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. …”

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. …”

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)

Ensemble deep learning for brain tumor detection by Shtwai Alsubai (15211962)

“…The analysis and mining of these data significantly influence the prediction, monitoring, diagnosis, and treatment of tumor disorders. …”

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

“…The integration of modern computer vision methods into clinical procedures offers the potential for enhancing the detection of skin cancer . The UNet model has gained prominence as a valuable tool for this objective, continuously evolving to tackle the difficulties associated with the inherent diversity of dermatological images. …”

Morphological changes in amblyopic eyes in choriocapillaris and Sattler’s layer in comparison to healthy eyes, and in retinal nerve fiber layer in comparison to fellow eyes through... by Masri, Oussama Samer

“…Results The method of measuring reflectivity is good to excellent reliability for all regions of interest except the fourth. The mean reflectivity of the choriocapillaris and Sattler’s layer in amblyopic eyes were significantly lower than in healthy eyes (p = 0.003 and p = 0.008 respectively). …”
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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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Evidence and gaps in clinical outcomes of novel pharmacologic therapies for sickle cell disease: A systematic literature review highlighting insights from clinical trials and real-... by Mohamed Yassin (4166515)

“…The high discontinuation rate and results of the STAND trial led to a significant decrease in the use of crizanlizumab. The HOPE trial demonstrated a 51 % improvement in hemoglobin response and a reduction in hemolytic markers in patients treated with voxelotor. …”

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. …”

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.…”

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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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. …”

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. …”

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. …”

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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Cutting‐edge technologies for detecting and controlling fish diseases: Current status, outlook, and challenges by Sk Injamamul Islam (14111241)

“…In an aquaculture environment, the high host density coupled with restricted water flow promotes pathogen spread. Early detection of disease is crucial for farmers as mortality rates can reach as high as 100% if left untreated. …”

Analysis of chlortetracycline by high performance liquid chromatography with postcolumn alkaline-induced fluorescence detection by Capomacchia, Anthony C.

“…The detection method was linear between 0.02 μg/mL and 4 μg/mL. …”
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A Novel Versatile Framework for Enabling Early Detection of Evolving Network-based Cyberattacks by THOMAS, RAJESH

“…In the thesis, two attack detection scenarios i.e. Botnet and Low-rate Denial of Service (LDoS), and four different low overhead techniques i.e. …”
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