Journal articles: 'Operation anaconda'

1

Caruso, David. "Operation Anaconda." Oral History Review 39, no. 2 (July 1, 2012): 334–36. http://dx.doi.org/10.1093/ohr/ohs065.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Midla, George S. "Lessons Learned: Operation Anaconda." Military Medicine 169, no. 10 (October 2004): 810–13. http://dx.doi.org/10.7205/milmed.169.10.810.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

HASTERT, PAUL. "Operation Anaconda: Perception Meets Reality in the Hills of Afghanistan." Studies in Conflict and Terrorism 28, no. 1 (January 15, 2005): 11–20. http://dx.doi.org/10.1080/10576100590524294.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Czarnecki, Jonathan E. "Not a Good Day to Die: The Untold Story of Operation Anaconda (review)." Journal of Military History 69, no. 4 (2005): 1264–66. http://dx.doi.org/10.1353/jmh.2005.0216.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Kelly, Michael A., Joseph M. Comberiate, Ethan S. Miller, and Larry J. Paxton. "Progress toward forecasting of space weather effects on UHF SATCOM after Operation Anaconda." Space Weather 12, no. 10 (October 2014): 601–11. http://dx.doi.org/10.1002/2014sw001081.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Samylkina, N. N., and I. A. Kalinin. "Exploring the operation of a neural network using the example of handwritten digits recognition in an advanced informatics course using the python 3.8 programming language." Informatics in school, no. 10 (December 24, 2021): 21–29. http://dx.doi.org/10.32517/2221-1993-2021-20-10-19-27.

Full text

Abstract:

The article discusses the possibility of including artificial intelligence topics in the informatics course at the level of secondary general education on the demonstration example of handwritten digits recognition using the Python 3.8 programming language and the TensorFlow package. A hands-on example of a classification problem using the classic MNIST (Modified National Institute of Standards and Technology) training example set is dealt with step-by-step.Most of the tools required for the work (language interpreter, basic libraries, and shell) are downloaded in the form of a single software distribution kit of Anaconda. The network is trained using different methods. When the model is trained, the optimization method, the loss function, and the metric for estimation are specified. Image processing by convolutional nets containing special layers, which "convolve" the image the way a bitmap filter does, is also demonstrated

APA, Harvard, Vancouver, ISO, and other styles

7

Echaiz Espinoza, German Alberto, Gabriel Pereira de Oliveira, Verivan Santos Lima, Diego Antonio de Moura Fonseca, Werbet Luiz Almeida da Silva, Carla Wilza Souza de Paula Maitelli, Elmer Rolando Llanos Villarreal, and Andrés Ortiz Salazar. "Thermal Profiles in Water Injection Wells: Reduction in the Systematic Error of Flow Measurements during the Transient Regime." Sensors 23, no. 23 (November 28, 2023): 9465. http://dx.doi.org/10.3390/s23239465.

Full text

Abstract:

This article presents an analytical solution for calculating the flow rate in water injection wells based on the established thermal profile along the tubing. The intent is to minimize the intrinsic systematic error of classic quasi-static methodologies, which assume that all thermal transience on well completion has passed. When these techniques are applied during the initial hours of injection well operation, it can result in errors higher than 20%. To solve this limitation, the first law of thermodynamics was used to define a mathematical model and a thermal profile was established in the injection fluid, captured by using distributed temperature systems (DTSs) installed inside the tubing. The geothermal profile was also established naturally by a thermal source in the earth to determine the thermal gradient. A computational simulation of the injection well was developed to validate the mathematical solution. The simulation intended to generate the fluid’s thermal profile, for which data were not available for the desired time period. As a result, at the cost of greater complexity, the systematic error dropped to values below 1% in the first two hours of well operation, as seen throughout this document. The code was developed in Phyton, version 1.7.0., from Anaconda Navigator.

