An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification by Khaoula Taji, Ali Sohail, Tariq Shahzad, Bilal Shoaib Khan, Muhammad Adnan Khan, Khmaies Ouahada

“…The automatic identification and categorization of illnesses affecting apple and maize plants is the focus of this paper’s investigation into the implementation of cutting-edge technical solutions, notably machine learning algorithms. Deep learning has recently made significant contributions to the automatic detection and classification of plants diseases specifically in fruits and vegetables. …”
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Research on Cross-Circuitry Fault Identification Method for AC/DC Transmission System Based on Blind Signal Separation Algorithm by Yan Tao, Xiangping Kong, Chenqing Wang, Junchao Zheng, Zijun Bin, Jinjiao Lin, Sudi Xu

“…Then, the electrical signals of the AC system and the DC system of the AC/DC power transmission system are collected, and the collected signals are extracted by the blind signal separation algorithm. Then, aiming at the cross-circuitry Fault problem of the DC system, the electrical quantities of the positive and negative poles on the rectifier side and the inverter side are collected, and the characteristics of the electrical quantities are analyzed by wavelet to determine the fault. …”
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Clinical performance of human papillomavirus based cervical cancer screening algorithm: The result of a large Danish implementation study by Sofie Lindquist, Susanne K. Kjær, Kirsten Frederiksen, Dorthe Ørnskov, Lone Kjeld Petersen, Christian Munk, Marianne Waldstrøm

“…Conclusions In this pilot implementation study, an algorithm for HPV‐based screening was evaluated in a Danish setting. …”
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Identifying major depressive disorder among US adults living alone using stacked ensemble machine learning algorithms by Zhao Chen, Hao Liu, Yao Zhang, Fei Xing, Jiabao Jiang, Zhou Xiang, Zhou Xiang, Xin Duan, Xin Duan

“…We constructed a SEML model for MDD detection, incorporating three conventional machine learning algorithms as base models and a Neural Network (NN) as the meta-model. …”
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Computer Network System for Fire Flame detection from Digital Video using Fractal Geometry by Laheeb Ibrahim, Khalil Alsaif, Hasan Ahmed

“…Applying the suggested algorithm on Various samples exceeding 300 colored digital images containing flames with different spectra have been selected. …”
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Assessing the performance of the Asian/Pacific islander identification algorithm to infer Hmong ethnicity from electronic health records in California by Susan L Stewart, May Ying N Ly, Katherine K Kim

“…Sensitivity, specificity, positive (PPV) and negative predictive values (NPVs) were calculated comparing the source data ethnicity inferred by the algorithm with the self-reported ethnicity from the questionnaire.Participants EHRs indicating Hmong, Chinese, Vietnamese and Korean ethnicity who met the original study inclusion criteria were analysed.Results The NAPIIA had a sensitivity of 78%, a specificity of 99.9%, a PPV of 96% and an NPV of 99%. …”
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Development of a deep learning model for automated detection of calcium pyrophosphate deposition in hand radiographs by Thomas Hügle, Elisabeth Rosoux, Guillaume Fahrni, Deborah Markham, Tobias Manigold, Fabio Becce

“…In the recently established ACR/EULAR CPPD classification criteria, calcification and OA features of the wrist and hand joints are substantial features.ObjectivesTo develop and test a deep-learning algorithm for automatically and reliably detecting CPPD features in hand radiographs, focusing on calcification of the triangular fibrocartilage complex (TFCC) and metacarpophalangeal (MCP)-2 and -3 joints, in separate or combined models.MethodsTwo radiologists independently labeled a dataset of 926 hand radiographs, yielding 319 CPPD positive and 607 CPPD negative cases across the three sites of interest after adjudicating discrepant cases. …”
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Detection of Mycobacterium tuberculosis in sputum in patients coinfected with HIV / tuberculosis by various methods (literature review) by I. B. Viktorova, S. Yu. Degtyareva, E. I. Kulabukhova, E. N. Beloborodova, V. N. Zimina

“…The  experience of different countries of the world on the effectiveness of  various diagnostic algorithms for detecting tuberculosis in HIV/TB  co-infection and the role of molecular-genetic methods in the  diagnosis of tuberculosis are discussed. …”
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Real-time Detection of Imperfect Wheat Grains on Wheat Pile Surface Based on IDS-YOLO by FAN Jiawei, WU Lan, YAN Jingjing

“…To address the high missed detection rate of imperfect grains in target detection algorithms and to enhance the model detection speed, this study optimized the lightweight network model YOLOV4-Tiny. …”
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Spectral monitoring technology for gas concentrations of multiple target components in oil and gas leakage detection by Zhirong ZHANG, Pengchao CHEN, Yongjun CAI, Wenyou DU, Zhonglin LI, Yuntao PENG, Pengshuai SUN, Bian WU

