NeuroSight: A Deep‐Learning Integrated Efficient Approach to Brain Tumor Detection by Shafayat Bin Shabbir Mugdha, Mahtab Uddin

“…The worst model seemed to be Inception‐v3, with 89.40% test accuracy, 97.89% training accuracy, and 0.4418 validation loss. …”
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Brain tumor segmentation by deep learning transfer methods using MRI images by E.Y. Shchetinin

“…Among such models, VGG16, VGG19, Mobilenetv2, Inception, Efficientnetb7, InceptionResnetV2, DenseNet201, DenseNet121 were used. …”
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A novel carbon emission monitoring method for power generation enterprises based on hybrid transformer model by Yuqiong Jiang, Zhaofang Mao

“…Inspired by them, this paper proposes a novel model, named ICEEMDAN-Inception-Transformer, to thoroughly explore the relationship between power data and carbon emissions, providing precise hourly carbon emission acquisition for power enterprises. …”
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An Overview of the Florida Bull Test by G. Cliff Lamb

“…Cliff Lamb, describes this test designed as an educational aid for the improvement of beef cattle, its history since inception in 2000, and summarizes the consignors, bulls, breeds, performance, and sale averages of all previous Florida Bull Tests. …”
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Hyperspectral Image-Based Identification of Maritime Objects Using Convolutional Neural Networks and Classifier Models by Dongmin Seo, Daekyeom Lee, Sekil Park, Sangwoo Oh

“…Among the CNN models, EfficientNet B0 and Inception V3 demonstrated the best performance, with Inception V3 achieving a category-specific accuracy of 97% when weights were excluded. …”
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Comparison of deep transfer learning models for classification of cervical cancer from pap smear images by Harmanpreet Kaur, Reecha Sharma, Jagroop Kaur

“…A comprehensive comparison of 16 pre-trained models (VGG16, VGG19, ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152, ResNet152V2, DenseNet121, DenseNet169, DenseNet201, MobileNet, XceptionNet, InceptionV3, and InceptionResNetV2) were carried out for cervical cancer classification by relying on the Herlev dataset and Sipakmed dataset. …”
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Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease. by Mabirizi, Vicent, Kawuma, Simon, Kyarisiima, Addah, Bamutura, David, Atwiine, Barnabas, Nanjebe, Deborah, Oyesigye, Adolf Mukama

“…In our study, we have discovered that Inception V3 yielded the highest accuracy of 97.3% followed by VGG19 at 97.0%, VGG16 at 91%, ResNet50 at 82% and ReNet at 67%, and the CNN-scratch model achieved 81% accuracy. …”
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An Overview of the Florida Bull Test by G. Cliff Lamb

“…Cliff Lamb, describes this test designed as an educational aid for the improvement of beef cattle, its history since inception in 2000, and summarizes the consignors, bulls, breeds, performance, and sale averages of all previous Florida Bull Tests. …”
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Beware of diffusion models for synthesizing medical images—a comparison with GANs in terms of memorizing brain MRI and chest x-ray images by Muhammad Usman Akbar, Wuhao Wang, Anders Eklund

“…However, commonly used metrics such as Frechet inception distance and inception score are not suitable for determining whether diffusion models are simply reproducing the training images. …”
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Application of Artificial Intelligence Virtual Image Technology in Photography Art Creation Under Deep Learning by Qiong Yao

“…Moreover, the cGANs + VAEs model demonstrates strong performance in Frechet Inception Distance (FID) and Inception Score (IS) across multiple datasets, yielding FID values of 13.67, 9.45, and 11.90 on the COCO, CelebA, and WikiArt datasets, respectively. …”
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Analysis of preprocessing for Generative Adversarial Networks: A case study on color fundoscopy to fluorescein angiography image-to-image translation by Veena K.M., Veena Mayya, Rashmi Naveen Raj, Sulatha V. Bhandary, Uma Kulkarni

