Genderly: a data-centric gender bias detection system by Wael Khreich, Jad Doughman

“…We also curated new datasets as community resources for bias detection and mitigation. Our experiments used diverse preprocessing, feature engineering, and hyperparameter optimization methods for traditional ML models and large language models (LLMs) as gender bias detectors, comparing these results to evaluate model performance. …”
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AI bot to detect fake COVID‐19 vaccine certificate by Muhammad Arif, Shermin Shamsudheen, F Ajesh, Guojun Wang, Jianer Chen

“…So, to avoid this huge problem, this paper focuses on detecting fake vaccine certificates using a bot powered by Artificial Intelligence and neurologically powered by Deep Learning in which the following are the stages: a) Data Collection, b) Preprocessing to remove noise from the data, and convert to grayscale and normalised, c) Error level analysis, d) Texture‐based feature extraction for extracting logo, symbol and for the signature we extract Crest‐Trough parameter, and e) Classification using DenseNet201 and thereby giving the results as fake/real certificate. …”
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Coral reef detection using ICESat-2 and machine learning by Gabrielle A. Trudeau, Kim Lowell, Jennifer A. Dijkstra

“…These obstacles were mitigated through the integration of algorithmically derived pseudo-rugosity and slope metrics as innovative proxies for seafloor complexity, significantly improving predictive performance. Feature importance analysis identified satellite-derived bathymetry (SDB) depth as the most critical predictor of coral presence, followed by pseudo-rugosity, slope, and various other depth measurements. …”
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Exploration of machine learning approaches for automated crop disease detection by Annu Singla, Ashima Nehra, Kamaldeep Joshi, Ajit Kumar, Narendra Tuteja, Rajeev K. Varshney, Sarvajeet Singh Gill, Ritu Gill

“…Recent advancements in machine learning (ML) offer promising alternatives by automating the disease detection processes with high precision and efficiency. …”
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YOLO-MECD: Citrus Detection Algorithm Based on YOLOv11 by Yue Liao, Lerong Li, Huiqiang Xiao, Feijian Xu, Bochen Shan, Hua Yin

“…This modification not only enhances feature extraction capabilities and detection accuracy for citrus fruits but also achieves a significant reduction in model parameters. …”
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AI in dermatology: a comprehensive review into skin cancer detection by Kavita Behara, Ernest Bhero, John Terhile Agee

“…Results AI-based models exhibit remarkable performance in skin cancer detection by leveraging advanced deep learning algorithms, image processing techniques, and feature extraction methods. …”
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Affordable droplet-based flow analyzer with peristaltic micro-pumps for fluorescent ammonium sensing by Mingtao Sun, Yiyu Jiang, Wenshan Liang, Hui Zeng, Huiwen Chen, Min Zhang

“…The analyzer provides a limit of detection of 0.02 μM (3σ) and an RSD of 0.15 % (n=10, 1 μM ammonium). …”
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Application of spectral characteristics of electrocardiogram signals in sleep apnea by Jiayue Hu, Liu Yang, Xintong Zhao, Haicheng Wei, Jing Zhao, Miaomiao Li

“…The random forest classifier achieved optimal performance, with 92.9% accuracy, 86.6% specificity, and 100% sensitivity.ConclusionThis study demonstrates that spectral features derived from single-lead ECG signals, combined with EEMD-ICA and time-frequency analysis, offer an efficient and accurate method for sleep apnea detection.…”
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AGILE Gamma-Ray Detection of the Exceptional GRB 221009A by Marco Tavani, Giovanni Piano, Andrea Bulgarelli, Luca Foffano, Alessandro Ursi, Francesco Verrecchia, Carlotta Pittori, Claudio Casentini, Andrea Giuliani, Francesco Longo, Gabriele Panebianco, Ambra Di Piano, Leonardo Baroncelli, Valentina Fioretti, Nicolò Parmiggiani, Andrea Argan, Alessio Trois, Stefano Vercellone, Martina Cardillo, Lucio Angelo Antonelli, Guido Barbiellini, Patrizia Caraveo, Paolo W. Cattaneo, Andrew W. Chen, Enrico Costa, Ettore Del Monte, Guido Di Cocco, Immacolata Donnarumma, Yuri Evangelista, Marco Feroci, Fulvio Gianotti, Claudio Labanti, Francesco Lazzarotto, Paolo Lipari, Fabrizio Lucarelli, Martino Marisaldi, Sandro Mereghetti, Aldo Morselli, Luigi Pacciani, Alberto Pellizzoni, Francesco Perotti, Piergiorgio Picozza, Maura Pilia, Massimo Rapisarda, Andrea Rappoldi, Alda Rubini, Paolo Soffitta, Massimo Trifoglio, Valerio Vittorini, Fabio D’Amico

“…Here we report on the detection by the AGILE satellite in the MeV–GeV energy range of the remarkable long-duration gamma-ray burst GRB 221009A. …”
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Identification method for wheel/rail tread defects based on integrated partial convolutional network by CHENG Xiang, HE Jing, ZHANG Changfan, JIA Lin

