Gene-specific machine learning model to predict the pathogenicity of BRCA2 variants by Mohannad N. Khandakji (13885434)

“…Local, gene-specific information have been shown to aid variant pathogenicity prediction; therefore, our aim was to develop a BRCA2-specific machine learning model to predict pathogenicity of all types of BRCA2 variants.</p><p><br></p><h3>Methods</h3><p dir="ltr">We developed an XGBoost-based machine learning model to predict pathogenicity of BRCA2 variants. …”

Designing Cellular Mobile Networks Using Non{Deterministic Iterative Heuristics by Sait, Sadiq M.

“…Hence, a randomized, heuristic algorithm, such as Simulated Evolution is used in this work to optimize the transmission costs in cellular networks. …”
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Systems biology analysis reveals NFAT5 as a novel biomarker and master regulator of inflammatory breast cancer by Andrea Remo (447375)

“…</p><h3>Methods</h3><p dir="ltr">In-silico modeling and Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) on IBC/non-IBC (nIBC) gene expression data (n = 197) was employed to identify novel master regulators connected to the IBC phenotype. …”

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

“…Cyberbullying involves the use of communication technology and data, including messages, photographs, and videos, to undertake aggressive negative actions to harm others. …”
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The political economy of AI: a content analysis of ChatGPT outputs and anti-Palestinian media bias by King, Gretchen

“…A theoretical framework for analysing digital Orientalism is advanced through a historicisation of media imperialism, the military–industrial–communications complex and the political economy of AI in occupied Palestine. Grounded in research-based teaching methods, and employing a mixed-method content and frequency analysis and comparison of ChatGPT outputs in English and Arabic, this article expands research on AI biases, algorithmic oppression and digital divides within the context of digital Orientalism and anti-Palestinianism. …”
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Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network by Monzure-Khoda Kazi (17191207)

“…These algorithms include random forest (RF) classification and artificial neural networks (ANN). …”

Comparative assessment of fracture risk among osteoporosis and osteopenia patients: a cross-sectional study by Snezana Tomasevic-Todorovic (5782682)

“…The assessment of the fracture risk was executed by applying the Fracture Assessment Risk (FRAX) index (an algorithm developed by the World Health Organization) based on clinical fracture risks or combination of clinical fracture risks and bone mineral density.…”

Current trends and future orientation in diagnosing lung pathologies: A systematic survey by Noorizadeh, Mohammad

“…These VOCs can aid in the diagnosis of lung pathologies such as pneumonia. The CV processing method involves the application of advanced imaging techniques and machine learning algorithms to scrutinize and diagnose lung pathologies and ventilator-associated pneumonia (VAP). …”
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Optimal Trajectory and Positioning of UAVs for Small Cell HetNets: Geometrical Analysis and Reinforcement Learning Approach by Mohammad Taghi Dabiri (16904658)

“…Then, using geometrical analysis and deep reinforcement learning (RL) method, we propose several algorithms to find the optimal trajectory and select an optimal pattern during the trajectory. …”

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

“…Leave-one-out cross-validation method was used in this work. 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>). …”

Multimodal feature fusion and ensemble learning for non-intrusive occupancy monitoring using smart meters by Sakib Mahmud (15302404)

“…We combine features from all three modes of MMF-NIOM to achieve a state-of-the-art non-intrusive occupancy classification performance of 91.5 % accuracy and 91.5 % f1-score, approximately, by an ensemble of fine-tuned classifiers on the electricity consumption & occupancy (ECO) dataset. The proposed method is sustainable, robust, adaptable to various households, and can be mass-implemented within smart meters at a much lower cost and effort compared to the traditional internet of things (IoT)-based intrusive systems.…”

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects by Mohamed Massaoudi (16888710)

“…Motivated by the outstanding success of DL-based prediction methods, this article attempts to provide a thorough review from a broad perspective on the state-of-the-art advances of DL in SG systems. …”

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

“…We thus propose Con-Detect—a Contribution based Detection method—for detecting adversarial attacks against NLP classifiers. …”
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Con-Detect: Detecting Adversarially Perturbed Natural Language Inputs to Deep Classifiers Through Holistic Analysis by Hassan Ali (3348749)

“…We thus propose Con-Detect—a Contribution based Detection method—for detecting adversarial attacks against NLP classifiers. …”

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

“…The proposed scheme monitors the signal strength level during the file transfer process and compresses data blocks on-the-fly only whenever energy reduction gain is expected. …”
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Barriers of Adopting Artificial Intelligence Tools in Engineering Construction Projects by ALKAABI, ABDULLA

“…The situation may cause concern and trepidation about integrating AI technologies and lack understanding of their optimal deployment and operation. Construction data management and integration are difficult. AI algorithms depend on data for training and analysis. …”
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Growing hierarchical self-organizing map for filtering intrusion detection alarms by Mansour, Nashat

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Dynamic single node failure recovery in distributed storage systems by Itani, M.

“…We selected fractional repetition coding due to its simple repair mechanism that minimizes the repair and disk access bandwidths together with the property of un-coded repair process. To minimize the system repair cost, we formulate our problem using incidence matrices and solve it heuristically using genetic algorithms for all possible cases of single node failures. …”
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ISSP by Zouein, P.P.

“…Many decision-support tools were developed to assist planners in space scheduling but these were limited to providing the user with a platform that ties spatial and temporal data in the project and left it up to the user to decide on positions of resources and schedule adjustments to solve spatial conflicts that may arise in the process of constructing site layouts over time.The ISSP system, presented here, provides a graphical user-interactive interface with underlying layout and scheduling algorithms that construct feasible layout and schedule solutions under 2-dimensional spatial constraints between resources. …”
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The use of semantic-based predicates implication to improve horizontal multimedia database fragmentation by Getahun, Fekade

“…We particularly discuss multimedia primary horizontal fragmentation and focus on semantic-based textual predicates implication required as a pre-process in current fragmentation algorithms in order to partition multimedia data efficiently. …”
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