Explainable AI-driven assessment of hydro climatic interactions shaping river discharge dynamics in a monsoonal basin by Prashant Parasar, Akhouri Pramod Krishna

“…This study presents an interpretable deep learning framework for daily river discharge forecasting in the Subarnarekha river basin (SRB), integrating Kolmogorov Arnold networks (KAN) with Shapley additive exPlanations (SHAP). …”
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gamUnet: designing global attention-based CNN architectures for enhanced oral cancer detection and segmentation by Jinyang Zhang, Hongxin Ding, Hongxin Ding, Runchuan Zhu, Weibin Liao, Weibin Liao, Junfeng Zhao, Junfeng Zhao, Min Gao, Xiaoyun Zhang

“…Its infiltrative growth patterns and poorly defined boundaries, coupled with the complex architecture of the oral cavity, make accurate segmentation particularly difficult. …”
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Functional connectivity in EEG: a multiclass classification approach for disorders of consciousness by Sreelakshmi Raveendran, Kala S, Ramakrishnan A G, Ramakrishnan A G, Raghavendra Kenchaiah, Jayakrushna Sahoo, Santhos Kumar, Farsana M K, Ravindranadh Chowdary Mundlamuri, Sonia Bansal, Binu V S, Subasree R

“…Multiclass classification is attempted using various models of artificial neural networks that include different multilayer perceptrons (MLP), recurrent neural networks, long-short-term memory networks, gated recurrent units, and a hybrid CNN-LSTM model that combines convolutional neural networks (CNN) and long-short-term memory network to validate the discriminative power of these FC features. …”
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Automatic Correction System for Learning Activities in Remote-Access Laboratories in the Mechatronics Area by Guido S. Machado, Thiago R. M. Salgado, Florindo A. C. Ayres, Iury V. Bessa, Renan L. P. Medeiros, Vicente F. Lucena

“…The application of CNNs serves to validate the results of the experiments through image analysis, whereas generative AI helps to identify patterns. The system was evaluated in a didactic plant, effectively correcting experiments with digital inputs and outputs. …”
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Combining Endpoint Detection and One-Dimensional CNN-Based Classifier for Non-Technical Loss Screening in Smart Grids by Ping-Tzan Huang, Feng-Chang Gu, Chia-Hung Lin, Chao-Lin Kuo, Neng-Sheng Pai, Yung-Chang Luo, Wen-Cheng Pu

“…These standard electricity-consumption models (SECM), along with their associated consumption patterns, can be further used for applications in load forecasting, technical loss (TL) analysis, and non-technical loss (NTL) detection. …”
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Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model by Zachary Barbre, Gang Li

“…Seasonal analysis revealed consistent prediction accuracy across varied meteorological patterns. The xLSTM model maintains linear computational complexity with respect to sequence length while offering enhanced capabilities in memory retention, state tracking, and long-range dependency modeling. …”
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Predicting CO₂ Emissions with Advanced Deep Learning Models and a Hybrid Greylag Goose Optimization Algorithm by Amel Ali Alhussan, Marwa Metwally, S. K. Towfek

“…First, experiments showed that ensemble machine learning models such as CatBoost and Gradient Boosting addressed static features effectively, while time-dependent patterns proved more challenging to predict. Transitioning to recurrent neural network architectures, mainly BIGRU, enabled the modeling of sequential dependence on emissions data. …”
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SpaCCC: Large Language Model-Based Cell-Cell Communication Inference for Spatially Resolved Transcriptomic Data by Boya Ji, Xiaoqi Wang, Debin Qiao, Liwen Xu, Shaoliang Peng

“…SpaCCC also infers known LR pairs concealed by existing aggregative methods and then identifies communication patterns for specific cell types and their signaling pathways. …”
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RelicNet: highly reliable wireless sensor system for microclimate monitoring in wildland cultural heritage sites by XIA Ming1, DONG Ya-bo1, LU Dong-ming1, XUE Ping2

“…The microclimate change patterns in caves were analyzed using the data col- lected by the system, and the reliability and long lifetime of the system were verified through network and battery performance evaluations.…”
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A Fuzzy-Neural Model for Personalized Learning Recommendations Grounded in Experiential Learning Theory by Christos Troussas, Akrivi Krouska, Phivos Mylonas, Cleo Sgouropoulou

