Lightweight Brain Tumor Segmentation Through Wavelet-Guided Iterative Axial Factorization Attention by Yueyang Zhong, Shuyi Wang, Yuqing Miao, Tao Zhang, Haoliang Li

“…Conventional deep learning methods, such as convolutional neural networks and transformer-based models, frequently introduce significant computational overhead or fail to effectively represent multi-scale features. …”
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Predicting Sugarcane Yield Through Temporal Analysis of Satellite Imagery During the Growth Phase by Julio Cezar Souza Vasconcelos, Caio Simplicio Arantes, Eduardo Antonio Speranza, João Francisco Gonçalves Antunes, Luiz Antonio Falaguasta Barbosa, Geraldo Magela de Almeida Cançado

“…This research investigates how to estimate sugarcane (<i>Saccharum officinarum</i> L.) yield at harvest by using an average satellite image time-series collected during the growth phase. …”
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Non-Linear Synthetic Time Series Generation for Electroencephalogram Data Using Long Short-Term Memory Models by Bakr Rashid Alqaysi, Manuel Rosa-Zurera, Ali Abdulameer Aldujaili

“…To overcome this drawback, long short-term memory (LSTM) networks are proposed to learn long-term dependencies in non-linear EEG time series and subsequently generate synthetic signals to enhance the training of detection systems. …”
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Technological Advancements in Human Navigation for the Visually Impaired: A Systematic Review by Edgar Casanova, Diego Guffanti, Luis Hidalgo

“…It was also found that AI systems employ deep learning and neural networks to optimize both navigation accuracy and energy efficiency. …”
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Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing by Shizhe Li, Chunzhi Fan, Ali Kargarandehkordi, Yinan Sun, Christopher Slade, Aditi Jaiswal, Roberto M. Benzo, Kristina T. Phillips, Peter Washington

“…Strategic selection of an optimal threshold enabled us to optimize either sensitivity or specificity while maintaining reasonable performance for the other metric. …”
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Cascaded metasurfaces enabling adaptive aberration corrections for focus scanning by Xiaotong Li, Xiaodong Cai, Chang Liu, Yeseul Kim, Trevon Badloe, Huanhuan Liu, Junsuk Rho, Shiyi Xiao

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OR-FCOS: an enhanced fully convolutional one-stage approach for growth stage identification of Oudemansiella raphanipes by Runze Fang, Huamao Huang, Nuoyan Guo, Haichuan Wei, Shiyi Wang, Haiying Hu, Ming Liu

“…A neural architecture search (NAS)-enhanced FCOS decoder replaces both the traditional feature pyramid networks (FPN) and prediction head in FCOS, optimizing feature fusion and prediction. …”
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Experimental and machine learning-driven assessment of IS 2062 steel under double-base propellant combustion conditions by Hari Singh, Dola Sundeep, C. Chandrasekhara Sastry, Eswaramoorthy K Varadharaj

“…To enhance predictive accuracy, machine learning models Linear Regression, Random Forest Regression, Support Vector Machines (SVM), K-Means Clustering, and Artificial Neural Networks (ANN) were employed to analyze combustion-induced degradation trends, confirming Test-06 as the optimal balance of stability and high performance. …”
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Enhancing charcoal Production: Improvements in the traditional brick kiln and product properties by Eleeyah Saniso, Lutfee Sueni, Muhammadkhoiri Hayibaka, Manus Suttikan

“…The dual-wall kilns maintained internal temperatures approximately 11.6 % higher than those recorded in the single-wall kiln (C1). Charcoal yield averaged 22.1 %, with moisture content ranging from 3.0 % to 5.1 %, and a calorific value reaching 32.17 MJ/kg in the optimal configuration (C4). …”
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Interference Fading Mitigation in Coherent <inline-formula><tex-math notation="LaTeX">$\Phi$</tex-math></inline-formula>-OTDR Based on Subband Phase-Shift Transform by Yafeng Cheng, Hanyong Wang, Desheng Li, Ying Qiu, Ming Luo, Xu Zhang, Jing Zhang, Zhichao Wu, Tianye Huang, Xiang Li

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Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image Analysis by Muyang Li, Tuo Yao, Jian Liu, Ziyi Liu, Zhenguo Gao, Junbo Gong

