Development of interpretable intelligent frameworks for estimating river water turbidity by Amin Gharehbaghi, Salim Heddam, Saeid Mehdizadeh, Sungwon Kim

“…Analysis of the SHAP graphs in a global level during the validation phase illustrated that river discharge was the most important input variable affecting the output results of the best-performing implemented models.…”
Get full text

Enhanced Pneumonia Detection from Chest X-rays Using Machine Learning and Deep Neural Architectures by Kamal Upreti, Anju Singh, Divakar Singh, Preety Shoran, Uma Shankar, Meenakshi Yadav, Rituraj Jain

“…The study aims to improve diagnostic precision, reduce interpretation discrepancies, and facilitate faster clinical decision-making by identifying the most effective machine learning approaches for real-world applications in healthcare settings. …”
Get full text

FPA-based weighted average ensemble of deep learning models for classification of lung cancer using CT scan images by Liang Zhou, Achin Jain, Arun Kumar Dubey, Sunil K. Singh, Neha Gupta, Arvind Panwar, Sudhakar Kumar, Turki A. Althaqafi, Varsha Arya, Wadee Alhalabi, Brij B. Gupta

“…Abstract Cancer is among the most dangerous diseases contributing to rising global mortality rates. …”
Get full text

Deep Learning-Based Ground-Penetrating Radar Inversion for Tree Roots in Heterogeneous Soil by Xibei Li, Xi Cheng, Yunjie Zhao, Binbin Xiang, Taihong Zhang

“…Additionally, a GPR simulation data set and a measured data set are built in this study, which were used to train inversion models and validate the effectiveness of GPR inversion methods.The introduced GPR inversion model is a pyramid convolutional network with vision transformer and edge inversion auxiliary task (PyViTENet), which combines pyramidal convolution and vision transformer to improve the diversity and accuracy of data feature extraction. …”
Get full text

An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival of hepatocellular carcinoma patients after stereotactic body r... by Yi Chen, David Pasquier, Damon Verstappen, Henry C. Woodruff, Philippe Lambin

“…Deep learning models, leveraging various convolutional neural networks (CNNs), were employed to effectively integrate both image and clinical data. …”
Get full text

A Systematic Review of Reimagining Fashion and Textiles Sustainability with AI: A Circular Economy Approach by Hiqmat Nisa, Rebecca Van Amber, Julia English, Saniyat Islam, Georgia McCorkill, Azadeh Alavi

“…The types of textiles captured were most commonly swatches of fabric, with 20 studies examining these, whereas whole garments were less frequently studied, with only 7 instances. …”
Get full text

Machine Learning-Based Analysis of Travel Mode Preferences: Neural and Boosting Model Comparison Using Stated Preference Data from Thailand’s Emerging High-Speed Rail Network by Chinnakrit Banyong, Natthaporn Hantanong, Supanida Nanthawong, Chamroeun Se, Panuwat Wisutwattanasak, Thanapong Champahom, Vatanavongs Ratanavaraha, Sajjakaj Jomnonkwao

“…CatBoost emerges as the top-performing model (area under the curve = 0.9113; accuracy = 0.7557), highlighting travel cost, service frequency, and waiting time as the most influential determinants. These findings underscore the effectiveness of machine learning approaches in capturing complex behavioral patterns, providing empirical evidence to guide high-speed rail policy development in low- and middle-income countries. …”
Get full text

xLSTM Interaction Multilevel SSM-Assisted Decoding Network for Remote Sensing Image Change Detection by Chunpeng Wu, Shuli Cheng, Anyu Du, Liejun Wang, Wenbin Tang

“…With the advancements of convolutional neural networks (CNNs) and Transformers in deep learning, the accuracy of RSCD has significantly improved. …”
Get full text

Deep Learning in Defect Detection of Wind Turbine Blades: A Review by Katleho Masita, Ali N. Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…The increasing adoption of wind turbines as a key component of renewable energy generation necessitates the development of efficient and reliable maintenance strategies to ensure their optimal performance and safety. Among the most critical aspects of turbine maintenance is detecting and classifying defects in wind turbine blades, which are constantly exposed to extreme environmental conditions. …”
Get full text

BCTDNet: Building Change-Type Detection Networks with the Segment Anything Model in Remote Sensing Images by Wei Zhang, Jinsong Li, Shuaipeng Wang, Jianhua Wan

