A Survey of Machine Learning Techniques Leveraging Brightness Indicators for Image Analysis in Biomedical Applications by Hajer Ghodhbani, Suvendi Rimer, Khmaies Ouahada, Adel M. Alimi

“…This paper presents a comprehensive survey of machine-learning techniques that leverage brightness indicators for image analysis within biomedical applications. By examining commonalities and challenges in brightness-based analysis, this survey provides insights into machine learning (ML) methods that enhance interpretability, noise reduction, and feature detection in the biomedical field. …”
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An exploratory analysis of longitudinal artificial intelligence for cognitive fatigue detection using neurophysiological based biosignal data by Sameer Nooh, Mahmoud Ragab, Rania Aboalela, Abdullah AL-Malaise AL-Ghamdi, Omar A. Abdulkader, Ghadah Alghamdi

“…Primarily, the EALAI-CFDNBD model utilized the linear scaling normalization (LSN) model to ensure that the input features were appropriately scaled for subsequent analysis. …”
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Video Anomaly Detection Methods: a Survey by WU Peichen, YUAN Lining, GUO Fang, LIU Zhao

“…By comparing the network architectures of different models, this paper summarizes the test datasets, use cases, advantages, and limitations of various abnormal behavior detection models. Furthermore, it compares and evaluates models using benchmark datasets and common evaluation standards such as frame-level and pixel-level standards, and conducts intra-class comparisons based on performance results, followed by analysis of the outcomes. …”
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Spatiotemporal Land Use Change Detection Through Automated Sampling and Multi-Feature Composite Analysis: A Case Study of the Ebinur Lake Basin by Yi Yang, Liang Zhao, Ya Guo, Shihua Liu, Xiang Qin, Yixiao Li, Xiaoqiong Jiang

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Machine Learning Approaches for Speech-Based Alzheimer’s Detection: A Comprehensive Survey by Ahmed Sharafeldeen, Justin Keowen, Ahmed Shaffie

“…This survey paper provides a comprehensive review of the current literature on the application of ML and DL techniques for AD detection through the analysis of a patient’s speech signal, utilizing various acoustic and textual features. …”
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Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis by Asif Rahman, Maqsood Hayat, Nadeem Iqbal, Fawaz Khaled Alarfaj, Salem Alkhalaf, Fahad Alturise

“…In addition, The SHAP analysis was used to identify the most important features in classification. …”
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Passive Radar Low Slow Small Detection Dataset (LSS-PR-1.0) and Multi-domain Feature Extraction and Analysis Methods by Xiaolong CHEN, Guilin RAO, Jian GUAN, Jinhao WANG, Hongyong WANG, Caisheng ZHANG, Jianxin YI, Xianrong WAN, Yunhua RAO

“…On this basis, four categories of ten multi-domain feature extraction and analysis methods are proposed, including time-domain features (relative average amplitude), frequency-domain features (spectral features, Doppler waterfall plot, and range Doppler features), time-frequency-domain features, and motion features (heading difference, trajectory parameters, speed variation interval, speed variation coefficient, and acceleration). …”
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Semantic knowledge graph fusion for fake news detection: Unifying content-based features and evidence-based analysis in the COVID-19 infodemic. by Rayees Ahmad Dar, Rana Hashmy, Muhammad Shahid Anwar, Patrik Böhm, Jaroslav Frnda

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Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring by Marcus Jenkins, Kirsty A. Franklin, Malcolm A. C. Nicoll, Nik C. Cole, Kevin Ruhomaun, Vikash Tatayah, Michal Mackiewicz

“…Traditional methods, which require extensive fieldwork, are increasingly being supplemented by time-lapse camera-trap imagery combined with an automatic analysis of the image data. The latter usually involves some object detector aimed at detecting relevant targets (commonly animals) in each image, followed by some postprocessing to gather activity and population data. …”
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A Study of Feature Combination for Vehicle Detection Based on Image Processing by Jon Arróspide, Luis Salgado

“…In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. …”
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Approach of detecting low-rate DoS attack based on combined features by Zhi-jun WU, Jing-an ZHANG, Meng YUE, Cai-feng ZHANG

