Journal articles: 'Automated networks'

1

Pingale, Subhash, and Sanjay R. Sutar. "Automated network intrusion detection using multimodal networks." International Journal of Computational Science and Engineering 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijcse.2022.10044448.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Hou, Tingting, and Zamira Madina. "Automatic Classification of Basic Nursing Teaching Resources Based on the Fusion of Multiple Neural Networks." Mathematical Problems in Engineering 2022 (February 21, 2022): 1–7. http://dx.doi.org/10.1155/2022/7176111.

Full text

Abstract:

Automatic classification is one of the hot topics in the field of information retrieval and natural language processing, but it still faces many problems to be solved. The classic automated classification approach has a sluggish classification speed and poor processing accuracy for resources with a large quantity of data. Based on this, an automated classification approach based on the integration of various neural networks for fundamental nursing teaching materials was presented. The automatic classification method of teaching resources was designed by extracting the characteristics of teaching resources, establishing the model of multiple neural network integration, and designing the classification index of basic nursing teaching resources. The experimental findings suggest that this technique has higher chi-square test parameters and better outcomes for the automated classification of large instructional materials than the classic rough set automatic classification method.

APA, Harvard, Vancouver, ISO, and other styles

3

Gurskiy, A. A., A. E. Goncharenko, and S. M. Dubna. "AUTOMATIC SYNTHESIS OF PETRI NETS AT TUNING UP OF THE COORDINATING AUTOMATIC CONTROL SYSTEMS." ELECTRICAL AND COMPUTER SYSTEMS 33, no. 108 (November 30, 2020): 34–44. http://dx.doi.org/10.15276/eltecs.32.108.2020.4.

Full text

Abstract:

The process of automated tuning for the coordinating automatic control system is considered in this paper. This process of tuning for the coordinating control system is linked to the automatic synthesis of Petri nets based on functioning of the artificial neural network. Thereby, we can automate the process of tuning and synthesis of system models and also solve the urgent task linked to the minimization of tuning time for the multilevel control systems. The purposes of the scientific work are time reduction of the tuning and automatization of the tuning for the multilevel coordinating systems of the automatic control. In order to achieve this purpose in the MATLAB \ Simulink software environment it is necessary to devel- op the system for automated tuning of the regulators of various levels for the coordinating automatic control system. The application of artificial neural network with automatic synthesis of Petri nets allows to introduce intelligent technology in the automated tuning system. In this work we have presented the corresponding block diagrams of considered automated tuning system and the principles of its functioning. The certain principle of the formation of Petri nets is proposed. These Petri nets represent the algorithms of tuning in the systems for analysis the corresponding processes. The formation of the composition in the scheme from Petri net during the functioning of the artificial neural network is presented in the paper. The results of experiment are presented in the final part of this work. This time characteristics of the pro- cess of setting up for the coordinating automatic control system of foodstuffs cooling in tunnel chamber. The experiments were conducted in the Matlab 2012a environment. Based on the results of the experiment we have depicted the process of synthesis of the Petri net representing the system tuning algorithm. The performed experiments have showed the principal suitability of the automated search system for the settings of the regulators of various levels of the coordinating control system. The technique of automatic synthesis of Petri nets based on the functioning of artificial neural networks has obtained the further devel- opment while performing the approved task in the scientific paper.

APA, Harvard, Vancouver, ISO, and other styles

4

Gerber, Mia, and Nelishia Pillay. "Automated Design of the Deep Neural Network Pipeline." Applied Sciences 12, no. 23 (November 29, 2022): 12215. http://dx.doi.org/10.3390/app122312215.

Full text

Abstract:

Deep neural networks have proven to be effective in various domains, especially in natural language processing and image processing. However, one of the challenges associated with using deep neural networks includes the long design time and expertise needed to apply these neural networks to a particular domain. The research presented in this paper investigates the automation of the design of the deep neural network pipeline to overcome this challenge. The deep learning pipeline includes identifying the preprocessing needed, the feature engineering technique, the neural network to use and the parameters for the neural network. A selection pertubative hyper-heuristic (SPHH) is used to automate the design pipeline. The study also examines the reusability of the generated pipeline. The effectiveness of transfer learning on the generated designs is also investigated. The proposed approach is evaluated for text processing—namely, sentiment analysis and spam detection—and image processing—namely, maize disease detection and oral lesion detection. The study revealed that the automated design of the deep neural network pipeline produces just as good, and in some cases better, performance compared to the manual design, with the automated design requiring less design time than the manual design. In the majority of instances, the design was not reusable; however, transfer learning achieved positive transfer of designs, with the performance being just as good or better than when transfer learning was not used.

APA, Harvard, Vancouver, ISO, and other styles

5

Al-Hazaimeh, Obaida M., and Ma'moun Al-Smadi. "Automated Pedestrian Recognition Based on Deep Convolutional Neural Networks." International Journal of Machine Learning and Computing 9, no. 5 (October 2019): 662–67. http://dx.doi.org/10.18178/ijmlc.2019.9.5.855.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Khanafer, R. M., B. Solana, J. Triola, R. Barco, L. Moltsen, Z. Altman, and P. Lazaro. "Automated Diagnosis for UMTS Networks Using Bayesian Network Approach." IEEE Transactions on Vehicular Technology 57, no. 4 (July 2008): 2451–61. http://dx.doi.org/10.1109/tvt.2007.912610.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Mapayi, Temitope, Pius A. Owolawi, and Adedayo O. Adio. "Automated Detection and Tortuosity Characterization of Retinal Vascular Networks." Journal of Biomimetics, Biomaterials and Biomedical Engineering 50 (April 2021): 89–102. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.50.89.

Full text

Abstract:

Automated retinal vascular network detection and analysis using digital retinal images continue to play a major role in the field of biomedicine for the diagnosis and management of various forms of human ailments like hypertension, diabetic retinopathy, retinopathy of prematurity, glaucoma and cardiovascular diseases. Although several literature have implemented different automatic approaches of detecting blood vessels in the retinal and also determining their tortuous states, the results obtained show that there are needs for further investigation on more efficient ways to detect and characterize the blood vessel network tortuosity states. This paper implements the use of an adaptive thresholding method based on local spatial relational variance (LSRV) for the detection of the retinal vascular networks. The suitability of a multi-layer perceptron artificial neural network (MLP-ANN) technique for the tortuosity characterization of retinal blood vascular networks is also presented in this paper. Some vessel geometric features of detected vessels are fed into ANN classifier for the automatic classification of the retinal vascular networks as being tortuous vessels or normal vessels. Experimental studies conducted on DRIVE and STARE databases show that the vascular network detection results obtained from the method implemented in this paper detects large and thin vascular networks in the retina. In comparison to preious methods in the literature, the proposed method for vascular network segmentation achieved better performance than several methods in the literature with a mean accuracy value of 95.04% and mean sensitivity value of 75.16% on DRIVE and mean accuracy value of 94.02% and average sensitivity value of 76.55% on STARE with computational processing time of 4.5 seconds and 9.4 seconds on DRIVE and STARE respectively. The MLP-ANN method proposed for the vascular network tortuosity characterization achieves promising accuracy rates of 77.5%, 80%, 83.33%, 85%, 86.67% and 100% for varying training sample sizes.

