Detecting performance difficulty of learners in colonoscopy: Evidence from eye-tracking by Xin Liu, Bin Zheng, Xiaoqin Duan, Wenjing He, Yuandong Li, Jinyu Zhao, Chen Zhao, Lin Wang

“…In this study, we tracked the eye movements of trainees during their colonoscopic performance in simulation. We applied deep learning algorithms to detect the eye-tracking metrics on the moments of navigation lost (MNL), a signature sign for performance difficulty during colonoscopy. …”
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Anomaly detection in cropland monitoring using multiple view vision transformer by Xuesong Liu, Yansong Liu, He Sui, Chuan Qin, Yuanxi Che, Zhaobo Guo

“…In the future, this study plans to explore integrating data from thermal, infrared, or LIDAR sensors, enhance the interpretability of the vision transformer model, and optimize the deep learning pipeline to reduce computational complexity.…”
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Integrating Information Gain and Chi-Square for Enhanced Malware Detection Performance by Fauzi Adi Rafrastara, Wildanil Ghozi, Ramadhan Rakhmat Sani, Lekso Budi Handoko, Abdussalam Abdussalam, Elkaf Rahmawan Pramudya, Faizal M. Abdollah

“…Recent studies have shown that this challenge can be addressed by employing machine learning algorithms for detection. Some studies have also implemented various feature selection methods to optimize detection efficiency. …”
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Mammography in 2022, from Computer-Aided Detection to Artificial Intelligence Applications by Filippo Pesapane, Chiara Trentin, Marta Montesano, Federica Ferrari, Luca Nicosia, Anna Rotili, Silvia Penco, Mariagiorgia Farina, Irene Marinucci, Francesca Abbate, Lorenza Meneghetti, Anna Bozzini, Antuono Latronico, Alessandro Liguori, Giuseppe Carrafiello, Enrico Cassano

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Global Feature Focusing and Information Enhancement Network for Occluded Pedestrian Detection by ZHENG Kaikui, JI Kangyou, LI Jun, LI Qiming

“…The experimental results on three benchmark datasets demonstrate the superiority of GFFIE-Net over existing methods, particularly in handling heavily occluded pedestrians.OtherFuture research could explore semi-supervised or self-supervised learning using limited labeled data. This method can reduce the dependence on large scale labeled datasets, improve the model's generalization ability, and further enhance the method's applicability and accuracy in different scenarios.…”
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基于脑影像及临床特征的机器学习模型预测缺血性卒中后心房颤动 A Machine Learning Model Based on Brain Imaging and Clinical Features for Predicting Atrial Fibrillation Detected after Stroke... by 张栗源1，刘涛2，姜勇1，李子孝1，3，王拥军1，3，杨晓萌1，3(ZHANG Liyuan1, LIU Tao2, JIANG Yong1, LI Zixiao1,3, WANG Yongjun1,3, YANG Xiaomeng1,3 )

“…Patients were divided into two groups according to the systematic collection of past medical history records, electrocardiogram, and 24-hour Holter monitoring results during hospitalization: the sinus rhythm group and the atrial fibrillation detected after stroke group. Firstly, a pre-trained nnUNet deep learning framework was applied for standardized preprocessing and automated lesion segmentation of DWI data. …”
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Method for Vocal Fold Paralysis Detection Based on Perceptual and Acoustic Assessment by Rafał HALAMA, Krzysztof SZKLANNY, Danijel KORŽINEK

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Explainable Anomaly Detection Based on Operational Sequences in Industrial Control Systems by Ka-Kyung Kim, Joon-Seok Kim, Ieck-Chae Euom

“…Many anomaly detection algorithms based on deep learning models have good performance but often involve complex neural network structures, creating a black-box issue where users cannot interpret the decisions made by the models. …”
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Intrusion Detection Framework for Internet of Things with Rule Induction for Model Explanation by Kayode S. Adewole, Andreas Jacobsson, Paul Davidsson

“…The results show that XGBoost outperformed the other ensemble algorithms on two publicly available datasets for intrusion detection. …”
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A Link-Quality Anomaly Detection Framework for Software-Defined Wireless Mesh Networks by Sotiris Skaperas, Lefteris Mamatas, Vassilis Tsaoussidis

“…In this paper, we propose an unsupervised machine learning (ML) online framework for link quality detection consisting of: 1) improved preprocessing clustering algorithm, based on elastic similarity measures, to efficiently characterize wireless links in terms of reliability, and 2) a novel change point (CP) detector for the real-time identification of anomalies in the quality of selected links, which minimizes the overestimation error through the incorporation of a rank-based test and a recursive max-type procedure. …”
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A Lightweight and Efficient Plant Disease Detection Method Integrating Knowledge Distillation and Dual-Scale Weighted Convolutions by Xiong Yang, Hao Wang, Qi Zhou, Lei Lu, Lijuan Zhang, Changming Sun, Guilu Wu

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Early Detection of Alzheimer’s Disease Using Generative Models: A Review of GANs and Diffusion Models in Medical Imaging by Md Minul Alam, Shahram Latifi

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IT Parkinson’s Disease Diagnostics Based on the Freezing of Gait Analysis Using Long Short Term Memory Neural Network by U. A. Vishniakou, Xia YiWei

“…Next, a data enhancement algorithm was used to balance the number of samples in the data sets. …”
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Application of Roadside Perception Method Based on Improved DeepSORT in Surface Mine by YUE Wei, LIN Jun, KANG Gaoqiang, YOU Jun, XU Yanghan, TONG Hao

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Generalizing location-centric variations to enhance contactless human activity recognition by Fawad Khan, Syed Yaseen Shah, Jawad Ahmad, Alanoud Al Mazroa, Adnan Zahid, Muhammed Ilyas, Qammer Hussain Abbasi, Syed Aziz Shah

“…To address this challenge, in this study, we present a novel federated learning (FL) algorithm designed to train a robust global model from local datasets in different localizations. …”
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Automatic location of surface-monitored microseismicity with deep learning by Zhaolong Gan, Xiao Tian, Xiong Zhang, Mengxue Dai

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Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder by Zahra Rastin, Gholamreza Ghodrati Amiri, Ehsan Darvishan

“…Most of the deep learning-based methods proposed for damage detection in civil structures are based on supervised algorithms that require data from the healthy state and different damaged states of the structure in the training phase. …”
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Complex Texture Contour Feature Extraction of Cracks in Timber Structures of Ancient Architecture Based on YOLO Algorithm by Jian Ma, Weidong Yan, Guoqi Liu, Shiyu Xing, Siqi Niu, Tong Wei

“…Deep learning has achieved good results in the crack detection of roads and bridges. …”
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Application of Machine Learning Techniques for Bearing Fault Diagnosis by Sarra Eddai, Nabih Feki, Ahmed Ghorbel, Abdelkhalak El Hami, Mohamed Haddar

“…This investigation provides a comprehensive analysis of the Case Western Reserve University (CWRU) dataset, data preprocessing procedures, feature extraction techniques, and machine learning algorithms utilized for fault detection. …”
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Mobile based deep CNN model for maize leaf disease detection and classification by Getnet Tigabie Askale, Achenef Behulu Yibel, Belayneh Matebie Taye, Gashaw Desalegn Wubneh

“…Recently, researchers have attempted to detect and classify maize leaf diseases using Deep Learning algorithms. …”
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