Fraud Detection on Crowdfunding Platforms Using Multiple Feature Selection Methods by Seunghun Lee, Heemin Park, Hyun-Chul Kim

“…In order to identify and comprehend the distinguishing characteristics of fraudulent campaigns, we first propose 1) using a wide variety of characteristics of campaign projects and project creators, including their profiles, behavior, social traits, and language; then, 2) we propose to use and combine three well-known multiple feature selection methods, which are based on Correlation-based Feature Selection (CFS), Pearson Correlation Coefficient (PCC), and Information Gain (IG), to identify representative features of fraudulent campaigns. …”
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Detection of Selected Heavy Metal Ions Using Organic Chromofluorescent Chemosensors by Samina Aslam, Iram Kousar, Sadia Rani, Wajiha Altaf, Sadia Bristy, Rachid Skouta

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Optimizing Feature Selection for IOT Intrusion Detection Using RFE and PSO by zahraa mehssen agheeb Alhamdawee

“…RFE notably improved kNN and DT accuracy, while SVM showed consistent results regardless of the feature of selection. These results highlight the importance of feature selection in optimizing classifiers for IoT intrusion detection , and achieved perfect scores (1,00) across all metrics.The aim from this paper is to enhance intrusion detection in iot networks by designing adual stage feature selection method based on RFE and PSO.…”
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Detection of wheat, chickpea, and sea foam in black pepper using hyperspectral imaging technique by Mohammad Hossein Nargesi, Jafar Amiri Parian, Kamran Kheiralipour

“…After feature extraction, sequential feature selection method was used to find efficient features and then artificial neural network was used for classification and prediction. …”
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Detection of LUAD-Associated Genes Using Wasserstein Distance in Multiomics Feature Selection by Shaofei Zhao, Siming Huang, Lingli Yang, Weiyu Zhou, Kexuan Li, Shige Wang

“…Using The Cancer Genome Atlas (TCGA) dataset, we integrated copy number alteration (CNA), mRNA expression, and DNA methylation data as predictors and applied our selected methods. …”
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EEG-Based Emotion Detection Using Roberts Similarity and PSO Feature Selection by Mustafa Hussein Mohammed, Mustafa Noaman Kadhim, Dhiah Al-Shammary, Ayman Ibaida

“…In this paper, a novel classifier based on Robert’s similarity measure is introduced for emotion detection using electroencephalogram (EEG) signals. …”
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Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques by Mohamed Ali Kazi

“…Subsequently, a methodology is proposed for detecting banking malware C&C communication traffic, and this methodology is tested using several feature selection algorithms to determine which feature selection algorithm performs the best. …”
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Replica Node Detection Using Enhanced Single Hop Detection with Clonal Selection Algorithm in Mobile Wireless Sensor Networks by L. S. Sindhuja, G. Padmavathi

“…This paper proposes to solve these issues by enhancing the Single Hop Detection (SHD) method using the Clonal Selection algorithm to detect the clones by selecting the appropriate witness nodes. …”
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Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers by Raja Azlina Raja Mahmood, AmirHossien Abdi, Masnida Hussin

“…The performance of the abovementioned classifiers using different features values was evaluated.  The experimental results indicate that the detection accuracy improves by approximately 1.55% when implemented using the PSO-based selected features than that of using GA-based selected features.  …”
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Enhanced Peer-to-Peer Botnet Detection Using Differential Evolution for Optimized Feature Selection by Sangita Baruah, Vaskar Deka, Dulumani Das, Utpal Barman, Manob Jyoti Saikia

“…Differential evolution is a population-based meta-heuristic technique which can be applied to nonlinear and non-differentiable optimization problems owing to its fast convergence and use of few control parameters. Apart from that, an ensemble learning algorithm is also employed to support and enhance the detection phase, providing a robust defense against the dynamic and sophisticated nature of modern P2P botnets. …”
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Improving Image Spam Detection Using a New Image Texture Features Selection by Azam Shekari Shahrak, Seyed javad Mirabedini, Nasser Mikaeilvand, Seyed Hamid Haj Seyed Javadi

“…The purpose of the research is to use image texture features to detect image spam. …”
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Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection by Mohammad Abrar, Abdu Salam, Ahmed Albugmi, Fahad Al-otaibi, Farhan Amin, Isabel de la Torre, Thania Candelaria Chio Montero, Perla Araceli Arroyo Gala

“…In the feature extraction stage, feature selection is performed using CAOA. In addition, classification is performed using a Support Vector Machine (SVM) based on features extracted through Multivariate Empirical Mode Function (MEMF) and entropy measures. …”
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Enhanced Anomaly Detection in IoT Networks Using Deep Autoencoders with Feature Selection Techniques by Hamza Rhachi, Younes Balboul, Anas Bouayad

“…More specifically, we propose and implement a model that uses DAE (deep autoencoder) to detect and classify the network data, with an ANOVA F-Test for the feature selection. …”
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Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks by Masoud Geravanchizadeh, Amir Shaygan Asl, Sebelan Danishvar

“…Finally, the corresponding EEG features of active electrodes are fed into the graph attention layers to detect auditory attention. The Fuglsang 2020 dataset is used in the experiments to train and test the proposed and baseline systems. …”
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Sensitive and Highly Selective Detection of Organophosphorus Pesticides Using Organic Field‐Effect Transistors by Yanan Lei, Haikuo Gao, Zhengsheng Qin, Jie Cheng, Can Gao, Dan Liu, Zhagen Miao, Xiangyu Tan, Pengsong Wang, Qingbin Li, Yu Zhang, Pu Wang, Xiaodan Ding, Ziyi Xie, Zhenling Liu, Jiaxin Yang, Yongshuai Wang, Yihan Zhang, Huanli Dong, Peilong Wang

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Intrusion detection system based on machine learning using least square support vector machine by Pratik Waghmode, Manideep Kanumuri, Hosam El-Ocla, Tanner Boyle

“…The hyperparameters of our model are tuned by utilizing those selected features to maximize the accuracy. The model developed is verified using three different datasets, which have been widely applied to intrusion detection. …”
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Rapid detection of illegal selective androgen receptor modulators in unregistered supplements using a combination of selected solid-state analytical methods by Izabela Jendrzejewska, Lubos Cehlarik, Tomasz Goryczka, Ewa Pietrasik, Natalia Pawlik, Josef Jampilek

“…For customs officers and police officers, minimal or no sample preparation before analysis is essential when detecting prohibited compounds/counterfeit products, ideally using on-site analysis. …”
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Selective protection of cereals using artificial neural networks by Wiesław Golka, Magdalena Szechyńska-Hebda, Jolanta Kowalska, Adrian Golka, Robert Grolik, Magdalena Jakubowska, Mateusz Góra, Aleksander Bomberski

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An effective method for anomaly detection in industrial Internet of Things using XGBoost and LSTM by Zhen Chen, ZhenWan Li, Jia Huang, ShengZheng Liu, HaiXia Long

“…Finally, combining the optimal threshold and loss function, we propose a model named MIX_LSTM for anomaly detection in IIoT. Experiments are conducted using the UNSW-NB15 and NSL-KDD datasets. …”
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Intelligent Cyber-Attack Detection in IoT Networks Using IDAOA-Based Wrapper Feature Selection by Mohammed Abdullah, Ryna Svyd

“…This study presents an innovative framework that integrates the Improved Dynamic Arithmetic Optimization Algorithm (IDAOA) with a Bagging technique to enhance the performance of intelligent cyber intrusion detection systems. The IDAOA serves as a wrapper-based feature selection method, optimizing the identification of the most impactful features while balancing local exploration and global exploitation. …”
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