EmoFusion: An integrated machine learning model leveraging embeddings and lexicons to improve textual emotion classification by Anjali Bhardwaj, Muhammad Abulaish

“…This paper presents EmoFusion, an integrated machine learning model that improves emotion classification in textual data by integrating pre-trained word embeddings and emotion lexicons. …”
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Classification of Arrhythmias Using a Pre-trained Deep Learning Model with Binary Images of Segmented ECG by H. Solieman, S. Sali

“…The performance of the model was evaluated for three cases: 12 surface ECG leads, orthogonal leads, and all leads. …”
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RL-Cervix.Net: A Hybrid Lightweight Model Integrating Reinforcement Learning for Cervical Cell Classification by Shakhnoza Muksimova, Sabina Umirzakova, Jushkin Baltayev, Young-Im Cho

“…<b>Methods:</b> RL-Cervix.Net combines the robust ResNet-50 architecture with a reinforcement learning module tailored for the unique challenges of cytological image analysis. The model was trained and validated using three extensive public datasets to ensure its effectiveness under realistic conditions. …”
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Presenting a Hybrid Model based on the Machine Learning for the Classification of Banking and Insurance Industry Common Customers by Hamidreza Amirhassankhani, Abbas Toloie Eshlaghy, Reza Radfar, Alireza pourebrahimi

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A two-stage CNN model for the classification and severity analysis of retinal and choroidal diseases in OCT images by Neetha George, Linu Shine, Ambily N, Bejoy Abraham, Sivakumar Ramachandran

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Optimized classification of dental implants using convolutional neural networks and pre-trained models with preprocessed data by Reza Ahmadi Lashaki, Zahra Raeisi, Nasim Razavi, Mehdi Goodarzi, Hossein Najafzadeh

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StrokeNeXt: an automated stroke classification model using computed tomography and magnetic resonance images by Evren Ekingen, Ferhat Yildirim, Ozgur Bayar, Erhan Akbal, Ilknur Sercek, Abdul Hafeez-Baig, Sengul Dogan, Turker Tuncer

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STUDY OF PREDICTION AND CLASSIFICATION MODELS IN THE PROBLEMS OF DIABETES AMONG PATIENTS WITH A STROKE IN DIFFERENT LIVING CONDITIONS by Nural Huliiev, Maksym Peretiaha, Artem Khovrat, Denys Teslenko, Alexei Nazarov

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Constructing a Classification Model for Cervical Cancer Tumor Tissue and Normal Tissue Based on CT Radiomics by Jinghong Pei BD, Jing Yu BD, Ping Ge BD, Liman Bao BD, Haowen Pang MS, Huaiwen Zhang MS

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Evaluation of Machine Learning Models for Stress Symptom Classification of Cucumber Seedlings Grown in a Controlled Environment by Kyu-Ho Lee, Samsuzzaman, Md Nasim Reza, Sumaiya Islam, Shahriar Ahmed, Yeon Jin Cho, Dong Hee Noh, Sun-Ok Chung

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An Innovative Stepwise C‐Means Clustering Approach for Classification of Adolescent Idiopathic Scoliosis by Jiale Gong, Zifang Zhang, Yunzhang Cheng, Liang Cheng, Yating Dong, Lin Sha, Qin Fan, Jian Chen, Chaomeng Wu, Wenyuan Sui, Yaqing Zhang, Fuyun Liu, Weiming Hu, Wenqing Wei, Junlin Yang

“…ABSTRACT Objective Existing 3D classification systems for scoliosis primarily guide surgical treatment, with limited application in conservative management. …”
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Evaluation of Modified Reflection Symmetry Decomposition Polarization Features for Sea Ice Classification by Tianlang Lan, Chengfei Jiang, Xiaofan Luo, Wentao An

“…Feature importance and SHapley Additive exPlanations (SHAP) analysis further demonstrate the physical significance of the MRSD polarization features and their role in model decision-making, suggesting that the scattered component power plays a crucial role in the model’s classification decision. …”
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Targeted Data Augmentation for Improving Model Robustness by Mikołajczyk-Bareła Agnieszka, Ferlin Maria, Grochowski Michał

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Improving Presentation Attack Detection Classification Accuracy: Novel Approaches Incorporating Facial Expressions, Backdrops, and Data Augmentation by Tayyaba Riaz, Adeel Anjum, Madiha Haider Syed, Semeen Rehman

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Greedy Algorithm for Deriving Decision Rules from Decision Tree Ensembles by Evans Teiko Tetteh, Beata Zielosko

“…They show that, as the value of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0</mn><mo>≤</mo><mi>α</mi><mo><</mo><mn>1</mn></mrow></semantics></math></inline-formula>, increases, shorter rules are obtained, and also it is possible to improve the classification accuracy of rule-based models.…”
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Raman Spectra Classification of Pharmaceutical Compounds: A Benchmark of Machine Learning Models with SHAP-Based Explainability by Dimitris Kalatzis, Alkmini Nega, Yiannis Kiouvrekis

“…Ensemble methods such as Random Forest and XGBoost also yielded high accuracies above 98.3%. In addition to strong predictive performance, SHAP (SHapley Additive exPlanations) analysis was employed to interpret model decisions. …”
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Iterative segmentation and classification for enhanced crop disease diagnosis using optimized hybrid U-Nets model by Malathi Chilakalapudi, Sheela Jayachandran

“…Along with this is the use of a Fuzzy U-Net++ model for image segmentation, whereby fuzzy decisions in generously instill an increase in performance for image segmentation. …”
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Severity Classification of Freezing of Gait Using Machine-Learning Algorithms: A Hidden State Model Approach by Aditi Site, Elena Simona Lohan, Jari Nurmi

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Blockchain enabled deep learning model with modified coati optimization for sustainable healthcare disease detection and classification by Heba G. Mohamed, Fadwa Alrowais, Fahd N. Al-Wesabi, Mesfer Al Duhayyim, Anwer Mustafa Hilal, Abdelwahed Motwakel

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Internal Fault Detection, Location, and Classification in Stator Winding of the Synchronous Generators Based on the Terminal Voltage Waveform by M. Fayazi, F. Haghjoo

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