Fall Detection in Q-eBall: Enhancing Gameplay Through Sensor-Based Solutions by Zeyad T. Aklah, Hussein T. Hassan, Amean Al-Safi, Khalid Aljabery

“…The automated fall detection process enables the game to take the required actions, such as transferring possession of the visual ball or applying fouls, without the need for manual intervention. …”
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Estimating Total Sediment Load Using Water Quality Parameters in the Sufi Chay River, Iran: A Comparative Analysis of Soft Computing and Dimensionless Approaches by Jafar Chabokpour

“…Besides, a nonlinear relationship between flow rate and both sediment concentration and discharge in this catchment reflects a complex erosion and transport process. The investigation also resulted in some important ion-parameter relationships, which are indicative of the geochemical factors operating on the water quality and sediment activity.…”
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Modelling of Regional Economy Growth Strategy by I. S. Mezhov, E. V. Kletskova

“…This approach allows us to take into account the production process of the GRP in the industry as much as possible, based on the inclusion in its production function of such characteristics as: basic production assets, capital productivity, profitability of costs, sensitivity to investment, consumption-output relationships. …”
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A Frequency-Shifting Variational Mode Decomposition-Based Approach to MI-EEG Signal Classification for BCIs by Haiqin Xu, Shahzada Ali Hassan, Waseem Haider, Youchao Sun, Xiaojun Yu

“…The FS-VMD method works by extracting and shifting the fundamental frequency of the EEG signal to a lower frequency range, followed by an iterative decomposition process that enhances signal clarity and reduces mode aliasing. …”
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The method of increasing the efficiency of the man-machine system functioning by improving the quality of decision support system software by О.В. Турінський, М.А. Павленко, Г.В. Пєвцов, С.В. Осієвський

“…It is proposed to use the methods for solving the problem of MMS control according to the vector criterion as the basis for solving this problem. …”
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Reverse Vaccinology Integrated with Biophysics Techniques for Designing a Peptide-Based Subunit Vaccine for Bourbon Virus by Taghreed N. Almanaa

“…The results of the in silico cloning process for <i>E. coli</i> indicated that the vaccine construct will try its utmost to express itself in the host, with a codon adaptation CAI value of 0.94. …”
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A systematic review on sleep stage classification and sleep disorder detection using artificial intelligence by Tayab Uddin Wara, Ababil Hossain Fahad, Adri Shankar Das, Md Mehedi Hasan Shawon

“…Artificial Intelligence has made the evaluation process of sleep stages and sleep disorders classification more efficient. …”
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Assessing the Performance of CareStart™ Malaria Rapid Diagnostic Tests in Northwest Ethiopia: A Cross-Sectional Study by Zelalem Dejazmach, Getaneh Alemu, Mulat Yimer, Banchamlak Tegegne, Abel Getaneh

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Parallel strategies for a multi-criteria GRASP algorithm Estratégias de paralelização para um algoritmo GRASP multicritério by Dalessandro Soares Vianna, José Elias Claudio Arroyo, Pedro Sampaio Vieira, Thiago Ribeiro de Azeredo

“…Each process finds a subset of efficient solutions. These subsets are joined using different strategies to obtain the final set of efficient solutions. …”
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“I LIGHT MY CANDLE FROM YOURS…”: ANTHROPOLOGICAL ASPECTS OF MODERN LIBRARY SERVICES FOR SCIENTISTS by T. О. Kolesnykova

“…They include shift of the priority vector towards the services for an author-scientist and support of the philosophy of open access to knowledge. …”
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Hybrid classical and quantum computing for enhanced glioma tumor classification using TCGA data by Emine Akpinar, Murat Oduncuoglu

“…While classical AI methods have shown promise in glioma classification, the growing volume of medical data, inherent noise, and limitations of classical vector spaces present significant challenges. However, quantum computing-based AI methods have the potential to process data in parallel by leveraging quantum properties such as superposition and entanglement, analyze higher-dimensional data more efficiently, and solve certain problems that classical methods struggle with more rapidly and effectively. …”
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Data-driven predictive models for sustainable smart buildings by Prabhu Rajaram, Gnana Swathika O․V․

