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Journal articles on the topic 'Detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Qian, Sen. "What Is Detection?" Detection 02, no. 02 (2014): 7–9. http://dx.doi.org/10.4236/detection.2014.22002.

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Sugiura, Hiroki, Shinichi Demura, Yoshinori Nagasawa, et al. "Relationship between Extent of Coffee Intake and Recognition of Its Effects and Ingredients." Detection 01, no. 01 (2013): 1–6. http://dx.doi.org/10.4236/detection.2013.11001.

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3

Brandão, Marcelo Luiz Lima, Carla de Oliveira Rosas, Silvia Maria Lopes Bricio, et al. "Preparation of Reference Material for Proficiency Test for Enumeration of Coliforms in Cheese Matrix." Detection 01, no. 01 (2013): 7–12. http://dx.doi.org/10.4236/detection.2013.11002.

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Zhang, Jianyong, Xiao Cai, and Xiaohu Mo. "On Two Cryogenic Systems of High Purity Germanium Detector." Detection 01, no. 02 (2013): 13–20. http://dx.doi.org/10.4236/detection.2013.12003.

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5

Chuto, Maneenuch, Sudkate Chaiyo, Weena Siangproh, and Orawon Chailapakul. "A Rapid Separation and Highly Determination of Paraben Species by Ultra-Performance Liquid Chromatography —Electrochemical Detection." Detection 01, no. 02 (2013): 21–29. http://dx.doi.org/10.4236/detection.2013.12004.

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Cardone, Fabio, Giovanni Cherubini, Walter Perconti, Andrea Petrucci, and Alberto Rosada. "Neutron Imaging by Boric Acid." Detection 01, no. 02 (2013): 30–35. http://dx.doi.org/10.4236/detection.2013.12005.

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7

Weng, Binbin, Jijun Qiu, Lihua Zhao, Caleb Chang та Zhisheng Shi. "Theoretical D* Optimization of N+-p Pb<sub>1-x</sub>Sn<sub>x</sub>Se Long-Wavelength (8 - 11 μm) Photovoltaic Detector at 77 K". Detection 02, № 01 (2014): 1–6. http://dx.doi.org/10.4236/detection.2014.21001.

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8

Żak, Dariusz, Jarosław Jureńczyk, and Janusz Kaniewski. "Zener Phenomena in InGaAs/InAlAs/InP Avalanche Photodiodes." Detection 02, no. 02 (2014): 10–15. http://dx.doi.org/10.4236/detection.2014.22003.

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Wu, Jun, Hao Fu, and Xiashi Zhu. "Separation/Analysis Rhodamine B by Anion Surfactant/Ionic Liquid Aqueous Two-Phase Systems Coupled with Ultraviolet Spectrometry." Detection 02, no. 03 (2014): 17–25. http://dx.doi.org/10.4236/detection.2014.23004.

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Saleh, Tawfik A. "Detection: From Electrochemistry to Spectroscopy with Chromatographic Techniques, Recent Trends with Nanotechnology." Detection 02, no. 04 (2014): 27–32. http://dx.doi.org/10.4236/detection.2014.24005.

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11

Najam, Laith A., Nada F. Tafiq, and Fouzey H. Kitah. "Estimation of Natural Radioactivity of Some Medicinal or Herbal Plants Used in Iraq." Detection 03, no. 01 (2015): 1–7. http://dx.doi.org/10.4236/detection.2015.31001.

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12

Majeed, Fouad A., Inaam H. Kadhim, Ali O. Muhsen, and Khalid H. Abass. "Determination of Alpha Particles Concentration in Toothpaste Using CR-39 Track Detector." Detection 03, no. 02 (2015): 9–13. http://dx.doi.org/10.4236/detection.2015.32002.

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Al-Jobouri, Hussain Ali. "Determination the Effect of Gamma Radiation and Thermal Neutron on PM-355 Detector by Using FTIR Spectroscopy." Detection 03, no. 03 (2015): 15–20. http://dx.doi.org/10.4236/detection.2015.33003.

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14

Hashim, Abdalsattar Kareem, and Laith Ahmed Najam. "Radium and Uranium Concentrations Measurements in Vegetables Samples of Iraq." Detection 03, no. 04 (2015): 21–28. http://dx.doi.org/10.4236/detection.2015.34004.

