Neutrosophic computational model for identifying trends in scientific articles using Natural Language Processing by Omar Mar Cornelio, Omar Mar Cornelio

“…The results demonstrate that the system is not only efficient in generating coherent summaries but also facilitates the identification of critical themes in scientific research, potentially guiding future research and applications in the field. …”
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Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments by Qifeng Yu, Yudong Han, Xinjia Gao, Wuguang Lin, Yi Han

“…To improve the detection performance of YOLO v5 for corrosion image features, this study investigates two enhanced models: EfficientViT-NWD-YOLO v5 and Gold-NWD-YOLO v5. …”
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A Hybrid Graph Hydrodynamic Method for Modelling Multiple Pipe Failure in Stormwater Networks by Aun Dastgir, Rahul Satish, Mohsen Hajibabaei, Martin Oberascher, Robert Sitzenfrei

“…In this context, this study proposes a hybrid graph hydrodynamic model (GHM) that combines the advantages of graph theory and hydrodynamic modelling to enhance the identification of critical pipe failures (i.e., computationally efficient and high accuracy). …”
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FPCAM: A Weighted Dictionary-Driven Model for Single-Cell Annotation in Pulmonary Fibrosis by Guojun Liu, Yan Shi, Hongxu Huang, Ningkun Xiao, Chuncheng Liu, Hongyu Zhao, Yongqiang Xing, Lu Cai

“…In summary, FPCAM provides an efficient, flexible, and accurate solution for cell-type identification and serves as a powerful tool for scRNA-seq research in pulmonary fibrosis and other related diseases.…”
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Comparative analysis of machine learning classifiers and deep learning models for categorization of Knee Osteoarthritis by Deo Arpit, Korde Manish, Khatri Amit, Jain Aman, Kumawat Ashish, Rathore Vineeta

“…The study evaluates two Machine Learning classifiers which were Support Vector Machine (SVM) and XGBoost which both are optimized through GridSearchCV for hyperparameter tuning and two deep learning models EfficientNetB6 and EfficientNetB7 which both were fine tuned. …”
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Deep Learning Methods and UAV Technologies for Crop Disease Detection by S. G. Mudarisov, I. R. Miftakhov

“…The paper also addresses challenges associated with the use of unmanned aerial vehicles, such as data quality limitations, the complexity of processing large volumes of images, and the need for the development of more advanced models. The paper proposes solutions to these issues, including algorithm optimization and improved data preprocessing techniques. …”
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Allosteric transitions of supramolecular systems explored by network models: application to chaperonin GroEL. by Zheng Yang, Peter Májek, Ivet Bahar

“…Motivated by the utility of elastic network models for describing the collective dynamics of biomolecular systems and by the growing theoretical and experimental evidence in support of the intrinsic accessibility of functional substates, we introduce a new method, adaptive anisotropic network model (aANM), for exploring functional transitions. …”
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Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models by Esra Eroğlu Demirkan, Murat Aydos

“…We present MalevisAsm, an enriched dataset that merges MaleVis malware samples with benign files, and propose a hybrid deep learning model that combines EfficientNetB0 and DenseNet121 for robust feature extraction. …”
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Cotton Leaf Disease Detection Using LLM-Synthetic Data and DEMM-YOLO Model by Lijun Gao, Tiantian Ran, Hua Zou, Huanhuan Wu

“…Cotton leaf disease detection is essential for accurate identification and timely management of diseases. It plays a crucial role in enhancing cotton yield and quality while promoting the advancement of intelligent agriculture and efficient crop harvesting. …”
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Prediction of microbe-drug associations using a CNN-Bernoulli random forest model by Zihao Song, Qingnuo Li, Jincheng Zhao, Qinggang Bu, Zekang Bian, Jia Qu

“…These models can facilitate the identification of novel microbe-drug associations and help counteract resistance mechanisms. …”
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Review of optimization problems, models and methods for airline disruption management from 2010 to 2024 by Yuzhen Hu, Sirui Wang, Song Zhang, Zhisheng Li

“…The last way is to study the research findings based on statistical analysis and perform future research direction identification in the areas of problems, models, and solution approaches. …”
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Advancing blood cell detection and classification: performance evaluation of modern deep learning models by Shilpa Choudhary, Sandeep Kumar, Pammi Sri Siddhaarth, Guntu Charitasri, Monali Gulhane, Nitin Rakesh, Feslin Anish Mon, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

“…This follows a two-step approach, where YOLO-based detection is first performed to locate blood cells, followed by classification using a hybrid CNN model to ensure accurate identification. We conducted a thorough and extensive comparison with other state-of-the-art models, including MobileNetV2, ShuffleNetV2, and DarkNet, for blood cell detection and classification. …”
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Assessing early childhood developmental functioning in the parental screening tool: an application of the Rasch model by Lucia Ráczová, Tomáš Urbánek, Erika Jurišová, Marta Popelková, Tomáš Sollár

“…ObjectivesEnsuring rapid and efficient detection of developmental difficulties in early childhood necessitates aligning screening tools with the timing of preventive examinations in each country, emphasizing the need for quick and effective unidimensional screening methods.AimThis study aims to assess the scalability and unidimensionality of developmental functioning in two- and three-year-old children using Guttman scaling and the Rasch model.MethodsThe anonymized data from 1,640 children aged 26 to 44 months, whose caregivers completed the S-PMV11 method, were gathered from routine pediatric preventive check-ups via the National Database.ResultsThe findings indicate the effective scalability of developmental functioning using the Guttman approach, thus enabling the early identification of children at risk. …”
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An enhanced deep learning model for accurate classification of ovarian cancer from histopathological images by Anik Kumar Saha, Muntezar Rabbani, Anika Saba Ibte Sum, M. F. Mridha, Md Mohsin Kabir

“…This model presents significant prospects for enhancing ovarian cancer early identification and diagnosis, which will ultimately enhance patient outcomes.…”
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Deep learning model for hair artifact removal and Mpox skin lesion analysis and detection by Edeh Michael Onyema, B. Gunapriya, Balasubramanian Prabhu Kavin, Mueen Uddin, Priyan Malarvizhi Kumar, Tehseen Mazhar, Mamoon M. Saeed

“…Abstract Accurate identification of Mpox is essential for timely diagnosis and treatment. …”
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ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images by Jing Zhang, Hao Zhou, Kunyu Liu, Yuguang Xu

“…Traditional manual monitoring methods are limited by efficiency bottlenecks and insufficient spatial coverage. …”
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Deep(er) reconstruction of imaging Cherenkov detectors with swin transformers and normalizing flow models by C Fanelli, J Giroux, J Stevens

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The value-added model of the supply chain of petrochemical industries with a sustainable development approach by ali amiri, seyed abbas heydari, vahidreza Mirabi

“…The benefits of using this model include improving efficiency and reducing waste in the supply chain, improving communication and coordination between supply chain members, and increasing the competitiveness of companies (Duncan et al, 2019). …”
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Modeling Topic-Specific Influential Users in QA Forums Using Association Rule Mining by Umar Ishfaq, Tassawar Ali, Ali Daud, Mohammed Alreshoodi, Azeem Irshad

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ParaAntiProt provides paratope prediction using antibody and protein language models by Mahmood Kalemati, Alireza Noroozi, Aref Shahbakhsh, Somayyeh Koohi

“…Built on the ProtTrans architecture, and utilizing pre-trained protein and antibody language models, we extract efficient embeddings for predicting paratope. …”
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