Detecting Changeover Events on Manufacturing Machines with Machine Learning and NC data by Bastian Engelmann, Anna-Maria Schmitt, Moritz Heusinger, Vladyslav Borysenko, Niklas Niedner, Jan Schmitt

“…The machine learning approach uses several algorithms to classify different phases of the changeover process. The changeover of a milling process was defined using different phase concepts (2-phases, 6-phases, 23-phases) to be applicable to other types of manufacturing machines. …”
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A New Approach for Brain Tumor Detection Using Machine Learning by Elsadek Hussien Ibrahim, Shaaban Ebrahim Abo-Youssef, Khaled El-Bahnasy, Khaled Ahmed Mohamed Fathy

“…Diagnosing brain tumors process is a time-consuming process requiring the expertise of radiologists. …”
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Leak detection and localization in water distribution systems via multilayer networks by Daniel Barros, Ariele Zanfei, Andrea Menapace, Gustavo Meirelles, Manuel Herrera, Bruno Brentan

“…The detection process involves correlating monitored data to create a temporal graph and classify vertices. …”
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Use of Explainable Artificial Intelligence for Analyzing and Explaining Intrusion Detection Systems by Pamela Hermosilla, Mauricio Díaz, Sebastián Berríos, Héctor Allende-Cid

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Medical Image Segmentation for Anomaly Detection Using Deep Learning Techniques by Bibat Thokar, Binod Sapkota, Babu R. Dawadi, Shashidhar R. Joshi

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Iterative Assessment of Edge Criticality: Efficiency Enhancement or Hidden Insufficiency Detection by Vasily Lubashevskiy, Hamza Ejjbiri, Ihor Lubashevsky

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Universal anomaly detection at the LHC: transforming optimal classifiers and the DDD method by Sascha Caron, José Enrique García Navarro, María Moreno Llácer, Polina Moskvitina, Mats Rovers, Adrián Rubio Jímenez, Roberto Ruiz de Austri, Zhongyi Zhang

“…We compare the performance of the DDD method with the Deep Robust One-Class Classification method (DROCC), which incorporates signals in the training process, and the Deep Support Vector Data Description (DeepSVDD) method, a well-established and well-performing method for anomaly detection. …”
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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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Comparison classification algorithms and the YOLO method for video analysis and object detection by Martin Magdin, Zoltán Balogh

“…Abstract This article focus on designing and programming an application for implementing the YOLO method v8 in the detection and subsequent classification of objects in video recordings. …”
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Detection algorithm of LSB hidden messages based local image stability by ZHANG Qiu-yu, LIU Hong-guo, YUAN Zhan-ting

“…Aimed at the characteristics of LSB steganogtaphy, an algorithm based on local image stability was proposed.Combined with the idea of pollution data analysis, the secret information was regarded as noise in the process of informa-tion transmission.Then using the noise analysis technique, and selecting appropriate critical point value to achieve the detection purpose of the secret information.The theoretic analysis and experimental results show that detection algorithm advances than traditional algorithm in low embedding rate.…”
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Deep learning-assisted terahertz intelligent detection and identification of cancer tissue by Xingyu Wang, Yafei Xu, Rong Wang, Nuoman Tian, Zhengpeng Zhu, Shuting Fan, Liuyang Zhang, Ruqiang Yan, Xuefeng Chen

“…The cancer identification and diagnosis process are transformed into one end-to-end classification process of THz signals reflected from cancer tissue. …”
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Enhanced Kidney Stone Detection and Classification Using SVM and LBP Features by Hawkar K. Hama, Hamsa D. Majeed, Goran Saman Nariman

“…The feature extraction comes into action through the LBP technique as a preparation step for the SVM classifier to complete the stone detection process. The approach introduced in this paper has the potential to enhance detection accuracy and efficiency. …”
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Developing and validating key performance indicators for breast, cervical, and colorectal cancer screening programs: a literature review and Delphi survey by Arezoo Jabbari, Zhila Najafpour, Sima Ourang, Maria Cheraghi

“…Finally, the outcome dimension features 12 indicators: Screening Coverage, All-Cause Mortality Rate, Cause-Specific Mortality Rate, Invasive Cancer Detection Rate, Interval Cancer Rate, Ductal Carcinoma in Situ (DCIS) Rate, Cancer Detection Rate, Polyp Detection Rate, Fecal Occult Blood Test (FOBt) Positivity Rate, Adenoma Detection Rate, Positive Predictive Value for Cancer Detection (PPV), and Episode Sensitivity.ConclusionThis study identified a robust set of 30 key performance indicators (KPIs) for breast, cervical, and colorectal cancer screening programs, with a high overall content validity index demonstrating strong expert consensus on their relevance and importance.…”
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Detection algorithm of LSB hidden messages based local image stability by ZHANG Qiu-yu, LIU Hong-guo, YUAN Zhan-ting

“…Aimed at the characteristics of LSB steganogtaphy, an algorithm based on local image stability was proposed.Combined with the idea of pollution data analysis, the secret information was regarded as noise in the process of informa-tion transmission.Then using the noise analysis technique, and selecting appropriate critical point value to achieve the detection purpose of the secret information.The theoretic analysis and experimental results show that detection algorithm advances than traditional algorithm in low embedding rate.…”
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Application of Sinusoidal Function in Financial Crisis Early Warning and Detection System by Xueyin Wang

“…Therefore, this paper studies the application of sine signal function in financial crisis early warning and detection system. According to the principle of “different frequencies are uncorrelated,” the derivation process of a single sinusoidal signal with noise in the crisis warning period is also applicable to the case of multiple sinusoidal signals, so the program can also be used to detect multiple sinusoidal signals in the crisis warning period at the same time. …”
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Evaluation of Artificial Intelligence Effectiveness in Detection of Lumbosacral Spine Degenerative Diseases by N. В. Nudnov, A. V. Korobov, А. А. Skachkov, T. V. Kulneva, V. V. Sherstoboev, L. А. Titova, A. S. Rusakov, V. V. Tumko, R. S. Sarbaev, N. А. Uspenskaya, E. А. Andrienko, M. Е. Ivannikov

“…Each archive consisted of a set of programs in two planes containing T2-TSE, T1-TSE and T2 sequences with fat suppression program. …”
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Skull fracture detection for point-of-care diagnostics using microwave technique by Mariella Särestöniemi, Daljeet Singh, Jarmo Reponen, Mikael von und zu Fraunberg, Teemu Myllylä

“…Additionally, frequent and safe monitoring of the healing process of skull fractures in smaller healthcare centers, as a new telemedicine solution, would enable early detection of potential problems. …”
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Les ailes du crime ou la modernité de Détective by Mélodie Simard-Houde

“…This is a paradoxal process, by which an object (aeronautics imaginaries) that has, at first view, very few things to do with criminality and “fait divers” is integrated to a periodic dedicated to this type of topics. …”
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Multiscale Motion Detection and Recognition Combining Still Images and Video Sequences by Zhuo Chen

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Using fuzzy clustering to reconstruct alert correlation graph of intrusion detection by MA Lin-ru1, YANG Lin2, WANG Jian-xin2, TANG Xin2

“…Causal correlation method was one of the most representative methods for instruction detection alert correla-tion. In some conditions, the correlation graph would be split because of loss of causal information. …”
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