Malaria Disease Prediction Based on Convolutional Neural Networks by Dhrgam AL Kafaf, Noor N. Thamir, Samara S. AL-Hadithy

“…The architectonics of the CNN model is meticulously devised, comprising of six blocks and three interconnected blocks, thereby rendering an efficient extraction of features and subsequent classification of the cells. …”
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Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery by Felix Wong, Aarti Krishnan, Erica J Zheng, Hannes Stärk, Abigail L Manson, Ashlee M Earl, Tommi Jaakkola, James J Collins

“…Abstract Efficient identification of drug mechanisms of action remains a challenge. …”
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EER-DETR: An Improved Method for Detecting Defects on the Surface of Solar Panels Based on RT-DETR by Jiajun Dun, Hai Yang, Shixin Yuan, Ying Tang

“…In the context of the rapid popularization of clean energy, the precise identification of surface defects on photovoltaic modules has become a core technical bottleneck limiting the operational efficiency of power stations. …”
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Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in Mammography by Alessandro Stefano, Fabiano Bini, Eleonora Giovagnoli, Mariangela Dimarco, Nicolò Lauciello, Daniela Narbonese, Giovanni Pasini, Franco Marinozzi, Giorgio Russo, Ildebrando D’Angelo

“…Additionally, a deep learning approach based on the EfficientNetB6 model was employed for comparison. <b>Results</b>: The LDA model achieved a mean validation AUC of 68.28% for microcalcifications and 61.53% for masses. …”
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A Multi-Scale Deep Learning Framework Combining MobileViT-ECA and LSTM for Accurate ECG Analysis by Abduljabbar S. Ba Mahel, Mehdhar S. A. M. Al-Gaashani, Reem Ibrahim Alkanhel, Dina S. M. Hassan, Mohammed Saleh Ali Muthanna, Ammar Muthanna, Ahmed Aziz

“…Our experimental findings show that the model attains high accuracy in ECG classification and AF identification, with average metrics including accuracy of 87.80%, recall of 87.80%, F1-score of 87.45%, and specificity of 95.66%. …”
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Use of Multimodal Artificial Intelligence in Surgical Instrument Recognition by Syed Ali Haider, Olivia A. Ho, Sahar Borna, Cesar A. Gomez-Cabello, Sophia M. Pressman, Dave Cole, Ajai Sehgal, Bradley C. Leibovich, Antonio Jorge Forte

“…Accurate identification of surgical instruments is crucial for efficient workflows and patient safety within the operating room, particularly in preventing complications such as retained surgical instruments. …”
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Power Equipment Image Recognition Method Based on Feature Extraction and Deep Learning by Shuang Lin

“…We plan to introduce a lightweight convolutional structure combined with a graph neural network mechanism to strengthen global context modeling and device structural awareness. This will enable efficient and interpretable identification and localization of power equipment in application scenarios such as automated substation inspections and real-time monitoring with drones.…”
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Enhanced CATBraTS for Brain Tumour Semantic Segmentation by Rim El Badaoui, Ester Bonmati Coll, Alexandra Psarrou, Hykoush A. Asaturyan, Barbara Villarini

“…The early and precise identification of a brain tumour is imperative for enhancing a patient’s life expectancy; this can be facilitated by quick and efficient tumour segmentation in medical imaging. …”
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Automated comprehensive evaluation of coronary artery plaque in IVOCT using deep learning by Pengfei Liu, Zang Lu, Wenqing Hou, Kaisaierjiang Kadier, Chunying Cui, Zhengyang Mu, Aikeliyaer Ainiwaer, Xinliang Peng, Gulinuer Wufu, Yitong Ma, Jianguo Dai, Xiang Ma

“…The median duration for each callback analysis was 18 s. EDA-UNet model serves as an efficient and accurate technological tool for plaque characterization and quantification.…”
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LeafDNet: Transforming Leaf Disease Diagnosis Through Deep Transfer Learning by Tofayet Sultan, Mohammad Sayem Chowdhury, Nusrat Jahan, M. F. Mridha, Sultan Alfarhood, Mejdl Safran, Dunren Che

