Evaluation of the precision and accuracy in the classification of breast histopathology images using the MobileNetV3 model by Kenneth DeVoe, Gary Takahashi, Ebrahim Tarshizi, Allan Sacker

“…This visual assessment is repeated on numerous slides taken at various sections through the resected tumor, each at different magnifications. Computer vision models have been proposed to assist human pathologists in classification tasks such as these. …”
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Models and methods for analyzing complex networks and social network structures by Ju. P. Perova, V. P. Grigoriev, D. O. Zhukov

“…Compared with other methods, the network approach has the undeniable advantage of operating with data at different levels of research to ensure its continuity. …”
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Multi-scale window transformer for cervical cytopathology image recognition by Jiaxiang Yi, Xiuli Liu, Shenghua Cheng, Li Chen, Shaoqun Zeng

“…We design multi-scale window multi-head self-attention (MW-MSA) to simultaneously integrate cell features of different scales. Small window self-attention is used to extract local cell detail features, and large window self-attention aims to integrate features from smaller-scale window attention to achieve window-to-window information interaction. …”
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Lightweight GAN-Assisted Class Imbalance Mitigation for Apple Flower Bud Detection by Wenan Yuan, Peng Li

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Multi-Layer Modeling and Visualization of Functional Network Connectivity Shows High Performance for the Classification of Schizophrenia and Cognitive Performance via Resting fMRI by Duc My Vo, Anees Abrol, Zening Fu, Vince D. Calhoun

“…In the first, a deep convolutional neural network (DCNN) is trained to produce heatmaps from multiple convolution layers. …”
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An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification by Khaoula Taji, Ali Sohail, Tariq Shahzad, Bilal Shoaib Khan, Muhammad Adnan Khan, Khmaies Ouahada

“…The ensemble feature vector is optimized using three different meta-heuristic algorithms that are Binary Dragonfly algorithm (BDA), Ant Colony Optimization algorithm and Moth Flame Optimization algorithm (MFO). …”
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Predicting microbe-disease associations via graph neural network and contrastive learning by Cong Jiang, Cong Jiang, Junxuan Feng, Junxuan Feng, Bingshen Shan, Bingshen Shan, Qiyue Chen, Jian Yang, Jian Yang, Gang Wang, Gang Wang, Xiaogang Peng, Xiaozheng Li, Xiaozheng Li

“…Next, we apply the feature encoder separately to the microbe similarity network, disease similarity network, and microbe-disease association network, and enhance the consistency of features for the same nodes across different association networks through contrastive learning. …”
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MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease Classification by Md. Eshmam Rayed, Jamin Rahman Jim, Md Juniadul Islam, M. F. Mridha, Md Mohsin Kabir, Md. Jakir Hossen

“…Our study focuses on introducing MangoLeafXNet, a customized Convolutional Neural Network (CNN) architecture specifically tailored for the classification of mango leaf diseases, along with a healthy class. …”
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Time-Domain Versus Frequency-Embedded EEG Sequences for Sensorimotor BCI Using 1D-CNN by Simanto Saha, Mathias Baumert, Alistair Mcewan

“…This study proposed a motor imagery (MI) classification pipeline featuring a 1−dimensional convolutional neural network (1D-CNN) with different time/frequency feature representation techniques. …”
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MMAgentRec, a personalized multi-modal recommendation agent with large language model by Xiaochen Xiao

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A Lightweight Fault Diagnosis Framework for Hydro-Turbine Main Shaft Bearing Under Noise Interference by Hongwei Zhang, Zhao Liu, Hansong Si, Kaipeng Yu, Shuaifang Li, Zhenwu Yan

“…Second, a parameter-free Light Global Attention mechanism is proposed, which distinguishes key features from noise interference by minimizing the energy differences among similar features. Comparative experiments demonstrate that the lightweight method proposed in this paper exhibits superior diagnostic performance and noise robustness.…”
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An Optimized Model for Identification of Cerebral Palsy Using Deep Learning by Md Anjar Ahsan, Hassan bin Mohammed, Abhilash Maroju, Nurhafizah Moziyana Mohd Yusop, Wan Su Emi Yusnita Wan Yusof, Najjah Salwa Abd Razak, Mohd Arif Dar

“…Individualized therapy and rehabilitation programs are necessary to treat these differences effectively. Therefore, early-stage CP categorization is crucial to ensuring timely and targeted treatment efforts. …”
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Assessment of the Diagnostic Accuracy of Artificial Intelligence Software in Identifying Common Periodontal and Restorative Dental Conditions (Marginal Bone Loss, Periapical Lesion... by Wael I. Ibraheem, Saurabh Jain, Mohammed Naji Ayoub, Mohammed Ahmed Namazi, Amjad Ismail Alfaqih, Aparna Aggarwal, Abdullah A. Meshni, Ammar Almarghlani, Abdulkareem Abdullah Alhumaidan

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Majorization Problems for Subclasses of Meromorphic Functions Defined by the Generalized <inline-formula><math display="inline"><semantics><mstyle mathvariant="bold"><mi mathvarian... by Ekram E. Ali, Rabha M. El-Ashwah, Teodor Bulboacă, Abeer M. Albalahi

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Multi-view Fusion 3D Model Classification by GAO Yuan, DING Bo, HE Yong-jun

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Discrimination of Types of Seizure Using Brain Rhythms Based on Markov Transition Field and Deep Learning by Anand Shankar, Samarendra Dandapat, Shovan Barma

“…For this purpose, the Markov transition field transformation technique has been employed for 2D image construction by preserving statistical dynamics characteristics of EEG signals, which are very important during the discrimination of different types of seizures. And, a convolution neural network (CNN) has been used for classification. …”
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Soil Porosity Detection Method Based on Ultrasound and Multi-Scale Feature Extraction by Hang Xing, Zeyang Zhong, Wenhao Zhang, Yu Jiang, Xinyu Jiang, Xiuli Yang, Weizi Cai, Shuanglong Wu, Long Qi

“…Since the collected ultrasonic signals belong to long-time series data and there are different frequency and sequence features, this study constructs a multi-scale CNN-LSTM deep neural network model using large convolution kernels based on the idea of multi-scale feature extraction, which uses multiple large convolution kernels of different sizes to downsize the collected ultra-long time series data and extract local features in the sequences, and combining the ability of LSTM to capture global and long-term dependent features enhances the feature expression ability of the model. …”
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Research on real-time monitoring method of mine personnel protective equipment with improved YOLOv8 by Lei ZHANG, Zhipeng SUN, Hongjing TAO, Shangkai HAO, Qianru YAN, Xiwei LI

“…Making convolution deformable, when sampling, it can more closely detect the true shape and size of the object, more robust, It effectively improves its feature acquisition ability for targets of different scales. …”
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An XNet-CNN Diabetic Retinal Image Classification Method by CHEN Yu, ZHOU Yujia, DING Hui

“…In this research,a retina image automatic recognition system based on Convolutional Neural Network (CNN) is proposed for the disadvantages of the traditional retina image processing process which is cumbersome and poor in robustness. …”
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ARO-GNN: Adaptive relation-optimized graph neural networks by Yong Lu, Zhengguo Lin

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