APA, Harvard, Vancouver, ISO, and other styles

8

Thomsen, Paul A. "Operation Anaconda: America's First Major Battle in Afghanistan. By Lester W. Grau and Dodge Billingsley. (Lawrence, KS: University Press of Kansas, 2012. Pp. 459. $39.95.)." Historian 75, no. 1 (March 1, 2013): 152–53. http://dx.doi.org/10.1111/hisn.12004_24.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Marcos-Vidal, José Miguel, Rafael González, Cristina García, and Carlos Soria. "Out-of-operating room anesthesia: use of the AnaConDa vaporizer with anesthesia." Journal of Clinical Anesthesia 24, no. 4 (June 2012): 346–47. http://dx.doi.org/10.1016/j.jclinane.2011.05.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Li, Yuxin, Yinggang Xie, and Xi Lu. "Multi-face recognition and dynamic tracking based on reinforcement learning algorithm." MATEC Web of Conferences 336 (2021): 06006. http://dx.doi.org/10.1051/matecconf/202133606006.

Full text

Abstract:

Aiming at the problem that the current low accuracy rate of face detection and target tracking, a reinforcement learning algorithm is proposed, which integrates face detection technology and target tracking technology organically, adopts the face detection algorithm based on Multi-Task Convolutional Neural Network (MTCNN) and target tracking algorithm based on Kalman filtering, so as to realize face detection, multiplayer face recognition and dynamic tracking of personnel movement. In this paper, the configuration environment is Anaconda, the operating platform is PyCharm, the video-based face detection and dynamic capture and rapid identification system has been designed and developed. The system consists of two modules: face detection module and target tracking module. The optimized face detection and dynamic capture algorithm improved the detection success rate by about 11.5%, the face detection success rate by about 15.2%, the dynamic capture success rate increased by about 12.0%, and the optimized system has a wider practicality.

APA, Harvard, Vancouver, ISO, and other styles

11

Kadhim, Rania R., and Mohammed Y. Kamil. "Comparison of machine learning models for breast cancer diagnosis." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (March 1, 2023): 415. http://dx.doi.org/10.11591/ijai.v12.i1.pp415-421.

Full text

Abstract:

<p><span lang="EN-US">Breast cancer is the most common cause of death among women worldwide. Breast cancer can be detected early, and the death rate can be reduced. Machine learning techniques are a hot topic for study and have proved influential in cancer prediction and early diagnosis. This study's objective is to predict and diagnose breast cancer using machine learning models and evaluate the most effective based on six criteria: specificity, sensitivity, precision, accuracy, F1-score and receiver operating characteristic curve. All work is done in the anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries, and pandas and matplotlib. This study used the Wisconsin diagnostic breast cancer dataset to test ten machine learning algorithms: decision tree, linear discriminant analysis, forests of randomized trees, gradient boosting, passive aggressive, logistic regression, naïve Bayes, nearest centroid, support vector machine, and perceptron. After collecting the findings, we performed a performance evaluation and compared these various classification techniques. Gradient boosting model outperformed all other algorithms, scoring 96.77% on the F1-score.</span></p>

APA, Harvard, Vancouver, ISO, and other styles

12

Filippi, Geneviève, Geneviève Saliou, and Patrice Pelle. "ANACONDAS: Data Analysis to Assess Work Activity in Simulated Control Rooms. Setting up an Observatory of Nuclear Power Plant Operation at EDF." IFAC Proceedings Volumes 31, no. 26 (September 1998): 377–82. http://dx.doi.org/10.1016/s1474-6670(17)40122-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Matveeva, A. M. "The Geostrategy of the Russian Naval Forces in the Face of Hybrid Challenges." Prepodavatel XXI vek, no. 3, 2020 (2020): 303–15. http://dx.doi.org/10.31862/2073-9613-2020-3-303-315.

