Journal articles: 'Data detection'

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P, Veeramuthu. "Analysis of Progressive Duplicate Data Detection." Journal of Computational Mathematica 3, no. 2 (December 30, 2019): 41–50. http://dx.doi.org/10.26524/cm53.

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Baidari, Dr Ishwar, and S. P. Sajjan. "Location Based Crime Detection Using Data Mining." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (October 31, 2016): 208–12. http://dx.doi.org/10.9756/bijsesc.8279.

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S., Geetha. "Big Data Analysis - Cybercrime Detection in Social Network." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 147–52. http://dx.doi.org/10.5373/jardcs/v12sp4/20201476.

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Sunjana and Azizah Zakiah. "Outlier Detection of Transaction Data Using DBSCAN Algorithm." International Journal of Psychosocial Rehabilitation 24, no. 02 (February 12, 2020): 3232–40. http://dx.doi.org/10.37200/ijpr/v24i2/pr200632.

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Dener, Murat, Gökçe Ok, and Abdullah Orman. "Malware Detection Using Memory Analysis Data in Big Data Environment." Applied Sciences 12, no. 17 (August 27, 2022): 8604. http://dx.doi.org/10.3390/app12178604.

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Malware is a significant threat that has grown with the spread of technology. This makes detecting malware a critical issue. Static and dynamic methods are widely used in the detection of malware. However, traditional static and dynamic malware detection methods may fall short in advanced malware detection. Data obtained through memory analysis can provide important insights into the behavior and patterns of malware. This is because malwares leave various traces on memories. For this reason, the memory analysis method is one of the issues that should be studied in malware detection. In this study, the use of memory data in malware detection is suggested. Malware detection was carried out by using various deep learning and machine learning approaches in a big data environment with memory data. This study was carried out with Pyspark on Apache Spark big data platform in Google Colaboratory. Experiments were performed on the balanced CIC-MalMem-2022 dataset. Binary classification was made using Random Forest, Decision Tree, Gradient Boosted Tree, Logistic Regression, Naive Bayes, Linear Vector Support Machine, Multilayer Perceptron, Deep Feed Forward Neural Network, and Long Short-Term Memory algorithms. The performances of the algorithms used have been compared. The results were evaluated using the Accuracy, F1-score, Precision, Recall, and AUC performance metrics. As a result, the most successful malware detection was obtained with the Logistic Regression algorithm, with an accuracy level of 99.97% in malware detection by memory analysis. Gradient Boosted Tree follows the Logistic Regression algorithm with 99.94% accuracy. The Naive Bayes algorithm showed the lowest performance in malware analysis with memory data, with an accuracy of 98.41%. In addition, many of the algorithms used have achieved very successful results. According to the results obtained, the data obtained from memory analysis is very useful in detecting malware. In addition, deep learning and machine learning approaches were trained with memory datasets and achieved very successful results in malware detection.

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Dalvi, Mr Sagar Ravindra, and Ms Shamika Rajendra Khatu. "Data Leakage Detection." IARJSET 4, no. 4 (January 27, 2017): 164–66. http://dx.doi.org/10.17148/iarjset/nciarcse.2017.48.

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Papadimitriou, Panagiotis, and Hector Garcia-Molina. "Data Leakage Detection." IEEE Transactions on Knowledge and Data Engineering 23, no. 1 (January 2011): 51–63. http://dx.doi.org/10.1109/tkde.2010.100.

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Sakr, Mohamed, Walid Atwa, and Arabi Keshk. "Genetic-based Summarization for Local Outlier Detection in Data Stream." International Journal of Intelligent Systems and Applications 13, no. 1 (February 8, 2021): 58–68. http://dx.doi.org/10.5815/ijisa.2021.01.05.

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Outlier detection is one of the important tasks in data mining. Detecting outliers over streaming data has become an important task in many applications, such as network analysis, fraud detections, and environment monitoring. One of the well-known outlier detection algorithms called Local Outlier Factor (LOF). However, the original LOF has many drawbacks that can’t be used with data streams: 1- it needs a lot of processing power (CPU) and large memory to detect the outliers. 2- it deals with static data which mean that in any change in data the LOF recalculates the outliers from the beginning on the whole data. These drawbacks make big challenges for existing outlier detection algorithms in terms of their accuracies when they are implemented in the streaming environment. In this paper, we propose a new algorithm called GSILOF that focuses on detecting outliers from data streams using genetics. GSILOF solve the problem of large memory needed as it has fixed memory bound. GSILOF has two phases. First, the summarization phase that tries to summarize the past data arrived. Second, the detection phase detects the outliers from the new arriving data. The summarization phase uses a genetic algorithm to try to find the subset of points that can represent the whole original set. our experiments have been done over real datasets. Our experiments confirming the effectiveness of the proposed approach and the high quality of approximate solutions in a set of real-world streaming data.

