UAV Detection Using Template Matching and Centroid Tracking by Muhammad Hanzla, Muhammad Ovais Yusuf, Touseef Sadiq, Naif Al Mudawi, Hameedur Rahman, Abdulwahab Alazeb, Aisha Ahmed AlArfaj, Asaad Algarni

“…In computer vision and image processing, vehicle detection and tracking in complicated aerial images have become important subjects. …”
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Detection of network intelligence features with the decision tree model by N. P. Sharaev, S. N. Petrov

“…A monitoring tool has been developed for detecting and analyzing the events, the results of which were used as the basis for a new dataset.The implementation of the decision tree method in the form of program code allowed to increase the speed of the module by about 2 times (0,147 ms). …”
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An Ontology-Enabled Approach for the Detection of Software Design Smells by Sabiyyah Sabir, Ghulam Rasool, Tariq Umer, Naoufel Kraiem, Yunyoung Nam

“…This paper introduces an innovative Ontology based Detection of Design Smell (ODDS) approach for detecting software design smells at the design level. …”
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Colorectal Cancer Detection Tool Developed with Neural Networks by Alex Ede Danku, Eva Henrietta Dulf, Alexandru George Berciu, Noemi Lorenzovici, Teodora Mocan

“…The objective of this study is to develop a practical, low-cost, AI-based decision-support tool that integrates clinical test data (blood/stool) and, if needed, colonoscopy images to help reduce misdiagnosis and improve early detection of colorectal cancer for clinicians. Convolutional neural networks (CNNs) and artificial neural networks (ANNs) are utilized in conjunction with a graphical user interface (GUI), which caters to individuals lacking programming expertise. …”
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Multitier ensemble classifiers for malicious network traffic detection by Jie WANG, Lili YANG, Min YANG

“…A malicious network traffic detection method based on multi-level distributed ensemble classifier was proposed for the problem that the attack model was not trained accurately due to the lack of some samples of attack steps for detecting attack in the current network big data environment,as well as the deficiency of the existing ensemble classifier in the construction of multilevel classifier.The dataset was first preprocessed and aggregated into different clusters,then noise processing on each cluster was performed,and then a multi-level distributed ensemble classifier,MLDE,was built to detect network malicious traffic.In the MLDE ensemble framework the base classifier was used at the bottom,while the non-bottom different ensemble classifiers were used.The framework was simple to be built.In the framework,big data sets were concurrently processed,and the size of ensemble classifier was adjusted according to the size of data sets.The experimental results show that the AUC value can reach 0.999 when MLDE base users random forest was used in the first layer，bagging was used in the second layer and AdaBoost classifier was used in the third layer.…”
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Multitier ensemble classifiers for malicious network traffic detection by Jie WANG, Lili YANG, Min YANG

“…A malicious network traffic detection method based on multi-level distributed ensemble classifier was proposed for the problem that the attack model was not trained accurately due to the lack of some samples of attack steps for detecting attack in the current network big data environment,as well as the deficiency of the existing ensemble classifier in the construction of multilevel classifier.The dataset was first preprocessed and aggregated into different clusters,then noise processing on each cluster was performed,and then a multi-level distributed ensemble classifier,MLDE,was built to detect network malicious traffic.In the MLDE ensemble framework the base classifier was used at the bottom,while the non-bottom different ensemble classifiers were used.The framework was simple to be built.In the framework,big data sets were concurrently processed,and the size of ensemble classifier was adjusted according to the size of data sets.The experimental results show that the AUC value can reach 0.999 when MLDE base users random forest was used in the first layer，bagging was used in the second layer and AdaBoost classifier was used in the third layer.…”
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The using nonlinear frequency signal for reduction the losses of the detection by G. Z. Akselrod, K. A. Elagina

“…There is usage of the processing of nonlinear frequency signals considered to reduce the losses of the detection. …”
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Domain Adaptation for Pedestrian Detection Based on Prediction Consistency by Yu Li-ping, Tang Huan-ling, An Zhi-yong

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Mechanism for detecting cause-and-effect relationships in court judgments by Łukasz Kurant

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Dataset Enhancement in Hair Follicle Detection: ESENSEI Challenge by Jan Jakubik

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Towards automated detection of adversarial attacks on tabular data by Piotr Biczyk, Łukasz Wawrowski

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Dealing with Imbalanced Data for GPS Trajectory Outlier Detection by Nguyen Van Chien, Van-Hau Nguyen, Le Van Quoc Anh

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Multi-Agents Indexing System (MAIS) for Plagiarism Detection by Samia Zouaoui, Khaled Rezeg

“…Developing such a system that can covers the content of all Arabic documents using ontology as a semantic resource would be a complex and time-consuming task and would require intelligent natural language-processing capabilities.In the context of Arabic plagiarism detection systems using an Arabic ontology and permitting these systems to support semantic representation to more efficiently verify the originality of the research and meet the needs of researchers, this paper presents a novel approach for addressing plagiarism named Multi-agents Indexing System. …”
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Weak Signal Detection Algorithm Based on Particle Filtering by Mao Senpeng, Wang Pengfei, Chen Wei, Guo Lei

“…To address the problems arising in the signal processing under continuous clutter scenarios and improve the weak signal detection performance of the particle filter, this paper proposes an improved particle filter algorithm. …”
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Auditory cortical detection and discrimination correlates with communicative significance. by Robert C Liu, Christoph E Schreiner

“…Plasticity studies suggest that behavioral relevance can change the cortical processing of trained or conditioned sensory stimuli. …”
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Fast Artificial Landmark Detection for Indoor Mobile Robots by Dmitriy Kartashov, Arthur Huletski, Kirill Krinkin

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Incident Detection with Pruned Residual Multilayer Perceptron Networks by Mohamad Soubra, Marek Kisiel-Dorohinicki, Marcin Kurdziel, Marek Zachara

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Improvement of signal detection based on using machine learning by Bassam Abd

“…The SVM is one of the most popular learning algorithms in different fields, such as signal processing, image processing, communication, and pattern recognition. …”
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Smart home with neural network based object detection by Agyan Panda, Sheila Maria Muniz

“…Household object detection is a brand-new computer technique that combines image processing and computer vision to recognise objects in the home. …”
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EFCNet for small object detection in remote sensing images by Yutong Wang, Zhensong Li, Shiliang Zhu, Xiaotan Wei

“…Abstract Object detection, as a crucial component of remote sensing image processing, has become one of the primary methods with the maturation of deep learning technologies. …”
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