Deep Learning-Based Coding Strategy for Improved Cochlear Implant Speech Perception in Noisy Environments by Billel Essaid, Hamza Kheddar, Noureddine Batel, Muhammad E. H. Chowdhury

“…The second strategy builds on this framework by additionally incorporating bidirectional gated recurrent units (Bi-GRU) alongside TCN and MHA layers, further refining sequence modeling and enhancing noise reduction. …”
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An Explainable Bayesian TimesNet for Probabilistic Groundwater Level Prediction by Zechen Peng, Shaoxing Mo, Alexander Y. Sun, Jichun Wu, Xiankui Zeng, Miao Lu, Xiaoqing Shi

“…BTimesNet transforms 1D time series data into 2D matrices based on periodicity, enhancing the capture of temporal patterns through convolutional filters. A Bayesian framework using Stein Variational Gradient Descent is implemented to quantify predictive uncertainties. …”
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The Robust Vessel Segmentation and Centerline Extraction: One-Stage Deep Learning Approach by Rostislav Epifanov, Yana Fedotova, Savely Dyachuk, Alexandr Gostev, Andrei Karpenko, Rustam Mullyadzhanov

“…The proposed end-to-end framework directly predicts the centerline as a polyline with real-valued coordinates, thereby eliminating the need for post-processing steps commonly required by previous methods that infer centerlines either implicitly or without ensuring point connectivity. …”
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Forecasting Electric Vehicle Charging Demand in Smart Cities Using Hybrid Deep Learning of Regional Spatial Behaviours by Muhammed Cavus, Huseyin Ayan, Dilum Dissanayake, Anurag Sharma, Sanchari Deb, Margaret Bell

“…This study presents a novel predictive framework for estimating electric vehicle (EV) charging demand in smart cities, contributing to the advancement of data-driven infrastructure planning through behavioural and spatial data analysis. …”
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A Dual-Stream Deep Learning Architecture With Adaptive Random Vector Functional Link for Multi-Center Ischemic Stroke Classification by Mahesh Anil Inamdar, Anjan Gudigar, U. Raghavendra, Massimo Salvi, Raja Rizal Azman Bin Raja Aman, Nadia Fareeda Muhammad Gowdh, Izzah Amirah Binti Mohd Ahir, Mohd Salahuddin Bin Kamaruddin, Khairul Azmi Abdul Kadir, Filippo Molinari, Ajay Hegde, Girish R. Menon, U. Rajendra Acharya

“…We demonstrate the strong generalization capabilities of the proposed framework by achieving 92.42% accuracy on a diverse, multi-center dataset of 7,842 CT images. …”
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Clinical Applicability and Cross-Dataset Validation of Machine Learning Models for Binary Glaucoma Detection by David Remyes, Daniel Nasef, Sarah Remyes, Joseph Tawfellos, Michael Sher, Demarcus Nasef, Milan Toma

“…These findings highlight the importance of proper evaluation frameworks, including external validation, to ensure the reliability of artificial intelligence tools for clinical use. …”
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Mechanism of Influence of Spatial Perception on Residents’ Emotion in Child-Friendly Urban Streets of Fuzhou City by Shaofeng CHEN, Zhengyan CHEN, Yuhan XU, Zheng DING

“…Three machine learning architectures are deployed: CNN-BiLSTM Hybrid Model, FCN-RF Semantic Segmentation, and XGBoost-SHAP Interpretability Framework. For FCN-RF Semantic Segmentation, street view images are processed by fully convolutional networks to quantify 10 spatial metrics, validated against human-scored safety perceptions via random forest-based adversarial training; for XGBoost-SHAP Interpretability Framework, the nonlinear relationships between 12 street environment indicators and emotional indices are modeled through extreme gradient boosting, with SHapley additive explanations (SHAP) decoding feature contributions and interaction effects. …”
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Aided Greenway Design Approach Based on Internet Big Data and AIGC Fine-Tuning Model by Yifan WU, Lu MENG, Liang LI

“…The research takes greenways as a typical example to verify the performance of the approach framework. Image and text data related to Beijing greenways from 2013 to 2022 are collected from Weibo platform as the original dataset. …”
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Socializing AI: Integrating Social Network Analysis and Deep Learning for Precision Dairy Cow Monitoring—A Critical Review by Sibi Chakravathy Parivendan, Kashfia Sailunaz, Suresh Neethirajan

“…We describe the transition from manual, observer-based assessments to automated, scalable methods using convolutional neural networks (CNNs), spatio-temporal models, and attention mechanisms. …”
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Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis by Ji Soo Park, Sa-Yoon Park, Jae Won Moon, Kwangsoo Kim, Dong In Suh

