High throughput assessment of blueberry fruit internal bruising using deep learning models by Chenjiao Tan, Changying Li, Penelope Perkins-Veazie, Heeduk Oh, Rui Xu, Massimo Iorizzo

“…Overall, this study presents an effective and efficient approach with a user-friendly interface to evaluate blueberry internal bruising using deep learning models, which could facilitate the breeding of blueberry genotypes optimized for machine harvesting. …”
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Chemical Space Exploration and Machine Learning-Based Screening of PDE7A Inhibitors by Yuze Li, Zhe Wang, Shengyao Ma, Xiaowen Tang, Hanting Zhang

“…The specific substructures that significantly enhance the activity of PDE7A inhibitors, including benzenesulfonamido, acylamino, and phenoxyl, were identified by an interpretable machine learning analysis. Subsequently, a machine learning model employing the Random Forest–Morgan pattern was constructed for the qualitative and quantitative prediction of PDE7A inhibitors. …”
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A White Dwarf Catalog from LAMOST DR11 Using Deep Learning by Shengwen Zhang, Yanxia Zhang, Chao Liu

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Blast loading prediction in a typical urban environment based on Bayesian deep learning by Weiwen Peng, Meilin Pan, Chunjiang Leng, Shufei Wang, Wei Zhong

“…Numerical study of blast loading prediction in a typical urban environment is presented to demonstrate the proposed method. The results show that the Bayesian deep learning-based prediction model can fulfil the blast loading prediction in this typical urban environment within 2.9 millisecond, and the mean absolute percentage error of blast loading prediction is controlled below 12.9%. …”
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Towards automatic US-MR fetal brain image registration with learning-based methods by Qi Zeng, Weide Liu, Bo Li, Ryne Didier, P. Ellen Grant, Davood Karimi

“…An essential step towards this integration is accurate automatic spatial alignment, which is technically very challenging due to the inherent differences in contrast and modality-specific imaging artifacts. In this work, we present a novel atlas-assisted multi-task learning technique to address this problem. …”
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Machine learning model to predicting synergy of ultrasonication and solvation impacts on crude oil viscosity by Nasir Khan, Mehdi Razavifar, Qazi Adnan Ahmad, Muhammad Siyar, Masoud Riazi, Waqar Khan, Jafar Qajar

“…In this context, combining traditional methods, such as solvent treatment, with ultrasonic radiation presents a promising, yet complex solution. In this study, we develop a machine learning-based algorithm to rigorously predict the synergistic effects of ultrasonication and solvation on crude oil viscosity. …”
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Estimation of Physical Stellar Parameters from Spectral Models Using Deep Learning Techniques by Esteban Olivares, Michel Curé, Ignacio Araya, Ernesto Fabregas, Catalina Arcos, Natalia Machuca, Gonzalo Farias

“…This article presents a new algorithm that uses techniques from the field of artificial intelligence to automatically estimate the physical parameters of massive stars from a grid of stellar spectral models. …”
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ST40 electromagnetic predictive studies supported by machine learning applied to experimental database by M. Scarpari, S. Minucci, G. Sias, R. Lombroni, P. F. Buxton, M. Romanelli, G. Calabrò

“…This paper aims at presenting preliminary predictive analyses of ST40 plasma scenarios by exploiting Machine Learning techniques applied to an experimental database populated by plasma pulses executed during the ST40 2021–2022 experimental campaign. …”
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Investigating the Effects of Socioscientific Issues-based Learning Module in Enhancing Students’ Physics Knowledge by Jeah May Badeo, Bee Ching Ong Kian Koc, Domarth Ace G. Duque, Russel L. Arnaldo

“…Results of this study showed that the implementation of SSI-based learning modules increases physics knowledge, which led students to explore the connection of the SSI to the presented physics topics. …”
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Application of peer-assisted learning under the LSPPDM mode in resident clinical skill training by Peipei Jin, Jinyu Chen, Ping Wang, Yadian Xie, Cen Zhang, Liqiong Shen, Li He, Yanqing Ye

