DenseNet-FPA: Integrating DenseNet and Flower Pollination Algorithm for Breast Cancer Histopathology Image Classification by Musa Adamu Wakili, Harisu Abdullahi Shehu, Mahdi Abdollahi, Badamasi Imam Ya'u, Md Haidar Sharif, Huseyin Kusetogullari

“…Breast cancer is one of the most prevalent and life-threatening diseases affecting women worldwide. …”
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Siamese text classification network (SiamTCN) for multi-class multi-label information extraction of typhoon disasters from social media data by Zhi He, Chengle Zhou, Liwei Zou, Suhong Zhou, Xueqiang Zhao

“…The SiamTCN leverages a dual-path architecture with shared weights, utilizing multi-head self-attention and convolution to extract hidden features from texts. A novel multi-class multi-label contrastive loss function is proposed to optimize the model. …”
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Boundary aware microscopic hyperspectral pathology image segmentation network guided by information entropy weight by Xueying Cao, Hongmin Gao, Ting Qin, Min Zhu, Ping Zhang, Ping Zhang, Peipei Xu, Peipei Xu

“…Specifically, we first propose a Laplacian of Gaussian operator convolution boundary feature extraction block, which encodes feature gradient information through the improved edge detection operators and emphasizes relevant boundary channel weights based on channel information entropy weighting. …”
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Efficient and Effective NDVI Time-Series Reconstruction by Combining Deep Learning and Tensor Completion by Ang Li, Menghui Jiang, Dong Chu, Xiaobin Guan, Jie Li, Huanfeng Shen

“…Considering the temporal continuity and spatial correlation of NDVI time-series data, we combine long short-term memory with a convolution (LSTM-Conv) structure and utilize residual learning and dense connection strategies to mine the spatiotemporal features in depth. …”
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Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images by Yuhang Zhang, Wuxia Zhang, Songtao Ding, Siyuan Wu, Xiaoqiang Lu

“…Moreover, Mixed Spatial Reasoning Convolution block (MixSrc) is presented to enrich the spatial information by extracting the multiscale features, thus improving the model's capability to interpret complex scenes. …”
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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

“…In addition, a residual structure-based self-guided spatial-channel adaptive convolution is introduced to accommodate diverse features within FASA adaptively. …”
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Predicting the Temperature of a Permanent Magnet Synchronous Motor: A Comparative Study of Artificial Neural Network Algorithms by Nabil El Bazi, Nasr Guennouni, Mohcin Mekhfioui, Adil Goudzi, Ahmed Chebak, Mustapha Mabrouki

“…The intent is to identify the most favorable model that balances high accuracy with low computational cost.…”
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Constraint-aware wind power forecasting with an optimized hybrid machine learning model by Md. Omer Faruque, Md. Alamgir Hossain, S.M. Mahfuz Alam, Muhammad Khalid

“…In response, this paper introduces a novel constraint aware forecasting framework formed by a convolutional neural network (CNN) integrated with a double layer of gated recurrent unit (GRU) and fully connected layers. …”
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A Review: Radar Remote-Based Gait Identification Methods and Techniques by Bruno Figueiredo, Álvaro Frazão, André Rouco, Beatriz Soares, Daniel Albuquerque, Pedro Pinho

“…In addition, the study indicates that simpler AI techniques, such as Convolutional Neural Network (CNN), are more effective at improving results.…”
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Re-Parameterization After Pruning: Lightweight Algorithm Based on UAV Remote Sensing Target Detection by Yang Yang, Pinde Song, Yongchao Wang, Lijia Cao

“…Furthermore, the entire model is pruned using the convolution kernel pruning method. After pruning, model parameters and floating-point operations (FLOPs) on VisDrone and DIOR datasets are reduced to 1.2 M and 1.5 M and 6.2 G and 6.5 G, respectively. …”
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Enhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based Encoder–Decoder Network by Salih Taha Alperen Özçelik, Hüseyin Üzen, Abdulkadir Şengür, Hüseyin Fırat, Muammer Türkoğlu, Adalet Çelebi, Sema Gül, Nebras M. Sobahi

