Remote Sensing Surveillance Using Multilevel Feature Fusion and Deep Neural Network by Laiba Zahoor, Haifa F. Alhasson, Mohammed Alnusayri, Mohammed Alatiyyah, Dina Abdulaziz Alhammadi, Ahmad Jalal, Hui Liu

“…Four significant features are employed to capture the complexity of human movement effectively: the incorporation of 3D point cloud data adds depth to the image and makes it feasible to construct a more detailed three-dimensional representation; measuring the angles between keypoints provides significant details on joint orientations which are essential for posture analysis; and geodesic distance measure the shortest paths along the surface of the body to provide useful insight into the spatial relationships between keypoints. …”
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Imaging of brain electric field networks with spatially resolved EEG by Lawrence R Frank, Vitaly L Galinsky, Olave Krigolson, Susan Tapert, Stephan Bickel, Antigona Martinez

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Survey of Quantum Generative Adversarial Networks (QGAN) to Generate Images by Mohammadsaleh Pajuhanfard, Rasoul Kiani, Victor S. Sheng

“…Quantum Generative Adversarial Networks (QGANs) represent a useful development in quantum machine learning, using the particular properties of quantum mechanics to solve the challenges of data analysis and modeling. …”
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A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis by Alali Abdulla Mousa Falah, Padmaja Dhyaram Lakshmi, Soni Mukesh, Khan Muhammad Attique, Khan Faheem, Ofori Isaac

“…Classification algorithms for MM illness diagnosis were carried out using computationally efficient data mining techniques. The support vector machine outperformed the multilayer perceptron ensembles (MLPE) neural network (NN) technique, yielding promising findings. …”
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STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data by Wei Zhang, Weiming Zeng, Hongyu Chen, Jie Liu, Hongjie Yan, Kaile Zhang, Ran Tao, Wai Ting Siok, Nizhuan Wang

“…STANet comprises the following steps: (1) Aggregate spatio-temporal information via independent component analysis (ICA). (2) Utilize multi-scale deep convolution to capture detailed features. (3) Balance data using the synthetic minority over-sampling technique (SMOTE) to generate new samples for minority classes. (4) Employ the attention-Fourier gate recurrent unit (AFGRU) classifier to capture long-term dependencies, with an adaptive weight assignment mechanism to enhance model generalization. …”
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Enhancing the Accuracy of Image Classification for Degenerative Brain Diseases with CNN Ensemble Models Using Mel-Spectrograms by Sang-Ha Sung, Michael Pokojovy, Do-Young Kang, Woo-Yong Bae, Yeon-Jae Hong, Sangjin Kim

“…Using voice data offers advantages in accessibility compared to brain imaging analysis. …”
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Advancing Early Detection of Major Depressive Disorder Using Multisite Functional Magnetic Resonance Imaging Data: Comparative Analysis of AI Models by Masab Mansoor, Kashif Ansari

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Analysis and Classification of Distress on Flexible Pavements Using Convolutional Neural Networks: A Case Study in Benin Republic by Crespin Prudence Yabi, Godfree F. Gbehoun, Bio Chéissou Koto Tamou, Eric Alamou, Mohamed Gibigaye, Ehsan Noroozinejad Farsangi

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Deep learning analysis of fMRI data for predicting Alzheimer's Disease: A focus on convolutional neural networks and model interpretability. by Xiao Zhou, Sanchita Kedia, Ran Meng, Mark Gerstein

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Effective Algorithm for Biomedical Image Segmentation by Zhao Di, Tang Yi, A. B. Gourinovitch

“…Biomedical image segmentation plays an important role in quantitative analysis, clinical diagnosis, and  medical manipulation. …”
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Classification Analysis of Blended Copper Concentrate Tablet Combustion Behavior by High-speed Imaging of Suspended Combustion Test and Convolutional Neural Network by Shungo NATSUI, Yuko GOTO, Jun-ichi TAKAHASHI, Hiroshi NOGAMI

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DeepCycle reconstructs a cyclic cell cycle trajectory from unsegmented cell images using convolutional neural networks by Luca Rappez, Alexander Rakhlin, Angelos Rigopoulos, Sergey Nikolenko, Theodore Alexandrov

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Feature Interaction and Adaptive Fusion Network With Spectral Modulation for Pansharpening by Lihua Jian, Jiabo Liu, Lihui Chen, Di Zhang, Gemine Vivone, Xichuan Zhou

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Optimizing binary neural network quantization for fixed pattern noise robustness by Francisco Javier Andreo-Oliver, Gines Domenech-Asensi, Jose Angel Diaz-Madrid, Ramon Ruiz-Merino, Juan Zapata-Perez

“…Abstract This work presents a comprehensive analysis of how extreme data quantization and fixed pattern noise (FPN) from CMOS imagers affect the performance of deep neural networks for image recognition tasks. …”
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More than just life and death: advances in imaging and analysis for 3D-bioprinted tissues by Erin R. Spiller, Daniela F. Duarte Campos

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Analysis of Deep Learning Techniques for Vehicle Detection and Reidentification Using Data from Multiple Drones and Public Datasets by FELIPE P.A. EUPHRÁSIO, RAFAEL M. DE ANDRADE, ELCIO H. SHIGUEMORI, LIANGRID L. SILVA, MOISÉS JOSÉ S. FREITAS, NATHAN AUGUSTO Z. XAVIER, ARGEMIRO S.S. SOBRINHO

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Application of a generative adversarial network algorithm to filling blank strips of fractures in formation microresistivity imaging images by Zhengming KANG, Chensheng WU, Guodong YANG, Disheng WU, Ruifei WANG, Xiangyu YANG, Wei GAN

“…Using a dataset constructed utilizing both simulated and actual data, this study proposed a generative adversarial network (GAN)-based method for filling the blank strips of fractures in FMI images. …”
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Mapping the Use of Artificial Intelligence–Based Image Analysis for Clinical Decision‐Making in Dentistry: A Scoping Review by Wei Chen, Monisha Dhawan, Jonathan Liu, Damie Ing, Kruti Mehta, Daniel Tran, Daniel Lawrence, Max Ganhewa, Nicola Cirillo

“…A full‐text review and analysis were performed according to predefined inclusion criteria, and data were extracted from eligible articles. …”
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A neural network classifier for detecting diabetic retinopathy from retinal images by M. M. Lukashevich

“…Predictive analytics can be based on the analysis of human retinal images using convolutional neural networks. …”
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Enhancing Brain Tumor Detection Through Custom Convolutional Neural Networks and Interpretability-Driven Analysis by Kavinda Ashan Kulasinghe Wasalamuni Dewage, Raza Hasan, Bacha Rehman, Salman Mahmood

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