Journal articles: 'Detection'

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, Shunsuke Yamaji, Tamotsu Kitabayashi, Shigeki Matsuda, Takayoshi Yamada, and Ning Xu. "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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Brandão, Marcelo Luiz Lima, Carla de Oliveira Rosas, Silvia Maria Lopes Bricio, Valéria de Mello Medeiros, Juliana de Castro Beltrão da Costa, Rodrigo Rollin Pinheiro, Paola Cardarelli-Leite, Marcus Henrique Campino de La Cruz, and Armi Wanderley da Nóbrega. "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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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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Weng, Binbin, Jijun Qiu, Lihua Zhao, Caleb Chang, and Zhisheng Shi. "Theoretical D* Optimization of N+-p Pb1-xSnxSe Long-Wavelength (8 - 11 μm) Photovoltaic Detector at 77 K." Detection 02, no. 01 (2014): 1–6. http://dx.doi.org/10.4236/detection.2014.21001.

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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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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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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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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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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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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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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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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(1H)-quinoxalinone." Detection 04, no. 01 (2016): 16–24. http://dx.doi.org/10.4236/detection.2016.41003.

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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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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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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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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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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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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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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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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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Shi, Xinghua, Quang Phan, Binbin Weng, Lance L. McDowell, Jijun Qiu, Zhihua Cai, and Zhisheng Shi. "Study on the Theoretical Limitation of the Mid-Infrared PbSe N+-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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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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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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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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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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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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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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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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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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Hamza Osman, Ahmed, Naomie Salim, and Albaraa Abuobieda. "Survey of Text Plagiarism Detection." Computer Engineering and Applications Journal 1, no. 1 (June 26, 2012): 37–45. http://dx.doi.org/10.18495/comengapp.v1i1.5.

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In this paper we are going to review and list the advantages and limitations of the significant effective techniques employed or developed in text plagiarism detection.Â It was found that many of the proposed methods for plagiarism detection have a weakness and lacking for detecting some types of plagiarized text. This paper discussed several important issues in plagiarism detection such as; plagiarism detection Tasks, plagiarism detection process and some of the current plagiarism detection techniques.DOI:Â 10.18495/comengapp.11.037045

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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 detected as an event. In our work, we propose a detection method capable of surpassing deterministic methods in detecting events on seismograms, successfully detecting a similar or more events with fewer false detections.

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Kim, Kyuho, Sumin Lee, and Jiman Hong. "A Detection Tool for Detecting Android Emulator Detection Techniques." KIISE Transactions on Computing Practices 29, no. 5 (May 31, 2023): 221–27. http://dx.doi.org/10.5626/ktcp.2023.29.5.221.

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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 (November 12, 2023): 3617–26. http://dx.doi.org/10.17485/ijst/v16i41.1580.

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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 (January 31, 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 gradient descent, logistic regression, random forest, decision trees, k-nearest neighbor, and naive Bayes. The system was trained using Common Objects in Context (COCO), the most challenging publicly available dataset. Notably, a yearly object detection challenge is held using COCO. The resulting technology is quick and precise, making it ideal for applications requiring an object detection accuracy of 97%

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Wang, Jie, and Hong Zhao. "Improved YOLOv8 Algorithm for Water Surface Object Detection." Sensors 24, no. 15 (August 5, 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 the backbone network, C2f_MLCA is used to enhance the robustness and stability of the model. The lightweight model SENetV2 is employed in the neck component to improve the model’s performance in detecting small targets and its anti-interference capability. The SIoU loss function enhances detection accuracy and bounding box regression precision through shape awareness and geometric information integration. Experiments on the publicly available dataset FloW-Img show that the improved algorithm achieves an mAP@0.5 of 87.9% and an mAP@0.5:0.95 of 47.6%, which are improvements of 5% and 2.6%, respectively, compared to the original model.

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Zhu, Juncai, Zhizhong Wang, Songwei Wang, and Shuli Chen. "Moving Object Detection Based on Background Compensation and Deep Learning." Symmetry 12, no. 12 (November 27, 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 specifically designed for small objects. Finally, the moving objects are determined by fusing the results obtained by motion detection and object detection. Missed detections are recalled according to the temporal and spatial information in adjacent frames. A dataset is not currently available specifically for moving object detection and recognition, and thus, we have released the MDR105 dataset comprising three classes with 105 videos. Our experiments demonstrated that the proposed algorithm can accurately detect moving objects in various scenarios with good overall performance.

