EZATECH: Design and development of Artificial Intelligence technologies for knowledge management throughout the life cycle of workers in organizations by Maria Ruiz, Juan Ignacio Igartua, Jabier Retegi, Aitor Uriondo, Jose Antonio Sudupe, Izaskun Heriz, Estibaliz Garate

“…This objective responds to the challenges derived from the existence of a multitude of approaches that have “significantly hindered” the practical development of Knowledge Management in the business environment: The majority of practical application cases published to date refer to large companies or service companies. Machine Learning, Learning Analytics and People Analytics are the techniques used for the development of the EZATECH architecture, which is a software system to unable Knowledge Management in Educational and Industrial sectors.…”
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Sub-bandgap charge harvesting and energy up-conversion in metal halide perovskites: ab initio quantum dynamics by Bipeng Wang, Weibin Chu, Yifan Wu, Wissam A. Saidi, Oleg V. Prezhdo

“…By combining ab initio time-dependent density functional theory, nonadiabatic molecular dynamics and machine learning, we advance quantum dynamics simulation to nanosecond timescale and demonstrate that large fluctuations of MHP defect energy levels extend light absorption to longer wavelengths and enable trapped charges to escape into bands. …”
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Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features by Emmanuel Adetiba, Oludayo O. Olugbara

“…This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their “nonensemble” variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. …”
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Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance by Romina Wild, Felix Wodaczek, Vittorio Del Tatto, Bingqing Cheng, Alessandro Laio

“…We demonstrate the usefulness of this approach on two benchmark molecular problems: (1) identifying collective variables that describe conformations of a biomolecule, and (2) selecting features for training a machine-learning force field. These results show the potential of DII in addressing feature selection challenges and optimizing dimensionality in various applications. …”
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ISLRWR: A network diffusion algorithm for drug-target interactions prediction. by Lu Sun, Zhixiang Yin, Lin Lu

“…Machine learning techniques and computer-aided methods are now widely used in the pre-discovery tasks of drug discovery, effectively improving the efficiency of drug development and reducing the workload and cost. …”
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Gaussian Mixture Model-Based Vector Approach to Real-Time Three-Dimensional Path Planning in Cluttered Environment by Abera Tullu, Yunsang Cho, Sangho Ko

“…Gaussian mixture model (GMM), a class of unsupervised machine learning, is employed for environment perception based on a proposed vector approach to obstacle-free path planning. …”
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MagNet: Automated Magnetic Mineral Grain Morphometry Using Convolutional Neural Network by Zhaowen Pei, Liao Chang, Pengfei Xue, Richard J. Harrison

“…Here, we present an easy‐to‐use machine learning framework MagNet for automated morphological recognition of magnetic mineral grains in microscopic images. …”
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Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications by Aditya Venkatraman, Mark A. Wilson, David Montes de Oca Zapiain

“…To address this, we develop reduced-order machine learning models that provide accurate and efficient predictions of charge density in corrosive environments. …”
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A Methodology for Clustering Items with Seasonal and Non-seasonal Demand Patterns for Inventory Management by Burak Kandemir

“…In this paper, we propose two algorithms, one based on statistical analysis and the other an unsupervised machine learning algorithm using K-means clustering, both of which differ-entiate seasonal and non-seasonal products where an item’s variation is evaluated with respect to seasonality of the product group it belongs to. …”
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A parallel CNN architecture for sport activity recognition based on minimal movement data by Huipeng Zhao

“…This is achieved through the parallel implementation of Convolutional Neural Networks (CNN) and machine learning methods. The methodology being proposed consists of four fundamental phases. …”
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A Model for Estimating Resiliency of AI-Based Classifiers Defending Against Cyber Attacks by Kousik Barik, Sanjay Misra, Luis Fernandez-Sanz

“…The study proposes an Egyptian Vulture Optimized Adaptive Elman Recurrent Neural Networks (EVO-AERNN) model to assess cybersecurity resilience and compare it with machine learning and deep learning-based classifiers. It illustrates the potential of using adversary-aware feature sampling to build more robust classifiers and use an optimized algorithm to maintain inherent resilience. …”
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Semisupervised Learning-Based Word-Sense Disambiguation Using Word Embedding for Afaan Oromoo Language by Tabor Wegi Geleta, Jara Muda Haro

“…To conduct the study, 456,300 Afaan Oromo words were taken from different sources and preprocessed for experimentation by the Natural Language Toolkit and Anaconda tool. The K-means machine learning algorithm was used to cluster similar word vocabulary. …”
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Potential Source Density Function: A New Tool for Identifying Air Pollution Sources by In Sun Kim, Yong Pyo Kim, Daehyun Wee

“…Abstract Potential source density function (PSDF) is developed to identify, that is, locate and quantify, source areas of ambient trace species based on Gaussian process regression (GPR), a machine-learning technique. The PSDF model requires backward trajectories and sampling data at a receptor site in the calculation as in the conventional model to locate source areas of ambient trace species, such as the potential source contribution function (PSCF). …”
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Developing Roadway Safety Models for Winter Weather Conditions Using a Feature Selection Algorithm by Bryce Hallmark, Jing Dong

“…This paper presents a feature selection framework using a machine-learning algorithm known as the Boruta algorithm and exhaustive search to select a list of variables to be included in a negative binomial crash frequency model. …”
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Alignment of vector fields on manifolds via contraction mappings by O.N. Kachan, Yu.A. Yanovich, E.N. Abramov

“…Manifold learning is a part of machine learning where an intrinsic data representation is uncovered based on the manifold hypothesis. …”
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Downscaled gridded global dataset for gross domestic product (GDP) per capita PPP over 1990–2022 by Matti Kummu, Maria Kosonen, Sina Masoumzadeh Sayyar

“…Downscaling with machine learning algorithms showed high performance (R2 = 0.79 for cross-validation, R2 = 0.80 for the test dataset) and accuracy against reported datasets (Pearson R = 0.88). …”
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An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam Detection by Umair Maqsood, Saif Ur Rehman, Tariq Ali, Khalid Mahmood, Tahani Alsaedi, Mahwish Kundi

“…In previous studies using machine learning (ML), spam detection using a small dataset has resulted in lower accuracy. …”
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Visualization of Multivariate Time-Series Characteristics of Ground Loss Caused by Shield Tunneling by Zhu Wen, Xiaoli Rong, Fei Gao, Zhen Wang, Dong An

“…However, the existing machine learning methods ignore the continuous and dynamic nature of EPB shield tunneling. …”
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Dynamic trajectories of land use and land cover changes in Lombok Island, West Nusa Tenggara, Indonesia by H. Oğuz Çoban, Miftahul Irsyadi Purnama

“…Utilizing Landsat satellite imagery and a combination of land cover classes from the Indonesian Ministry of Environment and Forestry (MoEF) with the machine learning-based Random Forest algorithm, we aimed to improve classification accuracy and model land cover transitions over time. …”
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Modeling Groundwater Resources in Data-Scarce Regions for Sustainable Management: Methodologies and Limits by Iolanda Borzì

“…This systematic review of Scopus-indexed papers identifies various approaches to address these challenges, including coupled hydrological-groundwater models, machine learning techniques, distributed hydrological models, water balance models, 3D groundwater flow modeling, geostatistical techniques, remote sensing-based approaches, isotope-based methods, global model downscaling, and integrated modeling approaches. …”
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