APA, Harvard, Vancouver, ISO, and other styles

8

Pavlov, A. A., I. O. Datyev, and M. G. Shishaev. "MULTI-HOP WIRELESS NETWORKS AUTOMATED SIMULATION." Informacionno-technologicheskij vestnik 13, no. 3 (September 30, 2017): 68–78. http://dx.doi.org/10.21499/2409-1650-2017-3-68-78.

Full text

Abstract:

Simulation is the main way for testing technologies in the field of multi-hop wireless networks (MWN). Creating a simulation model MWN - a time-consuming task associated with the use of specialized software tools, called network simulators. In this paper, the modern experience of modeling MWN and the main problems are formulated. One of the main problem is the comparative analysis' impossibility of the experiments results conducted by various researchers. This is due to the reasons associated with the models used for testing, the planning an imitation experiment and the principal differences in the network simulators. To solve this problem, authors propose a generalized conceptual model of MWN simulation and a specialized software package that automates the execution of experiment series in a heterogeneous modeling environment.

APA, Harvard, Vancouver, ISO, and other styles

9

Lurgi, Miguel, and David Robertson. "Automated experimentation in ecological networks." Automated Experimentation 3, no. 1 (2011): 1. http://dx.doi.org/10.1186/1759-4499-3-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Triantafyllis, N., E. Sokos, and A. Ilias. "Automatic moment tensor determination for the Hellenic Unified Seismic Network." Bulletin of the Geological Society of Greece 47, no. 3 (December 21, 2016): 1308. http://dx.doi.org/10.12681/bgsg.10912.

Full text

Abstract:

Modern seismic networks with broadband sensors and real time digital telemetry made Moment Tensor (MT) determination a routine procedure. Automatic MT’s are now provided by global networks and a few very dense regional networks, within minutes after a significant event. An automatic MT determination wasn’t possible for the broader Hellenic area since seismic station density wasn’t sufficient. The creation of the Hellenic Unified Seismic Network (HUSN) provided the opportunity to apply an automated MT procedure using the available broad band data from almost one hundred stations. Thus the ISOLA code was extended towards the automatic operation based on Linux OS shell scripts, stand alone Fortran codes and SAC2000. Software supports both manual and automatic mode; at the first case, the user manually runs the program with the desired input parameters while at the latter, the system monitors a mailbox or RSS feed and if it receives an appropriate notification triggers the MT inversion procedure based on certain conditions. As it is setup now it calculates automatically the moment tensor of earthquakes larger than 3.5M w using data from HUSN. Application of an automated MT inversion procedure for HUSN will provide important real time information for studies like ground motion evaluation, tsunami warning etc.

APA, Harvard, Vancouver, ISO, and other styles

11

Dimitris Koutras, Panos Dimitrellos, Panayiotis Kotzanikolaou, and Christos Douligeris. "Automated Wi-Fi intrusion detection tool on 802.11 networks." ITU Journal on Future and Evolving Technologies 5, no. 1 (March 12, 2024): 88–103. http://dx.doi.org/10.52953/lhxo3338.

Full text

Abstract:

Wi-Fi networks enable user-friendly network connectivity in various environments, ranging from home to enterprise networks. However, vulnerabilities in Wi-Fi implementations may allow nearby adversaries to gain an initial foothold into a network, e.g., in order to attempt further network penetration. In this paper we propose a methodology for the detection of attacks originating from Wi-Fi networks, along with a Wi-Fi Network Intrusion Detection (Wi-Fi-NID) tool, developed to automate the detection of such attacks at 802.11 networks. In particular, Wi-Fi-NID has the ability to detect and trace possible illegal network scanning attacks, which originate from attacks at the Wi-Fi access layer. We extend our initial implementation to increase the efficiency of detection, based on mathematical and statistical function techniques. A penetration testing methodology is defined, in order to discover the environmental security characteristics, related with the current configuration of the devices connected to the 802.11 network. The methodology covers known Wi-Fi attacks such as de-authentication attacks, capturing and cracking WPA-WPA/2 handshake, captive portal and WPA attacks, mostly based on various open source software tools, custom tools, as well as on specialized hardware.

APA, Harvard, Vancouver, ISO, and other styles

12

Chatterjee, Priyadarshini, and Dutta Sushama Rani. "A Survey on Techniques used in Medical Imaging Processing." Journal of Physics: Conference Series 2089, no. 1 (November 1, 2021): 012013. http://dx.doi.org/10.1088/1742-6596/2089/1/012013.

Full text

Abstract:

Abstract Automated diagnosis of diseases in the recent years have gain lots of advantages and potential. Specially automated screening of cancers has helped the clinicians over the time. Sometimes it is seen that the diagnosis of the clinicians is biased but automated detection can help them to come to a proper conclusion. Automated screening is implemented using either artificial inter connected system or convolutional inter connected system. As Artificial neural network is slow in computation, so Convolutional Neural Network has achieved lots of importance in the recent years. It is also seen that Convolutional Neural Network architecture requires a smaller number of datasets. This also provides them an edge over Artificial Neural Networks. Convolutional Neural Networks is used for both segmentation and classification. Image dissection is one of the important steps in the model used for any kind of image analysis. This paper surveys various such Convolutional Neural Networks that are used for medical image analysis.

APA, Harvard, Vancouver, ISO, and other styles

13

Marchant, Ross, Martin Tetard, Adnya Pratiwi, Michael Adebayo, and Thibault de Garidel-Thoron. "Automated analysis of foraminifera fossil records by image classification using a convolutional neural network." Journal of Micropalaeontology 39, no. 2 (October 15, 2020): 183–202. http://dx.doi.org/10.5194/jm-39-183-2020.

