Improved YOLO for long range detection of small drones by Sicheng Zhou, Lei Yang, Huiting Liu, Chongqin Zhou, Jiacheng Liu, Yang Wang, Shuai Zhao, Keyi Wang

“…Abstract The timely and accurate detection of unidentified drones is crucial for public safety. …”
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GE-YOLO for Weed Detection in Rice Paddy Fields by Zimeng Chen, Baifan Chen, Yi Huang, Zeshun Zhou

“…It introduces the Neck network with the Gold-YOLO feature aggregation and distribution network to enhance the network’s ability to fuse multi-scale features and detect weeds of different sizes. …”
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Recurrent Neural Network Optimized by Grasshopper for Accurate Audio Data-Based Diagnosis of Parkinson's Disease by Saif Wali Ali Alsudani, Ghassan Khudair Saud

“… Proposed here is a speech-based diagnostic framework for detecting Parkinson's disease that utilizes a Long Short-Term Memory neural network and the Grasshopper Optimization Algorithm. …”
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Lithium Battery Thermal Runaway Warning Method Based on Multi-Feature Fusion by Mingwei DAI, Chunfu ZHANG, Jiawu YANG

“…[Conclusion] The early warning algorithm is able to accurately identify lithium batteries with abnormal temperature rise rates, and can promptly and precisely detect the timing and location of the opening of the safety valve in the lithium battery. …”
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Enhancing radiomics robustness using bayesian penalized likelihood PET reconstruction: application to Phantom and non-small cell lung cancer patient studies by Zahra Valibeiglou, Jalil Pirayesh Islamian, Yunus Soleymani, Saeed Farzanehfar, Farahnaz Aghahosseini, Neda Gilani, Arman Rahmim, Peyman Sheikhzadeh

“…Abstract Purpose This study aims to enhance the diagnostic and prognostic capabilities of PET imaging through improved robustness of radiomics features, utilizing the Bayesian penalized likelihood (BPL) reconstruction algorithm. …”
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Integration of Machine Learning and Wavelet Algorithms for Processing Probing Signals: An Example of Oil Wells by Zukhra Abdiakhmetova, Zhanerke Temirbekova

“…A key innovation of this study is the development of an algorithm that processes low-amplitude high-frequency signals, which are often difficult to detect with conventional methods. …”
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A New Method for Weak Fault Feature Extraction Based on Improved MED by Junlin Li, Jingsheng Jiang, Xiaohong Fan, Huaqing Wang, Liuyang Song, Wenbin Liu, Jianfeng Yang, Liangchao Chen

“…Because of the characteristics of weak signal and strong noise, the low-speed vibration signal fault feature extraction has been a hot spot and difficult problem in the field of equipment fault diagnosis. …”
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Graph-Based Radiomics Feature Extraction From 2D Retina Images by Ofelio Jorreia, Nuno Goncalves, Rui Cortesao

“…Based on predicted bifurcation points and blood vessel segments, we use the Graph-Based Radiomics Feature Extraction Algorithm (Graph-BRFExtract) to extract the adjacency matrix. …”
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Time synchronization attack detection for industrial wireless network by Sichao ZHANG, Wei LIANG, Xudong YUAN, Yinlong ZHANG, Meng ZHENG

“…High-precision time synchronization is the basis for ensuring the secure and reliable transmission of industrial wireless network (IWN).Delay attacks, as a class of time synchronization attacks which cannot be solved by cryptographic techniques, seriously threaten the secure operation of IWN.Firstly, based on the in-depth analysis on the time synchronization mechanisms of IWN, three-time synchronization attack models were proposed, including the one-way full life cycle delay attack, two-way full life cycle delay attack, and one-way non-full-life cycle delay attack.Stealthier delay attacks could be realized by the attack models under the premise that target nodes were not captured.Secondly, considering the problem that existing detection algorithms are difficult to detect stealthier delay attacks without obvious changes in time features, an attack detection algorithm based on a Bayesian model was proposed that extracts four representative features, including transmission rate, transmission delay, transmission success rate and time synchronization interval.In addition, in order to ensure the accuracy of the attack detection and classification in the presence of noise interference, the noise model of wireless channel was introduced to the Bayesian feature information matrix.Experimental results show that the proposed algorithm can effectively detect three kinds of attacks in the presence of noise.…”
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Time synchronization attack detection for industrial wireless network by Sichao ZHANG, Wei LIANG, Xudong YUAN, Yinlong ZHANG, Meng ZHENG

