Simplified two-compartment neuron with calcium dynamics capturing brain-state specific apical-amplification, -isolation and -drive by Elena Pastorelli, Alper Yegenoglu, Alper Yegenoglu, Nicole Kolodziej, Nicole Kolodziej, Nicole Kolodziej, Willem Wybo, Francesco Simula, Sandra Diaz-Pier, Johan Frederik Storm, Pier Stanislao Paolucci

“…In contrast, classical models of learning in spiking networks are based on single-compartment neurons, lacking the ability to describe the integration of apical and basal/somatic information. …”
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Improved Multi-Grained Cascade Forest Model for Transformer Fault Diagnosis by Yiyi Zhang, Yuxuan Wang, Jiefeng Liu, Heng Zhang, Xianhao Fan, Dongdong Zhang

“…Firstly, in order to extract features more effectively and reduce memory consumption, the multi-grained scanning of gcForest is replaced by convolutional neural networks. …”
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Prediction of cholesterol level in patients with myocardial infarction based on medical data mining methods by Cemil Colak, Mehmet C. Colak, Necip Ermis, Nevzat Erdil, Ramazan Ozdemir

“…The current study was carried out to predict the cholesterol level in patients with MI usingdata mining methods, artificial neural networks (ANNs) and support vector machine (SVM) models. …”
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Unbalancing Datasets to Enhance CNN Models Learnability: A Class-Wise Metrics-Based Closed-Loop Strategy Proposal by Somayeh Shahrabadi, Victor Alves, Emanuel Peres, Raul Morais Dos Santos, Telmo Adao

“…Using these datasets, 72 models with varying configurations – including different convolutional neural network architectures, initial learning rates, and optimizers – were initially trained and then evaluated against imagery test sets. …”
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Coffee plant disease identification with an attentive multi-image segmentation framework (MISF) with CycleGAN by Savitri Kulkarni, P. Deepa Shenoy, K.R. Venugopal

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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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GNNs and ensemble models enhance the prediction of new sRNA-mRNA interactions in unseen conditions by Shani Cohen, Lior Rokach, Isana Veksler-Lublinsky

“…To test this, we developed models from two families: (1) graph neural networks (GNNs), including GraphRNA and kGraphRNA, that learn transformed representations of interacting sRNA-mRNA pairs via graph relationships, and (2) decision forests, sInterRF (Random Forest) and sInterXGB (XGBoost), which use various interaction features for prediction. …”
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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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Interpretable Deep Learning for Diabetic Retinopathy: A Comparative Study of CNN, ViT, and Hybrid Architectures by Weijie Zhang, Veronika Belcheva, Tatiana Ermakova

“…Deep learning models have been widely used for automated DR classification, with Convolutional Neural Networks (CNNs) being the most established approach. …”
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Sustainable AI for plant disease classification using ResNet18 in few-shot learning by Fareeha Naveed, Adven Masih, Jabar Mahmood, Moeez Ahmed, Aitizaz Ali, Aysha Saddiqa, Mohamed Shabbir Hamza Abdulnabi, Ebenezer Agbozo

“…The architecture incorporates a pre-training phase based on transfer learning as a feature extractor, followed by meta-learning using Prototypical Networks (ProtoNets) for class prototype computation and distance-based classification. …”
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Enhancing Hierarchical Classification in Tree-Based Models Using Level-Wise Entropy Adjustment by Olga Narushynska, Anastasiya Doroshenko, Vasyl Teslyuk, Volodymyr Antoniv, Maksym Arzubov

“…The model was trained and evaluated on two real-world datasets based on the GS1 Global Product Classification (GPC) system. …”
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Deep learning based bio-metric authentication system using a high temporal/frequency resolution transform by Sajjad Maleki Lonbar, Akram Beigi, Nasour Bagheri, Nasour Bagheri, Pedro Peris-Lopez, Carmen Camara

“…Notable datasets, such as the NSRDB and MITDB, are employed to evaluate the performance of the system. These datasets, however, contain inherent noise, which necessitates preprocessing. …”
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Advancing Skin Disease Diagnosis: A Multimodal Approach Utilizing Telegram Api Token Chatbot for Text and Image Analysis in Skin Disease Classification by Modigari Narendra, T. S. Harshini, L. Jani Anbarasi

“…ResNet50, with its residual connections, helps mitigate vanishing gradient issues, allowing for deeper networks with stable training leading to improved feature extraction and representation. …”
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The Importance of X-Ray in Examination of Lungs in Patients with Inhalation Trauma by E. A. Beresneva, T. G. Spiridonova, E. A. Zhirkova, M. V. Barinova, T. I. Semenova, P. A. Brygin, O. A. Zabavskaya, E. P. Sokolova, E. A. Lapshina, A. S. Orlov

“…Using a statistical evaluation, we showed that the presence of network deformation of the pulmonary pattern under the conditions of IT is an objective feature, confirmed with Cohen’s kappa coefficient (0.6±0.14; 95% CI [0.32–0.88]).…”
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Comparative Study of Person Re-Identification Techniques Based on Deep Learning Models by Mossaab Idrissi Alami, Abderrahmane Ez-zahout, Fouzia Omary

“…This study explores deep metric learning models, specifically Siamese and Triplet networks, to improve Re-ID performance. We evaluate these methods on the Market-1501 dataset using Cumulative Matching Characteristic (CMC) and Cumulative Distribution Function (CDF) curves. …”
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A Self-Attention Enhanced Deep CNN-LSTM-Based Irregular Surface Recognition Approach for Integration Into Lower Limb Prosthesis Systems to Ensure Safety Through Predictive Walking by Norazian Subari, Kamarul Hawari Ghazali, Yuanfa Ji

“…The model employs the strengths of convolutional and recurrent neural networks combined with a self-attention mechanism to enhance feature representation and improve classification accuracy. …”
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TTG-Text: A Graph-Based Text Representation Framework Enhanced by Typical Testors for Improved Classification by Carlos Sánchez-Antonio, José E. Valdez-Rodríguez, Hiram Calvo

“…Our evaluation on a text classification task using a graph convolutional network (GCN) demonstrates that TTG-Text achieves a 95% accuracy rate, surpassing conventional methods and BERT with fewer required training epochs. …”
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CANGuard: An Enhanced Approach to the Detection of Anomalies in CAN-Enabled Vehicles by Damilola Oladimeji, Razaq Jinad, Amar Rasheed, Mohamed Baza

“…A key enabler of this advancement is the Controller Area Network (CAN) bus, which facilitates seamless communication between vehicle components. …”
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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

“…Subsequently, the STFT is applied to analyze the frequency contains in the drastically changing time-domain data and then generates the visualization color feature patterns. With theses feature patterns, the 1D-CNN based classifier is used to identify the data into normal (Nor), suspected incidents (SI), fraud incidents (FI), and fault or power outage (OUT) events. …”
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APPROACH TO IMAGE ANALYSIS FOR COMPUTER VISION SYSTEMS by N. A. Iskra

“…Attention is paid to the selection of a neural network algorithm for object detection in an image, as a preliminary stage of model construction. …”
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