scholarly journals Accurate Identification of Agricultural Inputs Based on Sensor Monitoring Platform and SSDA-HELM-SOFTMAX Model

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Juan Zou ◽  
Hanjing Jiang ◽  
Qingxiu Wang ◽  
Ningxia Chen ◽  
Ting Wu ◽  
...  
Keyword(s):  
Accurate Identification ◽  
Detection Techniques ◽  
Discriminant Model ◽  
Artificial Intelligence Algorithm ◽  
Input Monitoring ◽  
Learning Machine ◽  
Agricultural Input ◽  
Hardware Circuit ◽  
Sensor Monitoring ◽  
Monitoring Platform

The unreliability of traceability information on agricultural inputs has become one of the main factors hindering the development of traceability systems. At present, the major detection techniques of agricultural inputs were residue chemical detection at the postproduction stage. In this paper, a new detection method based on sensors and artificial intelligence algorithm was proposed in the detection of the commonly agricultural inputs in Agastache rugosa cultivation. An agricultural input monitoring platform including software system and hardware circuit was designed and built. A model called stacked sparse denoising autoencoder-hierarchical extreme learning machine-softmax (SSDA-HELM-SOFTMAX) was put forward to achieve accurate and real-time prediction of agricultural input varieties. The experiments showed that the combination of sensors and discriminant model could accurately classify different agricultural inputs. The accuracy of SSDA-HELM-SOFTMAX reached 97.08%, which was 4.08%, 1.78%, and 1.58% higher than a traditional BP neural network, DBN-SOFTMAX, and SAE-SOFTMAX models, respectively. Therefore, the method proposed in this paper was proved to be effective, accurate, and feasible and will provide a new online detection way of agricultural inputs.

Otolith chemistry is more accurate than otolith shape in identifying cod species (genus Pseudophycis) in the diet of Australian fur seals (Arctocephalus pusillus doriferus)

2011 ◽  
Vol 68 (10) ◽  
pp. 1732-1743 ◽  
Author(s):  
Jodie Kemp ◽  
Stephen E. Swearer ◽  
Gregory P. Jenkins ◽  
Simon Robertson
Keyword(s):  
Fourier Analysis ◽  
Significant Variation ◽  
Prey Species ◽  
Fine Scale ◽  
Accurate Identification ◽  
Fish Prey ◽  
Otolith Shape ◽  
Discriminant Model ◽  
Fur Seals ◽  
Otolith Core

Fine-scale shape variation and the added effect of partial digestion often limits accurate identification of different teleost prey species in marine diet studies using otoliths. We evaluated the use of fine-scale shape and trace element variation in digested otoliths to identify fish prey species from the diet of predators. Fourier analysis of otolith shape revealed significant variation between red cod ( Pseudophycis bachus ) and bearded rock cod ( Pseudophycis barbata ) otoliths. Incorporating otoliths that had been consumed by Australian fur seals ( Arctocephalus pusillus doriferus ) into a Fourier analysis discriminant model identified 73% of otoliths as those of red cod and 27% as those of bearded rock cod. However, in vitro digestion of red cod and bearded rock cod otoliths resulted in incorrect classification of both cod species otoliths to varying degrees when using Fourier analysis shape descriptors. There was significant variation between red cod and bearded rock cod otolith core chemistry. Incorporating otoliths consumed by the seals into an otolith core chemistry discriminant model identified all otoliths as those of red cod. Using otolith core chemistry to identify prey species was found to be successful, and there is great potential for this technique to have wider applications in investigating ecosystem trophic interactions.

scholarly journals An Application and Performance Evaluation of Twin Extreme Learning Machine Classifier for Intrusion Detection

2019 ◽  
Vol 8 (9) ◽  
pp. 219-222
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Intrusion Detection System ◽  
Detection System ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Detection Techniques ◽  
Learning Machines ◽  
And Performance ◽  
Learning Machine

Network along with Security is most significant in the digitalized environment. It is necessary to secure data from hackers and intruders. A strategy involved in protection of information from hackers will be termed as Intrusion Detection System (IDS).By taking into nature of attack or the usual conduct of user, investigation along with forecasting activities of the clients will be performed by mentioned system.Variousstrategies are utilized for the intrusion detection system. For the purpose of identification of hacking activity, utilization of machine learning based approach might be considered as novel strategy.In this paper, for identification of the hacking activity will be carried out by Twin Extreme Learning Machines (TELM).Employing the concept of Twin Support Vector Machine with the fundamental structure of Extreme Learning Machine is considered in the establishment of Twin Extreme Learning Machine (TELM).Also, its performance and accuracy are compared with the other intrusion detection techniques

scholarly journals Development of an Open-Source Cloud-Connected Sensor-Monitoring Platform