Full text

Abstract:

Abstract. Manual identification of foraminiferal morphospecies or morphotypes under stereo microscopes is time consuming for micropalaeontologists and not possible for nonspecialists. Therefore, a long-term goal has been to automate this process to improve its efficiency and repeatability. Recent advances in computation hardware have seen deep convolutional neural networks emerge as the state-of-the-art technique for image-based automated classification. Here, we describe a method for classifying large foraminifera image sets using convolutional neural networks. Construction of the classifier is demonstrated on the publicly available Endless Forams image set with a best accuracy of approximately 90 %. A complete automatic analysis is performed for benthic species dated to the last deglacial period for a sediment core from the north-eastern Pacific and for planktonic species dated from the present until 180 000 years ago in a core from the western Pacific warm pool. The relative abundances from automatic counting based on more than 500 000 images compare favourably with manual counting, showing the same signal dynamics. Our workflow opens the way to automated palaeoceanographic reconstruction based on computer image analysis and is freely available for use.

APA, Harvard, Vancouver, ISO, and other styles

14

Balcas, Justas, Harvey Newman, Preeti P. Bhat, Frank Würthwein, Jonathan Guiang, Aashay Arora, Diego Davila, et al. "Automated Network Services for Exascale Data Movement." EPJ Web of Conferences 295 (2024): 01009. http://dx.doi.org/10.1051/epjconf/202429501009.

Full text

Abstract:

The Large Hadron Collider (LHC) experiments distribute data by leveraging a diverse array of National Research and Education Networks (NRENs), where experiment data management systems treat networks as a “blackbox” resource. After the High Luminosity upgrade, the Compact Muon Solenoid (CMS) experiment alone will produce roughly 0.5 exabytes of data per year. NREN Networks are a critical part of the success of CMS and other LHC experiments. However, during data movement, NRENs are unaware of data priorities, importance, or need for quality of service, and this poses a challenge for operators to coordinate the movement of data and have predictable data flows across multi-domain networks. The overarching goal of SENSE (The Software-defined network for End-to-end Networked Science at Exascale) is to enable National Labs and universities to request and provision end-to-end intelligent network services for their application workflows leveraging SDN (Software-Defined Networking) capabilities. This work aims to allow LHC Experiments and Rucio, the data management software used by CMS Experiment, to allocate and prioritize certain data transfers over the wide area network. In this paper, we will present the current progress of the integration of SENSE, Multi-domain end-to-end SDN Orchestration with QoS (Quality of Service) capabilities, with Rucio, the data management software used by CMS Experiment.

APA, Harvard, Vancouver, ISO, and other styles

15

Nikitin, N. A., Y. A. Orlova, V. L. Rozaliev, A. S. Kuznetsova, and V. V. Gilka. "Proposal of method for generating musical compositions of different genres." Journal of Physics: Conference Series 2060, no. 1 (October 1, 2021): 012023. http://dx.doi.org/10.1088/1742-6596/2060/1/012023.

Full text

Abstract:

Abstract The work is dedicated to the research and development of methods and algorithms that automate and support the process of technical creativity, from the point of automated creation of musical compositions with different genres. The method is based on the use of neural networks to predict composition, and also suggests using various models that have been trained by songs in certain genres to improve the quality of the resulting musical composition. This work describes the algorithm itself, the process of collecting and classifying data for training, the process of training a neural network. In addition, it describes the process of choosing a neural network architecture. Also, the work proposes a system architecture for the automated generation of compositions using the proposed algorithm.

APA, Harvard, Vancouver, ISO, and other styles

16

Son, Surak, and Yina Jeong. "An Automated Fish-Feeding System Based on CNN and GRU Neural Networks." Sustainability 16, no. 9 (April 27, 2024): 3675. http://dx.doi.org/10.3390/su16093675.

Full text

Abstract:

AI plays a pivotal role in predicting plant growth in agricultural contexts and in creating optimized environments for cultivation. However, unlike agriculture, the application of AI in aquaculture is predominantly focused on diagnosing animal conditions and monitoring them for users. This paper introduces an Automated Fish-feeding System (AFS) based on Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs), aiming to establish an automated system akin to smart farming in the aquaculture sector. The AFS operates by precisely calculating feed rations through two main modules. The Fish Growth Measurement Module (FGMM) utilizes fish data to assess the current growth status of the fish and transmits this information to the Feed Ration Prediction Module (FRPM). The FRPM integrates sensor data from the fish farm, fish growth data, and current feed ration status as time-series data, calculating the increase or decrease rate of ration based on the present fish conditions. This paper automates feed distribution within fish farms through these two modules and verifies the efficiency of automated feed distribution. Simulation results indicate that the FGMM neural network model effectively identifies fish body length with a minor deviation of less than 0.1%, while the FRPM neural network model demonstrates proficiency in predicting ration using a GRU cell with a structured layout of 64 × 48.

APA, Harvard, Vancouver, ISO, and other styles

17

Shahamiri, Seyed Reza, Wan M. N. Wan-Kadir, Suhaimi Ibrahim, and Siti Zaiton Mohd Hashim. "Artificial neural networks as multi-networks automated test oracle." Automated Software Engineering 19, no. 3 (September 24, 2011): 303–34. http://dx.doi.org/10.1007/s10515-011-0094-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Won, Jongbin, Jong-Woong Park, Soojin Jang, Kyohoon Jin, and Youngbin Kim. "Automated Structural Damage Identification Using Data Normalization and 1-Dimensional Convolutional Neural Network." Applied Sciences 11, no. 6 (March 15, 2021): 2610. http://dx.doi.org/10.3390/app11062610.

Full text

Abstract:

In the field of structural-health monitoring, vibration-based structural damage detection techniques have been practically implemented in recent decades for structural condition assessment. With the development of deep-learning networks that make automatic feature extraction and high classification accuracy possible, deep-learning-based structural damage detection has been gaining significant attention. The deep-learning neural networks come with fixed input and output size, and input data must be downsampled or cropped to the predetermined input size of the networks to obtain desired output of the network. However, the length of input data (i.e., sensing data) is associated with the excitation quality of a structure, adjusting the size of the input data while maintaining the excitation quality is critical to ensure high accuracy of the deep-learning-based structural damage detection. To address this issue, natural-excitation-technique-based data normalization and the use of 1-D convolutional neural networks for automated structural damage detection are presented. The presented approach converts input data to predetermined size using cross-correlation and uses convolutional network to extract damage-sensitive feature for automated structural damage identification. Numerical simulations were conducted on a simply supported beam model excited by random and traffic loadings, and the performance was validated under various scenarios. The proposed method successfully detected the location of damage on a beam under random and traffic loadings with accuracies of 99.90% and 99.20%, respectively.

