A multi-model approach integrating whole-slide imaging and clinicopathologic features to predict breast cancer recurrence risk by Manu Goyal, Jonathan D. Marotti, Adrienne A. Workman, Graham M. Tooker, Seth K. Ramin, Elaine P. Kuhn, Mary D. Chamberlin, Roberta M. diFlorio-Alexander, Saeed Hassanpour

“…Abstract Breast cancer is the most common malignancy affecting women worldwide and is notable for its morphologic and biologic diversity, with varying risks of recurrence following treatment. …”
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MCT-CNN-LSTM: A Driver Behavior Wireless Perception Method Based on an Improved Multi-Scale Domain-Adversarial Neural Network by Kaiyu Chen, Yue Diao, Yucheng Wang, Xiafeng Zhang, Yannian Zhou, Minming Gu, Bo Zhang, Bin Hu, Meng Li, Wei Li, Shaoxi Wang

“…Initially, a multi-channel convolutional neural network (CNN) combined with a Long Short-Term Memory Network (LSTM) is employed. …”
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Optimizing Cancer Detection: Swarm Algorithms Combined with Deep Learning in Colon and Lung Cancer using Biomedical Images by HariKrishna Pathipati, Lova Naga Babu Ramisetti, Desidi Narsimha Reddy, Swetha Pesaru, Mashetty Balakrishna, Thota Anitha

“…The histopathological recognition of such diseases is generally the most significant module in defining the finest progress of action. …”
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Enhanced estimation of reference evapotranspiration using hybrid deep learning models and remote sensing variables by Tze Ying Fong, Yuk Feng Huang, Ren Jie Chin, Chai Hoon Koo

“…They managed to improve the accuracy of the prediction in most of the cases, with the highest R2 = 0.805 and the lowest prediction errors, MAE = 0.265 mm/day, RMSE = 0.343 mm/day and NRMSE = 0.096. …”
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An Anchor-Free Method Based on Transformers and Adaptive Features for Arbitrarily Oriented Ship Detection in SAR Images by Bingji Chen, Chunrui Yu, Shuang Zhao, Hongjun Song

“…Ship detection is a crucial application of synthetic aperture radar (SAR). Most recent studies have relied on convolutional neural networks (CNNs). …”
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Deep Learning Models and Fusion Classification Technique for Accurate Diagnosis of Retinopathy of Prematurity in Preterm Newborn by Nazar Salih, Mohamed Ksantini, Nebras Hussein, Donia Ben Halima, ali Abdul Razzaq, Sohaib Ahmed

“…   Retinopathy of prematurity (ROP) is the most common cause of irreversible childhood blindness, and its diagnosis and treatment rely on subjective grading based on retinal vascular features. …”
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Attention Enhanced InceptionNeXt-Based Hybrid Deep Learning Model for Lung Cancer Detection by Burhanettin Ozdemir, Emrah Aslan, Ishak Pacal

“…Lung cancer is the most common cause of cancer-related mortality globally. …”
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InBRwSANet: Self-attention based parallel inverted residual bottleneck architecture for human action recognition in smart cities. by Yasir Khan Jadoon, Muhammad Attique Khan, Yasir Noman Khalid, Jamel Baili, Nebojsa Bacanin, MinKyung Hong, Yunyoung Nam

“…The learned weights of the first module are passed to self-attention, extract the most essential features, and can easily discriminate complex human actions. …”
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An extensive experimental analysis for heart disease prediction using artificial intelligence techniques by D. Rohan, G. Pradeep Reddy, Y. V. Pavan Kumar, K. Purna Prakash, Ch. Pradeep Reddy

“…Therefore, experimenting with various models to identify the most effective one for heart disease prediction is crucial. …”
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CGD-CD: A Contrastive Learning-Guided Graph Diffusion Model for Change Detection in Remote Sensing Images by Yang Shang, Zicheng Lei, Keming Chen, Qianqian Li, Xinyu Zhao

“…Self-supervised methods (SSL) for remote sensing image change detection (CD) can effectively address the issue of limited labeled data. However, most SSL algorithms for CD in remote sensing image rely on convolutional neural networks with fixed receptive fields as their feature extraction backbones, which limits their ability to capture objects of varying scales and model global contextual information in complex scenes. …”
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Dual-hybrid intrusion detection system to detect False Data Injection in smart grids. by Saad Hammood Mohammed, Mandeep S Jit Singh, Abdulmajeed Al-Jumaily, Mohammad Tariqul Islam, Md Shabiul Islam, Abdulmajeed M Alenezi, Mohamed S Soliman