2018 ◽  
Vol 08 (01) ◽  
pp. 1-11 ◽  
Author(s):  
Daniel K. Fisher ◽  
Reginald S. Fletcher ◽  
Saseendran S. Anapalli ◽  
H. C. Pringle III
Keyword(s):  
Open Source ◽  
Sensor Monitoring ◽  
Monitoring Platform

scholarly journals Novel Damage Detection Techniques for Structural Health Monitoring Using a Hybrid Sensor

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Dengjiang Wang ◽  
Jingjing He ◽  
Banglin Dong ◽  
Xiaopeng Liu ◽  
Weifang Zhang
Keyword(s):  
Damage Detection ◽  
Fatigue Testing ◽  
Fatigue Cracks ◽  
Probability Of Detection ◽  
Model Parameters ◽  
The Novel ◽  
Plate Structures ◽  
Experimental Procedure ◽  
Detection Techniques ◽  
Sensor Monitoring

This study presents a technique for detecting fatigue cracks based on a hybrid sensor monitoring system consisting of a combination of intelligent coating monitoring (ICM) and piezoelectric transducer (PZT) sensors. An experimental procedure using this hybrid sensor system was designed to monitor the cracks generated by fatigue testing in plate structures. A probability of detection (POD) model that quantifies the reliability of damage detection for a specific sensor or the nondestructive testing (NDT) method was used to evaluate the weight factor for the ICM and PZT sensors. To estimate the uncertainty of model parameters in this study, the Bayesian method was employed. Realistic data from fatigue testing was used to validate the overall method, and the results show that the novel damage detection technique using a hybrid sensor can quantify fatigue cracks more accurately than results obtained by conventional sensor methods.

scholarly journals Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sahil Dalal ◽  
Virendra P. Vishwakarma
Keyword(s):  
Well Being ◽  
Noise Removal ◽  
Discrete Wavelet ◽  
Human Beings ◽  
Accurate Identification ◽  
Classification Rate ◽  
Hybrid Classifier ◽  
Ecg Signals ◽  
Learning Machine ◽  
Ecg Data

AbstractEvery human being has a different electro-cardio-graphy (ECG) waveform that provides information about the well being of a human heart. Therefore, ECG waveform can be used as an effective identification measure in biometrics and many such applications of human identification. To achieve fast and accurate identification of human beings using ECG signals, a novel robust approach has been introduced here. The databases of ECG utilized during the experimentation are MLII, UCI repository arrhythmia and PTBDB databases. All these databases are imbalanced; hence, resampling techniques are helpful in making the databases balanced. Noise removal is performed with discrete wavelet transform (DWT) and features are obtained with multi-cumulants. This approach is mainly based on features extracted from the ECG data in terms of multi-cumulants. The multi-cumulants feature based ECG data is classified using kernel extreme learning machine (KELM). The parameters of multi-cumulants and KELM are optimized using genetic algorithm (GA). Excellent classification rate is achieved with 100% accuracy on MLII and UCI repository arrhythmia databases, and 99.57% on PTBDB database. Comparison with existing state-of-art approaches has also been performed to prove the efficacy of the proposed approach. Here, the process of classification in the proposed approach is named as evolutionary hybrid classifier.

scholarly journals APPLICATION OF ARTIFICIAL INTELLIGENCE TO BITCOIN COURSE MODELLING

2020 ◽  
pp. 14-20
Author(s):  
O. Liashenko ◽  
T. Kravets ◽  
Y. Repetskiyi
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Network Performance ◽  
Short Term Memory ◽  
Back Propagation ◽  
Absolute Deviation ◽  
Artificial Intelligence Algorithm ◽  
The Mean ◽  
Long Short Term Memory ◽  
Learning Machine

Artificial neural networks are modern methods suitable for solving the problem of nonlinear dependency approximation, which is successfully applied in many fields. This paper compares the predictive capabilities of Back Propagation, Radial Basis Function, Extreme Learning Machine, and Long-Short Term Memory neural networks to determine which artificial intelligence algorithm is best for modeling the price of Bitcoin opening. The criterion for comparing network performance was the standard deviation, the mean absolute deviation, and the accuracy of predicting the direction of change of course. At the same time, in the study of time series, it is recommended to perform a comprehensive data analysis using appropriate networks, depending on the length of the series and the specificity of the database.

