Design of acoustic absorbing metasurfaces using a data-driven approach by Hamza Baali (14603380)

Subjects:

Automatic and Intelligent Stressor Identification Based on Photoplethysmography Analysis by Sami Elzeiny (16891521)

Identification of phantom movements with an ensemble learning approach by Akhan Akbulut (17380285)

“…The ensemble learning-based approaches outperformed the SVM, Decision tree, and kNN methods. …”

EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach by Muhammad Adeel Asghar (6724982)

“…<p dir="ltr">Much attention has been paid to the recognition of human emotions with the help of electroencephalogram (EEG) signals based on machine learning technology. Recognizing emotions is a challenging task due to the non-linear property of the EEG signal. …”

Tensile Test Optimization Using the Design of Experiment and Soft Computing by Mehdi Moayyedian (14880358)

Recursive least-squares backpropagation algorithm for stop-and-go decision-directed blind equalization by Abrar, Shafayat

“…To overcome this problem, in this work, a fast converging recursive least squares (RLS)-based complex-valued backpropagation learning algorithm is derived for S&G-DD blind equalization. …”
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Joint energy-distortion aware algorithms for cooperative video streaming over LTE networks by Yaacoub, Elias

“…Videos with single description coding (SDC), multiple description coding (MDC), and scalable video coding (SVC) are considered. …”
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Adaptive Secure Pipeline for Attacks Detection in Networks with set of Distribution Hosts by ALSHAMSI, SUROUR

“…The research work carried out, in relation to attack detection ensemble learning, mainly aims to increase the performance of machine learning algorithms by combining their results. …”
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A machine learning approach for localization in cellular environments by Abdallah, Ali A.

“…A machine learning approach is developed for localization based on received signal strength (RSS) from cellular towers. …”
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Modulation With Metaheuristic Approach for Cascaded-MPUC49 Asymmetrical Inverter With Boosted Output by Kaif Ahmed Lodi (16855518)

An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization by Majdi Mansouri (16869885)

LDSVM: Leukemia Cancer Classification Using Machine Learning by Abdul Karim (417009)

“…This study proposes a novel method using machine learning algorithms based on microarrays of leukemia GSE9476 cells. …”

Using machine learning to support students’ academic decisions by ALLAH, AISHA QASIM GHAZAL FATEH

“…This research tests and compares the performance of Decision Trees, Random Forests, Gradient-Boosted trees, and Deep Learning machine learning regression algorithms to predict student GPA. …”
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Cyberbullying Detection in Arabic Text using Deep Learning by ALBAYARI, REEM RAMADAN SA’ID

“…Data-driven approaches, such as machine learning (ML), particularly deep learning (DL), have shown promising results. …”
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Wavelet Analysis- Singular Value Decomposition Based Method for Precise Fault Localization in Power Distribution Networks Using k-NN Classifier by Abhishek Raj (7245425)

The Detection of Dysarthria Severity Levels Using AI Models: A Review by Afnan Al-Ali (16888695)

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

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Single channel speech denoising by DDPG reinforcement learning agent by Sania Gul (18272227)

“…In this paper, a novel SD algorithm is presented based on the deep deterministic policy gradient (DDPG) agent; an off-policy reinforcement learning (RL) agent with a continuous action space. …”

Analysis of Using Machine Learning to Enhance the Efficiency of Facilities Management in the UAE by ULLAH, SAAD

“…This study addresses these issues by Implementing Machine Learning (ML) algorithms using data from Building Management Systems (BMS) and FM maintenance reports, focussing on predictive maintenance for Fresh Air Handling Units. …”
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The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review by Zainab Jan (17306614)

“…We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). …”