An Algorithm for Simplifying 3D Building Models with Consideration for Detailed Features and Topological Structure by Zhenglin Li, Zhanjie Zhao, Wujun Gao, Li Jiao

“…To tackle problems such as the destruction of topological structures and the loss of detailed features in the simplification of 3D building models, we propose a 3D building model simplification algorithm that considers detailed features and topological structures. …”
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Using pseudo-AI submissions for detecting AI-generated code by Shariq Bashir

“…Previous studies have explored ways to detect AI-generated text, such as analyzing structural differences, embedding watermarks, examining specific features, or using fine-tuned language models. …”
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An image and text-based fake news detection with transfer learning. by Esther Irawati Setiawan, Patrick Sutanto, Christian Nathaniel Purwanto, Joan Santoso, F X Ferdinandus, Nemuel Daniel Pah, Mauridhi Hery Purnomo

“…Fake news has emerged as a significant problem in today's information age, threatening the reliability of information sources. …”
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Automatic Detection of Motorcycle on the Road using Digital Image Processing by sutikno sutikno, Helmie Arif Wibawa, Ragil Saputra

“…One of them is traffic accidents on motorcyclist not wearing helmets. To overcome this problem, several researchers have developed detection system of motorcyclist not wear helmet. …”
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BLSTM based night-time wildfire detection from video. by Ahmet K Agirman, Kasim Tasdemir

“…Distinguishing fire from non-fire objects in night videos is problematic if only spatial features are to be used. Those features are highly disrupted under low-lit environments because of several factors, such as the dynamic range limitations of the cameras. …”
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Machine learning techniques for imbalanced multiclass malware classification through adaptive feature selection by Binayak Panda, Sudhanshu Shekhar Bisoyi, Sidhanta Panigrahy, Prithviraj Mohanty

“…This article proposes an Adaptive Multiclass Malware Classification (AMMC) framework that trains base machine learning models with fewer computational resources to detect malware. Furthermore, this work proposes a novel adaptive feature selection (AFS) technique using the greedy strategy on term frequency and inverse document frequency (TF-IDF) feature weights to address the selection of influential features and ensure better performance metrics in imbalanced multiclass malware classification problems. …”
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An XAI Approach to Melanoma Diagnosis: Explaining the Output of Convolutional Neural Networks with Feature Injection by Flavia Grignaffini, Enrico De Santis, Fabrizio Frezza, Antonello Rizzi

“…The most explored medical application is cancer detection, for which several CAD systems have been proposed. …”
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Modern epidemiological features of viral acute intestinal infections in children and adolescents of Sakhalin region by E. Yu. Sapega, L. V. Butakova, O. E. Trotsenko

“…Age-adjusted rotavirus infection incidence showed predominance of the infection among preschool-aged children; norovirus infection was frequently detected among schoolchildren and adolescents. An increase in the focality index may indicate ongoing problems in the public catering sector, non-compliance with sanitary rules and hygiene standards, and untimely detection and isolation of patients with acute intestinal infections.Conclusion. …”
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Multipath Suppression and High-precision Angle Measurement Method Based on Feature Game Preprocessing by Houhong XIANG, Yongliang WANG, Yuxi LI, Yufeng CHEN, Fengyu WANG, Xiaolu ZENG

“…The meter-wave radar, known for its wide beamwidth, often faces challenges in detecting low-elevation targets due to interference from multipath signals. …”
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Unveiling key pathomic features for automated diagnosis and Gleason grade estimation in prostate cancer by Valentina Brancato, Mario Verdicchio, Carlo Cavaliere, Francesco Isgrò, Marco Salvatore, Marco Aiello

“…Focusing on the problem of prostate cancer (PCa) diagnosis and grading, this study aims to detect which are the most discriminant features for distinguishing malignant from non-malignant tissue and Gleason patterns, leaving the evaluation of models’ classification performances as a secondary goal. …”
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Detection of Fake Instagram Accounts via Machine Learning Techniques by Stefanos Chelas, George Routis, Ioanna Roussaki

“…This paper focuses on the detection of fake accounts on Instagram and proposes a novel solution that aims to address this problem. …”
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EXPERIMENTAL STUDY OF IMMUNOMODULATORY FEATURES OF CEREBROSPINAL FLUID IN CASE OF BACTERIAL PURULENT MENINGITIS IN CHILDREN by L. A. Alekseeva, N. V. Skripchenko, G. F. Zheleznikova, N. E. Monakhova, A. A. Zhirkov, T. V. Bessonova

“…Pediatric Research and Clinical Center of Infectious Diseases under the Federal Medical Biological Agency, Saint Petersburg, Russia Investigation of the role of cerebrospinal fluid components in the processes of patho- and sanogenesis of central nervous system diseases is a fundamental problem of medicine and biology. The work aim was to study immunomodulatory features of cerebrospinal fluid in case of bacterial purulent meningitis (BPM) in children by an in vitro experiment.There were studied immunomodulatory features of high-molecular and low-molecular fractions of cerebrospinal fluid of 33 children with bacterial purulent meningitis of different etiology and 12 children without meningitis by phytohemagglutinin (PHA)-stimulated reaction of leukocyte blast transformation. …”
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An Interpretable Method for Anomaly Detection in Multivariate Time Series Predictions by Shijie Tang, Yong Ding, Huiyong Wang

