iSeizdiag: toward the framework development of epileptic seizure detection for healthcare by Ashish Sharma, Ashish Sharma, Akshat Saxena, Mradul Agrawal, Kunal Kishor, Deepti Kaushik, Prateek Jain, Arvind R. Yadav, Manob Jyoti Saikia, Manob Jyoti Saikia

“…Different neurological disorders are represented as different waves on EEG records.MethodThis paper involves the detection of Epilepsy which appears as rapid spiking on electroencephalogram signals, using feature extraction and machine learning techniques. …”
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The Possibilities of Modern Ultrasound Scanning in Uveal Melanoma Extrascleral Growth Detection by A. G. Amiryan, S. V. Saakyan, G. P. Zakharova, A. Yu. Tsygankov

“…In patients with EG, domed and irregular tumors were more often detected. The results of densitometric analysis of the tumor in patients with and without EG showed significantly higher acoustic density in patients with EG in the area of the base (A1) and central part (A2) of the tumor. …”
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Deep Learning in Defect Detection of Wind Turbine Blades: A Review by Katleho Masita, Ali N. Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…Furthermore, this review discusses the role of advanced data acquisition techniques, such as drone-based imaging, thermographic analysis, and LiDAR (Light Detection and Ranging), in generating high-resolution and multi-spectral data for improved detection accuracy. …”
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Leveraging U-Net and ASPP for effective fault detection in photovoltaic modules by Khalfalla Awedat, Masoud Alajmi, Mustafa Elfituri

“…This study presents a novel deep-learning-based approach to enhance fault detection in PV systems by customizing the Atrous Spatial Pyramid Pooling (ASPP) module within a U-Net architecture. …”
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CFR-YOLO: A Novel Cow Face Detection Network Based on YOLOv7 Improvement by Guohong Gao, Yuxin Ma, Jianping Wang, Zhiyu Li, Yan Wang, Haofan Bai

“…In order to solve these problems, this study explores the application of cattle face detection technology in cattle individual detection to improve the accuracy of detection, an approach that is particularly important in smart animal husbandry and animal behavior analysis. …”
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FEATURE EXTRACTION METHOD OF SOUND SIGNAL TO ROLLING BEARING BASED ON BLIND SOURCE SEPARATION AND MORLET WAVELET by LI JingJiao, CHEN EnLi, LIU YongQiang

“…It is a new method of sound signal testing and analysis,but the SNR is too low,so we raise a feature extraction method of sound signal to rolling bearing based on blind source separation and adaptive Morlet wavelet. …”
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Study of Detection of Typical Pesticides in Paddy Water Based on Dielectric Properties by Shuanggen Huang, Mei Yang, Junshi Huang, Longwei Shang, Qi Chen, Fang Peng, Muhua Liu, Yan Wu, Jinhui Zhao

“…Principal component analysis (PCA) and competitive adaptive reweighted sampling (CARS) were used to extract characteristic frequencies. …”
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Advanced hybrid deep learning model for enhanced evaluation of osteosarcoma histopathology images by Arezoo Borji, Arezoo Borji, Arezoo Borji, Gernot Kronreif, Bernhard Angermayr, Bernhard Angermayr, Sepideh Hatamikia, Sepideh Hatamikia

“…BackgroundRecent advances in machine learning are transforming medical image analysis, particularly in cancer detection and classification. …”
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Composite fault feature extraction for gears based on MCKD-EWT adaptive wavelet threshold noise reduction by Yanchang LV, Jingyue Wang, Chengqiang Zhang, Jianming Ding

“…The results of experimental data analysis show that compared with the feature extraction methods such as spatial scale threshold EWT-MCKD and Complete Ensemble Empirical Mode Decomposition (CEEMDAN)-MCKD, the proposed method is more suitable for the diagnosis of gear composite faults in a strong background noise environment, the noise interference is effectively suppressed, and the extraction effect of gear composite fault features is more obvious.…”
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Improved Liquefaction Hazard Assessment via Deep Feature Extraction and Stacked Ensemble Learning on Microtremor Data by Oussama Arab, Soufiana Mekouar, Mohamed Mastere, Roberto Cabieces, David Rodríguez Collantes

