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. …”
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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

“…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. …”
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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. …”
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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. …”
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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. …”
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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. …”
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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. …”
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SAM-Net: Spatio-Temporal Sequence Typhoon Cloud Image Prediction Net with Self-Attention Memory by Yanzhao Ren, Jinyuan Ye, Xiaochuan Wang, Fengjin Xiao, Ruijun Liu

“…In this process, the changes in time and space are crucial for spatio-temporal sequence prediction models. However, most models now rely on stacking convolutional layers to obtain local spatial features. …”
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Spectral estimation of the aboveground biomass of cotton under water–nitrogen coupling conditions by Shunyu Qiao, Jiaqiang Wang, Fuqing Li, Jing Shi, Chongfa Cai

“…Conclusions In terms of model testing, the RT model was found to be the most accurate for estimating cotton AGB, outperforming SVM and CNN.…”
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Automated Arrhythmia Classification System: Proof-of-Concept With Lightweight Model on an Ultra-Edge Device by Namho Kim, Seongjae Lee, Seungmin Kim, Sung-Min Park

“…Electrocardiograms are the most reliable measure for detecting arrhythmia. This study aims to implement a practical ultra-edge-computing system for automated arrhythmia classification, incorporating a lightweight deep neural network-based model and low-power wearable electrocardiogram sensing device. …”
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Deep learning super-resolution for temperature data downscaling: a comprehensive study using residual networks by Shailesh Kumar Jha, Vivek Gupta, Priyank J. Sharma, Anurag Mishra, Saksham Joshi

“…Complex terrain areas, such as the Himalayas and the Tibetan Plateau, benefit the most from these advancements. These findings suggest that advanced deep learning models employing residual networks, such as VDSR and EDSR, significantly enhance temperature data accuracy over SRCNN. …”
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Continuous Arabic Sign Language Recognition Models by Nahlah Algethami, Raghad Farhud, Manal Alghamdi, Huda Almutairi, Maha Sorani, Noura Aleisa

“…This study is the first to use the Temporal Convolutional Network (TCN) model for Arabic Sign Language (ArSL) recognition. …”
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Importance Analysis of Vegetation Change Factors in East Africa Based on Machine Learning by Zhang Xiumei, Ma Bo, Zhang Yijie

“…Precipitation was the most important climatic factor affecting vegetation changes. …”
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On the usage of artificial intelligence in leprosy care: A systematic literature review. by Hilson Gomes Vilar de Andrade, Elisson da Silva Rocha, Kayo H de Carvalho Monteiro, Cleber Matos de Morais, Danielle Christine Moura Dos Santos, Dimas Cassimiro Nascimento, Raphael A Dourado, Theo Lynn, Patricia Takako Endo

“…It can result in physical disabilities and functional loss and is particularly prevalent amongst the most vulnerable populations in tropical and subtropical regions worldwide. …”
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A Model for Diagnosing Mild Nutrient Stress in Facility-Grown Tomatoes Throughout the Entire Growth Cycle by Yunpeng Yuan, Guoxiang Sun, Guangyu Chen, Qihua Zhang, Lingwei Liang

“…The study concludes that, given complete training data, the CNN + LSTM model can effectively diagnose mild nutrient stress (N, K, and Ca) in facility-grown tomatoes in most scenarios.…”
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GGLA-NeXtE2NET: A Dual-Branch Ensemble Network With Gated Global-Local Attention for Enhanced Brain Tumor Recognition by Adnan Saeed, Khurram Shehzad, Shahzad Sarwar Bhatti, Saim Ahmed, Ahmad Taher Azar

“…Simultaneously, local information is captured through multiple convolutions with a gating layer. The gating mechanism within the GGLA dynamically balances the contributions of global and local information, enabling the model to adaptively focus on the most relevant features for accurate classification. …”
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Development of metastasis and survival prediction model of luminal and non-luminal breast cancer with weakly supervised learning based on pathomics by Hui Liu, Linlin Ying, Xing Song, Xueping Xiang, Shumei Wei

“…Objective Breast cancer stands as the most prevalent form of cancer among women globally. …”
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Wearable Regionally Trained AI-Enabled Bruxism-Detection System by Anusha Ishtiaq, Jahanzeb Gul, Zia Mohy Ud Din, Azhar Imran, Khalil El Hindi

“…This study purposely found which muscle varies most with bruxism activity. The EMG signals’ data of 30 regional subjects, with 5 trials each, have been acquired and pre-processed using filters and three data oversampling techniques, SMOTE, SMOTE-ENN, and ADSYN. …”
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Binding Affinity Prediction for Pancreatic Ductal Adenocarcinoma Using Drug-Target Descriptors and Artificial Intelligence by Pragya, A. Amalin Prince, Jac Fredo Agastinose Ronickom

“…Pancreatic ductal adenocarcinoma (PDAC) is the most common and aggressive form of pancreatic cancer, accounting for 90% of all pancreatic malignancies. …”
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Green Apple Detection Method Based on Multidimensional Feature Extraction Network Model and Transformer Module by Wei Ji, Kelong Zhai, Bo Xu, Jiawen Wu

“…Experimental results show that compared with the original DETR model, the proposed algorithm has improved in AP, AP50, and AP75 indicators, especially in the AP50 indicator, which has the most obvious improvement reaching a detection accuracy of 97.12%. …”
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