Detection method of LDoS attacks based on combination of ANN ＆ KPCA by Zhijun WU, Liang LIU, Meng YUE

“…Low-rate denial-of-service (LDoS) attack is a new type of attack mode for TCP protocol.Characteristics of low average rate and strong concealment make it difficult for detection by traditional DoS detecting methods.According to characteristics of LDoS attacks,a new LDoS queue future was proposed from the router queue,the kernel principal component analysis (KPCA) method was combined with neural network,and a new method was present to detect LDoS attacks.The method reduced the dimensionality of queue feature via KPCA algorithm and made the reduced dimension data as the inputs of neural network.For the good sell-learning ability,BP neural network could generate a great LDoS attack classifier and this classifier was used to detect the attack.Experiment results show that the proposed approach has the characteristics of effectiveness and low algorithm complexity,which helps the design of high performance router.…”
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FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices. by Junjie Lu, Yuchen Zheng, Liwei Guan, Bing Lin, Wenzao Shi, Junyan Zhang, Yunping Wu

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. Firstly, the FasterNext module is proposed to reduce computational cost and enhance detection precision through lightweight design. …”
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An Adaptive Threshold-Based Pixel Point Tracking Algorithm Using Reference Features Leveraging the Multi-State Constrained Kalman Filter Feature Point Triangulation Technique for D... by Zohaib Wahab Memon, Yu Chen, Hai Zhang

“…Although feature points can be extracted and tracked using traditional techniques followed by the MSCKF feature point triangulation algorithm, the number of feature points in the image is often insufficient to capture the depth of the entire environment. …”
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A Crowd Density Detection Algorithm for Tourist Attractions Based on Monitoring Video Dynamic Information Analysis by Lina Li

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Intelligent intrusion detection system based on crowd search optimization for attack classification in network security by Chetan Gupta, Amit Kumar, Neelesh Kumar Jain

“…In comparison to the various state-of-the-art classifiers, the CSO method was applied to the NSL-KDD dataset and the ROSPaCe dataset for the detection of the attacks. In the proposed work, we have used a random forest technique to perform feature selection (FS) to improve the effectiveness and efficiency of intrusion detection. …”
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Defect Detection in the DR Images of Aluminum and Magnesium Alloy Castings Based on the Improved YOLOv8 Algorithm by Mingfei CHEN, Shuai TIAN

“…Existing algorithms can miss detecting small target defects owing to the complex background structure and noise in the DR images of aluminum and magnesium alloy castings. …”
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A novel approach to intrusion detection system using hybrid flower pollination and cheetah optimization algorithm by Deepshikha Kumari, Prashant Pranav, Abhinav Sinha, Sandip Dutta

“…A novel hybrid IDS model integrating the Flower Pollination Algorithm (FPA), Cheetah Optimization Algorithm (COA), and Artificial Neural Networks (ANN) is proposed to enhance detection accuracy, reduce false positives, and optimize feature selection, anomaly detection, and rule adaptation. …”
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Improved Aerial Surface Floating Object Detection and Classification Recognition Algorithm Based on YOLOv8n by Lili Song, Haixin Deng, Jianfeng Han, Xiongwei Gao

“…The water surface environment is highly complex, and floating objects in aerial images often occupy a minimal proportion, leading to significantly reduced feature representation. These challenges pose substantial difficulties for current research on the detection and classification of water surface floating objects. …”
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A Lightweight Citrus Ripeness Detection Algorithm Based on Visual Saliency Priors and Improved RT-DETR by Yutong Huang, Xianyao Wang, Xinyao Liu, Liping Cai, Xuefei Feng, Xiaoyan Chen

“…To address this, we constructed a citrus ripeness detection dataset under complex orchard conditions, proposed a lightweight algorithm based on visual saliency priors and the RT-DETR model, and named it LightSal-RTDETR. …”
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ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection by N. I. Md. Ashafuddula, Rafiqul Islam

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GCS-YOLO: A Lightweight Detection Algorithm for Grape Leaf Diseases Based on Improved YOLOv8 by Qiang Hu, Yunhua Zhang

