A Mean Weighted Squared Error-based Neural Classifier for Intelligent Pattern Recognition in Smart Grids by Mehdi Khashei, Mehrnaz Ahmadi, Fatemeh Chahkoutahi

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Monitoring and forecasting of land use/land cover (LULC) in Al-Hassa Oasis, Saudi Arabia based on the integration of the Cellular Automata (CA) and the Cellular Automata-Markov Mod... by Ashraf Abdelkarim

“…The maximum likelihood classifier (MLC) method was used as a supervised classification algorithm to super control classification coefficients. …”
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Explainable deep learning-based meta-classifier approach for multi-label classification of retinal diseases by Md. Moniruzzaman Hemal, Suman Saha

“…To develop and train the models, we apply a transfer learning approach to several state-of-the-art deep learning models, including MobileNetV2, InceptionV3, NASNetMobile, DenseNet169, EfficientNetB4, DenseNet121, ConvNeXt, and Xception. …”
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An improved deep learning approach for automated detection of multiclass eye diseases by Feudjio Ghislain, Saha Tchinda Beaudelaire, Romain Atangana, Tchiotsop Daniel

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Electroencephalography-Based Recognition of Low Mental Resilience Using Multi-Condition Decision-Level Fusion Approach by Rumaisa Abu Hasan, Tong Boon Tang, Muhamad Saiful Bahri Yusoff, Syed Saad Azhar Ali

“…Fusion of SVM scores from the eyes-closed, eyes-open and task conditions improved the classification accuracy to more than 85%.Conclusion: The pilot trial reveals the EC as the most promising EEG feature type in assessing mental resilience due to its measure of causality in brain activity, and demonstrates that the fusion of decisions among different mental conditions can help improve the recognition of low mental resilience. …”
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A Hybrid Deep Learning Approach for Enhanced Sentiment Classification and Consistency Analysis in Customer Reviews by Shaymaa E. Sorour, Abdulrahman Alojail, Amr El-Shora, Ahmed E. Amin, Amr A. Abohany

“…The WDE-CNN-LSTM model consistently outperformed standalone CNN, LSTM, and WDE-LSTM models regarding precision, recall, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>F</mi><mn>1</mn></msub></semantics></math></inline-formula>-score, achieving up to 98.26% in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>F</mi><mn>1</mn></msub></semantics></math></inline-formula>-score for three-class classification. …”
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Sample Denoising and Optimization Technique Based on Noise Filtering and Evolutionary Algorithms for Imbalanced Data Classification by Fhira Nhita, Asniar, Isman Kurniawan, Adiwijaya

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CLASSIFICATION OF IRRIGATION MANAGEMENT PRACTICES IN MAIZE HYBRIDS USING MULTISPECTRAL SENSORS AND MACHINE LEARNING TECHNIQUES by João L. G de Oliveira, Dthenifer C. Santana, Izabela C de Oliveira, Ricardo Gava, Fábio H. R. Baio, Carlos A da Silva Junior, Larissa P. R. Teodoro, Paulo E. Teodoro, Job T de Oliveira

“…Three accuracy metrics were utilized to evaluate the algorithms in the classification of irrigation management: correct classifications (CC), Kappa coefficient and F-Score. …”
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Gated-LNN: Gated Liquid Neural Networks for Accurate Water Quality Index Prediction and Classification by Sreeni Chadalavada, Suleyman Yaman, Abdulkadir Sengur, Abdul Hafeez-Baig, Ru-San Tan, Prabal Datta Barua, Ravinesh C. Deo, Makiko Kobayashi, U. Rajendra Acharya

“…The proposed gated-LNN model achieved a high R2 of 0.9995 for WQI prediction and 99.74% accuracy for three-class water quality classification into &#x201C;Good,&#x201D; &#x201C;Poor,&#x201D; and &#x201C;Unsuitable&#x201D; classes, outperforming state-of-the-art models in both regression and classification tasks. …”
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Multi-CNN Deep Feature Fusion and Stacking Ensemble Classifier for Breast Ultrasound Lesion Classification by Kemal PANÇ, Sümeyye SEKMEN

“…While promising for clinical decision support, the model’s lower sensitivity for malignant lesions necessitates further refinement.…”
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Machine-Learning-Based Biomechanical Feature Analysis for Orthopedic Patient Classification with Disc Hernia and Spondylolisthesis by Daniel Nasef, Demarcus Nasef, Viola Sawiris, Peter Girgis, Milan Toma

“…These models are trained on two open-source datasets, using the PyCaret library in Python. (3) <b>Results</b>: The findings suggest that an ensemble of Random Forest and Logistic Regression models performs best for the 2C classification, while the Extra Trees classifier performs best for the 3C classification. …”
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Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography. by MinJae Woo, Linglin Zhang, Beatrice Brown-Mulry, InChan Hwang, Judy Wawira Gichoya, Aimilia Gastounioti, Imon Banerjee, Laleh Seyyed-Kalantari, Hari Trivedi

“…Subgroup analysis was conducted using univariate and multivariate regression models to control for confounding effects. The classification model achieved an AUC of 0.975 and a recall of 0.927. …”
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Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis – a systematic review by Filip Orzan, Ştefania D. Iancu, Laura Dioşan, Zoltán Bálint

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Enhancing Support Vector Classification for Diabetes Prediction with Novel Optimization Algorithms of Intelligent Health Services by Debojani Paul Chowdhury, Aditi Paul Chowdhury, Apurba Das, Pinki Pinki

“…The fundamental model was combined with 3 optimization approaches to create the hybrid models: SVC + QIO (SVQI), SVC + AVOA (SVAV), and SVC + TSA (SVTS). …”
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Robotics Classification of Domain Knowledge Based on a Knowledge Graph for Home Service Robot Applications by Yiqun Wang, Rihui Yao, Keqing Zhao, Peiliang Wu, Wenbai Chen

“…This designed knowledge graph contributes to the state of the art by improving the accuracy and efficiency of service decision making. The lightweight network MobileNetV3 is used to pre-train the model, and a lightweight convolution method with good feature extraction performance is selected. …”
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Deep Learning-Based Classification of Canine Cataracts from Ocular B-Mode Ultrasound Images by Sanghyeon Park, Seokmin Go, Seonhyo Kim, Jaeho Shim

“…A dataset of 3155 ultrasound images (comprising 1329 No cataract, 614 Cortical, 1033 Mature, and 179 Hypermature cases) was used to train and validate four widely used deep learning models (AlexNet, EfficientNetB3, ResNet50, and DenseNet161). …”
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Classification prediction of load losses in power stations using machine learning multilayer stack ensemble by Bathandekile M. Boshoma, Oluwole S. Akinola, Peter Olukanmi, Peter Olukanmi

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Hybrid CNN-Transformer Architecture With Xception-Based Feature Enhancement for Accurate Breast Cancer Classification by Alireza Zeynali, Mohammad Ali Tinati, Behzad Mozaffari Tazehkand

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Advancing breast cancer diagnosis: token vision transformers for faster and accurate classification of histopathology images by Mouhamed Laid Abimouloud, Khaled Bensid, Mohamed Elleuch, Mohamed Ben Ammar, Monji Kherallah

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EmbryoNet-VGG16 framework for deep learning-based embryo classification with Otsu segmentation by M. Saraniya, J. Anitha Ruth

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