Relevant bibliographies by topics / Data detection

Academic literature on the topic 'Data detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Data detection.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Data detection"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Data detection"

1

Weis, Melanie. "Duplicate detection in XML data." Duisburg Köln WiKu, 2007. http://d-nb.info/987676849/04.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Cao, Lei. "Outlier Detection In Big Data." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/82.

Full text
Abstract:
The dissertation focuses on scaling outlier detection to work both on huge static as well as on dynamic streaming datasets. Outliers are patterns in the data that do not conform to the expected behavior. Outlier detection techniques are broadly applied in applications ranging from credit fraud prevention, network intrusion detection to stock investment tactical planning. For such mission critical applications, a timely response often is of paramount importance. Yet the processing of outlier detection requests is of high algorithmic complexity and resource consuming. In this dissertation we investigate the challenges of detecting outliers in big data -- in particular caused by the high velocity of streaming data, the big volume of static data and the large cardinality of the input parameter space for tuning outlier mining algorithms. Effective optimization techniques are proposed to assure the responsiveness of outlier detection in big data. In this dissertation we first propose a novel optimization framework called LEAP to continuously detect outliers over data streams. The continuous discovery of outliers is critical for a large range of online applications that monitor high volume continuously evolving streaming data. LEAP encompasses two general optimization principles that utilize the rarity of the outliers and the temporal priority relationships among stream data points. Leveraging these two principles LEAP not only is able to continuously deliver outliers with respect to a set of popular outlier models, but also provides near real-time support for processing powerful outlier analytics workloads composed of large numbers of outlier mining requests with various parameter settings. Second, we develop a distributed approach to efficiently detect outliers over massive-scale static data sets. In this big data era, as the volume of the data advances to new levels, the power of distributed compute clusters must be employed to detect outliers in a short turnaround time. In this research, our approach optimizes key factors determining the efficiency of distributed data analytics, namely, communication costs and load balancing. In particular we prove the traditional frequency-based load balancing assumption is not effective. We thus design a novel cost-driven data partitioning strategy that achieves load balancing. Furthermore, we abandon the traditional one detection algorithm for all compute nodes approach and instead propose a novel multi-tactic methodology which adaptively selects the most appropriate algorithm for each node based on the characteristics of the data partition assigned to it. Third, traditional outlier detection systems process each individual outlier detection request instantiated with a particular parameter setting one at a time. This is not only prohibitively time-consuming for large datasets, but also tedious for analysts as they explore the data to hone in on the most appropriate parameter setting or on the desired results. We thus design an interactive outlier exploration paradigm that is not only able to answer traditional outlier detection requests in near real-time, but also offers innovative outlier analytics tools to assist analysts to quickly extract, interpret and understand the outliers of interest. Our experimental studies including performance evaluation and user studies conducted on real world datasets including stock, sensor, moving object, and Geolocation datasets confirm both the effectiveness and efficiency of the proposed approaches.
APA, Harvard, Vancouver, ISO, and other styles
3

Abghari, Shahrooz. "Data Modeling for Outlier Detection." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16580.

Full text
Abstract:
This thesis explores the data modeling for outlier detection techniques in three different application domains: maritime surveillance, district heating, and online media and sequence datasets. The proposed models are evaluated and validated under different experimental scenarios, taking into account specific characteristics and setups of the different domains. Outlier detection has been studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. Outlier detection techniques are often domain-specific. The main challenge in outlier detection relates to modeling the normal behavior in order to identify abnormalities. The choice of model is important, i.e., an incorrect choice of data model can lead to poor results. This requires a good understanding and interpretation of the data, the constraints, and the requirements of the problem domain. Outlier detection is largely an unsupervised problem due to unavailability of labeled data and the fact that labeled data is expensive. We have studied and applied a combination of both machine learning and data mining techniques to build data-driven and domain-oriented outlier detection models. We have shown the importance of data preprocessing as well as feature selection in building suitable methods for data modeling. We have taken advantage of both supervised and unsupervised techniques to create hybrid methods. For example, we have proposed a rule-based outlier detection system based on open data for the maritime surveillance domain. Furthermore, we have combined cluster analysis and regression to identify manual changes in the heating systems at the building level. Sequential pattern mining for identifying contextual and collective outliers in online media data have also been exploited. In addition, we have proposed a minimum spanning tree clustering technique for detection of groups of outliers in online media and sequence data. The proposed models have been shown to be capable of explaining the underlying properties of the detected outliers. This can facilitate domain experts in narrowing down the scope of analysis and understanding the reasons of such anomalous behaviors. We have also investigated the reproducibility of the proposed models in similar application domains.
Scalable resource-efficient systems for big data analytics
APA, Harvard, Vancouver, ISO, and other styles
4

Payne, Timothy Myles. "Remote detection using fused data /." Title page, abstract and table of contents only, 1994. http://web4.library.adelaide.edu.au/theses/09PH/09php3465.pdf.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Forstén, Andreas. "Unsupervised Anomaly Detection in Receipt Data." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215161.

