Intelligent integration of AI and IoT for advancing ecological health, medical services, and community prosperity by Abdulrahman Alzahrani, Patty Kostkova, Hamoud Alshammari, Safa Habibullah, Ahmed Alzahrani

“…CNN (convolutional neural networks) with transfer learning enabled by Res-Net provides high-accuracy image recognition, which can be used for waste classification. …”
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Deep learning-based occlusion-aware face mask detection for airborne disease control by Teshome Ayechiluhem Yalew, Sosina M. Gashaw, Aleka Melese Ayalew, Mourad Oussalah

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Occupational dimension of the INFODORADCA+ project by Stefan Kwiatkowski

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

“…Notable approaches like YOLO (You Only Look Once) and its variants have shown exceptional performance in detecting defects with varying scales and complexities, leveraging innovations such as feature pyramid networks and efficient loss functions. Furthermore, this review discusses the role of advanced data acquisition techniques, such as drone-based imaging, thermographic analysis, and LiDAR (Light Detection and Ranging), in generating high-resolution and multi-spectral data for improved detection accuracy. …”
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Sensing the Cultural Significance with AI for Social Inclusion by Nan Bai

“…This thesis explores the use of AI in a methodological framework to include the contribution of a larger and more diverse group of participants with user-generated data. It is an interdisciplinary study integrating methods and knowledge from heritage studies, computer science, social sciences, network science, and spatial analysis. …”
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Work-based learning as a shift towards improved labour market skills in VET in Latvia by Biruta Sloka, Ilze Buligina

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A multitask framework based on CA-EfficientNetV2 for the prediction of glioma molecular biomarkers by Qian Xu, Feng Ning Liang, Ya Ru Cao, Jin Duan, Teng Cui, Teng Zhao, Hong Zhu

“…Accurate, non-invasive prediction of these biomarkers using MRI is of significant clinical value.Materials and methodsWe proposed a novel multitask deep learning framework based on Coordinate Attention-EfficientNetV2 (CA-EfficientNetV2) to simultaneously predict IDH mutation and MGMT promoter methylation status based on MRI data. Initially, unlabeled MR images were annotated using K-means clustering to generate pseudolabels, which were subsequently refined using a Vision Transformer (ViT) network to improve labeling accuracy. …”
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Evolving Strategies in Machine Learning: A Systematic Review of Concept Drift Detection by Gurgen Hovakimyan, Jorge Miguel Bravo

“…Key findings highlight the effectiveness of diverse methodologies such as drift detection methods, window-based methods, unsupervised statistical methods, and neural network techniques. However, challenges remain, particularly with imbalanced data, computational efficiency, and the application of concept drift detection to non-tabular data like images. …”
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ReLTER: An R interface for environmental observation in long term ecological research by Alessandro Oggioni, Micha Silver, Paolo Tagliolato, Arnon Karnieli

“…More than forty functions have been implemented that allow the retrieval of species occurrence data from third-party online networks, such as GBIF, iNaturalist, and OBIS, as well as earth observation images from various internet archives, and to harmonize the downloaded data (in terms of structure, semantics, taxonomy, etc.) for combining and linking them in subsequent analyses. …”
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Genetic neurocognitive profile of autism unveiled with gene transcription by Yingxing Zhang, Fangfang Li, Hongli Wu, Wei Du, Jianhua Shu, Anqing Wang, Chunsheng Xu, Chuanfu Li, Ya Wang, Sheng Hu

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A Method for Detecting Foreign Objects in Pastries Based on Deep Learning by Haoqi TANG, Jun YANG, Rongchang CHEN

“…In this study, we propose a foreign-object detection method based on the U-Net network, which is trained using CT data from the same type of samples and foreign objects. …”
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AI may help to predict thyroid nodule malignancy based on radiomics features from [18F]FDG PET/CT by Krystian Ślusarz, Mikolaj Buchwald, Adrian Szczeszek, Szymon Kupinski, Anna Gramek-Jedwabna, Wojciech Andrzejewski, Juliusz Pukacki, Robert Pękal, Marek Ruchała, Rafał Czepczyński, Cezary Mazurek