“…LDoS (low-rate denial of service) attack is a kind of RoQ (reduction of quality) attack which has the characteristics of low average rate and strong concealment.These characteristics pose great threats to the security of cloud computing platform and big data center.Based on network traffic analysis,three intrinsic characteristics of LDoS attack flow were extracted to be a set of input to BP neural network,which is a classifier for LDoS attack detection.Hence,an approach of detecting LDoS attacks was proposed based on novel combined feature value.The proposed approach can speedily and accurately model the LDoS attack flows by the efficient self-organizing learning process of BP neural network,in which a proper decision-making indicator is set to detect LDoS attack in accuracy at the end of output.The proposed detection approach was tested in NS2 platform and verified in test-bed network environment by using the Linux TCP-kernel source code,which is a widely accepted LDoS attack generation tool.The detection probability derived from hypothesis testing is 96.68%.Compared with available researches,analysis results show that the performance of combined features detection is better than that of single feature,and has high computational efficiency.…”
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JDroid: Android malware detection using hybrid opcode feature vector by Recep Sinan Arslan

“…In this study, we propose a tool called JDroid that treats opcodes (Dalvik Opcode and Java ByteCode) as features based on static analysis. The proposed tool aims to detect malicious applications with a unique ensemble model in a stacked generalised structure that uses different opcode sequences as a hybrid, and where each feature is first trained separately and then used by an ensemble decision. …”
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SPECIFIC FEATURES OF NON-TUBERCULOUS PULMONARY DISEASES DETECTION IN TB HOSPITALS by Z. A. Laushkina, V. A. Krasnov

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The Bright Feature Transform for Prominent Point Scatterer Detection and Tone Mapping by Gregory D. Vetaw, Suren Jayasuriya

“…This paper introduces a fast image-processing method to visually identify and detect point scatterers in synthetic aperture imagery using the bright feature transform (BFT). …”
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Detecting Driver Drowsiness Using Hybrid Facial Features and Ensemble Learning by Changbiao Xu, Wenhao Huang, Jiao Liu, Lang Li

“…To address these issues, we propose a drowsiness detection method that combines an ensemble model with hybrid facial features. …”
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Detection of Road Rage in Vehicle Drivers Based on Speech Feature Fusion by Xiaofeng Feng, Chenhui Liu, Ying Chen

“…To improve the detection efficiency, principal component analysis (PCA) is used to reduce the dimensionality of the fused features. …”
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Hyperbolic geometry enhanced feature filtering network for industrial anomaly detection by Yanjun Feng, Jun Liu, Yonggang Gai

“…This mapping leverages the unique geometric properties of hyperbolic space, particularly the hyperbolic distance metric, to represent the distances between features more effectively. Next, the most relevant features for anomaly detection are selected through the anomaly-aware feature subset selection module, enhancing anomaly detection performance. …”
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Unsupervised Feature Representation Based on Deep Boltzmann Machine for Seizure Detection by Tengzi Liu, Muhammad Zohaib Hassan Shah, Xucun Yan, Dongping Yang

“…Visualization of EEG data in low-dimensional feature space can ease the annotation to support subsequent supervised learning for seizure detection. …”
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An optimized ensemble model with advanced feature selection for network intrusion detection by Afaq Ahmed, Muhammad Asim, Irshad Ullah, Zainulabidin, Abdelhamied A. Ateya

“…We conducted a comprehensive evaluation of the proposed approach against several well-known machine learning models, including AdaBoostM1 (AbM1), K-nearest neighbor (KNN), J48-Decision Tree (J48), multilayer perceptron (MLP), stochastic gradient descent (SGD), naïve Bayes (NB), and logistic model tree (LMT). The comparative analysis demonstrates the effectiveness and superiority of our method across various performance metrics, highlighting its potential to significantly enhance the capabilities of network intrusion detection systems.…”
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Can behavioral features reveal lying in an online personality questionnaire? The impact of mouse dynamics and speech by Eduard Kuric, Peter Demcak, Peter Smrecek, Stefan Benus

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