YOLO-SAG: An improved wildlife object detection algorithm based on YOLOv8n by Lingli Chen, Gang Li, Shunkai Zhang, Wenjie Mao, Mei Zhang

“…This paper proposes a wildlife target detection algorithm based on improved YOLOv8n - YOLO-SAG, which aims to balance accuracy and speed. …”
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A detection algorithm for small surface floating objects based on improved YOLOv5s by Xusheng YUE, Jun LI, Yaohong WANG, Penghao ZHU, Zhexing WANG, Xuanhao XU

“…ObjectiveTo address the challenges of false detection and missed detection in identifying floating bottles on the water surface in unmanned surface vehicle applications, this study proposes an improved small floating object detection algorithm based on YOLOv5s. …”
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Investigating the Performance of Open-Vocabulary Classification Algorithms for Pathway and Surface Material Detection in Urban Environments by Kauê de Moraes Vestena, Silvana Phillipi Camboim, Maria Antonia Brovelli, Daniel Rodrigues dos Santos

“…The second experiment evaluates standardized pavement classification using the Deep Pavements dataset and a fine-tuned CLIP algorithm optimized for detecting OSM-compliant pavement categories. …”
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Small target detection algorithm based on SAHI-Improved-YOLOv8 for UAV imagery: A case study of tree pit detection by Xiuhao Liang, Jun Xiang, Sheng Qin, Yundan Xiao, Lifen Chen, Dongxia Zou, Honglun Ma, Dong Huang, Yongxin Huang, Wei Wei

“…To address these challenges, this paper proposes a small target detection algorithm based on SAHI-ImprovedYOLOv8 for detecting tree pits, which includes Slicing Aided Hyper Inference (SAHI), Focal loss, Spatial Pyramid Pooling Concurrent Spatial Pyramid Convolution (SPPCSPC), and Convolutional Block Attention Module (CBAM). …”
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Distributed Denial of Service Attack Detection in Software-Defined Networks Using Decision Tree Algorithms by Ali Zaman, Salman A. Khan, Nazeeruddin Mohammad, Abdelhamied A. Ateya, Sadique Ahmad, Mohammed A. ElAffendi

“…There is abundant literature on the application of such algorithms on conventional networks. However, because SDNs are relatively new, they lack such abundance both in terms of novel algorithms and effective datasets when it comes to DDoS attack detection. …”
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OS-PCMM: Automatic Registration Algorithm for Optical and SAR Image Based on Phase Congruency and Multimoment Feature by Yuan Liang, Tao Su, Ruiqiu Wang, Jiangtao Liu

“…In this article, we propose an automatic registration algorithm for optical and SAR images based on phase congruency and multimoment features. …”
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DMSF-YOLO: Cow Behavior Recognition Algorithm Based on Dynamic Mechanism and Multi-Scale Feature Fusion by Changfeng Wu, Jiandong Fang, Xiuling Wang, Yudong Zhao

“…Because of the complex background, multi-scale behavior changes of dairy cows, similar behavior, and difficulty in detecting small targets in the actual dairy farm environment, this study proposes a dairy cow behavior recognition algorithm, DMSF-YOLO, based on dynamic mechanism and multi-scale feature fusion, which can quickly and accurately identify the lying, standing, walking, eating, drinking and mounting behaviors of dairy cows. …”
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Hybrid Methods Random Forest and FOX-Inspired Optimization Algorithm for Selecting Features in Cervical Cancer Data by Afidatul Masbakhah, Umu Sa'adah, Mohamad Muslikh

“…SI is able to combine new algorithms to improve performance in feature selection. …”
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Feature selection using a multi-strategy improved parrot optimization algorithm in software defect prediction by Qi Fei, Guisheng Yin, Zhian Sun

“…This study proposes a novel feature selection and defect prediction classification algorithm based on a multi-strategy enhanced Parrot Optimization (PO) algorithm. …”
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A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer Detection by Ahmed Adil Nafea, Manar AL-Mahdawi, Khattab M Ali Alheeti, Mustafa S. Ibrahim Alsumaidaie, Mohammed M AL-Ani

