A deep learning model with machine vision system for recognizing type of the food during the food consumption by Pouya Bohlol, Soleiman Hosseinpour, Mahmoud Soltani Firouz

“…An essential step involved updating hyperparameters, including image size, batch size, learning rate, and optimizer settings, to enhance convergence rates and accuracy. …”
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Nondestructive egg freshness assessment using hyperspectral imaging and deep learning with distance correlation wavelength selection by Pauline Ong, Shih-Yen Chiu, I-Lin Tsai, Yen-Chou Kuan, Yu-Jen Wang, Yung-Kun Chuang

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Environmental factors contributing to southern house mosquito presence in Clark County, Nevada, using machine learning by Chibuike Chiedozie Ibebuchi, Itohan-Osa Abu, Somtochukwu Stella Onwah

“…In this study, we applied a multi-step machine learning approach to investigate environmental factors influencing the annual presence of Culex quinquefasciatus in Clark County, Nevada, using data from the Global Biodiversity Information Facility spanning 1980 to 2023. …”
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Automatic priority analysis of emergency response systems using internet of things (IoT) and machine learning (ML) by Abu S.M. Mohsin, Shadab H. Choudhury, Munyem Ahammad Muyeed

“…By integrating machine learning (ML) and the Internet of Things (IoT), automatic priority analysis of emergency response systems could revolutionise this vital process, save life and minimize damages. …”
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Deep learning-based dual optimization framework for accurate thyroid disease diagnosis using CNN architectures by Zeeshan Ali Haider, Nasser A Alsadhan, Fida Muhammad Khan, Waleed Al-Azzawi, Inam Ullah Khan, Inam Ullah

“…We present Dual-OptNet, a new hybrid deep learning architecture that effectively merges skip connections of ResNet with multi-scale feature extraction based on InceptionV3 for lung classification tasks. …”
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Impact of provoked stress on model-free and model-based reinforcement learning in individuals with alcohol use disorder by Florent Wyckmans, Armand Chatard, Charles Kornreich, Damien Gruson, Nemat Jaafari, Xavier Noël

“…Acute social stress was induced through the socially evaluated cold pressor task (SECPT), followed by engagement in a Two-Step Markov task to assess MB and MF learning tendencies. …”
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Identification of Plasma Proteins Associated with Alzheimer's Disease Using Feature Selection Techniques and Machine Learning Algorithms by Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik

“…Detecting Alzheimer’s using plasma proteins is a critical step toward improving treatment results for this disease. …”
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Surface-Related Multiple Suppression Based on Field-Parameter-Guided Semi-Supervised Learning for Marine Data by Jiao Qi, Siyuan Cao, Zhiyong Wang, Yankai Xu, Qiqi Zhang

“…Surface-related multiple suppression is a critical step in seismic data processing, while traditional adaptive matching subtraction methods often distort primaries, resulting in either the leakage of primaries or the residue of surface-related multiples. …”
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Parallel Local and Global Context Modeling of Deep Learning-Based Monaural Speech Source Separation Techniques by Swati Soni, Lalita Gupta, Rishav Dubey

“…The novel deep learning-based time domain single channel speech source separation methods have shown remarkable progress. …”
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Deep Learning Framework Using Transformer Networks for Multi Building Energy Consumption Prediction in Smart Cities by Samuel Moveh, Emmanuel Alejandro Merchán-Cruz, Maher Abuhussain, Yakubu Aminu Dodo, Saleh Alhumaid, Ali Hussain Alhamami

“…This research develops a novel transformer-based deep learning framework for multi-building energy consumption forecasting. …”
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A novel optimization framework for natural gas transportation pipeline networks based on deep reinforcement learning by Zemin Eitan Liu, Wennan Long, Zhenlin Chen, James Littlefield, Liang Jing, Bo Ren, Hassan M. El-Houjeiri, Amjaad S. Qahtani, Muhammad Y. Jabbar, Mohammad S. Masnadi