APA, Harvard, Vancouver, ISO, and other styles

19

Haddad, Majed, Piotr Wiecek, Habib B. A. Sidi, and Eitan Altman. "An Automated Dynamic Offset for Network Selection in Heterogeneous Networks." IEEE Transactions on Mobile Computing 15, no. 9 (September 1, 2016): 2151–64. http://dx.doi.org/10.1109/tmc.2015.2492560.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Pursky, Oleg, Valery Kozlov, Tetyana Tomashevska, Volodymyr Dyvak, Nataliia Hordiiko, and Mykola Sinitsky. "Information resources distribution between automated workstations in local corporative networks." PROBLEMS IN PROGRAMMING, no. 3-4 (December 2022): 23–31. http://dx.doi.org/10.15407/pp2022.03-04.023.

Full text

Abstract:

This article focuses on the problem of optimal distribution of related information resources between automated workstations in local corporate networks. In this work we present a mathematical description of the algorithm for quasi-optimal distribution of related information resources at designing automated workstations in a local corporate network. The undirected graph describing the task of information resources optimal distribution is presented. The method of quasi-optimal distribution of related resources at designing automated workstations in the local corporative network is proposed based on the developed algorithm. Using conditional organization as an example the modeling of optimal distribution of related information resources has been considered in local corporative network. The described algorithm provides an opportunity to optimally distribute the information resource in the local corporate network, as well as solve the task of building reliable and efficient local networks. The proposed method of quasi-optimal distribution of related information resources can be used in corporation of any type.

APA, Harvard, Vancouver, ISO, and other styles

21

Strakhov, S. Yu, A. A. Karasev, and N. V. Sotnikova. "FORMALIZATION AND THE CONSTRUCTION OF A NETWORK MODEL OF TESTING OF THE ELECTRONIC ONBOARD EQUIPMENT OF THE SPACECRAFT WITH THE HELP OF PETRI NETS." Issues of radio electronics, no. 7 (July 20, 2018): 51–58. http://dx.doi.org/10.21778/2218-5453-2018-7-51-58.

Full text

Abstract:

In this work the question of creation and application of formal models in the form of varieties of Petri nets to process of the automated electric tests of the automatic spacecraft is considered. At present, automatic spacecraft for various purposes are required to have a long active life. This implies the use of modern electronic components that are resistant to external factors affecting outer space. Electronic equipment, consisting of these components, forms information networks on Board the spasecraft, which leads to the need for a comprehensive electrical inspection of the operation of onboard instruments and systems of such networks in an automated mode. Modeling of the processes taking place in the on-Board electronic units, and the control and verification equipment interacting with it, is of separate interest, as it allows to develop automated testing methods based on the obtained data of the model analysis.

APA, Harvard, Vancouver, ISO, and other styles

22

Talbi, El-Ghazali. "Automated Design of Deep Neural Networks." ACM Computing Surveys 54, no. 2 (April 2021): 1–37. http://dx.doi.org/10.1145/3439730.

Full text

Abstract:

In recent years, research in applying optimization approaches in the automatic design of deep neural networks has become increasingly popular. Although various approaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this hot research topic. In this article, we propose a unified way to describe the various optimization algorithms that focus on common and important search components of optimization algorithms: representation, objective function, constraints, initial solution(s), and variation operators. In addition to large-scale search space, the problem is characterized by its variable mixed design space, it is very expensive, and it has multiple blackbox objective functions. Hence, this unified methodology has been extended to advanced optimization approaches, such as surrogate-based, multi-objective, and parallel optimization.

APA, Harvard, Vancouver, ISO, and other styles

23

Römmele, Stefan. "Automated Driving Calls for Secure Networks." ATZelektronik worldwide 10, no. 2 (April 2015): 12–17. http://dx.doi.org/10.1007/s38314-015-0517-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Almaini, A. E. A., J. F. Miller, and L. Xu. "Automated synthesis of digital multiplexer networks." IEE Proceedings E Computers and Digital Techniques 139, no. 4 (1992): 329. http://dx.doi.org/10.1049/ip-e.1992.0048.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Orlova, Yuliia, Ivan Kryven, and Piet D. Iedema. "Automated reaction generation for polymer networks." Computers & Chemical Engineering 112 (April 2018): 37–47. http://dx.doi.org/10.1016/j.compchemeng.2018.01.022.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Wilson, Zachary T., and Nikolaos V. Sahinidis. "Automated learning of chemical reaction networks." Computers & Chemical Engineering 127 (August 2019): 88–98. http://dx.doi.org/10.1016/j.compchemeng.2019.05.020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Lancaster, Jack L., Angela R. Laird, P. Mickle Fox, David E. Glahn, and Peter T. Fox. "Automated analysis of meta-analysis networks." Human Brain Mapping 25, no. 1 (2005): 174–84. http://dx.doi.org/10.1002/hbm.20135.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Potvin, Jean-Yves, Yu Shen, and Jean-Marc Rousseau. "Neural networks for automated vehicle dispatching." Computers & Operations Research 19, no. 3-4 (April 1992): 267–76. http://dx.doi.org/10.1016/0305-0548(92)90048-a.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Le, Truong-Minh, Bao-Thien Nguyen-Tat, and Vuong M. Ngo. "Automated evaluation of Tuberculosis using Deep Neural Networks." EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 8, no. 30 (April 14, 2022): e4. http://dx.doi.org/10.4108/eetinis.v8i30.478.

Full text

Abstract:

INTRODUCTION: Tuberculosis (TB) is a chronic, progressive infection that often has a latent period after the initial infection period. Early awareness from those period to have better prevention steps becomes an indispensable part for patients who want to lengthen their lives. Hence, applying cutting-edge technologies to support the medical business domain plays a key role in improving speed and accuracy in methods of diagnosis. Deep Neural Network-based technique (DNN) is one of such methods which offers positive results by leveraging the advantages of analyzing deeply the data, especially image data format via tons of deep layers of the neural networks. Our study was wrapped up by objectively assessing the performance of modern Deep Neural Network approaches and suggesting a model offering good results in terms of the selected metrics as defined later. In order to achieve optimized results, the chosen model must adapt well to the datasets but require less hardware and computational resources.OBJECTIVES: Our objective is to pick up and train a Deep Neural Network architecture which is highly trusted and flexibly fitted and applied to various datasets with minimum configurations. This will be used to produce good predictions based on the input data which are Chest X-ray images retrieved from the published datasets.METHODS: We have been approaching this problem by using the recognized datasets which have already been published before, then resizing them to the consistent input data for training purposes. In terms of Deep Neural Networks, we picked up VGG16 as the baseline network architecture, then use other ones which are state-of-the-art networks for comparison purposes. After all, we recommend the neural network architecture offering the most positive results based on accuracy and recall measurements. So that, this network architecture will show flexibility when fitting into diverse datasets representing different areas in the world that suffered from Tuberculosis before.RESULTS: After conducting the experiments, we observed that the Mobilenet model produced great results based on the predefined metrics for most of the proposed datasets. It shows the versatility which is applicable to all CXR datasets, especially for the Tuberculosis ones.CONCLUSION: Tuberculosis is still one of the most dangerous illnesses in the world that needs vital methods to prevent and detect soon so that patients are able to keep their lives longer. After this research, we are constantly improving the current accuracy of the models and applying the current results of this research for later problems such as detecting the Tuberculosis areas in real-time and supporting doctors to make decisions based on the current status of patients.