“…By streamlining the feature selection process, ANOVA not only enhances the performance of the models but also conserves computational resources, making the approach more efficient overall. …”
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Design and Implementation of a Physically Secure Open-Source FPGA and Toolchain by Sergej Meschkov, Daniel Lammers, Mehdi B. Tahoori, Amir Moradi

“…A practical security evaluation of our securefabric implementation on a real FPGA target board, using Test Vector LeakageAssessment (TVLA), demonstrated no SCA leakage over 100 million traces. …”
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Insertional mutagenesis and deep profiling reveals gene hierarchies and a Myc/p53-dependent bottleneck in lymphomagenesis. by Camille A Huser, Kathryn L Gilroy, Jeroen de Ridder, Anna Kilbey, Gillian Borland, Nancy Mackay, Alma Jenkins, Margaret Bell, Pawel Herzyk, Louise van der Weyden, David J Adams, Alistair G Rust, Ewan Cameron, James C Neil

“…Progression driven by RIM was characterised as a Darwinian process of clonal competition engaging proliferation control networks downstream of cytokine and T-cell receptor signalling. …”
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K-Gen PhishGuard: an Ensemble Approach for Phishing Detection with K-Means and Genetic Algorithm by Ali Al-Hafiz, Adnan Jabir, Shamala Subramaniam

“…In the second phase, the best set of features in each group is identified through the Genetic algorithm to enhance the classification process. Finally, a voting ensemble technique is applied, in which the Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Adaptive boosting (AdaBoost) models are combined. …”
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Investigating factors affecting the quality of water resources by multivariate analysis and soft computing approaches by Bing Cheng, Xinyu Liu, Keke Guo, Ahmad Rastegarnia

“…Therefore, Na+, Cl+, Na%, CO3 −, and SO4 2− were used as input variables (independent variables), and EC, TDS, and SAR were used as output variables (dependent variables). Support vector machine (SVM) with various kernel functions, multilayer perceptron artificial neural network (MLP-ANN) with various training algorithms, random forest algorithm (RFA), Gaussian process regression (GPR), and statistical analysis methods were used for modeling. …”
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Range Space and Similarity Transformation Using Symbolic Mathematics in Python by Chainarong Wisassakwichai, Prasert Phaochoo

“…These theorems are transformed into abstract data models and represented as programmatic objects using object-oriented programming to process input and output data. Two classes, &#x2018;rangeSpace&#x2019; and &#x2018;similarTrnsfrm,&#x2019; were created to handle computations related to range space and similarity transformation, respectively. …”
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A Set of Proximal Regulatory Elements Contribute to the Transcriptional Activity of the Human Lipoprotein Lipase Promoter by Nasmah K. Bastaki, Taybha A. Albarjes, Afnan K. Mohamed, Noorhan H. Sabri, Suzanne A. Al-Bustan

“…The transcription of <i>LPL</i> in response to cell types and regulatory factors is a complex process that starts with its promoter. In previous studies, several proximal regulatory elements within the human <i>LPL</i> promoter were individually characterized. …”
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Predictors of length of hospital stay in patients with acute COVID-19 by E. S. Pan’ko, S. V. Zhavoronok, A. M. Solovchuk, S. S. Pan’ko, S. V. Pan’ko

“…Conclusion: The proposed model for determining of the hypoxia vector by assessing the severity of ventilation-perfusion dissociation with indicators of oxygenation and ventilation showed that the MVV/MV ratio and SpO2/FiO2 ratio can be used alone and especially in combination, as effective outcome predictors of the acute phase of the infectious process caused by SARS-CoV-2.…”
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Prognosis of air quality index and air pollution using machine learning techniques by Mostafa M. Abdelmalek, Hatem Mahmoud, Hassan Shokry

“…This study utilizes five machine learning (ML) models, which are Gaussian Process Regression (GPR), Ensemble Regression (ER), Support Vector Machine (SVM), Regression Tree (RT), and Kernel Approximation Regression (KAR), which are developed and compared to predict the Air Quality Index (AQI). …”
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