygosity and concentrated rare alleles that aid gene discovery yet challenge clinical interpretation. Finally, we argue for coordinated, region-wide pangenome initiatives, spanning ethnolinguistic and tribal groups, to create equitable genome references, improve mapping and variant calling for Middle Eastern haplotypes, and accelerate precision medicine across the region.

Food security is highly reliant on agricultural activity to drive the world economy. However, this activity is in great danger due to climatic changes and improper use of irrigation techniques. Consequently, the lives of numerous individuals worldwide are in jeopardy. In light, this paper investigates the promise of smart irrigation systems based on new technology. To meet the growing demand for water in agriculture, this study presents an intelligent irrigation system that uses cutting-edge technologies of (1) cloud computing, (2) embedded systems, and (3) Internet-of- Things (IoT). The main objective is to demonstrate how this innovative strategy can effectively manage water resources, supporting food security through cutting-edge agricultural technology. This paper proposes a smart irrigation system based on cutting-edge technologies like the embedded system, Internet of Things (IoT), and cloud computing as a groundbreaking strategy to improve food security through the implementation of advanced agricultural technology. This system supervises real-time monitoring of crucial environmental factors such as (1) moisture, (2) humidity, (3) temperature, and (4) water levels, in smart agriculture practices. In addition, this system employs the latest sensors, including the module (DHT22), water level sensor, and moisture sensors, which are connected to the widely used embedded system (ESP32). The system uses the ThingSpeak cloud and ThingView app to enable wireless communication between the device and the farm owner, enhancing their interaction. The automated control of the two water pumps is based on the readings of various environmental factors. Moreover, this will also present a mathematical-driven function known as linear interpolation to calibrate the water level sensor in percentage. This system was created using the V-model software development approach. and conclusion. Farmers can access comprehensive farm data from anywhere in the world as the sensor data is transmitted in real-time to both the ThingSpeak cloud and the ThingView. This capability allows for more precise crop irrigation and increased production. The study’s findings demonstrate a striking 70% reduction in water consumption for soil irrigation when utilizing the proposed smart irrigation system. This paper underscores the significant promise of smart irrigation systems, driven by IoT, embedded systems, and cloud computing, to conserve water resources and advance food security. This article proposes an innovative solution that reduces soil irrigation water consumption by 70% compared to traditional methods. It explores how smart irrigation can improve the sustainability of agriculture and positively influence food security.

• Advanced data analytics and PySpark improve fake product review detection. • Classification and clustering methods identify patterns and reduce deceptive reviews. • Visual data exploration aids anomaly detection in review characteristics. • Techniques protect consumers and foster trust in e-commerce platforms. • Study bridges theoretical research and practical spam detection applications. In e-commerce, online reviews significantly influence consumer purchasing behavior, with authenticity directly impacting business revenues and consumer trust. This study addresses the critical issue of fake product reviews (FPRs) by analyzing their effects on consumer choice and the overall reliability of online marketplaces. To improve FPR detection, we employed PySpark and advanced data science techniques to analyze a labeled dataset of user reviews, uncovering patterns and anomalies indicative of review manipulation. By integrating classification methods such as K-Nearest Neighbors (KNN), the study demonstrates how machine learning can effectively identify and mitigate the impact of FPRs, thereby enhancing the credibility of online reviews. The results contribute to ensuring fair competition, consumer protection, and the long-term integrity of digital commerce. Future research may expand this framework by incorporating additional datasets, alternative classification algorithms, and deeper linguistic or sentiment-based analyses.

Gut microbiome dysbiosis is a critical determinant for autologous fecal microbiota transplantation (Auto-FMT) eligibility, yet current classification approaches rely predominantly on supervised learning with manually annotated sequencing labels, which are often scarce. This study proposes DynaBiome, a framework designed to predict gut dysbiosis by leveraging unsupervised learning and clinical phenotypic proxies as a scalable alternative to ground-truth genomic labeling. Our framework employs an LSTM autoencoder architecture to capture temporal microbiome dynamics within 14-day windows. The model reconstructs normal microbiome patterns, where high reconstruction errors signal potential dysbiosis. To ensure rigorous evaluation and prevent data leakage, the dataset was partitioned via a strict patient-level split. Unsupervised anomaly signals were refined via phenotypic proxy labels (e.g., fever, neutropenia) via weak supervision, and ensemble learning methods were applied to optimize classification performance. The initial LSTM autoencoder successfully flagged dysbiotic sequences but required refinement to reduce false positives. Ensemble learning significantly enhanced predictive accuracy. The stacked ensemble (with Logistic Regression meta-learner) demonstrated optimal performance with an ROC AUC of 0.8908 and a Weighted F1-score of 0.7909. This approach significantly outperformed the standard One-Class SVM baseline (ROC AUC 0.6033), confirming the superiority of deep temporal modeling over static anomaly detection. Critically, the model achieved performance levels comparable to fully supervised baselines, confirming the efficacy of the proxy-label framework. Integrating unsupervised temporal feature extraction with stacked ensemble methods provides a viable framework for dysbiosis prediction. These results demonstrate that leveraging phenotypic via weak supervision can effectively approximate supervised baselines, thereby reducing the reliance on comprehensive metagenomic annotations for longitudinal patient monitoring.

Abstract Background The genetic architecture of Breast Cancer (BC) in Arab populations remains largely understudied, limiting the precision of current prevention and screening programs. The Emirati Genome Program (EGP), one of the world’s first nation-wide sequencing initiatives, offers an unprecedented opportunity to study inherited BC risk across an entire population. Methods We analyzed 436,780 EGP individuals, including 229,309 women, integrating whole-genome sequencing (WGS) with electronic health records (EHRs). We quantified the prevalence and penetrance of pathogenic and likely pathogenic (P/LP) variants across 13 NCCN-recommended BC genes, evaluated the performance of established polygenic risk scores (PRS), and reconstructed >48,000 pedigrees to measure familial aggregation. Results P/LP variants were identified in 0.84% of women, accounting for 5.2% of BC cases (mean age of 45.9±11.1 years). Highly penetrant BRCA1 c.4065_4068del (p.Asn1355fs) and BRCA2 c.2808_2811del (p.Ala938Profs) variants showed age-specific cumulative risks of 37.6% and 31% by age 60, respectively, and allele frequencies up to tenfold higher in the Emirati population than in global reference datasets. The European-derived PRS model (PGS000004) demonstrated strong performance, advancing 10-year BC risk onset by a decade for women in the top decile. Family-based PRS discriminated affected from unaffected individuals, revealing higher polygenic risk even within sister pairs. Conclusions Nation-scale genome sequencing reveals, for the first time, the comprehensive landscape of inherited BC susceptibility within a Middle Eastern population. The integration of monogenic, polygenic, and familial data establishes a national framework for genomic risk stratification, transforming population genomics into a foundation for precision prevention and early detection in the UAE and beyond.