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Manoj, V. V. R., V. Aditya Rama Narayana, and A. Bhargavi A. Lakshmi Prasanna Md Aakhila Bhanu. "Outlier Detection using Reverse Neares Neighbor for Unsupervised Data." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1511–13. http://dx.doi.org/10.31142/ijtsrd11406.

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S, Umadevi, and Nirmala Sugirtha Rajini S. "Detection of Traffic Violation Crime Using Data Mining Algorithms." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 982–87. http://dx.doi.org/10.5373/jardcs/v11/20192660.

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Mishra, Suchismita, Srikant Pattnaik, and Bibhuti Bhusan Mishra. "Detection of Online Consumer Satisfaction through Data Mining Technique." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (November 29, 2019): 25–32. http://dx.doi.org/10.5373/jardcs/v11sp11/20192924.

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Li, Li, and Ye Yuan. "Data Preprocessing for Network Intrusion Detection." Applied Mechanics and Materials 20-23 (January 2010): 867–71. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.867.

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Most of IDS(Intrusion Detection System) are very particular about data source which might be asked to be categorical data or need to be correctly labeled. Therefore, the data preprocessing is an indispensable part in intrusion detecting. KDD Cpu 1999 Dataset is usually used for experimental data. This paper briefly introduces the features and the structure of the KDD Cpu 1999 Dataset and presents the method of the data preprocessing at Intrusion Detection System based on the neural network clustering’s algorithm.

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Pelich, Ramona, Marco Chini, Renaud Hostache, Patrick Matgen, Carlos Lopez-Martinez, Miguel Nuevo, Philippe Ries, and Gerd Eiden. "Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data." Remote Sensing 11, no. 9 (May 7, 2019): 1078. http://dx.doi.org/10.3390/rs11091078.

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This research addresses the use of dual-polarimetric descriptors for automatic large-scale ship detection and characterization from synthetic aperture radar (SAR) data. Ship detection is usually performed independently on each polarization channel and the detection results are merged subsequently. In this study, we propose to make use of the complex coherence between the two polarization channels of Sentinel-1 and to perform vessel detection in this domain. Therefore, an automatic algorithm, based on the dual-polarization coherence, and applicable to entire large scale SAR scenes in a timely manner, is developed. Automatic identification system (AIS) data are used for an extensive and also large scale cross-comparison with the SAR-based detections. The comparative assessment allows us to evaluate the added-value of the dual-polarization complex coherence, with respect to SAR intensity images in ship detection, as well as the SAR detection performances depending on a vessel’s size. The proposed methodology is justified statistically and tested on Sentinel-1 data acquired over two different and contrasting, in terms of traffic conditions, areas: the English Channel the and Pacific coastline of Mexico. The results indicate a very high SAR detection rate, i.e., >80%, for vessels larger than 60 m and a decrease of detection rate up to 40 % for smaller size vessels. In addition, the analysis highlights many SAR detections without corresponding AIS positions, indicating the complementarity of SAR with respect to cooperative sources for detecting dark vessels.

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Hu, Xiao Bing. "Abnormal Events Detection in Traffic Data." Advanced Materials Research 779-780 (September 2013): 525–29. http://dx.doi.org/10.4028/www.scientific.net/amr.779-780.525.

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Detecting abnormal events such as crash is a practical problem that is important to Intelligent Transportation System. By taking advantage of the data recorded by the remote sensors which are deployed along the road, we can perform data mining techniques to see whether there are abnormal events happening on the road. This paper aims at proposing an abnormal-events-detecting method based on the traffic data, which first utilizes outlier detection to generate a fuzzy result set from source data, and then through the time series mining techniques to filter that to obtain an accurate experimental one. Experiment with real-world data shows that our method works satisfactorily in detecting abnormalities such crash, stall and hazard on the road.

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Kumar, Sandeep, Muhammad Badruddin Khan, Mozaherul Hoque Abul Hasanat, Abdul Khader Jilani Saudagar, Abdullah AlTameem, and Mohammed AlKhathami. "An Anomaly Detection Framework for Twitter Data." Applied Sciences 12, no. 21 (November 1, 2022): 11059. http://dx.doi.org/10.3390/app122111059.

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An anomaly indicates something unusual, related to detecting a sudden behavior change, and is also helpful in detecting irregular and malicious behavior. Anomaly detection identifies unusual events, suspicious objects, or observations that differ significantly from normal behavior or patterns. Discrepancies in data can be observed in different ways, such as outliers, standard deviation, and noise. Anomaly detection helps us understand the emergence of specific diseases based on health-related tweets. This paper aims to analyze tweets to detect the unusual emergence of healthcare-related tweets, especially pre-COVID-19 and during COVID-19. After pre-processing, this work collected more than 44 thousand tweets and performed topic modeling. Non-negative matrix factorization (NMF) and latent Dirichlet allocation (LDA) were deployed for topic modeling, and a query set was designed based on resultant topics. This query set was used for anomaly detection using a sentence transformer. K-means was also employed for clustering outlier tweets from the cleaned tweets based on similarity. Finally, an unusual cluster was selected to identify pandemic-like healthcare emergencies. Experimental results show that the proposed framework can detect a sudden rise of unusual tweets unrelated to regular tweets. The new framework was employed in two case studies for anomaly detection and performed with 78.57% and 70.19% accuracy.