Full text
Abstract:
With the progress of data handling methods and computing power comes the possibility of automating tasks that are not necessarily handled by humans. This study was done in cooperation with a company that digitalizes receipts for companies. We investigate the possibility of automating the task of finding anomalous receipt data, which could automate the work of receipt auditors. We study both anomalous user behaviour and individual receipts. The results indicate that automation is possible, which may reduce the necessity of human inspection of receipts.
Med de framsteg inom datahantering och datorkraft som gjorts så kommer också möjligheten att automatisera uppgifter som ej nödvändigtvis utförs av människor. Denna studie gjordes i samarbete med ett företag som digitaliserar företags kvitton. Vi undersöker möjligheten att automatisera sökandet av avvikande kvittodata, vilket kan avlasta revisorer. Vti studerar både avvikande användarbeteenden och individuella kvitton. Resultaten indikerar att automatisering är möjligt, vilket kan reducera behovet av mänsklig inspektion av kvitton
APA, Harvard, Vancouver, ISO, and other styles
6

Tian, Xuwen, and 田旭文. "Data-driven textile flaw detection methods." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hdl.handle.net/10722/196091.

Full text
Abstract:
This research develops three efficient textile flaw detection methods to facilitate automated textile inspection for the textile-related industries. Their novelty lies in detecting flaws with knowledge directly extracted from textile images, unlike existing methods which detect flaws with empirically specified texture features. The first two methods treat textile flaw detection as a texture classification problem, and consider that defect-free images of a textile fabric normally possess common latent images, called basis-images. The inner product of a basis-image and an image acquired from this fabric is a feature value of this fabric image. As the defect-free images are similar, their feature values gather in a cluster, whose boundary can be determined by using the feature values of known defect-free images. A fabric image is considered defect-free, if its feature values lie within this boundary. These methods extract the basis-images from known defect-free images in a training process, and require less consideration than existing methods on the degree of matching of a textile to the texture features specified for the textile. One method uses matrix singular value decomposition (SVD) to extract these basis-images containing the spatial relationship of pixels in rows or in columns. The alternative method uses tensor decomposition to find the relationship of pixels in both rows and columns within each training image and the common relationship of these training images. Tensor decomposition is found to be superior to matrix SVD in finding the basis-images needed to represent these defect-free images, because extracting and decomposing the tri-lateral relationship usually generates better basis-images. The third method solves the textile flaw detection problem by means of texture segmentation, and is suitable for online detection because it does not require texture features specified by experience or found from known defect-free images. The method detects the presence of flaws by using the contrast between regions in the feature images of a textile image. These feature images are the output of a filter bank consisting of Gabor filters with scales and rotations. This method selects the feature image with maximal image contrast, and partitions this image into regions with morphological watershed transform to facilitate faster searching of defect-free regions and to remove isolated pixels with exceptional feature values. Regions with no flaws have similar statistics, e.g. similar means. Regions with significantly dissimilar statistics may contain flaws and are removed iteratively from the set which initially contains all regions. Removing regions uses the thresholds determined by using Neyman-Pearson criterion and updated along with the remaining regions in the set. This procedure continues until the set only contains defect-free regions. The occurrence of the removed regions indicates the presence of flaws whose extents are decided by pixel classification using the thresholds derived from the defect-free regions. A prototype textile inspection system is built to demonstrate the automatic textile inspection process. The developed methods are proved reliable and effective by testing them with a variety of defective textile images. These methods also have several advantages, e.g. less empirical knowledge of textiles is needed for selecting texture features.
published_or_final_version
Industrial and Manufacturing Systems Engineering
Doctoral
Doctor of Philosophy
APA, Harvard, Vancouver, ISO, and other styles
7

Siddiqui, Muazzam. "DATA MINING METHODS FOR MALWARE DETECTION." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2783.

