Identify suitable artificial groundwater recharge zones using hybrid deep learning models by Navaz Khalillollahi, Mohsen Isari, Hamed Faroqi, Kaywan Othman Ahmed, Kamran Nobakht Vakili, Miklas Scholz, Saad Sh. Sammeng

“…Identifying groundwater recharge zones is crucial for sustainable water resource management in water-scarce environments, such as Kurdistan, Iran. This study evaluated four deep learning models for delineating groundwater recharge zones: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and hybrid deep learning Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU). …”
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MIMO Lyapunov Theory-Based RBF Neural Classifier for Traffic Sign Recognition by King Hann Lim, Kah Phooi Seng, Li-Minn Ang

“…The performance comparisons between the proposed classifier and some existing conventional techniques are evaluated using traffic sign patterns. Simulation results reveal that our proposed system achieved better performance with lower number of training iterations.…”
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An Enhanced Distribution System Performance with Optimization Techniques for Location of Electrical Vehicle Charging Stations by Sainadh Singh Kshatri, Venkata Anjani Kumar G, Chilakapati Lenin Babu, Palepu Suresh Babu

“…The results demonstrate the GWO algorithm's superiority in minimizing power losses and enhancing voltage profiles across the distribution network. Furthermore, a probabilistic assessment is conducted to evaluate the robustness of the optimal EVCS placement under uncertain EV charging patterns. …”
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Requirements for Becoming a Startup: A Study with an Economic Sociology Approach (Case Study: Managers of Startups in Isfahan City) by Rahmatollah Torkan, Seyed Samad Beheshty, Ramin Moradi

“…Materials & MethodsThis qualitative research grounded in the interpretive paradigm and utilizing the thematic network approach (Attride-Stirling, 2001) was designed to uncover meaningful patterns within the data. …”
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Predicting sport event outcomes using deep learning by Jianxiong Gao, Yi Cheng, Jianwei Gao

“…The 1D CNN effectively captures local spatial patterns in structured match data, while the Transformer leverages self-attention mechanisms to model long-range dependencies. …”
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Lightweight Anomaly-Based Detection Using Cuckoo Search Algorithm and Decision Tree to Mitigate Man-in-the-Middle Attacks in DNS by Ramahlapane Lerato Moila, Mthulisi Velempini

“…The proposed model utilizes machine learning algorithms and statistical analysis techniques to ensure that the analysis of DNS query patterns can efficiently detect anomalies associated with the MitM. …”
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Advanced Hybrid Transformer-CNN Deep Learning Model for Effective Intrusion Detection Systems with Class Imbalance Mitigation Using Resampling Techniques by Hesham Kamal, Maggie Mashaly

“…The Transformer-CNN model focuses on three primary objectives to enhance detection accuracy and performance: (1) reducing false positives and false negatives, (2) enabling real-time intrusion detection in high-speed networks, and (3) detecting zero-day attacks. We evaluate our proposed model, Transformer-CNN, using the NF-UNSW-NB15-v2 and CICIDS2017 benchmark datasets, and assess its performance with metrics such as accuracy, precision, recall, and F1-score. …”
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Dynamic Threshold-Based Resource Management for Fog Computing Environments by Jui-Pin Yang

“…Fog computing extends cloud services to the network edge, thereby reducing latency and bandwidth usage for time-sensitive applications. …”
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Enhancing Urban Road Safety: A 5G NR Model for Vulnerable Road Users Awareness by Alexis Yanez, Felipe Salas, Cesar A. Azurdia-Meza, Jorge Ignacio Sandoval, Sandra Cespedes

“…The model assumes full VRU connectivity to the 5G network and simulates 5G NR operating in communication mode 2, incorporating realistic urban mobility patterns (via SUMO traces) and signal propagation effects, including building-induced obstructions. …”
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Investigation of the HLA locus in autopsy-confirmed progressive supranuclear palsy by Jinguo Wang, Shelley L. Forrest, Sathish Dasari, Hidetomo Tanaka, Ekaterina Rogaeva, M. Carmela Tartaglia, Susan Fox, Anthony E. Lang, Subha Kalyaanamoorthy, Gabor G. Kovacs

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Paraphrase detection for Urdu language text using fine-tune BiLSTM framework by Muhammad Ali Aslam, Khairullah Khan, Wahab Khan, Sajid Ullah Khan, Abdullah Albanyan, Shabbab Ali Algamdi