APA, Harvard, Vancouver, ISO, and other styles

30

Jareño, Javier, Guillermo Bárcena-González, Jairo Castro-Gutiérrez, Remedios Cabrera-Castro, and Pedro L. Galindo. "Enhancing Fish Auction with Deep Learning and Computer Vision: Automated Caliber and Species Classification." Fishes 9, no. 4 (April 13, 2024): 133. http://dx.doi.org/10.3390/fishes9040133.

Full text

Abstract:

The accurate labeling of species and size of specimens plays a pivotal role in fish auctions conducted at fishing ports. These labels, among other relevant information, serve as determinants of the objectivity of the auction preparation process, underscoring the indispensable nature of a reliable labeling system. Historically, this task has relied on manual processes, rendering it vulnerable to subjective interpretations by the involved personnel, therefore compromising the value of the merchandise. Consequently, the digitization and implementation of an automated labeling system are proposed as a viable solution to this ongoing challenge. This study presents an automatic system for labeling species and size, leveraging pre-trained convolutional neural networks. Specifically, the performance of VGG16, EfficientNetV2L, Xception, and ResNet152V2 networks is thoroughly examined, incorporating data augmentation techniques and fine-tuning strategies. The experimental findings demonstrate that for species classification, the EfficientNetV2L network excels as the most proficient model, achieving an average F-Score of 0.932 in its automatic mode and an average F-Score of 0.976 in its semi-automatic mode. Concerning size classification, a semi-automatic model is introduced, where the Xception network emerges as the superior model, achieving an average F-Score of 0.949.

APA, Harvard, Vancouver, ISO, and other styles

31

Gasparyan, Manvel, Arnout Van Messem, and Shodhan Rao. "An Automated Model Reduction Method for Biochemical Reaction Networks." Symmetry 12, no. 8 (August 7, 2020): 1321. http://dx.doi.org/10.3390/sym12081321.

Full text

Abstract:

We propose a new approach to the model reduction of biochemical reaction networks governed by various types of enzyme kinetics rate laws with non-autocatalytic reactions, each of which can be reversible or irreversible. This method extends the approach for model reduction previously proposed by Rao et al. which proceeds by the step-wise reduction in the number of complexes by Kron reduction of the weighted Laplacian corresponding to the complex graph of the network. The main idea in the current manuscript is based on rewriting the mathematical model of a reaction network as a model of a network consisting of linkage classes that contain more than one reaction. It is done by joining certain distinct linkage classes into a single linkage class by using the conservation laws of the network. We show that this adjustment improves the extent of applicability of the method proposed by Rao et al. We automate the entire reduction procedure using Matlab. We test our automated model reduction to two real-life reaction networks, namely, a model of neural stem cell regulation and a model of hedgehog signaling pathway. We apply our reduction approach to meaningfully reduce the number of complexes in the complex graph corresponding to these networks. When the number of species’ concentrations in the model of neural stem cell regulation is reduced by 33.33%, the difference between the dynamics of the original model and the reduced model, quantified by an error integral, is only 4.85%. Likewise, when the number of species’ concentrations is reduced by 33.33% in the model of hedgehog signaling pathway, the difference between the dynamics of the original model and the reduced model is only 6.59%.

APA, Harvard, Vancouver, ISO, and other styles

32

Суханова, Наталия, and Nataliya Sukhanova. "METHOD DEVELOPMENT AND INVESTIGATION FOR CONTROL OF AUTOMATED CONTROL SYSTEM WORKING CAPACITY BASED ON ARTIFICIAL NEURAL NETWORKS." Bulletin of Bryansk state technical university 2018, no. 7 (October 4, 2018): 91–98. http://dx.doi.org/10.30987/article_5ba8a190c4b385.22437052.

Full text

Abstract:

The purpose of the work consists in the im-provement of known control methods of automated control system working capacity. A possibility for con-trol of automated control system working capacity based on artificial neural networks (ANN) is shown. ANN must be taught. ANN training requires a large training sample, substantial costs, computer resources, time and has high labor intensity. Methods of investigation are modeling, methods of artificial intelligence, artificial neural networks. As a result of the investigation there is devel-oped and investigated a method of automated control system (ACS) working capacity control with the use of a neural network.

APA, Harvard, Vancouver, ISO, and other styles

33

Luntovsky, A. O., and I. V. Melnyk. "Automated design of wireless computer rooms networks in the CANDY Framework system." Electronics and Communications 16, no. 1 (March 28, 2011): 174–80. http://dx.doi.org/10.20535/2312-1807.2011.16.1.274244.

Full text

Abstract:

The paper discusses architecture of a computer-aided design (CAD) of combined networks CANDY Framework for offices and building automation systems based on diverse wired and wireless network standards. The requirements to modern radio networks design have been examined. A developed planning tool purposed to support of PHY-layer design of wireless nets by standards IEEE 802.11 (Wireless LAN), 802.16 (WiMAX), 802.15.4 (Wireless Sensor Networks ZigBee) is represented. The design requirements on these networks are often contradictive and often have to consider such factors as performance, energy and cost efficiency for a network solution altogether.

APA, Harvard, Vancouver, ISO, and other styles

34

Jesus, Thiago, Paulo Portugal, Francisco Vasques, and Daniel Costa. "Automated Methodology for Dependability Evaluation of Wireless Visual Sensor Networks." Sensors 18, no. 8 (August 10, 2018): 2629. http://dx.doi.org/10.3390/s18082629.

