Performance of artificial intelligence models in estimating blood glucose level among diabetic patients using non-invasive wearable device data by Arfan Ahmed (17541309)

“…One of the key aspects of WDs with machine learning (ML) algorithms is to find specific data signatures, called Digital biomarkers, that can be used in classification or gaging the extent of the underlying condition. …”

Finetuning Analytics Information Systems for a Better Understanding of Users: Evidence of Personification Bias on Multiple Digital Channels by Bernard J. Jansen (7434779)

“…The results show that despite using the same data and algorithm, varying the number of personas strongly biases the information system’s personification of the user population. …”

An Efficient Prediction System for Diabetes Disease Based on Deep Neural Network by Tawfik Beghriche (19563184)

“…In this work, an efficient medical decision system for diabetes prediction based on Deep Neural Network (DNN) is presented. Such algorithms are state‐of‐the‐art in computer vision, language processing, and image analysis, and when applied in healthcare for prediction and diagnosis purposes, these algorithms can produce highly accurate results. …”

A Fuzzy Low-Dimensional Intersection Graph Representation Approach for Graph Compression and Anonymization by Yamout, Youssef

“…We examine several heuristic algorithms in an attempt to achieve these two main objectives and conduct a thorough experimental analysis providing evidence of the effectiveness of our graph mapping approach. …”
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Creating and detecting fake reviews of online products by Joni Salminen (7434770)

A Hybrid Approach for Predicting Critical Machining Conditions in Titanium Alloy Slot Milling Using Feature Selection and Binary Whale Optimization Algorithm by Amirsajjad Rahmani (17541453)

“…The t-test and the binary whale optimization algorithm (BWOA) were applied to choose the best features and train the support vector machine (SVM) model with validation and training data. …”

Simultaneous identification of robust synergistic subnetwork markers for effective cancer prognosis by Navadon Khunlertgit (3480926)

“…<p>Accurate prediction of cancer prognosis based on gene expression data is generally difficult, and identifying robust prognostic markers for cancer remains a challenging problem. …”

Content-Aware Adaptive Video Streaming Using Actor-Critic Deep Reinforcement Learning by Amer, Hala Nagi

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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. …”

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

“…A self-organizing map is one of the well-known unsupervised neural network algorithms used for preserving typologies during mapping from the input space (high-dimensional) to the display (low-dimensional).An algorithm called Local Adaptive Receptive Field Dimension Selective Self-Organizing Map 2 is a modified form of a self-organizing Map to cater different data types in the dataset. …”

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

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Joint computing, communication and cost-aware task offloading in D2D-enabled Het-MEC by Abbas, Nadine

“…In this work, we propose a joint computing, communication and cost-aware task offloading optimization problem aiming at maximizing the number of completed tasks, while minimizing energy consumption and monetary cost in D2D-enabled heterogeneous MEC networks. Our proposed scheme allows partial offloading where a requester mobile terminal offloads different parts of its data task simultaneously to multiple peer mobile terminals (MTs), edge servers and cloud. …”
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Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques by Abu Zitar, Raed

“…The complexity of this variety of energy depends on its coverage of large sizes of data and parameters, which have to be investigated thoroughly. …”
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Large language models for code completion: A systematic literature review by Rasha Ahmad Husein (19744756)

Deep learning-based user experience evaluation in distance learning by Rahim Sadigov (17714301)

A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation by Khaled Dhibi (16891524)

“…The proposed approach deals with system uncertainties (current/voltage variability, noise, measurement errors, ⋯) by using an interval-valued data representation, and with large-scale systems by using a dataset size-reduction framework. …”

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

“…The proposed approach makes advantage of data augmentation to generate newly synthesized images, which are subsequently processed using a watershed mask. …”

Distributed DRL-Based Downlink Power Allocation for Hybrid RF/VLC Networks by Bekir Sait Ciftler (17541801)

“…In this paper, we formulate a distributed downlink power allocation problem to optimize the transmit power for users to reach target data rates in hybrid RF/VLC networks. Then, we propose a distributed DRL-based algorithm Deep Deterministic Policy Gradient (DDPG), to solve the formulated computationally-intensive problem. …”

Acoustic Based Localization of Partial Discharge Inside Oil-Filled Transformers by Hamidreza Besharatifard (16904823)

“…<p dir="ltr">This paper addresses the localization of Partial Discharge through a 3D Finite Element Method analysis of acoustic wave propagation inside a 3-phase 35kV transformer with the help of COMSOL Multiphysics software. …”

Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test by Hasan T. Abbas (8115014)

“…The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. …”