A Novel Genetic Algorithm Optimized Adversarial Attack in Federated Learning for Android-Based Mobile Systems by Faria Nawshin (21841598)

“…<p dir="ltr">Federated Learning (FL) is gaining traction in Android-based consumer electronics, enabling collaborative model training across decentralized devices while preserving data privacy. However, the increasing adoption of FL in these devices exposes them to adversarial attacks that can compromise user data and device security. …”

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

“…The objective of this study is to predict plasma vitamin C using machine learning. The NHANES dataset was used to predict plasma vitamin C in a cohort of 2952 American adults using regression algorithms and clustering in a way that a hypothetical health application might. …”

Stochastic Search Algorithms for Exam Scheduling by Mansour, Nashat

“…Then, we empirically compare the three proposed algorithms and FESP using realistic data. Our experimental results show that SA and GA produce good exam schedules that are better than those of FESP heuristic procedure. …”
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Sentiment Mining of Arabic Twitter Data by Ahmed, Soha Galalaldin Khider

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Robust Control Of Sampled Data Systems by AL-Sunni, Fouad

“…They then present a numerical controller design algorithm based on the derived bounds. Examples are used for demonstration.…”
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Robust Control Of Sampled Data Systems by AL-Sunni, Fouad

“…They then present a numerical controller design algorithm based on the derived bounds. Examples are used for demonstration.…”
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Robust Control Of Sampled Data Systems by AL-Sunni, Fouad

“…They then present a numerical controller design algorithm based on the derived bounds.Examples are used for demonstration.…”
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Online Recruitment Fraud (ORF) Detection Using Deep Learning Approaches by Natasha Akram (20749538)

“…In recent studies, traditional machine learning and deep learning algorithms have been implemented to detect fake job postings; this research aims to use two transformer-based deep learning models, i.e., Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT-Pretraining Approach (RoBERTa) to detect fake job postings precisely. …”

Oversampling techniques for imbalanced data in regression by Samir Brahim Belhaouari (9427347)

“…For tabular data we conducted a comprehensive experiment using various models trained on both augmented and non-augmented datasets, followed by performance comparisons on test data. …”

Bee Colony Algorithm for Proctors Assignment. by Mansour, Nashat

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A Novel Steganography Technique for Digital Images Using the Least Significant Bit Substitution Method by Shahid Rahman (16904613)

“…<p>Communication has become a lot easier in this era of technology, development of high-speed computer networks, and the inexpensive uses of Internet. Therefore, data transmission has become vulnerable to and unsafe from different external attacks. …”

Improvement of Kernel Principal Component Analysis-Based Approach for Nonlinear Process Monitoring by Data Set Size Reduction Using Class Interval by Mohammed Tahar Habib Kaib (21633176)

“…<p dir="ltr">Fault detection and diagnosis (FDD) systems play a crucial role in maintaining the adequate execution of the monitored process. One of the widely used data-driven FDD methods is the Principal Component Analysis (PCA). …”

Multi-Cluster Jumping Particle Swarm Optimization for Fast Convergence by Atiq Ur Rehman (8843024)

“…Keeping in view the need of an optimization algorithm with fast convergence speed, suitable for high dimensional data space, this article proposes a novel concept of Multi-Cluster Jumping PSO. …”

Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy by Haitao Xu (435549)

“…Adjusting the weight and bias of the ANN model using an optimization algorithm is known as the training process. …”

Data Redundancy Management in Connected Environments by Mansour, Elio

“…., building) equipped with sensors that produce and exchange raw data. Although the sensed data is considered to contain useful and valuable information, yet it might include various inconsistencies such as data redundancies, anomalies, and missing values. …”
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Unsupervised outlier detection in multidimensional data by Atiq ur Rehman (14153391)

“…<p>Detection and removal of outliers in a dataset is a fundamental preprocessing task without which the analysis of the data can be misleading. Furthermore, the existence of anomalies in the data can heavily degrade the performance of machine learning algorithms. …”

Android Malware Detection Using Machine Learning by Al Ali, Shaikha

“…Detecting and preventing malware is crucial for several reasons, including the security of personal information, data loss and tampering, system disruptions, financial losses, and reputation damage. …”
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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. …”

Automated Deep Learning BLACK-BOX Attack for Multimedia P-BOX Security Assessment by Zakaria Tolba (16904718)

“…This paper provides a deep learning-based decryptor for investigating the permutation primitives used in multimedia block cipher encryption algorithms.We aim to investigate how deep learning can be used to improve on previous classical works by employing ciphertext pair aspects to maximize information extraction with low-data constraints by using convolution neural network features to discover the correlation among permutable atoms to extract the plaintext from the ciphered text without any P-box expertise. …”

Delay Optimization in LoRaWAN by Employing Adaptive Scheduling Algorithm With Unsupervised Learning by Zulfiqar Ali (117651)

“…This paper aims to optimize the delay in LoRaWAN by using an Adaptive Scheduling Algorithm (ASA) with an unsupervised probabilistic approach called Gaussian Mixture Model (GMM). …”