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CoroYOLO: a novel colorectal cancer detection method based on the Mamba framework by Wenfei Chen, Fengrui Hou, Yika Shen

“…In recent years, object detection methods based on convolutional neural networks (CNNs) and transformers have made significant progress in medical image analysis. …”
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Assessment of economic well-being in South Africa based on remote sensing transfer learning by L. Wang, L. Wang, T. Long, T. Long, E. Adam

“…However, comprehensive and spatially clear data on environmental conditions and socio-economic well-being remain scarce, preventing a thorough analysis of intersecting inequalities. …”
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Risk Factors for Gout in Taiwan Biobank: A Machine Learning Approach by Liu YR, Nfor ON, Zhong JH, Lin CY, Liaw YP

“…Five machine learning models were used: Bayesian Network (BN), Random Forest (RF), Gradient Boosting (GB), Logistic Regression (LR), and Neural Network (NN). …”
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Evidence of a persistent altered neural state in people with fibromyalgia syndrome during functional MRI studies and its relationship with pain and anxiety. by Patrick W Stroman, Roland Staud, Caroline F Pukall

“…Altered neural signaling in fibromyalgia syndrome (FM) was investigated with functional magnetic resonance imaging (fMRI). We employed a novel fMRI network analysis method, Structural and Physiological Modeling (SAPM), which provides more detailed information than previous methods. …”
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Methods and Results of the Study of the Phenomenon of Soviet Nostalgia in the Russian Media Space by Ivan V. Suslov, Sofya V. Tikhonova

“…The authors emphasize the changing balance between communicative and cultural memory in the era of digital technologies, as well as the need to develop new methods of analysis to work with huge amounts of data. The review of current studies of Soviet nostalgia in the media space allows us to identify the main vectors of development of Russian digital humanities, as well as analyze the reasons for the continued interest in nostalgic discussions in online communities, social networks and other media platforms. …”
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Logging Evaluation of Mixed-sediments Reservoirs of the Lower Cambrian Canglangpu Formation in Penglai Gas Area, Central Sichuan Basin by WANG Haiqing, WANG Zeyu, LAI Qiang, LI Shurong, WU Yuyu, ZHANG Mingyong, WANG Yaqing

“…From an overall perspective, the regularity of porosity to permeability is poor, the electrical differentiation of gas abundance is fuzzy, so the traditional parameter calculation model is not applicable, and it is urgent to study an effective logging evaluation method. Using the core analysis data such as cast thin section, XRD, physical property, logging data and gas testing data, the paper studied a systematic evaluation method for calculating the mineral component by dimensionality reduction, neural network analysis and element content to mineral content inversion method, calculating porosity by variable matrix parameters on the basis of tri-porosity logging curves, calculating permeability by lithologic subdivision model, and quantitative evaluation of reservoir property, connectivity and permeability by array acoustic wave combined with electrical imaging logging, then determined the lower limit of effective reservoir and the classification standard of reservoir. …”
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Enhanced Classification of Phonocardiograms Using Modified Deep Learning by Awais Mahmood, Habib Dhahri, Mousa Alhajlah, Abdulaziz Almaslukh

“…Instead of handicraft features, we converted the speech files into spectrograms and then fed them to the Convolutional neural network (CNN) model as images. The innovative approach involves a modified CNN integrated with dual classifiers: a SoftMax classifier and a Support Vector Machine (SVM), The proposed model demonstrates remarkable proficiency, achieving 97.85% accuracy with the SoftMax classifier and 98.28% accuracy with the SVM, surpassing the former. …”
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Non-linear relationship between built environment and non-motorized travel efficiency under the traffic micro-circulation model. by Xiaoyuan Dong, Lining Wang, Sen Du, Bicheng Qian, Jiaxin Wang

“…This paper focuses on the core area of Chengguan District in Lanzhou City, utilizing multi-source big data including POI, OpenStreetMap, street view images, and built environment data. …”
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