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15

Al-Jobouri, Hussain A., Mustafa Y. Rajab, and Laith A. Najam. "Analysis of Nuclear Track Parameters of CN-85 Detector Irradiated to Thermal Neutrons by Using MATLAB Program." Detection 03, no. 04 (2015): 29–36. http://dx.doi.org/10.4236/detection.2015.34005.

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Tawfiq, Nada F., and Jaafar Jaleel. "Radon Concentration in Soil and Radon Exhalation Rate at Al-Dora Refinery and Surrounding Area in Baghdad." Detection 03, no. 04 (2015): 37–44. http://dx.doi.org/10.4236/detection.2015.34006.

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17

Najam, Liath Ahmed, Hazim Louis Mansour, Nada Fadhil Tawfiq, and Mahmood Salim Karim. "Measurement of Radon Gas Concentrations in Tap Water Samples for Thi-Qar Governorate Using Nuclear Track Detector (CR-39)." Detection 04, no. 01 (2016): 1–8. http://dx.doi.org/10.4236/detection.2016.41001.

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18

Zhang, Ronglan. "Anti-Corrosion Test on Basal Slope Protection Materials." Detection 04, no. 01 (2016): 9–15. http://dx.doi.org/10.4236/detection.2016.41002.

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19

Higashi, Yasuhiko. "Development of Simultaneous HPLC-Fluorescence Assay of Phenol and Chlorophenols in Tap Water after Pre-Column Derivatization with 3-Chlorocarbonyl-6,7-dimethoxy-1- methyl-2(1<i>H</i>)-quinoxalinone." Detection 04, no. 01 (2016): 16–24. http://dx.doi.org/10.4236/detection.2016.41003.

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20

Zhang, Peipei, and Shuyu Liu. "Solvent Effects on the UV Absorption Spectrum of Carmofur." Detection 04, no. 01 (2016): 25–31. http://dx.doi.org/10.4236/detection.2016.41004.

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21

Najam, Laith Ahmed, Abdalsattar Kareem Hashim, Hussein Abdulkareem Ahmed, and Israa M. Hassan. "Study the Attenuation Coefficient of Granite to Use It as Shields against Gamma Ray." Detection 04, no. 02 (2016): 33–39. http://dx.doi.org/10.4236/detection.2016.42005.

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22

Karim, Mahmood S., Hasan H. Daroysh, and Ali N. Mohammed. "Assessment of Indoor Radon Concentrations in Dwellings for Baghdad Governorate by Using RAD-7 Detector." Detection 04, no. 02 (2016): 40–44. http://dx.doi.org/10.4236/detection.2016.42006.

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23

Kadhum, Nada Farhan, Layth Abdulhakeem Jebur, and Ali A. Ridha. "Studying Different Etching Methods Using CR-39 Nuclear Track Detector." Detection 04, no. 03 (2016): 45–53. http://dx.doi.org/10.4236/detection.2016.43007.

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24

Ridha, Ali A., and Hasan A. Hasan. "Cancer Risk Due to the Natural Radioactivity in Cigarette Tobacco." Detection 04, no. 03 (2016): 54–65. http://dx.doi.org/10.4236/detection.2016.43008.

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25

Fu, Hanzhuo. "A Mass Spectrometric Study of Kratom Compounds by Direct Infusion Electrospray Ionization Triple Quadrupole Mass Spectrometry." Detection 04, no. 03 (2016): 66–72. http://dx.doi.org/10.4236/detection.2016.43009.

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26

Abt, Iris, Lucia Garbini, and Oliver Schulz. "Measurement of the Potassium Content of Different Food Samples with High Purity Germanium Detectors." Detection 04, no. 03 (2016): 73–85. http://dx.doi.org/10.4236/detection.2016.43010.

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27

Liao, Hailin, Lingfeng Jiang, Chunjie Wei, Hua Huang, Jing Pan, and Chuntao Luo. "Applied Orthogonal Experiment Design for the Optimum Extraction Conditions of High Concentration Selenium from Maifanite." Detection 05, no. 01 (2017): 1–4. http://dx.doi.org/10.4236/detection.2017.51001.

