Real-time mobile broadband quality of service prediction using AI-driven customer-centric approach by Ayokunle A. Akinlabi, Folasade M. Dahunsi, Jide J. Popoola, Lawrence B. Okegbemi

“…The highlighted gap can be addressed by machine learning (ML), as it has been effectively used in the past to support the analysis and knowledge discovery of communication systems’ traffic data through identification of intricate and hidden patterns. …”
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Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings by David Cabezuelo, Izar Lopez-Ramirez, June Urkizu, Ander Goikoetxea

“…This study investigates the impact of data collection frequency and model selection on the predictive accuracy of power consumption in two distinct building types: an Academic one with 15-min interval data and an Industrial one with hourly data. Various machine learning models, including Support Vector Machine (SVM) with Radial and Sigmoid kernels, Random Forest (RF), and Deep Neural Networks (DNNs), across different data splits and feature sets, were considered. …”
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Advances in Neural Network assisted Tool Pressure Prediction by Göltl Florian, Harst Felix, Birkert Arndt, Stache Nicolaj C.

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Clustering Library Loan Books Using K-Means Clustering by Mawar Indah Tanjung, Sriani Sriani

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The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy by Szandra László, Ádám Nagy, József Dombi, Emőke Adrienn Hompoth, Emese Rudics, Zoltán Szabó, András Dér, András Búzás, Zsolt János Viharos, Anh Tuan Hoang, Vilmos Bilicki, István Szendi

“…By applying model-explaining tools to the well-performing models, we could conclude the movement patterns and characteristics of the groups. Our study indicates that in the PSF liability phase of schizophrenia, actigraphic features related to sleep are most significant, but as the disease progresses, both sleep and daytime activity patterns are crucial. …”
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An application of acoustic measurements to quality control of low power electrical motors by M. Gibiec

“…Its performance was compared to results of the minimum distance method based on averaged spectrum patterns.…”
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Introduction to Computational Creativity by Anna Longo, ChatGPT

“…By deconstructing the cognitive processes involved in human creativity, researchers can design algorithms that simulate these processes. This involves machine learning, neural networks, evolutionary algorithms, and other AI techniques that enable computers to recognize patterns, generate new ideas, and refine them through iterative processes.  …”
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Inferring free surface disturbance properties from Kelvin wakes using convolutional neural network by Xuanting Hao

“…Kelvin wakes are fluid motions generated by a moving disturbance at a free surface. We present a machine learning-based framework for inferring the properties of such moving disturbances from the Kelvin-wake patterns. …”
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A Backdoor Approach With Inverted Labels Using Dirty Label-Flipping Attacks by Orson Mengara

“…Audio-based machine learning systems frequently use public or third-party data, which might be inaccurate. …”
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ARTIFICIAL INTELLIGENCE IN AGRICULTURE: CURRENT TRENDS AND INNOVATIONS by Jonathan Masasi, John N. Ng’ombe, Blessing Masasi

“…AI technologies, including machine learning, computer vision, and precision agriculture, are explored. …”
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Potential Applications and Ethical Considerations for Artificial Intelligence in Traumatic Brain Injury Management by Kryshawna Beard, Ashley M. Pennington, Amina K. Gauff, Kelsey Mitchell, Johanna Smith, Donald W. Marion

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Computational intelligence investigations on evaluation of salicylic acid solubility in various solvents at different temperatures by Adel Alhowyan, Wael A. Mahdi, Ahmad J. Obaidullah

“…Abstract This research shows the utilization of various tree-based machine learning algorithms with a specific focus on predicting Salicylic acid solubility values in 13 solvents. …”
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On the role of knowledge graphs in AI-based scientific discovery by Mathieu d’Aquin

“…Indeed, the ability of such models to extrapolate from data, seemingly finding unknown patterns relating implicit features of the objects under study to their properties can, at the very least, help accelerate and scale up those studies as demonstrated in fields such as molecular biology and chemistry. …”
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Electricity demand from national interconnected system of paraguay and meteorological datasetMendeley Data by Félix Morales-Mareco, Matías Verdún, Diego H. Stalder, Carlos Sauer, Sebastián Grillo

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Advancing Real-Time Remote Learning: A Novel Paradigm for Cognitive Enhancement Using EEG and Eye-Tracking Analytics by Nuraini Jamil, Abdelkader Nasreddine Belkacem

“…This study explores the convergence of biometric analytics and machine learning in online education, where the level of student participation directly impacts academic achievement. …”
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Advanced explainable models for strength evaluation of self-compacting concrete modified with supplementary glass and marble powders by Khan Kaffayatullah, Khan Muhammad Ehsan Ullah, Al-Naghi Ahmed A. Alawi, Amin Muhammad Nasir, Iftikhar Bawar, Qadir Muhammad Tahir

“…This motivates the adoption of machine learning (ML) techniques, which can efficiently analyze complex datasets and identify patterns that influence concrete performance. …”
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Evaluating the Accuracy of Land-Use Change Models for Predicting Vegetation Loss Across Brazilian Biomes by Macleidi Varnier, Eliseu José Weber

“…While machine-learning-based models use computational methods and spatial variables to identify patterns that explain the occurrence of changes. …”
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Interpretable Transformer Neural Network Prediction of Diverse Environmental Time Series Using Weather Forecasts by Enrique Orozco López, David Kaplan, Anna Linhoss

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An Autism Spectrum Disorder Identification Method Based on 3D-CNN and Segmented Temporal Decision Network by Zhiling Liu, Ye Chen, Xinrui Dong, Jing Liu

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Density matrix emulation of quantum recurrent neural networks for multivariate time series prediction by J D Viqueira, D Faílde, M M Juane, A Gómez, D Mera

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