Data mining approach to predict student's selection of program majors by SIDDARTHA, SHARMILA

“…Cross validation is applied to train and test performance of models. Naïve Bayes, Decision Trees offered the best predictions across the different sampling techniques. …”
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CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children by Jayakanth, Kunhoth

“…The extracted CNN features are then fused in different combinations. Three machine learning algorithms support vector machine (SVM), AdaBoost, and Random forest are employed to assess the performance of the CNN features and fused CNN features. …”
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Behavior-Based Machine Learning Approaches to Identify State-Sponsored Trolls on Twitter by Saleh Alhazbi (16869960)

“…Based on these features, we developed four classification models to identify political troll accounts, these models are based on decision tree, random forest, Adaboost, and gradient boost algorithms. …”

Use data Mining Techniques to Predict Users’ Engagement on the Social Network Posts in The Period Before, During and After Ramadan by AL RAWASHDEH, HANEEN MOHAMMAD

“…In this thesis, the effectiveness of using a data mining classifier helps to predict users’ engagement with a social media post before publishing and anticipating the best type of media for the post. Different classification algorithms were applied to the dataset using the Rapidminer tool. …”
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Design and analysis of efficient and secure elliptic curve cryptoprocessors. by Al-Somani, Turki Faisal

“…The proposed architectures have been modeled using VHDL and implemented on FPGA platform.…”
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Con-Detect: Detecting Adversarially Perturbed Natural Language Inputs to Deep Classifiers Through Holistic Analysis by Hassan Ali (3348749)

“…We experiment with multiple attackers—Text-bugger, Text-fooler, PWWS—on several architectures—MLP, CNN, LSTM, Hybrid CNN-RNN, BERT—trained for different classification tasks—IMDB sentiment classification, fake-news classification, AG news topic classification—under different threat models—Con-Detect-blind attacks, Con-Detect-aware attacks, and Con-Detect-adaptive attacks—and show that Con-Detect can reduce the attack success rate (ASR) of different attacks from 100% to as low as 0% for the best cases and ≈70% for the worst case. …”

A lightweight adaptive compression scheme for energy-efficient mobile-to-mobile file sharing applications by Sharafeddine, Sanaa

“…However, the computational as well as memory access requirements of compression algorithms could consume more energy than simply transmitting data uncompressed. …”
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Integration of nonparametric fuzzy classification with an evolutionary-developmental framework to perform music sentiment-based analysis and composition by Abboud, Ralph

“…Over the past years, several approaches have been developed to create algorithmic music composers. Most existing solutions focus on composing music that appears theoretically correct or interesting to the listener. …”
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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. …”

Sentiment Analysis for Arabic Social media Movie Reviews Using Deep Learning by MEZAHEM, FATEMA HAMAD

“…The two movies are Wahed Tani which translates to (someone else) and Amahom which translate to (their uncle), Three datasets were employed, and several categorization models were compared across them. Prior to performing sentiment analysis, it is necessary to prepare the data so that it may be used to train machine learning (ML) algorithms. …”
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Transformations for Variants of the Travelling Salesman Problem and Applications by Assaf, Mustafa Jamil

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CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children by Jayakanth Kunhoth (14158908)

“…The extracted CNN features are then fused in different combinations. Three machine learning algorithms support vector machine (SVM), AdaBoost, and Random forest are employed to assess the performance of the CNN features and fused CNN features. …”

Artificial Intelligence for Skin Cancer Detection: Scoping Review by Abdulrahman Takiddin (14153181)

“…</p><h3>Conclusions</h3><p dir="ltr">This paper examined multiple AI-based skin cancer detection models. However, a direct comparison between methods was hindered by the varied use of different evaluation metrics and image types. …”

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. …”

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

“…In the current study, we utilized ensemble learning algorithms for the recognition and classification of phantom movements of the different amputation levels of the upper and lower extremity. …”

HVAC system attack detection dataset by Mariam Elnour (14147790)

“…It aims to promote and support the research in the field of cybersecurity of HVAC systems in smart buildings by facilitating the validation of attack detection and mitigation strategies, benchmarking the performance of different data-driven algorithms, and studying the impact of attacks on the HVAC system.…”

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

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The role of Reinforcement Learning in software testing by Amr Abo-eleneen (17032284)

“…</p><h3>Results</h3><p dir="ltr">This study highlights different software testing types to which RL has been applied, commonly used RL algorithms and architecture for learning, challenges faced, advantages and disadvantages of using RL, and the performance comparison of RL-based models against other techniques.…”

MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network by Sakib Mahmud (15302404)

“…The performance of the deep learning models is measured using three well-known performance matrices viz. mean absolute error (MAE)-based construction error, the difference in the signal-to-noise ratio (ΔSNR), and percentage reduction in motion artifacts (<i>η</i>). …”

Predicting Compression Modes and Split Decisions for HEVC Video Coding Using Machine Learning Techniques by Hassan, Mahitab Alaaeldin

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