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Zhang, Minghu, Jianwen Guo, Xin Li, and Rui Jin. "Data-Driven Anomaly Detection Approach for Time-Series Streaming Data." Sensors 20, no. 19 (October 2, 2020): 5646. http://dx.doi.org/10.3390/s20195646.

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Recently, wireless sensor networks (WSNs) have been extensively deployed to monitor environments. Sensor nodes are susceptible to fault generation due to hardware and software failures in harsh environments. Anomaly detection for the time-series streaming data of sensor nodes is a challenging but critical fault diagnosis task, particularly in large-scale WSNs. The data-driven approach is becoming essential for the goal of improving the reliability and stability of WSNs. We propose a data-driven anomaly detection approach in this paper, named median filter (MF)-stacked long short-term memory-exponentially weighted moving average (LSTM-EWMA), for time-series status data, including the operating voltage and panel temperature recorded by a sensor node deployed in the field. These status data can be used to diagnose device anomalies. First, a median filter (MF) is introduced as a preprocessor to preprocess obvious anomalies in input data. Then, stacked long short-term memory (LSTM) is employed for prediction. Finally, the exponentially weighted moving average (EWMA) control chart is employed as a detector for recognizing anomalies. We evaluate the proposed approach for the panel temperature and operating voltage of time-series streaming data recorded by wireless node devices deployed in harsh field conditions for environmental monitoring. Extensive experiments were conducted on real time-series status data. The results demonstrate that compared to other approaches, the MF-stacked LSTM-EWMA approach can significantly improve the detection rate (DR) and false rate (FR). The average DR and FR values with the proposed approach are 95.46% and 4.42%, respectively. MF-stacked LSTM-EWMA anomaly detection also achieves a better F2 score than that achieved by other methods. The proposed approach provides valuable insights for anomaly detection in WSNs by detecting anomalies in the time-series status data recorded by wireless sensor nodes.

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MULLER, PRIYA SHIRLEY. "Early Detection of Diabetes during Pregnancy Using Data Mining Tool." Journal of Research on the Lepidoptera 51, no. 2 (June 25, 2020): 936–45. http://dx.doi.org/10.36872/lepi/v51i2/301147.

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Kurien, K. Leena, and Dr Ajeet Chikkamannur. "A Survey of Methodaology of Fraud Detection Using Data Mining." International Journal of Trend in Scientific Research and Development Volume-1, Issue-6 (October 31, 2017): 38–42. http://dx.doi.org/10.31142/ijtsrd2482.

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Bauer, Peter, Dirk Burose, and Jörg Schulz. "Rain detection over land surfaces using passive microwave satellite data." Meteorologische Zeitschrift 11, no. 1 (March 5, 2002): 37–48. http://dx.doi.org/10.1127/0941-2948/2002/0011-0037.

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Fawaiq, Mohammad Nur, Ema Utami, and Dhani Ariatmanto. "Rice Plant Disease Detection with Data Augmentation Using Transfer Learning." International Journal of Research Publication and Reviews 4, no. 4 (April 8, 2023): 2195–99. http://dx.doi.org/10.55248/gengpi.2023.4.4.35530.

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Shabtai, Asaf, Maya Bercovitch, Lior Rokach, and Yuval Elovici. "Optimizing Data Misuse Detection." ACM Transactions on Knowledge Discovery from Data 8, no. 3 (June 2, 2014): 1–23. http://dx.doi.org/10.1145/2611520.

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Wester, Benjamin, David Devecsery, Peter M. Chen, Jason Flinn, and Satish Narayanasamy. "Parallelizing data race detection." ACM SIGARCH Computer Architecture News 41, no. 1 (March 29, 2013): 27–38. http://dx.doi.org/10.1145/2490301.2451120.

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Shrivastava, Pranav, Prerna Agarwal, Rahul, and Monika. "WiFi Data Leakage Detection." IOP Conference Series: Materials Science and Engineering 804 (June 17, 2020): 012042. http://dx.doi.org/10.1088/1757-899x/804/1/012042.

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Wester, Benjamin, David Devecsery, Peter M. Chen, Jason Flinn, and Satish Narayanasamy. "Parallelizing data race detection." ACM SIGPLAN Notices 48, no. 4 (April 23, 2013): 27–38. http://dx.doi.org/10.1145/2499368.2451120.

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Chen, Pei Jiang. "Headlight Detection and Error Correction of Measurement Data." Applied Mechanics and Materials 740 (March 2015): 535–38. http://dx.doi.org/10.4028/www.scientific.net/amm.740.535.

