Identifying influential nodes in weighted complex networks by considering the importance of shortest paths by Xiaohong Wang, Zhenyu Wang

“…By integrating the theory of average shortest paths, SSPW ensures an efficient and precise identification process, even in large-scale networks with significant complexity. …”
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Post-Disaster Building Damage Segmentation Using Convolutional Neural Networks by Revaldi Rahmatmulya, Agung Teguh Wibowo Almais, Mokhamad Amin Hariyadi

“…The impacts of natural disasters are significant and varied across various sectors, including the economy, health, and primarily, infrastructure. Effective and efficient actions are needed to assist in the recovery following natural disasters, one of which is aiding in the identification of building damage levels post-disaster. …”
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Emergence of AI—Impact on Building Condition Index (BCI) by Jye West, Milind Siddhpura, Ana Evangelista, Assed Haddad

“…This allows for a more efficient resource allocation and helps maximise the lifespan and functionality of buildings and their assets. …”
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Exploiting the Regularized Greedy Forest Algorithm Through Active Learning for Predicting Student Grades: A Case Study by Maria Tsiakmaki, Georgios Kostopoulos, Sotiris Kotsiantis

“…This synergy aims to improve the predictive performance of the model while minimizing the labeling effort, making the approach both efficient and scalable. …”
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Bridging Urban Mobility Gaps: Integrating Rapid Transit Railways into Lusaka City’s Transport Network by Mwiza Nalomba, Erastus Misheng’u Mwanaumo, Penjani Hopkins Nyimbili, Wellington Didibhuku Thwala

“…Using Google Earth and QGIS for station identification, mapping and distance analysis, the study delineates a 92.8 km main corridor with 13 branch-off lines spanning 51.6 km, including two direct airport connections, designed to enhance accessibility and efficiency. …”
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An anatomically enhanced and clinically validated framework for lung abnormality classification using deep features and KL divergence by Suresh Kumar Samarla, Maragathavalli P

“…Quantitative Analysis with Kullback-Leibler (KL) divergence: The model enhances precise identification of abnormal tissue by comparing the probability distributions of healthy lungs and abnormal areas…”
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Detection of out-of-seam and out-of-scope mining using double filtering method by Chong Wang, Pengzhi Cui, Guodong Song, Zhiming Xu, Jiajun Qin

“…This approach enables the precise and efficient identification of out-of-seam and out-of-scope mining activities, a capability validated through experimental trials.…”
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Analysis of IoT-Collected Radish Growth Data Using Deep Neural Networks by Ivan P. Malashin, Vadim S. Tynchenko, Andrei P. Gantimurov, Vladimir A. Nelyub, Aleksei S. Borodulin

“…Through the identification of optimal growing conditions, this study supports sustainable practices in agriculture by facilitating resource-efficient and environmentally friendly crop management.…”
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Application of artificial intelligence in the diagnosis and treatment of lacrimal disorders: challenges and opportunities by PENG Xintong, LI Guangyu

“…AI has the ability to provide more precise disease identification and treatment strategies through efficient image analysis, multimodal data fusion, and deep learning algorithms. …”
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Cloning and functional analysis of the TERE1 gene using the Gal4-UaS system in S2 cells: A streamlined approach for human gene functional genomics by Maytham. A. Dragh, Zainab. S. Al-Allak, Zainab. Z. Allami

“…This simplified method presents an efficient alternative to traditional mutagenesis screens, facilitating gene function studies and aiding the identification of disease-related genes.…”
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AGW-YOLO-Based UAV Remote Sensing Approach for Monitoring Levee Cracks by HU Weibo, ZHOU Shaoliang, ZHAO Erfeng, ZHAO Xueqiang

“…ObjectiveRecently, the identification of cracks in river levees primarily relies on manual inspections, which suffer from low efficiency, high costs, and significant safety risks. …”
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Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer’s Disease Detection by Opeyemi Taiwo Adeniran, Blessing Ojeme, Temitope Ezekiel Ajibola, Ojonugwa Oluwafemi Ejiga Peter, Abiola Olayinka Ajala, Md Mahmudur Rahman, Fahmi Khalifa

