ADFCNN-BiLSTM: A Deep Neural Network Based on Attention and Deformable Convolution for Network Intrusion Detection by Bin Li, Jie Li, Mingyu Jia

“…Many existing intrusion detection studies often fail to fully extract the spatial features of network traffic and make reasonable use of temporal features. …”
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A Classification System to Detect Congestive Heart Failure Using Second-Order Difference Plot of RR Intervals by R. A. Thuraisingham

“…The classification procedure uses the k-nearest neighbor algorithm and uses features from the second-order difference plot (SODP) obtained from Holter monitor cardiac RR intervals. …”
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Android malware detection via efficient application programming interface call sequences extraction and machine learning classifiers by Tanjie Wang, Yueshen Xu, Xinkui Zhao, Zhiping Jiang, Rui Li

“…We also propose a pruning search, which further reduces the number of paths to be searched. Our algorithm greatly reduces the time complexity. We generate the transition matrix as classification features and investigate three types of machine learning classifiers to complete the malware detection task. …”
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Cnidaria herd optimized fuzzy C-means clustering enabled deep learning model for lung nodule detection by R. Hari Prasada Rao, Agam Das Goswami

“…Furthermore, statistical and texture descriptors extract the significant features that aid in improving the detection accuracy. …”
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Detection of Melamine in Soybean Meal Using Near-Infrared Microscopy Imaging with Pure Component Spectra as the Evaluation Criteria by Zengling Yang, Lujia Han, Chengte Wang, Jing Li, Juan A. Fernández Pierna, Pierre Dardenne, Vincent Baeten

“…However, there are problems with using near-infrared (NIR) spectroscopy for detecting samples with low contaminant concentration because of instrument noise and sampling issues. …”
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Highly accurate anomaly based intrusion detection through integration of the local outlier factor and convolutional neural network by Rahimullah Rabih, Hamed Vahdat-Nejad, Wathiq Mansoor, Javad Hassannataj Joloudari

“…This research proposes a novel approach to enhance the accuracy of anomaly-based intrusion detection systems (IDS). This approach involves combining the Local outlier factor (LOF) algorithm for outlier detection and the Convolutional neural network (CNN) for classification. …”
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Anterior cruciate ligament tear detection based on Res2Net modified by improved Lévy flight distribution by Peiji Yang, Yanan Liu, Fei Liu, Mingxia Han, Yadegar Abdi

“…This study introduces a new diagnostic approach by combining of the deep learning architecture Res2Net with an improved version of the Lévy flight distribution (ILFD) to improve the detection of ACL tears in knee MRI images. The Res2Net model is known for its ability to extract important features and classify them effectively. …”
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A Driver Behavior Detection Model for Human-Machine Co-Driving Systems Based on an Improved Swin Transformer by Junhua Cui, Yunxing Chen, Zhao Wu, Huawei Wu, Wanghao Wu

“…The results show that the proposed model algorithm has a better performance in 10 classifications of driver behavior detection, with an accuracy of 99.42%, which is improved by 3.8% and 1.68% compared to Vgg16 and MobileNetV2, respectively. …”
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Driving Anger States Detection Based on Incremental Association Markov Blanket and Least Square Support Vector Machine by Ping Wan, Chaozhong Wu, Yingzi Lin, Xiaofeng Ma

“…., traffic congestion, vehicles weaving/cutting in line, jaywalking and red light waiting in real traffic environment; (2) apply incremental association Markov blanket (IAMB) algorithm to select typical features related to driving anger states; (3) employ least square support vector machine (LSSVM) to identify different driving anger states based on the selected features. …”
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Scalable Detection of Underground Water Leaks in Dense Urban Environments Using L-Band SAR and Machine Learning by E. Ali, E. Ali, L. Xie, A. Sani-Mohammed, W. Xu, T. Zayed

“…Features extracted via Gray-Level Co-occurrence Matrix (GLCM) metrics and backscattering coefficients were used to train various machine learning, deep learning, and ensemble learning models, with hyperparameter optimization performed using a grid search algorithm. …”
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Subsystem-Based Fault Detection in Robotics via <italic>L2</italic> Norm and Random Forest Models by Elham Abdullah Alamoudi

“…Our approach involves dividing the system into subsystems, processing signals from a single sensor type, and using optimal features derived from the <inline-formula> <tex-math notation="LaTeX">$L2$ </tex-math></inline-formula> norm for prediction via a random forest model for detection. …”
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Combined L-Band Polarimetric SAR and GPR Data to Develop Models for Leak Detection in the Water Pipeline Networks by Yuyao Zhang, Hongliang Guan, Fuzhou Duan