“…High-precision time synchronization is the basis for ensuring the secure and reliable transmission of industrial wireless network (IWN).Delay attacks, as a class of time synchronization attacks which cannot be solved by cryptographic techniques, seriously threaten the secure operation of IWN.Firstly, based on the in-depth analysis on the time synchronization mechanisms of IWN, three-time synchronization attack models were proposed, including the one-way full life cycle delay attack, two-way full life cycle delay attack, and one-way non-full-life cycle delay attack.Stealthier delay attacks could be realized by the attack models under the premise that target nodes were not captured.Secondly, considering the problem that existing detection algorithms are difficult to detect stealthier delay attacks without obvious changes in time features, an attack detection algorithm based on a Bayesian model was proposed that extracts four representative features, including transmission rate, transmission delay, transmission success rate and time synchronization interval.In addition, in order to ensure the accuracy of the attack detection and classification in the presence of noise interference, the noise model of wireless channel was introduced to the Bayesian feature information matrix.Experimental results show that the proposed algorithm can effectively detect three kinds of attacks in the presence of noise.…”
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Investigate the Use of Deep Learning in IoT Attack Detection by Mohamed Saddek Ghozlane, Adlen Kerboua, Smaine Mazouzi, Lakhdar Laimeche

“…This study contributes a comprehensive comparative analysis of deep learning models for IoT security, focusing on the effectiveness of weighted features in improving detection accuracy. The results provide valuable information for the advancement of real-time IoT attack detection systems.…”
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Change Detection Network Based on Transformer and Transfer Learning by Hua Li, Jingyu Li, Guanghao Luo, Liang Zhou, Hao Wu, Zhangcai Yin

“…Currently, deep learning based change detection algorithms have achieved excellent detection results through the meticulous design of feature extraction and change judgment modules. …”
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Evaluation of different spectral indices for wheat lodging assessment using machine learning algorithms by Shikha Sharda, Sumit Kumar, Raj Setia, Prince Dhiman, N. R. Patel, Brijendra Pateriya, Ali Salem, Ahmed Elbeltagi

“…This study presented a systematic approach for detecting the wheat lodging occurred during the end of March and April 2023 in the Ludhiana district of Punjab (India) from multi-temporal Sentinel-2 data using the machine learning algorithms. …”
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Detecting Textual Propaganda Using Machine Learning Techniques by Akib Mohi Ud Din Khanday, Qamar Rayees Khan, Syed Tanzeel Rabani

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Exploration of geo-spatial data and machine learning algorithms for robust wildfire occurrence prediction by Svetlana Illarionova, Dmitrii Shadrin, Fedor Gubanov, Mikhail Shutov, Usman Tasuev, Ksenia Evteeva, Maksim Mironenko, Evgeny Burnaev

“…The goal of this study is to explore the potential of predicting wildfire occurrences using various available environmental parameters - meteorological, geo-spatial, and anthropogenic - and machine learning (ML) algorithms. We developed a unified pipeline for data acquisition and subsequent ML-based algorithm development. …”
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A Novel Method for Community Detection in Bipartite Networks by Ali Khosrozadeh, Ali Movaghar, Mohammad Mehdi Gilanian Sadeghi, Hamidreza Mahyar

“…Over the past years, community detection has drawn a lot of attention. Numerous methods for community detection have been put forth. …”
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Detecting Unbalanced Network Traffic Intrusions With Deep Learning by S. Pavithra, K. Venkata Vikas

“…Furthermore, the Random Forest Regressor is used to ascertain the importance of features for enhancing detection accuracy and interpretability. …”
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Automatic Fall Risk Detection Based on Imbalanced Data by Yen-Hung Liu, Patrick C. K. Hung, Farkhund Iqbal, Benjamin C. M. Fung

“…In this paper, we propose a pose estimation-based fall detection algorithm to detect fall risks. We use body ratio, acceleration and deflection as key features instead of using the body keypoints coordinates. …”
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Improved RT-DETR Framework for Railway Obstacle Detection by Peng Li, Yanhui Peng, Su-Mei Wang, Cheng Zhong

“…However, existing algorithms face challenges related to insufficient multiscale object detection, high model redundancy, and poor real-time performance. …”
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Research on Improved YOLOv7 for Traffic Obstacle Detection by Yifan Yang, Song Cui, Xuan Xiang, Yuxing Bai, Liguo Zang, Hongshan Ding

“…Object detection and recognition algorithms are widely used in applications such as real-time monitoring and autonomous driving. …”
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