Full text
Abstract:
This research investigates the use of data mining methods for malware (malicious programs) detection and proposed a framework as an alternative to the traditional signature detection methods. The traditional approaches using signatures to detect malicious programs fails for the new and unknown malwares case, where signatures are not available. We present a data mining framework to detect malicious programs. We collected, analyzed and processed several thousand malicious and clean programs to find out the best features and build models that can classify a given program into a malware or a clean class. Our research is closely related to information retrieval and classification techniques and borrows a number of ideas from the field. We used a vector space model to represent the programs in our collection. Our data mining framework includes two separate and distinct classes of experiments. The first are the supervised learning experiments that used a dataset, consisting of several thousand malicious and clean program samples to train, validate and test, an array of classifiers. In the second class of experiments, we proposed using sequential association analysis for feature selection and automatic signature extraction. With our experiments, we were able to achieve as high as 98.4% detection rate and as low as 1.9% false positive rate on novel malwares.
Ph.D.
Other
Sciences
Modeling and Simulation PhD
APA, Harvard, Vancouver, ISO, and other styles
8

Mohd, Ali Azliza. "Anomalous behaviour detection using heterogeneous data." Thesis, Lancaster University, 2018. http://eprints.lancs.ac.uk/125026/.

Full text
Abstract:
Anomaly detection is one of the most important methods to process and find abnormal data, as this method can distinguish between normal and abnormal behaviour. Anomaly detection has been applied in many areas such as the medical sector, fraud detection in finance, fault detection in machines, intrusion detection in networks, surveillance systems for security, as well as forensic investigations. Abnormal behaviour can give information or answer questions when an investigator is performing an investigation. Anomaly detection is one way to simplify big data by focusing on data that have been grouped or clustered by the anomaly detection method. Forensic data usually consists of heterogeneous data which have several data forms or types such as qualitative or quantitative, structured or unstructured, and primary or secondary. For example, when a crime takes place, the evidence can be in the form of various types of data. The combination of all the data types can produce rich information insights. Nowadays, data has become ‘big’ because it is generated every second of every day and processing has become time-consuming and tedious. Therefore, in this study, a new method to detect abnormal behaviour is proposed using heterogeneous data and combining the data using data fusion technique. Vast challenge data and image data are applied to demonstrate the heterogeneous data. The first contribution in this study is applying the heterogeneous data to detect an anomaly. The recently introduced anomaly detection technique which is known as Empirical Data Analytics (EDA) is applied to detect the abnormal behaviour based on the data sets. Standardised eccentricity (a newly introduced within EDA measure offering a new simplified form of the well-known Chebyshev Inequality) can be applied to any data distribution. Then, the second contribution is applying image data. The image data is processed using pre-trained deep learning network, and classification is done using a support vector machine (SVM). After that, the last contribution is combining anomaly result from heterogeneous data and image recognition using new data fusion technique. There are five types of data with three different modalities and different dimensionalities. The data cannot be simply combined and integrated. Therefore, the new data fusion technique first analyses the abnormality in each data type separately and determines the degree of suspicious between 0 and 1 and sums up all the degrees of suspicion data afterwards. This method is not intended to be a fully automatic system that resolves investigations, which would likely be unacceptable in any case. The aim is rather to simplify the role of the humans so that they can focus on a small number of cases to be looked in more detail. The proposed approach does simplify the processing of such huge amounts of data. Later, this method can assist human experts in their investigations and making final decisions.
APA, Harvard, Vancouver, ISO, and other styles
9

Pellissier, Muriel. "Anomaly detection technique for sequential data." Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENM078/document.

