13 research outputs found
Green Space Quality Analysis Using Machine Learning Approaches
No full textGreen space is any green infrastructure consisting of vegetation. Green space is linked with improving mental and physical health, providing opportunities for social interactions and physical activities, and aiding the environment. The quality of green space refers to the condition of the green space. Past machine learning-based studies have emphasized that littering, lack of maintenance, and dirtiness negatively impact the perceived quality of green space. These methods assess green spaces and their qualities without considering the human perception of green spaces. Domain-based methods, on the other hand, are labour-intensive, time-consuming, and challenging to apply to large-scale areas. This research proposes to build, evaluate, and deploy a machine learning methodology for assessing the quality of green space at a human-perception level using transfer learning on pre-trained models. The results indicated that the developed models achieved high scores across six performance metrics: accuracy, precision, recall, F1-score, Cohen’s Kappa, and Average ROC-AUC. Moreover, the models were evaluated for their file size and inference time to ensure practical implementation and usage. The research also implemented Grad-CAM as means of evaluating the learning performance of the models using heat maps. The best-performing model, ResNet50, achieved 98.98% accuracy, 98.98% precision, 98.98% recall, 99.00% F1-score, a Cohen’s Kappa score of 0.98, and an Average ROC-AUC of 1.00. The ResNet50 model has a relatively moderate file size and was the second quickest to predict. Grad-CAM visualizations show that ResNet50 can precisely identify areas most important for its learning. Finally, the ResNet50 model was deployed on the Streamlit cloud-based platform as an interactive web application
Commentary, Margin and Notes On The Work "Al-Manzuma"
No full textThe development of hadith science in the 16th and 17th centuries led to an increase in the number of rich heritages of hadith scholars. Among these, it is appropriate to mention Toho Bayquni’s poem “Manzumatu-l-Bayquniya”. Despite the fact that his poetic text is short, it has attracted the attention of scholars of hadith until now. In particular, the number of comments written on the poem is close to a hundred, which indicates that the author is at a high level in the science of hadith. Finding the manuscript of the comments in the world’s manuscript funds, the scientific legacy of the author will give high spirituality to the next generations
Improving microvascular brain analysis with adversarial learning for OCT–TPM vascular domain translation
No full textLearning-based MRI response predictions from OCT microvascular models to replace simulation-based frameworks
No full textABSTRACT: Computational quantification of magnetic resonance imaging (MRI) response from neurovascular structures is used to investigate potential biomarkers for different types of cerebrovascular deteriorations at the microscopic scale. Simulation-based MRI requires fully resolved microvascular structures, with geometric and physiological parameters, from tissue volumes captured using microscopic imaging modalities, e.g., optical coherence tomography (OCT). The preparation of such input models hinders large cohort studies and requires extensive manual effort. Here, we propose using 3D neural networks as an alternative learning-based solution over MRI simulation schemes. We trained state-of-the-art 3D neural networks to predict the spin echo (SE) MRI response from OCT microvascular volumes. By validating against simulated signals, our result demonstrates that the 3D ResNet-based regression network achieves a high accuracy to predict MRI signals with an average mean square error (MSE) <1%, R2 of 82.8% and explained variance score of 82.9%
DJAMAL KAMAL - AN EXPERIENCED TRANSLATOR
Full text linkThe article is dedicated to the brief biography of Jamal Kamal, the great poet of Uzbekistan, skilled translator and publicist, a public man, a literary critic, and the candidate of philological sciences. Along with his creative activity, the author translated the finest pieces of world literature into the Uzbek language. It is also an ancient tradition to translate samples of Persian literature into Turkic or, conversely, Turkic works of art into Persian. Taking into account all above said, Jamal Kamal was one of the first in Uzbekistan to translate the work of Jaloliddin Rumi “Masnaviy Manaviy” into Uzbek. In order to confirm our opinion, the original Uzbek translations have been studied comparatively
An expert rule-based approach for identifying infantile-onset Pompe disease patients using retrospective electronic health records
No full textPompe disease (OMIM #232300), a rare genetic disorder, leads to glycogen buildup in the body due
to an enzyme defciency, particularly harming the heart and muscles. Infantile-onset Pompe disease
(IOPD) requires urgent treatment to prevent mortality, but the unavailability of these methods often
delays diagnosis. Our study aims to streamline IOPD diagnosis in the UAE using electronic health
records (EHRs) for faster, more accurate detection and timely treatment initiation. This study utilized
electronic health records from the Abu Dhabi Healthcare Company (SEHA) healthcare network in the
UAE to develop an expert rule-based screening approach operationalized through a dashboard. The
study encompassed six diagnosed IOPD patients and screened 93,365 subjects. Expert rules were
formulated to identify potential high-risk IOPD patients based on their age, particular symptoms,
and creatine kinase levels. The proposed approach was evaluated using accuracy, sensitivity, and
specifcity. The proposed approach accurately identifed fve true positives, one false negative, and
four false positive IOPD cases. The false negative case involved a patient with both Pompe disease and
congenital heart disease. The focus on CHD led to the overlooking of Pompe disease, exacerbated by
no measurement of creatine kinase. The false positive cases were diagnosed with Mitochondrial DNA
depletion syndrome 12-A (SLC25A4 gene), Immunodefciency-71 (ARPC1B mutation), Niemann–Pick
disease type C (NPC1 gene mutation leading to frameshift), and Group B Streptococcus meningitis.
