AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery by Ahmad Almadhor, Hafiz Tayyab Rauf, Muhammad Ikram Ullah Lali, Robertas Damaševičius, Bader Alouffi, Abdullah Alharbi

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Small Lesions Evaluation Based on Unsupervised Cluster Analysis of Signal-Intensity Time Courses in Dynamic Breast MRI by A. Meyer-Baese, T. Schlossbauer, O. Lange, A. Wismueller

“…An application of an unsupervised neural network-based computer-aided diagnosis (CAD) system is reported for the detection and characterization of small indeterminate breast lesions, average size 1.1 mm, in dynamic contrast-enhanced MRI. …”
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MAMNet: Lightweight Multi-Attention Collaborative Network for Fine-Grained Cropland Extraction from Gaofen-2 Remote Sensing Imagery by Jiayong Wu, Xue Ding, Jinliang Wang, Jiya Pan

“…To address the issues of high computational complexity and boundary feature loss encountered when extracting farmland information from high-resolution remote sensing images, this study proposes an innovative CNN–Transformer hybrid network, MAMNet. …”
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MDSCNN: Remote Sensing Image Spatial–Spectral Fusion Method via Multi-Scale Dual-Stream Convolutional Neural Network by Wenqing Wang, Fei Jia, Yifei Yang, Kunpeng Mu, Han Liu

“…This paper proposes a remote sensing spatial–spectral fusion method based on a multi-scale dual-stream convolutional neural network, which includes feature extraction, feature fusion, and image reconstruction modules for each scale. …”
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Complex Indoor Human Detection with You Only Look Once: An Improved Network Designed for Human Detection in Complex Indoor Scenes by Yufeng Xu, Yan Fu

“…The method proposed in this article combines the spatial pyramid pooling of the backbone with an efficient partial self-attention, enabling the network to effectively capture long-range dependencies and establish global correlations between features, obtaining feature information at different scales. …”
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Linear regressive weighted Gaussian kernel liquid neural network for brain tumor disease prediction using time series data by Firoz Khan, Sardar Irfanullah Amanullah, Shitharth Selvarajan

“…Finally, the classification process is performed using the selected significant features with the Gaussian Kernelized Liquid Neural Network. …”
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Vision Transformer-Based Unhealthy Tree Crown Detection in Mixed Northeastern US Forests and Evaluation of Annotation Uncertainty by Durga Joshi, Chandi Witharana

“…By comparing the performance of traditional convolutional neural network (CNN) models (U-Net and DeepLabv3+) with a state-of-the-art Vision Transformer (SegFormer), we aimed to determine the optimal approach for detecting unhealthy tree crowns (UTC) using a publicly available data source. …”
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Evaluation of the Screening Performance of Ultrasonographic Abdominal Circumference and Estimated Fetal Weight in Predicting Small for Gestational Age Newborns by Yusuf Dal, Fatih Akkus, Sebnem Karagun, Ahmet Zeki Nessar, Sefanur Gamze Karaca, Hakan Aytan, Ayhan Coskun

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Model Semantic Attention (SemAtt) With Hybrid Learning Separable Neural Network and Long Short-Term Memory to Generate Caption by Agus Nursikuwagus, Rinaldi Munir, Masayu L. Khodra, Deshinta Arrova Dewi

“…Shapes, colors, and structures are to be focused on to get the image’s features. The problem faced is how the separable neural network (SNN) and long short-term memory (LSTM) have an impact on the caption that can meet the geologist’s description. …”
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Enhancement and evaluation for deep learning-based classification of volumetric neuroimaging with 3D-to-2D knowledge distillation by Hyemin Yoon, Do-Young Kang, Sangjin Kim

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Design of a Drivable Area Segmentation Network Using a Field Programmable Gate Array Based on Light Detection and Ranging by Xue-Qian Lin, Jyun-Yu Jhang, Cheng-Jian Lin, Sheng-Fu Liang

“…To enable effective identification of drivable areas on the basis of environmental information, this study designed a drivable area segmentation network named DASNet. The proposed DASNet utilizes depthwise separable convolution as a basis/platform for feature extraction to enable features to be efficiently extracted to reduce both the computational load and required network parameters. …”
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Evaluation of the River Salinity Changes using Hydrochemical Method in Arid Area (Case Study: Namaklan River, Ilam) by Maryam Mousavi, Noredin Rostami, Haji Karimi