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28

Shi, Xinghua, Quang Phan, Binbin Weng, et al. "Study on the Theoretical Limitation of the Mid-Infrared PbSe N<sup>+</sup>-P Junction Detectors at High Operating Temperature." Detection 06, no. 01 (2018): 1–16. http://dx.doi.org/10.4236/detection.2018.61001.

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29

Lawal, Jamiu Idowu. "Natural Radionuclides Content in Granites from Operational Quarry Sites." Detection 07, no. 01 (2019): 1–15. http://dx.doi.org/10.4236/detection.2019.71001.

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30

Agba, Dabo S. I., Koudou Djagouri, Bogbe D. L. H. Gogon, and Aka A. Koua. "Bulk Etch Rate of LR 115 Polymeric Radon Detector." Detection 08, no. 01 (2021): 1–8. http://dx.doi.org/10.4236/detection.2021.81001.

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31

Mejri, Rafika, and Taoufik Aguili. "Modeling of Radiating Aperture Using the Iterative Method." Detection 09, no. 03 (2022): 29–36. http://dx.doi.org/10.4236/detection.2022.93003.

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32

Huang, Weiwei, Yanan Hu, Jinjun Zhu, Zenan Cen, and Jiali Bao. "The Measurement and Evaluation of the Electromagnetic Environment from 5G Base Station." Detection 09, no. 01 (2022): 1–11. http://dx.doi.org/10.4236/detection.2022.91001.

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33

Zhang, Cuixian. "Direct Conversion X-Ray Detectors with High Sensitivity at Low Dose Rate Based on All-Inorganic Lead-Free Perovskite Wafers." Detection 09, no. 02 (2022): 13–27. http://dx.doi.org/10.4236/detection.2022.92002.

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34

Russell, Daniel Nelson. "20 ppm Anhydrous Ammonia Odor Agent Proposed for Hydrogen Fuel for Safe Detection of Leaks." Detection 10, no. 01 (2023): 1–6. http://dx.doi.org/10.4236/detection.2023.101001.

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35

Anyikwa, Sylvester Obum, Obineche Charles Ndukwe, Theresa Chinwendu Umeojiakor, Pat Chukwudi Nnaji, and Ndidiamaka Martina Amadi. "Monitoring and Evaluation of Air Pollution at Ohaji/Egbema Flow Station and Its Environs via GPS in Ohaji Egbema Lga, Imo State Nigeria." Detection 09, no. 04 (2022): 37–49. http://dx.doi.org/10.4236/detection.2022.94004.

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36

Agba, Dabo S. I., Ponaho Kezo, and Issa Konaté. "Comparative Alpha Tracks Counting Using an Optical Microscope and a Spark Counter." Detection 10, no. 02 (2023): 7–18. http://dx.doi.org/10.4236/detection.2023.102002.

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37

Liu, Yi, Jing Wang, Jingjing Liu, Xiangying Pi, Zumei Gao, and Qian Sun. "Clinical Application and Evaluation of Rapid Detection Technology for Airborne Microorganisms in Operating Rooms." Detection 11, no. 01 (2025): 1–11. https://doi.org/10.4236/detection.2025.111001.

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38

Ulyanov, N. A., S. V. Yaskevich, Dergach P. A., and A. V. YablokovAV. "Detection of records of weak local earthquakes using neural networks." Russian Journal of Geophysical Technologies, no. 2 (January 13, 2022): 13–23. http://dx.doi.org/10.18303/2619-1563-2021-2-13.

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Manual processing of large volumes of continuous observations produced by local seismic networks takes a lot of time, therefore, to solve this problem, automatic algorithms for detecting seismic events are used. Deterministic methods for solving the problem of detection, which do an excellent job of detecting intensive earthquakes, face critical problems when detecting weak seismic events (earthquakes). They are based on principles based on the calculation of energy, which causes multiple errors in detection: weak seismic events may not be detected, and high-amplitude noise may be mistakenly d

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39

Hajiarbabi, Mohammadreza, and Arvin Agah. "Techniques for Skin, Face, Eye and Lip Detection using Skin Segmentation in Color Images." International Journal of Computer Vision and Image Processing 5, no. 2 (2015): 35–57. http://dx.doi.org/10.4018/ijcvip.2015070103.