“…Given the difficulties associated with accurately detecting minor wheelset damages, an enhanced adaptive spatial feature fusion (E-ASFF) detection approach was introduced. …”
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Ultrasound super resolution imaging for accurate uterus tumor detection and malignancy prediction by Ashwini Sawant, Sujata Kulkarni, Milind Sawant

“…Enhanced ultrasound images can reach even higher accuracy, cementing them as plausible alternatives to MRI. A comparative analysis of copious relevant image de-speckling, image enhancement, segmentation, and feature extraction methods are carried out. …”
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Improving brain tumor classification: An approach integrating pre-trained CNN models and machine learning algorithms by Mohamed R. Shoaib, Jun Zhao, Heba M. Emara, Ahmed S. Mubarak, Osama A. Omer, Fathi E. Abd El-Samie, Hamada Esmaiel

“…This study introduces a novel approach to brain tumor classification by exploring three pre-trained convolutional neural network (CNN) models: DenseNet201, EfficientNetB5, and InceptionResNetV2, combined with softmax activation for feature extraction. These features are then subjected to Principal Component Analysis (PCA) for dimensionality reduction. …”
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Classification of chest radiographs into healthy/pneumonia using Harris-Hawks Algorithm optimized deep-features by K. Vijayakumar, Mohammad Nazmul Hasan Maziz, Swaetha Ramadasan, Seifedine Kadry, S. Arunmozhi

“…The experimental outcome authenticates that this PDL-tool helps to offer improved accuracy with the HHA-optimized features. This work provided an accuracy of 99.3750% during healthy/pneumonia detection with FFV and Support Vector Machine (SVM), and detection accuracy of 88.5417% during viral/bacterial pneumonia detection with FFV and SVM.…”
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Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models by Ahmad Chaddad

“…GMM features demonstrated the best performance by the comparative study using principal component analysis (PCA) and wavelet based features. …”
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Prevalence and Possible Causes of Infertility in the Perm Region by A. P. Godovalov, N. V. Nikolaeva, T. I. Karpunina

“…The long-term dynamics of general morbidity, distribution of the patients by age, sex, social status, morbidity in combination with HIV infection, the detection of gonococcal infection by various specialists and methods of laboratory diagnostics were studied using the method of retrospective epidemiological analysis. …”
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Predictive modeling of adolescent suicidal behavior using machine learning: Key features and algorithmic insights by Priya Metri, Swetta Kukreja

“…Most studies relied on survey-based data (68 %) and employed PHQ-9 or GAD-7 scales for input features. This review highlights existing gaps in cross-cultural generalization and calls for the development of interpretable and hybrid models for improved risk prediction.This review aims to conduct a comprehensive examination of the etiological factors contributing to the development of suicidal thoughts in students, with the goal of enabling early detection through the application of AI and machine learning techniques.This paper aims to review the current state-of-the-art, highlight the limitations, and emphasizes the need to shift toward hybrid and ensemble deep learning models, which have shown early promise but lack extensive analysis in current literature.…”
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Research on the Motion Features Model for Underwater Targets with Multiple Highlights and Multiple Micro-Motion Forms by Tong-jing SUN, Zihan ZHOU, Dongliang PENG

“…Finally, a time-frequency analysis method is employed to extract motion features and estimate target parameters, thereby validating the accuracy and effectiveness of the proposed model. …”
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XGBoost models based on non imaging features for the prediction of mild cognitive impairment in older adults by Miguel A. Fernández-Blázquez, José M. Ruiz-Sánchez de León, Rubén Sanz-Blasco, Emilio Verche, Marina Ávila-Villanueva, María José Gil-Moreno, Mercedes Montenegro-Peña, Carmen Terrón, Cristina Fernández-García, Jaime Gómez-Ramírez

“…Model performance improved with the inclusion of cognitive assessments, with the most comprehensive model (Model 5) achieving the highest accuracy (86%) and area under the curve (AUC = 0.8359). Feature importance analysis revealed that variables such as memory tests, depressive symptoms, and age were significant predictors of MCI conversion. …”
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Research of the clinical features, risk factors, and surgical diagnosis of intramural stones in patients with gallbladder stones by Xiaobing Luo, Hongying Cai, Xiaofeng Wang, Ruihong Ma, Gang Wang, Sangui Wang, Tie Qiao

“…Abstract Crystals or stones within the gallbladder wall in patients with gallbladder stones (GBS) have been occasionally reported, but their clinical features and aetiology remain unclear. This retrospective study analysed 323 consecutive patients with GBS who underwent rigid choledochoscopic gallbladder-preserving cholecystolithotomy to determine the detection rate, clinical features, and potential risk factors of gallbladder intramural stones (IS). …”
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Clinical characteristics of patients with acute pulmonary embolism complicated with antiphospholipid syndrome: a case control study by Jianyuan Wan, Shuanglan Xu, Jingchao Yang, Qian Liu, Rongping Yang, Wei Kang, Jiao Yang, Xiqian Xing

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