Developing an online hate classifier for multiple social media platforms by Joni Salminen (7434770)

“…Although researchers have found that hate is a problem across multiple platforms, there is a lack of models for online hate detection using multi-platform data. …”

ML-Based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks by Muhammad Asif Khan (7367468)

“…In this paper, we propose data-driven machine learning (ML) schemes to efficiently solve these problems in wireless LAN (WLAN) networks. …”

Detecting and Predicting Archaeological Sites Using Remote Sensing and Machine Learning—Application to the Saruq Al-Hadid Site, Dubai, UAE by Ben Romdhane, Haifa

“…The validation of these results was performed using previous archaeological works as well as geological and geomorphological field surveys. The modelling and prediction accuracies are expected to improve with the insertion of a neural network and backpropagation algorithms based on the performed cluster groups following more recent field surveys. …”
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Precision nutrition: A systematic literature review by Daniel Kirk (17302798)

“…Therefore, we carried out a Systematic Literature Review (SLR) to provide an overview of where and how machine learning has been used in Precision Nutrition from various aspects, what such machine learning models use as input features, what the availability status of the data used in the literature is, and how the models are evaluated. …”

Study of the Impact of Land Use and Land Cover (LULC) Changes on local climate in Six Mega Global Cities in the 21st Century by Sabobeh, Lara Ali

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PROVOKE: Toxicity trigger detection in conversations from the top 100 subreddits by Hind Almerekhi (7434776)

“…Before finding toxicity triggers, we built and evaluated various machine learning models to detect toxicity from Reddit comments. Subsequently, we used our best-performing model, a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model that achieved an area under the receiver operating characteristic curve (AUC) score of 0.983 to detect toxicity. …”

Clustering and Stochastic Simulation Optimization for Outpatient Chemotherapy Appointment Planning and Scheduling by Majed Hadid (17148364)

“…A Stochastic Discrete Simulation-Based Multi-Objective Optimization (SDSMO) model is developed and linked to clustering algorithms using an iterative sequential approach. …”

From Collatz Conjecture to chaos and hash function by Masrat Rasool (17807813)

“…The effectiveness and dependability of the proposed hash function are evaluated by comparing it with two well-known hash algorithms, namely SHA-3 and SHA-2, as well as several other Chaos-based hash algorithms. …”

Detecting and Predicting Archaeological Sites Using Remote Sensing and Machine Learning—Application to the Saruq Al-Hadid Site, Dubai, UAE by Ben-Romdhane, Haïfa

“…The validation of these results was performed using previous archaeological works as well as geological and geomorphological field surveys. The modelling and prediction accuracies are expected to improve with the insertion of a neural network and backpropagation algorithms based on the performed cluster groups following more recent field surveys. …”
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Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images by Rehan Raza (17019105)

“…The class imbalance issue was handled through multiple data augmentation methods to overcome the biases. …”

Engineering the advances of the artificial neural networks (ANNs) for the security requirements of Internet of Things: a systematic review by Yasir Ali (799969)

“…In this question, we also determined the various models, frameworks, techniques and algorithms suggested by ANNs for the security advancements of IoT. …”

Nonlinear Friction Identification of A Linear Voice Coil DC Motor by Takrouri, Mohannad Hashem

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Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks by Mohammed Almehdhar (22046597)

“…Our systematic review covers a range of AI algorithms, including traditional ML, and advanced neural network models, such as Transformers, illustrating their effectiveness in IDS applications within IVNs. …”

An Auction-Based Scheduling Approach for Minimizing Latency in Fog Computing Using 5G Infrastructure by Fahmy, Ahmed

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Deep Neural Networks for Electromagnetic Inverse Scattering Problems in Microwave Imaging by Maricar, Mohammed Farook

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Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis by Jithin Kalathikudiyil Sreedharan (18268894)

“…<h3>Introduction</h3><p dir="ltr">Artificial intelligence has come to be the highlight in almost all fields of science. It uses various models and algorithms to detect patterns and specific findings to diagnose a disease with utmost accuracy. …”

Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study by Muhammad Atif Butt (10849980)

“…Since the success of AI is to be measured ultimately in terms of how it benefits human beings, and that the data driving the deep learning-based edge AI algorithms are intricately and intimately tied to humans, it is important to look at these AI technologies through a human-centric lens. …”

A Fusion-Based Approach for Skin Cancer Detection Combining Clinical Images, Dermoscopic Images, and Metadata by Siddiqui, Ansah Juned

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Software-Defined-Networking-Based One-versus-Rest Strategy for Detecting and Mitigating Distributed Denial-of-Service Attacks in Smart Home Internet of Things Devices by Neder Karmous (19743430)

“…We conducted a comparative analysis of various models and algorithms used in the related works. The results indicated that our proposed approach outperforms others, showcasing its effectiveness in both detecting and mitigating DDoS attacks within SDNs. …”

Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases by Muhammad Ali Muzammil (17910611)

“…These AI algorithms are effective non-invasive biomarkers for cardiovascular illnesses because they can identify subtle patterns and signals in the ECG that may not be readily apparent to human interpreters. …”