LADOS: Aerial Imagery Dataset for Oil Spill Detection, Classification, and Localization Using Semantic Segmentation by Konstantinos Gkountakos, Maria Melitou, Konstantinos Ioannidis, Konstantinos Demestichas, Stefanos Vrochidis, Ioannis Kompatsiaris

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A Model-Based Substructuring Method for Local Damage Detection of Structure by Eun-Taik Lee, Hee-Chang Eun

“…It is impractical to collect the full set of data in finite element model for investigating the structural performance. …”
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From Community Detection to Mentor Selection in Rating-Free Collaborative Filtering by Armelle Brun, Sylvain Castagnos, Anne Boyer

“…We propose to take inspiration from local community detection algorithms to form communities of users and deduce the set of mentors of a given user. …”
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Approach of detecting low-rate DoS attack based on combined features by Zhi-jun WU, Jing-an ZHANG, Meng YUE, Cai-feng ZHANG

“…LDoS (low-rate denial of service) attack is a kind of RoQ (reduction of quality) attack which has the characteristics of low average rate and strong concealment.These characteristics pose great threats to the security of cloud computing platform and big data center.Based on network traffic analysis,three intrinsic characteristics of LDoS attack flow were extracted to be a set of input to BP neural network,which is a classifier for LDoS attack detection.Hence,an approach of detecting LDoS attacks was proposed based on novel combined feature value.The proposed approach can speedily and accurately model the LDoS attack flows by the efficient self-organizing learning process of BP neural network,in which a proper decision-making indicator is set to detect LDoS attack in accuracy at the end of output.The proposed detection approach was tested in NS2 platform and verified in test-bed network environment by using the Linux TCP-kernel source code,which is a widely accepted LDoS attack generation tool.The detection probability derived from hypothesis testing is 96.68%.Compared with available researches,analysis results show that the performance of combined features detection is better than that of single feature,and has high computational efficiency.…”
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Reinforcement Learning-Based Generative Security Framework for Host Intrusion Detection by Yongsik Kim, Su-Youn Hong, Sungjin Park, Huy Kang Kim

“…When calculating the reward in reinforcement learning, we used the comparison value with the pre-trained Seq2Seq model, the malware log sequence detected by the rule set based on reinforcement learning, and the false positive value generated by the normal data to create its own rule set. …”
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Nondestructive Detection of Blackheart in Potato by Visible/Near Infrared Transmittance Spectroscopy by Zhu Zhou, Songwei Zeng, Xiaoyu Li, Jian Zheng

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A call to action for adverse drug event (ADE) detection and prevention by John McCue, C. David Butler, Raymond C. Love, Shelly Spiro, Roy Guharoy, the United States Pharmacopeia (USP) Allergy & Intolerance Classification Expert Panel

“…Efforts to prevent and detect ADEs within healthcare systems are complicated by data quality, lack of data standardization, and actionable clinical decision support systems. …”
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An intrusion detection mechanism for IPv6-based wireless sensor networks by Min Wei, Chunmeng Rong, Erxiong Liang, Yuan Zhuang

“…This mechanism trains an intrusion detection algorithm using a feature data set to create a normal profile. …”
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Multiple Sclerosis Lesion Detection Using Constrained GMM and Curve Evolution by Oren Freifeld, Hayit Greenspan, Jacob Goldberger

“…Experimental results on both standard brain MRI simulation data and real data indicate that the proposed method outperforms previously suggested approaches, especially for highly noisy data.…”
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Detecting Current Transformer Measurement Errors Using Double Decomposition Method by Bolun Du, Yinglong Diao, Huan Wang, Xiujuan Zeng, Tong Liu, Yiyi Peng

“…Meanwhile, wavelet decomposition is applied to decompose the residuals in multiple layers, and the approximate coefficient set of each phase is screened. Then, t-distributed stochastic neighbor embedding (t-SNE) is utilized to extract CT deterioration feature from the approximate coefficient set. …”
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A generalized deep learning model to detect and classify volcano seismicity by David Fee, Darren Tan, John Lyons, Mariangela Sciotto, Andrea Cannata, Alicia Hotovec-Ellis, Társilo Girona, Aaron Wech, Diana Roman, Matthew Haney, Silvio De Angelis