“…We created a synthetic dataset of 1000 samples using realistic feature ranges that mimic the Rif data region to test model performance and conduct sensitivity analysis. …”
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Hybrid Paddy disease classification using optimized statistical feature based transformation technique with explainable AI by M Amudha, Brindha K

“…The next step is to use a Cat Boost classifier to sort the features. Using SHAP analysis and the Cat Boost classifier, this study delves deeper into the model’s classification process and the effects of each feature on the model’s operation. …”
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Bag of Feature-Based Ensemble Subspace KNN Classifier in Muscle Ultrasound Diagnosis of Diabetic Peripheral Neuropathy by Kadhim K. Al-Barazanchi, Ali H. Al-Timemy, Zahid M. Kadhim

“…This work develops a computer-aided diagnostic (CAD) system based on muscle ultrasound that integrates the bag of features (BOF) and an ensemble subspace k-nearest neighbor (KNN) algorithm for DPN detection. …”
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FEBE-Net: Feature Exploration Attention and Boundary Enhancement Refinement Transformer Network for Bladder Tumor Segmentation by Chao Nie, Chao Xu, Zhengping Li

“…The automatic and accurate segmentation of bladder tumors is a key step in assisting urologists in diagnosis and analysis. At present, existing Transformer-based methods have limited ability to restore local detail features and insufficient boundary segmentation capabilities. …”
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FEPA-Net: A Building Extraction Network Based on Fusing the Feature Extraction and Position Attention Module by Yuexin Liu, Yulin Duan, Xuya Zhang, Wen Zhang, Chang Wang

“…This substitution aims to broaden the receptive field, with the primary intention of enabling the output of each convolution layer to incorporate a broader spectrum of feature information. Additionally, a feature extraction module is added to mitigate the loss of detailed features. …”
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ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning by Raiyan Jahangir, Muhammad Nazrul Islam, Md. Shofiqul Islam, Md. Motaharul Islam

“…As an outcome, the stack clas- sifier with XGBoost as the meta-classifier, trained with 65 important features determined by the Principal Component Analysis (PCA) technique, achieved the best performance among all the models. …”
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Machine learning-based feature selection for ultra-high-dimensional survival data: a computational approach by Nahid Salma, Majid Khan Majahar Ali, Raja Aqib Shamim

“…Gene interaction network analysis confirmed their role in RCC progression. Despite SCAD’s strong performance, it left 31% of data variability unexplained, suggesting hybrid ML models that integrate ensemble learning, two-component regression structures, and deep learning-based feature selection could further enhance gene selection and predictive accuracy. …”
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Exploring Feature Selection with Deep Learning for Kidney Tissue Microarray Classification Using Infrared Spectral Imaging by Zachary Caterer, Jordan Langlois, Connor McKeown, Mikayla Hady, Samuel Stumo, Suman Setty, Michael Walsh, Rahul Gomes

“…Chromophobe renal cell carcinoma is a rarer form of RCC that poses significant challenges to accurate diagnosis, as it shares many histologic features with Oncocytoma, a benign renal tumor. Biopsies for histopathological and immunohistochemical analysis have limitations in distinguishing chromophobe RCC from Oncocytoma. …”
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Cloud-edge collaborative data anomaly detection in industrial sensor networks. by Tao Yang, Xuefeng Jiang, Wei Li, Peiyu Liu, Jinming Wang, Weijie Hao, Qiang Yang

“…Nevertheless, the majority of existing methodologies face challenges in simultaneously and comprehensively analyzing both features. To solve the limitations above, this paper develops a cloud-edge collaborative data anomaly detection approach for industrial sensor networks that mainly consists of a sensor data detection model deployed at individual edges and a sensor data analysis model deployed in the cloud. …”
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Machine learning to detect melanoma exploiting nuclei morphology and Spatial organization by Giulia Veronesi, Nico Curti, Aldo Gardini, Giulia Querzoli, Gastone Castellani, Emi Dika

“…The images were first processed using a multi-resolution image processing pipeline with the aim of segmenting nuclei, evaluating their geometrical and morphological features, as well as their spatial organization. To characterize each slide, these features were synthesized into 44 variables, which were then subjected to Linear Discriminant Analysis. …”
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Enhanced water saturation estimation in hydrocarbon reservoirs using machine learning by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Dmitriy A. Martyushev

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