Network Congestion Tracking and Detection in Banking Industry Using Machine Learning Models by Kingsley Ifeanyi Chibueze, Nwamaka Georgenia Ezeji, Nnenna Harmony Nwobodo-Nzeribe

“…This research evaluates various ML algorithms, including Support Vector Machines, Decision Trees, and Random Forests, to identify the most effective approach for congestion detection. …”
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CHDPL-Net: a lightweight network for Chinese herbal decoction pieces detection by Chuhe Lin, Zhijun Xie, Xing Jin, Hangjuan Lin, Renguang Shan

“…However, the performance of current algorithms remains unsatisfactory. To address this, we have constructed a diverse CHDP dataset and proposed a lightweight network for CHDP detection, named CHDPL-Net. …”
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Advanced Classifiers and Feature Reduction for Accurate Insomnia Detection Using Multimodal Dataset by Ameya Chatur, Mostafa Haghi, Nagarajan Ganapathy, Nima TaheriNejad, Ralf Seepold, Natividad Martinez Madrid

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Leveraging SARs and Advanced Deep Learning Techniques for Oil Spill Detection in UAE by M. Alkhalifa, A. Alowais, A. Manousakis, A. Manousakis

“…In aim to solve this issue, we suggest a system that is designed to detect oil spills on the ocean surface and provide information for taking appropriate measures to contain the spill. …”
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A Study of Real-time Detection Methods for Solar Radio Burst Identification by YongLin Yu, ChengLong Qiao, Shuo Xu, QinZheng Li, YanRui Su, HongQiang Song, FaBao Yan

“…Spectrum analysis conducted manually or with traditional image processing algorithms is limited by low efficiency and accuracy. …”
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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

“…Industrial defect detection is crucial for ensuring product quality and production efficiency, playing a pivotal role in advancing smart manufacturing. …”
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Metabolic profile changes in patients with rheumatoid arthritis detected using mass spectrometry by Xue Wu, Qi Yang, Shanshan Liu, Peng Yang, Zhengqi Liu, Zhitu Zhu

“…Methods A total of 533 participants, including 382 healthy individuals and 151 RA patients, were recruited to explore altered metabolic profiles through the analysis of dried blood spot samples by mass spectrometry. Multiple algorithms were applied to identify potential biomarkers. …”
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SChanger: Change Detection From a Semantic Change and Spatial Consistency Perspective by Ziyu Zhou, Keyan Hu, Yutian Fang, Xiaoping Rui

“…Recently, deep learning methods have demonstrated strong performance and widespread application. However, change detection faces data scarcity due to the labor-intensive process of accurately aligning remote sensing images of the same area, which limits the performance of deep learning algorithms. …”
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Detectable changes in the blood transcriptome are present after two weeks of antituberculosis therapy. by Chloe I Bloom, Christine M Graham, Matthew P R Berry, Katalin A Wilkinson, Tolu Oni, Fotini Rozakeas, Zhaohui Xu, Jose Rossello-Urgell, Damien Chaussabel, Jacques Banchereau, Virginia Pascual, Marc Lipman, Robert J Wilkinson, Anne O'Garra

“…Two quantitative scoring algorithms were applied to measure the changes in the transcriptional response. …”
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Fraud Detection in Privacy Preserving Health Insurance System Using Blockchain Technology by Md. Mazharul Islam, Mubasshir Ahmed, Rajesh Palit, Mohammad Shahriar Rahman, Salekul Islam

“…Smart contract algorithms are utilized in two scenarios: patient‐submitted claims and healthcare provider‐submitted claims, ensuring accurate processing and validation while detecting fraudulent activities such as duplicate claims, inflated medical bills, billing for unprovided services, falsifying patient records, and submitting claims for nonexistent treatments. …”
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Efficient Implementation of Mahalanobis Distance on Ferroelectric FinFET Crossbar for Outlier Detection by Musaib Rafiq, Yogesh Singh Chauhan, Shubham Sahay

