Investigation of Forming a Framework to shortlist contractors in the tendering phase by DABASH, MOHANNAD SALAH

“…The aim of this research is to create a framework that can predict the best contractor to be awarded a construction contract by a consultant/client using a different set of variables known as “Decision factors.” This research was conducted to improve the traditional tendering process, the model was used to predict the “Success Rate” for the project by assessing each contractor’s possibility of completing the project successfully using their compatibility with the project. …”
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Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review by Zainab Jan (17306614)

“…Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). …”

A hybrid cryptosystem based on WEP for 802.11 networks. (c2006) by Horr, Walid Majed

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A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition by Hanif Heidari (22467148)

“…Most improved RF versions modify attribute selection processes or combine them with other machine learning algorithms, increasing their complexity. …”

Wearable wrist to finger photoplethysmogram translation through restoration using super operational neural networks based 1D-CycleGAN for enhancing cardiovascular monitoring by Sakib Mahmud (15302404)

“…<h3>Background and Motivations</h3><p dir="ltr">Physiological signals, such as the Photoplethysmogram (PPG) collected through wearable devices, consistently encounter significant motion artifacts. Current signal processing techniques, and even state-of-the-art machine learning algorithms, frequently struggle to effectively restore the inherent bodily signals amidst the array of randomly generated distortions. …”

Target Detection Using Learning Methods by Sahnoon, Mohammad Moufeed

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Predicting Plasma Vitamin C Using Machine Learning by Daniel Kirk (17302798)

“…The low R-squared scores obtained by the models are likely to be due to the low resolution of the NHANES data, particularly the dietary data. This emphasizes the need for high-quality data sets in Precision Nutrition research.…”

Digital twin in energy industry: Proposed robust digital twin for power plant and other complex capital-intensive large engineering systems by Ahmad K. Sleiti (14778229)

“…Data-driven algorithms with capabilities to predict the system’s dynamic behavior still need to be developed. …”

DoFP-ML: A Machine Learning Approach to Food Quality Monitoring Using a DoFP Polarization Image Sensor by Maen Takruri (10873368)

Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review by Avneet Kaur (712349)

“…It has been learned that image-processing techniques overwhelm the existing research and have the potential to integrate meteorological data. …”

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

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Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems by Tarek Berghout (16905132)

Enhancing e-learning through AI: advanced techniques for optimizing student performance by Rund Mahafdah (21399854)

Various Faults Classification of Industrial Application of Induction Motors Using Supervised Machine Learning: A Comprehensive Review by Rehaan Hussain (22302742)

“…In current literature, there are a number of papers that address all these faults using different methods, and this paper compiles the information from the written works for ease of access. Machine learning algorithms are a set of data-driven rules that are able to classify specific faults in induction motors, which will be explained further in this review paper. …”

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. …”

Machine Learning–Based Approach for Identifying Research Gaps: COVID-19 as a Case Study by Alaa Abd-alrazaq (17058018)

“…</p><h3>Methods</h3><p dir="ltr">We conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. …”

Ensemble Deep Random Vector Functional Link Neural Network for Regression by Minghui Hu (2457952)

Deep and transfer learning for building occupancy detection: A review and comparative analysis by Aya Nabil Sayed (17317006)

“…This work investigates occupancy detection methods to develop an efficient system for processing sensor data while providing accurate occupancy information. …”

Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UAV Swarms by Marwan Dhuheir (19170898)

“…A key requirement in these applications is minimizing the latency of data processing, particularly for time-sensitive tasks like image classification of IIoT device data. …”

Fast Transient Stability Assessment of Power Systems Using Optimized Temporal Convolutional Networks by Mohamed Massaoudi (16888710)

“…The proposed algorithm unlocks scalability and system adaptability to operational variability by adopting numeric imputation and missing-data-tolerant techniques. …”