Full text

Abstract:

The article considers the main threats to the security of Russia from the coasts, seas and oceans, which for three centuries came from the Anglo-Saxon world. It raises issues of general maritime defense geo-strategy as well as maritime defense geo-strategy for Russia in modern conditions of local wars and hybrid operations. The author develops an idea that the Russian statehood, formed in the center of the Eurasian continent, in severe climatic conditions, unprotected by natural barriers from aggressive neighbors, from a certain point could withstand and develop only by gaining access to the seas and oceans. Having acquired the status of the Sea Power, Russia has finally established itself as one of the greatest empires. For our continental country, the sea has become a geostrategic shield and a basis for economic growth. Hostile to Russia, the states of the Ocean, primarily the U.S., are trying to launch a reverse force mechanism. Anglo-Saxon geo-politicians know that by throwing Russia away from the outlets to the seas and oceans, its existence as a geopolitical phenomenon will be under threat. The article focuses on the analysis of Anglo-Saxon tactics of “anaconda rings” to push Russia away from the coasts in conditions of modern forms of hybrid counteraction.

APA, Harvard, Vancouver, ISO, and other styles

14

Pedruzzi, W. O., W. B. Silva, and J. C. S. Dutra. "INFLUENCE OF THERMOPHYSICAL PROPERTIES ON TRANSFER OF HEAT IN MULTI-LAYER DUCTS AND TEMPERATURE CONTROL USING ELECTRIC HEATING." Revista de Engenharia Térmica 22, no. 1 (April 28, 2023): 18. http://dx.doi.org/10.5380/reterm.v22i1.90967.

Full text

Abstract:

Ensuring oil production flow in offshore systems is a critical aspect of oil exploration operations. Any interruption in the production process, whether partial or complete, can result in significant financial losses and cause solid deposition in the production line. Such deposition is due to the crystallization of paraffin and hydrates, a common problem caused by low temperatures in deep waters. Among various mitigation strategies, the Pipe-in-Pipe (PIP) system with active heating is a technological solution to address this issue. This work aimed to perform a numerical simulation of the PIP system using the Finite Volume Method with an implicit formulation, considering the effect of temperature on fluid properties and the system's dynamic response. A control loop using a PI velocity algorithm was developed to maintain the temperature above the critical point. Such simulation studies were performed using the Python programming language in the Anaconda suite. The results showed that the fluid properties greatly influence the dynamic response. The PI control maintained the temperature in the desired condition, demonstrating its operational effectiveness in preventing solid deposition and delivering stable and low-oscillatory behavior. This research emphasizes the significance of taking temperature's impact on fluid properties into account when simulating offshore oil production systems and demonstrates the effectiveness of implementing feedback control.

APA, Harvard, Vancouver, ISO, and other styles

15

Sun, Weijia, and Brian L. N. Kennett. "Common-Reflection-Point-Based Prestack Depth Migration for Imaging Lithosphere in Python: Application to the Dense Warramunga Array in Northern Australia." Seismological Research Letters 91, no. 5 (July 15, 2020): 2890–99. http://dx.doi.org/10.1785/0220200078.

Full text

Abstract:

Abstract We exploit estimates of P-wave reflectivity from autocorrelation of transmitted teleseismic P arrivals and their coda in a common reflection point (CRP) migration technique. The approach employs the same portion of the vertical-component seismogram, as in standard Ps receiver function analysis. This CRP prestack depth migration approach has the potential to image lithospheric structures on scales as fine as 4 km or less. The P-wave autocorrelation process and migration are implemented in open-source software—the autocorrelogram calculation (ACC) package, which builds on the widely used the seismological Obspy toolbox. The ACC package is written in the open-source and free Python programming language (3.0 or newer) and has been extensively tested in an Anaconda Python environment. The package is simple and friendly to use and runs on all major operating systems (e.g., Windows, macOS, and Linux). We utilize Python multiprocessing parallelism to speed up the ACC on a personal computer system, or servers, with multiple cores and threads. The application of the ACC package is illustrated with application to the closely spaced Warramunga array in northern Australia. The results show how fine-scale structures in the lithospheric can be effectively imaged at relatively high frequencies. The Moho ties well with conventional H−κ receiver analysis and deeper structure inferred from stacked autocorrelograms for continuous data. CRP prestack depth migration provides an important complement to common conversion point receiver function stacks, since it is less affected by surface multiples at lithospheric depths.