“…Deep learning-based in situ imaging and analysis for crystallization process are essential for optimizing product qualities, reducing experimental costs through real-time monitoring, and controlling the process. …”
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Deep learning techniques for detecting freezing of gait episodes in Parkinson’s disease using wearable sensors by Mosleh Hmoud Al-Adhaileh, Mosleh Hmoud Al-Adhaileh, Asim Wadood, Theyazn H. H. Aldhyani, Theyazn H. H. Aldhyani, Safeer Khan, M. Irfan Uddin, Abdullah H. Al-Nefaie, Abdullah H. Al-Nefaie

“…The methodology combines CNNs for spatial feature extraction, BiLSTM networks for temporal modeling, and an attention mechanism to enhance interpretability and focus on critical gait features. …”
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Tracking dustbathing behavior of cage-free laying hens with machine vision technologies by Bidur Paneru, Ramesh Bist, Xiao Yang, Lilong Chai

“…The objectives of this study were to (1) develop and test a deep learning model for detecting DB behavior and find out the optimal model; and (2) assess the performance of the optimal model in detecting DB behavior at different growing phases. …”
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Inductive and Transfer Learning‐Based Hybrid Model Techniques for Accurate and Automated Diagnosis of Neurological Diseases by Saroj Kumar Pandey, Yogesh Kumar Rathore, Sunakshi Mehra, Anurag Sinha, Tarun Raj Kumar, Ankit Kumar, Rekh Ram Janghel, Ayodele Lasisi, Quadri Noorulhasan Naveed, Md. Sazid Reza

“…Methods NeuroDL utilizes convolutional neural networks (CNNs) trained on two publicly available, annotated MRI datasets. …”
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Cities in transition: Krakow‘s social, economic and spatial transformation within the last thirty years (selected aspects) by Anna Karwińska, Dorota Jopek, Michał Kudłacz, Michał Wiśniewski

“…The world has become a networked and digitized entity susceptible to the influence of innovation. …”
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THz-Enabled UAV Communications Under Pointing Errors: Tractable Statistical Channel Modeling and Security Analysis by Mohammad Javad Saber, Mazen Hasna, Osamah S. Badarneh

“…These results provide important guidelines for optimizing future wireless networks using UAVs and THz frequencies to ensure secure and reliable data transmission in dynamic environments.…”
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Lightweight Deep Learning Model for Fire Classification in Tunnels by Shakhnoza Muksimova, Sabina Umirzakova, Jushkin Baltayev, Young-Im Cho

“…This approach enhances the model generalization capabilities, enabling it to handle diverse fire scenarios, including those with low visibility, high smoke density, and variable ventilation conditions. Deployment optimizations, such as quantization and layer fusion, ensure computational efficiency, achieving an average inference time of 12ms/frame, making it suitable for resource-constrained environments like IoT and edge devices. …”
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Graph-based analysis of histopathological images for lung cancer classification using GLCM features and enhanced graph by Imam Dad, JianFeng He, Zulqarnain Baloch

“…This study advances computational pathology by unifying Graph Neural Networks (GNN) with interpretable feature engineering, offering a scalable, efficient solution for cancer subtype classification. …”
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Exploring the achievements and forecasting of SDG 3 using machine learning algorithms: Bangladesh perspective. by Md Maeen Molla, Md Sifat Hossain, Md Ayub Ali, Md Raqibul Islam, Mst Papia Sultana, Dulal Chandra Roy

“…Additionally, Machine Learning (ML) models, including Bidirectional Recurrent Neural Networks (BRNN) and Elastic Neural Networks (ENET), were employed for all the indicators.…”
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A Study on Canopy Volume Measurement Model for Fruit Tree Application Based on LiDAR Point Cloud by Na Guo, Ning Xu, Jianming Kang, Guohai Zhang, Qingshan Meng, Mengmeng Niu, Wenxuan Wu, Xingguo Zhang

“…During model construction, the study optimized the hyperparameters of partial least squares regression (PLSR), backpropagation (BP) neural networks, and gradient boosting decision trees (GBDT) to build canopy volume measurement models tailored to the dataset. …”
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