Joint BS Assignment and End-to-End Scheduling for Wireless Cellular Networks with Heterogeneous Services حسب Saad, Walid

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Estimation of power grid topology parameters through pilot signals حسب Hargossian, H.

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Robustness testing of composed real-time systems حسب Fouchal, Hacène

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A machine learning approach for localization in cellular environments حسب Abdallah, Ali A.

"…The proposed approach only assumes knowledge of RSS fingerprints of the environment, and does not require knowledge of the cellular base transceiver station (BTS) locations, nor uses any RSS mathematical model. The proposed localization scheme integrates a weighted K-nearest neighbor (WKNN) and a multilayer neural network. …"
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Real-Time Social Robot’s Responses to Undesired Interactions Between Children and their Surroundings حسب Ahmad Yaser Alhaddad (7017434)

"…The findings of this work can be used by social robot developers to address undesirable interactions in their robotic designs.…"

Proactive machine learning-based solution for advanced manageability of multi-persona mobile computing حسب Tout, Hanine

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The architecture of a highly reconfigurable RISC dataflow array processor حسب Sait, Sadiq M.

"…This processor forms an element of a processor array which possess the features of both static and dynamic dataflow models. The array can be programmed to execute arbitrary algorithms in both static and dynamic manner. …"
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Lightweight blockchain solutions: Taxonomy, research progress, and comprehensive review حسب Mershad, Khaleel

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Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review حسب Alaa Abd-alrazaq (17058018)

"…The most frequently used data set from open sources was Depresjon. The most commonly used algorithm was random forest, followed by support vector machine.…"

Impact of birth weight to placental weight ratio and other perinatal risk factors on left ventricular dimensions in newborns: a prospective cohort analysis حسب Ashraf Gad (17040114)

"…Chi-squared and one-way analysis of variance were used to compare BW/PW groups and the best regression model was selected using a genetic and backward stepwise algorithm.…"

Con-Detect: Detecting adversarially perturbed natural language inputs to deep classifiers through holistic analysis حسب 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 حسب 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. …"

Recent advances on artificial intelligence and learning techniques in cognitive radio networks حسب Abbas, Nadine

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A pragmatic approach for testing robustness on real-time component based systems حسب Tarhini, Abbas

"…Each tester is dedicated to test a single SUT component. A test execution algorithm with an approach to handle testers coordination and execution delay is presented. …"
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Integration of nonparametric fuzzy classification with an evolutionary-developmental framework to perform music sentiment-based analysis and composition حسب Abboud, Ralph

"…Unlike existing solutions, MUSEC is: (i) a hybrid crossover between supervised learning (SL, to learn sentiments from music) and evolutionary computation (for music composition, MC), where SL serves at the fitness function of MC to compose music that expresses target sentiments, (ii) extensible in the panel of emotions it can convey, producing pieces that reflect a target crisp sentiment (e.g., love) or a collection of fuzzy sentiments (e.g., 65% happy, 20% sad, and 15% angry), compared with crisp-only or two-dimensional (valence/arousal) sentiment models used in existing solutions, (iii) adopts the evolutionary-developmental model, using an extensive set of specially designed music-theoretic mutation operators (trille, staccato, repeat, compress, etc.), stochastically orchestrated to add atomic (individual chord-level) and thematic (chord pattern-level) variability to the composed polyphonic pieces, compared with traditional evolutionary solutions producing monophonic and non-thematic music. …"
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Deep Neural Networks for Electromagnetic Inverse Scattering Problems in Microwave Imaging حسب Maricar, Mohammed Farook

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Systems biology analysis reveals NFAT5 as a novel biomarker and master regulator of inflammatory breast cancer حسب 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. …"

An Infrastructure-Assisted Crowdsensing Approach for On-Demand Traffic Condition Estimation حسب Abdul Rahman, Sawsan

"…Our approach combines the strengths of mobile crowdsensing, with the support of the mobile infrastructure, a multi-criteria algorithm for the participants' selection, and a deductive rule-based model for traffic condition estimation. …"
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Active distribution network type identification method of high proportion new energy power system based on source-load matching حسب Qinlin, Shi

"…Firstly, the typical daily output scenarios of DG are extracted by clustering method, and the generalized load curve model is solved by the optimization algorithm to obtain the source load operation data; Secondly, calculate the source-load matching indicators (including matching performance, matching degree, and matching rate) according to the source load data of each region, and identify the distribution network type according to the range of the index values; Finally, several indicators are introduced to quantify the characteristics of different types of distribution networks. …"
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DAP: A dataset-agnostic predictor of neural network performance حسب Sui Paul Ang (18460605)

"…To this end, we propose a dataset-agnostic regression framework that uses a novel dual-LSTM model and a new dataset difficulty feature. …"