A hybrid 3D CNN-LSTM model with soft spatial attention mechanism for accurate hyperspectral image classification by Mohamed Sultan Mohamed Ali (17317003)

“…<p>Hyperspectral imaging (HSI) plays a pivotal role in remote sensing, enabling precise material identification through spectral data across many bands. Despite its advantages, challenges like high dimensionality, spectral mixing, and limited labelled data hinder its classification accuracy. …”

Modeling electricity consumption patterns during the COVID-19 pandemic across six socioeconomic sectors in the State of Qatar by Ammar Abulibdeh (15785928)

Spatial and temporal assessment of metal pollution in the sediments of the Qaraoun reservoir, Lebanon by Wazne, Mahmoud

“…Total metal concentrations and the environmental indices indicated increased pollution with time. Total organic carbon data showed a remarkable and significant increase in the organic fraction in 2013 relative to previous years. …”
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Urban expansion in Greater Irbid Municipality, Jordan: the spatial patterns and the driving factors by Muheeb M. Awawdeh (21633620)

“…The local perspective depicted significant spatial disparities in coefficients, highlighting variations in magnitude and direction. …”

Analysing Satellite Imagery Classification under Spatial Domain Shift across Geographic Regions by Sara A. Al-Emadi (22827935)

Seasonal and spatial variations in concentration, diversity, and antibiotic resistance of ambient bioaerosols in an arid region by Bilal Sajjad (17017749)

“…Air samples were collected using a sampler from ten geographically or functionally distinct locations during a period of one year. Spatial and seasonal variations significantly impacted microbial concentrations, with the highest average concentrations observed at 514 ± 77 CFU/m³ for bacteria over the dry-hot summer season and 134 ± 31 CFU/m³ for fungi over the mild winter season. …”

Spatial modelling of contribution of individual level risk factors for mortality from Middle East respiratory syndrome coronavirus in the Arabian Peninsula by Oyelola A. Adegboye (4287826)

“…Health-care workers were significantly less likely to die from the disease compared with non-health workers [OR = 0.372, 95% CI: 0.151, 0.827]. …”

Geospatial assessment of the carbon footprint of water and electricity consumption in residential buildings in Doha, Qatar by Ammar, Abulibdeh

“…The study employs the Multi-Regional Input-Output Life Cycle Assessment (MRIO-LCA) model to calculate and convert the water and electricity consumption data into the CF of these buildings. Further, the study employs various methods for statistical and spatial statistical analysis of CF emissions, including geographically weighted regression (GWR), Ordinary Least Squares (OLS), and hotspot and cold spot assessments. …”
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Multi-IsnadSet MIS for Sahih Muslim Hadith with chain of narrators, based on multiple ISNAD by Aziz Mehmood Farooqi (20748788)

Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRS by Khan, Malik Nasir Afzal

“…In the time series data, statistically significant differences were noticed in the data before and after BB stimulation, which showed an improvement in the brain state, in line with the classification results. …”
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Mapping subnational HIV mortality in six Latin American countries with incomplete vital registration systems by Local Burden of Disease HIV Collaborators (13279180)

“…</p><h3>Conclusions</h3><p dir="ltr">Our subnational estimates of HIV mortality revealed significant spatial variation and diverging local trends in HIV mortality over time and by age. …”

Effects of the Phantom Shape on the Gradient Artefact of Electroencephalography (EEG) Data in Simultaneous EEG–fMRI by Muhammad Chowdhury (3664999)

“…Moreover, a paired t-test showed that the head-shaped phantom’s and the spherical phantom’s data were significantly different (p < 0.005) from the subjects’ data, whereas the difference between the head-shaped phantom’s and the spherical phantom’s data was not significant (p = 0.07). …”

MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model by Md Abrar Jahin (20108252)

“…MCDFN utilizes Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs) to extract spatial and temporal features from time series data. …”

Deep learning-based marine big data fusion for ocean environment monitoring: Towards shape optimization and salient objects detection by Sulaiman Khan (12585349)

“…For the categorization of spatial data, AlexNet is utilized, whereas Inception V-4 is employed for temporal data (environment monitoring). …”

An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study by Ayman Hassan (14426412)

Video steganography: recent advances and challenges by Jayakanth Kunhoth (14158908)

Prediction of transportation index for urban patterns in small and medium-sized Indian cities using hybrid RidgeGAN model by Thottolil, Rahisha

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From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors by Michael R. Giordano (9976173)

“…Low-cost PM sensors are especially beneficial in low and middle-income countries where few, if any, reference grade measurements exist and in areas where the concentration fields of air pollutants have significant spatial gradients. Unfortunately, low-cost PM sensors also come with a number of challenges that must be addressed if their data products are to be used for anything more than a qualitative characterization of air quality. …”

Unveiling the Nexus Between Land Use, Land Surface Temperature, and Carbon Footprint: A Multi-Scale Analysis of Building Energy Consumption in Arid Urban Areas by Ammar Abulibdeh (15785928)

“…The research utilizes electricity consumption data from residential, commercial, and government buildings in conjunction with remote sensing data (Landsat 8) and climatical data (ERA5) to estimate LST and vegetation health. …”

GIS-based spatiotemporal analysis for road traffic crashes; in support of sustainable transportation Planning by Semira Mohammed (15294167)

“…The study employed various methods, including Time-Space Cube analysis, Geographically Weighted Regression (GWR), Emerging Hot Spot analysis, and Spatial Autocorrelation analysis, with historical traffic crash data from 2015 and 2019. …”