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Face detection is a challenging and important problem in Computer Vision. In most of the face recognition systems, face detection is used in order to locate the faces in the images. There are different methods for detecting faces in images. One of these methods is to try to find faces in the part of the image that contains human skin. This can be done by using the information of human skin color. Skin detection can be challenging due to factors such as the differences in illumination, different cameras, ranges of skin colors due to different ethnicities, and other variations. Neural networks h

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40

Tao, Hongjing, Lei Zhang, Zhipeng Sun, Xinchao Cui, and Weixun Yi. "Multi-Strategy Improvement of Coal Gangue Recognition Method of YOLOv11." Sensors 25, no. 7 (2025): 1983. https://doi.org/10.3390/s25071983.

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The current methods for detecting coal gangue face several challenges, including low detection accuracy, a high probability of missed detections, and inadequate real-time performance. These issues stem from the complexities associated with diverse industrial environments and mining conditions, such as the mixing of coal gangue and insufficient illumination within coal mines. A detection model, referred to as EBD-YOLO, is proposed based on YOLOv11n. First, the C3k2-EMA module is integrated with the EMA attention mechanism within the C3k2 module of the backbone network, thereby enhancing the mod

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41

Archana, Kande, and Kamakshi Prasad. "Object Detection Using Region-Conventional Neural Network (RCNN) and OpenCV." International Journal of Distributed Artificial Intelligence 14, no. 2 (2023): 1–9. http://dx.doi.org/10.4018/ijdai.315277.

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Object detection is used in almost every real-world application such as autonomous traversal, visual system, face detection, and even more. This paper aims at applying object detection technique to assist visually impaired people. It helps visually impaired people to know about the objects around them to enable them to walk free. A prototype has been implemented on a Raspberry PI3 using OpenCV libraries, and satisfactory performance is achieved. In this paper, a detailed review has been carried out on object detection using region-conventional neural network (RCNN)-based learning systems for a

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42

S. Hasan, Athraa, Jianjun Yi, Haider M. AlSabbagh, and Liwei Chen. "Multiple Object Detection-Based Machine Learning Techniques." Iraqi Journal for Electrical and Electronic Engineering 20, no. 1 (2024): 149–59. http://dx.doi.org/10.37917/ijeee.20.1.15.

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Object detection has become faster and more precise due to improved computer vision systems. Many successful object detections have dramatically improved owing to the introduction of machine learning methods. This study incorporated cutting- edge methods for object detection to obtain high-quality results in a competitive timeframe comparable to human perception. Object-detecting systems often face poor performance issues. Therefore, this study proposed a comprehensive method to resolve the problem faced by the object detection method using six distinct machine learning approaches: stochastic

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43

Preethi, B. Meena, C. Sunitha, M. Parameshvar, B. Dharshini, and S. Gokul. "Emotion Detection using HOG for Crime Detection." Indian Journal Of Science And Technology 16, no. 41 (2023): 3617–26. http://dx.doi.org/10.17485/ijst/v16i41.1580.

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44

Wang, Jie, and Hong Zhao. "Improved YOLOv8 Algorithm for Water Surface Object Detection." Sensors 24, no. 15 (2024): 5059. http://dx.doi.org/10.3390/s24155059.

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To address the issues of decreased detection accuracy, false detections, and missed detections caused by scale differences between near and distant targets and environmental factors (such as lighting and water waves) in surface target detection tasks for uncrewed vessels, the YOLOv8-MSS algorithm is proposed to be used to optimize the detection of water surface targets. By adding a small target detection head, the model becomes more sensitive and accurate in recognizing small targets. To reduce noise interference caused by complex water surface environments during the downsampling process in t

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45

Gao, Xiang, Min Yu, Jian Guo Jiang, Xin Liang Qiu, and Chao Liu. "A Combined Malicious Documents Detecting Method Based on Emulators." Applied Mechanics and Materials 602-605 (August 2014): 1707–12. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1707.

