Novel Immune Microlens Imaging for Detection of Antigen and Antibody by Jiahui Liang, Xiaotian Ye, Jiang He, Jing Liu, Yaoxiong Huang, Feiyue Xing

“…Detection and analysis of antigen-antibody reaction is one of the most critical detection techniques in the fields of medicine, biology, environmental science, and food safety. …”
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Image recoloring detection based on inter-channel correlation by Nuo CHEN, Shuren QI, Yushu ZHANG, Mingfu XUE, Zhongyun HUA

“…Image recoloring is an emerging editing technique that can change the color style of an image by modifying pixel values.With the rapid proliferation of social networks and image editing techniques, recolored images have seriously hampered the authenticity of the communicated information.However, there are few works specifically designed for image recoloring.Existing recoloring detection methods still have much improvement space in conventional recoloring scenarios and are ineffective in dealing with hand-crafted recolored images.For this purpose, a recolored image detection method based on inter-channel correlation was proposed for conventional recoloring and hand-crafted recoloring scenarios.Based on the phenomenon that there were significant disparities between camera imaging and recolored image generation methods, the hypothesis that recoloring operations might destroy the inter-channel correlation of natural images was proposed.The numerical analysis demonstrated that the inter-channel correlation disparities can be used as an important discriminative metric to distinguish between recolored images and natural images.Based on such new prior knowledge, the proposed method obtained the inter-channel correlation feature set of the image.The feature set was extracted from the channel co-occurrence matrix of the first-order differential residuals of the differential image.In addition, three detection scenarios were assumed based on practical situations, including scenarios with matching and mismatching between training-testing data, and scenario with hand-crafted recoloring.Experimental results show that the proposed method can accurately identify recolored images and outperforms existing methods in all three hypothetical scenarios, achieving state-of-the-art detection accuracy.In addition, the proposed method is less dependent on the amount of training data and can achieve fairly accurate prediction results with limited training data.…”
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Toward an Onboard Anomaly Detection and Identification Method for Satellites by Shun Katsube, Hironori Sahara

“…Feature contribution analysis using orthogonal arrays and signal-to-noise ratios was used to enhance explainability, attempting to identify the type and location of detected anomalies. …”
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Role of EEG as Biomarker in the Early Detection and Classification of Dementia by Noor Kamal Al-Qazzaz, Sawal Hamid Bin MD. Ali, Siti Anom Ahmad, Kalaivani Chellappan, Md. Shabiul Islam, Javier Escudero

“…The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. …”
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Neurophysiological Approaches to Lie Detection: A Systematic Review by Bewar Neamat Taha, Muhammet Baykara, Talha Burak Alakuş

“…The goal is to summarize commonly used EEG signal processing techniques, feature extraction methods, and classification algorithms, identifying those that yield the highest accuracy in lie detection tasks. …”
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A robust deep learning framework for multiclass skin cancer classification by Burhanettin Ozdemir, Ishak Pacal

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Image Processing for Laser Impact Detection in Shooting Simulators by José Antonio García Torres, Daniel Guzmán Pérez, Jhon Fredy Rincón Morantes, Daniel Felipe Molina Martínez, Cristian Camilo García Rodríguez, Jhonnatan Eduardo Zamudio Palacios

“…The methodology was structured in four phases: (1) system requirements analysis, (2) hardware and software development, (3) system integration with a real firearm, and (4) functional testing in both controlled and open environments. …”
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Intelligent Recognition Method for Ferrography Wear Debris Images Using Improved Mask R-CNN Methods by Xiangwen Xiao, Weixuan Zhang, Qing Wang, Yuan Liu, Yishou Wang

“…The improvement on the Mask R-CNN focuses on two key aspects: enhancing feature extraction through the feature pyramid network structure and integrating attention mechanisms. …”
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A Proposed Method for Deep Learning-Based Automatic Tracking with Minimal Training Data for Sports Biomechanics Research by Daichi Yamashita, Minoru Matsumoto, Takeo Matsubayashi

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A Multi-Parameter Persistence Algorithm for the Automatic Energy Calibration of Scintillating Radiation Sensors by Guglielmo Ferranti, Chiara Rita Failla, Paolo Finocchiaro, Alessandro Pluchino, Andrea Rapisarda, Salvatore Tudisco, Gianfranco Vecchio

