Sustainable production and consumption: a bibliometric analysis of SDG-12 literature through a financial management lens by Amol S. Dhaigude, Anshul Verma, Gurudutt Nayak

“…It provides positive and actionable implications for both scholars and practitioners to navigate the convolution of SDG-12 vis-à-vis financial management.…”
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AI in Endoscopic Gastrointestinal Diagnosis: A Systematic Review of Deep Learning and Machine Learning Techniques by Jovita Relasha Lewis, Sameena Pathan, Preetham Kumar, Cifha Crecil Dias

“…Gastrointestinal (GI) diseases are most common worldwide and the death rate can be reduced by early detection. …”
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A deep fusion‐based vision transformer for breast cancer classification by Ahsan Fiaz, Basit Raza, Muhammad Faheem, Aadil Raza

“…Abstract Breast cancer is one of the most common causes of death in women in the modern world. …”
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Automatic detection of sleep spindles by neural networks algorithms by Jan Rychlík, Roman Mouček

“…The learning algorithms underwent training using meticulously annotated data from the Montreal Archive of Sleep Studies (MASS) data center. The convolutional neural network emerged as the most effective classification model, achieving an accuracy surpassing 67 %. …”
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Multi‐Wound Classification: Exploring Image Enhancement and Deep Learning Techniques by Prince Odame, Maxwell Mawube Ahiamadzor, Nana Kwaku Baah Derkyi, Kofi Agyekum Boateng, Kelvin Sarfo‐Acheampong, Eric Tutu Tchao, Andrew Selasi Agbemenu, Henry Nunoo‐Mensah, Dorothy Araba Yakoba Agyapong, Jerry John Kponyo

“…The approaches used included Contrast Limited Adaptive Histogram Equalization (CLAHE) with machine and deep learning models, Discrete Wavelet Transformations (DWT) with a novel Gated Wavelet Convolutional Neural Network (CNN) model, and FixCaps, an improved version of Capsule Networks utilizing Convolutional Block Attention Module (CBAM) to reduce spatial information loss. …”
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Deep Learning Based on Facial Expression Recognition from Images to Videos by Deng Rui

“…Facial expressions, as a vital conduit for human emotional expression, are among the most observable features of machines in the field of computer vision. …”
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Assessment of Bone Aging—A Comparison of Different Methods for Evaluating Bone Tissue by Paweł Kamiński, Aleksander Gali, Rafał Obuchowicz, Michał Strzelecki, Adam Piórkowski, Marcin Kociołek, Elżbieta Pociask, Joanna Kwiecień, Karolina Nurzyńska

“…Automatically selecting radiomic features for machine learning models achieves a MAE of 7.99, whereas utilizing well-known convolutional architectures on the original image results in a system efficacy of 7.96. …”
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Local Auxiliary Spatial–Spectral Decoupling Transformer Network for Cross-Scene Hyperspectral Image Classification by Qiusheng Chen, Zhuoqun Fang, Zhaokui Li, Qian Du, Shizhuo Deng, Tong Jia, Dongyue Chen

“…The feature-level domain alignment based on deep learning techniques has greatly improved the performance of unsupervised domain adaptation (UDA) for hyperspectral image (HSI) classification. However, most of these methods leverage convolutional neural networks to capture local features, overlooking the comparable spatial global (SaG) and spectral global (SeG) information shared by both the source and target domains. …”
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Unlocking chickpea flour potential: AI-powered prediction for quality assessment and compositional characterisation by Ali Zia, Muhammad Husnain, Sally Buck, Jonathan Richetti, Elizabeth Hulm, Jean-Philippe Ral, Vivien Rolland, Xavier Sirault

“…Using a dataset comprising 136 chickpea varieties, the research compares the performance of several state-of-the-art deep learning models, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Graph Convolutional Networks (GCNs), and compares the most effective model, CNN, against the traditional Partial Least Squares Regression (PLSR) method. …”
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Racism Detection by Analyzing Differential Opinions Through Sentiment Analysis of Tweets Using Stacked Ensemble GCR-NN Model by Ernesto Lee, Furqan Rustam, Patrick Bernard Washington, Fatima El Barakaz, Wajdi Aljedaani, Imran Ashraf