Full text

Abstract:

Wireless sensor networks have been considered as an effective solution to a wide range of applications due to their prominent characteristics concerning information retrieving and distributed processing. When visual information can be also retrieved by sensor nodes, applications acquire a more comprehensive perception of monitored environments, fostering the creation of wireless visual sensor networks. As such networks are being more often considered for critical monitoring and control applications, usually related to catastrophic situation prevention, security enhancement and crises management, fault tolerance becomes a major expected service for visual sensor networks. A way to address this issue is to evaluate the system dependability through quantitative attributes (e.g., reliability and availability), which require a proper modeling strategy to describe the system behavior. That way, in this paper, we propose a methodology to model and evaluate the dependability of wireless visual sensor networks using Fault Tree Analysis and Markov Chains. The proposed modeling strategy considers hardware, battery, link and coverage failures, besides considering routing protocols on the network communication behavior. The methodology is automated by a framework developed and integrated with the SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) tool. The achieved results show that this methodology is useful to compare different network implementations and the corresponding dependability, enabling the uncovering of potentially weak points in the network behavior.

APA, Harvard, Vancouver, ISO, and other styles

35

Waldherr, Annie, Daniel Maier, Peter Miltner, and Enrico Günther. "Big Data, Big Noise." Social Science Computer Review 35, no. 4 (May 9, 2016): 427–43. http://dx.doi.org/10.1177/0894439316643050.

Full text

Abstract:

In this article, we focus on noise in the sense of irrelevant information in a data set as a specific methodological challenge of web research in the era of big data. We empirically evaluate several methods for filtering hyperlink networks in order to reconstruct networks that contain only webpages that deal with a particular issue. The test corpus of webpages was collected from hyperlink networks on the issue of food safety in the United States and Germany. We applied three filtering strategies and evaluated their performance to exclude irrelevant content from the networks: keyword filtering, automated document classification with a machine-learning algorithm, and extraction of core networks with network-analytical measures. Keyword filtering and automated classification of webpages were the most effective methods for reducing noise, whereas extracting a core network did not yield satisfying results for this case.

APA, Harvard, Vancouver, ISO, and other styles

36

Tsai, Fa-Ta, Van-Tung Nguyen, The-Phong Duong, Quoc-Hung Phan, and Chi-Hsiang Lien. "Tomato Fruit Detection Using Modified Yolov5m Model with Convolutional Neural Networks." Plants 12, no. 17 (August 26, 2023): 3067. http://dx.doi.org/10.3390/plants12173067.

Full text

Abstract:

The farming industry is facing the major challenge of intensive and inefficient harvesting labors. Thus, an efficient and automated fruit harvesting system is required. In this study, three object classification models based on Yolov5m integrated with BoTNet, ShuffleNet, and GhostNet convolutional neural networks (CNNs), respectively, are proposed for the automatic detection of tomato fruit. The various models were trained using 1508 normalized images containing three classes of cherry tomatoes, namely ripe, immature, and damaged. The detection accuracy for the three classes was found to be 94%, 95%, and 96%, respectively, for the modified Yolov5m + BoTNet model. The model thus appeared to provide a promising basis for the further development of automated harvesting systems for tomato fruit.

APA, Harvard, Vancouver, ISO, and other styles

37

P, Mr Chavan Pauras, Mr Dhapare Pradnyesh P. P, Mr Joshi Paresh S, Mr Karande Pratik S, and Prof Kolwankar Mansi S. "Automated Pothole Filling Machine." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 4116–18. http://dx.doi.org/10.22214/ijraset.2023.51213.

Full text

Abstract:

Abstract: This paper gives an overview of various methods and techniques used for pothole detection and repairing. In Indian roads, one often encounters potholes, which can be either dry or water-filled. Accordingly, to ensure safe driving, it is important detect potholes and forecast their depths in all conditions. A road network is a way of sharing and transporting goods and services locally to the community. Roads networks are also the means of communication in some parts of the world. Therefore, access to good roads improves the life quality and work of the people living in the community. Because of the poor condition of the design and development of the road network along with natural disasters like heavy rains have created many unwanted potholes on the roads which is unsafe for commuters and other road users. In addition, the lack of a proper road maintenance increases the number of potholes that jeopardizes transport and road safety

APA, Harvard, Vancouver, ISO, and other styles

38

Xue, Yu, Pengcheng Jiang, Ferrante Neri, and Jiayu Liang. "A Multi-Objective Evolutionary Approach Based on Graph-in-Graph for Neural Architecture Search of Convolutional Neural Networks." International Journal of Neural Systems 31, no. 09 (July 24, 2021): 2150035. http://dx.doi.org/10.1142/s0129065721500350.

Full text

Abstract:

With the development of deep learning, the design of an appropriate network structure becomes fundamental. In recent years, the successful practice of Neural Architecture Search (NAS) has indicated that an automated design of the network structure can efficiently replace the design performed by human experts. Most NAS algorithms make the assumption that the overall structure of the network is linear and focus solely on accuracy to assess the performance of candidate networks. This paper introduces a novel NAS algorithm based on a multi-objective modeling of the network design problem to design accurate Convolutional Neural Networks (CNNs) with a small structure. The proposed algorithm makes use of a graph-based representation of the solutions which enables a high flexibility in the automatic design. Furthermore, the proposed algorithm includes novel ad-hoc crossover and mutation operators. We also propose a mechanism to accelerate the evaluation of the candidate solutions. Experimental results demonstrate that the proposed NAS approach can design accurate neural networks with limited size.

APA, Harvard, Vancouver, ISO, and other styles

39

Liu, Wenzhe, Jiehua Zhang, Zhuo Su, Zhongzhu Zhou, and Li Liu. "Binary Neural Network for Automated Visual Surface Defect Detection." Sensors 21, no. 20 (October 16, 2021): 6868. http://dx.doi.org/10.3390/s21206868.

Full text

Abstract:

As is well-known, defects precisely affect the lives and functions of the machines in which they occur, and even cause potentially catastrophic casualties. Therefore, quality assessment before mounting is an indispensable requirement for factories. Apart from the recognition accuracy, current networks suffer from excessive computing complexity, making it of great difficulty to deploy in the manufacturing process. To address these issues, this paper introduces binary networks into the area of surface defect detection for the first time, for the reason that binary networks prohibitively constrain weight and activation to +1 and −1. The proposed Bi-ShuffleNet and U-BiNet utilize binary convolution layers and activations in low bitwidth, in order to reach comparable performances while incurring much less computational cost. Extensive experiments are conducted on real-life NEU and Magnetic Tile datasets, revealing the least OPs required and little accuracy decline. When classifying the defects, Bi-ShuffleNet yields comparable results to counterpart networks, with at least 2× inference complexity reduction. Defect segmentation results indicate similar observations. Some network design rules in defect detection and binary networks are also summarized in this paper.

APA, Harvard, Vancouver, ISO, and other styles

40

Alaiad, Ahmad, Aya Migdady, Ra’ed M. Al-Khatib, Omar Alzoubi, Raed Abu Zitar, and Laith Abualigah. "Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images." Journal of Imaging 9, no. 3 (March 8, 2023): 64. http://dx.doi.org/10.3390/jimaging9030064.