“…Risk of bias was assessed using a modified Quality Assessment of Diagnostic Accuracy Studies (version 2) framework. Data were extracted on study design, dataset, ML methods, feature extraction, and classification tasks. …”
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A Predictive Method for Greenhouse Soil Pore Water Electrical Conductivity Based on Multi-Model Fusion and Variable Weight Combination by Jiawei Zhao, Peng Tian, Jihong Sun, Xinrui Wang, Changjun Deng, Yunlei Yang, Haokai Zhang, Ye Qian

“…Importantly, it provides agricultural researchers and greenhouse managers with a deployable and generalizable framework for digital, precise, and intelligent management of soil water and nutrients in protected horticulture systems.…”
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Channel and Spatial Attention in Chest X-Ray Radiographs: Advancing Person Identification and Verification with Self-Residual Attention Network by Hazem Farah, Akram Bennour, Neesrin Ali Kurdi, Samir Hammami, Mohammed Al-Sarem

“…For the network backbone, a self-residual attention block (SRAB) was implemented within a ResNet50 framework, forming a Siamese network trained with triplet loss to improve feature embedding for identity identification and verification. …”
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MSCPUnet: A multi-task neural network for plot-level crop classification in complex agricultural areas by Kedi Fang, Shengwei Zhang, Yongting Han, Lin Yang, Meng Luo, Lu Liu, Qian Zhang, Bo Wang

“…This paper first proposes a novel multi-task neural network, Multi-task Multi-Scale Convolutional Pooling Unet (MSCPUnet), for extracting vector plots. …”
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Accurate estimation of permeability reduction resulted from low salinity water flooding in clay-rich sandstones by Xiaojuan Zhang, Muntadher Abed Hussein, Tarak Vora, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Farzona Alimova, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Ahmad Khalid

“…This work provides a comprehensive, validated, and highly accurate ML framework specifically tailored for predicting complex permeability alterations, offering a superior alternative to conventional approaches and enhancing decision-making in EOR projects.…”
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Enhancing Water Bodies Detection in the Highland and Coastal Zones Through Multisensor Spectral Data Fusion and Deep Learning by Xiaofei Han, Nazih Y. Rebouh, Yasmeen Ahmed, Muhammad Nasar Ahmad, Zainab Tahir, Yahia Said, Ishfaq Gujree

“…This study presents a deep-learning-based semantic segmentation framework that leverages multiband Sentinel-2 imagery for delineating glaciers and coastal lakes. …”
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Predictive performance and uncertainty analysis of ensemble models in gully erosion susceptibility assessment by Congtan Liu, Haoming Fan, Yixuan Wang

“…This study aims to identify the optimal feature datasets and to quantify the uncertainty associated with gully erosion prediction models by developing a novel methodological framework based on ensembles of the three machine learning models: Random Forest (RF), Convolutional Neural Network (CNN), and Transformer models. …”
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Apple Yield Estimation Method Based on CBAM-ECA-Deeplabv3+ Image Segmentation and Multi-Source Feature Fusion by Wenhao Cui, Yubin Lan, Jingqian Li, Lei Yang, Qi Zhou, Guotao Han, Xiao Xiao, Jing Zhao, Yongliang Qiao

“…This study establishes a reliable framework for precise fruit tree image segmentation and early yield estimation, advancing precision agriculture applications.…”
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Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer’s Disease or Mild Cognitive Impairment by Lanlan Li, Yeying Yang, Qi Zhang, Jiao Wang, Jiehui Jiang, Alzheimer’s Disease Neuroimaging Initiative

“…The proposed deep-learning genomics (DLG) approach consists of three steps: quality control, coding of single-nucleotide polymorphisms, and classification. The ResNet framework was used for the DLG model, and the results were compared with classifications by simple convolutional neural network structure. …”
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Concept of a geoinformation platform for landmines and other explosive objects detection and mapping with UAV by Mykhailo Popov, Sergey Stankevich, Sergey Mosov, Stanislav Dugin, Stanislav Golubov, Artem Andreiev, Artur Lysenko, Ievgen Saprykin

“…The results of this study provide a general framework, i.e., a geoinformation platform for landmine detection and mapping. …”
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An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images by Md. Romzan Alom, Fahmid Al Farid, Muhammad Aminur Rahaman, Anichur Rahman, Tanoy Debnath, Abu Saleh Musa Miah, Sarina Mansor

“…This research introduces the Deep Neural Breast Cancer Detection (DNBCD) model, an explainable AI-based framework that utilizes deep learning methods for classifying breast cancer using histopathological and ultrasound images. …”
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