A New Model Selection Metric for Biomarker Detection Algorithms and Tools by Bo Feng, Yubo Sun, Benny Zee

“…We proposed a new model selection metric that estimates the above two clinical utilities of biomarker detection algorithms without the need for a real drug clinical trial. …”
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Comparison between Statistical Approaches and Data Mining Algorithms for Outlier Detection by Annisa Putri Utami, Anwar Fitrianto, Khairil Anwar Notodiputro

“…The presence of outliers in data can have a negative impact on research but can contain important information for other research. …”
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Development and validation of multimodal deep learning algorithms for detecting pulmonary hypertension by Wei Zhao, Zhihua Huang, Xiaolin Diao, Zhan Yang, Zhihui Zhao, Yun Xia, Qing Zhao, Zhaohong Sun, Qunying Xi, Yanni Huo, Ou Xu, Jiahui Geng, Xin Li, Anqi Duan, Sicheng Zhang, Luyang Gao, Yijia Wang, Sicong Li, Qin Luo, Zhihong Liu

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Graph-Regularized Orthogonal Non-Negative Matrix Factorization with Itakura–Saito (IS) Divergence for Fault Detection by Yabing Liu, Juncheng Wu, Jin Zhang, Man-Fai Leung

“…This paper presents a novel approach to fault detection in industrial processes, called Graph-Regularized Orthogonal Non-negative Matrix Factorization with Itakura–Saito Divergence (GONMF-IS). …”
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Deep Learning Multi-Modal Melanoma Detection: Algorithm Development and Validation by Nithika Vivek, Karthik Ramesh

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A suggested algorithm for detection of multi drug-resistant tuberculosis in Zimbabwe by Beauty Makamure, Salome Makumbirofa, Tsitsi Bandason, Paul Leccese, Reggie Mutetwa, Val Robertson, Peter Mason

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Improving lameness detection in cows: A machine learning algorithm application by Elma Dervić, Caspar Matzhold, Christa Egger-Danner, Franz Steininger, Peter Klimek

“…A Random Forest classifier, using input features selected by the Boruta algorithm, was used for the prediction task; effects of individual features were further assessed using partial dependence plots. …”
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Innovative multi objective optimization based automatic fake news detection by Cebrail Barut, Suna Yildirim, Bilal Alatas, Gungor Yildirim

“…In order to minimize the negative effects of fake news, it has become a critical necessity to detect them quickly and effectively. …”
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Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers by Ruslan Abdulkadirov, Pavel Lyakhov, Denis Butusov, Nikolay Nagornov, Dmitry Reznikov, Anatoly Bobrov, Diana Kalita

“…In this paper, we propose the Yolov8 architecture with decomposed layers via canonical polyadic and Tucker methods for accelerating the solving of the object detection problem in satellite images. Our positive–negative momentum approaches enabled a reduction in the loss in precision and recall assessments for the proposed neural network. …”
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Diagnostic algorithm for the detection of carbapenemases and extended-spectrum β-lactamases in carbapenem-resistant Pseudomonas aeruginosa by Stefano Mancini, Laia Garcia-Verellen, Helena M. B. Seth-Smith, Peter M. Keller, Natalia Kolesnik-Goldmann, Muhammad Ali Syed, Irfan Ullah, Vladimira Hinic, Tim Roloff, Adrian Egli, Oliver Nolte

“…For the remaining C-T positive but I-R negative isolates, C-E showed 75% sensitivity and 78.6% specificity in detecting ESBL production. …”
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Anomaly detection algorithm based on Gaussian mixture variational auto encoder network by Huahua CHEN, Zhe CHEN, Chunsheng GUO, Na YING, Xueyi YE, Jianwu ZHANG

“…Anomalous data, which deviates from a large number of normal data, has a negative impact and contains a risk on various systems.Anomaly detection can detect anomalies in the data and provide important support for the normal operation of various systems, which has important practical significance.An anomaly detection algorithm based on Gaussian mixture variational auto encoder network was proposed, in which a variational autoencoder was built to extract the features of the input data based on Gaussian mixture distribution, and using this variational autoencoder to construct a deep support vector network to compress the feature space and find the minimum hyper sphere to separate the normal data and the abnormal data.Anomalies can be detected by the score from the Euclidean distance from the feature of data to the center of the hypersphere.The proposed algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST, and the corresponding average AUC are 0.954 and 0.937 respectively.The experimental results show that the proposed algorithm achieves preferable effects.…”
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Parameter Optimisation of Support Vector Machine using Genetic Algorithm for Cyberbullying Detection by Mohd Qorib Alqowiy, Ema Utami

