Combining Transfer Learning and Ensemble Algorithms for Improved Citrus Leaf Disease Classification by Hongyan Zhu, Dani Wang, Yuzhen Wei, Xuran Zhang, Lin Li

“…These findings conclusively illustrate that deep learning model fusion networks combining transfer learning and integration algorithms can automatically extract image features, enhance the automation and accuracy of disease recognition, demonstrate the significant potential and application value in citrus leaf disease classification, and potentially drive the development of smart agriculture.…”
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Multi-Task Perception Algorithm for Rail Transit Scenarios Based on Triplet Attention by GAO Rui, XIONG Yanping, WEI Chenfeng, XIE Guotao, GAO Ming

“…Aiming at the challenges of insufficient object detection accuracy and low detection speeds, and the pursuit of an accuracy-speed balance in environmental perception within rail transit scenarios, this paper proposes a multi-task perception model that features simultaneous detection and segmentation. …”
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A lightweight personnel detection method for underground coal mines by Shuai WANG, Wei YANG, Yuxiang LI, Jiaqi WU, Wei YANG

“…Commonly used detection algorithms have large parameter counts, high requirements on equipment arithmetic, and are not satisfactory for application in low illumination environments in coal mines. …”
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DGA malicious domain name identification based on XGBoost and particle swarm optimization algorithm by CHEN Zesheng, ZHOU Min, FENG Lichun, CHEN Weijie

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An Extrinsic Calibration Method for LiDAR and Camera Based on Feature Matching by CHEN Meilin, JIANG Guotao, PI Zhichao, HE Qian, TAN Bin, LUO Shuo, YANG Hailang

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One-Class Anomaly Detection for Industrial Applications: A Comparative Survey and Experimental Study by Davide Paolini, Pierpaolo Dini, Ettore Soldaini, Sergio Saponara

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ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection by Nadia Rashid, Rashid Mehmood, Fahad Alqurashi, Saad Alqahtany, Juan M. Corchado

“…It is trained using 139 datasets built upon 60 base datasets from 11 diverse domains (finance, healthcare, network security) and 80 ML and DL models composed of 22 base anomaly detection algorithms. It uses meta-features and correlation functions to evaluate 300 features. …”
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Intelligent classification models for food products basis on morphological, colour and texture features by Narendra Veernagouda Ganganagowder, Priya Kamath

“…The Correlation-based Feature Selection (CFS) algorithm and 2nd derivative pre-treatments of the Morphological, Colour and Texture features are used to train the models for classification and detection. …”
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Malware detection approach based on improved SOINN by Bin ZHANG, Lixun LI, Shuqin DONG

“…To deal with the problems of dynamic update of detection model and high computation costs in malware detection model based on batch learning,a novel malware detection approach is proposed by combing SOINN and supervised classifiers,to reduce computation costs and enable the detection model to update dynamically with the assistance of SOINN′s incremental learning characteristic.Firstly,the improved SOINN was given.According to the whole alignment algorithm,search the adjusted weights of neurons under all input sequences in the learning cycle and then calculate the average value of all adjusted weights as the final result,to avoid SOINN′s stability under different input sequences and representativeness of original data,therefore improve malware detection accuracy.Then a data preprocessing algorithm was proposed based on nonnegative matrix factor and Z-score normalization to transfer the malware behavior feature vector from high dimension and high order to low dimension and low order,to speed up and avoid overfitting and further improve detection accuracy.The results of experiments show that proposed approach supports dynamic updating of detection model and has a significantly higher accuracy of detecting unknown new samples and lower computation costs than tradition methods.…”
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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

“…Consequently, the accuracy of an ML model likely declines when irrelevant features are included from a vast dataset. In this paper, the exhaustive feature selection algorithm is employed to assess every possible combination of features in a dataset to evaluate its performance. …”
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Toward lightweight intrusion detection systems using the optimal and efficient feature pairs of the Bot-IoT 2018 dataset by Erman Özer, Murat İskefiyeli, Jahongir Azimjonov

