Digital-Twin-Based Diagnosis and Tolerant Control of T-Type Three-Level Rectifiers حسب Ali Sharida (17947847)

"…To develop the DT, a dense deep neural network (DNN) machine learning approach is used. …"

Applications of artificial intelligence in ultrasound imaging for carpal-tunnel syndrome diagnosis: a scoping review حسب Yosra Magdi Mekki (21673721)

"…The majority of studies utilized deep learning approaches, particularly CNNs (<i>n</i> = 15), and some focused on radiomics features (<i>n</i> = 5) and traditional machine learning techniques.…"

Smart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k-nearest neighbors classifier حسب Yassine Himeur (14158821)

"…Furthermore, an improved k-nearest neighbors (IKNN) algorithm is presented to reduce the learning computation time and improve the classification performance. …"

LungVision: X-ray Imagery Classification for On-Edge Diagnosis Applications حسب Aldamani, Raghad

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Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR) حسب Elaine Beller (44602)

"…Recent advances in natural language processing, text mining and machine learning have produced new algorithms that can accurately mimic human endeavour in systematic review activity, faster and more cheaply. …"

Design and Analysis of Secure Digital Twin Architecture حسب Shaikh, Eman

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Regression testing C# programs. (c2007) حسب Staitieh, Wael

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Nonlinear analysis of shell structures using image processing and machine learning حسب M.S. Nashed (16392961)

"…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …"

Design and analysis of entropy-constrained reflected residual vector quantization حسب Mousa, W.A.H.

"…Jointly optimized RVQ (JORVQ) is an effective design algorithm for minimizing the overall quantization error. …"
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Design and Analysis of Lightweight Authentication Protocol for Securing IoD حسب Saeed Ullah Jan (9079260)

"…We proposed a hash message authentication code/secure hash algorithmic (HMACSHA1) based robust, improved and lightweight authentication protocol for securing IoD. …"

Role of authentication factors in Fin-tech mobile transaction security حسب Habib Ullah Khan (12024579)

"…In addition, this also ensures the user is legitimate using advanced technologies and algorithms to predict and discover transaction risks and discourage fraudsters from trying.…"

Deepfakes Signatures Detection in the Handcrafted Features Space حسب Hamadene, Assia

"…In the Handwritten Signature Verification (HSV) literature, several synthetic databases have been developed for data-augmentation purposes, where new specimens and new identities were generated using bio-inspired algorithms, neuromotor synthesizers, Generative Adversarial Networks (GANs) as well as several deep learning methods. …"
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DASSI: differential architecture search for splice identification from DNA sequences حسب Shabir Moosa (14153316)

"…The demand for robust algorithms over the recent years has brought huge success in the field of Deep Learning (DL) in solving many difficult tasks in image, speech and natural language processing by automating the manual process of architecture design. …"

Depthwise Separable Convolutions and Variational Dropout within the context of YOLOv3 حسب Chakar, Joseph

"…Deep learning algorithms have demonstrated remarkable performance in many sectors and have become one of the main foundations of modern computer-vision solutions. …"
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Energy-Efficient VoI-Aware UAV-Assisted Data Collection in Wireless Sensor Networks حسب Kalash, Saeddin

"…To address these objectives, our proposed approach incorporates deep reinforcement learning (RL-DQN) techniques to optimize UAV deployment, minimizing the number of UAVs while maximizing the number of successfully collected SNs with non-redundant data. …"
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Developing an online hate classifier for multiple social media platforms حسب Joni Salminen (7434770)

"…We then experiment with several classification algorithms (Logistic Regression, Naïve Bayes, Support Vector Machines, XGBoost, and Neural Networks) and feature representations (Bag-of-Words, TF-IDF, Word2Vec, BERT, and their combination). …"

Large-scale annotation dataset for fetal head biometry in ultrasound images حسب Mahmood Alzubaidi (15740693)

"…It is also compatible with multiple medical imaging software and deep learning frameworks. The reliability of the annotations is verified through a two-step validation process involving a Senior Attending Physician and a Radiologic Technologist. …"

Exploring new horizons in neuroscience disease detection through innovative visual signal analysis حسب Nisreen Said Amer (17984077)

"…To address this, our study focuses on visualizing complex EEG signals in a format easily understandable by medical professionals and deep learning algorithms. We propose a novel time–frequency (TF) transform called the Forward–Backward Fourier transform (FBFT) and utilize convolutional neural networks (CNNs) to extract meaningful features from TF images and classify brain disorders. …"

Diagnostic performance of artificial intelligence in detecting and subtyping pediatric medulloblastoma from histopathological images: A systematic review حسب 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. …"

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

"…However, the ECG can be interpreted differently by humans depending on the interpreter's level of training and experience, which could make diagnosis more difficult. Using AI, especially deep learning convolutional neural networks (CNNs), to look at single, continuous, and intermittent ECG leads that has led to fully automated AI models that can interpret the ECG like a human, possibly more accurately and consistently. …"