APA, Harvard, Vancouver, ISO, and other styles

16

Alhayani, Mohammed H. "Real-Time Objects Detection, Tracking, and Counting Using Image Processing Techniques." Al-Nahrain Journal for Engineering Sciences 26, no. 1 (February 20, 2023): 24–30. http://dx.doi.org/10.29194/njes.26010024.

Full text

Abstract:

As a result of the tremendous development taking place in modern systems and technologies in the field of electronic monitoring. Intelligent monitoring, decision making, and automated response systems have become common subjects at this time, especially after the development of machines responsible for these processes. Traffic surveillance is a trend goal nowadays using different techniques and equipment. In this article, real-time Object detection and tracking techniques were proposed for traffic surveillance using image processing techniques. A state was specifically examined for its ability to detect and count passing motorcycles on a highway in a specific area. The results showed good reliability, with a frame processing time of approximately about (30 ms) and the achievement of real-time performance. The main contribution of this article is reaching the best result implemented by the performance the real-time process using image process technique and tracking the object by depending on the sequencing of frames and can stands with rationally not so powerful machines. Several tools have been used for different types of necessary tasks that will be part of the required application such as Python 3.7; which was used to build the basic algorithms,Visual studio code (VSC) as an Integrated Development Environment (IDE), and Anaconda navigator for downloading many useful libraries. The specifications of the used device were Intel(R) Core (TM) i7- 10750H CPU @ 2.60GHz 2.59 GHz, RAM 16.0 GB, NVIDIA GeForce GTX 1650 GPU, 64-bit operating system, x64-based processor.

APA, Harvard, Vancouver, ISO, and other styles

17

Adetunji, Olusogo Julius, Ibrahim Adepoju X. Ibrahim Adepoju Adeyanju, Adebimpe Omolayo Esan, and Adedayo Aladejobi Sobowale. "Flood Image Classification using Convolutional Neural Networks." ABUAD Journal of Engineering Research and Development (AJERD) 6, no. 2 (October 20, 2023): 113–21. http://dx.doi.org/10.53982/ajerd.2023.0602.11-j.

Full text

Abstract:

Flood disaster is a natural disaster that leads to loss of lives, properties damage, devastating effects on the economy and environment; therefore, there should be effective predictive measures to curb this problem. Between the years 2002- 2023, flood has caused death of over 200,000 people globally and occurred majorly in resource poor countries and communities. Different machine learning approaches have been developed for the prediction of floods. This study develops a novel model using convolutional neural networks (CNN) for the prediction of floods. Important parameters such as standard deviation and variance were incorporated in the parameters tuned CNN model that performed flood images feature extraction and classification for better predictive performance. The enhanced model was assessed with accuracy and loss measurement and compared with the existing model. The model leverage on the unique features of region of Interest aligns to resolve the issues of misalignments caused by the use of region of Interest pooling engaged in the traditional Faster-RCNN. The techniques and the developed system were implemented using a Python-based integrated development environment called “Anaconda Navigator” on Intel Core i5 with 8G Ram hardware of Window 10 operating system. The developed model achieved optimal accuracy at 200 epochs with 99.80% and corresponding loss of 0.0890. The results confirmed that predictive performance of a model can be improved by incorporating standard deviation and variance on model, coupled with its parameters tunning approach before classification.

