Deep Learning Model for Feature Extraction and Anomaly Recognition in High-Dimensional Energy Metering Data by Huakun Que, Zetao Jiang, Zhifeng Zhou, Yongsheng He, Xin Liu

“…Objectives: This study aims to develop a deep learning-based method to detect anomalies in high-dimensional energy metering data, overcoming the limitations of existing techniques that struggle with data complexity and lack effective contextual analysis. …”
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Using pseudo-AI submissions for detecting AI-generated code by Shariq Bashir

“…Previous studies have explored ways to detect AI-generated text, such as analyzing structural differences, embedding watermarks, examining specific features, or using fine-tuned language models. …”
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Isfahan Artificial Intelligence Event 2023: Reflux Detection Competition by Azra Rasouli Kenari, Ahmadreza Montazerolghaem, Zahra Zojaji, Mehdi Ghatee, Behnam Yousefimehr, Amin Rahmani, Mahdi Kalani, Farnoush Kiyanpour, Mohamad Kiani-Abari, Mohammad Yasin Fakhar, Safiyeh Rezaei, Mojtaba Tahernia, Mohammad Hossein Vafaie, Hamidreza Besharatnezhad, Vahid Rahimi Bafrani, Mohamad Taghi Tofighi, Peyman Adibi Sedeh, Maryam Soheilipour, Hossein Rabbani

“…Achieving success necessitates the seamless collaboration of two key components: a reflux definition criteria protocol established by gastrointestinal experts and a comprehensive analysis of MII data for reflux detection. Method: In an endeavor to address this challenge, our team assembled a dataset comprising 201 MII episodes. …”
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A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking by Mohamed Mahmoud, Mahmoud SalahEldin Kasem, Hyun-Soo Kang

“…This survey paper presents a comprehensive analysis of the challenges and advancements in recognizing and detecting individuals with masked faces, which has seen innovative shifts due to the necessity of adapting to new societal norms. …”
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Detection of OSA Through the Application of Deep Learning on Polysomnography Data by Hasan Ulutas, Recep Sinan Arslan, Muhammet Emin Sahin, Halil Ibrahim Cosar, Cagri Arisoy, Ahmet Sertol Koksal, Mehmet Bakir, Bulent Ciftci

“…The proposed methodology focusses on the use of deep neural networks (DNNs) to enhance the accuracy and reliability of sleep apnea detection. By employing meticulous data collection, preprocessing, and analysis, the study demonstrates the potential of DNNs to capture intricate and high-dimensional features within complex sleep data, allowing precise and reliable diagnosis. …”
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A Comprehensive Joint Learning System to Detect Skin Cancer by Lubna Riaz, Hafiz Muhammad Qadir, Ghulam Ali, Mubashir Ali, Muhammad Ahsan Raza, Anca D. Jurcut, Jehad Ali

“…This research offers a joint learning system using Convolutional Neural Networks (CNN) and Local Binary Pattern (LBP) followed by its concatenation of all the extracted features through CNN and LBP architecture. The proposed system is trained and tested using the widely used publicly accessible dataset for skin cancer detection to solve multiclass skin disease issues. …”
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Mixture-of-experts graph transformers for interpretable particle collision detection by Donatella Genovese, Alessandro Sgroi, Alessio Devoto, Samuel Valentine, Lennox Wood, Cristiano Sebastiani, Simone Scardapane, Monica D’Onofrio, Stefano Giagu

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Robust Resilience Blocks Detection Problem in Dynamic Social Networks by Yuxin Gao, Jianming Zhu, Peikun Ni, Guoqing Wang

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Use of satellite data for detecting icebergs and evaluating the iceberg threats by I. A. Bychkova, V. G. Smirnov

“…Te developed method of iceberg detection is based on statistical criteria for fnding gradient zones in the analysis of two-dimensional felds of satellite images. …”
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Fusion feature-based hybrid methods for diagnosing oral squamous cell carcinoma in histopathological images by Jiaxing Li

“…ObjectiveThis study is experimental in nature and assesses the effectiveness of the Cross-Attention Vision Transformer (CrossViT) in the early detection of Oral Squamous Cell Carcinoma (OSCC) and proposes a hybrid model that combines CrossViT features with manually extracted features to improve the accuracy and robustness of OSCC diagnosis.MethodsWe employed the CrossViT architecture, which utilizes a dual attention mechanism to process multi-scale features, in combination with Convolutional Neural Networks (CNN) technology for the effective analysis of image patches. …”
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Detecting Keratoconus in Adolescents with Anterior Segment Optical Coherence Tomography by Burcu Yücekul, Anika Förster, H. Burkhard Dick, Suphi Taneri