“…Our approach employs word embeddings and text preprocessing techniques like tokenization, stop-word removal, and label encoding to effectively handle Urdu’s morphological variations. The BiLSTM network sequentially processes the input, leveraging both forward and backward contextual information to encode the complex syntactic and semantic patterns inherent in Urdu text. …”
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Quantifying Urban Vitality in Guangzhou Through Multi-Source Data: A Comprehensive Analysis of Land Use Change, Streetscape Elements, POI Distribution, and Smartphone-GPS Data by Hongjin Chen, Jingyi Ge, Wei He

“…The results reveal the spatial distribution patterns of urban vitality and the varying significance of different determinants, providing data-driven insights and policy implications for urban planning and development.…”
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Robust Classification of UWB NLOS/LOS Using Combined FCE and XGBoost Algorithms by Shoude Wang, Nur Syazreen Ahmad

“…Preliminary NLOS identification and classification are performed using Fuzzy Comprehensive Evaluation (FCE), with subsequent optimization of FCE weights via the Pattern Search algorithm (PSa). …”
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Deep learning model for grading carcinoma with Gini-based feature selection and linear production-inspired feature fusion by Shreyan Kundu, Souradeep Mukhopadhyay, Rahul Talukdar, Dmitrii Kaplun, Alexander Voznesensky, Ram Sarkar

“…Traditional deep learning methods often struggle with the intricate and complex patterns seen in histopathology images of RCC and HCC, leading to inaccuracies in classification. …”
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Defect Prediction in CWDM Optical Modules Using Multimodal Learning by Kyu-Jeong Choi, Jia Yang, Botambu Collins, Sung-Geun Kim, Do-Jin Lim, Jin-Taek Seong

“…Reliable defect detection in coarse-wavelength division multiplexing (CWDM) optical modules is critical for ensuring stable high-speed optical communication and minimizing network disruptions. Traditional inspection methods, such as manual video screening and optical testing, are labor-intensive, prone to human error, and often fail to identify underlying defect patterns, thereby necessitating multiple rounds of testing. …”
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SPEED (SPlEnic Embolisation Decisions) study-Decision to treat acute traumatic splenic artery injury in the context of trauma protocol. by Paul Jenkins, Jim Zhong, James Harding, Lexy Sorrell, Jason Smith, Victoria Allgar, Carl Roobottom

“…Data available from the TARN database and then correlated with data that will be collected at each MTC, where detail as to the embolisation technique, specific injury pattern, imaging based follow up and patient survival will be available. …”
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Transcriptome analysis of ovarian cancer uncovers association between tumor-related inflammation/immunity and patient outcome by Jingfang Wang, Wenrui Zhu, Xia Li, Yuanyuan Wu, Wenhui Ma, Yangzhou Wang, Weihong Zhao, Fang Wei, Wenhao Wang

“…The immune infiltration microenvironment, clinicopathological features, and survival rates differed significantly between two inflammatory gene expression patterns identified using cluster and immune microenvironment analyses. …”
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Spatial and spatio-temporal analysis of cancer mortality in Iran: gender disparities and implications for health policy by Alireza Zangeneh, Arash Ziapour, Seyede Negin Hoseini, Babak Nazari, Homa Molavi, Farid Najafi, Ardeshir Khosravi, Reza Heidari Moghadam

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Optical Coherent Tomography-Angiography in the Research of Myopia Complicated by Choroidal Neovascularization by E. A. Drozdova, O. V. Zhiliaeva

“…Two mCNV phenotypes were established: glomerular (44.83 %) and flat (55.17 %) as regularized (18.75 %) and tangled (81.25 %) vascular network patterns. Hypointensive peripheral halo during the entire period of the neovascular complex activity was visualized regardless of the phenotype. …”
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ADHD detection from EEG signals using GCN based on multi-domain features by Ling Li, Xueyang Guo, Zihan Yang, Yanping Zhao, Xu Liu, Junxian Yang, Yanyan Chen, Xinxian Peng, Lina Han

“…Finally, a GCN model integrates these time-frequency features with topological patterns from the connectivity matrix for ADHD classification.ResultsEvaluated on two EEG datasets, the proposed method achieved average accuracies of 97.29% and 96.67%, outperforming comparative models (XGBoost, LightGBM, AdaBoost, random forest). …”
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