“…The evaluation utilized Frechet Inception Distance (FID) and Kernel Inception Distance (KID) metric scores to measure the performance of the GAN variants. …”
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Modified MobileNetV2 transfer learning model to detect road potholes by Neha Tanwar, Anil V. Turukmane

“…Fourteen models were evaluated, including MobileNet, MobileNetV2, NASNetMobile, DenseNet121, DenseNet169, InceptionV3, DenseNet201, ResNet152V2, EfficientNetB0, InceptionResNetV2, Xception, and EfficientNetV2M. …”
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Use of Hospital Patient and Family Advisory Councils: A Scoping Study by Barbara Lewis MBA, Chris Cochran PhD, Erika Marquez PhD, MPH, Neeraj Bhandari PhD, Jennifer Pharr PhD, Soumya Upadhyay PhD, Stowe Shoemaker PhD

“…With the aim of fostering patient-centered care, Patient and Family Advisory Councils (PFACs) have emerged as a way for hospitals to garner input for initiatives and programs from patients and patients’ families who have used the hospitals’ services. Despite their inception in the early 1980s, only 54% of United States hospitals field a PFAC. …”
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Comparative Analysis of Pre-trained based CNN-RNN Deep Learning Models on Anomaly-5 Dataset for Action Recognition by Fayaz Ahmed Memon, Umair Ali Khan, Pardeep Kumar, Imtiaz Ali Halepoto, Farida Memon

“…In this paper, we present an in-depth comparative analysis of five CNN-RNN models based on pre-trained networks such as InceptionV3, VGG16, MobileNetV2, ResNet152V2 and InceptionResNetV2 with recurrent LSTM units for action recognition on Anomaly-5 dataset. …”
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Editorial by Crystal Fulton

“…Notably, the journal still follows an open access publication model as it did from its inception at a time when ideas around open science tended not to be at the heart of research and publishing.  …”
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Deep Learning-Based Damage Assessment in Cherry Leaves by Burakhan Cubukcu, Hazel BOZCU

“…On the PlantVillage dataset, AlexNet, VGG-16, MobileNet-V2, Inception-V3, and CNN models were compared. Due to the low performance of AlexNet and the long training time of VGG-16, MobileNet-V2, Inception-V3, CNN, and two different CNN+RNN models were compared on the Kozlu dataset. …”
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Frequency and Texture Aware Multi-Domain Feature Fusion for Remote Sensing Scene Classification by Russo Ashraf, Kang-Hyun Jo

“…Delving into these complexities, the Efficient Spectral Inception Former (ESIF) architecture is proposed, which introduces a novel paradigm to RS scene classification by integrating multi-domain feature fusion of the spatial, texture, and spectral (frequency) domains. …”
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A real time monitoring system for accurate plant leaves disease detection using deep learning by Kazi Naimur Rahman, Sajal Chandra Banik, Raihan Islam, Arafath Al Fahim

“…Subsequently, the dataset was partitioned for individual plant disease detection, applying nine different CNN models (custom CNN, VGG16, VGG19, InceptionV3, MobileNet, DenseNet121, Xception, and two hybrid models) to each plant type. …”
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WDRIV-Net: a weighted ensemble transfer learning to improve automatic type stratification of lumbar intervertebral disc bulge, prolapse, and herniation by Ichiro Nakamoto, Hua Chen, Rui Wang, Yan Guo, Wei Chen, Jie Feng, Jianfeng Wu

“…We devised a weighted transfer learning framework WDRIV-Net by ensembling four pre-trained models including Densenet169, ResNet101, InceptionV3, and VGG19. The proposed approach was applied to the clinical data and achieved 96.25% accuracy, surpassing the benchmark ResNet101 (87.5%), DenseNet169 (82.5%), VGG19 (88.75%), InceptionV3 (93.75%), and other state-of-the-art (SOTA) ensemble deep learning models. …”
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American Polka in the Media: From Next to Nothing to 24/7 by Richard March

“…From its very inception as a nineteenth century popular culture fad, the media presence of polka music has been crucial to its diffusion. …”
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