“…Our generalized VOISS-Net model achieves an accuracy of 87 % on the test set. We apply this model to continuous data from several volcanoes and eruptions included within and outside our training set, and find that multiple types of tremor, explosions, earthquakes, long-period events, and noise are successfully detected and classified. …”
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Detection of SSL/TLS protocol attacks based on flow spectrum theory by Shize GUO, Fan ZHANG, Zhuoxue SONG, Ziming ZHAO, Xinjie ZHAO, Xiaojuan WANG, Xiangyang LUO

“…Network attack detection plays a vital role in network security.Existing detection approaches focus on typical attack behaviors, such as Botnets and SQL injection.The widespread use of the SSL/TLS encryption protocol arises some emerging attack strategies against the SSL/TLS protocol.With the network traffic collection environment that built upon the implements of popular SSL/TLS attacks, a network traffic dataset including four SSL/TLS attacks, as well as benign flows was controlled.Considering the problems that limited observability of existing detection and limited separation of the original-flow spatiotemporal domains, a flow spectrum theory was proposed to map the threat behavior in the cyberspace from the original spatiotemporal domain to the transformed domain through the process of “potential change” and obtain the “potential variation spectrum”.The flow spectrum theory is based on a set of separable and observable feature representations to achieve efficient analysis of network flows.The key to the application of flow spectrum theory in actual cyberspace threat behavior detection is to find the potential basis matrix for a specific threat network flow under the condition of a given transformation operator.Since the SSL/TLS protocol has a strong timing relationship and state transition process in the handshake phase, and there are similarities between some SSL/TLS attacks, the detection of SSL/TLS attacks not only needs to consider timing context information, but also needs to consider the high-separation representation of TLS network flows.Based on the flow spectrum theory, the threat template idea was used to extract the potential basis matrix, and the potential basis mapping based on the long-short-term memory unit was used to map the SSL/TLS attack network flow to the flow spectrum domain space.On the self-built SSL/TLS attack network flow data set, the validity of the flow spectrum theory is verified by means of classification performance comparison, potential variation spectrum dimensionality reduction visualization, threat behavior feature weight evaluation, threat behavior spectrum division assessment, and potential variation base matrix heatmap visualization.…”
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Acoustic recognition of individuals in closed and open bird populations by Lifi Huang, Rohan H. Clarke, Daniella Teixeira, André Chiaradia, Bernd Meyer

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Detection of Graduation Potential in Prospective Students using the Random Forest Algorithm by Puguh Hasta Gunawan, Irving Vitra Paputungan

“…A total of 396 student records were used in this study and processed through a series of preprocessing steps, including the removal of irrelevant data and the encoding of categorical variables. The model was developed using the Random Forest algorithm with parameters set to max_depth = 15 and random_state = 42. …”
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Automatic Detection and Unsupervised Clustering-Based Classification of Cetacean Vocal Signals by Yinian Liang, Yan Wang, Fangjiong Chen, Hua Yu, Fei Ji, Yankun Chen

“…In the ocean environment, passive acoustic monitoring (PAM) is an important technique for the surveillance of cetacean species. Manual detection for a large amount of PAM data is inefficient and time-consuming. …”
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Automated Detection of Poor-Quality Scintigraphic Images Using Machine Learning by Anil K. Pandey, Akshima Sharma, Param D. Sharma, Chandra S. Bal, Rakesh Kumar

“…These 32 feature vectors of each image were used for the classification of images into poor or good quality using machine learning algorithm (multivariate adaptive regression splines [MARS]). The data were split into two sets, that is, training set and test set in the ratio of 60:40. …”
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Panel defect detection algorithm based on improved Faster R-CNN by Chen Wanqin, Tang Qingshan, Huang Tao

“…It also analyzes the difference in the aspect ratio of the defect data set, sets the generation size of the aiming window, and combines the DIoU-NMS suggestion frame screening mechanism to improve the matching rate of the prior frame and the target frame. …”
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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 Tunnel Surface Diseases Detection Algorithm Based on YOLOv4 by LI Jia, QIU Xinhua, JI Yuwen

“…In the established surface image data set of high speed railway tunnel, the mAP reaches 65.1%, and the defect detection rate is 90.1%, which verifies the high efficiency of the algorithm.…”
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A Review of Artificial Intelligence-Based Down Syndrome Detection Techniques by Mujeeb Ahmed Shaikh, Hazim Saleh Al-Rawashdeh, Abdul Rahaman Wahab Sait

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