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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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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Region and Sample Level Domain Adaptation for Unsupervised Infrared Target Detection in Aerial Remote Sensing Images by Lianmeng Jiao, Haifeng Wei, Quan Pan

“…Finally, the proposed region and sample level domain adaptation framework is realized based on the advanced YOLOv7 one-stage detection backbone. We conducted comprehensive experiments based on the VEDAI and DroneVehicle aerial remote sensing datasets, and the experimental results demonstrate that our algorithm achieves better performance than those state-of-the-art unsupervised domain adaptation target detection algorithms. …”
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Random forest algorithm identifies miRNA signatures for breast cancer detection and classification from patient urine samples by Jochen Maurer, Matthias Rübner, Chao-Chung Kuo, Birgit Klein, Julia Franzen, Julia Wittenborn, Tomas Kupec, Laila Najjari, Peter Fasching, Elmar Stickeler

“…Results and conclusion: Using a random forest algorithm, we identified a signature of 275 miRNAs that allows the detection of invasive breast cancer in urine. …”
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A Novel Interest Detection-Based Video Dissemination Algorithm under Flash Crowd in Mobile Ad Hoc Networks by Shijie Jia, Shengli Jiang, Yuanchen Li, Xihu Zhi, Mu Wang

“…The peer-to-peer-based video resource dissemination is important for handling extreme conditions such as flash crowds which severely break the balance between supply and demand of video content and bring negative effects for quality of service (QoS). In this paper, we propose a novel interest detection-based video dissemination algorithm under flash crowd in mobile ad hoc networks (IDVD). …”
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Highly sensitive molecular assay based on Identical Multi-Repeat Sequence (IMRS) algorithm for the detection of Trichomonas vaginalis infection. by Clement Shiluli, Shwetha Kamath, Bernard N Kanoi, Racheal Kimani, Bernard Oduor, Hussein M Abkallo, Michael Maina, Harrison Waweru, Moses Kamita, Nicole Pamme, Joshua Dupaty, Catherine M Klapperich, Srinivasa Raju Lolabattu, Jesse Gitaka

“…However, these diagnostic techniques have numerous disadvantages such as high infrastructure costs, false negative and positive results, and interstrain variation among others. …”
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FP-YOLOv8: Surface Defect Detection Algorithm for Brake Pipe Ends Based on Improved YOLOv8n by Ke Rao, Fengxia Zhao, Tianyu Shi

“…To address the limitations of existing deep learning-based algorithms in detecting surface defects on brake pipe ends, a novel lightweight detection algorithm, FP-YOLOv8, is proposed. …”
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Advancing Real-Time Food Inspection: An Improved YOLOv10-Based Lightweight Algorithm for Detecting Tilapia Fillet Residues by Zihao Su, Shuqi Tang, Nan Zhong

“…The model demonstrates the best overall performance among many mainstream detection algorithms with a small model size (3.3 MB), a high frame rate (77FPS), and an excellent <i>mAP</i> (0.942). …”
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Lightweight malicious domain name detection model based on separable convolution by Luhui YANG, Huiwen BAI, Guangjie LIU, Yuewei DAI

“…The application of artificial intelligence in the detection of malicious domain names needs to consider both accuracy and calculation speed,which can make it closer to the actual application.Based on the above considerations,a lightweight malicious domain name detection model based on separable convolution was proposed.The model uses a separable convolution structure.It first applies depthwise convolution on every input channel,and then performs pointwise convolution on all output channels.This can effectively reduce the parameters of convolution process without impacting the effectiveness of convolution feature extraction,and realize faster convolution process while keeping high accuracy.To improve the detection accuracy considering the imbalance of the number and difficulty of positive and negative samples,a focal loss function was introduced in the training process of the model.The proposed algorithm was compared with three typical deep-learning-based detection models on a public data set.Experimental results denote that the proposed algorithm achieves detection accuracy close to the state-of-the-art model,and can significantly improve model inference speed on CPU.…”
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A deep learning based intrusion detection system for CAN vehicle based on combination of triple attention mechanism and GGO algorithm by Hongwei Yang, Mehdi Effatparvar

