Detection of the Pigment Distribution of Stacked Matcha During Processing Based on Hyperspectral Imaging Technology by Qinghai He, Zhiyuan Liu, Xiaoli Li, Yong He, Zhi Lin

“…This paper presents the use of hyperspectral imaging technology (HSI), combined with machine learning algorithms, to detect chlorophyll a, chlorophyll b, and carotenoids in stacked matcha tea during processing. …”
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Identification and grading method of HCAs in gas pipelines based on multisource data fusion by Haoran TANG, Junnan XIONG, Zhiwei YONG, Wenjie CHEN, Aoru LIU, Qisheng WANG, Huiwen XIAO, Rongkang WANG

“…The spatial analysis feature and attribute processing capabilities of GIS were utilized to calculate building areas and integrate spatial and attribute data. Moreover, an algorithm for identifying and grading HCAs in gas pipelines was devised. …”
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Learning Transferable Convolutional Proxy by SMI-Based Matching Technique by Wei Jin, Nan Jia

“…We design an iterative algorithm to update the parameters alternately and test it over benchmark data sets of abnormal behavior detection in video, Amazon product reviews sentiment analysis, etc.…”
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Deep Space Insights: Machine Learning Revolutionizing Astrophysical Discoveries by Dutta Samya, Paul Prithwineel

“…This paper examines the transformative role of machine learning (ML) in astrophysics. With the exponential growth of astronomical data, traditional methods are often insufficient for effective data management and analysis. …”
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Utilizing Footstep Sound Event Detection by Using CNN Techniques for Assuring Property Security by Furkan Y. Yavuz, Nejat Yumusak

“…This sound event-based approach enables a more compact installation and allows for the use of affordable microphones, offering an alternative to visual-based security systems. We propose deep learning based on a convolutional neural network (CNN) algorithm for classification of footstep sound events. …”
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Divide and Conquer: Automatic Detection of the Thalamus to Empower DBS Physiological Navigation to the Subthalamic Region by Stefanie Glowinsky, Zvi Israel, Sami Heymann, Hagai Bergman

“…Our aim was to identify thalamic electrophysiological activity along the trajectory to the subthalamic region using micro-electrode recordings in deep brain stimulation (DBS) surgery by integrating site- and sequence-based approaches, without compromising subthalamic nucleus (STN) detection accuracy. We used electrophysiological data from 29,735 recording sites across 112 patients to develop algorithms for automatic detection of the thalamus, STN, and non-cellular brain areas. …”
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Detecting cyber attacks in vehicle networks using improved LSTM based optimization methodology by C. Jayasri, V. Balaji, C. M. Nalayini, S. Pradeep

“…Traditional intrusion detection systems struggle to cope with the dynamic and high-volume nature of vehicular data, often leading to high false positives and limited adaptability. …”
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StegGPT: A Novel Foundation-Model-Based Character-Level Linguistic Steganography Method Utilizing Large Language Models by Omer Farooq Ahmed Adeeb, Seyed Jahanshah Kabudian

“…Conventional methodologies for obfuscating data within textual covers exhibit inherent limitations and susceptibility to detection. …”
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Deep Learning for Spectroscopic X‐ray Nano‐Imaging Denoising by Tianyu Fu, Kai Zhang, Qingxi Yuan, Jizhou Li, Piero Pianetta, Yijin Liu

“…To address this issue, a deep learning‐based algorithm is developed for suppressing the image noise, grounded in self‐supervised learning principles. …”
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Unsupervised fake news detection on social media using hybrid Gaussian Mixture Model. by Sajida Perveen, Muhammad Shahbaz, Sami S Albouq, Khlood Shinan, Hanan E Alhazmi, Fatmah Alanazi, M Usman Ashraf, Rehan Ashraf

“…This research proposes an unsupervised approach using clustering algorithms, including Gaussian Mixture Model (GMM), K-means, and K-medoids, to eliminate the need for manual labeling in detecting fake news. …”
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Graph neural network-based water contamination detection from community housing information by Raphael Anaadumba, Yigit Bozkurt, Connor Sullivan, Madhavi Pagare, Pradeep Kurup, Benyuan Liu, Mohammad Arif Ul Alam

“…Specifically, the GAT achieved an accuracy of 0.80, precision of 0.71, and recall of 0.93, outperforming XGBoost, a classical machine learning algorithm, which had an accuracy of 0.70, precision of 0.66, and recall of 0.67.Discussion: In addition to its predictive capabilities, the GAT model identifies key factors contributing to lead contamination, enabling more precise targeting of at-risk areas. …”
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A Cost-Sensitive Small Vessel Detection Method for Maritime Remote Sensing Imagery by Zhuhua Hu, Wei Wu, Ziqi Yang, Yaochi Zhao, Lewei Xu, Lingkai Kong, Yunpei Chen, Lihang Chen, Gaosheng Liu

“…This enriches the diversity of target samples per image and mitigates the impact of data imbalance on the detection task. At the algorithm level, we introduce the Normalized Wasserstein Distance (NWD) to compute the similarity between bounding boxes. …”
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Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach by Rasool Jader, Sadegh Aminifar

“…Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. …”
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An enhanced time efficient technique for image watermarking using ant colony optimization and light gradient boosting algorithm by Vipul Sharma, Roohie Naaz Mir

“…This paper proposes a time efficient optimization method based on machine learning algorithms to detect the best embedding parameter for image watermarking with both robustness and imperceptibility. …”
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Evaluation on nature-connected environment in building embedded landscape: theory, detection, and case design by Shu Zhong, Jiao Ren

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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

“…Hyperspectral anomaly detection (HAD) aims to classify each pixel in a hyperspectral image as either background or anomaly without requiring labeled data. …”
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Dual‐fluorescence imaging and automated trophallaxis detection for studying multi‐nutrient regulation in superorganisms by Lior Baltiansky, Einav Sarafian‐Tamam, Efrat Greenwald, Ofer Feinerman

“…Additionally, our image‐based deep learning algorithm for automatic detection of ant trophallaxis events efficiently yields a detailed record of all food‐transfer interactions. …”
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A General Framework for CFAR Detection in PolSAR Imagery Based on Quadratic Statistics by Ziyuan Yang, Liguo Liu, Xiaoyang Hou, Yinghui Quan, Xian Zhang, Tao Liu

“…Given the challenges faced by deep learning methods in scenarios with limited labeled data and insufficient prior information, CFAR detection techniques remain vital in resource-constrained environments requiring rapid response. …”
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Regression with right-censored high-dimensional data: An application with different imputation techniques by Ersin Yılmaz, Dursun Aydın, S. Ejaz Ahmed

“…To solve the censorship problem, two imputation techniques are considered based on machine learning algorithms kNN and Sliding-windows methods respectively. …”
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The diagnostic efficacy of seven autoantibodies in early detection of ground-glass nodular lung adenocarcinoma by Hua Guo, Wei Zhao, Chunsun Li, Zhen Wu, Ling Yu, Miaoyu Wang, Yuanhui Wei, Zirui Wang, Shangshu Liu, Yue Yin, Zhen Yang, Liangan Chen

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