Unlocking health insights: exploring intention to adopt district health information systems in Bahir Dar City, northwest Ethiopia by Habtamu Alganeh Guadie, Amarech Kindie, Hunegnaw Almaw Derseh, Desta Debalkie Atnafu

“…BackgroundDistrict Health Information System version 2 (DHIS2) is an open-source platform designed for data collection, processing, analysis, and visualization within healthcare systems. However, there is limited empirical evidence regarding health professionals’ intentions to use district health information systems. …”
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Potential Benefits of Polar Transformation of Time–Frequency Electrocardiogram (ECG) Signals for Evaluation of Cardiac Arrhythmia by Hanbit Kang, Daehyun Kwon, Yoon-Chul Kim

“…Prediction performance and visualization quality were evaluated across various image resolutions. …”
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MEN: leveraging explainable multimodal encoding network for precision prediction of CYP450 inhibitors by Abena Achiaa Atwereboannah, Wei-Ping Wu, Mugahed A. Al-antari, Sophyani B. Yussif, Chukwuebuka J. Ejiyi, Edwin K. Tenagyei, Grace-Mercure B. Kissanga, Gyarteng S. A. Emmanuel, Yeong Hyeon Gu, Emmanuel Ahene

“…An explainable AI (XAI) module is incorporated into the model to support biological interpretation, using visualization techniques such as heatmaps. The model was trained and validated using two datasets: chemical structures in SMILES format from PubChem and protein sequences of five CYP450 isoforms (1A2, 2C9, 2C19, 2D6, and 3A4) obtained from the Protein Data Bank (PDB). …”
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Development and validation of a prediction model for gestational diabetes mellitus risk among women from 8 to 14 weeks of gestation in Western China by Jiani Zhang, Qi Cao, Chihui Mao, Jinfeng Xu, Yaqian Li, Yi Mu, Guiqiong Huang, Daijuan Chen, Xixi Deng, Tingting Xu, Fan Zhou, Xiaodong Wang

“…Visualization of the model was achieved through a static nomogram and a risk-scoring model. …”
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A deep learning model combining circulating tumor cells and radiological features in the multi-classification of mediastinal lesions in comparison with thoracic surgeons: a large-s... by Feng Wang, Minwei Bao, Bo Tao, Fugui Yang, Guangxue Wang, Lei Zhu

“…The predictive abilities were compared with thoracic resident physicians, attending physicians, and chief physicians by the area under the receiver operating characteristic (ROC) curve, and diagnostic results were visualized in the heatmap. Results For binary classification, the predictive performances of DMFN (AUC = 0.941, 95% CI 0.901–0.982) were better than the monomodal CNN model (AUC = 0.710, 95% CI 0.664–0.756). …”
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Rethinking timing residuals: advancing PET detectors with explicit TOF corrections by Stephan Naunheim, Stephan Naunheim, Luis Lopes de Paiva, Luis Lopes de Paiva, Vanessa Nadig, Yannick Kuhl, Yannick Kuhl, Stefan Gundacker, Stefan Gundacker, Florian Mueller, Volkmar Schulz, Volkmar Schulz, Volkmar Schulz, Volkmar Schulz

“…PET is a functional imaging method that can visualize metabolic processes and relies on the coincidence detection of emitted annihilation quanta. …”
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Point rotation invariant features and attention fusion network for point cloud registration of 3D shapes by Zeyang Liu, Zhiguo Lu, Yancong Shan

“…The experimental results show that the method performs well in registration accuracy. Visualization experiments further illustrate the exceptional performance of our network in point cloud registration tasks.…”
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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

“…Results demonstrated strong model performance, with validation accuracy reaching 97.44% and a macro-average F1-score of approximately 97.12%. …”
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Leveraging Neural Radiance Fields for Large-Scale 3D Reconstruction from Aerial Imagery by Max Hermann, Hyovin Kwak, Boitumelo Ruf, Martin Weinmann

“…In addition, we analyze the effects of using multiple sub-modules, estimating the visibility by an additional neural network and varying the density threshold for the extraction of the point cloud. For performance evaluation, we use benchmark datasets that correspond to the setting off standard flight campaigns and therefore typically have nadir camera perspective and relatively little image overlap, which can be challenging for NeRF-based approaches that are typically trained with significantly more images and varying camera angles. …”
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Projection and assessment of future droughts in Iowa: developing a machine learning model and an interactive application by Ingrid Cintura, Antonio Arenas

“…Moreover, this research developed an interactive application for visualizing future drought conditions in Iowa. This tool aids users in making informed water management decisions by providing stakeholders with detailed visualizations and technical information.…”
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AI Knows Aesthetics: AI-Generated Interior Design Identification Using Deep Learning Algorithms by Fei Liu, Kailing Deng