“…By leveraging spectral analysis for complex systems and employing a design approach focused on background noise removal, traditional tunable diode laser absorption spectroscopy (TDLAS) technology was integrated with partial least squares (PLS) algorithm, non-negative least squares (NNLS) algorithm, and artificial intelligence models. …”
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Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India) by Olivia K. Botonis, Jonathan Mendley, Shreya Aalla, Nicole C. Veit, Michael Fanton, JongYoon Lee, Vikrant Tripathi, Venkatesh Pandi, Akash Khobragade, Sunil Chaudhary, Amitav Chaudhuri, Vaidyanathan Narayanan, Shuai Xu, Hyoyoung Jeong, John A. Rogers, Arun Jayaraman

“…An Extreme Gradient Boosting algorithm was trained to discriminate between COVID-19 infected individuals (n = 295) and COVID-19 negative healthy controls (n = 172) and achieved an F1-Score of 0.80 (95% CI = [0.79, 0.81]). …”
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Large language models for accurate disease detection in electronic health records: the examples of crystal arthropathies by Kim Lauper, Denis Mongin, Nils Bürgisser, Samia Mehouachi, Clement P. Buclin, Delphine S. Courvoisier, Etienne Chalot

“…The framework was further validated on 600 paragraphs assessing ‘Calcium Pyrophosphate Deposition Disease (CPPD)’.Results The LLM-based algorithm outperformed the regex method, achieving a 92.7% (88.7%–95.4%) positive predictive value, a 96.6% (94.6%–97.8%) negative predictive value and an accuracy of 95.4% (93.6%–96.7%) for gout. …”
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Screening potential diagnostic biomarkers for PLA2R‑associated idiopathic membranous nephropathy by WGCNA analysis and LASSO algorithm by Jinxu Huang, Yaqing Huang, Xiaoling Zeng, Yuhong Zhang, Junneng Zhang, Qingchu Hong, Yongtiao Peng

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Detection Method for Safety Helmet Wearing on Construction Sites Based on UAV Images and YOLOv8 by Xin Jiao, Cheng Li, Xin Zhang, Jian Fan, Zhenwei Cai, Zhenglong Zhou, Ying Wang

“…To address these issues, this study proposes a helmet detection method based on unmanned aerial vehicles (UAVs) and the YOLOv8 object detection algorithm. …”
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Advances in Skeleton-Based Fall Detection in RGB Videos: From Handcrafted to Deep Learning Approaches by Van-Ha Hoang, Jong Weon Lee, Md. Jalil Piran, Chun-Su Park

“…In this paper, we examine the most recent advances in skeleton-based fall detection in RGB videos, from handcrafted feature-based methods to advanced deep learning algorithms. …”
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Two‐stage video anomaly detection based on dual‐stream networks and multi‐instance learning by Dejun Zhang, Wenbo Fang, Yuhang Liu, Zirong Lyu, Chen Xiong, Zhan Wang

“…However, watching surveillance footage is a labor‐intensive task, and prolonged observation can lead to visual fatigue and lack of concentration, which in turn results in missed detections and false positives [1]. Therefore, it is crucial to develop intelligent algorithms for video anomaly detection. …”
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Research on load frequency control system attack detection method based on multi-model fusion by Feng Zheng, Weixun Li, Huifeng Li, Libo Yang, Zengjie Sun

“…A multi-model fusion attack detection framework is proposed, integrating (Long Short-Term Memory) LSTM supervised learning and autoencoder unsupervised learning algorithms, with an adaptive weight adjustment mechanism that dynamically optimizes detection strategies. …”
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Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing by Krzysztof Pastuszak, Michał Sieczczyński, Marta Dzięgielewska, Rafał Wolniak, Agata Drewnowska, Marcel Korpal, Laura Zembrzuska, Anna Supernat, Anna J. Żaczek

“…We developed four tree-based models and we trained and tested them on a dataset consisting of Smart-Seq2 sequenced data from primary tumor sections of breast cancer patients and PBMCs and on a public dataset with manually annotated CTC expression profiles from 34 metastatic breast patients, including triple-negative breast cancer. Our best models achieved about 95% balanced accuracy on the CTC test set on per cell basis, correctly detecting 133 out of 138 CTCs and CTC-PBMC clusters. …”
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Detection of Right and Left Ventricular Dysfunction in Pediatric Patients Using Artificial Intelligence–Enabled ECGs by Scott Anjewierden, Donnchadh O'Sullivan, Kathryn E. Mangold, Grace Greason, Itzhak Zachi Attia, Francisco Lopez‐Jimenez, Paul A. Friedman, Samuel J. Asirvatham, Jason Anderson, Benjamin W. Eidem, Jonathan N. Johnson, Shisheer Havangi Prakash, Talha Niaz, Malini Madhavan

“…We aimed to develop novel artificial intelligence–enabled ECG algorithms for LVSD and RVSD detection in pediatric patients. …”
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Curvature-Based Change Detection in Road Segmentation: Ascending Hierarchical Clustering vs. K-Means by David Jaurès Fotsa-Mbogne, Addie Bernice Nguensie-Wakponou, Jean Michel Nlong, Marcellin Atemkeng, Maurice Tchuente

“…Given the high frequency of data collection by accelerometers, the resulting large datasets pose a computational challenge for anomaly detection using supervised classification algorithms. …”
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