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Headlight detection was an important item of vehicle safety testing which main detection contents included light intensity and beam irradiation direction. It was to ensure the safe operation of vehicle at night or in adverse visual conditions. The basic concepts and testing standards of headlight were introduced, and the reasons of high failure rate for headlight detection were discussed. The main error correction methods of vehicle parking position in headlight detection were compared, and their advantages and disadvantages were analyzed. An error correction system of headlight testing measurement data was designed based on machine vision, and the process of system realization was given. It could provide a method to get more accurate measurement results of automobile headlight detecting.

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Dudczyk, Janusz, Roman Czyba, and Krzysztof Skrzypczyk. "Multi-Sensory Data Fusion in Terms of UAV Detection in 3D Space." Sensors 22, no. 12 (June 7, 2022): 4323. http://dx.doi.org/10.3390/s22124323.

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The paper focuses on the problem of detecting unmanned aerial vehicles that violate restricted airspace. The main purpose of the research is to develop an algorithm that enables the detection, identification and recognition in 3D space of a UAV violating restricted airspace. The proposed method consists of multi-sensory data fusion and is based on conditional complementary filtration and multi-stage clustering. On the basis of the review of the available UAV detection technologies, three sensory systems classified into the groups of passive and active methods are selected. The UAV detection algorithm is developed on the basis of data collected during field tests under real conditions, from three sensors: a radio system, an ADS-B transponder and a radar equipped with four antenna arrays. The efficiency of the proposed solution was tested on the basis of rapid prototyping in the MATLAB simulation environment with the use of data from the real sensory system obtained during controlled UAV flights. The obtained results of UAV detections confirmed the effectiveness of the proposed method and theoretical expectations.

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Daza Santacoloma, Genaro, Julio Fernando Suárez Cifuentes, and Germán Castellanos Domínguez. "Biosignal data preprocessing: a voice pathology detection application." Ingeniería e Investigación 29, no. 3 (September 1, 2009): 92–96. http://dx.doi.org/10.15446/ing.investig.v29n3.15189.

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A methodology for biosignal data preprocessing is presented. Experiments were mainly carried out with voice signals for automatically detecting pathologies. The proposed methodology was structured on 3 elements: outlier detection, normality verification and distribution transformation. It improved classification performance if basic assumptions about data structure were met. This entailed a more accurate detection of voice pathologies and it reduced the computational complexity of classification algorithms. Classification performance improved by 15%.

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Jia, Xue Fei, Ning Bu, Shang Gao, and Tao Li. "Sensitive Data Leak Detection Based on Boundary Detection." Advanced Materials Research 1049-1050 (October 2014): 1154–58. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1154.

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Sensitive data has a great influence on our daily lives. Once the leakage of sensitive data occurred, the timely detection and response is of great importance. This article puts forward the concept of boundary detection based on the black box testing. And with the idea of boundary detection, a cross-platform sensitive data leakage detection system is built.

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N.Holambe, Sushilkumar, Ulhas B. Shinde, and Archana U. Bhosale. "The Guilt Detection Approach in Data Leakage Detection." International Journal of Computer Applications 119, no. 8 (June 18, 2015): 38–43. http://dx.doi.org/10.5120/21091-3786.

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Elvidge, Christopher, Mikhail Zhizhin, Kimberly Baugh, Feng Hsu, and Tilottama Ghosh. "Extending Nighttime Combustion Source Detection Limits with Short Wavelength VIIRS Data." Remote Sensing 11, no. 4 (February 15, 2019): 395. http://dx.doi.org/10.3390/rs11040395.

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The Visible Infrared Imaging Radiometer Suite (VIIRS) collects low light imaging data at night in five spectral bands. The best known of these is the day/night band (DNB) which uses light intensification for imaging of moonlit clouds in the visible and near-infrared (VNIR). The other four low light imaging bands are in the NIR and short-wave infrared (SWIR), designed for daytime imaging, which continue to collect data at night. VIIRS nightfire (VNF) tests each nighttime pixel for the presence of sub-pixel IR emitters across six spectral bands with two bands each in three spectral ranges: NIR, SWIR, and MWIR. In pixels with detection in two or more bands, Planck curve fitting leads to the calculation of temperature, source area, and radiant heat using physical laws. An analysis of January 2018 global VNF found that inclusion of the NIR and SWIR channels results in a doubling of the VNF pixels with temperature fits over the detection numbers involving the MWIR. The addition of the short wavelength channels extends detection limits to smaller source areas across a broad range of temperatures. The VIIRS DNB has even lower detection limits for combustion sources, reaching 0.001 m2 at 1800 K, a typical temperature for a natural gas flare. Comparison of VNF tallies and DNB fire detections in a 2015 study area in India found the DNB had 15 times more detections than VNF. The primary VNF error sources are false detections from high energy particle detections (HEPD) in space and radiance saturation on some of the most intense events. The HEPD false detections are largely eliminated in the VNF output by requiring multiband detections for the calculation of temperature and source size. Radiance saturation occurs in about 1% of the VNF detections and occurs primarily in the M12 spectral band. Inclusion of the radiances affected by saturation results in temperature and source area calculation errors. Saturation is addressed by identifying the presence of saturation and excluding those radiances from the Planck curve fitting. The extremely low detection limits for the DNB indicates that a DNB fire detection algorithm could reveal vast numbers of combustion sources that are undetectable in longer wavelength VIIRS data. The caveats with the DNB combustion source detection capability is that it should be restricted to pixels that are outside the zone of known VIIRS detected electric lighting.