“…The study explores a few commonly used perturbation-based interpretability (LIME) and gradient-based interpretability (Saliency and Grad-CAM) approaches for visualizing and explaining the dataset, models, and decisions of MRI image-based Alzheimer’s disease identification using the diagnostic and predictive strengths of an ensemble framework comprising Convolutional Neural Networks (CNNs) architectures (Custom multi-classifier CNN, VGG-19, ResNet, MobileNet, EfficientNet, DenseNet), and a Vision Transformer (ViT). …”
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Multimodal Deep Learning for Violence Detection: VGGish and MobileViT Integration With Knowledge Distillation on Jetson Nano by Mohammed, Antara Labiba Swapnil, Marilyn Dip Peris, Istiaque Hasan Nihal, Riasat Khan, Mohammad Abdul Matin

“…Finally, the proposed distilled MobileViT model has been implemented in a Jetson Nano edge device for immediate identification at an average frame rate of 5–10 frames per second. …”
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Innovative deep learning approach for cross-crop plant disease detection: A generalized method for identifying unhealthy leaves by Imane Bouacida, Brahim Farou, Lynda Djakhdjakha, Hamid Seridi, Muhammet Kurulay

“…The key idea is to focus on recognizing the diseased small leaf regions rather than the overall appearance of the diseased leaf, along with determining the disease’s prevalence rate on the entire leaf. For efficient classification and to leverage the excellence of the Inception model in disease recognition, we employ a small Inception model architecture, which is suitable for processing small regions without compromising performance. …”
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MRDDA: a multi-relational graph neural network for drug–disease association prediction by Congzhou Chen, Yaozheng Zhou, Yinghong Li, Jin Xu, Demin Li, Lingfeng Wang

“…The model’s ability to integrate multi-relational data with greater accuracy paves the way for more efficient identification of potential therapeutic uses for existing drugs, ultimately contributing to the acceleration of drug development and reduced costs in the pharmaceutical industry.…”
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Deep Learning-Based Instance Segmentation of Galloping High-Speed Railway Overhead Contact System Conductors in Video Images by Xiaotong Yao, Huayu Yuan, Shanpeng Zhao, Wei Tian, Dongzhao Han, Xiaoping Li, Feng Wang, Sihua Wang

“…It integrates the ECA (Efficient Channel Attention) mechanism for the adaptive augmentation of conductor characteristics and utilizes the FL (Focal Loss) function to mitigate the class-imbalance issue between positive and negative samples, hence enhancing the model’s sensitivity to conductors. …”
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A hybrid hierarchical health monitoring solution for autonomous detection, localization and quantification of damage in composite wind turbine blades for tinyML applications by Nikhil Holsamudrkar, Shirsendu Sikdar, Akshay Prakash Kalgutkar, Sauvik Banerjee, Rakesh Mishra

“…TinyML hardware can run ML models efficiently with low power consumption. This paper presents a Hybrid Hierarchical Machine-Learning Model (HHMLM) that leverages acoustic emission (AE) data to identify, classify, and locate different types of damage using the single unified model. …”
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Research on Concrete Crack Damage Assessment Method Based on Pseudo-Label Semi-Supervised Learning by Ming Xie, Zhangdong Wang, Li’e Yin

“…In complex crack scenarios, the model attains a recall rate (Re = 0.882), surpassing U-Net by 13.9%, with significantly enhanced edge reconstruction precision. …”
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Association analysis of sepsis progression to sepsis-induced coagulopathy: a study based on the MIMIC-IV database by Jian-Yue Yang, Li-Li Li, Su-Zhen Fu

“…The model’s predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC), and its calibration was assessed through a calibration curve. …”
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Enhancing Dongba Pictograph Recognition Using Convolutional Neural Networks and Data Augmentation Techniques by Shihui Li, Lan Thi Nguyen, Wirapong Chansanam, Natthakan Iam-On, Tossapon Boongoen

“…This study provides a robust and efficient solution for automatically classifying Dongba pictographs, contributing to their digital preservation and scholarly research. …”
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