“…Owing to the superior performance of deep learning, a stacked ensemble deep learning model is assembled by combining gated recurrent unit (GRU), convolutional neural networks (CNN), and recurrent neural networks RNN, called, Gated Convolutional Recurrent- Neural Networks (GCR-NN). …”
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Advanced hybrid deep learning model for enhanced evaluation of osteosarcoma histopathology images by Arezoo Borji, Arezoo Borji, Arezoo Borji, Gernot Kronreif, Bernhard Angermayr, Bernhard Angermayr, Sepideh Hatamikia, Sepideh Hatamikia

“…Techniques such as deep learning, especially convolutional neural networks (CNNs) and vision transformers (ViTs), are now enabling the precise analysis of complex histopathological images, automating detection, and enhancing classification accuracy across various cancer types. …”
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Dynamic spatiotemporal graph network for traffic accident risk prediction by Pengcheng Zhang, Wen Yi, Yongze Song, Penggao Yan, Peng Wu, Ammar Shemery, Keith Hampson, Albert P. C. Chan

“…Our model uses channel-wise convolutional neural networks to detect spatial accident patterns across weekly, daily, and hourly time scales with automatic weight learning, simultaneously employing graph convolutional networks to process road network features, population feature while integrating external data like weather and dates. …”
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Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks by Magdalena Fafrowicz, Marcin Tutajewski, Igor Sieradzki, Jeremi K. Ochab, Jeremi K. Ochab, Anna Ceglarek-Sroka, Koryna Lewandowska, Tadeusz Marek, Barbara Sikora-Wachowicz, Igor T. Podolak, Paweł Oświęcimka, Paweł Oświęcimka, Paweł Oświęcimka

“…The best performance was achieved by the LGBM classifier with 1-time point input data during memory retrieval and a convolutional neural network during the encoding phase. …”
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AI-enhanced real-time monitoring of marine pollution: part 1-A state-of-the-art and scoping review by Navya Prakash, Navya Prakash, Oliver Zielinski, Oliver Zielinski

“…This review synthesizes 53 recent studies on Artificial Intelligence applications in marine pollution detection, focusing on different model architectures, sensing technologies and preprocessing methods. The most deployed models of Random Forest, U-Network, Generative Adversarial Networks, Mask Region-based Convolution Neural Network and You Only Look Once demonstrated high prediction rate for detecting oil spills and marine litter. …”
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Building Footprint Extraction from High Resolution Aerial Images Using Generative Adversarial Network (GAN) Architecture by Arnick Abdollahi, Biswajeet Pradhan, Shilpa Gite, Abdullah Alamri

“…Thus, we introduce an end-to-end convolutional neural network called Generative Adversarial Network (GAN) in this study to tackle these issues. …”
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A Comprehensive Design of Hybrid Residual (2+1)-Dimensional CNN and Dense Networks With Multi-Modal Sensor for Fish Appetite Detection by Infall Syafalni, Agape D'Sky, Nana Sutisna, Trio Adiono

“…Fish is one of the most demanding protein sources in the food industry. …”
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Fusing satellite imagery and ground-based observations for PM2.5 air pollution modeling in Iran using a deep learning approach by Zohreh Sohrabi, Jamshid Maleki

“…In this study, we applied deep learning techniques, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Convolutional Long Short-Term Memory (ConvLSTM), to model PM2.5 concentrations for continuous monthly distribution estimation. …”
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AFDR-Det: Adaptive Feature Dual-Refinement Oriented Detector for Remote Sensing Object Detection by Jie Yang, Li Zhou, Yongfeng Ju

“…In remote sensing images, objects are distributed in arbitrary orientations, while convolutional features are inherently axis-aligned. This inevitably leads to spatial misalignment between the heuristically defined anchors and the convolutional features. …”
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SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals by Davide Lillini, Carlo Aironi, Lucia Migliorelli, Leonardo Gabrielli, Stefano Squartini

“…Our proposed SiCRNN processes Mel spectrograms using a Siamese approach, integrating a convolutional neural network (CNN) backbone and a bidirectional gated recurrent unit (GRU). …”
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Assessment of the Energy Security of EU Countries in Light of the Expansion of Renewable Energy Sources by Aleksandra Kuzior, Yevhen Kovalenko, Inna Tiutiunyk, Larysa Hrytsenko

“…This research employs non-linear (piecewise linear) normalization and the multiplicative convolution method, analyzing data from 2000 to 2021. …”
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