“…Our method transforms the interpretation of anomalous features into solving an optimization problem in a normal “reference” state. …”
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A privacy-enhanced framework with deep learning for botnet detection by Guangli Wu, Xingyue Wang

“…Based on this problem, this article proposes a privacy-enhanced framework with deep learning for botnet detection. …”
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YOLO-WAD for Small-Defect Detection Boost in Photovoltaic Modules by Yin Wang, Wang Yun, Gang Xie, Zhicheng Zhao

“…Subsequently, an additional detection layer is added to the neck, and C2f is replaced by C2f-EMA (CSP bottleneck with two convolutions–efficient multi-scale attention mechanism), which can redistribute feature weights and prioritize relevant features and spatial details across image channels to improve feature extraction. …”
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Tampered text detection via RGB and frequency relationship modeling by Yuxin WANG, Boqiang ZHANG, Hongtao XIE, Yongdong ZHANG

“…In recent years, the widespread dissemination of tampered text images on the Internet constitutes an important threat to the security of text images.However, the corresponding tampered text detection (TTD) methods have not been sufficiently explored.The TTD task aims to locate all text regions in an image while judging whether the text regions have been tampered with according to the authenticity of the texture.Thus, different from the general text detection task, TTD task further needs to perceive the fine-grained information for real-world and tampered text classification.TTD task has two main challenges.One the one hand, due to the high similarity in texture between real-world texts and tampered texts, TTD methods that only learn from RGB domain features have limited capability to distinguish these two-category texts well.On the other hand, as the different detecting difficulty exists in real-world texts and tampered texts, the network cannot well balance the learning process of the two-category texts, resulting in the imbalance detection performance between real-world and tampered texts.Compared with RGB domain features, the discontinuity of text texture in frequency domain can help the network to identify the authenticity of text instances.Accordingly, a new TTD method based on RGB and frequency information relationship modeling was proposed.The features in the RGB and frequency domains were extracted by independent feature extractors respectively.Thus, the identification ability of tampered texture can be enhanced by introducing frequency information during the texture perception.Then, a global RGB-frequency relationship module (GRM) was introduced to model the texture authenticity relationship between different text instances.GRM referred to the RGB-frequency features of other text instances in the same image to assist in judging the authenticity of the current text instance, which solved the problem of imbalanced detection performance.Furthermore, a new TTD dataset (Tampered-SROIE) was proposed to evaluate the effectiveness of proposed method, which contains 986 images (626 training images and 360 test images).By evaluating on the Tampered-SROIE, the proposed method obtains 95.97% and 96.80% in F-measure for real-world and tampered texts respectively and reduces the imbalanced detection accuracy by 1.13%.The proposed method will give new insights to the TTD community from the perspective of network structure and detection strategy.Tampered-SROIE also provides an evaluation benchmark for future TTD methods.…”
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Intrusion detection model based on fuzzy theory and association rules by Jianwu ZHANG, Jiasen HUANG, Di ZHOU

“…An intrusion detection model based on fuzzy theory and improved Apriori algorithm was proposed.The BV-Apriori algorithm was used to generate the matching rule base,and the problem of excessive boundary in the continuous data partitioning process was solved by fuzzy set technology.The real-time analysis of the relationship between features and the update of the rule base were completed,and the intrusion detection model BVA-IDS (Boolean vector Apriori-intrusion detection system) was built.The results show that the mining efficiency of the BV-Apriori algorithm is significantly improved when compared with the existing Apriori-BR algorithm,in addition,the BVA-IDS model also performs well on intrusion detection indicators with high detection accuracy,and low false positive rate and false negative rate.…”
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A Hybrid Vision-Map Method for Urban Road Detection by Carlos Fernández, David Fernández-Llorca, Miguel A. Sotelo

“…A hybrid vision-map system is presented to solve the road detection problem in urban scenarios. The standardized use of machine learning techniques in classification problems has been merged with digital navigation map information to increase system robustness. …”
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Intrusion detection model based on fuzzy theory and association rules by Jianwu ZHANG, Jiasen HUANG, Di ZHOU

“…An intrusion detection model based on fuzzy theory and improved Apriori algorithm was proposed.The BV-Apriori algorithm was used to generate the matching rule base,and the problem of excessive boundary in the continuous data partitioning process was solved by fuzzy set technology.The real-time analysis of the relationship between features and the update of the rule base were completed,and the intrusion detection model BVA-IDS (Boolean vector Apriori-intrusion detection system) was built.The results show that the mining efficiency of the BV-Apriori algorithm is significantly improved when compared with the existing Apriori-BR algorithm,in addition,the BVA-IDS model also performs well on intrusion detection indicators with high detection accuracy,and low false positive rate and false negative rate.…”
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A SVM Approach of Aircraft Conflict Detection in Free Flight by Xu-rui Jiang, Xiang-xi Wen, Ming-gong Wu, Ze-kun Wang, Xi Qiu

“…In this article, aircraft conflict detection is considered as a binary classification problem; therefore, it can be solved by a pattern recognition method. …”
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