Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View Clustering by Saadia Jamil (22045946)

“…It calculates the dimension relevance with various data instances. These further place the relevant dimension samples in one group. …”

Assessment of four dose calculation algorithms using IAEA-TECDOC-1583 with medium dependency correction factor (K_med) application by Aram Rostami (22045274)

“…K_med is calculated for D_m.m and D_w.walgorithm types in bone and lung media for both photon beams. …”

Large language models for code completion: A systematic literature review by Rasha Ahmad Husein (19744756)

A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs by Alabd-Aljabar, Ahmed

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Impact of AI and Dynamic Ensemble Techniques in Enhancing Healthcare Services: Opportunities and Ethical Challenges by Haseeb Javed (18576340)

A multi-pretraining U-Net architecture for semantic segmentation by Cagla Copurkaya (22502042)

“…In this research, we propose and evaluate a modified version of a deep learning algorithm called U-Net architecture for partitioning histopathological images. …”

Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations by Shameem A. Puthiya Parambath (14150997)

“…CIP can be used in conjunction with relevance ranking metrics like NDCG and MAP to measure the effectiveness of the cold-start recommendation algorithm.…”

C-3PA: Streaming Conformance, Confidence and Completeness in Prefix-Alignments by Raun, Kristo

“…Further, no indication is given of how close the trace is to termination—a highly relevant measure in a streaming setting. This paper introduces a novel approximate streaming conformance checking algorithm that enriches prefix-alignments with confidence and completeness measures. …”
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Data Embedding in HEVC Video by Modifying the Partitioning of Coding Units by Shanableh, Tamer

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MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection by Sui Paul Ang (18460605)

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review by Zainab Jan (17306614)

“…PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. …”

Nested ensemble selection: An effective hybrid feature selection method by Kamalov, Firuz

“…It has been shown that while feature selection algorithms are able to distinguish between relevant and irrelevant features, they fail to differentiate between relevant and redundant and correlated features. …”
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Multi-Robot Map Exploration Based on Multiple Rapidly-Exploring Randomized Trees by Umari, Hassan Abdul-Rahman

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Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection by Serkan Kiranyaz (3762058)

Multi Agent Reinforcement Learning Approach for Autonomous Fleet Management by Alhusin, Mohammed Omer Alamin

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Frontiers and trends of supply chain optimization in the age of industry 4.0: an operations research perspective by Zhitao Xu (2426023)

“…It contributes to the literature by identifying the four OR innovations to typify the recent advances in SC optimization: new modeling conditions, new inputs, new decisions, and new algorithms. Furthermore, we recommend four promising research avenues in this interplay: (1) incorporating new decisions relevant to data-enabled SC decisions, (2) developing data-enabled modeling approaches, (3) preprocessing parameters, and (4) developing data-enabled algorithms. …”

Brain Source Localization in the Presence of Leadfield Perturbations by Momin, Rabiya Nakhat

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

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An integrated partitioning and synthesis system for dynamically reconfigurable Multi-FPGA architectures by Ouaiss, Iyad

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A Survey of Deep Learning Approaches for the Monitoring and Classification of Seagrass by Uzma Nawaz (21980708)

“…This study not only examines the well-known challenges such as limited availability of data but provides a novel, structured taxonomy of deep learning techniques tailored for the monitoring of seagrass, highlighting their unique advantages and limitations within diverse marine environments. By synthesizing findings across various data sources and model architectures, we offer critical insights into the selection of context-aware algorithms and identify key research gaps, an essential step for advancing the reliability and applicability of AI-driven seagrass conservation efforts.…”