Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases by Muhammad Ali Muzammil (17910611)

“…To guarantee a safe and successful deployment in clinical practice, the use of AI in cardiology must be done with a thorough understanding of the algorithms and their limits. …”

Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review by Arfan Ahmed (17541309)

“…Wearable devices (WDs) make use of sensors historically reserved for hospital settings. …”

Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study by Muhammad Atif Butt (10849980)

“…Secondly, to illustrate the gravity of these issues, we present a case study on the task of human facial emotion recognition (FER) based on AffectNet dataset, where we analyze the effects of widely used input quantization on the security, robustness, fairness, and trustworthiness of an EML model. …”

A comparative analysis to forecast carbon dioxide emissions by Md. Omer Faruque (17545671)

“…The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are used to analyze and compare the performances of the predictive models. …”

Data Endowment as a Digital Waqf: An Islamic Ethical Framework for AI Development by Abdulqadir J. Nashwan (11659453)

“…<p dir="ltr">In the era of artificial intelligence (AI), data is often called the new oil—an essential asset for training algorithms and fueling intelligent systems. However, the ethical considerations surrounding this data’s ownership, use, and benefits remain contentious. …”

SemIndex: Semantic-Aware Inverted Index by Chbeir, Richard

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A FeedForward–Convolutional Neural Network to Detect Low-Rate DoS in IoT by Harun Surej Ilango (17545728)

“…The performance of the models is measured using the metrics accuracy, precision, recall, F1 score, detection time per flow, and ROC curves. …”

Depthwise Separable Convolutions and Variational Dropout within the context of YOLOv3 by Chakar, Joseph

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Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures by Iryna Haponchyk (19691701)

“…Most interestingly, we show that such functions can be (i) automatically learned also from controversial but commonly accepted coreference measures, e.g., MELA, and (ii) successfully used in learning algorithms. The accurate model comparison on the standard CoNLL-2012 setting shows the benefit of more expressive loss functions.…”

Humanizing AI in medical training: ethical framework for responsible design by Mohammed Tahri Sqalli (18420840)

“…The transparency pillar highlights the crucial role of maintaining the explainabilty of AI algorithms, while the fairness and justice pillar emphasizes on addressing biases in healthcare data and designing models that prioritize equitable medical training outcomes. …”

Traffic Offloading with Channel Allocation in Cache-Enabled Ultra-Dense Wireless Networks by Abbas, Nadine

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Dynamic multiple node failure recovery in distributed storage systems by Itani, May

“…The contribution of this paper is four-fold: i. we generate an optimized block distribution scheme that minimizes the total system repair cost of all dependent and independent multiple node failure scenarios; ii. we address the practical scenario of having newly arriving blocks and allocate those blocks to existing nodes without any modification to the original on-node block distribution; iii. we consider new-comer nodes and generate an updated optimized block distribution; iv. we consider optimized storage and recovery of blocks with varying priority using variable fractional repetition codes. The four problems are modeled using incidence matrices and solved heuristically. …”
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A Cyber-Physical System and Graph-Based Approach for Transportation Management in Smart Cities by Muhammad Mazhar Rathore (17051745)

“…In this paper, we proposed a transport-control model that exploits cyber-physical systems (CPS) and sensor-technology to continuously monitor and mine the big city data for smart decision-making. …”

Energy-Efficient VoI-Aware UAV-Assisted Data Collection in Wireless Sensor Networks by Kalash, Saeddin

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

“…The studies used up to 11 evaluation metrics to assess the proposed models, where 39 studies used accuracy as the primary evaluation metric. …”

Application of Metastructures for Targeted Low-Frequency Vibration Suppression in Plates by Ratiba F. Ghachi (14152455)

“…The thin plate and the zigzag cutouts are modelled using the finite element method, and the optimal location and optimal tip mass of the zigzag cutouts are obtained using genetic algorithms through iterative simulations. …”

Dynamic Cyber Resilience of Interdependent Critical Information Infrastructures by JUMA, MAZEN GHAZI

“…The technology stack of the proposed solution was also implemented with four algorithms and eight protocols. The evaluation results of the proposed solution were compared to the results of standard solutions under different cyberattack scenarios using quantitative research methods involving computing simulations, emulation experiments, and analytical modeling. …”
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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.…”

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

“…Deep Learning (DL) algorithms have shown wonders in many Natural Language Processing (NLP) tasks such as language-to-language translation, spam filtering, fake-news detection, and comprehension understanding. …”
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Con-Detect: Detecting Adversarially Perturbed Natural Language Inputs to Deep Classifiers Through Holistic Analysis by Hassan Ali (3348749)

“…<p>Deep Learning (DL) algorithms have shown wonders in many Natural Language Processing (NLP) tasks such as language-to-language translation, spam filtering, fake-news detection, and comprehension understanding. …”