“…Assessing the applicability of an algorithm developed for keratoconus detection in adolescents. This algorithm relies on optical coherence tomography (OCT) and incorporates features related to corneal pachymetric and epithelial thickness alterations. …”
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Improved CSW-YOLO Model for Bitter Melon Phenotype Detection by Haobin Xu, Xianhua Zhang, Weilin Shen, Zhiqiang Lin, Shuang Liu, Qi Jia, Honglong Li, Jingyuan Zheng, Fenglin Zhong

“…Furthermore, the effectiveness of the improvements was validated through heatmap analysis and ablation experiments, demonstrating that the CSW-YOLO model can more accurately focus on target features, reduce false detection rates, and enhance generalization capabilities. …”
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Methodology for Feature Selection of Time Domain Vibration Signals for Assessing the Failure Severity Levels in Gearboxes by Antonio Pérez-Torres, René-Vinicio Sánchez, Susana Barceló-Cerdá

“…Early failure detection in gear systems reduces unplanned downtime and associated maintenance costs in rotating machinery. …”
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360 Using machine learning to analyze voice and detect aspiration by Cyril Varghese, Jianwei Zhang, Sara A. Charney, Abdelmohaymin Abdalla, Stacy Holyfield, Adam Brown, Hunter Stearns, Michelle Higgins, Julie Liss, Nan Zhang, David G. Lott, Victor E. Ortega, Visar Berisha

“…Methods/Study Population: Retrospectively recorded [i] phonations from 163 unique ENT patients were analyzed for acoustic features including jitter, shimmer, harmonic to noise ratio (HNR), etc. …”
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Milling Machine Fault Diagnosis Using Acoustic Emission and Hybrid Deep Learning with Feature Optimization by Muhammad Umar, Muhammad Farooq Siddique, Niamat Ullah, Jong-Myon Kim

“…AE signals, capturing the dynamic responses of machine components, are transformed into continuous wavelet transform (CWT) scalograms for further analysis. Gaussian filtering is applied to enhance the clarity of these scalograms, effectively reducing noise while maintaining essential features. …”
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Early detection of fungal infection of Arabidopsis and brassica by Raman spectroscopy by Song-Yi Kuo, Ling-Ying Chiu, Ekta Jain, Gajendra Pratap Singh, Muhammad Nabil Syafiq Bin Jamaludin, Rajeev J. Ram, Rajeev J. Ram, Nam-Hai Chua, Nam-Hai Chua

“…Principal component analysis differentiated Raman spectral features associated with fungal and bacterial infections, emphasizing their unique profiles and reinforcing the utility of Raman spectroscopy for early detection of pathogen-related plant stress. …”
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An interpretable XAI deep EEG model for schizophrenia diagnosis using feature selection and attention mechanisms by Ahmad Almadhor, Stephen Ojo, Thomas I. Nathaniel, Shtwai Alsubai, Abdullah Alharthi, Abdullah Al Hejaili, Gabriel Avelino Sampedro

“…In addition to fine-tuning input dimensionality, F-test feature selection increases learning efficiency.ResultsThrough the integration of feature importance analysis and conventional performance measures, this study presents valuable insights into the discriminative neurophysiological patterns associated with Schizophrenia, advancing both diagnostic and neuroscientific expertise. …”
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A Hybrid Brain Stroke Prediction Framework: Integrating Feature Selection, Classification, and Hyperparameter Optimization by Mohammad Amin, Khalid M. O. Nahar, Hasan Gharaibeh, Rabia Emhamed Al Mamlook, Ahmad Nasayreh, Nesrine Atitallah, Ali Gharaibeh, Raneem Hamad, Raed Abu Zitar, Aseel Smerat, Laith Abualigah

“…We used a publicly available Harvard Stroke Prediction Data Warehouse dataset, applying multiple feature selection methods: ANOVA, chi‐square, mutual information classification, and analysis of variance to identify relevant features. …”
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EXAMINING THE IMPACT OF FEATURE SELECTION TECHNIQUES ON MACHINE AND DEEP LEARNING MODELS FOR THE PREDICTION OF COVID-19 by Hafiza Zoya Mojahid, Jasni Mohamad Zain, Marina Yusoff, Abdul Basit, Abdul Kadir Jumaat, Mushtaq Ali

“…This study delves into key variable selection methods—specifically Recursive Feature Elimination (RFE), Principal Component Analysis (PCA) and Least Absolute Shrinkage and Selection Operator (LASSO). …”

Local Outlier Detection Method Based on Improved K-means by Yu ZHOU, Hao XIA, Xuezhen YUE, Peichong WANG

“…The task of outlier detection involves identifying these points and analyzing their potential abnormal information through the analysis of data attribute features. …”
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