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Lu, Tianyuan, Lei Wang, and Xiaoyong Zhao. "Review of Anomaly Detection Algorithms for Data Streams." Applied Sciences 13, no. 10 (May 22, 2023): 6353. http://dx.doi.org/10.3390/app13106353.

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With the rapid development of emerging technologies such as self-media, the Internet of Things, and cloud computing, massive data applications are crossing the threshold of the era of real-time analysis and value realization, which makes data streams ubiquitous in all kinds of industries. Therefore, detecting anomalies in such data streams could be very important and full of challenges. For example, in industries such as electricity and finance, data stream anomalies often contain information that can help avoiding risks and support decision making. However, most traditional anomaly detection algorithms rely on acquiring global information about the data, which is hard to apply to stream data scenarios. Currently, the reviews of the algorithm in the field of anomaly detection, both domestically and internationally, tend to focus on the exposition of anomaly detection algorithms in static data environments, while lacking in the induction and analysis of anomaly detection algorithms in the context of streaming data. As a result, unlike the existing literature reviews, this review provides the current mainstream anomaly detection algorithms in data streaming scenarios and categorizes them into three types on the basis of their fundamental principles: (1) based on offline learning; (2) based on semi-online learning; (3) based on online learning. This review discusses the current state of research on data stream anomaly detection and studies the key issues in various algorithms for detecting anomalies in data streams on the basis of concise summarization. Moreover, the review conducts a detailed comparison of the pros and cons of the algorithms. Finally, the future challenges in the field are analyzed, and future research directions are proposed.

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Burgos, Julian M., and John K. Horne. "Sensitivity analysis and parameter selection for detecting aggregations in acoustic data." ICES Journal of Marine Science 64, no. 1 (October 25, 2006): 160–68. http://dx.doi.org/10.1093/icesjms/fsl007.

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Abstract Burgos, J. M., and Horne, J. K. 2007. Sensitivity analysis and parameter selection for detecting aggregations in acoustic data. ICES Journal of Marine Science, 64: 160–168. A global sensitivity analysis was conducted on the algorithm implemented in the Echoview ® software to detect and describe aggregations in acoustic backscatter. Multiple aggregation detections were performed using walleye pollock (Theragra chalcogramma) data from the eastern Bering Sea. Walleye pollock form distinct aggregations and dense and diffuse layers. In each aggregation detection, input parameters defining minimum size, density, and distance to other aggregations were selected at random using a Latin hypercube sampling design. Sensitivity was quantified by testing for correlation among input parameters and a series of aggregation descriptors. In all, 336 correlation tests were performed, corresponding to a combination of seven detection input parameters, eight aggregation descriptors, and six transects. Among these, 181 tests were significant, indicating sensitivity between input parameters and aggregation descriptors. The aggregation-detection algorithm is sensitive to changes in threshold and minimum size, but less sensitive to changes in the connectivity criterion among aggregations.

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Amro, Ahmed, Aybars Oruc, Vasileios Gkioulos, and Sokratis Katsikas. "Navigation Data Anomaly Analysis and Detection." Information 13, no. 3 (February 23, 2022): 104. http://dx.doi.org/10.3390/info13030104.

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Several disruptive attacks against companies in the maritime industry have led experts to consider the increased risk imposed by cyber threats as a major obstacle to undergoing digitization. The industry is heading toward increased automation and connectivity, leading to reduced human involvement in the different navigational functions and increased reliance on sensor data and software for more autonomous modes of operations. To meet the objectives of increased automation under the threat of cyber attacks, the different software modules that are expected to be involved in different navigational functions need to be prepared to detect such attacks utilizing suitable detection techniques. Therefore, we propose a systematic approach for analyzing the navigational NMEA messages carrying the data of the different sensors, their possible anomalies, malicious causes of such anomalies as well as the appropriate detection algorithms. The proposed approach is evaluated through two use cases, traditional Integrated Navigation System (INS) and Autonomous Passenger Ship (APS). The results reflect the utility of specification and frequency-based detection in detecting the identified anomalies with high confidence. Furthermore, the analysis is found to facilitate the communication of threats through indicating the possible impact of the identified anomalies against the navigational operations. Moreover, we have developed a testing environment that facilitates conducting the analysis. The environment includes a developed tool, NMEA-Manipulator that enables the invocation of the identified anomalies through a group of cyber attacks on sensor data. Our work paves the way for future work in the analysis of NMEA anomalies toward the development of an NMEA intrusion detection system.