The proposed approach of integrating expert rules with a dashboard facilitated efcient data
visualization and automated patient screening, which aids in the early detection of Pompe disease.
Future studies are encouraged to investigate the application of machine learning methodologies to
enhance further the precision and efciency of identifying patients with IOPD
Integrating AI-based and conventional cybersecurity measures into online higher education settings: Challenges, opportunities, and prospects
No full textThe rapid adoption of online learning in higher education has resulted in significant cybersecurity challenges. As educational institutions increasingly rely on digital platforms, they are facing cyber threats that can compromise sensitive data and disrupt operations. This systematic literature review explores the integration of artificial intelligence (AI) into traditional methods to address cybersecurity risks in online higher education. The review integrates a qualitative synthesis of relevant literature and a quantitative meta-analysis using PRISMA guidelines, ensuring comprehensive insights into the integration process. The most prevalent cybersecurity threats are examined, and the effectiveness of AI-based and conventional approaches in mitigating these challenges is compared. Additionally, the most effective AI techniques in cybersecurity solutions are analyzed, and their performance is compared across different contexts. Furthermore, the review considers the key ethical and technical considerations associated with integrating AI into traditional cybersecurity methods. The findings reveal that while AI-based techniques offer promising solutions for threat detection, authentication, and privacy preservation, their successful implementation requires careful consideration of data privacy, fairness, transparency, and robustness. The importance of interdisciplinary collaboration, continuous monitoring of AI models—by automated systems and humans—and the need for comprehensive guidelines to ensure responsible and ethical use of AI in cybersecurity are highlighted. The findings of this review provide actionable insights for educational institutions, educators, and students, helping to facilitate the development of secure and resilient online learning environments. The identified ethical and technical considerations can serve as a foundation for the responsible integration of AI into cybersecurity within the online higher-education sector
IMPORTANCE INTERNET IN EDUCATION
No full textThis article discusses the role of the Internet in modern education. The author discusses the benefits of the Internet and its benefits for students
Adversarial Approaches to Tackle Imbalanced Data in Machine Learning
No full textReal-world applications often involve imbalanced datasets, which have different distributions of examples across various classes. When building a system that requires a high accuracy, the performance of the classifiers is crucial. However, imbalanced datasets can lead to a poor classification performance and conventional techniques, such as synthetic minority oversampling technique. As a result, this study proposed a balance between the datasets using adversarial learning methods such as generative adversarial networks. The model evaluated the effect of data augmentation on both the balanced and imbalanced datasets. The study evaluated the classification performance on three different datasets and applied data augmentation techniques to generate the synthetic data for the minority class. Before the augmentation, a decision tree was applied to identify the classification accuracy of all three datasets. The obtained classification accuracies were 79.9%, 94.1%, and 72.6%. A decision tree was used to evaluate the performance of the data augmentation, and the results showed that the proposed model achieved an accuracy of 82.7%, 95.7%, and 76% on a highly imbalanced dataset. This study demonstrates the potential of using data augmentation to improve the classification performance in imbalanced datasets
