Beat-aligned motor synergies and kinematic beat detection in street dance movements by Keli Shen, Jun-ichiro Hirayama

“…Abstract Dance is a rich artistic expression that combines intricate human movements with music, emotion, and cultural elements. However, the analysis of complex dance movements poses significant challenges because of the lack of comprehensive motion capture data and efficient computational techniques for feature extraction. …”
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Temporal Logical Attention Network for Log-Based Anomaly Detection in Distributed Systems by Yang Liu, Shaochen Ren, Xuran Wang, Mengjie Zhou

“…Detecting anomalies in distributed systems through log analysis remains challenging due to the complex temporal dependencies between log events, the diverse manifestation of system states, and the intricate causal relationships across distributed components. …”
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Early feature study of Yunnan pine pinewood nematode disease based on hyperspectral remote sensing of ground objects by Xiao Zhang, Yingqun Gao, Lianjin Fu, Yiran Zhang, Zeyu Li, Qingtai Shu

“…Therefore, this study proposes a new approach for early detection of PWD using hyperspectral data combined with measured physiological parameters to obtain diagnostic spectra and optimal biochemical parameters for early detection. …”
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Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques by Uwe Knauer, Sebastian Warnemünde, Patrick Menz, Bonito Thielert, Lauritz Klein, Katharina Holstein, Miriam Runne, Wolfgang Jarausch

“…Data processing consists of preprocessing for segmentation of the leaf area, feature extraction, classification and subsequent analysis of relevance of spectral bands. …”
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Object detection with afordable robustness for UAV aerial imagery: model and providing method by Viacheslav Moskalenko, Artem Korobov, Yuriy Moskalenko

“…The model includes a transformer-based backbone, gate units, and simplified feature pyramid network with a RetinaNet detection head. …”
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Advancements in Hematologic Malignancy Detection: A Comprehensive Survey of Methodologies and Emerging Trends by Rajashree Nambiar, Ranjith Bhat, Balachandra Achar H V

“…This survey systematically examines the state-of-the-art in blood cancer detection through image-based analysis, aimed at identifying the most effective computational strategies and highlighting emerging trends. …”
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An AI-Based Horticultural Plant Fruit Visual Detection Algorithm for Apple Fruits by Bin Yan, Xiameng Li, Rongshan Yan

“…The detection algorithm proposed in the study can be extended to the intelligent measurement of apple biological and physical characteristics, including for size measurement, shape analysis, and color analysis. …”
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Detection of Leaf Miner Infestation in Chickpea Plants Using Hyperspectral Imaging in Morocco by Mohamed Arame, Issam Meftah Kadmiri, Francois Bourzeix, Yahya Zennayi, Rachid Boulamtat, Abdelghani Chehbouni

“…This study addresses the problem of early detection of leaf miner infestations in chickpea crops, a significant agricultural challenge. …”
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Review of Surface-Defect Detection Methods for Industrial Products Based on Machine Vision by Quan Wang, Mengnan Wang, Jiadong Sun, Deji Chen, Pei Shi

“…It also offers a detailed analysis of recent developments and practical applications of large models in industry defect detection. …”
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Modality-based Modeling with Data Balancing and Dimensionality Reduction for Early Stunting Detection by Yohanes Setiawan, Mohammad Hamim Zajuli Al Faroby, Mochamad Nizar Palefi Ma’ady, I Made Wisnu Adi Sanjaya, Cisa Valentino Cahya Ramadhani

“…The main contributions of this research are the development of a comprehensive framework for modality-based analysis, the application of advanced data preprocessing techniques, and the comparison of various machine learning algorithms to identify the best model for stunting detection. …”
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Optimized Breast Cancer Classification Using PCA-LASSO Feature Selection and Ensemble Learning Strategies With Optuna Optimization by Prabhat Kumar Sahu, Taiyaba Fatma