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Yang, Fei, Zhenxing Yao, and Peter J. Jin. "GPS and Acceleration Data in Multimode Trip Data Recognition Based on Wavelet Transform Modulus Maximum Algorithm." Transportation Research Record: Journal of the Transportation Research Board 2526, no. 1 (January 2015): 90–98. http://dx.doi.org/10.3141/2526-10.

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The GPS-based travel survey is an emerging data collection method in transportation planning. The survey's application in trip mode detection has been explored in many studies. Most research on trip mode detection methods based on GPS data has been developed and tested with data collected from European and American countries. The methods cannot be easily adapted to Asian countries such as China, India, and Japan, which have much higher population densities, more complex road networks, and highly mixed travel modes during daily commuting. Furthermore, for trip segment division in multimode travel, existing algorithms use travel time and distance thresholds that are highly dependent on local travel behavior and lack universality across traffic environments. This paper proposes an innovative framework for detecting trip modes in complex urban environments. First, a smartphone application, GPSurvey, was developed to collect passive GPS trace data. Then a wavelet transform modulus maximum algorithm was developed for trip segment division. The algorithm has outstanding capabilities for identifying singularity features of a signal; this factor suits the task of detecting mode changes in a complex traffic environment. A neural network module was developed for mode detection on the basis of cell phone GPS location and acceleration data. The results indicate that the proposed method has promising performance. The average absolute detection error of mode transfer time was within 1 min, and the accuracy for detecting all modes was greater than 85%.

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Mao, Jiang Kun, and Fan Zhan. "Study on Intrusion Detection System Based on Data Mining." Applied Mechanics and Materials 713-715 (January 2015): 2499–502. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2499.

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Intrusion detection system as a proactive network security technology, is necessary and reasonable to add a static defense. However, the traditional exceptions and errors detecting exist issues of leakage police, the false alarm rate or maintenance difficult. In this paper, The intrusion detection system based on data mining with statistics, machine learning techniques in the detection performance, robustness, self-adaptability has a great advantage. The system improves the K-means clustering algorithm, focus on solving two questions of the cluster center node selection and discriminating of clustering properties, the test shows that the system further enhance the detection efficiency of the system.

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Yadav, R., A. Nascetti, and Y. Ban. "BUILDING CHANGE DETECTION USING MULTI-TEMPORAL AIRBORNE LIDAR DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 31, 2022): 1377–83. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-1377-2022.

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Abstract. Building change detection is essential for monitoring urbanization, disaster assessment, urban planning and frequently updating the maps. 3D structure information from airborne light detection and ranging (LiDAR) is very effective for detecting urban changes. But the 3D point cloud from airborne LiDAR(ALS) holds an enormous amount of unordered and irregularly sparse information. Handling such data is tricky and consumes large memory for processing. Most of this information is not necessary when we are looking for a particular type of urban change. In this study, we propose an automatic method that reduces the 3D point clouds into a much smaller representation without losing necessary information required for detecting Building changes. The method utilizes the Deep Learning(DL) model U-Net for segmenting the buildings from the background. Produced segmentation maps are then processed further for detecting changes and the results are refined using morphological methods. For the change detection task, we used multi-temporal airborne LiDAR data. The data is acquired over Stockholm in the years 2017 and 2019. The changes in buildings are classified into four types: ‘newly built’, ‘demolished’, ‘taller’ and ’shorter’. The detected changes are visualized in one map for better interpretation.

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J.Jabez, J. Jabez, and Dr G. S. Anandha Mala. "Multi-Level Security System for Anomaly Detection in Cloud Based Data." Indian Journal of Applied Research 4, no. 4 (October 1, 2011): 114–17. http://dx.doi.org/10.15373/2249555x/apr2014/33.

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S.N., Sithi Shamila. "Performance Analysis of Detection of Video for Secured Cloud Data Service." International Journal of Psychosocial Rehabilitation 24, no. 5 (April 20, 2020): 4232–46. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020139.

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Lixia Ji, Lixia Ji, Xiao Zhang Lixia Ji, Yao Zhao Xiao Zhang, and Zongkun Li Yao Zhao. "Anomaly Detection of Dam Monitoring Data based on Improved Spectral Clustering." 網際網路技術學刊 23, no. 4 (July 2022): 749–59. http://dx.doi.org/10.53106/160792642022072304010.

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In response to the abnormal data mining in dam safety monitoring, and based on the traditional spectral clustering, this paper presents an anomaly detection method based on improved spectral clustering. This method applies a distance and density adaptive similarity measure. The natural eigenvalue is introduced to adaptively select the neighbors of data points, and the similarity is redefined to be combined with the natural k-nearest neighbor. Furthermore, the shared neighbor is introduced to adjust the similarity between the monitoring data samples according to the regional density. Moreover, considering the distribution of dam monitoring data, the initialization of clustering centers is optimized according to both the density and distance feature. This method can prevent the algorithm from local optimum, better adapt to the density of non-convex dataset, reduce the number of iterations, and enhance the efficiencies of clustering and anomaly detection. Taking the dam slab monitoring data as the research object, experimental datasets are formed. Experiments on these datasets further verify that the method of this paper can effectively adapt to discrete distribution datasets and is superior to the classical spectral clustering method in both clustering and anomaly detection.

