MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target Detection by Jingcui Ma, Nian Pan, Dengyu Yin, Di Wang, Jin Zhou

“…To address the issues of sparse feature loss for small targets during the down-sampling phase of the traditional U-Net network and the semantic gap in the feature fusion process, a multilevel feature extraction and fusion attention network (MEFA-Net) is designed. …”
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Deepfake Face Detection and Adversarial Attack Defense Method Based on Multi-Feature Decision Fusion by Shanzhong Lei, Junfang Song, Feiyang Feng, Zhuyang Yan, Aixin Wang

“…To improve the accuracy of deepfake face detection models and strengthen their resistance to adversarial attacks, this manuscript introduces a method for detecting forged faces and defending against adversarial attacks based on a multi-feature decision fusion. …”
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An Improved YOLOP Lane-Line Detection Utilizing Feature Shift Aggregation for Intelligent Agricultural Machinery by Cundeng Wang, Xiyuan Chen, Zhiyuan Jiao, Shuang Song, Zhen Ma

“…This approach addresses the challenge faced by using embedded devices mounted on AGVs, which are unable to run multiple models for different tasks in parallel due to limited computational resources. For lane-line detection tasks, we also propose an improved YOLOP lane-line detection algorithm based on feature shift aggregation. …”
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Fish Freshness Detection Through Artificial Intelligence Approaches: A Comprehensive Study by Sabire Kılıçarslan, Meliha Merve Hız Çiçekliyurt, Serhat Kılıçarslan

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Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images by Vahid Faghih Dinevari, Ghader Karimian Khosroshahi, Mina Zolfy Lighvan

“…This method will utilize the advantages of the discrete wavelet transform (DWT) and singular value decomposition (SVD) algorithms to extract features from different color channels of the WCE images. …”
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Android malware detection method based on deep neural network by Fan CHAO, Zhi YANG, Xuehui DU, Yan SUN

“…Android is increasingly facing the threat of malware attacks.It is difficult to effectively detect large-sample and multi-class malware for traditional machine learning methods such as support vector machine,method for Android malware detection and family classification based on deep neural network was proposed.Based on the comprehensive extraction of application components,Intent Filter,permissions,and data flow,the method performed an effective feature selection to reduce dimensions,and conducted a large-sample detection and multi-class classification for malware based on deep neural network.The experimental results show that the method can conduct an effective detection and classification.The accuracy of binary classification between benign and malicious Apps is 97.73%,and the accuracy of family multi-class classification can reach 93.54%,which is higher than other machine learning algorithms.…”
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Comprehensive empirical evaluation of feature extractors in computer vision by Murat ISIK

“…Feature detection and matching are fundamental components in computer vision, underpinning a broad spectrum of applications. …”
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Deep Learning Algorithm for Optimized Sensor Data Fusion in Fault Diagnosis and Tolerance by M. Elhoseny, Deepak Dasaratha Rao, Bala Dhandayuthapani Veerasamy, Noha Alduaiji, J. Shreyas, Piyush Kumar Shukla

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A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study by Maria Habib, Victor Vicente-Palacios, Pablo García-Sánchez

“…This paper introduces a multi-objective bio-inspired, AI-based optimization approach for the automated detection of voice disorders. Different multi-objective evolutionary algorithms (the Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-II), and the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D)) have been compared to detect voice disorders by optimizing two conflicting objectives: error rate and the number of features. …”
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Intrusion Detection Framework for Internet of Things with Rule Induction for Model Explanation by Kayode S. Adewole, Andreas Jacobsson, Paul Davidsson

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Multi-Level Intertemporal Attention-Guided Network for Change Detection in Remote Sensing Images by Shuo Liu, Qinyu Zhang, Yuhang Zhang, Xiaochen Niu, Wuxia Zhang, Fei Xie

“…Through comprehensive testing on two datasets, the MIANet algorithm proves to be effective and robust, achieving detection results that are either better or at least comparable with current methods in terms of accuracy and reliability.…”
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A Hybrid PSO-GA Optimized Approach for COVID-19 Detection Using CT Scan by Abdelghany Fathy, Hatem Abdelkader, Samia Allaoua Chelloug, Mohammed Saleh Ali Muthanna, Amira Abdelatey

“…In the first step, pre-trained convolutional neural networks (CNNs), including VGG-16, ResNet-50, and MobileNet-v2, are utilized to extract critical features from COVID-19-affected lung images. In the second step, a hybrid particle swarm optimization (PSO) and genetic algorithm (GA) optimized approach, called Hybrid PSO-GA, is developed and used to select the optimal features that can increase the accuracy of COVID-19 detection. …”
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Integrating Information Gain and Chi-Square for Enhanced Malware Detection Performance by Fauzi Adi Rafrastara, Wildanil Ghozi, Ramadhan Rakhmat Sani, Lekso Budi Handoko, Abdussalam Abdussalam, Elkaf Rahmawan Pramudya, Faizal M. Abdollah

“…Recent studies have shown that this challenge can be addressed by employing machine learning algorithms for detection. Some studies have also implemented various feature selection methods to optimize detection efficiency. …”
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Automated Detection of Gastrointestinal Diseases Using Resnet50*-Based Explainable Deep Feature Engineering Model with Endoscopy Images by Veysel Yusuf Cambay, Prabal Datta Barua, Abdul Hafeez Baig, Sengul Dogan, Mehmet Baygin, Turker Tuncer, U. R. Acharya

“…This work aims to develop a novel convolutional neural network (CNN) named ResNet50* to detect various gastrointestinal diseases using a new ResNet50*-based deep feature engineering model with endoscopy images. …”
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MA-YOLO: A Pest Target Detection Algorithm with Multi-Scale Fusion and Attention Mechanism by Yongzong Lu, Pengfei Liu, Chong Tan

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A Hybrid Algorithm for Contour Thinning in Image Processing by Mamatov Narzullo, Niyozmatova Nilufar, Jalelova Malika, Samijonov Abdurashid, Samijonov Boymirzo

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High resolution remote sensing image object detection algorithm based on improved YOLOv8 by ZHANG Xia, QIAO Huanyu, CAO Feng

“…An improved efficient decoupled detection head was designed. Finally, combining NWD and WIoU loss functions, a new loss function NWD-WIoU was designed to accelerate the convergence speed of algorithm detection and improve the detection performance of the algorithm for small objects. …”
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Pillar-X: Integrating Self-Learned Image Features to Improve 3D Object Detection by Mihaly Csontho, Andras Rovid

“…The proposed model achieves this while maintaining a speed similar to the baseline algorithm (¿20 Hz). By comparing the network using both PASCAL and a specific criterion, it can be concluded that Pillar-X is able to improve accuracy and reliability in 3D object detection tasks.…”
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Study on the Transient Extraction Transform Algorithm for Defect Detection in Welded Plates Based on Laser Vibrometer by Yu Du, Xinke Xu, Longbiao Zhao, Dijian Yuan, Jinwen Wang

“…This paper addresses the issue of detecting welding defects in steel plates during the welding process by proposing a method that combines the laser vibrometer with transient feature extraction technology. …”
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Cervical Cancer Detection Using Deep Neural Network and Hybrid Waterwheel Plant Optimization Algorithm by Sarah A. Alzakari, Amel Ali Alhussan, S.K. Towfek, Marwa Metwally, Dina Ahmed Salem

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