A FeedForward–Convolutional Neural Network to Detect Low-Rate DoS in IoT by Harun Surej Ilango (17545728)

“…An iterative wrapper-based feature selection using Support Vector Machine (SVM) is used to derive the significant features required for detection. The performance of FFCNN is compared to the machine learning algorithms-J48, Random Forest, Random Tree, REP Tree, SVM, and Multi-Layer Perceptron (MLP). …”

Assessing uncertainty in image-based monitoring: addressing false positives, false negatives, and base rate bias in structural health evaluation by Vagelis Plevris (14158863)

“…However, its reliability is impacted by challenges such as false positives, false negatives, and environmental variability, particularly in low base rate damage scenarios. The Base Rate Bias plays a significant role, as low probabilities of actual damage often lead to misinterpretation of positive results. …”

A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance by Krishnamoorthy Natarajan (22047464)

Diagnostic accuracy of interleukin-6 (IL-6) as a significant biomarker in late-onset neonatal sepsis: an updated systematic review and meta-analysis by Amani N. Alansari (19743643)

“…Subgroup and sensitivity analyses confirmed the robustness of the findings, and no significant threshold effect was detected. However, suspected publication bias was noted. …”

Growth hormone treatment modulates active ghrelin levels in rats by Haddad, Haytham

“…Genotropin treatment caused a dose dependent decrease in active ghrelin concentration in stomach, kidney and pancreas with a concomitant increase in plasma, and reaching significance at 20 and 100 µg/rat/day doses. …”
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Ensemble deep learning for brain tumor detection by Shtwai Alsubai (15211962)

“…Since it has a wide range of traits, a low survival rate, and an aggressive nature, brain tumor is regarded as the deadliest and most devastating disease. …”

LSCS-Net: A lightweight skin cancer segmentation network with densely connected multi-rate atrous convolution by Sadia Din (18136919)

“…This network comprises an encoder–decoder architecture, a novel feature extraction block, and a densely connected multi-rate Atrous convolution block. We evaluated the performance of the proposed lightweight skin cancer segmentation network (LSCS-Net) on three widely used benchmark datasets for skin lesion segmentation: ISIC 2016, ISIC 2017, and ISIC 2018. …”

Evaluating chemiluminescent immunoassays for syphilis detection: A comparative analysis by Ahmed, Ismail

“…BackgroundSyphilis, caused by Treponema pallidum (TP), remains a significant global public health concern, with approximately 8 million new cases annually. …”
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The Anti-Tumor Agent Sodium Selenate Decreases Methylated PP2A, Increases GSK3βY216 Phosphorylation, Including Tau Disease Epitopes and Reduces Neuronal Excitability in SHSY-5Y Neu... by Wesal Habbab (17346961)

“…Somewhat surprisingly, the catalytically active form, methylated PP2A (mePP2A) was significantly decreased. In close correlation to these data, the phosphorylation state of two substrate proteins, sensitive to PP2A activity, GSK3β and Tau were found to be increased. …”

IoT empowered smart cybersecurity framework for intrusion detection in internet of drones by Syeda Nazia Ashraf (17541222)

“…In recent years, there has been a significant increase in the utilization of IoT-enabled drone data management technology. …”

EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory Analysis by Yahya, Fares

“…The strength and directionality of information flow in the connectivity network by RWTE/PDC and the GTA measures significantly decrease with vigilance decrement, p<0.05. …”
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Bridging Innovation and Security: Advancing Cyber-Threat Detection in Sustainable Smart Infrastructure by Duaa Shoukat (20961200)

“…Our evaluation results demonstrate the superior performance of the proposed Cu-GRULSTM model, achieving an exceptional accuracy rate of 99.62% with a minimal False Alarms Rate (FAR) of 0.0003. …”

Automated Detection of Colorectal Polyp Utilizing Deep Learning Methods With Explainable AI by Md. Faysal Ahamed (21842396)

“…These polyps cause severe conditions in the colon or rectum, presenting a significant diagnostic challenge. Traditional manual detection through medical imaging is not only bulky and prone to errors but also incurs substantial costs, requiring expert endoscopist. …”

A Machine Learning Approach on Chest X-Rays for Pediatric Pneumonia Detection by Barakat, Natali Imad

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Using machine learning algorithm for detection of cyber-attacks in cyber physical systems by Almajed, Rasha

“…In addition, the framework outperforms conventional detection algorithms in words of detection rate, the rate of the false positive, and calculation time, respectively.…”
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A machine learning approach on chest X-rays for pediatric pneumonia detection by Barakat, Natali Imad

“…Thus, there is a significant need for automating the detection process to minimize the potential human error. …”
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TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection by Zina Chkirbene (16869987)

“…Consequently, TICDS has better performance than the state of art techniques in terms of accuracy detection, while providing good detection and false alarm rates.…”

Mulberry Leaf Disease Detection Using CNN-Based Smart Android Application by Abdus Salam (1918387)

“…Leveraging computer vision for early disease detection and classification can mitigate up to 90% of production losses. …”

Intravenous administration of gold nanoparticles in rats exhibit alterations in sphingomyelins, bile acids, sphingolipids, and cholesterol esters levels by Haya Al-Sulaiti (5449256)

“…Furthermore, treatment with AuNPs caused significant alterations in cholesterol ester levels, essential for lipid storage and transport, indicating disruptions in these mechanisms. …”

Association of microRNAs With Embryo Development and Fertilization in Women Undergoing Subfertility Treatments: A Pilot Study by Alexandra E. Butler (6189536)

“…A beta-regression model identified miR-1260a, miR-486-5p, and miR-132-3p (p < 0.03, p = 0.0003, p < 0.00001, respectively) as the most predictive for fertilization rate. Notably, changes in detectable miRNAs were not linked to cleavage rate, top quality embryos (G3D3), and blastocyst or antral follicle count. …”