“…The proposed methodology combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) for hybrid feature selection, ensuring the selection of the most relevant features for detecting FDIAs. Additionally, the IDS employs a hybrid deep learning classifier that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture the smart grid data's spatial and temporal features. …”
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Revealing Depression Through Social Media via Adaptive Gated Cross-Modal Fusion Augmented With Insights From Personality Traits by Gede Aditra Pradnyana, Wiwik Anggraeni, Eko Mulyanto Yuniarno, Mauridhi Hery Purnomo

“…An extensive ablation study reveals that the most substantial performance gain occurs when DeXMAG is augmented with insights from Myers–Briggs Type Indicator (MBTI) personality traits in conjunction with textual and visual features. …”
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Towards precision diagnosis: a novel hybrid DC-CAD model for lung disease detection leveraging multi-scale capsule networks and temporal dynamics by Esther Stacy E. B. Aggrey, Qin Zhen, Seth Larweh Kodjiku, Linda Delali Fiasam, Collins Sey, Chiagoziem C. Ukwuoma, Evans Aidoo, Emmanuel Osei-Mensah

“…The model consists of three main contributions: (1) Dilated Capsule Networks for improved multi-scale context aggregation, which captures subtle textural variations, (2) a Channel-wise Attention Mechanism to focus on the most relevant regions of interest, minimizing the impact of irrelevant features, and (3) Distanced LSTM layers to model temporal dependencies across sequential CT scans, providing insights into disease progression. …”
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Gully Erosion Susceptibility Prediction Using High-Resolution Data: Evaluation, Comparison, and Improvement of Multiple Machine Learning Models by Heyang Li, Jizhong Jin, Feiyang Dong, Jingyao Zhang, Lei Li, Yucheng Zhang

“…The optimized XGBOOST model achieved the highest performance with an AUC-ROC of 0.9909, and through SHAP analysis, we identified roughness as the most significant factor affecting local gully erosion, with a relative importance of 0.277195. …”
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Global Aerosol Climatology from ICESat-2 Lidar Observations by Shi Kuang, Matthew McGill, Joseph Gomes, Patrick Selmer, Grant Finneman, Jackson Begolka

“…Marine aerosol belts are most prominent in the tropics, contrasting with earlier reports of the Southern Ocean maxima. …”
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Fano Resonance Mach–Zehnder Modulator Based on a Single Arm Coupled with a Photonic Crystal Nanobeam Cavity for Silicon Photonics by Enze Shi, Guang Chen, Lidan Lu, Yingjie Xu, Jieyu Yang, Lianqing Zhu

“…When the applied voltage of the MZM is biased at 4.3 V and the non-return-to-zero on–off keying (NRZ-OOK) signal at a data rate of 10 Gbit/s is modulated, the sharpest asymmetric resonant peak and the most remarkable Fano line shape can be obtained around a wavelength of 1550.68 nm. …”
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Human-Centric Cognitive State Recognition Using Physiological Signals: A Systematic Review of Machine Learning Strategies Across Application Domains by Kaizhe Jin, Adrian Rubio-Solis, Ravi Naik, Daniel Leff, James Kinross, George Mylonas

“…Electrocardiogram (ECG) is the most utilised modality, with convolutional neural networks (CNNs) being the primary DL approach. …”
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Identifying native grasslands and key phenological stages using time series Sentinel-2 data and deep learning models by Yihan Pu, Amy Nixon, Beatriz Prieto, Xulin Guo

“…Canadian prairies are among the world’s most endangered ecosystems, and the identification of native grasslands is crucial for supporting grassland management and wildlife conservation. …”
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Bayesian Q-learning in multi-objective reward model for homophobic and transphobic text classification in low-resource languages: A hypothesis testing framework in multi-objective... by Vivek Suresh Raj, Ruba Priyadharshini, Saranya Rajiakodi, Bharathi Raja Chakravarthi

“…Most Reinforcement Learning (RL) algorithms optimize a single-objective function, whereas real-world decision-making involves multiple aspects. …”
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Enhancing anomaly detection in plant disease recognition with knowledge ensemble by Jiuqing Dong, Jiuqing Dong, Jiuqing Dong, Heng Zhou, Alvaro Fuentes, Alvaro Fuentes, Sook Yoon, Dong Sun Park, Dong Sun Park

“…Plant diseases pose a significant threat to agriculture, impacting food security and public health. Most existing plant disease recognition methods operate within closed-set settings, where disease categories are fixed during training, making them ineffective against novel diseases. …”
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