A Machine Learning Approach to Predicting Diabetes Complications by Jian, Yazan Khaled

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A multi-class discriminative motif finding algorithm for autosomal genomic data. (c2015) by Wehbe, Gioia Wahib

“…Aiming to tackle these obstacles, we have derived a new computational method in order to identify conserved regions of Single Nucleotide Polymorphisms (SNPs) on autosomal chromosomes that are differentiable in different populations. Our algorithm first performs a feature selection step to define differentiable SNPs. …”
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Predicting Patient ICU Readmission Using Recurrent Neural Networks With Long Short-Term Memory by Nadia Rasheed (22997257)

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

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Hybrid Deep Learning-based Models for Crop Yield Prediction by Alexandros Oikonomidis (12050497)

“…In this study, we developed deep learning-based models to evaluate how the underlying algorithms perform with respect to different performance criteria. …”

Cyberbullying Detection Model for Arabic Text Using Deep Learning by Albayari, Reem

“…Therefore, in this study, we conduct a performance evaluation and comparison for various DL algorithms (LSTM, GRU, LSTM-ATT, CNN-BLSTM, CNN-LSTM and LSTM-TCN) on different datasets of Arabic cyberbullying to obtain more precise and dependable findings. …”
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Cyberbullying Detection Model for Arabic Text Using Deep Learning by Albayari, Reem

“…Therefore, in this study, we conduct a performance evaluation and comparison for various DL algorithms (LSTM, GRU, LSTM-ATT, CNN-BLSTM, CNN-LSTM and LSTM-TCN) on different datasets of Arabic cyberbullying to obtain more precise and dependable findings. …”
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The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review by Zainab Jan (17306614)

“…The most commonly used data belonged to the clinical category (19, 58%). 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%). …”

PSYCHOLOGICAL EMOTION RECOGNITION OF STUDENTS USING MACHINE LEARNING BASED CHATBOT by Khalil Assayed, Suha

“…The authors applied Machine Learning algorithms, Support Vector Machines (SVM) and the Naïve Bayes (NB) and accordingly they compared the accuracy between them. …”
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PSYCHOLOGICAL EMOTION RECOGNITION OF STUDENTS USING MACHINE LEARNING BASED CHATBOT by Khalil Assayed, Suha

“…The authors applied Machine Learning algorithms, Support Vector Machines (SVM) and the Naïve Bayes (NB) and accordingly they compared the accuracy between them. …”
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Nonlinear analysis of shell structures using image processing and machine learning by M.S. Nashed (16392961)

“…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …”

Unsupervised Deep Learning for Classification Of Bats Calls Using Acoustic Data by Arshad, Muhammad Arbab

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Recent advances on artificial intelligence and learning techniques in cognitive radio networks by Abbas, Nadine

“…The literature survey is organized based on different artificial intelligence techniques such as fuzzy logic, genetic algorithms, neural networks, game theory, reinforcement learning, support vector machine, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. …”
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Reinforcement R-learning model for time scheduling of on-demand fog placement by Farhat, Peter

“…Our model aims to decrease the cloud’s load by utilizing the maximum available fogs resources over different locations. An implementation of our proposed R-learning model is provided in the paper, followed by a series of experiments on a real dataset to prove its efficiency in utilizing fog resources and minimizing the cloud’s load. …”
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Machine learning approach for the classification of corn seed using hybrid features by Aqib Ali (19680145)

“…The purpose of this study was to examine the feasibility of a machine learning (ML) approach for classifying different types of corn seeds. …”

Machine Learning Solutions for the Security of Wireless Sensor Networks: A Review by Yazeed Yasin Ghadi (16667109)

“…The main objective of the article is to know about different machine learning algorithms that are used to solve the security issues of wireless sensor networks. …”

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. …”

Detecting Arabic Cyberbullying Tweets in Arabic Social Using Deep Learning by ALFALASI, FARIS Jr

“…A sizable dataset of electronic text data was gathered from multiple social media platforms like Twitter, Instagram, YouTube, and many more sites in order to examine cyberbullying in social media using machine learning and deep learning techniques. The data needs to be initially prepared so that deep learning algorithms may be trained on it before cyberbullying analysis can be done. …”
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Multi-Cluster Jumping Particle Swarm Optimization for Fast Convergence by Atiq Ur Rehman (8843024)

A learning approach for prioritized handoff channel allocation in mobile multimedia networks by El-Alfy, E.-S.M.

“…In this paper we study the application of a reinforcement-learning algorithm to develop an alternative channel allocation scheme in mobile cellular networks that supports multiple heterogeneous traffic classes. …”
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