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ShellCode injections with malicious JavaScript code in documents are becoming more prevalent and dangerous. However, the existing methods have some limitations in detecting this kind of attacks. In this article, we explore the detections of malicious documents and propose an approach of detecting malicious documents that contains JavaScript ShellCode. In our approach, we provide an impact factor which represents the reliability of the document being malicious. We use both static detections and dynamic detections and then combine the results of the two different methods. Therefore, we can get a

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46

Zhu, Juncai, Zhizhong Wang, Songwei Wang, and Shuli Chen. "Moving Object Detection Based on Background Compensation and Deep Learning." Symmetry 12, no. 12 (2020): 1965. http://dx.doi.org/10.3390/sym12121965.

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Detecting moving objects in a video sequence is an important problem in many vision-based applications. In particular, detecting moving objects when the camera is moving is a difficult problem. In this study, we propose a symmetric method for detecting moving objects in the presence of a dynamic background. First, a background compensation method is used to detect the proposed region of motion. Next, in order to accurately locate the moving objects, we propose a convolutional neural network-based method called YOLOv3-SOD for detecting all objects in the image, which is lightweight and specific

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47

Yang, Wanhong, Zhenlin Yang, Meiyun Wu, Gui Zhang, Yinfang Zhu, and Yurong Sun. "SIMCB-Yolo: An Efficient Multi-Scale Network for Detecting Forest Fire Smoke." Forests 15, no. 7 (2024): 1137. http://dx.doi.org/10.3390/f15071137.

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Abstract: Forest fire monitoring plays a crucial role in preventing and mitigating forest disasters. Early detection of forest fire smoke is essential for a timely response to forest fire emergencies. The key to effective forest fire monitoring lies in accounting for the various levels of forest fire smoke targets in the monitoring images, enhancing the model’s anti-interference capabilities against mountain clouds and fog, and reducing false positives and missed detections. In this paper, we propose an improved multi-level forest fire smoke detection model based on You Only Look Once v5s (Yol

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48

Kumar, Sanjeev, Ravendra Singh, Mohammad Zubair Khan, and Abdulfattah Noorwali. "Design of adaptive ensemble classifier for online sentiment analysis and opinion mining." PeerJ Computer Science 7 (August 5, 2021): e660. http://dx.doi.org/10.7717/peerj-cs.660.

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DataStream mining is a challenging task for researchers because of the change in data distribution during classification, known as concept drift. Drift detection algorithms emphasize detecting the drift. The drift detection algorithm needs to be very sensitive to change in data distribution for detecting the maximum number of drifts in the data stream. But highly sensitive drift detectors lead to higher false-positive drift detections. This paper proposed a Drift Detection-based Adaptive Ensemble classifier for sentiment analysis and opinion mining, which uses these false-positive drift detect

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49

Schiano, J. L., A. J. Blauch, and M. D. Ginsberg. "Optimization of NQR Pulse Parameters using Feedback Control." Zeitschrift für Naturforschung A 55, no. 1-2 (2000): 67–73. http://dx.doi.org/10.1515/zna-2000-1-213.

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A new method for increasing the probability of detecting nuclear resonance signals is demon-strated experimentally. It is well known that the detection of signals with a low signal to noise ratio (SNR) results in missed detections of false alarms. In situations where the noise is correlated or where limited data is averaging, it may not be possible to achieve a desired SNR through averaging alone. We present an alternative approach in which a feedback algorithm automatically adjusts pulse parameters so that the SNR and probability of correct detection are increased. Experimental results are pr

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50

Chan, Hsiao-Lung, Yuan Ouyang, Rou-Shayn Chen, et al. "Deep Neural Network for the Detections of Fall and Physical Activities Using Foot Pressures and Inertial Sensing." Sensors 23, no. 1 (2023): 495. http://dx.doi.org/10.3390/s23010495.

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Fall detection and physical activity (PA) classification are important health maintenance issues for the elderly and people with mobility dysfunctions. The literature review showed that most studies concerning fall detection and PA classification addressed these issues individually, and many were based on inertial sensing from the trunk and upper extremities. While shoes are common footwear in daily off-bed activities, most of the aforementioned studies did not focus much on shoe-based measurements. In this paper, we propose a novel footwear approach to detect falls and classify various types

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