“…The CBAM attention mechanism is added to the model to improve the extraction of subtle features of lesions in complex environments. Cross-scale shared convolution parameters and separated batch normalization techniques are used to optimize the detection head, achieving a lightweight design and improving the detection efficiency of the algorithm. …”
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A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients by Alla Ahmad Hassan, Tarik A Rashid

“…Based on the comparison, this paper grouped the top seven ML models such as Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian Networks algorithms and measured feature importance, and other, to justify the differences between classification models. …”
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A multilayer deep autoencoder approach for cross layer IoT attack detection using deep learning algorithms by K. Saranya, A. Valarmathi

“…This technology effectively safeguards against various cyber threats, including Man-in-the-Middle attacks at the network layer and Distributed Denial of Service (DDoS) attacks at the transport layer of IoT networks. To improve detection and adapt to emerging attack methods, the M-LDAE system employs deep learning algorithms such as RNNs, GNNs, and TCNs. …”
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Privacy protection risk identification mechanism based on automated feature combination by CAI Minchao, YAO Hongwei, WANG Yang, QIN Zhan, CHEN Shaomeng, REN Kui

“…In practice, the anomaly detection (AD) algorithm usually faced technical challenges such as difficulty in optimizing feature combinations, difficulty in improving classifier accuracy, and low model application efficiency. …”
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Optimizing Cancer Detection: Swarm Algorithms Combined with Deep Learning in Colon and Lung Cancer using Biomedical Images by HariKrishna Pathipati, Lova Naga Babu Ramisetti, Desidi Narsimha Reddy, Swetha Pesaru, Mashetty Balakrishna, Thota Anitha

“…Eventually, the whale optimization algorithm (WOA) is used to optimally choose the hyperparameters of the CNN‐BiGRU model. …”
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A Fine-Grained Aircraft Target Recognition Algorithm for Remote Sensing Images Based on YOLOV8 by Xiao-Nan Jiang, Xiang-Qian Niu, Fan-Lu Wu, Yao Fu, He Bao, Yan-Chao Fan, Yu Zhang, Jun-Yan Pei

“…This article addresses the issues of missed and false detections in existing aircraft target fine-grained recognition algorithms for remote sensing images by proposing an improved algorithm based on YOLOv8, called FD-YOLOv8 (Focus Detail-YOLOv8). …”
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Multimodal anomaly detection in complex environments using video and audio fusion by Yuanyuan Wang, Yijie Zhao, Yanhua Huo, Yiping Lu

“…Abstract Due to complex environmental conditions and varying noise levels, traditional models are limited in their effectiveness for detecting anomalies in video sequences. Aiming at the challenges of accuracy, robustness, and real-time processing requirements in the field of image and video processing, this study proposes an anomaly detection and recognition algorithm for video image data based on deep learning. …”
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An enhanced YOLOv8 model for accurate detection of solid floating waste by Juxing Di, Kaikai Xi, Yang Yang

“…The new model optimizes the feature fusion strategy in the neck, constructing a refined “160-80-40-20” multiscale detection frame work. …”
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Optimized deep residual networks for early detection of myocardial infarction from ECG signals by Pon Bharathi A, Madavan R, Sakthivel E

“…Later, the signal feature, transform feature, medical feature and statistical feature are extracted by the feature extraction phase followed by data augmentation that consists of permutation, random generation and re-sampling processes and finally, detection is accomplished by the SSS-DRN. …”
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Automated detection of spreading depolarizations in electrocorticography by Sreekar Puchala, Ethan Muchnik, Anca Ralescu, Jed A. Hartings

“…This model was applied to continuous electrocorticography data by generating a time series of SD probability [P SD (t)], and threshold P SD (t) values to trigger SD predictions were determined empirically. The developed algorithm was then tested on a novel dataset of 10 patients, resulting in 1,252 true positive detections (/1,953; 64% sensitivity) and 323 false positives (6.5/day). …”
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