APA, Harvard, Vancouver, ISO, and other styles

18

Sackey, P. V., P. J. Radell, F. Granath, and C. R. Martling. "Bispectral Index as a Predictor of Sedation Depth during Isoflurane or Midazolam Sedation in ICU Patients." Anaesthesia and Intensive Care 35, no. 3 (June 2007): 348–56. http://dx.doi.org/10.1177/0310057x0703500305.

Full text

Abstract:

Bispectral index (BIS) is used for monitoring anaesthetic depth with inhaled anaesthetic agents in the operating room but has not been evaluated as a monitor of sedation depth in the intensive care unit (ICU) setting with these agents. If BIS could predict sedation depth in ICU patients, patient disturbances could be reduced and oversedation avoided. Twenty ventilator-dependent ICU patients aged 27 to 80 years were randomised to sedation with isoflurane via the AnaConDa® or intravenous midazolam. BIS (A-2000 XP, version 3.12), electromyogram activity (EMG) and Signal Quality Index were measured continuously. Hourly clinical evaluation of sedation depth according to Bloomsbury Sedation Score (Bloomsbury) was performed. The median BIS value during a 10-minute interval prior to the clinical evaluation at the bedside was compared with Bloomsbury. Nurses performing the clinical sedation scoring were blinded to the BIS values. End-tidal isoflurane concentration was measured and compared with Bloomsbury. Correlation was poor between BIS and Bloomsbury in both groups (Spearman's rho 0.012 in the isoflurane group and -0.057 in the midazolam group). Strong correlation was found between BIS and EMG (Spearman's rho 0.74). Significant correlation was found between end-tidal isoflurane concentration and Bloomsbury (Spearman's rho 0.47). In conclusion, BIS XP does not reliably predict sedation depth as measured by clinical evaluation in non-paralysed ICU patients sedated with isoflurane or midazolam. EMG contributes significantly to BIS values in isoflurane or midazolam sedated, non-paralysed ICU patients. End-tidal isoflurane concentration appeared to be a better indicator of clinical sedation depth than BIS.

APA, Harvard, Vancouver, ISO, and other styles

19

"Operation Anaconda: America's first major battle in Afghanistan." Choice Reviews Online 49, no. 10 (June 1, 2012): 49–5928. http://dx.doi.org/10.5860/choice.49-5928.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

"Linear Attribute Projection and Performance Assessment for Signifying the Absenteeism at Work using Machine Learning." International Journal of Recent Technology and Engineering 8, no. 3 (September 30, 2019): 1262–67. http://dx.doi.org/10.35940/ijrte.c4405.098319.

Full text

Abstract:

In recent times, with the technological advancement the industry and organization are transforming all their inflow and outflow operations into digital identity. At the outset, the name of the organization is also in the hands of the employee. One of the major needs of the employee in the working environment is to avail leave or vacation based on their family circumstances. Based on the health condition and need of the employee, the organization must extend their leave for the satisfaction of the employee. The performance of the employee is also predicted based on the working days in the organization. With this view, this paper attempts to analyze the performance of the employee and the number of working hours by using machine learning algorithms. The Absenteeism at work dataset from UCI machine learning Repository is used for prediction analysis. The prediction of absent hours is achieved in three ways. Firstly, the correlation between each of the dataset attributes are found and depicted as a histogram. Secondly, the top most high correlated features are identified which are directly fitted to the regression models like Linear regression, SRD regression, RANSAC regression, Ridge regression, Huber regression, ARD Regression, Passive Aggressive Regression and Theilson Regression. Thirdly, the Performance analysis is done by analyzing the performance metrics like Mean Squared Error, Mean Absolute Error, R2 Score, Explained Variance Score and Mean Squared Log Error. The implementation is done by python in Anaconda Spyder Navigator Integrated Development Environment. Experimental Result shows that the Passive Aggressive Regression have achieved the effective prediction of number of absent hours with minimum MSE of 0.04, MAE of 0.16, EVS of 0.03, MSLE of 0.32 and reasonable R2 Score of 0.89.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Operation anaconda'

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