Kohonen’s algorithm in problems of classification of defects in printed circuit assemblies by S. U. Uvaysov, V. V. Chernoverskaya, An Kuan Dao, Van Tuan Nguyen

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Enhancing Decision-Making in Piezoelectric Energy Harvesting Systems through Neutrosophic Logic by A.Salam, Mohamed A. Mohamed, Hanan M. Amer, Musallam Matar Jeailan Hzam AlZubi, Huda E. Khalid, Ahmed K. Essa

“…The proposed approach follows a three-valued neutrosophic logic models with truth, indeterminacy, and falsity, that is used for sensor data classification and energy control i.e., to determine the energy to save in desktop environment and to reinforce fault diagnosis ability. …”
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Evaluating Optimal Deep Learning Models for Freshness Assessment of Silver Barb Through Technique for Order Preference by Similarity to Ideal Solution with Linear Programming by Atchara Choompol, Sarayut Gonwirat, Narong Wichapa, Anucha Sriburum, Sarayut Thitapars, Thanakorn Yarnguy, Noppakun Thongmual, Waraporn Warorot, Kiatipong Charoenjit, Ronnachai Sangmuenmao

“…Three lightweight deep learning architectures, MobileNetV2, MobileNetV3, and EfficientNet Lite2, were analyzed across 18 different configurations, varying model size (Small, Medium, Large) and preprocessing methods (With and Without Preprocessing). …”
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Enhanced Gold Ore Classification: A Comparative Analysis of Machine Learning Techniques with Textural and Chemical Data by Fabrizzio Rodrigues Costa, Cleyton de Carvalho Carneiro, Carina Ulsen

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Large language models as a diagnostic support tool in neuropathology by Katherine J Hewitt, Isabella C Wiest, Zunamys I Carrero, Laura Bejan, Thomas O Millner, Sebastian Brandner, Jakob Nikolas Kather

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Justifying the selection of a neural network linguistic classifier by Olessia Barkovska, Kseniia Voropaieva, Oleksandr Ruskikh

“…The following results were obtained: The LSTM model demonstrated superior classification accuracy across all three training sample sizes when compared to CNN. …”
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Justifying the selection of a neural network linguistic classifier by Олеся Барковська, Ксенія Воропаєва, Олександр Руських

“…The following results were obtained: The LSTM model demonstrated superior classification accuracy across all three training sample sizes when compared to CNN. …”
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Wind-induced transmission line interruption fragility models: An adaptive GAN-augmented probabilistic classification approach for extremely unbalanced data by Mazin Al-Mahrouqi, Abdollah Shafieezadeh, Jieun Hur, Jae-Wook Jung, Jeong-Gon Ha, Daegi Hahm

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Structural health monitoring of railway truss bridge under moving train load using decision tree models and residual neural networks by Sharan Kumar Sunchikala, S. C. Mohan, Sai Dheeraj Gopala, P. Swetha

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The utility of an artificial intelligence model based on decision tree and evolution algorithm to evaluate steatotic liver disease in a primary care setting by A.C. Goulart, A.P. Alencar, G. Tunes, L.L.T. Bianchi, M.H. Miname, C.M. Padilha, J.M.S. Pescuma, A.L.C.C. Rodrigues, B.B. Henares, M.S. de Almeida, T.A.O. Machado, D.H. Syllos, Y.P. Wang, M. Rienzo

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Development and validation of automated three-dimensional convolutional neural network model for acute appendicitis diagnosis by Minsung Kim, Taeyong Park, Jaewoong Kang, Min-Jeong Kim, Mi Jung Kwon, Bo Young Oh, Jong Wan Kim, Sangook Ha, Won Seok Yang, Bum-Joo Cho, Iltae Son

“…In stage 2, the IA model exhibited 76.1% accuracy (70.3–81.9%), 82.6% sensitivity (62.9–90.9%), 74.2% specificity (67.0–80.3%), and an AUC of 0.827 (0.820–0.833), differentiating simple and complicated appendicitis. …”
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An Ensemble Classification Method Based on a Stacking Strategy for Ship Type Classification with AIS Data by Lei Deng, Shichen Yang, Limin Jia, Danyang Geng

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An explainable AI-driven transformer model for spoofing attack detection in Internet of Medical Things (IoMT) networks by Mohammad A. Alsharaiah, Mohammed Amin Almaiah, Rami Shehab, Mansour Obeidat, Fuad Ali El-Qirem, Theyazn Aldhyani

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Prediction of Metastasis in Paragangliomas and Pheochromocytomas Using Machine Learning Models: Explainability Challenges by Carmen García-Barceló, David Gil, David Tomás, David Bernabeu

“…Understanding the factors driving predictions is essential not only for validating their reliability but also for enabling their integration into clinical decision-making. In this paper, we propose an architecture that combines data mining, machine learning, and explainability techniques to improve predictions of metastatic disease in these types of cancer and enhance trust in the models. …”
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Evaluating Security Anomalies by Classifying Traffic Using a Multi-Layered Model by Mohammadreza Samadzadeh, Najmeh Farajipour Ghohroud

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Oral mucosal lesions triage via YOLOv7 models by Yu Hsu, Cheng-Ying Chou, Yu-Cheng Huang, Yu-Chieh Liu, Yong-Long Lin, Zi-Ping Zhong, Jun-Kai Liao, Jun-Ching Lee, Hsin-Yu Chen, Jang-Jaer Lee, Shyh-Jye Chen

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Generating Human-Interpretable Rules from Convolutional Neural Networks by Russel Pears, Ashwini Kumar Sharma

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Opinion mining using ensemble model for restaurant feedback analysis by Rohan S. Kamath, M. R. Kaushik, M. Ramakrishna

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A novel attention-based deep learning model for improving sentiment classification after the case of the 2023 Kahramanmaras/Turkey earthquake on Twitter by Serpil Aslan, Muhammed Yildirim

“…This ensures careful consideration of semantic dependencies in sentiment classification. The proposed model operates in three stages: (i) MConv—Local Contextual Feature Extraction, (ii) bidirectional long short-term memory (BiLSTM)—sequence learning, and (iii) Global Attention Mechanism (GAM)—Attention Mechanism. …”
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Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification by Umesh Kumar Lilhore, Yogesh Kumar Sharma, Brajesh Kumar Shukla, Muniraju Naidu Vadlamudi, Sarita Simaiya, Roobaea Alroobaea, Majed Alsafyani, Abdullah M. Baqasah

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