XSE-TomatoNet: An explainable AI based tomato leaf disease classification method using EfficientNetB0 with squeeze-and-excitation blocks and multi-scale feature fusion by Md Assaduzzaman, Prayma Bishshash, Md. Asraful Sharker Nirob, Ahmed Al Marouf, Jon G. Rokne, Reda Alhajj

“…This study introduces XSE-TomatoNet, an enhanced version of EfficientNetB0, incorporating Squeeze-and-Excitation (SE) blocks and multi-scale feature fusion to boost classification performance. …”
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Foreign object recognition for mine conveyor belt iron separators based on transfer learning with EfficientNet by Hailong YANG, Yiping YUAN, Panpan FAN, Lu XIAO, Feiyang ZHAO, Shaoke YUAN

“…Comparative experiments were conducted, and results show that the proposed model achieves faster stable iterations and lower loss values, outperforming other existing convolutional neural network models across various performance metrics. …”
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Research on the Evaluation of the Node Cities of China Railway Express Based on Machine Learning by Chenglin Ma, Mengwei Zhou, Wenchao Kang, Haolong Wang, Jiajia Feng

“…By comparing the evaluation performance of six machine learning models, an optimal decision-making model is identified, and the evaluation indicators are rigorously screened to provide robust decision-support for the establishment of CR Express assembly centers. …”
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Machine Learning for Health Insurance Prediction in Nigeria by Victor Enemona Ochigbo, Oluwasogo Adekunle Okunade, Emmanuel Gbenga Dada, Oluyemi Mikail Olaniyi, Oluwatoyosi Victoria Oyewande

“…Furthermore, the performance metrics uutilized to rate the predictive capabilities of the models are Accuracy, Precision, Sensitivity, F Score, and area under the Receiver Operating Characteristic (AUC & ROC Curve). …”
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TMBO-AOD: Transparent Mask Background Optimization for Accurate Object Detection in Large-Scale Remote-Sensing Images by Tianyi Fu, Hongbin Dong, Benyi Yang, Baosong Deng

“…However, large-scale remote-sensing images typically feature extensive and complex backgrounds with small and sparsely distributed objects, which pose significant challenges to detection performance. …”
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Design of a Conveyer Trough Bolt Signal Acquisition System and Bayesian Ensemble Identification Method for Working State by Yi Lian, Bangzhui Wang, Meiyan Sun, Kexin Que, Sijia Xu, Zhong Tang, Zhilong Huang

“…Finally, after comparing machine learning algorithms, Support Vector Machine was chosen for its superior performance. Using a one-vs.-one strategy and data from critical points, an operational condition identification model was developed. …”
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A Comprehensive Framework for Parkinson's Disease Detection Using Spiral Drawings and Advanced Machine Learning Techniques by Mohamed J. Saadh, Waleed K. Abdulsahib, Hardik Doshi, Anupam Yadav, J. Gowrishankar, Mayank Kundlas, Nargiza Mansurova, Kamal Kant Joshi, Fadhil Feez Sead, Bagher Farhood

“…To enhance model performance, four feature selection techniques were applied: Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and ANOVA. …”
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A Hybrid Approach for Forecasting Occupancy of Building’s Multiple Space Types by Iqra Rafiq, Anzar Mahmood, Ubaid Ahmed, Ahsan Raza Khan, Kamran Arshad, Khaled Assaleh, Naeem Iqbal Ratyal, Ahmed Zoha

“…Moreover, to highlight the importance of LGBM as a feature selection technique, the XgBoost model is also trained with all features. …”
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Enhance Social Network Bullying Detection Using Multi-Teacher Knowledge Distillation With XGBoost Classifier by Sathit Prasomphan

“…A key contribution of this study is the successful adaptation of XGBoost, traditionally used for structured/tabular data, for a natural language classification task by using rich semantic features extracted via pre-trained NLP models. Additionally, although the selected datasets (Wisesight, Thai Toxic Tweet, and 40 Thai Children Stories) are often used for sentiment analysis, we reframe and preprocess them for the purpose of cyberbullying classification by focusing on toxic, harmful, or aggressive linguistic patterns. …”
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MUF-Net: A Novel Self-Attention Based Dual-Task Learning Approach for Automatic Left Ventricle Segmentation in Echocardiography by Juan Lyu, Jinpeng Meng, Yu Zhang, Sai Ho Ling