“…The results demonstrate an accuracy improvement, with the genetic algorithm achieving an accuracy of 86%. This highlights the effectiveness of genetic algorithms in optimizing SVM parameters for cyberbullying detection.…”
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Enhanced APT detection with the improved KAN algorithm: capturing interdependencies for better accuracy by Weiwu Ren, Hewen Zhang, Yu Hong, Zhiwei Wang

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Analysis of Possibilities to Automate Detection of Unscrupulous Microfinance Organizations based on Machine learning Methods by Yu. M. Beketnova

“…However, the engagement of microfinance organizations in illegal financial transactions associated with fraud, illegal creditors, money laundering, significantly limits their potential and has negative impact on their development. The aim of the paper is to study the possibilities to automate detection of unscrupulous microfinance organizations based on machine learning methods in order to promptly identify and suppress illegal activities by regulatory authorities. …”
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Attentive ink MLP droplet detection algorithm based on the satellite droplets threshold domain by Yin Wang, Yali Qi, Likun Lu, Qingtao Zeng

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Temporal Community Detection and Analysis with Network Embeddings by Limengzi Yuan, Xuanming Zhang, Yuxian Ke, Zhexuan Lu, Xiaoming Li, Changzheng Liu

“…To address these issues, we propose TCDA-NE, a novel TCD algorithm that combines evolutionary clustering with convex non-negative matrix factorization (Convex-NMF). …”
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An Immunology Inspired Flow Control Attack Detection Using Negative Selection with -Contiguous Bit Matching for Wireless Sensor Networks by Muhammad Zeeshan, Huma Javed, Amna Haider, Aumbareen Khan

“…This paper implemented an improved, decentralized, and customized version of the Negative Selection Algorithm (NSA) for data flow anomaly detection with learning capability. …”
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Detection of <i>Aspergillus flavus</i> in Figs by Means of Hyperspectral Images and Deep Learning Algorithms by Cristian Cruz-Carrasco, Josefa Díaz-Álvarez, Francisco Chávez de la O, Abel Sánchez-Venegas, Juan Villegas Cortez

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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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Adversarial patch defense algorithm based on PatchTracker by Zhenjie XIAO, Shiyu HUANG, Feng YE, Liqing HUANG, Tianqiang HUANG

“…The application of deep neural networks in target detection has been widely adopted in various fields.However, the introduction of adversarial patch attacks, which add local perturbations to images to mislead deep neural networks, poses a significant threat to target detection systems based on vision techniques.To tackle this issue, an adversarial patch defense algorithm based on PatchTracker was proposed, leveraging the semantic differences between adversarial patches and image backgrounds.This algorithm comprised an upstream patch detector and a downstream data enhancement module.The upstream patch detector employed a YOLOV5 (you only look once-v5) model with attention mechanism to determine the locations of adversarial patches, thereby improving the detection accuracy of small-scale adversarial patches.Subsequently, the detected regions were covered with appropriate pixel values to remove the adversarial patches.This module effectively reduced the impact of adversarial examples without relying on extensive training data.The downstream data enhancement module enhanced the robustness of the target detector by modifying the model training paradigm.Finally, the image with removed patches was input into the downstream YOLOV5 target detection model, which had been enhanced through data augmentation.Cross-validation was performed on the public TT100K traffic sign dataset.Experimental results demonstrated that the proposed algorithm effectively defended against various types of generic adversarial patch attacks when compared to situations without defense measures.The algorithm improves the mean average precision (mAP) by approximately 65% when detecting adversarial patch images, effectively reducing the false negative rate of small-scale adversarial patches.Moreover, compared to existing algorithms, this approach significantly enhances the accuracy of neural networks in detecting adversarial samples.Additionally, the method exhibited excellent compatibility as it does not require modification of the downstream model structure.…”
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