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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 (July 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 have been used for detecting human skin. Different methods have been applied to neural networks in order to increase the detection rate of the human skin. The resulting image is then used in the detection phase. The resulting image consists of several components and in the face detection phase, the faces are found by just searching those components. If the components consist of just faces, then the faces can be detected using correlation. Eye and lip detections have also been investigated using different methods, using information from different color spaces. The speed of face detection methods using color images is compared with other face detection methods.

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Archana, Kande, and Kamakshi Prasad. "Object Detection Using Region-Conventional Neural Network (RCNN) and OpenCV." International Journal of Distributed Artificial Intelligence 14, no. 2 (July 10, 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 real-world application. This paper explores the various process of detecting objects using various object detections methods and walks through detection including a deep neural network for SSD implemented using Caffee model.

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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 an acceptable overhead and make the detection immune to obfuscation. We have implemented a proof-of-concept prototype of the detection system on a Linux platform. We also have evaluated the accuracy and the performance overhead on the test platform. The results show that the system reports very few faults with an acceptable overhead.

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Singh Tomar, Apoorv, and Brijesh Kumar Chaurasia. "Intrusion Detection System and Its Attacks Detection: Comparative." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 1 (January 30, 2017): 1–6. http://dx.doi.org/10.23956/ijarcsse/v7i1/0147.

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48

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 (June 29, 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 (Yolov5s) called SIMCB-Yolo. This model aims to achieve high-precision detection of forest fire smoke at various levels. First, to address the issue of low precision in detecting small target smoke, a Swin transformer small target monitoring head is added to the neck of Yolov5s, enhancing the precision of small target smoke detection. Then, to address the issue of missed detections due to the decline in conventional target smoke detection accuracy after improving small target smoke detection accuracy, we introduced a cross stage partial network bottleneck with three convolutional layers (C3) and a channel block sequence (CBS) into the trunk. These additions help extract more surface features and enhance the detection accuracy of conventional target smoke. Finally, the SimAM attention mechanism is introduced to address the issue of complex background interference in forest fire smoke detection, further reducing false positives and missed detections. Experimental results demonstrate that, compared to the Yolov5s model, the SIMCB-Yolo model achieves an average recognition accuracy (mAP50) of 85.6%, an increase of 4.5%. Additionally, the mAP50-95 is 63.6%, an improvement of 6.9%, indicating good detection accuracy. The performance of the SIMCB-Yolo model on the self-built forest fire smoke dataset is also significantly better than that of current mainstream models, demonstrating high practical value.

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Feng, Jingxuan. "Different Detection Methods for Dark Matter." Theoretical and Natural Science 5, no. 1 (May 25, 2023): 268–74. http://dx.doi.org/10.54254/2753-8818/5/20230452.

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Dark matter has been proposed to fulfill the missing mass from Astro-observation. Many theories have been raised to explain dark matter, and weakly interacting massive particles (WIMPs) are one of them. In recent decades, dark matter detection sensitivity has improved significantly. However, solid evidence for their existence has not come yet. This paper outlines some methods for detecting dark matter, including direct detections, collider searches with the ATLAS detector at LHC, and collider searches with CEPC.

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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 detections to benefit and minimize the negative impact of false-positive drift detection signals. The proposed method creates and adds a new classifier to the ensemble whenever a drift happens. A weighting mechanism is implemented, which provides weights to each classifier in the ensemble. The weight of the classifier decides the contribution of each classifier in the final classification results. The experiments are performed using different classification algorithms, and results are evaluated on the accuracy, precision, recall, and F1-measures. The proposed method is also compared with these state-of-the-art methods, OzaBaggingADWINClassifier, Accuracy Weighted Ensemble, Additive Expert Ensemble, Streaming Random Patches, and Adaptive Random Forest Classifier. The results show that the proposed method handles both true positive and false positive drifts efficiently.

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Journal articles on the topic 'Detection'

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