“…The optimization control problem in steady state is formulated into a one-step Markov decision process (MDP) and solved by DRL. …”
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StructureNet: Physics-Informed Hybridized Deep Learning Framework for Protein–Ligand Binding Affinity Prediction by Arjun Kaneriya, Madhav Samudrala, Harrish Ganesh, James Moran, Somanath Dandibhotla, Sivanesan Dakshanamurthy

“…Accurately predicting protein–ligand binding affinity is an important step in the drug discovery process. Deep learning (DL) methods have improved binding affinity prediction by using diverse categories of molecular data. …”
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Learning the factors controlling mineral dissolution in three-dimensional fracture networks: applications in geologic carbon sequestration by Aleksandra A. Pachalieva, Aleksandra A. Pachalieva, Jeffrey D. Hyman, Daniel O’Malley, Gowri Srinivasan, Hari Viswanathan

“…This study is a first step towards characterizing the parameters that control carbon mineralization using an approach with integrates computational physics and machine learning.…”
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Advancing In Vivo Molecular Bioimaging With Optimal Frequency Offset Selection and Deep Learning Reconstruction for CEST MRI by Adarsha Bhattarai, Chathumi Samaraweera, Mariano Uberti, Aditya N. Bade, Yutong Liu, Dongming Peng

“…Thus, herein, we accelerate CEST MRI data acquisition using a two-step approach. Firstly, we use an optimization algorithm to identify a set of optimal sparse frequency offsets for data collection. …”
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Predicting the compressive strength of concrete incorporating waste powders exposed to elevated temperatures utilizing machine learning by Islam N. Fathy, Hany A. Dahish, Mohammed K. Alkharisi, Alaa A. Mahmoud, Hala Emad Elden Fouad

“…Abstract The addition of powders from waste construction materials as partial cement substitute in concrete represents a significant step toward green concrete construction. High temperatures have a substantial influence on concrete strength, resulting in a reduction in mechanical properties. …”
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Data-Driven Fault Detection and Diagnosis in Cooling Units Using Sensor-Based Machine Learning Classification by Amilcar Quispe-Astorga, Roger Jesus Coaquira-Castillo, L. Walter Utrilla Mego, Julio Cesar Herrera-Levano, Yesenia Concha-Ramos, Erwin J. Sacoto-Cabrera, Edison Moreno-Cardenas

“…This research is based on data-driven models and machine learning, where a specific strategy is proposed for five types of system failures. …”
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Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma by Juan I. Pisula, Doris Helbig, Lucas Sancéré, Oana-Diana Persa, Corinna Bürger, Anne Fröhlich, Carina Lorenz, Sandra Bingmann, Dennis Niebel, Konstantin Drexler, Jennifer Landsberg, Roman Thomas, Katarzyna Bozek, Johannes Brägelmann

“…Abstract Predicting cancer patient disease progression is a key step towards personalized medicine and secondary prevention. …”
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A novel diagnostic framework for breast cancer: Combining deep learning with mammogram-DBT feature fusion by Nishu Gupta, Jan Kubicek, Marek Penhaker, Mohammad Derawi

“…Materials and methods: Mammogram images from the CBIS-DDSM dataset and DBT images from the Breast-Cancer-Screening-DBT dataset were used. The preprocessing step involved Extended-Tuned Adaptive Frost Filtering (Ext-AFF) to enhance image quality by reducing noise. …”
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Unveiling new insights into migraine risk stratification using machine learning models of adjustable risk factors by Yu-Chen Liu, Ye-Hai Liu, Hai-Feng Pan, Wei Wang

“…Methods We developed a three-step analytical framework combining causal inference, predictive modeling, and burden projection to systematically evaluate modifiable factors associated with migraine. …”
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Machine Learning Models for Tracking Blood Loss and Resuscitation in a Hemorrhagic Shock Swine Injury Model by Jose M. Gonzalez, Ryan Ortiz, Lawrence Holland, Austin Ruiz, Evan Ross, Eric J. Snider

“…Here, we apply these advanced metrics developed using data from a canine model to data collected from a swine model of controlled hemorrhage as an interim step towards showing their relevance to human medicine. …”
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