Full text

Abstract:

Automated deep learning is promising in artificial intelligence (AI). However, a few applications of automated deep learning networks have been made in the clinical medical fields. Therefore, we studied the application of an open-source automated deep learning framework, Autokeras, for detecting smear blood images infected with malaria parasites. Autokeras is able to identify the optimal neural network to perform the classification task. Hence, the robustness of the adopted model is due to it not needing any prior knowledge from deep learning. In contrast, the traditional deep neural network methods still require more construction to identify the best convolutional neural network (CNN). The dataset used in this study consisted of 27,558 blood smear images. A comparative process proved the superiority of our proposed approach over other traditional neural networks. The evaluation results of our proposed model achieved high efficiency with impressive accuracy, reaching 95.6% when compared with previous competitive models.

APA, Harvard, Vancouver, ISO, and other styles

41

Pham, D. T., and E. J. Bayro-Corrochano. "Neural Classifiers for Automated Visual Inspection." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 208, no. 2 (April 1994): 83–89. http://dx.doi.org/10.1243/pime_proc_1994_208_166_02.

Full text

Abstract:

This paper discusses the application of a back-propagation multi-layer perceptron and a learning vector quantization network to the classification of defects in valve stem seals for car engines. Both networks were trained with vectors containing descriptive attributes of known flaws. These attribute vectors (‘signatures’) were extracted from images of the seals captured by an industrial vision system. The paper describes the hardware and techniques used and the results obtained.

APA, Harvard, Vancouver, ISO, and other styles

42

Ransom, Elliot, Xiyuan Chen, and Fu-Kuo Chang. "Design of a Robust Tool for Deploying Large-Area Stretchable Sensor Networks from Microscale to Macroscale." Sensors 22, no. 13 (June 27, 2022): 4856. http://dx.doi.org/10.3390/s22134856.

Full text

Abstract:

An investigation was conducted to develop an effective automated tool to deploy micro-fabricated stretchable networks of distributed sensors onto the surface of large structures at macroscale to create “smart” structures with embedded distributed sensor networks. Integrating a large network of distributed sensors with structures has been a major challenge in the design of so-called smart structures or devices for cyber-physical applications where a large amount of usage data from structures or devices can be generated for artificial intelligence applications. Indeed, many “island-and-serpentine”-type distributed sensor networks, while promising, remain difficult to deploy. This study aims to enable such networks to be deployed in a safe, automated, and efficient way. To this end, a scissor-hinge controlled system was proposed as the basis for a deployment mechanism for such stretchable sensor networks (SSNs). A model based on a kinematic scissor-hinge mechanism was developed to simulate and design the proposed system to automatically stretch a micro-scaled square network with uniformly distributed sensor nodes. A prototype of an automatic scissor-hinge stretchable tool was constructed during the study with an array of four scissor-hinge mechanisms, each belt-driven by a single stepper motor. Two micro-fabricated SSNs from a 100 mm wafer were fabricated at the Stanford Nanofabrication Facility for this deployment study. The networks were designed to be able to cover an area 100 times their manufacturing size (from a 100 mm diameter wafer to a 1 m2 active area) once stretched. It was demonstrated that the proposed deployment tool could place sensor nodes in prescribed locations efficiently within a drastically shorter time than in current labor-intensive manual deployment methods.

APA, Harvard, Vancouver, ISO, and other styles

43

Hanh, Nguyen Thi Hong, Cuong Vu Xuan, and Nguyen Ngoc Thy. "Automated Generalization of Road Networks for Topographic Base Map." Modern Environmental Science and Engineering 8, no. 2 (February 8, 2022): 126–31. http://dx.doi.org/10.15341/mese(2333-2581)/02.08.2022/007.

Full text

Abstract:

Automated generalization is a very challenging problem for map producers and plays an important role in map creation. Road networks are important features on topographic base maps. They are sensitive to scale change, therefore multiple representations are required to maintain visual and geographic logic at smaller scales. This paper focuses on the characteristics, hierachies, constraint parameters and automated generalization of the road features on the topographic base map at different scales. The generalization steps were implemented in ArcGIS Model Builder using mentioned out-of-the-box functionality, which removes features by feature hierarchy and network connectivity, yet preserves characteristic urban local density patterns that can be lost through simple category removals. The tool and constraint parameters are used to automatically generalize road networks from maps scale of 1: 2,000 to scales of 1:10,000 area of Ho Chi Minh City, Viet Nam. Research result shows that using reasonable parameters and tools could provide a good way to generalize and create base map layers meeting different demands and building multi-purpose map database in the future. Key words: GIS, map generalization, topographic base map, road network, automated generalization

APA, Harvard, Vancouver, ISO, and other styles

44

Prashar, Deepak, Nishant Jha, Muhammad Shafiq, Nazir Ahmad, Mamoon Rashid, Shoeib Amin Banday, and Habib Ullah Khan. "Blockchain-Based Automated System for Identification and Storage of Networks." Security and Communication Networks 2021 (February 19, 2021): 1–7. http://dx.doi.org/10.1155/2021/6694281.

Full text

Abstract:

Network topology is one of the major factors in defining the behavior of a network. In the present scenario, the demand for network security has increased due to an increase in the possibility of attacks by malicious users. In this paper, a blockchain-based system is suggested for securely discovering and storing networks. Techniques such as cloud-based storage systems are not efficient and are lacking in trust, privacy, security, and data control. The blockchain-based technique suggested in this paper is capable of resolving these challenges. Experiments were performed using Mininet, Cisco Packet Tracer, and Ethereum blockchain with the network inference algorithm. This algorithm is capable of inferring the network topology even when only partial information regarding the network is available. The results obtained clearly show that the network is resistant to malicious users and various external attacks, making the network robust.

APA, Harvard, Vancouver, ISO, and other styles

45

Akbar, F., A. Ghosh, S. Young, S. Akhter, Z. Ahmad Dar, V. Ansari, M. V. Ascencio, et al. "Vertex finding in neutrino-nucleus interaction: a model architecture comparison." Journal of Instrumentation 17, no. 08 (August 1, 2022): T08013. http://dx.doi.org/10.1088/1748-0221/17/08/t08013.

Full text

Abstract:

Abstract We compare different neural network architectures for machine learning algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package “Multi-node Evolutionary Neural Networks for Deep Learning” (MENNDL), developed at Oak Ridge National Laboratory. While the domain-expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed as well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.

