Improving EEG based brain computer interface emotion detection with EKO ALSTM model by R. Kishore Kanna, Preety Shoran, Meenakshi Yadav, Mohammad Nadeem Ahmed, Shrikant Burje, Garima Shukla, Anurag Sinha, Mohammad Rashid Hussain, Saifullah Khalid

“…To overcome these issues, this study proposed a novel EKO-ALSTM for emotion detection in EEG-based brain–computer interfaces. The proposed study comprises EEG-based signals that record the electrical activity of the brain connected to various emotional states, which are gathered as real-time acquired EEG signals for emotion detection. …”
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Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning by Younes Karmoude, Soufiane Idbraim, Souad Saidi, Antoine Masse, Manuel Arbelo

“…This study monitors changes in an argan forest near Agadir, Morocco, from 2017 to 2023 using Sentinel-2 satellite imagery and advanced image processing algorithms. Various machine learning models were evaluated for argan tree detection, with LightGBM achieving the highest accuracy when trained on a dataset integrating spectral bands, temporal features, and vegetation indices information. …”
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WDS-YOLO: A Marine Benthos Detection Model Fusing Wavelet Convolution and Deformable Attention by Jiahui Qian, Ming Chen

“…Finally, the SF-PAFPN feature fusion structure was designed to enhance the model’s ability to detect smaller object features while mitigating false positives and missed detections. …”
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MSF-GhostNet: Computationally Efficient YOLO for Detecting Drones in Low-Light Conditions by Maham Misbah, Misha Urooj Khan, Zeeshan Kaleem, Ali Muqaibel, Muhamad Zeshan Alam, Ran Liu, Chau Yuen

“…The proposed solution also outperformed several other state-of-the-art algorithms exists in the literature.…”
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Estimation of the High-Frequency Feature Slope in Gravitational Wave Signals from Core Collapse Supernovae Using Machine Learning by Alejandro Casallas-Lagos, Javier M. Antelis, Claudia Moreno, Ramiro Franco-Hernández

“…We conducted an in-depth exploration of the use of different machine learning (ML) for regression algorithms, including Linear, Ridge, LASSO, Bayesian Ridge, Decision Tree, and a variety of Deep Neural Network (DNN) architectures, to estimate the slope of the high-frequency feature (HFF), a prominent emergent feature found in the gravitational wave (GW) signals of core collapse supernovae (CCSN). …”
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Soft-computing-based false alarm reduction for hierarchical data of intrusion detection system by Parminder Singh, Sujatha Krishnamoorthy, Anand Nayyar, Ashish Kr Luhach, Avinash Kaur

“…The proposed method enhances the intrusion detection systems that can work with data with dependent and independent features. …”
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Application of a dynamic optimization-based multi-attribute fusion method for fault detection. by Chen Ma, Handong Huang, Youcai Tang, Suo Cheng, Chao Wang, Xin Wang

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Predictive analysis of clinical features for HPV status in oropharynx squamous cell carcinoma: A machine learning approach with explainability by Emily Diaz Badilla, Ignasi Cos, Claudio Sampieri, Berta Alegre, Isabel Vilaseca, Simone Balocco, Petia Radeva

“…This study aims to provide a comprehensive method based on pre-treatment clinical data for predicting the patient’s HPV status over a large OPSCC patient cohort and employing explainability techniques to interpret the significance and effects of the features. Materials and Methods:: We employed the RADCURE dataset clinical information to train six Machine Learning algorithms, evaluating them via cross-validation for grid search hyper-parameter tuning and feature selection as well as a final performance measurement on a 20% sample test set. …”
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Traffic light detection and recognition based on deep learning for autonomous-rail rapid tram by XIONG Qunfang, LIN Jun, YUAN Xiwen, XU Yanghan, YUE Wei, LI Yuanzhengyu

“…This paper presents study efforts in this field through the application of a deep learning algorithm. Firstly, regions of interest (RoIs) for traffic lights were determined using high-precision map information to narrow the detection range and improve the detection speed. …”
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Context guided transformer enhanced YOLOv8 for accurate juvenile abalone detection and counting by Dapeng Cheng, Ji Ruan, Xinhao Li, Feng Zhao, Shoudu Zhang, Guofan Zhang, Fucun Wu