“…These two tasks are then jointly trained using a temporal consistency mechanism to extract spatio-temporal features across frames. Experimental results demonstrate that our model outperforms existing segmentation methods. …”
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Parcel-scale crop planting structure extraction combining time-series of Sentinel-1 and Sentinel-2 data based on a semantic edge-aware multi-task neural network by Zhiguang Tang, Xiangdong Wang, Qin Jiang, Haizhu Pan, Gang Deng, He Chen, Yuanhong You, Sijia Li, Haiyan Hou

“…Subsequently, the Global Separability Index (GSI) was used to optimize feature selection. Ultimately, a parcel-scale random forest (RF) model was developed to enable accurate crop type classification and spatial delineation of crop planting structures. …”
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Integrating Message Content and Propagation Path for Enhanced False Information Detection Using Bidirectional Graph Convolutional Neural Networks by Jie Hu, Mei Yang, Bingbing Tang, Jianjun Hu

“…The bidirectional graph convolutional neural network subsequently learns the feature representations of the event propagation network during information dissemination, merging these representations with the original text content features to achieve comprehensive disinformation detection. …”
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Cross-Modal Compositional Learning for Multilabel Remote Sensing Image Classification by Jie Guo, Shuchang Jiao, Hao Sun, Bin Song, Yuhao Chi

“…The multilabel image classification task is modeled as the feature distance measurement task, and the visual features of images and the semantic information of labels are mutually promoted by the method. …”
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FFAE-UNet: An Efficient Pear Leaf Disease Segmentation Network Based on U-Shaped Architecture by Wenyu Wang, Jie Ding, Xin Shu, Wenwen Xu, Yunzhi Wu

“…The AGM module effectively suppresses background noise interference by reconstructing features and accurately capturing spatial and channel relationships, while the FESM module enhances the model’s responsiveness to disease features at different scales through channel aggregation and feature supplementation mechanisms. …”
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Camera Absolute Pose Estimation Using Hierarchical Attention in Multi-Scene by Xinhua Lu, Jingui Miao, Qingji Xue, Hui Wan, Hao Zhang

“…The multi-scene camera pose estimation approach aims to recover the camera pose from any given scene, catering to the demands of real-life mobile devices to perform tasks. Facing the challenge that it is difficult to extract efficient features in training multi-scene models, we present a modified model named Hierarchical Attention Absolute Pose Regression(H-AttnAPR) which can obtain different scales of feature dependencies. …”
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Low transthyretin is associated with the poor prognosis of colorectal cancer by Zhe Zhang, Zhe Zhang, Zhe Zhang, Chenhao Hu, Chenhao Hu, Chenhao Hu, Feiyu Shi, Feiyu Shi, Feiyu Shi, Lei Zhang, Lei Zhang, Lei Zhang, Ya Wang, Ya Wang, Yujie Zhang, Yujie Zhang, Yujie Zhang, Xiaojiang Zhang, Xiaojiang Zhang, Xiaojiang Zhang, Junjun She, Junjun She, Junjun She

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Lightweight detection of cotton leaf diseases using StyleGAN2-ADA and decoupled focused self-attention by Henghui Mo, Linjing Wei

“…Post-pruning, the model’s parameters are reduced to 4.9 million (M), with a computational demand of 31.5 Giga Floating-Point Operations Per Second (GFLOPs), showing superior performance over existing models. …”
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Enhancing prediction of wildfire occurrence and behavior in Alaska using spatio-temporal clustering and ensemble machine learning by A. Ahajjam, M. Allgaier, R. Chance, E. Chukwuemeka, J. Putkonen, T. Pasch

“…This ensemble model’s performance is benchmarked across four prediction horizons (same-day, +7 days, +30 days, +90 days) and against various conventional ML and deep learning techniques. …”
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COMPARATIVE ANALYSIS OF NON-COOPERATIVE SPECTRUM SENSING TECHNIQUES IN COGNITIVE RADIO NETWORK by HAMMED OYEBAMIJI LASISI, OLAJIDE MICHEAL ODOFIN, MUHAMMED BABAJIDE HAMMED, IDOWU OLAMIDE HUSSEIN

“…Matlab Simulink was used as modeling and simulating tool for the evaluations. From the results, energy detector was simpler and faster but unlike CFD exhibited poor performance in corrupt channels. …”
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Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization by Usharani Bhimavarapu, Gopi Battineni, Nalini Chintalapudi

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