“…This classification is essential for ensuring authenticity in design visualization, improving recommendation systems, and enhancing virtual and augmented reality applications in architectural planning. …”
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Adverse to Normal Image Reconstruction Using Inverse of StarGAN for Autonomous Vehicle Control by Maryam Asad Samani, Mohammad Farrokhi

“…To address this limitation, we propose an inverse version of StarGAN that processes images captured by the vehicle’s camera and transforms them into clear visuals simulating normal conditions. Initially, the model is trained with explicit weather domain labels and later with shuffled labels, a strategy that reduces dependence on specific label severities and enhances the model’s adaptability in dynamic environments. …”
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A SIMBA CoMICs Initiative to Cocreating and Disseminating Evidence-Based, Peer-Reviewed Short Videos on Social Media: Mixed Methods Prospective Study by Maiar Elhariry, Kashish Malhotra, Kashish Goyal, Marco Bardus, SIMBA and CoMICs Team, Punith Kempegowda

“…The SIMBA-CoMICs (Simulation via Instant Messaging for Bedside Application–Combined Medical Information Cines) initiative provides a structured process where medical concepts are simplified and converted to visually engaging videos. The initiative recruited medical students interested in making visually appealing and scientifically accurate videos for social media. …”
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Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging by Yoshihiro Obata, Dilworth Y. Parkinson, Daniël M. Pelt, Claire Acevedo

“…After convolutional neural networks were trained, Noise2Inverse performance on all dose simulations was assessed visually and by analyzing bone microstructural features. …”
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Classification Model of Clock Drawing Test Based on Contrastive Learning Using Multi-Channel Features With Channel-Spatial Attention by Changsu Kang, Bohyun Wang, J. S. Lim

“…As far as our knowledge extends, this study represents the first instance of utilizing supervised contrastive learning, acquiring features from multiple channels, for classifying CDT images, and we achieved superior performance compared to other models. Furthermore, the model visualized the attention clock elements to provide evidence for the inference results and presents the potential of utilizing artificial intelligence to classify CDT images.…”
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Feasibility of relaxation along a fictitious field in the 2nd rotating frame (TRAFF2) mapping in the human myocardium at 3 T by Joao Tourais, Joao Tourais, Joao Tourais, Maša Božić-Iven, Maša Božić-Iven, Maša Božić-Iven, Yidong Zhao, Qian Tao, Iain Pierce, Iain Pierce, Christian Nitsche, Christian Nitsche, George D. Thornton, George D. Thornton, Lothar R. Schad, Lothar R. Schad, Thomas A. Treibel, Thomas A. Treibel, Sebastian Weingärtner, Mehmet Akçakaya, Mehmet Akçakaya

“…PurposeEvaluate the feasibility of quantification of Relaxation Along a Fictitious Field in the 2nd rotating frame (RAFF2) relaxation times in the human myocardium at 3 T.MethodsTRAFF2 mapping was performed using a breath-held ECG-gated acquisition of five images: one without preparation, three preceded by RAFF2 trains of varying duration, and one preceded by a saturation prepulse. …”
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Risk prediction of stroke-associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning models by Tai Su, Peng Zhang, Peng Zhang, Bingyin Zhang, Zihao Liu, Zexing Xie, Xiaomei Li, Jixiang Ma, Tao Xin

“…The optimal prediction model was visualized using a nomogram. In this study, SAP was identified in 10.16% of cases. …”
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A Combined MobileNetV2 and CBAM Model to Improve Classifying the Breast Cancer Ultrasound Images by Muhammad Rakha, Mahmud Dwi Sulistiyo, Dewi Nasien, Muhammad Ridha

“…CBAM is proven to improve MobileNetV2 performance with an 11% increase in accuracy. Grad-CAM visualization shows that MobileNetV2-CBAM can better focus on localizing important regions in breast cancer images, providing clearer explanations and assisting medical personnel in diagnosis. …”
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Detection of <i>Tagosodes orizicolus</i> in Aerial Images of Rice Crops Using Machine Learning by Angig Rivera-Cartagena, Heber I. Mejia-Cabrera, Juan Arcila-Diaz

“…A dataset of 1500 images was constructed and utilized to train deep learning models based on VGG16 and ResNet50. …”
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Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition by Reham Al-Majed, Muhammad Hussain

“…Automatic clothes pattern recognition is important to assist visually impaired people and for real-world applications such as e-commerce or personal fashion recommendation systems, and it has attracted increased interest from researchers. …”
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