“…However, due to the small size, dense distribution, and frequent occlusion among individuals during the rearing period, existing detection algorithms often demonstrate low precision in identifying abalones. …”
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Detection of Cross-Line Successive Faults in Non-Effective Neutral Grounding Distribution Networks by Yuxuan Jin, Bin Wang, Mingming Xu, Ruirui Xie, Zhi Li, Xuan Dong

“…Based on these distinctive features, the paper proposes a detection method for successive cross-line grounding faults with varying fault phases. …”
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Real-time crop row detection using computer vision- application in agricultural robots by Md. Nazmuzzaman Khan, Adibuzzaman Rahi, Veera P. Rajendran, Mohammad Al Hasan, Sohel Anwar

“…The experiments conducted on real-time video featuring challenging scenarios show that our proposed algorithm exhibits a detection accuracy of over 90% and is a viable option for real-time implementation. …”
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2HR-Net VSLAM: Robust visual SLAM based on dual high-reliability feature matching in dynamic environments. by Wang Yang, Huang Chao, Zhang Yi, Tan Shuyi

“…This paper innovatively proposes a dynamic adaptive VSLAM system based on the High-repeatability and High-reliability feature matching network (2HR-Net), which improves localization accuracy in dynamic environments through three key innovations: First, the 2HR feature detection network is designed, integrating the K-Means clustering algorithm into L2-Net to achieve feature point detection with both high repeatability and high reliability. …”
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Onboard LiDAR–Camera Deployment Optimization for Pavement Marking Distress Fusion Detection by Ciyun Lin, Wenjian Sun, Ganghao Sun, Bown Gong, Hongchao Liu

“…First, LiDAR and camera sensors’ detection capability was assessed based on the sensors’ built-in features. …”
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GSF-YOLOv8: A Novel Approach for Fire Detection Using Gather-Distribute Mechanism and SimAM Attention by Caixiong Li, Dali Wu, Xing Zhang, Peng Wu

“…To address the current challenges in fire detection algorithms, including insufficient feature extraction, high computational complexity, limited deployment on resource-constrained devices, missed detections, false detections, and low accuracy, we developed a high-precision algorithm named GSF-YOLOv8. …”
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A Nonlinear Compensation Method for Enhancing the Detection Accuracy of Weak Targets in FMCW Radar by Bo Wang, Tao Lai, Qingsong Wang, Haifeng Huang

“…To achieve precise detection of target geometric features, Ka/W/sub-terahertz band imaging radar systems with ultra-wide instantaneous bandwidth have been developed. …”
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Unified Dynamic Dictionary and Projection Optimization With Full-Rank Representation for Hyperspectral Anomaly Detection by Hongran Li, Chao Wei, Yizhou Yang, Zhaoman Zhong, Ming Xu, Dongqing Yuan

“…These improvements result in a more accurate background representation, thereby enhancing anomaly detection performance. Experimental results on several hyperspectral datasets demonstrate that the proposed algorithm excels in anomaly detection tasks, offering new insights and approaches for HAD.…”
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InvMOE: MOEs Based Invariant Representation Learning for Fault Detection in Converter Stations by Hao Sun, Shaosen Li, Hao Li, Jianxiang Huang, Zhuqiao Qiao, Jialei Wang, Xincui Tian

“…To overcome these issues, we propose InvMOE, a novel fault detection algorithm with two core components: (1) invariant representation learning, which captures task-relevant features and mitigates background noise interference, and (2) multi-task training using a mixture of experts (MOE) framework to adaptively optimize feature learning across tasks and address label sparsity. …”
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ECG Signal Detection and Classification of Heart Rhythm Diseases Based on ResNet and LSTM by Qiyang Xie, Xingrui Wang, Hongyu Sun, Yongtao Zhang, Xiang Lu

“…Based on previous research on electrocardiogram (ECG) automatic detection and classification algorithm, this paper uses the ResNet34 network to learn the morphological characteristics of ECG signals and get the significant information of signals, then passes into a three-layer stacked long-term and short-term memory network to get the context dependency of the features. …”
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Automated inflammatory bowel disease detection using wearable bowel sound event spotting by Annalisa Baronetto, Annalisa Baronetto, Sarah Fischer, Sarah Fischer, Markus F. Neurath, Markus F. Neurath, Oliver Amft, Oliver Amft

“…A deep-learning-based audio pattern spotting algorithm was trained to retrieve BS events. Subsequently, features were extracted around detected BS events and a Gradient Boosting Classifier was trained to classify patients with IBD vs. healthy controls. …”
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