Full text
Abstract:
De nos jours, beaucoup de données peuvent être facilement accessibles. Mais toutes ces données ne sont pas utiles si nous ne savons pas les traiter efficacement et si nous ne savons pas extraire facilement les informations pertinentes à partir d'une grande quantité de données. Les techniques de détection d'anomalies sont utilisées par de nombreux domaines afin de traiter automatiquement les données. Les techniques de détection d'anomalies dépendent du domaine d'application, des données utilisées ainsi que du type d'anomalie à détecter.Pour cette étude nous nous intéressons seulement aux données séquentielles. Une séquence est une liste ordonnée d'objets. Pour de nombreux domaines, il est important de pouvoir identifier les irrégularités contenues dans des données séquentielles comme par exemple les séquences ADN, les commandes d'utilisateur, les transactions bancaires etc.Cette thèse présente une nouvelle approche qui identifie et analyse les irrégularités de données séquentielles. Cette technique de détection d'anomalies peut détecter les anomalies de données séquentielles dont l'ordre des objets dans les séquences est important ainsi que la position des objets dans les séquences. Les séquences sont définies comme anormales si une séquence est presque identique à une séquence qui est fréquente (normale). Les séquences anormales sont donc les séquences qui diffèrent légèrement des séquences qui sont fréquentes dans la base de données.Dans cette thèse nous avons appliqué cette technique à la surveillance maritime, mais cette technique peut être utilisée pour tous les domaines utilisant des données séquentielles. Pour notre application, la surveillance maritime, nous avons utilisé cette technique afin d'identifier les conteneurs suspects. En effet, de nos jours 90% du commerce mondial est transporté par conteneurs maritimes mais seulement 1 à 2% des conteneurs peuvent être physiquement contrôlés. Ce faible pourcentage est dû à un coût financier très élevé et au besoin trop important de ressources humaines pour le contrôle physique des conteneurs. De plus, le nombre de conteneurs voyageant par jours dans le monde ne cesse d'augmenter, il est donc nécessaire de développer des outils automatiques afin d'orienter le contrôle fait par les douanes afin d'éviter les activités illégales comme les fraudes, les quotas, les produits illégaux, ainsi que les trafics d'armes et de drogues. Pour identifier les conteneurs suspects nous comparons les trajets des conteneurs de notre base de données avec les trajets des conteneurs dits normaux. Les trajets normaux sont les trajets qui sont fréquents dans notre base de données.Notre technique est divisée en deux parties. La première partie consiste à détecter les séquences qui sont fréquentes dans la base de données. La seconde partie identifie les séquences de la base de données qui diffèrent légèrement des séquences qui sont fréquentes. Afin de définir une séquence comme normale ou anormale, nous calculons une distance entre une séquence qui est fréquente et une séquence aléatoire de la base de données. La distance est calculée avec une méthode qui utilise les différences qualitative et quantitative entre deux séquences
Nowadays, huge quantities of data can be easily accessible, but all these data are not useful if we do not know how to process them efficiently and how to extract easily relevant information from a large quantity of data. The anomaly detection techniques are used in many domains in order to help to process the data in an automated way. The anomaly detection techniques depend on the application domain, on the type of data, and on the type of anomaly.For this study we are interested only in sequential data. A sequence is an ordered list of items, also called events. Identifying irregularities in sequential data is essential for many application domains like DNA sequences, system calls, user commands, banking transactions etc.This thesis presents a new approach for identifying and analyzing irregularities in sequential data. This anomaly detection technique can detect anomalies in sequential data where the order of the items in the sequences is important. Moreover, our technique does not consider only the order of the events, but also the position of the events within the sequences. The sequences are spotted as anomalous if a sequence is quasi-identical to a usual behavior which means if the sequence is slightly different from a frequent (common) sequence. The differences between two sequences are based on the order of the events and their position in the sequence.In this thesis we applied this technique to the maritime surveillance, but this technique can be used by any other domains that use sequential data. For the maritime surveillance, some automated tools are needed in order to facilitate the targeting of suspicious containers that is performed by the customs. Indeed, nowadays 90% of the world trade is transported by containers and only 1-2% of the containers can be physically checked because of the high financial cost and the high human resources needed to control a container. As the number of containers travelling every day all around the world is really important, it is necessary to control the containers in order to avoid illegal activities like fraud, quota-related, illegal products, hidden activities, drug smuggling or arm smuggling. For the maritime domain, we can use this technique to identify suspicious containers by comparing the container trips from the data set with itineraries that are known to be normal (common). A container trip, also called itinerary, is an ordered list of actions that are done on containers at specific geographical positions. The different actions are: loading, transshipment, and discharging. For each action that is done on a container, we know the container ID and its geographical position (port ID).This technique is divided into two parts. The first part is to detect the common (most frequent) sequences of the data set. The second part is to identify those sequences that are slightly different from the common sequences using a distance-based method in order to classify a given sequence as normal or suspicious. The distance is calculated using a method that combines quantitative and qualitative differences between two sequences
APA, Harvard, Vancouver, ISO, and other styles
10

Al-Bataineh, Hussien Suleiman. "Islanding Detection Using Data Mining Techniques." Thesis, North Dakota State University, 2015. https://hdl.handle.net/10365/27634.

