Integrity verification and behavioral classification of a large dataset applications pertaining smart OS via blockchain and generative models

2020 ◽  
Author(s):  
Salman Jan ◽  
Shahrulniza Musa ◽  
Toqeer Ali ◽  
Mohammad Nauman ◽  
Sajid Anwar ◽  
...  
Keyword(s):  
Generative Models ◽  
Large Dataset ◽  
Integrity Verification ◽  
Behavioral Classification

scholarly journals Minimization of the Line Resistance Impact on Memdiode-Based Simulations of Multilayer Perceptron Arrays Applied to Pattern Recognition

2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Fernando Leonel Aguirre ◽  
Nicolás M. Gomez ◽  
Sebastián Matías Pazos ◽  
Félix Palumbo ◽  
Jordi Suñé ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Ex Situ ◽  
Multilayer Perceptrons ◽  
Spice Simulation ◽  
Large Dataset ◽  
High Line ◽  
Calibration Algorithm ◽  
The Impact ◽  
Line Resistance

In this paper, we extend the application of the Quasi-Static Memdiode model to the realistic SPICE simulation of memristor-based single (SLPs) and multilayer perceptrons (MLPs) intended for large dataset pattern recognition. By considering ex-situ training and the classification of the hand-written characters of the MNIST database, we evaluate the degradation of the inference accuracy due to the interconnection resistances for MLPs involving up to three hidden neural layers. Two approaches to reduce the impact of the line resistance are considered and implemented in our simulations, they are the inclusion of an iterative calibration algorithm and the partitioning of the synaptic layers into smaller blocks. The obtained results indicate that MLPs are more sensitive to the line resistance effect than SLPs and that partitioning is the most effective way to minimize the impact of high line resistance values.

Research of Trusted Network Security Technology

2011 ◽  
Vol 52-54 ◽  
pp. 713-716
Author(s):  
Xiao Ying Gan ◽  
Bin Liu
Keyword(s):  
Data Transmission ◽  
Network Access ◽  
End User ◽  
Security Technology ◽  
Resources Management ◽  
Integrity Verification ◽  
Reliable Model ◽  
User Data ◽  
Repair Techniques

Based on the TNC architecture, using a trusted network of repair techniques in the trusted network access scenario does not meet the requirements of integrity verification solution for end users. Put forward a credible fix the overall network design, reliable model restoration and repair services, network workflow. The system is in need of restoration to provide safe and reliable repair end-user data transmission, providing a humane, reasonable repair services to ensure the credibility of fixed network and the isolation effect of the terminal to be repaired and strengthened the security of fixed server. Realized the classification of various types of repair resources management, restoration of resources in ensuring the transfer of fast, reliable, based on the performance with a certain extension.

Behavioral classification of data from collars containing motion sensors in grazing cattle

2015 ◽  
Vol 110 ◽  
pp. 91-102 ◽  
Author(s):  
L.A. González ◽  
G.J. Bishop-Hurley ◽  
R.N. Handcock ◽  
C. Crossman
Keyword(s):  
Motion Sensors ◽  
Grazing Cattle ◽  
Behavioral Classification

scholarly journals Three-Dimensional Trajectory Construction and Observation of Group Behavior of Wild Bats During Cave Emergence

2021 ◽  
Vol 33 (3) ◽  
pp. 556-563
Author(s):  
Emyo Fujioka ◽  
Mika Fukushiro ◽  
Kazusa Ushio ◽  
Kyosuke Kohyama ◽  
Hitoshi Habe ◽  
...  
Keyword(s):  
Quantitative Estimation ◽  
Three Dimensional ◽  
Experimental System ◽  
High Sensitivity ◽  
Group Behavior ◽  
Three Dimensions ◽  
Detection And Tracking ◽  
External Objects ◽  
Behavioral Classification

