Federated XAI IDS: An Explainable and Safeguarding Privacy Approach to Detect Intrusion Combining Federated Learning and SHAP by Kazi Fatema, Samrat Kumar Dey, Mehrin Anannya, Risala Tasin Khan, Mohammad Mamunur Rashid, Chunhua Su, Rashed Mazumder

“…Furthermore, UDP has been found to be the most significant feature of the network layer from the SHAP analysis. …”
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Impact-Induced Delamination Detection of Composites Based on Laser Ultrasonic Zero-Lag Cross-Correlation Imaging by Yun-Kyu An

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DconnLoop: a deep learning model for predicting chromatin loops based on multi-source data integration by Junfeng Wang, Kuikui Cheng, Chaokun Yan, Huimin Luo, Junwei Luo

“…However, current mainstream detection methods rely primarily on single-source data, such as Hi-C, which limits these methods’ ability to capture the diverse features of chromatin loop structures. …”
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Analyzing the Impact of Binaural Beats on Anxiety Levels by a New Method Based on Denoised Harmonic Subtraction and Transient Temporal Feature Extraction by Devika Rankhambe, Bharati Sanjay Ainapure, Bhargav Appasani, Avireni Srinivasulu, Nicu Bizon

“…Existing methods struggle to effectively reduce harmonics and capture the fine-grained temporal dynamics of EEG signals, leading to inaccurate feature extraction. Hence, a novel Denoised Harmonic Subtraction and Transient Temporal Feature Extraction is proposed to improve the analysis of the impact of binaural beats on anxiety levels. …”
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Developing Innovative Feature Extraction Techniques from the Emotion Recognition Field on Motor Imagery Using Brain–Computer Interface EEG Signals by Amr F. Mohamed, Vacius Jusas

“…Statistical, wavelet analysis, Hjorth parameters, higher-order spectra, fractal dimensions (Katz, Higuchi, and Petrosian), and a five-dimensional combination of all five feature sets were implemented. …”
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Day-to-day dynamics of fetal heart rate variability to detect chorioamnionitis in preterm premature rupture of membranes. by Aline Taoum, Guy Carrault, Caroline Tesson, Maxime Esvan, Bruno Laviolle, Linda Lassel

“…Then, each set of features was analyzed separately using the multiple factor analysis, where three groups were defined as the feature set for days 0, -1 and -2 prior to birth. …”
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Automated Detection and Biomarker Identification Associated with the Structural and Functional Progression of Glaucoma on Longitudinal Color Fundus Images by Iyad Majid, Zubin Mishra, Ziyuan Chris Wang, Vikas Chopra, Dale Heuer, Zhihong Jewel Hu

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Structural Vibration Detection Using the Optimized Optical Flow Technique and UAV After Removing UAV’s Motions by Xin Bai, Rongliang Xie, Ning Liu, Zi Zhang

“…Advances in computer vision technology have driven the research on vision-based structural vibration analysis and damage identification. In this study, an optimized Lucas–Kanade optical flow algorithm is proposed, and it integrates feature point trajectory analysis with an adaptive thresholding mechanism, and improves the accuracy of the measurements through an innovative error vector filtering strategy. …”
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Adaptive neuro-fuzzy inference systems for improved mastitis classification and diagnosis by Javad Shirani Shamsabadi, Saeid Ansari Mahyari, Mostafa Ghaderi-Zefrehei

“…The aim of this study was to compare the performance of three adaptive neuro-fuzzy inference systems (ANFIS) classification methodologies in classifying mastitis in Holstein dairy cattle: gradient descent (GD)-based ANFIS (GD-ANIFIS), particle swarm optimization (PSO)-based ANFIS (PSO-ANFIS) and genetic algorithm (GA)-based ANFIS (GA-ANFIS). Two feature reduction techniques were used to reduce data dimensions and improve model performance: Pearson correlation and principal component analysis. …”
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A multi-filter deep transfer learning framework for image-based autism spectrum disorder detection by Rodrigo Colnago Contreras, Monique Simplicio Viana, Victor José Souza Bernardino, Francisco Lledo dos Santos, Önsen Toygar, Rodrigo Capobianco Guido