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Desai, Vinod, and H. A. Dinesh. "Efficient Reputation-based Cyber Attack Detection Mechanism for Big Data Environment." Indian Journal of Science and Technology 15, no. 13 (April 5, 2022): 592–602. http://dx.doi.org/10.17485/ijst/v15i13.2102.

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Abid, Mahwish, Muhammad Usman, and Muhammad Waleed Ashraf. "Plagiarism Detection Process using Data Mining Techniques." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 5, no. 4 (December 20, 2017): 68. http://dx.doi.org/10.3991/ijes.v5i4.7869.

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As the technology is growing very fast and usage of computer systems is increased as compared to the old times, plagiarism is the phenomenon which is increasing day by day. Wrongful appropriation of someone else’s work is known as plagiarism. Manually detection of plagiarism is difficult so this process should be automated. There are various tools which can be used for plagiarism detection. Some works on intrinsic plagiarism while other work on extrinsic plagiarism. Data mining the field which can help in detecting the plagiarism as well as can help to improve the efficiency of the process. Different data mining techniques can be used to detect plagiarism. Text mining, clustering, bi-gram, tri-grams, n-grams are the techniques which can help in this process

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How, Jason Richard, and Simon de Lestang. "Acoustic tracking: issues affecting design, analysis and interpretation of data from movement studies." Marine and Freshwater Research 63, no. 4 (2012): 312. http://dx.doi.org/10.1071/mf11194.

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Acoustic telemetry systems are an increasingly common way to examine the movement and behaviour of marine organisms. However, there has been little published on the methodological and analytical work associated with this technology. We tested transmitters of differing power outputs simultaneously in several trials, some lasting ~50 days, to examine the effects of power output and environmental factors (water movement, temperature, lunar cycle and time of day). There were considerable and volatile changes in detections throughout all trials. Increased water movement and temperature significantly reduced detection rates, whereas daytime and full-moon periods had significantly higher detection rates. All nine transmitters (from seven transmitter types tested) showed a sigmoidal trend between detection frequency and distance. Higher-powered transmitters had a prolonged detection distance with near-maximal detections, whereas lower-powered transmitters showed an almost immediate decline. Variation of detection frequency, transmitter type and the modelled relationship between distance and detection frequency were incorporated into a positioning trial which resulted in markedly improved position estimates over previous techniques.

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Fayad, Ibrahim, Nicolas Baghdadi, Hassan Bazzi, and Mehrez Zribi. "Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data." Remote Sensing 12, no. 12 (June 19, 2020): 1976. http://dx.doi.org/10.3390/rs12121976.

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Short-term freeze/thaw cycles, which mostly occur in the northern hemisphere across the majority of land surfaces, are reported to cause severe economic losses over broad areas of Europe and North America. Therefore, in order to assess the extent of frost damage in the agricultural sector, the objective of this study is to build an operational approach capable of detecting frozen plots at the plot scale in a near real-time scenario using Sentinel-1 (S1) data. C-band synthetic aperture radar (SAR) data show high potential for the detection of freeze/thaw surface states due to the significant alterations to the dielectric properties of the soil, which are distinctly observable in the backscattered signal. In this study, we propose an approach that relies on change detection in the high-resolution Sentinel-1 C-band SAR backscattered coefficients, to determine surface states at the plot scale as either frozen or unfrozen. A threshold analysis is first performed in order to determine the best thresholds for three distinct land cover classes, and for each polarization mode (VH, and VV). S-1 SAR data are then used to detect a plot’s surface state as either unfrozen, mild-to-moderately frozen or severely frozen. A temperature-based filter has also been applied at the end of the detection chain to eliminate false detections in the freezing detection algorithm due mainly to rainfall, irrigation, tillage, or signal noise. Our approach has been tested over two study sites in France, and the output results, using either VH or VV, compared qualitatively well with both in situ air temperature data and soil temperature data provided by ERA5-Land. Overall, our algorithm was able to detect all freezing episodes over the analyzed S-1 SAR time series, and with no false detections. Moreover, given the high-resolution aspect of S-1 SAR data, our algorithm is capable of mapping the local variation of freezing episodes at plot scale. This is in contrast with previous products that only offer coarser results across larger areas (low spatial resolution).

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Periyasamy, A. R. Pon, and E. Thenmozhi. "Data Leakage Detection and Data Prevention Using Algorithm." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 4 (April 30, 2017): 251–56. http://dx.doi.org/10.23956/ijarcsse/v7i4/0121.