Echolocating bats perceive the surrounding environment by processing echoes of their ultrasound emissions. Echolocation enables bats to avoid colliding with external objects in complete darkness. In this study, we sought to develop a method for measuring the collective behavior of echolocating bats (Miniopterus fuliginosus) emerging from their roost cave using high-sensitivity stereo-camera recording. First, we developed an experimental system to reconstruct the three-dimensional (3D) flight trajectories of bats emerging from the roost for nightly foraging. Next, we developed a method to automatically track the 3D flight paths of individual bats so that quantitative estimation of the population in proportion to the behavioral classification could be conducted. Because the classification of behavior and the estimation of population size are ecologically important indices, the method established in this study will enable quantitative investigation of how individual bats efficiently leave the roost while avoiding colliding with each other during group movement and how the group behavior of bats changes according to weather and environmental conditions. Such high-precision detection and tracking will contribute to the elucidation of the algorithm of group behavior control in creatures that move in groups together in three dimensions, such as birds.

scholarly journals Quantile contours and allometric modelling for risk classification of abnormal ratios with an application to asymmetric growth-restriction in preterm infants

2019 ◽  
Vol 29 (7) ◽  
pp. 1769-1786
Author(s):  
Marco Geraci ◽  
Nansi S Boghossian ◽  
Alessio Farcomeni ◽  
Jeffrey D Horbar
Keyword(s):  
Risk Assessment ◽  
Preterm Infants ◽  
Operational Definition ◽  
Risk Classification ◽  
Large Dataset ◽  
Maternal Hypertension ◽  
Asymmetric Growth ◽  
Definition Of

We develop an approach to risk classification based on quantile contours and allometric modelling of multivariate anthropometric measurements. We propose the definition of allometric direction tangent to the directional quantile envelope, which divides ratios of measurements into half-spaces. This in turn provides an operational definition of directional quantile that can be used as cutoff for risk assessment. We show the application of the proposed approach using a large dataset from the Vermont Oxford Network containing observations of birthweight (BW) and head circumference (HC) for more than 150,000 preterm infants. Our analysis suggests that disproportionately growth-restricted infants with a larger HC-to-BW ratio are at increased mortality risk as compared to proportionately growth-restricted infants. The role of maternal hypertension is also investigated.

scholarly journals An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xinman Zhang ◽  
Qi Xiong ◽  
Yixuan Dai ◽  
Xuebin Xu ◽  
Guokun Song
Keyword(s):  
Extreme Learning Machine ◽  
Binary Classification ◽  
Tongue Movement ◽  
Common Spatial Pattern ◽  
Large Dataset ◽  
Electrocorticogram Ecog ◽  
Learning Machine ◽  
Bci System ◽  
System Approaches

In order to improve the accuracy of brain signal processing and accelerate speed meanwhile, we present an optimal and intelligent method for large dataset classification application in this paper. Optimized Extreme Learning Machine (OELM) is introduced in ElectroCorticoGram (ECoG) feature classification of motor imaginary-based brain-computer interface (BCI) system, with common spatial pattern (CSP) to extract the feature. When comparing it with other conventional classification methods like SVM and ELM, we exploit several metrics to evaluate the performance of all the adopted methods objectively. The accuracy of the proposed BCI system approaches approximately 92.31% when classifying ECoG epochs into left pinky or tongue movement, while the highest accuracy obtained by other methods is no more than 81%, which substantiates that OELM is more efficient than SVM, ELM, etc. Moreover, the simulation results also demonstrate that OELM will significantly improve the performance with p value being far less than 0.001. Hence, the proposed OELM is satisfactory in addressing ECoG signal.

scholarly journals Classification of Pedestrian using Convoluted Neural Network

2019 ◽  
Vol 8 (9) ◽  
pp. 1071-1075
Keyword(s):  
Neural Network ◽  
Pedestrian Detection ◽  
Extraction Techniques ◽  
Large Dataset ◽  
Major Work ◽  
Algorithm Performance ◽  
Single Person ◽  
Set Up ◽  
Window Approach

We present our work based on classification of pedestrians into a single person and group of people using Convoluted Neural Network (CNN). Major work was done on classification-based feature extraction techniques before CNN is applied to it. CNN can classify objects without extracting the features. Here, we have set up a complete channel for pedestrian detection using sliding window approach and classification using a CNN network. Alex Net and ResNet are the two architectures used in CNN for implementing the classification algorithm. Performance is evaluated on the PET and Caltech dataset which consists of a number of people who are walking with a group or separately in the scene. We got the optimistic results in case of small dataset used for testing. We have also tested our algorithm over large dataset to verify its performance with the help of performance evaluation metrics.

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