Structural similarity evaluation between XML documents and DTDs by Tekli, J.

“…Nonetheless, several operations based on the structure of XML data have not yet received strong attention. Among these is the process of matching XML documents with XML grammars, useful in various applications such as documents classification, retrieval and selective dissemination of information. …”
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Performance Analysis of Artificial Neural Networks in Forecasting Financial Time Series by Lasfer, Assia

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EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach by Muhammad Adeel Asghar (6724982)

“…The proposed model achieves better classification accuracy compared to the recently reported work when validated on SJTU SEED and DEAP data sets. …”

A microscale evaluation of pavement roughness effects for asset management by El Khoury, John

“…In this paper, the authors present a method to automate data collection and mapping of pavement roughness on various functional classification roadways and to quantify fuel consumption for enhanced benefit-to-cost analysis, asset management and infrastructure investment strategies. …”
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Video features with impact on user quality of experience by Abbas, Nadine

“…The feature evaluation is performed first using forward elimination feature selection algorithm, and second using decision tree classification to extract and sort the features that highly affect the subjective QoE. …”
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Detecting Arabic Cyberbullying Tweets in Arabic Social Using Deep Learning by 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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DNA Base-Calling Techniques by Odeh, Fadi

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Plant disease detection using drones in precision agriculture by Ruben Chin (17725986)

“…The most used drone type is the quadcopter and the most applied machine learning task is classification. Color-infrared (CIR) images are the most preferred data used and field images are the main focus. …”

A conjugate self-organizing migration (CSOM) and reconciliate multi-agent Markov learning (RMML) based cyborg intelligence mechanism for smart city security by S. Shitharth (12017480)

“…Moreover, the Reconciliate Multi-Agent Markov Learning (RMML) based classification algorithm is used to predict the intrusion with its appropriate classes. …”

Smart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k-nearest neighbors classifier by Yassine Himeur (14158821)

“…Specifically, short local histograms are drawn to represent individual appliance consumption signatures and robustly extract appliance-level data from the aggregated power signal. Furthermore, an improved k-nearest neighbors (IKNN) algorithm is presented to reduce the learning computation time and improve the classification performance. …”

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

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A novel few shot learning derived architecture for long-term HbA1c prediction by Marwa Qaraqe (10135172)

“…Finally, a K-nearest neighbor (KNN) model with majority voting is implemented for the final classification task. The proposed FSL-derived algorithm provides a prediction accuracy of 93.2%.…”

Automated skills assessment in open surgery: A scoping review by Hawa Hamza (17707224)

“…The assessment input for the automation algorithms were predominantly hand movement. Around 37.5 % (<i>n </i>= 15) of the studies assessed algorithm performance using classification accuracy. …”

Decomposition-based wind power forecasting models and their boundary issue: An in-depth review and comprehensive discussion on potential solutions by Yinsong Chen (16685508)

“…These methods generally disaggregate the original time series data into sub-time-series with better stationarity, and then the target data is predicted based on the sub-series. …”

A hybrid model to predict the pressure gradient for the liquid-liquid flow in both horizontal and inclined pipes for unknown flow patterns by Md Ferdous Wahid (13485799)

“…Statistical analysis showed that the selected features for liquids' and pipe's properties using the BGWOPSO algorithm were adequate to attain superior performance for both models. …”

Cyberbullying Detection in Arabic Text using Deep Learning by ALBAYARI, REEM RAMADAN SA’ID

“…Therefore, this study aims to evaluate several versions of Recurrent Neural Networks (RNNs) and Feedforward Neural Networks (FNNs) for detecting cyberbullying in the Arabic language. Although these algorithms are widely used in text classification and outperform the performance of classical classifiers, many have been extensively investigated in other domains such as sentiment analysis and dialect identification, as well as cyberbullying detection in English text. …”
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Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose by Atiq Ur Rehman (8843024)

“…These electronic instruments rely on Machine Learning (ML) algorithms for recognizing the sensed odors. The effect of long-term drift influences the performance of ML algorithms and the models those are trained on drift free data fail to perform on the drifted data. …”

Predicting User Behavior over the Web by Al Safadi, Amal Adnan

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

“…However, the meta-analysis shows that ML approaches, particularly DL, have not been extensively studied for the Arabic text classification of cyberbullying. 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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Multimodal feature fusion and ensemble learning for non-intrusive occupancy monitoring using smart meters by Sakib Mahmud (15302404)

“…In this study, we introduce the multimodal feature fusion for non-intrusive occupancy monitoring (MMF-NIOM) framework, which leverages both classical and deep machine learning algorithms to achieve state-of-the-art occupancy detection performance using smart meter data. …”