“…However, there are some limitations regarding accuracy in detection. This study introduces an approach that utilizes 1D CNN as feature extraction and employs machine learning (ML) algorithms such as XGBoost, random forests (RF), decision trees (DT) support vector machines (SVM) and k nearest neighbor (KNN) to classify samples as either benign or malignant aiming to enhance accuracy. …”
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Anomaly-Based Intrusion Detection System in Wireless Sensor Networks Using Machine Learning Algorithms by Belal Al-Fuhaidi, Zainab Farae, Farouk Al-Fahaidy, Gawed Nagi, Abdullatif Ghallab, Abdu Alameri

“…Experimental results show that the proposed model can detect intrusions using different ML algorithms with high accuracy. …”
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Evaluating and Enhancing Face Anti-Spoofing Algorithms for Light Makeup: A General Detection Approach by Zhimao Lai, Yang Guo, Yongjian Hu, Wenkang Su, Renhai Feng

“…The experimental outcomes confirm that the proposed algorithm exhibits superior detection capabilities when handling light makeup faces.…”
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Investigating Optimal EEG Channels and Features for Brain-Computer Interfaces: An Exploration using Evolutionary Algorithms and Machine Learning by Ali Ekhlasi, Hessam Ahmadi, Mohammad Saleh Hoseinzadeh

“…The Imperialist Competitive Algorithm (ICA) and Cuckoo Optimization Algorithm (COA) were utilized for feature selection. …”
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Lung Cancer Classification Using the Extreme Gradient Boosting (XGBoost) Algorithm and Mutual Information for Feature Selection by Regitha Zizilia, Yulison Herry Chrisnanto, Gunawan Abdillah

“…This study aims to evaluate the impact of feature selection using Mutual Information on the performance of lung cancer classification with the XGBoost algorithm. …”
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Computational intelligence to detect bearing faults using optimal features from motor current signals by G. Geetha, P. Geethanjali

“…The selection of optimal features is initially performed using three algorithms: the equilibrium optimizer, the emperor penguin optimizer (EPO), and the butterfly optimization method. …”
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Efficacy of swarm-based neural networks in automated depression detection by Alwan Atta, Dina ElSayad, Doaa Ezzat, Safaa Amin, Mahmoud ElGamal

“…Abstract As depression becomes a global pandemic, this research paper presents a comprehensive study for depression diagnosis using a custom-crafted deep learning model optimized with various swarm intelligence algorithms. Three different optimization algorithms-dragonfly algorithm, Firefly Algorithm, and Moth Flame Optimization Algorithm-have been employed for feature selection and dimensionality reduction. …”
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Heart Disease Prediction Using a Hybrid Feature Selection and Ensemble Learning Approach by Isha Gupta, Anu Bajaj, Manav Malhotra, Vikas Sharma, Ajith Abraham

“…This paper proposed an advanced prediction method that combines feature selection using a hybrid of Genetic Algorithm (GA) and Cuckoo Search Optimization (CSO) with a majority voting ensemble of Convolutional Neural Network and Random Forest. …”
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GIRH-Unet: Improved Residual Tobacco Segmentation Algorithm Based on GhostNetV3-Unet by Jianhua Ye, Yunda Zhang, Pan Li, Ze Guo

“…These factors contribute to the reduced accuracy and robustness of visual detection technologies based on segmentation algorithms within tobacco intelligent production systems, highlighting the need for a targeted segmentation model. …”
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RETRACTED: Computerized Detection of Calcium Oxalate Crystal Progression by Hanan A. Hosni Mahmoud

“…A new fast search algorithm—ODS: One Direction Search—is proposed to detect calcium oxalate crystal progression. …”
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Integrated artificial immune system for intrusion detection by Yue-bing CHEN, Chao FENG, Quan ZHANG, Chao-jing TANG

“…According to the practical requirements of intrusion detection,an integrated artificial immune system (IAIS) was proposed.The system combined dendritic cell algorithm(DCA)and negative selection algorithm(NSA).DCA was used to detect behavioral features.NSA was used to detect structural features.IAIS was validated on KDD 99 dataset.Comparisons to other approaches were made.The experimental results show that the detection performance of IAIS is comparable to classic classification algorithm.IAIS does not rely on labeled data to train detectors.It combines behavioral features and structural features to detect intrusions in real-time mode.…”
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