“…Peak detection is a fundamental task in spectral and time-series data analysis across diverse scientific and engineering disciplines, yet traditional approaches are highly sensitive to the choice of algorithm parameters, complicating reliable and consistent interpretation. …”
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Accurate Indoor Home Location Classification through Sound Analysis: The 1D-ILQP Approach by Umut Erman, Türker Tuncer, Nura Abdullahi, Sengul Dogan, Erhan Akbal

“…Subsequently, we selected the top 100 features using the Neighborhood Component Analysis (NCA) technique. …”
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Recurrent Neural Network Optimized by Grasshopper for Accurate Audio Data-Based Diagnosis of Parkinson's Disease by Saif Wali Ali Alsudani, Ghassan Khudair Saud

“…Incorporated within the system is a dimensionality reduction technique called Linear Discriminant Analysis. This approach allows the system to optimally utilize the features extracted from the given speech signal while ensuring that all the conditions necessary for successful classification are met. …”
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Continuous robust sound event classification using time-frequency features and deep learning. by Ian McLoughlin, Haomin Zhang, Zhipeng Xie, Yan Song, Wei Xiao, Huy Phan

“…It then benchmarks several high performing isolated sound classifiers to operate with continuous sound data by incorporating an energy-based event detection front end. Results are reported for each tested system using the new task, to provide the first analysis of their performance for continuous sound event detection. …”
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Human Pose Estimation and Event Recognition via Feature Extraction and Neuro-Fuzzy Classifier by Muhammad Hanzla, Naif S. Alshammari, Shuaa S. Alharbi, Wasim Wahid, Nouf Abdullah Almujally, Ahmad Jalal, Hui Liu

“…Key point features are characterized through the degree of freedom, human landmark detection via the HSV algorithm, and angular point analysis using the media pipe algorithm. …”
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Series-arc-fault diagnosis using feature fusion-based deep learning model by Won-Kyu Choi, Se-Han Kim, Ji-Hoon Bae

“…Because the characteristics of series arc faults change considerably depending on the load type, their accurate detection and analysis are difficult. We propose a series-arc-fault detector that uses a transfer learning (TL)-based feature fusion model. …”
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Clinical Findings Associated with a De Novo Partial Trisomy 10p11.22p15.3 and Monosomy 7p22.3 Detected by Chromosomal Microarray Analysis by Omid Kohannim, Jane Peredo, Katrina M. Dipple, Fabiola Quintero-Rivera

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EFINet: Efficient Feature Interaction Network for Real-Time RGB-D Semantic Segmentation by Zhe Yang, Baozhong Mu, Mingxun Wang, Xin Wang, Jie Xu, Baolu Yang, Cheng Yang, Hong Li, Rongqi Lv

“…Real-time RGB-D semantic segmentation is crucial for tasks like dynamic environment analysis of mobile robots or autonomous vehicles, real-time and efficient segmentation results can enhance the accuracy of subsequent tasks such as free space detection, mapping and navigation. …”
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Multiple model visual feature embedding and selection method for an efficient ocular disease classification by Isha Kansal, Vikas Khullar, Preeti Sharma, Supreet Singh, Junainah Abd Hamid, A. Johnson Santhosh

“…To optimize computational efficiency, a novel two-level feature selection framework combining Linear Discriminant Analysis (LDA) and advanced neural network classifiers—Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM)—was introduced. …”
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Deep learning and hyperspectral features for seedling stage identification of barnyard grass in paddy field by Siqiao Tan, Qiang Xie, Wenshuai Zhu, Yangjun Deng, Lei Zhu, Xiaoqiao Yu, Zheming Yuan, Zheming Yuan, Yuan Chen, Yuan Chen

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TF-LIME : Interpretation Method for Time-Series Models Based on Time–Frequency Features by Jiazhan Wang, Ruifeng Zhang, Qiang Li

“…The TFHS algorithm achieves precise homogeneous segmentation of the time–frequency matrix through peak detection and clustering analysis, incorporating the distribution characteristics of signals in both frequency and time dimensions. …”
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