Full text
Abstract:
Connection of the distributed generators (DGs), poses new challenges for operation and management of the distribution system. An important issue is that of islanding, where a part of the system gets disconnected from the DG. This thesis explores the use of several data-mining, and machine learning techniques to detect islanding. Several cases of islanding and non- islanding are simulated with a standard test-case: the IEEE 13 bus test distribution system. Different types of DGs are connected to the system and disturbances are introduced. Several classifiers are tested for their effectiveness in identifying islanded conditions under different scenarios. The simulation results show that the random forest classifier consistently outperforms the other methods for a diverse set of operating conditions, within an acceptable time after the onset of islanding. These results strengthen the case for machine-driven based tools for quick and accurate detection of islanding in microgrids.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Data detection"

1

Varshney, Pramod K. Distributed Detection and Data Fusion. New York, NY: Springer New York, 1997.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Gupta, Manish, Jing Gao, Charu Aggarwal, and Jiawei Han. Outlier Detection for Temporal Data. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-01905-0.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Varshney, Pramod K. Distributed Detection and Data Fusion. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1904-0.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Varshney, Pramod K. Distributed detection and data fusion. Edited by Burrus C. S. Berlin: Springer, 1996.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Varshney, Pramod K. Distributed detection and data fusion. Edited by Burrus C. S. New York: Springer, 1997.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Samy, Ihab, and Da-Wei Gu. Fault Detection and Flight Data Measurement. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24052-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Latifur, Khan, and Thuraisingham Bhavani M, eds. Data mining tools for malware detection. Boca Raton, FL: CRC Press, 2012.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Yang, Chenggang. Precipitation detection with satellite microwave data. Washington, D.C: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1988.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Chen, Zhiwen. Data-Driven Fault Detection for Industrial Processes. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-16756-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

M, Breipohl Arthur, ed. Random signals: Detection, estimation, and data analysis. New York: Wiley, 1988.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Data detection"

1

Sengupta, Nandita, and Jaya Sil. "Data Reduction." In Intrusion Detection, 47–82. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2716-6_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Batarseh, Feras A. "Anomaly Detection." In Encyclopedia of Big Data, 25–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-32010-6_223.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Batarseh, Feras A. "Anomaly Detection." In Encyclopedia of Big Data, 1–3. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-32001-4_223-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Boroojeni, Kianoosh G., M. Hadi Amini, and S. S. Iyengar. "Bad Data Detection." In Smart Grids: Security and Privacy Issues, 53–68. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45050-6_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Falsafi, Babak, Samuel Midkiff, JackB Dennis, JackB Dennis, Amol Ghoting, Roy H. Campbell, Christof Klausecker, et al. "Data Race Detection." In Encyclopedia of Parallel Computing, 524. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_2248.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Chen, Dewang, and Ruijun Cheng. "Error Data Detection." In Intelligent Processing Algorithms and Applications for GPS Positioning Data of Qinghai-Tibet Railway, 87–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-58970-0_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Bertino, Elisa. "Anomaly Detection." In Data Protection from Insider Threats, 37–45. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-01890-9_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Dissertori, Günther. "Data Analysis." In Handbook of Particle Detection and Imaging, 83–101. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-13271-1_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Dissertori, Günther. "Data Analysis." In Handbook of Particle Detection and Imaging, 1–25. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-47999-6_4-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Dissertori, Günther. "Data Analysis." In Handbook of Particle Detection and Imaging, 91–116. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-93785-4_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Data detection"

1

Peng, Chubing, M. Mansuripur, Kenichi Nagata, and Takeo Ohta. "Edge detection readout signal and cross-talk in phase-change optical data storage." In Optical Data Storage. Washington, D.C.: Optica Publishing Group, 1998. http://dx.doi.org/10.1364/ods.1998.tub.3.

Full text
Abstract:
Conventionally, readout signal is obtained by differential detection in magneto-optical storage or by direct integration of the reflected light in phase-change optical storage. Mark edges are usually determined by slicing the level detection signal at the standard level, suffering from intersymbol interference when reading densely recorded short marks. Edge detection is a direct optical detection for mark edges. The readout signal is the difference signal from a split detector. Theoretically, edge detection has advantages over conventional level detection, such as high contrast and ability to identify edges of densely spaced marks. These features need to be confirmed experimentally. In magneto-optical storage [1], edge-shift of short marks using edge detection was found to be lower than that using differential level detection [2]. But in other aspects, such as signal and noise levels, edge detection was inferior to differential level detection [2, 3]. In phase-change optical storage [4], theoretical analysis indicates that edge detection has a potential superiority over conventional detection (hereafter referred to as sum detection). Experimentally, edge detection noise level has been confirmed to be lower than sum detection, especially at low and high spatial frequencies. In this work we present results for edge detection readout signal, carrier-to-noise ratio (CNR), and cross-talk characteristics in the scheme of land-groove as well as comparison with sum detection for phase-change optical storage.
APA, Harvard, Vancouver, ISO, and other styles
2

Milster, Tom D., and Robert S. Upton. "Complex Plane Description of Differential Phase Detection in Optical Data Storage." In Optical Data Storage. Washington, D.C.: Optica Publishing Group, 1998. http://dx.doi.org/10.1364/ods.1998.pdp.9.