“…With the advancement of computer vision and pattern recognition techniques, new possibilities are emerging to automate and enhance the detection of characteristics associated with ASD, particularly in the analysis of facial features. …”
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Construction of feature selection and efficacy prediction model for transformation therapy of locally advanced pancreatic cancer based on CT, 18F-FDG PET/CT, DNA mutation, and CA19... by Liang Qi, Xiang Li, Jiayao Ni, Yali Du, Qing Gu, Baorui Liu, Jian He, Juan Du

“…We used short-term and long-term treatment efficacy as the prediction outcomes and performed statistical and machine learning-based feature selection and COX regression analysis to identify potentially predictive features. …”
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Small Scale Multi-Object Segmentation in Mid-Infrared Image Using the Image Timing Features–Gaussian Mixture Model and Convolutional-UNet by Meng Lv, Haoting Liu, Mengmeng Wang, Dongyang Wang, Haiguang Li, Xiaofei Lu, Zhenhui Guo, Qing Li

“…The approach integrates the Image Timing Features–Gaussian Mixture Model (ITF-GMM) and Convolutional-UNet (Con-UNet) to improve the accuracy of target detection. …”
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Diagnosis of Alzheimer's disease using non-linear features of ERP signals through a hybrid attention-based CNN-LSTM model by Elias Mazrooei Rad, Sayyed Majid Mazinani, Seyyed Ali Zendehbad

“…The result of this approach is to show that the combination of non-linear EEG analysis with advanced deep learning methods could provide early and precise AD detection.…”
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Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis by Ji Soo Park, Sa-Yoon Park, Jae Won Moon, Kwangsoo Kim, Dong In Suh

“…Data were extracted on study design, dataset, ML methods, feature extraction, and classification tasks. Bivariate meta-analysis was performed for binary classification tasks, including wheezing and abnormal lung sound detection. …”
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Intrusion detection in cluster‐based wireless sensor networks: Current issues, opportunities and future research directions by Ayuba John, Ismail Fauzi Bin Isnin, Syed Hamid Hussain Madni, Muhammed Faheem

“…In addition, this research conducted several comparative studies of feature selection techniques and machine learning methodologies in the development of intrusion detection systems. …”
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Rapid and Low-Cost Detection of Thyroid Dysfunction Using Raman Spectroscopy and an Improved Support Vector Machine by Xiangxiang Zheng, Guodong Lv, Guoli Du, Zhengang Zhai, Jiaqing Mo, Xiaoyi Lv

“…Principal component analysis (PCA) was used for feature extraction and reduced the dimension of high-dimension spectral data; then, SVM was employed to establish an effective discriminant model. …”
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GLIDE: A Game Theory and Data-Driven Mimicking Linkage Intrusion Detection for Edge Computing Networks by Qianmu Li, Jun Hou, Shunmei Meng, Huaqiu Long

“…Due to the dual constraints of the hybrid feature of edge computing networks and the uncertainty of new attack features, implementing active defence measures such as detection, evasion, trap, and control is essential for the security protection of edge computing networks with high real-time and continuity requirements. …”
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Machine learning and artificial intelligence based Diabetes Mellitus detection and self-management: A systematic review by Jyotismita Chaki, S. Thillai Ganesh, S.K Cidham, S. Ananda Theertan

“…This review delivers an analysis of the detection, diagnosis, and self-management techniques of DM from six different facets viz., datasets of DM, pre-processing methods, feature extraction methods, machine learning-based identification, classification, and diagnosis of DM, artificial intelligence-based intelligent DM assistant and performance measures. …”
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Tree-Related High-Impedance Fault in Distribution Systems: Modeling, Detection, and Ignition Risk Assessment (Review) by Chunlan Yang, Wenhai Zhang, Rui Tang, Xianyong Xiao

“…Detailed reviews and comparisons are conducted on THIF detection methods, encompassing fault analysis, fault feature extraction, and fault identification. …”
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DAFDM: A Discerning Deep Learning Model for Active Fire Detection Based on Landsat-8 Imagery by Xu Gao, Wenzhong Shi, Min Zhang, Lukang Wang

“…Traditional methods for detecting AFs rely on the statistical analysis of AF radiance and background features. …”
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