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Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors. by Montaser Abdelsattar, Mohamed A Ismeil, Karim Menoufi, Ahmed AbdelMoety, Ahmed Emad-Eldeen

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Deep image semantic communication model for 6G by Feibo JIANG, Yubo PENG, Li DONG

“…Current semantic communication models still have some parts that can be improved in processing image data, including effective image semantic codec, efficient semantic model training, and accurate image semantic evaluation.Hence, a deep image semantic communication (DeepISC) model was proposed.The vision transformer-based autoencoder (ViTA) network was used to achieve high-quality image semantic encoding and decoding.Then, an autoencoder realized channel codec to ensure the transmission of semantics on the channel.Furthermore, the discriminator network (DSN) and ViTA’s dual network architecture were used to jointly train, thus improving the semantic accuracy of the reconstructed image.Finally, for different downstream vision tasks, different evaluation indicators of image semantics were presented.Simulation results show that compared with other schemes, DeepISC can more effectively restore the semantic features of the transmitted image, so that the reconstructed image can show the same or similar semantic results as the original image in various downstream tasks.…”
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Graphical Empirical Mode Decomposition–Convolutional Neural Network-Based Expert System for Early Corrosion Detection in Truss-Type Bridges by Alan G. Lujan-Olalde, Angel H. Rangel-Rodriguez, Andrea V. Perez-Sanchez, Martin Valtierra-Rodriguez, Jose M. Machorro-Lopez, Juan P. Amezquita-Sanchez

“…The approach employs graphical empirical mode decomposition (GEMD) to decompose vibration signals into their intrinsic mode functions, extracting relevant structural features. These features are then transformed into grayscale images and classified using a Convolutional Neural Network (CNN) to automatically differentiate between a healthy structure and one affected by corrosion. …”
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A Novel Framework for Real ICMOS Image Denoising: LD-NGN Noise Modeling and a MAST-Net Denoising Network by Yifu Luo, Ting Zhang, Ruizhi Li, Bin Zhang, Nan Jia, Liping Fu

“…By capturing multi-scale features of image pixels, MAST-Net effectively removes complex noise. …”
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Exploring the Spatial Distribution Characteristics and Correlation Factors of Wayfinding Performance on City-Scale Road Networks Based on Massive Trajectory Data by Jun Li, Yan Zhu, Zhenwei Li, Wenle Lu, Yang Ji, Xiao Sang

“…In addition, a systematic index set of road network features are constructed for correlation analysis. …”
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HD-MVCNN: High-density ECG signal based diabetic prediction and classification using multi-view convolutional neural network by D. Santhakumar, K. Dhana Shree, M. Buvanesvari, A. Saran Kumar, Ayodeji Olalekan Salau

“…Thus, HD-MVCNN shows promise as a powerful method for classifying features in diabetes clinical data.…”
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GreenNet: A dual-encoder network for urban green space classification using high-resolution remotely sensed images by Ke Chen, Yang Wang, Cunrui Huang, Jing Wang, Sabrina L. Li, Haiyan Guan, Lingfei Ma

“…The proposed GreenNet was evaluated on a self-built urban green space dataset, covering the whole area of Nanshan District, Shenzhen City, China, achieving an overall accuracy (OA) of 88.88 %, a mean F1-score (mF1) of 74.06 %, and a mean Intersection over Union (mIoU) of 60.77 %, respectively, demonstrating its superior performance to state-of-the-art networks on green space classification tasks.…”
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Auto Machine Learning and Convolutional Neural Network in Diabetes Mellitus Research—The Role of Histopathological Images in Designing and Exploring Experimental Models by Iulian Tătaru, Simona Moldovanu, Oana-Maria Dragostin, Carmen Lidia Chiţescu, Alexandra-Simona Zamfir, Ionut Dragostin, Liliana Strat, Carmen Lăcrămioara Zamfir

“…The second comprises image classification with a custom-built convolutional neural network (CB-CNN), the extraction of textural features (contrast, entropy, energy, and homogeneity), and their classification with PyCaret Auto Machine Learning (AutoML). …”
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