Full text
Abstract:
Differential phase detection (DPD) utilizes the rotation of the total diffraction pattern that is collected in the exit pupil of an optical data storage system. By placing a quadrant cell detector in the exit pupil of the system, the DPD signal may be calculated as the phase difference that arises between the diagonal detector currents. Figure 1 is a schematic representing how the DPD signal is formed.
APA, Harvard, Vancouver, ISO, and other styles
3

Chernyavsky, Irina, Aparna S. Varde, and Simon Razniewski. "CSK-Detector: Commonsense in object detection." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020915.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Pham, Vung, Chau Pham, and Tommy Dang. "Road Damage Detection and Classification with Detectron2 and Faster R-CNN." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378027.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Dutta, Nishan, and M. Indumathy. "Sign Language Detection Using Action Recognition." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-oswg04.

Full text
Abstract:
Sign language detection technique is a part of technology which is of extreme importance to the society. Sign languages is used by deaf and dumb people who are unable to communicate directly using sound since they lack the ability to produce or recognize sound waves which enable us to communicate easily. The proposed project aims in decreasing the distance between the sign language detection techniques which only focuses on detecting the meaning of letters like ASL and not actions provided by the users. The project detects sign languages by using key holes as the position locator and then trains the system to detect accordingly. Keyholes are used to find the position of gesture to use LSTM throughout coaching of the information. Experimental results demonstrate the efficaciousness of the planned methodology in sign language detection task
APA, Harvard, Vancouver, ISO, and other styles
6

Wang, Yanbo J., Ming Ding, Shichao Kan, Shifeng Zhang, and Chenyue Lu. "Deep Proposal and Detection Networks for Road Damage Detection and Classification." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622599.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Ljevar, Vanja, James Goulding, Alexa Spence, and Gavin Smith. "Perception detection using Twitter." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378293.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Palsetia, Diana, Md Mostofa Ali Patwary, William Hendrix, Ankit Agrawal, and Alok Choudhary. "Clique guided community detection." In 2014 IEEE International Conference on Big Data (Big Data). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.2014.7004267.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Zhang, Wenshan, and Xi Zhang. "Cross-Lingual Propaganda Detection." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10021059.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Eslami, Mohammed, George Zheng, Hamed Eramian, and Georgiy Levchuk. "Anomaly detection on bipartite graphs for cyber situational awareness and threat detection." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258527.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Data detection"

1

Anderson-Cook, Christine, Dan Archer, Mark Bandstra, Joseph Curtis, James Ghawaly, Tenzing Joshi, Kary Myers, Andrew Nicholson, and Brian Quiter. Radiation Detection Data Competition Report. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1778748.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Lee, Wenke, and Salvatore J. Stolfo. Data Mining Approaches for Intrusion Detection. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada401496.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Varshney, Pramod K. Distributed Detection Theory and Data Fusion. Fort Belvoir, VA: Defense Technical Information Center, December 1999. http://dx.doi.org/10.21236/ada374837.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Varshney, Pramod K. Distributed Detection Theory and Data Fusion. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada280410.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Varshney, Pramod K. Distributed Detection Theory and Data Fusion. Fort Belvoir, VA: Defense Technical Information Center, July 1995. http://dx.doi.org/10.21236/ada301116.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Wegman, Edward J., and Don R. Faxon. Intrusion Detection Using Data Mining Techniques. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada421061.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Williams, A. Trace Chemical Mine Detection Data Collection. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada462212.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Widder, Edith A., and Charles L. Frey. Bioluminescence Truth Data Measurement and Signature Detection. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada549795.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Touzi, R. Calibrated Polarimetric SAR Data for Ship Detection. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2000. http://dx.doi.org/10.4095/219697.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Damiano, B., E. D. Blakeman, and L. D. Phillips. Detection and location of mechanical system degradation by using detector signal noise data. Office of Scientific and Technical Information (OSTI), June 1994. http://dx.doi.org/10.2172/10158070.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Data detection' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography