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Relevant bibliographies by topics / Detection approach

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Academic literature on the topic 'Detection approach'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Detection approach"

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Yeh, Cheng-Yu, and Shaw-Hwa Hwang. "Efficient Detection Approach for DTMF Signal Detection." Applied Sciences 9, no. 3 (2019): 422. http://dx.doi.org/10.3390/app9030422.

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A novel tone detection approach, designated as the multi-frequency detecting (MFD) algorithm, is presented in this work as an alternative to conventional single point detection approaches but it is an efficient way to achieve the aim of further computational load reduction for a dual-tone multi-frequency (DTMF) signal detection. The idea is that an optimal phase search is performed over the frequency band of interest in each tone detection, and then the optimal frequency response of a detector is built accordingly. In this manner, a DTMF detection task is done following one-time detection comp

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R, Vijaya Geetha, Chetan S, K. Deepika, Kishore R, and Likitha S. "A MULTISCALE APPROACH BASED AUTOMATIC SHIP DETECTION." International Journal of Current Engineering and Scientific Research 6, no. 6 (2019): 33–39. http://dx.doi.org/10.21276/ijcesr.2019.6.6.7.

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Lijo, V. P., and Hari Seetha. "A Distributed Approach for Tweet Polarity Detection." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 891–900. http://dx.doi.org/10.5373/jardcs/v11sp10/20192884.

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Pervin, Tamanna, Sharmin Akter, Sadia Afrin, et al. "A Hybrid CNN-LSTM Approach for Detecting Anomalous Bank Transactions: Enhancing Financial Fraud Detection Accuracy." American Journal of Management and Economics Innovations 07, no. 04 (2025): 116–23. https://doi.org/10.37547/tajmei/volume07issue04-15.

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Detecting fraudulent bank transactions is crucial for maintaining the integrity of financial institutions and preserving customer trust. This study introduces a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model designed to enhance the accuracy and efficiency of fraud detection systems. Utilizing the European Credit Card Fraud Detection dataset comprising 284,807 transactions with significant class imbalance, extensive preprocessing techniques were applied, including Min-Max scaling and Synthetic Minority Over-sampling Technique (SMOTE). Recursive Feature Elimination (

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Pervin, Tamanna, Sharmin Akter, Sadia Afrin, et al. "A Hybrid CNN-LSTM Approach for Detecting Anomalous Bank Transactions: Enhancing Financial Fraud Detection Accuracy." International Journal of Economics Finance & Management Science 10, no. 04 (2025): 47–54. https://doi.org/10.55640/ijefms/volume10issue04-04.

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Detecting fraudulent bank transactions is crucial for maintaining the integrity of financial institutions and preserving customer trust. This study introduces a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model designed to enhance the accuracy and efficiency of fraud detection systems. Utilizing the European Credit Card Fraud Detection dataset comprising 284,807 transactions with significant class imbalance, extensive preprocessing techniques were applied, including Min-Max scaling and Synthetic Minority Over-sampling Technique (SMOTE). Recursive Feature Elimination (

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Latha, Ch Madhavi. "Intrusion Detection on Smart Hotel through HISI Approach." Journal of Advanced Research in Dynamical and Control Systems 12, SP8 (2020): 393–401. http://dx.doi.org/10.5373/jardcs/v12sp8/20202537.

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Savenko, Oleg, Anatoliy Sachenko, Sergii Lysenko, George Markowsky, and Nadiia Vasylkiv. "BOTNET DETECTION APPROACH BASED ON THE DISTRIBUTED SYSTEMS." International Journal of Computing 19, no. 2 (2020): 190–98. http://dx.doi.org/10.31891/1727-6209/2020/19/2-190-198.

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P, Dinesh, Aakash Prabhu P, and Rajaesh Kanna R. "Modeling of Social Distancing Detection UsingDeep Learning Approach." International Journal of Research Publication and Reviews 4, no. 5 (2023): 1646–52. http://dx.doi.org/10.55248/gengpi.234.5.40503.

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I, I., I. I, and I. I. "Detection of Traditional Costumes: A Computer Vision Approach." Korean Institute of Smart Media 12, no. 11 (2023): 125–33. http://dx.doi.org/10.30693/smj.2023.12.11.125.

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Traditional attire has assumed a pivotal role within the contemporary fashion industry. The objective of this study is to construct a computer vision model tailored to the recognition of traditional costumes originating from five distinct countries, namely India, Korea, Japan, Tanzania, and Vietnam. Leveraging a dataset comprising 1,608 images, we proceeded to train the cutting-edge computer vision model YOLOv8. The model yielded an impressive overall mean average precision (MAP) of 96%. Notably, the Indian sari exhibited a remarkable MAP of 99%, the Tanzanian kitenge 98%, the Japanese kimono

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Kaur, Amandeep, and Mandeep Singh Sandhu. "SOFTWARE CODE CLONE DETECTION MODEL USING HYBRID APPROACH." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 2 (2012): 275–78. http://dx.doi.org/10.24297/ijct.v3i2b.2875.

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The aspiration of this study is to understand and analyze the concept of software Cloning and its detection. Software cloning is an acuity in which source code is duplicated. Software cloning and its detection is one of emerging and most dominant area of research in the field of software engineering. There exist numbers of techniques to detect clone in software. The focus of this study will be given on acquiring and analyzing the concept of hybrid clone detection technique. We will try to devise an algorithm for detecting duplicacy in the software by using hybrid software clone detection techn

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Dissertations / Theses on the topic "Detection approach"

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McGuinness, Kevin. "Cough detection. a physiological approach." Thesis, University of Manchester, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.528494.

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Yerra, Rajiv. "Detecting Similar HTML Documents Using A Sentence-Based Copy Detection Approach." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd977.pdf.

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Yadav, Kamna. "Improving Accuracy of the Edgebox Approach." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7326.

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Object region detection plays a vital role in many domains ranging from self-driving cars to lane detection, which heavily involves the task of object detection. Improving the performance of object region detection approaches is of great importance and therefore is an active ongoing research in Computer Vision. Traditional sliding window paradigm has been widely used to identify hundreds of thousands of windows (covering different scales, angles, and aspect ratios for objects) before the classification step. However, it is not only computationally expensive but also produces relatively low acc

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Fischer, Anja, and Zak Blacher. "An Ontological Approach to SIP DoS Detection." Thesis, Karlstad University, Faculty of Economic Sciences, Communication and IT, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-5100.

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<p>Traditional public switched telephone networks (PSTN) are replaced more and more by VoIP services these days. Although it is good for saving costs, the disadvantage of this development is that VoIP networks are less secure than the traditional way of transmitting voice. Because VoIP networks are being deployed in open environments and rely on other network services, the VoIP service itself becomes vulnerable to potential attacks against its infrastructure or other services it relies on.</p><p>This thesis will present a discussion of security issues of the Session Initiation Protocol (SI

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Morales, Jose Andre. "A Behavior Based Approach to Virus Detection." FIU Digital Commons, 2008. http://digitalcommons.fiu.edu/etd/41.

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Fast spreading unknown viruses have caused major damage on computer systems upon their initial release. Current detection methods have lacked capabilities to detect unknown virus quickly enough to avoid mass spreading and damage. This dissertation has presented a behavior based approach to detecting known and unknown viruses based on their attempt to replicate. Replication is the qualifying fundamental characteristic of a virus and is consistently present in all viruses making this approach applicable to viruses belonging to many classes and executing under several conditions. A form of replic

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Eriksson, André. "Anomaly Detection inMachine-Generated Data:A Structured Approach." Thesis, KTH, Matematisk statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-121549.

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Anomaly detection is an important issue in data mining and analysis, with applications in almost every area in science, technology and business that involves data collection. The development of general anomaly detection techniques can therefore have a large impact on data analysis across many domains. In spite of this, little work has been done to consolidate the different approaches to the subject. In this report, this deficiency is addressed in the target domain of temporal machine-generated data. To this end, new theory for comparing and reasoning about anomaly detection tasks and methods i

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Robinson, J. D. "Statistical approach to fault detection of gears." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/27285.

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Alsallal, M. "A machine learning approach for plagiarism detection." Thesis, Coventry University, 2016. http://curve.coventry.ac.uk/open/items/7e903a56-4845-4852-b1a8-2849b1cdb08a/1.

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Plagiarism detection is gaining increasing importance due to requirements for integrity in education. The existing research has investigated the problem of plagrarim detection with a varying degree of success. The literature revealed that there are two main methods for detecting plagiarism, namely extrinsic and intrinsic. This thesis has developed two novel approaches to address both of these methods. Firstly a novel extrinsic method for detecting plagiarism is proposed. The method is based on four well-known techniques namely Bag of Words (BOW), Latent Semantic Analysis (LSA), Stylometry and

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Wang, Xi Ph D. Massachusetts Institute of Technology. "A differential approach to undefined behavior detection." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/93071.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 61-65).<br>This thesis studies undefined behavior arising in systems programming languages such as C/C++. Undefined behavior bugs lead to unpredictable and subtle systems behavior, and their effects can be further amplified by compiler optimizations. Undefined behavior bugs are present in many systems, including the Linux kernel and the Postgres database. The consequences range from incorre

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Lee, Ann Ph D. Massachusetts Institute of Technology. "A comparison-based approach to mispronunciation detection." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/75660.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 89-92).<br>This thesis focuses on the problem of detecting word-level mispronunciations in nonnative speech. Conventional automatic speech recognition-based mispronunciation detection systems have the disadvantage of requiring a large amount of language-specific, annotated training data. Some systems even require a speech recognizer in the target language and another one in the students' native lang

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Books on the topic "Detection approach"

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H, Cotton Richard G., Edkins E, and Forrest S, eds. Mutation detection: A practical approach. IRL Press at Oxford University Press, 1998.

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-P, Tsai Jeffrey J., ed. Intrusion detection: A machine learning approach. Imperial College Press, 2011.

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Robinson, J. D. Statistical approach to fault detection of gears. Naval Postgraduate School, 1989.

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Centre, Bhabha Atomic Research, ed. Seismic signal detection by fractal dimension approach. Bhabha Atomic Research Centre, 2003.

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Abo-Amer, Aly E. Molecular approach for detection of waterborne pathogens. Nova Science Publisher's, 2011.

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Moyer, Ellen E. Economics of leak detection: A case study approach. American Water Works Association, 1985.

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Bonnie, Brandl, ed. Elder abuse detection and intervention: A collaborative approach. Springer, 2007.

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Cho, C. K. Practical approach to prevention and detection of fraud. South Eastern Publishers, 2012.

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Ning, Peng. Intrusion Detection in Distributed Systems: An Abstraction-Based Approach. Springer US, 2004.

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Wallin, P. Foreign body prevention, detection, and control: A practical approach. Blackie Academic & Professional, 1998.

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Book chapters on the topic "Detection approach"

1

Tuzlukov, Vyacheslav P. "Generalized Approach." In Signal Detection Theory. Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8_4.

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Tuzlukov, Vyacheslav P. "Generalized Approach: Communications." In Signal Detection Theory. Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8_6.

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Angelov, Plamen P., and Xiaowei Gu. "Anomaly Detection—Empirical Approach." In Empirical Approach to Machine Learning. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02384-3_6.

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Eichmann, David, and Padmini Srinivasan. "A Cluster-Based Approach to Broadcast News." In Topic Detection and Tracking. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0933-2_8.

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Vrij, Aldert. "A Cognitive Approach to Lie Detection." In Detecting Deception. John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118510001.ch9.

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Chen, Hsin-Hsi, and Lun-Wei Ku. "An NLP & IR Approach to Topic Detection." In Topic Detection and Tracking. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0933-2_12.

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Adolph, Karen E., Justine E. Hoch, and Ori Ossmy. "James Gibson’s Ecological Approach to Locomotion and Manipulation." In Perception as Information Detection. Routledge, 2019. http://dx.doi.org/10.4324/9780429316128-14.

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Angelov, Plamen P., and Xiaowei Gu. "Applications of Autonomous Anomaly Detection." In Empirical Approach to Machine Learning. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02384-3_10.

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Hao, Chengpeng, Danilo Orlando, Jun Liu, and Chaoran Yin. "Adaptive Radar Detection: Classical Approach." In Advances in Adaptive Radar Detection and Range Estimation. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6399-4_2.

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Shaw, Robert, and Jeffrey Kinsella-Shaw. "The Challenge of an Ecological Approach to Event Perception." In Perception as Information Detection. Routledge, 2019. http://dx.doi.org/10.4324/9780429316128-7.

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Conference papers on the topic "Detection approach"

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Manzoor, Er Suhail, Radhika Malhotra, Rahul Bhat, and Shivam Shekhar. "Apple Detection: A CNN Approach for Diseases Detection." In 2024 Second International Conference on Advances in Information Technology (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690497.

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Pang, Hongbo, and Changcheng Huang. "Pedestrian Detection: An Explainable Approach." In 2024 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00146.

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Kanna, R. Kishore, R. Anitha, K. Sundaramoorthy, Ayodeji Olalekan Salau, Nirdesh Sikarwar, and Tuhina Panda. "Computational Approach for Cardiac Defect Detection using CNN Approach." In 2024 IEEE 5th International Conference on Electro-Computing Technologies for Humanity (NIGERCON). IEEE, 2024. https://doi.org/10.1109/nigercon62786.2024.10927174.

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Köhler, Jelena, Aurélien Benoit-Lévy, Pablo Correa, et al. "Next-Generation Triggering: A Novel Event-Level Approach." In 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities. Sissa Medialab, 2024. http://dx.doi.org/10.22323/1.470.0061.

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Blum, Ashlae. "A Temporal Approach to Unsupervised Anomaly Detection." In A Temporal Approach to Unsupervised Anomaly Detection. US DOE, 2021. http://dx.doi.org/10.2172/1825324.

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Simango, Doubt, Tawanda Mushiri, Abid Yahya, and Jacqueline Kiwa. "An automated wound detection system." In ADVANCES IN INTELLIGENT APPLICATIONS AND INNOVATIVE APPROACH. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0126327.

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Usenov, Iskander, Tatiana Sakharova, Alexander Novikov, Andrey Bogomolov, Alexey Bocharnikov, and Viacheslav Artyushenko. "Fiber sensors with multispectral approach for biomedical and industrial applications." In Optical Sensing and Detection VII, edited by Francis Berghmans and Ioanna Zergioti. SPIE, 2022. http://dx.doi.org/10.1117/12.2624472.

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McCartney, C. T. "New vehicle security assessment - a practical insurance approach." In European Convention on Security and Detection. IEE, 1995. http://dx.doi.org/10.1049/cp:19950505.

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Ayyavu, Udhaya Kumar, Sathish Kumar Shanmugam, Aarul Venkatesan, Gokul Alagarasan, Hariharan Selvakumar, and Karthick Palanisamy. "Underground cable fault detection using Raspberry Pi." In ADVANCES IN INTELLIGENT APPLICATIONS AND INNOVATIVE APPROACH. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0141635.

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Alminshid, Khalid, and Mohd Nizam Omar. "Detecting backdoor using stepping stone detection approach." In 2013 Second International Conference on Informatics & Applications (ICIA 2013). IEEE, 2013. http://dx.doi.org/10.1109/icoia.2013.6650235.

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Reports on the topic "Detection approach"

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Michels, James H. A Parametric Detection Approach Using Multichannel Processes. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada219328.

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Taylor, John Aaron. An axial approach to detection in capillary electrophoresis. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10116795.

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Devaney, A. J., R. Raghavan, H. Lev-Ari, E. Manolakos, and M. Kokar. Automatic Target Detection And Recognition: A Wavelet Based Approach. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada329696.

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Dixit, Arati M., Harpreet Singh, and Thomas Meitzler. Soft Computing Approach to Crack Detection and FPGA Implementation. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada570058.

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Gupta, Vijay. Control of Sensors for Sequential Detection: A Stochastic Approach. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada592031.

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Richardson, Thomas J., and Sanjoy K. Mitter. Scaling Results for the Variational Approach to Edge Detection. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada459531.

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Panta, Manisha, Md Tamjidul Hoque, Kendall Niles, Joe Tom, Mahdi Abdelguerfi, and Maik Flanagin. Deep learning approach for accurate segmentation of sand boils in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49460.

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Sand boils can contribute to the liquefaction of a portion of the levee, leading to levee failure. Accurately detecting and segmenting sand boils is crucial for effectively monitoring and maintaining levee systems. This paper presents SandBoilNet, a fully convolutional neural network with skip connections designed for accurate pixel-level classification or semantic segmentation of sand boils from images in levee systems. In this study, we explore the use of transfer learning for fast training and detecting sand boils through semantic segmentation. By utilizing a pretrained CNN model with ResNe

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Baltzell, Kimberly. Exploring Early Detection Methods: Using the Intraductal Approach to Predict Breast Cancer. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada442994.

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Candy, J. V. CHIRP-Like Signals: Estimation, Detection and Processing A Sequential Model-Based Approach. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1297653.

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Baltzell, Kimberly, and Marylin Dodd. Exploring Early Detection Methods: Using the Intraductal Approach to Predict Breast Cancer. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada457467.

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