Spatial Localization of Broadleaf Species in Mixed Forests in Northern Japan Using UAV Multi-Spectral Imagery and Mask R-CNN Model by Nyo Me Htun, Toshiaki Owari, Satoshi N. Suzuki, Kenji Fukushi, Yuuta Ishizaki, Manato Fushimi, Yamato Unno, Ryota Konda, Satoshi Kita

“…Precise spatial localization of broadleaf species is crucial for efficient forest management and ecological studies. …”
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An Improved Model for Detecting the Presence of Pesticide Residues in Edible Parts of Tomatoes, Cabbages, Carrots, and Green Pepper Vegetables Using Batch Image Analysis by Nabaasa, Evarist, Natumanya, Deborah, Mabirizi, Vicent

“…Our results highlight the potential influence of this model on agricultural food safety practices by indicating that it can be used for the quick and extensive identification of pesticide residues. …”
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Recovery in personality disorders: the development and preliminary testing of a novel natural language processing model to identify recovery in mental health electronic records by Giouliana Kadra-Scalzo, Jaya Chaturvedi, Oliver Dale, Richard D. Hayes, Lifang Li, Shaza Mahmood, Jonathan Monk-Cunliffe, Angus Roberts, Paul Moran

“…However, the models performed less acceptably in correctly identifying all those who recovered, generally missing at least 50% of the population of those who had recovered.ConclusionIt is feasible to develop NLP models for the identification of recovery domains for individuals with a diagnosis of personality disorder. …”
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Fault Management in Speed Control Systems of Hydroelectric Power Plants Through Petri Nets Modeling: Case Study of the Alazán Power Plant, Ecuador by Cristian Fernando Valdez-Zumba, Luis Fernando Guerrero-Vásquez

“…Traditional diagnostic approaches often rely on manual inspection and expert intuition, and they lack formal mechanisms to model concurrent or asynchronous system behavior—leading to delays and reduced accuracy in fault identification. …”
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Construction and validation of a nomogram prediction model for antiviral efficacy based on clinical characteristics and intestinal microflora distribution in patients with chronic... by Hongjie Wu, Mingqiang Yue, Tianbao Wang, Xiaoxia Wei, Yanping Wang, Changyun Si

“…In the training set, multivariate logistic regression was used to analyze the risk factors for the failure of antiviral therapy and the nomogram prediction model was constructed. The ROC curve and calibration curve were drawn to evaluate the prediction efficiency of the nomogram model and were verified in the verification set.ResultsThere was no significant difference in the incidence, clinical characteristics and distribution parameters of intestinal flora between the training set and the verification set (p > 0.05). …”
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Structural Damage Detection Using an Unmanned Aerial Vehicle-Based 3D Model and Deep Learning on a Reinforced Concrete Arch Bridge by Mary C. Alfaro, Rodrigo S. Vidal, Rick M. Delgadillo, Luis Moya, Joan R. Casas

“…These networks are effective in detecting complex patterns, improving the accuracy and efficiency of damage identification based on simple visual inspection. …”
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A Blockchain-Based Architecture of Web3.0: A Comprehensive Decentralized Model With Relay Nodes, Unique IDs and P2P by Hyunjoo Yang, Sejin Park

“…These findings highlight the model’s adaptability and efficiency, offering a secure, scalable, and resilient solution for decentralized networks while addressing critical challenges in trust and reputation management.…”
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Development and validation of an explainable machine learning model for predicting postoperative pulmonary complications after lung cancer surgery: a machine learning studyResearch... by Shaolin Chen, Ting Deng, Qing Yang, Jin Li, Juanyan Shen, Xu Luo, Juan Tang, Xulian Zhang, Jordan Tovera Salvador, Junliang Ma

“…Twelve independent ML models and 26 stacking ensemble models were developed. …”
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Associations between perceived stress profiles, social connection and work engagement in clinical registered nurses: a mediation analysis and generalized additive models by Yuan Liao, Miaochun Huang, Zhimin Gu, Chun Li, Yan Yu, Qimei Zhang, Xiangyu Lai, Jialin Liu, Kang He, Huiyun Chu, Yao Zhao, Xinyu Wu, Lihua Wu, Yu Li, Sujuan Fang