“…However, buildings have large intra-class variance and high similarity with other objects, limiting the generalization ability of models in diverse scenarios. Moreover, most existing methods only detect whether changes have occurred but ignore change types, such as new construction and demolition. …”
Get full text

A Comprehensive Evaluation of Monocular Depth Estimation Methods in Low-Altitude Forest Environment by Jiwen Jia, Junhua Kang, Lin Chen, Xiang Gao, Borui Zhang, Guijun Yang

“…The results indicate that most Transformer-based models, such as DepthAnything and Metric3D, outperform traditional CNN-based models in complex forest environments by capturing detailed tree structures and depth discontinuities. …”
Get full text

Review of Recent Advances in Remote Sensing and Machine Learning Methods for Lake Water Quality Management by Ying Deng, Yue Zhang, Daiwei Pan, Simon X. Yang, Bahram Gharabaghi

“…This review also discusses the effectiveness of these models in predicting various water quality parameters, offering insights into the most appropriate model–satellite combinations for different monitoring scenarios. …”
Get full text

Accurate bladder cancer diagnosis using ensemble deep leaning by Rana A. El-Atier, M. S. Saraya, Ahmed I. Saleh, Asmaa H. Rabie

“…Abstract There are an estimated 1.3 million cases of cancer globally each year, making it one of the most serious types of urinary tract cancer. The methods used today for diagnosing and monitoring bladder cancer are intrusive, costly, and time-consuming. …”
Get full text

Locomotion Joint Angle and Moment Estimation With Soft Wearable Sensors for Personalized Exosuit Control by Luying Feng, Lianghong Gui, Wenzhu Xu, Xiang Wang, Canjun Yang, Yaochu Jin, Wei Yang

“…Recent advancements in flexible sensing and machine learning have positioned soft sensors as promising alternatives to traditional methods for human posture detection. However, most research has centered on calibration, with limited progress in practical applications due to the challenges posed by diverse users and complex scenarios such as human-robot interaction. …”
Get full text

Automated Models for Predicting Software Defects in Hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) Parallel Programs Using Deep Learning by Amani Saad Althiban, Hajar M. Alharbi, Lama A. Al Khuzayem, Fathy Elbouraey Eassa

“…The results reveal that Clang-token-based representation provided the most effective input for defect prediction, enabling CNN models to achieve an accuracy of 97%. …”
Get full text

Img2Neuro: brain-trained neural activity encoders for enhanced object recognition by Mona A Aboelnaga, Mohamed W El-Kharashi, Seif Eldawlatly

“…Therefore, rather than using the brain as an inspiration, in this paper, we introduce Img2Neuro; a convolutional neural network model feature extractor that predicts the visual brain’s response to images by encoding neural activity. …”
Get full text

Early breast cancer detection via infrared thermography using a CNN enhanced with particle swarm optimization by Riyadh M. Alzahrani, Mohamed Yacin Sikkandar, S. Sabarunisha Begum, Ahmed Farag Salem Babetat, Maryam Alhashim, Abdulrahman Alduraywish, N. B. Prakash, Eddie Y. K. Ng

“…Abstract Breast cancer remains the most prevalent cause of cancer-related mortality among women worldwide, with an estimated incidence exceeding 500,000 new cases annually. …”
Get full text

Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer by Taishi Nishizawa, Takouhie Maldjian, Zhicheng Jiao, Tim Q. Duong

“…The model integrates 3D convolutional neural networks and self-attention to capture spatial and cross-modal interactions. …”
Get full text

Plant Leaf Disease Detection Using Deep Learning: A Multi-Dataset Approach by Manjunatha Shettigere Krishna, Pedro Machado, Richard I. Otuka, Salisu W. Yahaya, Filipe Neves dos Santos, Isibor Kennedy Ihianle

“…Detecting plant diseases accurately in diverse and uncontrolled environments remains challenging, as most current detection methods rely heavily on lab-captured images that may not generalise well to real-world settings. …”
Get full text

A Novel Open Circuit Fault Diagnosis for a Modular Multilevel Converter with Modal Time-Frequency Diagram and FFT-CNN-BIGRU Attention by Ziyuan Zhai, Ning Wang, Siran Lu, Bo Zhou, Lei Guo

“…Fault diagnosis is one of the most important issues for a modular multilevel converter (MMC). …”
Get full text