“…In this paper, we combine the SAOCOM-1A L-band synthetic-aperture radar (SAR) and the ground-penetrating radar (GPR) data to develop regression models that predict the SSRDC values. The model features are selected with the Boruta wrapper algorithm based on the SAOCOM-1A images after pre-processing, and the SSRDC values at sampling locations within the research area are calculated with the reflected wave method based on the GPR data. …”
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A Multicomponent Collaborative Fossil Fuel Power Plants Detection Framework Based on Geographic Analysis in Wide Areas by Ning Li, Min Jing, Wanxuan Geng, Shengkun Dongye, Hui Chen, Chen Ji, Liang Cheng

“…Next, we constructed a comprehensive FFPP dataset, including plants and their components, and trained two separate object detection models for FFPPs and their components. Subsequently, the FFPP model was used to perform coarse detection, followed by the refined detection of primary features (chimneys, square chimneys, and cooling towers) and auxiliary features (substations and storage tanks). …”
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GTSD: glimmer and thermal infrared remote sensing dataset for night-time ship detection under thin clouds by Wencong Zhang, Qiyao Wang, Liyuan Li, Hongyu Chen, Zhuoyue Hu, Bihong Fu, Fansheng Chen

“…To address the above issues, this paper proposes a new method based on SGFusion to fuse glimmer and thermal infrared remote sensing images, then use YOLOv9 algorithm to realize ship detection under cloud at night. …”
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Improved Transfer Learning for Detecting Upper-Limb Movement Intention Using Mechanical Sensors in an Exoskeletal Rehabilitation System by Ahnryul Choi, Tae Hyong Kim, Seungheon Chae, Joung Hwan Mun

“…The objective of this study was to propose a novel strategy for detecting upper-limb motion intentions from mechanical sensor signals using deep and heterogeneous transfer learning techniques. …”
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Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine Learning by Zhuo Wang, Avia Noah, Valentina Graci, Emily A. Keshner, Madeline Griffith, Thomas Seacrist, John Burns, Ohad Gal, Allon Guez

“…Additionally, a real-time algorithm was developed to integrate the EEG and accelerometer data, which enabled accurate fall detection in under 400 ms and achieved an over 99% accuracy in detecting unexpected falls. …”
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Addressing significant challenges for animal detection in camera trap images: a novel deep learning-based approach by Margarita Mulero-Pázmány, Sandro Hurtado, Cristóbal Barba-González, María Luisa Antequera-Gómez, Francisco Díaz-Ruiz, Raimundo Real, Ismael Navas-Delgado, José F. Aldana-Montes

“…Subsequently, we train a specialized deep-learning expert model for each animal group to detect similar features. This approach leverages Transfer Learning from the MegaDetectorV5 (YOLOv5 version) model, already pre-trained on various animal species and ecosystems. …”
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A pregeneration–recognition method of detecting weak seafloor echoes for full-waveform airborne LiDAR bathymetry by Yadong Guo, Wenxue Xu, Yanxiong Liu, Fanlin Yang, Xue Ji, Yikai Feng, Qiuhua Tang

“…However, weak seafloor echoes in full-waveform data induced by environmental and device characteristics are confused with noise signals, leading to difficulties in seafloor detection. This paper proposes a pregeneration–recognition method of detecting weak seafloor echoes for ALB. …”
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Drowsiness Detection of Construction Workers: Accident Prevention Leveraging Yolov8 Deep Learning and Computer Vision Techniques by Adetayo Olugbenga Onososen, Innocent Musonda, Damilola Onatayo, Abdullahi Babatunde Saka, Samuel Adeniyi Adekunle, Eniola Onatayo

“…The proposed method leverages computer vision techniques to analyze facial and eye features, enabling the early detection of signs of drowsiness, effectively preventing accidents, and enhancing on-site safety. …”
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Fresh Tea Leaf-Grading Detection: An Improved YOLOv8 Neural Network Model Utilizing Deep Learning by Zejun Wang, Yuxin Xia, Houqiao Wang, Xiaohui Liu, Raoqiong Che, Xiaoxue Guo, Hongxu Li, Shihao Zhang, Baijuan Wang

“…The incorporation of an Efficient Multi-Scale Attention Module with Cross-Spatial Learning serves to attenuate the influence of irrelevant features in complex backgrounds, which in turn, elevates the model’s detection Precision. …”
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