The Monitoring Platform for Campus Security Based on Intelligent Assisted Vision

2014 ◽  
Vol 543-547 ◽  
pp. 981-984
Author(s):  
Xing Na He ◽  
Yin Fu Zhang
Keyword(s):  
State Monitoring ◽  
Fusion Algorithm ◽  
Accurate Identification ◽  
Security Monitoring ◽  
Multiple Sensors ◽  
Campus Security ◽  
System Information ◽  
Security Monitoring System ◽  
Monitoring Platform ◽  
Output Snr

Rapid, accurate identification and location of abnormal situation are significant for campus security. The traditional abnormal detections on campus are mainly artificial, which is difficult to conduct accurate and timely action. This paper proposes an Android monitoring platform for campus security based on visual multi-state monitoring states. It uses multiple sensors to collect system information thus realizing advantages complementary, uses wavelet transform in data processing, based on campus feature extraction, to realize image fusion in the pixel domain, greatly improving the output SNR, which improve the results of identification and location of the abnormal situation on campus. Finally, we verify the algorithm by the disaster source in actual disaster environment. Results show that the fusion algorithm in this paper greatly improves performance of campus security monitoring system to identify and locate the source disaster.

Immunolabelling of glomerular deposits in kidney biopsies routinely fixed in glutaraldehyde

1989 ◽  
Vol 47 ◽  
pp. 910-911
Author(s):  
Ś Lhoták ◽  
I. Alexopoulou ◽  
G. T. Simon
Keyword(s):  
Uv Light ◽  
Kidney Diseases ◽  
Light Chains ◽  
Microscopical Level ◽  
Accurate Identification ◽  
Light Microscopical Level ◽  
Dense Deposits ◽  
Cacodylate Buffer ◽  
Glomerular Deposits ◽  
Prognostic Importance

Various kidney diseases are characterized by the presence of dense deposits in the glomeruli. The type(s) of immunoglobulins (Igs) present in the dense deposits are characteristic of the disease. The accurate Identification of the deposits is therefore of utmost diagnostic and prognostic importance. Immunofluorescence (IF) used routinely at the light microscopical level is unable to detect and characterize small deposits found in early stages of glomerulonephritis. Although conventional TEM is able to localize such deposits, it is not capable of determining their nature. It was therefore attempted to immunolabel at EM level IgG, IgA IgM, C3, fibrinogen and kappa and lambda Ig light chains commonly found in glomerular deposits on routinely fixed ( 2% glutaraldehyde (GA) in 0.1M cacodylate buffer) kidney biopsies.The unosmicated tissue was embedded in LR White resin polymerized by UV light at -10°C. A postembedding immunogold technique was employed

Project BRAIN: Working Together to Improve Educational Outcomes for Students With Traumatic Brain Injury

2012 ◽  
Vol 22 (3) ◽  
pp. 106-118 ◽  
Author(s):  
Paula Denslow ◽  
Jean Doster ◽  
Kristin King ◽  
Jennifer Rayman
Keyword(s):  
Traumatic Brain Injury ◽  
At Risk ◽  
Brain Injury ◽  
Educational Outcomes ◽  
School Personnel ◽  
Department Of Education ◽  
Health Providers ◽  
Accurate Identification ◽  
Transition Intervention ◽  
Working Together

Children and youth who sustain traumatic brain injury (TBI) are at risk for being unidentified or misidentified and, even if appropriately identified, are at risk of encountering professionals who are ill-equipped to address their unique needs. A comparison of the number of people in Tennessee ages 3–21 years incurring brain injury compared to the number of students ages 3–21 years being categorized and served as TBI by the Department of Education (DOE) motivated us to create this program. Identified needs addressed by the program include the following: (a) accurate identification of students with TBI; (b) training of school personnel; (c) development of linkages and training of hospital personnel; and (d) hospital-school transition intervention. Funded by Health Services and Resources Administration (HRSA) grants with support from the Tennessee DOE, Project BRAIN focuses on improving educational outcomes for students with TBI through the provision of specialized group training and ongoing education for educators, families, and health professionals who support students with TBI. The program seeks to link families, hospitals, and community health providers with school professionals such as speech-language pathologists (SLPs) to identify and address the needs of students with brain injury.

Sign in / Sign up

Export Citation Format

Share Document