“…The results show that this method outperforms certain machine learning algorithms in error rate and false negative for DoS and drive gear and RPM spoofing attack with accuracy of 96.3%, recall of 96.1%, F1-Score of 96.2%, specificity of 97.2%, accuracy of 96.3%, AUC-ROC of 0.97, and MCC of 0.92 for DoS attacks. …”
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Artificial-Intelligence Bio-Inspired Peptide for Salivary Detection of SARS-CoV-2 in Electrochemical Biosensor Integrated with Machine Learning Algorithms by Marcelo Augusto Garcia-Junior, Bruno Silva Andrade, Ana Paula Lima, Iara Pereira Soares, Ana Flávia Oliveira Notário, Sttephany Silva Bernardino, Marco Fidel Guevara-Vega, Ghabriel Honório-Silva, Rodrigo Alejandro Abarza Munoz, Ana Carolina Gomes Jardim, Mário Machado Martins, Luiz Ricardo Goulart, Thulio Marquez Cunha, Murillo Guimarães Carneiro, Robinson Sabino-Silva

“…Developing affordable, rapid, and accurate biosensors is essential for SARS-CoV-2 surveillance and early detection. We created a bio-inspired peptide, using the SAGAPEP AI platform, for COVID-19 salivary diagnostics via a portable electrochemical device coupled to Machine Learning algorithms. …”
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Intersection of Performance, Interpretability, and Fairness in Neural Prototype Tree for Chest X-Ray Pathology Detection: Algorithm Development and Validation Study by Hongbo Chen, Myrtede Alfred, Andrew D Brown, Angela Atinga, Eldan Cohen

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Crops Disease Detection, from Leaves to Field: What We Can Expect from Artificial Intelligence by Youssef Lebrini, Alicia Ayerdi Gotor

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Development of load constant current model using feedback‐controlling resonant switching algorithm for overload protection by Hsiung‐Cheng Lin, Kai‐Chun Hsiao

“…On the basis of a negative feedback‐control mechanism, the proposed model can detect the load current and thus generate an appropriate switch signal fast and accurately. …”
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Modelling trial-by-trial changes in the mismatch negativity. by Falk Lieder, Jean Daunizeau, Marta I Garrido, Karl J Friston, Klaas E Stephan

“…The mismatch negativity (MMN) is a differential brain response to violations of learned regularities. …”
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Diagnostic value of the Risk of Ovarian Malignancy Algorithm (ROMA) index in the detection of ovarian cancer in postmenopausal women: a systematic review and meta-analysis by Ali Fathi, Mohammad Heidari, Javad Rasouli, Hojat Ghasemnejad-Berenji

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LULC change detection and future LULC modelling using RF and MLPNN-Markov algorithms in the uMngeni catchment, KwaZulu-Natal, South Africa by Orlando Bhungeni, Michael Gebreslasie, Ashadevi Ramjatan

“…However, the trajectory of Land Cover and Land Use Changes (LULC-C change poses a significant threat to water catchment areas, negatively affecting water quality. Thus, the adoption of remote sensing data and Machine Learning Algorithms (MLAs) is a novel approach that provides spatiotemporal data on the environmental changes resulting from LULC dynamics. …”
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Optimizing deep belief network for concrete crack detection via a modified design of ideal gas molecular dynamics by Tan Qin, Gongxing Yan, Huaguo Jiang, Minqi Shen, Andrea Settanni

“…Abstract Concrete structures are prone to developing cracks, which can have a negative impact on their overall performance and longevity. …”
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Intelligent recognition algorithm and application of coal mine overhead passenger device based on multiscale feature fusion by Beijing XIE, Heng LI, Hang DONG, Zheng LUAN, Ben ZHANG, Xiaoxu LI

“…The YOLOv8n single-stage object detection algorithm was used as the baseline model, and a coal mine cmopd intelligent recognition algorithm based on multi-scale feature fusion was proposed.In the image preprocessing stage, adaptive histogram equalization was employed to enhance image quality, and random rectangle masking was applied to simulate real scenarios where cmopd is occluded by underground objects during operation. …”
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Establishment and assessment of an early screening model for cervical cancer based on single-cell Raman spectroscopy combined with machine learning algorithms by MA Dongmei, ZHAO Wenjie, LIU Shihai, XU Haicang, CAI Duo, JI Yuetong, XU Jian, GUO Cancan, MA Bo, PAN Huazheng

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