“…Statistical analyses were performed utilizing latent profile examination, mediation analysis and generalized additive models. Results (1) The analysis revealed heterogeneity in stress levels among nurses, resulting in the identification of three distinct groups: low stress-high self-demand group (23.4%), high tension-low out-of-control group (57.5%), and high stress-low efficiency group (18.2%). (2) Clinical registered nurses that obtained support from their families were more inclined to be placed in the Low stress-high self-demand group. (3) Social connection significantly mediated the relationship between nurses’ work engagement and perceived stress. (4) Work engagement demonstrated a non-linear relationship with both perceived stress and social connection. …”
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Designing a new tomato leaf disease classification framework using ran-based adaptive fuzzy c-means with heuristic algorithm model by Kanti Rongali Divya, Rao Gottapu Sasibhushana, Aruna Singam

“…Therefore, the TLD classification and identification model is developed to solve the above problems. …”
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Application of Concentration-Area fractal modeling and artificial neural network to identify Cu, Zn±Pb geochemical anomalies in Hashtjin area, NW of Iran by Ali Imamalipour, Hamed Ebrahimi, Amir reza Abdollahpur

“…Introduction Over the past few decades, the identification of geochemical anomalies has played an important role in mineral exploration (Coates et al., 2011; Lecun et al., 2015; Bergen et al., 2019) Various methods have been used to identify geochemical anomalies in the last few decades, including statistical analysis, geostatistical approaches (Nabavi, 1976), fractal modeling (Ziaii et al., 2009; Ziaii et al., 2012) and many other methods. …”
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Robotic training complex by Yu. N. Matrunchyk

“…It is proposed to solve the following problems: development of a robotic training complex (hereinafter – RTС), its simulation and mathematical model; identification and optimization of the model; development of an electrical circuit diagram of the RTС; creation of a simulation model of the manipulator; development of a training and methodological complex for training personnel in the basics of designing and programming industrial manipulators with equipment for production tasks of varying complexity. …”
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MSKd_Net: Multi-Head Attention-Based Swin Transformer for Kidney Diseases Classification by H. Sharen, Modigari Narendra, L. Jani Anbarasi

“…The MSKd_Net architecture integrates Swin Transformer-based hierarchical learning, efficiently capturing both local and global features, along with a customized convolved model enhanced with a multi-head attention layer. …”
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A deep learning model based on self-supervised learning for identifying subtypes of proliferative hepatocellular carcinoma from dynamic contrast-enhanced MRI by Hui Qu, Shuairan Zhang, Xuedan Li, Yuan Miao, Yuxi Han, Ronghui Ju, Xiaoyu Cui, Yiling Li

“…The model analyzes temporal and spatial patterns in DCE-MRI data to identify the proliferative subtype efficiently and accurately. …”
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Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing by Katarzyna Antosz, Lucia Knapčíková, Jozef Husár

“…The BT model demonstrated stability in its predictions with a slower prediction time, while the SVM model exhibited superior training speed, though with slightly lower accuracy. …”
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LiSA-MobileNetV2: an extremely lightweight deep learning model with Swish activation and attention mechanism for accurate rice disease classification by Yongqi Xu, Dongcheng Li, Changcheng Li, Zheming Yuan, Zhijun Dai

“…In the context of intelligent agriculture in China, rapid and accurate identification of crop diseases is essential for ensuring food security and improving crop yield. …”
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Peer review in the global digital age: perspectives of publishing industry stakeholders by Dikran Toroser, Muhammad Sarwar, Lisa DeTora, Laura Dormer, Maryam Sayab

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Advanced clustering and transfer learning based approach for rice leaf disease segmentation and classification by Samia Nawaz Yousafzai, Fahd N. Al-Wesabi, Hadeel Alsolai, Shouki A. Ebad, Inzamam Mashood Nasir, Emad Fadhal, Adel Thaljaoui

“…Rice, the world’s most important food crop, requires an early and accurate identification of the diseases that infect rice panicles and leaves to increase production and reduce losses. …”
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Detection of Leaf Diseases in Banana Crops Using Deep Learning Techniques by Nixon Jiménez, Stefany Orellana, Bertha Mazon-Olivo, Wilmer Rivas-Asanza, Iván Ramírez-Morales

“…Due to the high computational demands of ResNet50 and VGG19, training was performed with EfficientNetB0. The models—EfficientNetB0, ResNet50, and VGG19—demonstrated the ability to identify leaf diseases in bananas, with accuracies of 88.33%, 88.90%, and 87.22%, respectively. …”
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DeepB3P: A transformer-based model for identifying blood-brain barrier penetrating peptides with data augmentation using feedback GAN by Qiang Tang, Wei Chen

“…Methods: A transformer-based deep learning model, DeepB3P, was proposed for predicting BBBP. …”
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