Semantic ECG hash similarity graph by Yixian Fang, Shilin Zhang, Yuwei Ren

“…Abstract Graph-based methods have made significant progress in addressing the dependent correlations among ECG time series variables. However, most existing graph structures primarily focus on local similarity while overlooking global semantic correlation. …”
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Advancing Hate Speech Detection in Indonesian Language Using Graph Neural Networks and TF-IDF by Syaikha Amirah Zikrina, Fitriyani

“…Most of the hate speech and abusive content on social media, particularly in the Indonesian language, presents significant challenges for content moderation systems. …”
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Integrating the CNN Model with the Web for Indonesian Sign Language (BISINDO) Recognition by Enisda Libra Kelana, Muhammad Riko Anshori Prasetya, Mambang ., Muhammad Zulfadhilah

“…Effective communication is challenging for deaf individuals in Indonesia, most of whom use Indonesian Sign Language (BISINDO). …”
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Comparison of CNN Architectures for Pre-Cancerous Cervical Lesion Classification Based on Colposopy Images Using IARC and AnnoCerv Datasets by Sigit Prasetyo Noprianto, Siti Nurmaini, Dian Palupi Rini

“…Based on these findings, MobileNetV2 is the most optimal model for classifying colposcopy images in this study. …”
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Optimizing Natural Image Quality Evaluators for Quality Measurement in CT Scan Denoising by Rudy Gunawan, Yvonne Tran, Jinchuan Zheng, Hung Nguyen, Rifai Chai

“…The result was obtained using the library of good images. Most are also part of the Convolutional Neural Network (CNN) training dataset against the testing dataset, and a new dataset shows an optimum patch size and contrast levels suitable for the task. …”
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Lighting System Control for Cataract Maturity Imaging Device by Katon Bagus Setiawan, I Komang Somawirata, Irmalia Suryani Faradisa

“…In cataract detection, artificial intelligence-based methods such as Convolutional Neural Networks (CNN) have proven very effective in detecting cataracts. …”
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Using Vision Transformers for Classifying Surgical Tools in Computer Aided Surgeries by El Moaqet Hisham, Janini Rami, Abdulbaki Alshirbaji Tamer, Aldeen Jalal Nour, Möller Knut

“…Nevertheless, it faces challenges due to complex surgical scenes and limited annotated data. Most of the existing methods for classifying surgical tools in laparoscopic surgeries rely on conventional deep learning methods such as convolutional and recurrent neural networks. …”
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MRSNet: Multi-Resolution Scale Feature Fusion-Based Universal Density Counting Network by Yi Zhang, Wei Song, Mingyue Shao, Xiangchun Liu

“…Thus, preserving features that carry the most relevant information at each scale is crucial for improving counting precision. …”
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Pilot Maneuvering Performance Analysis and Evaluation with Deep Learning by Shiwen Zhang, Zhimei Huo, Yanjin Sun, Fujuan Li, Bo Jia

“…However, traditional evaluation methods suffer from the inability to consider multiple parameters simultaneously, and most of them need to select features manually in advance. …”
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CoroYOLO: a novel colorectal cancer detection method based on the Mamba framework by Wenfei Chen, Fengrui Hou, Yika Shen

“…Colorectal cancer (CRC) is one of the most common malignant tumors worldwide, and early detection is crucial for improving cure rates. …”
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Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models by Ching-Ting Chung, Josh Jia-Ching Ying

“…In recent years, most Explainable Artificial Intelligence methods have primarily focused on image classification. …”
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FruitNet: Lightweight CNN for High-Throughput Image-Based Fruit Yield Estimation by Yadav Kamlesh Kumar, Tandan Gajendra

“…Innovations in this work include the most lightweight Convolutional Neural Network (CNN) named FruitNet proposed for achieving high throughput and image based estimation offrait yield. …”
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Task-Related EEG as a Biomarker for Preclinical Alzheimer’s Disease: An Explainable Deep Learning Approach by Ziyang Li, Hong Wang, Lei Li

“…Task-related EEG has been rarely used in Alzheimer’s disease research, as most studies have focused on resting-state EEG. An interpretable deep learning framework—Interpretable Convolutional Neural Network (InterpretableCNN)—was utilized to identify AD-related EEG features. …”
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IchthyNet: An Ensemble Method for the Classification of In Situ Marine Zooplankton Shadowgraph Images by Brittney Slocum, Bradley Penta

“…This study explores the use of machine learning for the automated classification of the ten most abundant groups of marine organisms (in the size range of 5–12 cm) plus marine snow found in the ecosystem of the U.S. east coast. …”
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Distributed Denial of Services (DDoS) attack detection in SDN using Optimizer-equipped CNN-MLP. by Sajid Mehmood, Rashid Amin, Jamal Mustafa, Mudassar Hussain, Faisal S Alsubaei, Muhammad D Zakaria

“…By assisting in the identification of which features are most essential to spot the incidents, the approach aids in the process of enhancing precision and flammability. …”
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A dual self-attentive transformer U-Net model for precise pancreatic segmentation and fat fraction estimation by Ashok Shanmugam, Prianka Ramachandran Radhabai, Kavitha KVN, Agbotiname Lucky Imoize

“…Calculating the fat fraction aids in the investigation of β-cell malfunction and insulin resistance. The most widely used pancreas segmentation technique is a U-shaped network based on deep convolutional neural networks (DCNNs). …”
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Enhanced Transformer Network With High-Dimensional Attention Mechanism for Diabetic Retinopathy Classification by M. Rizvana, Sathiya Narayanan

“…Prominent among them are Convolutional Neural Networks (CNN), Recurrent Neural Networks, Generative Adversarial Networks, and the Vision Transformer (ViT). …”
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HDCTfusion: Hybrid Dual-Branch Network Based on CNN and Transformer for Infrared and Visible Image Fusion by Wenqing Wang, Lingzhou Li, Yifei Yang, Han Liu, Runyuan Guo

“…The effectiveness of the proposed method in this paper is verified on different experimental datasets, and it is better than most of the current advanced fusion algorithms.…”
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Deblurring Method of Face Recognition AI Technology Based on Deep Learning by Weilong Li, Jie Li, Junhui Zhou

“…As a common method of deep learning, a convolutional neural network (CNN) shows excellent performance in face recognition. …”
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Performance Comparison of ResNet50, VGG16, and MobileNetV2 for Brain Tumor Classification on MRI Images by Muhammad Bayu Kurniawan, Ema Utami

“…Overall, this study confirms that VGG16 is the most efficient and effective model for MRI-based brain tumor classification.…”
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