TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection حسب Zina Chkirbene (16869987)

"…By using a comprehensive dataset with multiple attack types, a well-trained model can be created to improve the anomaly detection performance. …"

Adaptive Secure Pipeline for Attacks Detection in Networks with set of Distribution Hosts حسب 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 Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI حسب Oishi Jyoti (21593819)

Developing an online hate classifier for multiple social media platforms حسب 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. …"

Accurate multiple network alignment through context-sensitive random walk حسب Hyundoo Jeong (3840013)

"…In this work, we propose a novel multiple network alignment algorithm based on a context-sensitive random walk model. …"

Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tr... حسب Elmahdy, Samy

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Con-Detect: Detecting adversarially perturbed natural language inputs to deep classifiers through holistic analysis حسب Hassan, Ali

"…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. …"
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Con-Detect: Detecting Adversarially Perturbed Natural Language Inputs to Deep Classifiers Through Holistic Analysis حسب 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. …"

Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems حسب Tarek Berghout (16905132)

Artificial Intelligence for Skin Cancer Detection: Scoping Review حسب 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. …"

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

"…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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Detecting protein complexes in protein interaction networks using Mapper and Graph Convolution Networks حسب Daou, Leonardo

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

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Information reconciliation through agent controlled graph model. (c2018) حسب Saba, Rita

"…Multiple models have been proposed and different techniques and data structures were used. …"
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An Artificial Intelligence Approach for Predictive Maintenance in Electronic Toll Collection System حسب Alkhatib, Osama

"…Historical data of Dubai Toll Collection System is utilized to investigate multiple machine learning algorithms. Experiment is performed using Azure Machine Learning (ML) platform to test and assess the most efficient model that would predict the failure of system elements and predict the abnormality of the operation. …"
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Improving INS/GPS Integration for Mobile Robotics Applications حسب AbuHashim, Tariq Salman

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Artificial Intelligence–Driven Serious Games in Health Care: Scoping Review حسب Alaa Abd-alrazaq (17058018)

"…Accuracy was the most commonly used metric for evaluating the performance of AI models.</p><h3>Conclusions</h3><p dir="ltr">The last decade witnessed an increase in the development of AI-driven serious games for health care purposes, targeting various health conditions, and leveraging multiple AI algorithms; this rising trend is expected to continue for years to come. …"

Portable Electromagnetic Surface Profiling System حسب Al Homssi, Bassel Mohamad

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MSLP: mRNA subcellular localization predictor based on machine learning techniques حسب Saleh Musleh (15279190)

"…</p><h3>Results</h3><p dir="ltr">Considering the combination of the above-mentioned features, ennsemble-based models achieved state-of-the-art results in mRNA subcellular localization prediction tasks for multiple benchmark datasets. …"

Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images حسب Rehan Raza (17019105)

"…Considering these shortcomings, computational methods especially machine learning and deep learning algorithms are leveraged as an alternative to accelerate the accurate detection of CT scans as cancerous, and non-cancerous. …"