“…The present study aims to explore the role of peer-assisted learning under the LSPPDM mode in improving the clinical competency of residents. …”
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Multi-model deep learning approach for the classification of kidney diseases using medical images by Waleed Obaid, Abir Hussain, Tamer Rabie, Dhafar Hamed Abd, Wathiq Mansoor

“…Chronic kidney disease often presents without noticeable symptoms initially, leading to cases remaining untreated until advanced stages. …”
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Virtual Physical Education: Google Meet as an alternative platform for learning skill-based concepts by Joseph Lobo

“…The findings of this study would be used by the Physical Education Department and the college administration to examining further if the said videoconferencing platform may continuously be used in all skill-based courses in PE since the current setting of the investigation is still in a full-online learning modality. Proposals concerning the students, teachers, and future research directions are also presented.…”
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Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs by Sayed Gomaa, Ahmed Ashraf Soliman, Mohamed Mansour, Fares Ashraf El Salamony, Khalaf G. Salem

“…Predicting the overall oil recovery resulting from waterflooding in oil reservoirs is crucial for effective reservoir management and appropriate decision-making. Machine learning (ML) techniques present resourceful and fast-track tools, aiding in predicting oil recovery, which is time-consuming and costly to accomplish by simulation studies. …”
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FedEmerge: An Entropy-Guided Federated Learning Method for Sensor Networks and Edge Intelligence by Koffka Khan

“…<b>Introduction:</b> Federated Learning (FL) is a distributed machine learning paradigm where a global model is collaboratively trained across multiple decentralized clients without exchanging raw data. …”
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Cognitive functions and their effect on disability in learning process and knowledge application in patients with arterial hypertension by S. Ya. Yanovska

“…The aim of the investigation is to determine the influence of cognitive functions on disability in learning process and knowledge application in patients with hypertension. …”
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Empowering geoportals HCI with task-oriented chatbots through NLP and deep transfer learning by Mohammad H. Vahidnia

“…The notion of deep transfer learning (DTL) was then put into practice by customizing a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model for our particular aim and creating a task-oriented conversational agent. …”
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SHM System for Composite Material Based on Lamb Waves and Using Machine Learning on Hardware by Gracieth Cavalcanti Batista, Carl-Mikael Zetterling, Johnny Öberg, Osamu Saotome

“…For this reason, the present paper introduces an FPGA-based intelligent SHM system that processes Lamb wave signals using piezoelectric sensors to detect, classify, and locate damage in composite structures. …”
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Cryptocurrency forensics automation: a deep learning and NLP-based approach for mobile platforms by Abhishek Bhattarai, Abdulhadi Sahin, Maryna Veksler, Ahmet Kurt, Devrim Aras, Carlos Imery, Kemal Akkaya

“…Therefore, in this paper, we present a comprehensive framework that incorporates various machine learning (ML), image processing, and natural language processing (NLP) approaches to enable fast and automated extraction/triage of crypto-related artifacts from Android and iOS devices. …”
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Trust-driven approach to enhance early forest fire detection using machine learning by Tayyab Khan, Karan Singh, Bhoopesh Singh Bhati, Khaleel Ahmad, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

“…The detrimental impacts of forest fires, such as the exacerbation of the greenhouse effect, the hastening of global warming, and the modification of climatic patterns, underscore the urgent necessity for the creation of efficient detection systems. This study presents a real-time Universal Trust Model (UTM) that is specifically designed for the early forest fires detection (FFD) using an intelligent wireless sensor network and machine learning approaches. …”
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Learning to Cycle: Body Composition and Balance Challenges in Balance Bikes Versus Training Wheels by Cristiana Mercê, David Catela, Rita Cordovil, Mafalda Bernardino, Marco Branco

“…<b>Background/Objectives:</b> Empowering our children and youth to cycle empowers them to pursue a healthier, fuller, and more responsible life. The present study implemented the Learning to Cycle program with the following aims: (i) to promote learning to cycle; (ii) to investigate and compare the use of different learning bicycles, i.e., balance bicycle (BB) and bicycle with training wheels (BTW); (iii) to investigate the influence of body composition during this learning process. …”
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