“…A huge amount of data is being collected in this era of big data, predominantly for AI/ML algorithms and emerging applications. Considering such voluminous quantities, the collected data may contain a substantial number of outliers which must be detected before utilizing them for data mining or computations. …”
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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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Analysis of student’s final qualification theses using text loans detection systems by D. A. Petrusevich, K. D. Shakhardin

“…Popularity of the text loan detection systems increases. Automatic plagiarism detection systems are intended to make educational process better, the text drawing search easier, to support the copyright laws and academical honesty. …”
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PREDICTIVE MODELS FOR EARLY DETECTION OF PARKINSON’S DISEASE: A MACHINE LEARNING APPROACH by S. Jeyantha Jafna Juliet, D. Jasmine David, J. S. Raj Kumar, Angelin Jeba P., R. Golden Nancy, M. Selvarathi, T. Jemima Jebaseeli

“…To diagnose PD, the proposed method uses two different data sets. Algorithms for machine learning are also capable of helping in producing specific details from such data. …”
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Emerging analytical frontiers in microplastic detection: From spectroscopy to smart sensor technologies by Jen-Tai Lin, Ya-Ping Chung, Yen-Yi Lee, Tong-Lin Wu, Thao Thi Thu Huynh, Phung Thi Nguyen, Mei-Jou Lu, Bo-Wun Huang, Balasubramanian Sriram, Sea-Fue Wang, Guo-Ping Chang-Chien, Sakthivel Kogularasu, Wan-Ching Lin

“…Additionally, the integration of artificial intelligence and machine learning algorithms with high-dimensional spectral and electrochemical datasets is discussed as a transformative approach for enhancing classification accuracy, reducing analysis time, and facilitating automated 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

“…Therefore, the potential of hyperspectral imaging in combination with data analysis by machine learning algorithms was investigated to detect the symptoms solely based on the spectral signature of collected leaf samples. …”
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Machine learning applications for early detection of esophageal cancer: a systematic review by Farhang Hosseini, Farkhondeh Asadi, Hassan Emami, Rayan Ebnali Harari

“…However, the current literature is limited, and more studies are needed to investigate the clinical applications of these methods in early detection of EC. Furthermore, many studies suffer from class imbalance and biases, highlighting the need for validation of detection algorithms across organizations in longitudinal studies.…”
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Detection of Coffee Leaf Miner Using RGB Aerial Imagery and Machine Learning by Emerson Ferreira Vilela, Cileimar Aparecida da Silva, Jéssica Mayara Coffler Botti, Elem Fialho Martins, Charles Cardoso Santana, Diego Bedin Marin, Agnaldo Roberto de Jesus Freitas, Carolina Jaramillo-Giraldo, Iza Paula de Carvalho Lopes, Lucas de Paula Corrêdo, Daniel Marçal de Queiroz, Giuseppe Rossi, Gianluca Bambi, Leonardo Conti, Madelaine Venzon

“…A set of four machine learning algorithms was utilized: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD). …”
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Detecting and localizing cervical lesions in colposcopic images with deep semantic feature mining by Li Wang, Ruiyun Chen, Jingjing Weng, Huiping Li, Shi Ying, Jinghui Zhang, Zehao Yu, Chengbin Peng, Siming Zheng

“…The segmentation results were then coherently mapped back onto the original images, ensuring an integrated visualization of the findings.ResultsExperimental findings demonstrated that compared to algorithms solely based on image segmentation or classification, the proposed approach exhibited superior accuracy in distinguishing between normal and lesioned colposcopic images. …”
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RGB and RGNIR image dataset for machine learning in plastic waste detectionZENODO by Owen Tamin, Ervin Gubin Moung, Jamal Ahmad Dargham, Samsul Ariffin Abdul Karim, Ashraf Osman Ibrahim, Nada Adam, Hadia Abdelgader Osman

“…It is hopeful that these datasets will contribute to the advancement of the field of plastic waste detection and encourage further research in this area.…”
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