Leveraging Machine and Deep Learning Algorithms for hERG Blocker Prediction by Syed Mohammad (21075689)

Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition by Dabeeruddin Syed (16864260)

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

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

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Developing a framework for using face recognition in transit payment transactions by HABEH, ORABI MOHAMMAD ABDULLAH

“…The proposed framework involves the hardware, software, algorithms, and system specification requirements. Further, it provides a detailed end-to-end systems integration and transaction flow between the account-based ticketing, face recognition, and banking systems. …”
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Blue collar laborers’ travel pattern recognition: Machine learning classifier approach by Aya Hasan Alkhereibi (17151070)

“…<p dir="ltr">This paper proposes a pattern recognition model to develop clusters of homogenous activities for blue-collar workers in the State of Qatar. …”

Decision-level fusion for single-view gait recognition with various carrying and clothing conditions by Al-Tayyan, Amer

“…The experimental results show that the classification accuracy of the proposed methods is encouraging and outperforms several state-of-the-art gait recognition approaches reported in the literature.…”
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Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectives by Billel Essaid (22047578)

“…The process involves receiving the speech signal in analog form, followed by various signal processing algorithms to make it compatible with devices of limited capacities, such as cochlear implants (CIs). …”

Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features by Mariam Bahameish (19255789)

“…This study employed supervised learning algorithms to classify stress and relaxation states using HRV measures. …”

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

“…For optimal classification performance, we use a support vector machine (SVM) and k-nearest neighbor (k-NN) to classify the extracted features for the different emotional states of the two data sets. The BoDF model achieves 93.8% accuracy in the SEED data set and 77.4% accuracy in the DEAP data set, which is more accurate compared to other state-of-the-art methods of human emotion recognition.…”

Decision-level Gait Fusion for Human Identification at a Distance by Al-Tayyan, Amer

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Single channel speech denoising by DDPG reinforcement learning agent by Sania Gul (18272227)

“…<p dir="ltr">Speech denoising (SD) covers the algorithms that suppress the background noise from the contaminated speech and improve its clarity. …”

Deep Learning-Based Coding Strategy for Improved Cochlear Implant Speech Perception in Noisy Environments by Billel Essaid (22047578)

“…Experimental results on the TIMIT and Harvard Sentences datasets, enriched with diverse noise sources from the DEMAND database, yielded high intelligibility scores with a short-time objective intelligibility (STOI) of 0.8345, word recognition score (WRS) of 99.2636, and an near correlation coefficient (LCC) of 0.9607, underscoring the model’s capability to enhance speech perception in noisy CI environments, ensuring a balance between model size and speech quality, and surpassing the existing state-of-the-art techniques.…”

DASSI: differential architecture search for splice identification from DNA sequences by Shabir Moosa (14153316)

“…We have extensively benchmarked DASSI model with other state-of-the-art models and assessed its computational efficiency. …”

Gene selection for microarray data classification based on Gray Wolf Optimizer enhanced with TRIZ-inspired operators by Abu Zitar, Raed

“…The outcomes of the DNA microarray is a table/matrix, called gene expression data. Pattern recognition algorithms are widely applied to gene expression data to differentiate between health and cancerous patient samples. …”
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