“…Next, 10 full-feature-based intrusion detection systems were developed by training the 10 machine learning algorithms with the 12 full features. …”
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Multi-Target Vehicle Tracking Algorithm Based on Improved DeepSORT by Dudu Guo, Zhuzhou Li, Hongbo Shuai, Fei Zhou

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Image Matching Algorithm for Transmission Towers Based on CLAHE and Improved RANSAC by Ruihua Chen, Pan Yao, Shuo Wang, Chuanlong Lyu, Yuge Xu

“…To address the lack of robustness against illumination and blurring variations in aerial images of transmission towers, an improved image matching algorithm for aerial images is proposed. The proposed algorithm consists of two main components: an enhanced AKAZE algorithm and an improved three-stage feature matching strategy, which are used for feature point detection and feature matching, respectively. …”
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Automated Quality Control of Candle Jars via Anomaly Detection Using OCSVM and CNN-Based Feature Extraction by Azeddine Mjahad, Alfredo Rosado-Muñoz

“…Two anomaly detection strategies are explored: (1) a baseline model using convolutional neural networks (CNNs) as an end-to-end classifier and (2) a hybrid approach where features extracted by CNNs are fed into One-Class classification (OCC) algorithms, including One-Class SVM (OCSVM), One-Class Isolation Forest (OCIF), One-Class Local Outlier Factor (OCLOF), One-Class Elliptic Envelope (OCEE), One-Class Autoencoder (OCAutoencoder), and Support Vector Data Description (SVDD). …”
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Application of 4PCS and KD-ICP Alignment Methods Based on ISS Feature Points for Rail Wear Detection by Jie Shan, Hao Shi, Zhi Niu

“…The experimental results show that when the number of ISS feature points extracted is 4496, the 4PCS coarse alignment algorithm based on ISS feature points is higher than the original 4PCS algorithm as well as the other algorithms in terms of alignment accuracy; the ICP fine alignment algorithm based on the kd-tree is less than the original ICP algorithm as well as the other algorithms in terms of the time consumed. …”
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A comparative study of convolutional neural networks and traditional feature extraction techniques for adulteration detection in ground beef by Leila Bahmani, Saied Minaei, Alireza Mahdavian, Ahmad Banakar, Mahmoud Soltani Firouz

“…In order to identify the most appropriate feature extraction algorithm and classify samples having various levels of adulteration, Local Binary Pattern (LBP), Gray Level Co-occurrence Matrixes (GLCM) and Gabor filter were compared. …”
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ARSOD-YOLO: Enhancing Small Target Detection for Remote Sensing Images by Yijuan Qiu, Xiangyue Zheng, Xuying Hao, Gang Zhang, Tao Lei, Ping Jiang

“…The Adaptive Selective Feature Enhancement Module (AFEM) dynamically adjusts feature weights using GhostModule and sigmoid functions, thereby enhancing the accuracy of small target detection. …”
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Automated Tomato Leaf Disease Detection Using Image Processing: An SVM-Based Approach with GLCM and SIFT Features by Rashid Khan, Nasir Ud Din, Asim Zaman, Bingding Huang

“…Therefore, we proposed an approach that employs robust feature extraction methods, including the gray level co-occurrence matrix (GLCM) and scale-invariant feature transform (SIFT), coupled with a support vector machine (SVM) for adequate classification. …”
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Diabetic Retinopathy Detection Using DL-Based Feature Extraction and a Hybrid Attention-Based Stacking Ensemble by Sanjana Rajeshwar, Shreya Thaplyal, Anbarasi M., Siva Shanmugam G.

“…This paper addresses this challenge by utilizing advanced deep learning (DL) algorithms with established image processing techniques to enhance accuracy and efficiency in detection. …”
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AI-Based Ransomware Detection: A Comprehensive Review by Jannatul Ferdous, Rafiqul Islam, Arash Mahboubi, Md Zahidul Islam

“…This study contributes significantly to the development of a systematic evaluation framework that evaluates each component of the AI-based detection model framework using specific criteria and methodologies and analyzes how various AI algorithms respond to different ransomware attacks, thereby providing insights for more effective and robust detection methods. …”
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