“…In contrast, AI-based approaches such as artificial neural networks (ANNs) have high adaptability but lack interpretability. …”
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Employee Turnover Prediction Model Based on Feature Selection and Imbalanced Data Handling by Yuan Fang, Zhongqiu Zhang

“…To address the class imbalance issue, we applied both Synthetic Minority Over-sampling Technique (SMOTE) and Generative Adversarial Network (GAN-based) oversampling techniques. Eight models—Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and a hybrid model (RF+DNN) —were evaluated under different scenarios, including before and after feature selection and imbalance treatment. …”
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AN OVERVIEW OF INTEGRATING THE INTERNET OF THINGS AND ADVANCED AUTOMATION TECHNOLOGIES FOR SUSTAINABLE CROP GROWTH AND MONITORING IN PRECISION AGRICULTURE by Vishwas S, Nithesh Naik, Suhas Kowshik, Shilpa Suresh

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Ensemble Learning Approaches for Multi-Class Intrusion Detection Systems for the Internet of Vehicles (IoV): A Comprehensive Survey by Manal Alharthi, Faiza Medjek, Djamel Djenouri

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Characterizing Breast Tumor Heterogeneity Through IVIM-DWI Parameters and Signal Decay Analysis by Si-Wa Chan, Chun-An Lin, Yen-Chieh Ouyang, Guan-Yuan Chen, Chein-I Chang, Chin-Yao Lin, Chih-Chiang Hung, Chih-Yean Lum, Kuo-Chung Wang, Ming-Cheng Liu

“…The methodology involved several steps: acquisition of multi-b-value IVIM-DWI images, image pre-processing, including correction for motion and intensity inhomogeneity, treating the multi-b-value data as hyperspectral image stacks, applying hyperspectral techniques like band expansion, and evaluating three tumor detection methods: kernel-based constrained energy minimization (KCEM), iterative KCEM (I-KCEM), and deep neural networks (DNNs). …”
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Short-Term Energy Consumption Forecasting Analysis Using Different Optimization and Activation Functions with Deep Learning Models by Mehmet Tahir Ucar, Asim Kaygusuz

“…Regression methods, machine learning, and deep learning methods are used to learn different patterns from data and develop a consumption prediction model. …”
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Infections among adults hospitalized in intensive care after the 2023 earthquake in the southeastern part of Türkiye: a multi-center observational study by Esat Kivanc Kaya, Burcin Halacli, Goksel Guven, Mehmet Yildirim, Asir Eren Seven, Emin Gemcioglu, Meltem Simsek, Begum Erdemir Sullu, Recep Civan Yuksel, Ahmet Safa Kaynar, Aliye Esmaoglu, Banu Kilicaslan, Seda Banu Akinci, Kursat Gundogan, Ebru Ortac Ersoy, Jordi Rello, Arzu Topeli, On behalf of the Turkish Intensive Care Studies-Network (TRICS-Net)

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MACHINE LEARNING TECHNIQUES FOR RETINOPATHY DETECTION IN DIABETIC PATIENTS by Ajay Kushwaha, Ahankari Sachin Suresh, Chennoju Phanindra, Anil Kumar Sahu, Devanand Bhonsle, Yamini Chouhan

“…The core technology involves deep learning algorithms, particularly Convolutional Neural Networks (CNNs), which are designed to identify complex patterns and features within retinal images. …”
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: Keeping Pedestrian Phone Addicts from Dangers Using Mobile Phone Sensors by Zhengjuan Zhou

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Distributed Abnormal Activity Detection in Smart Environments by Chengliang Wang, Qian Zheng, Yayun Peng, Debraj De, Wen-Zhan Song

“…Firstly, DetectingAct finds the normal activity patterns through duration-dependent frequent pattern mining algorithm (DFPMA), which adopts unsupervised learning instead of supervised learning. …”
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User-Independent Activity Recognition via Three-Stage GA-Based Feature Selection by Theresia Ratih Dewi Saputri, Adil Mehmood Khan, Seok-Won Lee

“…Although the technology supports monitoring activity patterns, enabling applications to recognize activities user-independently is still a main concern. …”
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