Application of updated joint detection algorithm for the analysis of drilling parameters of roof bolters in multiple joints conditions by Liu, Wenpeng

“…For this purpose, computer programs based on updated pattern recognition algorithms were developed for joint-detection and classification of rock types to offer an estimated strength. …”
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A machine learning model for early detection of diabetic foot using thermogram images by Amith Khandakar (14151981)

“…Several studies have reported that thermogram images may help to detect an increase in plantar temperature prior to DFU. …”

AI-Aided Robotic Wide-Range Water Quality Monitoring System by Awwad, Ameen

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Type 2 Diabetes Mellitus Automated Risk Detection Based on UAE National Health Survey Data: A Framework for the Construction and Optimization of Binary Classification Machine Learn... by Mohamed, AlShuweihi

“…ML based informal diagnostic and decision support systems can provide a first line of detection to alert patients about potential disease risk. …”
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Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers by Yousef, Hibba

“…In this study, we developed an interpretable machine learning model leveraging baseline levels of biomarkers of oxidative stress (OS), inflammation, and mitochondrial dysfunction (MD) for identifying individuals at risk of developing T2DM. In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. …”
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Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression by Alaa Abd-Alrazaq (17430900)

“…<p dir="ltr">Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one of the technologies that have been exploited to detect or predict depression. The current review aimed at examining the performance of wearable AI in detecting and predicting depression. …”

Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection by Ahmad Yaser Alhaddad (7017434)

“…This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. …”

Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey by Faria Nawshin (21841598)

“…As new <u>malware</u> gets introduced frequently by <u>malware developers</u>, it is very challenging to come up with comprehensive algorithms to detect this malware. There are many machine-learning and deep-learning algorithms have been developed by researchers. …”

Diagnostic performance of artificial intelligence in detecting and subtyping pediatric medulloblastoma from histopathological images: A systematic review by Hiba Alzoubi (18001609)

“…</p><h3>Conclusion</h3><p dir="ltr">AI algorithms show promise in detecting and subtyping medulloblastomas, but the findings are limited by overreliance on one dataset, small sample sizes, limited study numbers, and lack of meta-analysis Future research should develop larger, more diverse datasets and explore advanced approaches like deep learning and foundation models. …”

Privacy-preserving energy optimization via multi-stage federated learning for micro-moment recommendations by Md Mosarrof Hossen (21399056)

Software-Defined-Networking-Based One-versus-Rest Strategy for Detecting and Mitigating Distributed Denial-of-Service Attacks in Smart Home Internet of Things Devices by Neder Karmous (19743430)

“…We conducted a comparative analysis of various models and algorithms used in the related works. The results indicated that our proposed approach outperforms others, showcasing its effectiveness in both detecting and mitigating DDoS attacks within SDNs. …”

IntruSafe: a FCNN-LSTM hybrid IoMT intrusion detection system for both string and 2D-spatial data using sandwich architecture by Moutaz Alazab (17730060)

“…The IntruSafe combines FCNN and LSTM to ensure the detection of both malicious text and image data. It detects and simultaneously protects the IoMT network from further intrusion with only a 0.18% service interruption rate. …”

Application of Data Mining to Predict and Diagnose Diabetic Retinopathy by Haniyeh, Maryam

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The use of multi-task learning in cybersecurity applications: a systematic literature review by Shimaa Ibrahim (22155739)

“…Five critical applications, such as network intrusion detection and malware detection, were identified, and several tasks used in these applications were observed. …”

Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier by Syed Ali Jafar Zaidi (19563178)

“…Modern ML models are used for prediction, prioritization, and decision making. Multiple ML algorithms are used to improve decision-making at different aspects after forecasting. …”

The Effectiveness of Supervised Machine Learning in Screening and Diagnosing Voice Disorders: Systematic Review and Meta-analysis by Ghada Al-Hussain (18295426)

“…Studies that examined the performance (accuracy, sensitivity, and specificity) of any ML algorithm in detecting pathological voice samples were included. …”

Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis by Jithin Kalathikudiyil Sreedharan (18268894)

“…It uses various models and algorithms to detect patterns and specific findings to diagnose a disease with utmost accuracy. …”

Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases by Muhammad Ali Muzammil (17910611)

“…The use of AI in ECG analysis has several benefits, including the quick and precise detection of problems like arrhythmias, silent cardiac illnesses, and left ventricular failure. …”

Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques by Amith Khandakar (14151981)

“…Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. …”

Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review by Sarah Aqel (17787809)

“…Deep learning is gaining prominence in radiomics and population health for disease risk prediction. There’s a significant focus on the integration of AI and ML in prehospital emergency care, particularly in using ML algorithms for predicting outcomes in COVID-19 patients and enhancing the recognition of out-of-hospital cardiac arrest (OHCA). …”