APA, Harvard, Vancouver, ISO, and other styles

46

Sukhanova, Nataliya. "DEVELOPING AND APPLYING NEURAL NETWORK MODELS IN AUTOMATING EQUIPMENT CONTROL AND TECHNOLOGICAL PROCESSES." Automation and modeling in design and management, no. 1 (March 17, 2022): 24–32. http://dx.doi.org/10.30987/2658-6436-2022-1-24-32.

Full text

Abstract:

Models of equipment and technological processes based on neural networks with a commutator structure are developed. The article proposes to use neural network models in automated control systems (ACS). Neural network models make it possible to implement automated control systems with a flexible programmable switching structure, quickly connect and disconnect new technological equipment, change the operation order in technological processes, and adapt the system to changing conditions and the external environment. The object of study is ACS. The method is modelling. The aim is to reduce the cost of developing and using automated control systems.

APA, Harvard, Vancouver, ISO, and other styles

47

Schnoor, Tyler T., Matthew C. Kelley, and Benjamin V. Tucker. "Automated accent rating using deep neural networks." Journal of the Acoustical Society of America 150, no. 4 (October 2021): A357. http://dx.doi.org/10.1121/10.0008581.

Full text

Abstract:

Automated accentedness rating has the potential to improve many human-computer interactions involving speech, including the adaptation of automatic speech recognition or other artificial intelligence models to the speaker's accent. Accent ratings may also be used as a metric by which language learners can quantify their progress. This study employs bidirectional long short-term memory layers in a neural network to predict human ratings of the accentedness of recorded speech. Speech data are extracted in 5-s segments from over 2000 first- and second-language English speakers from multiple corpora. Human ratings are obtained in an online experiment where participants rate the accentedness of a given speech recording on a 9-point Likert scale. Mel-frequency cepstral coefficients and mel-filterbank energy features are tested as speech input representations for the neural network. When inference is tested using 10-fold cross validation, the mean correlation between the model’s predictions and human ratings is high (r = 0.74). While previous methods attained a similar correlation by automatically comparing speech that has been transcribed [Wieling et al., Lang. Dyn. Chang. 4, 253–269 (2014)] or by making accent-specific Gaussian mixture models [Cheng et al., Interspeech 2013 (2013), pp. 2574–2578], the present model requires no transcription and can perform accent-general inference.

APA, Harvard, Vancouver, ISO, and other styles

48

Sarmadi, Sorena, James J. Winkle, Razan N. Alnahhas, Matthew R. Bennett, Krešimir Josić, Andreas Mang, and Robert Azencott. "Stochastic Neural Networks for Automatic Cell Tracking in Microscopy Image Sequences of Bacterial Colonies." Mathematical and Computational Applications 27, no. 2 (March 2, 2022): 22. http://dx.doi.org/10.3390/mca27020022.

Full text

Abstract:

Our work targets automated analysis to quantify the growth dynamics of a population of bacilliform bacteria. We propose an innovative approach to frame-sequence tracking of deformable-cell motion by the automated minimization of a new, specific cost functional. This minimization is implemented by dedicated Boltzmann machines (stochastic recurrent neural networks). Automated detection of cell divisions is handled similarly by successive minimizations of two cost functions, alternating the identification of children pairs and parent identification. We validate the proposed automatic cell tracking algorithm using (i) recordings of simulated cell colonies that closely mimic the growth dynamics of E. coli in microfluidic traps and (ii) real data. On a batch of 1100 simulated image frames, cell registration accuracies per frame ranged from 94.5% to 100%, with a high average. Our initial tests using experimental image sequences (i.e., real data) of E. coli colonies also yield convincing results, with a registration accuracy ranging from 90% to 100%.

APA, Harvard, Vancouver, ISO, and other styles

49

Gao, Jerry, Peng Du, Greg O'Grady, Rosalind Archer, Gianrico Farrugia, Simon J. Gibbons, and Leo K. Cheng. "Numerical metrics for automated quantification of interstitial cell of Cajal network structural properties." Journal of The Royal Society Interface 10, no. 86 (September 6, 2013): 20130421. http://dx.doi.org/10.1098/rsif.2013.0421.

Full text

Abstract:

Depletion of interstitial cells of Cajal (ICC) networks is known to occur in several gastrointestinal motility disorders. Although confocal microscopy can effectively image and visualize the spatial distribution of ICC networks, current descriptors of ICC depletion are limited to cell numbers and volume computations. Spatial changes in ICC network structural properties have not been quantified. Given that ICC generate electrical signals, the organization of a network may also affect physiology. In this study, six numerical metrics were formulated to automatically determine complex ICC network structural properties from confocal images: density , thickness , hole size , contact ratio , connectivity and anisotropy . These metrics were validated and applied in proof-of-concept studies to quantitatively determine jejunal ICC network changes in mouse models with decreased ( 5-HT 2 B receptor knockout (KO)) and normal ( Ano1 KO) ICC numbers, and during post-natal network maturation. Results revealed a novel remodelling phenomenon occurring during ICC depletion, namely a spatial rearrangement of ICC and the preferential longitudinal alignment. In the post-natal networks, an apparent pruning of the ICC network was demonstrated. The metrics developed here enabled the first detailed quantitative analyses of structural changes that may occur in ICC networks during depletion and development.

APA, Harvard, Vancouver, ISO, and other styles

50

Salido, Jesús, Carlos Sánchez, Jesús Ruiz-Santaquiteria, Gabriel Cristóbal, Saul Blanco, and Gloria Bueno. "A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms." Applied Sciences 10, no. 17 (August 31, 2020): 6033. http://dx.doi.org/10.3390/app10176033.

Full text

Abstract:

Currently, microalgae (i.e., diatoms) constitute a generally accepted bioindicator of water quality and therefore provide an index of the status of biological ecosystems. Diatom detection for specimen counting and sample classification are two difficult time-consuming tasks for the few existing expert diatomists. To mitigate this challenge, in this work, we propose a fully operative low-cost automated microscope, integrating algorithms for: (1) stage and focus control, (2) image acquisition (slide scanning, stitching, contrast enhancement), and (3) diatom detection and a prospective specimen classification (among 80 taxa). Deep learning algorithms have been applied to overcome the difficult selection of image descriptors imposed by classical machine learning strategies. With respect to the mentioned strategies, the best results were obtained by deep neural networks with a maximum precision of 86% (with the YOLO network) for detection and 99.51% for classification, among 80 different species (with the AlexNet network). All the developed operational modules are integrated and controlled by the user from the developed graphical user interface running in the main controller. With the developed operative platform, it is noteworthy that this work provides a quite useful toolbox for phycologists in their daily challenging tasks to identify and classify diatoms.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Automated networks'

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