“…This architectural enhancement enables the model to capture complex, subtle patterns within plant leaf images, contributing to more robust disease identification. …”
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Research on Wind Turbine Main Shaft Bearing Fault Diagnosis Method Based on Unity 3D and Transfer Learning by Shuai Wang, Wenlei Sun, Han Liu, Shenghui Bao, Yunhao Wang, Xin Zhao

“…The processed signal is then fed into an improved residual network model with an efficient channel attention mechanism. …”
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Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections by Asadulla Y. Ashurov, Mehdhar S. A. M. Al-Gaashani, Nagwan A. Samee, Reem Alkanhel, Ghada Atteia, Hanaa A. Abdallah, Mohammed Saleh Ali Muthanna

“…These findings emphasize the model’s potential as a practical tool for disease identification in agricultural applications, supporting timely and informed decision-making for crop protection.…”
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Disassembly of Distribution Transformers Based on Multimodal Data Recognition and Collaborative Processing by Li Wang, Feng Chen, Yujia Hu, Zhiyao Zheng, Kexin Zhang

“…By integrating 2D images and 3D point cloud data captured by RGB-D cameras, the system enables the precise recognition and efficient disassembly of transformer covers and internal components through multimodal data fusion, deep learning models, and control technologies. …”
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Use of E-Learning Platforms in the Training of Personnel in the Air Transport Industry by IONUȚ-CLAUDIU POPA

“…With the massive expansion and development of technology in the world, the study is beginning to develop quickly with steps from the classic model, on paper, to an online model, more efficient. …”
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Design and Implementation of a Hardware-in-the-Loop Simulation System for a Tilt Trirotor UAV by Li Yu, Guang He, Shulong Zhao, Xiangke Wang, Lincheng Shen

“…First, the tilt trirotor UAV configuration and dynamic model are presented, and the parameters are obtained by conducting identification experiments. …”
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A Decomposition-Integration Framework of Carbon Price Forecasting Based on Econometrics and Machine Learning Methods by Zhehao Huang, Benhuan Nie, Yuqiao Lan, Changhong Zhang

“…Then, Lempel–Ziv complexity and dispersion entropy algorithms are applied to analyze these components, facilitating the identification of their unique frequency attributes. The framework subsequently employs GARCH models for predicting high-frequency components and a gated recurrent unit (GRU) neural network optimized by the grey wolf algorithm for low-frequency components. …”
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ECOLOGICAL CULTURE IN THE CONTEXT OF THE CONCEPT OF SUSTAINABLE DEVELOPMENT: THE UKRAINIAN REALITIES AND PROSPECTS by Maryna Bratitsel

“…It includes: the improvement of living standards by protecting human health, preserving the environment, efficient use of resources and ensuring long-term economic competitiveness. …”
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Multi-Object Tracking With Memory Fusion in UAV Videos by Yibo Cui, Shangsheng Li, Xin Yang, Gang Wang

“…This design enables robust multi-frame dependency modeling and efficient memory propagation, thereby improving object association and re-identification performance. …”
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Hybrid Deep Learning Framework for High-Accuracy Classification of Morphologically Similar Puffball Species Using CNN and Transformer Architectures by Eda Kumru, Güney Ugurlu, Mustafa Sevindik, Fatih Ekinci, Mehmet Serdar Güzel, Koray Acici, Ilgaz Akata

“…In this study, five different deep learning models (ConvNeXt-Base, Swin Transformer, ViT, MaxViT, EfficientNet-B3) were comparatively evaluated on a balanced dataset of eight puffball species. …”
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Improved Butterfly Optimizer-Configured Extreme Learning Machine for Fault Diagnosis by Helong Yu, Kang Yuan, Wenshu Li, Nannan Zhao, Weibin Chen, Changcheng Huang, Huiling Chen, Mingjing Wang

“…An efficient intelligent fault diagnosis model was proposed in this paper to timely and accurately offer a dependable basis for identifying the rolling bearing condition in the actual production application. …”
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