“…The publicly available Breast Cancer Wisconsin (Diagnostic) dataset was utilized and underwent preprocessing steps, including dimensionality reduction and feature selection methods such as Principal Component Analysis (PCA) combined with Least Absolute Shrinkage and Selection Operator (LASSO). …”
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Introducing a Method for Backflow Detection in Lobe Blowers through Ultrasonic Technique by Ali Monazami, Somaye Mohammadi, Mehdi Behzad

“…Data analysis results show that the ultrasonic technique can detect the presence of backflow. …”
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Machine Learning based estimation of Chlorophyll and Flavonoid content in Bitter Leaf using Color and GLCM Texture Features by Retno Damayanti, Yusuf Hendrawan, Sandra Sandra

“…Leaves at three developmental stages were analyzed using standard spectrophotometric methods for validation. GLCM features (contrast, correlation, energy, and homogeneity) were extracted from normalized leaf images, and feature selection techniques were employed to identify the most predictive variables. …”
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Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals by Shuting Xu, Ravinesh C. Deo, Oliver Faust, Prabal D. Barua, Jeffrey Soar, Rajendra Acharya

“…The successful implementation of a dual-sound analysis approach promises advancements in the early detection and management of asthma and COPD.We conclude that the proposed model holds strong potential to transform asthma diagnostic practices and support clinicians in their respiratory healthcare practices.…”
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A novel pedestrian detection algorithm based on data fusion of face images by Jianhu Zheng, Jinshuan Peng

“…Face recognition is the most popular method to detect and track pedestrian movement. During the face recognition process, feature classification ability and reliability are determined by the feature extraction methods. …”
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Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders by Vahid Babaey, Hamid Reza Faragardi

“…Additionally, a structured tokenization strategy based on character-level analysis is employed to enhance input consistency and reduce feature dimensionality. …”
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Wi-Pest:a method for detecting stored grain pests based on CSI by Shaowei SHAN, Weidong YANG, Le XIAO, Ke WANG

“…The environmental and biological factors that affect the food security during the food storage,such as the food temperature,environment humidity,moisture,gas,mildew,pests and others pose a threat to the food storage security,among which the pest is an important factor threatening food storage security.Therefore,a fast and effective detection method is needed to detect stored grain pests.Some of the existing methods are time consuming,using expensive equipment,potentially harmful to health and inefficient.A non-contact,fast and low-cost detection method for stored grain pests based on the amplitude of the channel state information (CSI) was proposed,namely,wireless-pest (Wi-Pest).The feasibility of the pest detection in the stored grain was verified by using CSI amplitude data.On this basis,a Wi-Pest detection method was designed.Firstly,the amplitude data of CSI was preprocessed by outliers removal,data normalization and noise elimination.Then the principal component analysis (PCA) was used to compress the data and extract the main feature components.Finally,random forest (RF) classification method was used to detect stored grain pests.Experiments show that the abnormal density of live pests in grain heaps can be detected under the line of sight (LOS) scenario,and the detection accuracy of the proposed method can reach 97% on average.…”
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Applicability of Artificial Intelligence in Smart Healthcare Systems for Automatic Detection of Parkinson’s Disease by Harikumar Pallathadka, S.J.R.K. Padminivalli V., M. Vasavi, P. Nancy, Mohd Naved, Harish Kumar, Samrat Ray

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Cross-Scale Hypergraph Neural Networks with Inter–Intra Constraints for Mitosis Detection by Jincheng Li, Danyang Dong, Yihui Zhan, Guanren Zhu, Hengshuo Zhang, Xing Xie, Lingling Yang

“…Our model introduces a block-based feature extraction mechanism to efficiently capture deep representations. …”
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Transportation Mode Detection Using Learning Methods and Self-Contained Sensors: Review by Ilhem Gharbi, Fadoua Taia-Alaoui, Hassen Fourati, Nicolas Vuillerme, Zebo Zhou

“…Compared to these works, the proposed review provides some novelties such as an in-depth analysis of the current state-of-the-art techniques in TMD systems, relying on recent references and focusing particularly on the major existing problems, and an evaluation of existing methodologies for detecting travel modes using smartphone IMUs (including dataset structures, sensor data types, feature extraction, etc.). …”
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