“…Background: Dental disorders are one of the most important health problems, affecting billions of people all over the world. …”
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AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view by Mostafa Jalilifar, Mahdi Sadeghi, Alireza Emami-Ardekani, Ahmad Bitarafan-Rajabi, Kouhyar Geravand, Parham Geramifar

“…Four AI algorithms- multilayer perceptron (MLP), linear regression, support vector regression model, decision tree, convolution neural network, and U-Net were used for dose estimation. …”
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A Deep Learning Approach for Crop Disease and Pest Classification Using Swin Transformer and Dual-Attention Multi-Scale Fusion Network by R. Karthik, Armaano Ajay, Akshaj Singh Bisht, T. Illakiya, K. Suganthi

“…Current diagnostic methods are mostly manual, which is time-consuming and requires domain expertise. …”
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Investigation of ensembles of deep learning models for improved chronic kidney diseases detection in CT scan images by I.I. Ayogu, C.F. Daniel, B.A. Ayogu, J.N. Odii, C.L. Okpalla, E.C. Nwokorie

“…In general, nonetheless, kidney stone was the most difficult disease to detect for all the models investigated. …”
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Enhanced Workload Prediction in Data Centers Using Two-Stage Decomposition and Hybrid Parallel Deep Learning by Dalal Alqahtani, Hamidreza Imani, Tarek El-Ghazawi

“…Workload prediction is one of the most basic requirements in developing cost and energy-efficient Cloud Data Centers (CDCs). …”
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Evaluating the efficacy and site-specific performance of machine learning approaches: A comprehensive review of autism detection models by Deblina Mazumder Setu, Tania Islam, Md Maklachur Rahman, Samrat Kumar Dey, Tazizur Rahman

“…From them, 18 studies are based on 14 popular machine learning (ML) models to identify the most effective prediction methods. And four of them are more progressive, sophisticated methods including the convolutional neural network (CNN) model, diagnostic autism spectrum disorder (DASD) strategy, Ensemble Diagnosis Methodology (EKNN), and Self-Organizing Maps (SOM). …”
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AI and Data Analytics in the Dairy Farms: A Scoping Review by Osvaldo Palma, Lluis M. Plà-Aragonés, Alejandro Mac Cawley, Víctor M. Albornoz

“…To this end, a scoping review was carried out, which resulted in 151 articles of interest. Among the most important results, we found that (i) the identified studies are relatively recent with an average publication time of 5.95 years; (ii) the scope of the selected studies is mostly concentrated on milk and prediction (29%), early detection of lameness (26%), and timely detection of mastitis (13%); (iii) the type of analysis is mostly predictive (87%), and prescriptive is barely present (3%); (iv) the types of input data used in the studies are preferably historical (70%), and real-time data (25%) are used less frequently; (v) we found that the method of artificial neural networks (47%) and the convolutional neural networks (24%) are the most used for the studies regarding bovine milk output predictions. …”
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Development and evaluation of deep neural networks for the classification of subtypes of renal cell carcinoma from kidney histopathology images by Amit Kumar Chanchal, Shyam Lal, Shilpa Suresh

“…Abstract Kidney cancer is a leading cause of cancer-related mortality, with renal cell carcinoma (RCC) being the most prevalent form, accounting for 80–85% of all renal tumors. …”
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Deep Learning-Based Sign Language Recognition Using Efficient Multi-Feature Attention Mechanism by Esma Yenisari, Sirma Yavuz

“…These features are adaptively weighted using an attention mechanism and focus on the most critical information for the classification task. …”
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Impact of eye fundus image preprocessing on key objects segmentation for glaucoma identification by Sandra Virbukaitė, Jolita Bernatavičienė

“…The impact of image preprocessing on OD and OC segmentation was evaluated using three convolutional neural networks Attention U-Net, Residual Attention U-Net (RAUNET), and U-Net++. …”
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