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Dhawase, Shubhangi G., Bhagyashri J. Chaudhari, Neha S. Kolambe, and Poonam S. Masare. "Data Leakage Detection and Prevention of Confidential Data." International Journal of Computer Sciences and Engineering 6, no. 6 (June 30, 2018): 213–18. http://dx.doi.org/10.26438/ijcse/v6i6.213218.

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Siu, Y. M., C. K. Chan, and K. L. Ho. "Teletext data change detection and noiseless data compression." IEEE Transactions on Consumer Electronics 41, no. 4 (1995): 1061–68. http://dx.doi.org/10.1109/30.477224.

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Malki, Oliver, Frank Przygodda, Heiko Trautner, and Hartmut Richter. "Data Detection Methods for Holographic Data Storage Systems." Japanese Journal of Applied Physics 49, no. 8 (August 20, 2010): 08KD11. http://dx.doi.org/10.1143/jjap.49.08kd11.

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Togbe, Maurras Ulbricht, Yousra Chabchoub, Aliou Boly, Mariam Barry, Raja Chiky, and Maroua Bahri. "Anomalies Detection Using Isolation in Concept-Drifting Data Streams." Computers 10, no. 1 (January 19, 2021): 13. http://dx.doi.org/10.3390/computers10010013.

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Detecting anomalies in streaming data is an important issue for many application domains, such as cybersecurity, natural disasters, or bank frauds. Different approaches have been designed in order to detect anomalies: statistics-based, isolation-based, clustering-based, etc. In this paper, we present a structured survey of the existing anomaly detection methods for data streams with a deep view on Isolation Forest (iForest). We first provide an implementation of Isolation Forest Anomalies detection in Stream Data (IForestASD), a variant of iForest for data streams. This implementation is built on top of scikit-multiflow (River), which is an open source machine learning framework for data streams containing a single anomaly detection algorithm in data streams, called Streaming half-space trees. We performed experiments on different real and well known data sets in order to compare the performance of our implementation of IForestASD and half-space trees. Moreover, we extended the IForestASD algorithm to handle drifting data by proposing three algorithms that involve two main well known drift detection methods: ADWIN and KSWIN. ADWIN is an adaptive sliding window algorithm for detecting change in a data stream. KSWIN is a more recent method and it refers to the Kolmogorov–Smirnov Windowing method for concept drift detection. More precisely, we extended KSWIN to be able to deal with n-dimensional data streams. We validated and compared all of the proposed methods on both real and synthetic data sets. In particular, we evaluated the F1-score, the execution time, and the memory consumption. The experiments show that our extensions have lower resource consumption than the original version of IForestASD with a similar or better detection efficiency.

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Bazylevych, Volodymyr, and Maria Prybytko. "FAKE NEWS DETECTION SYSTEM BASED ON DATA SCIENCE." Technical Sciences and Technologies, no. 4(22) (2020): 91–95. http://dx.doi.org/10.25140/2411-5363-2020-4(22)-91-95.

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Urgency of the research. Today, the task of analyzing the veracity of information in the news, which filled all existing channels for obtaining information, is relevant. Its urgency is related to the need to prevent panic by obtaining inaccurate information, debunking pseudo-scientific facts that can threaten people's lives, combating political propaganda and others.Target settingThis article focuses on the concept of developing a system for detecting fake news, analysis of existing systems and their principles of operation, principles of construction of their algorithms and features of their use.Actual scientific researches and issues analysis.Recent open publications, statistics, and corporate reports were reviewed.Uninvestigated parts of general matters defining.File analysis will be performed using three methods / classifiers and without the use of PassiveAgressive classifier. The calculation and derivation of results is performed by constructing error matrices and calculating accuracy.The research objective.The main purpose of the work is to create a system for detecting fake news on the basis of the considered materials and to achieve the highest possible accuracy.Presenting main material. Input data for the study were selected, prepared and analyzed. Data were studied using the meth-ods /classifiers of Logistic Regression, Decision Tree and Random Forest. The accuracy of detecting fake news is calculated.Conclusions.The proposed system allows to classify news as “fake”or “true ”with an accuracy of 98-99%

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Syed Abd Mutalib, Sharifah Sakinah, Siti Zanariah Satari, and Wan Nur Syahidah Wan Yusoff. "A Review on Outliers-Detection Methods for Multivariate Data." Journal of Statistical Modelling and Analytics 3, no. 1 (July 1, 2021): 1–15. http://dx.doi.org/10.22452/josma.vol3no1.1.

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Data in practice are often of high dimension and multivariate in nature. Detection of outliers has been one of the problems in multivariate analysis. Detecting outliers in multivariate data is difficult and it is not sufficient by using only graphical inspection. In this paper, a nontechnical and brief outlier detection method for multivariate data which are projection pursuit method, methods based on robust distance and cluster analysis are reviewed. The strengths and weaknesses of each method are briefly discussed.

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Journal articles on the topic 'Data detection'

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