scholarly journals A comparative study of motion detection with FMG and sEMG methods for assistive applications

2020 ◽  
Vol 7 ◽  
pp. 205566832093858
Author(s):  
Muhammad Raza Ul Islam ◽  
Asim Waris ◽  
Ernest Nlandu Kamavuako ◽  
Shaoping Bai
Keyword(s):  
Motion Detection ◽  
Classification Accuracy ◽  
Detection Method ◽  
Daily Basis ◽  
Detection Methods ◽  
Feed Forward Neural Network ◽  
Training Scheme ◽  
Training Schemes ◽  
And Training

Introduction While surface-electromyography (sEMG) has been widely used in limb motion detection for the control of exoskeleton, there is an increasing interest to use forcemyography (FMG) method to detect motion. In this paper, we review the applications of two types of motion detection methods. Their performances were experimentally compared in day-to-day classification of forearm motions. The objective is to select a detection method suitable for motion assistance on a daily basis. Methods Comparisons of motion detection with FMG and sEMG were carried out considering classification accuracy (CA), repeatability and training scheme. For both methods, classification of motions was achieved through feed-forward neural network. Repeatability was evaluated on the basis of change in CA between days and also training schemes. Results The experiments shows that day-to-day CA with FMG can reach 84.9%, compared with a CA of 77.8% with sEMG, when the classifiers were trained only on the first day. Moreover, the CA with FMG can reach to 86.5%, comparable to CA of 84.1% with sEMG, if classifiers were trained daily. Conclusions Results suggest that data recorded from FMG is more repeatable in day-to-day testing and therefore FMG-based methods can be more useful than sEMG-based methods for motion detection in applications where exoskeletons are used as needed on a daily basis.

scholarly journals An Efficient Classification of Intrusion in Bigdata based on Chaos Game Optimization and Ensemble SVM

2021 ◽  
Author(s):  
A Ponmalar ◽  
V Dhanakoti
Keyword(s):  
Big Data ◽  
Classification Accuracy ◽  
High Speed ◽  
Performance Metrics ◽  
Daily Basis ◽  
Network Services ◽  
Detection Methods ◽  
Svm Model ◽  
Chaos Game ◽  
Big Data Classification

Abstract The growing popularity of the internet and network services has resulted in an increase in data in all fields. The data are increasing on the daily basis with high speed. This also creates some daunting issues such as security, storage, and so on. Meanwhile, the detection of intrusion from the big data in the ultra-high-speed environment is a critical task. Several intrusion detection methods are carried out to classify the big data based on intrusion and without intrusion. The optimum accuracy of big data classification, however, has yet to be achieved. Hence we proposed a novel ensemble SVM Model, in which the ensemble SVM is incorporated with the Chaos Game Optimization (CGO) algorithm, which can be exploited to enhance the classification accuracy. Our method also classifies the intrusion based on their types. It also classifies almost nine attacks as, Exploits, DoS, Backdoor, Generic, Worms, Analysis, Fuzzers, Shellcode, Reconnaissance. The experimental analysis is carried on the UNSW-NB15 big data dataset. The performance metrics precision, accuracy, recall, F-score are analyzed and compared with the state-of-art works such as BAMS-OIF, SAD, SMLsmBDA, and BDPM. The outcomes depict that the proposed work outperforms all the other existing works in terms of classification accuracy.

scholarly journals Quality Inspection of Digital Archives of Application and Installation in Power Business Expanding based on Artificial Intelligence Technology

2021 ◽  
Vol 2079 (1) ◽  
pp. 012030
Author(s):  
Haihong Liang ◽  
Ling Zeng ◽  
Xiaozhou Shen ◽  
Weiwei Shi ◽  
Jiujiao Cang
Keyword(s):  
Artificial Intelligence ◽  
Classification Accuracy ◽  
Detection Method ◽  
Detection Methods ◽  
Digital Archive ◽  
Quality Inspection ◽  
Digital Archives ◽  
Evaluation Standard ◽  
Quality Detection ◽  
Artificial Intelligence Technology

Abstract The existing quality detection methods of business expansion digital archives have the problem of fuzzy evaluation standard, which leads to low classification accuracy. This paper designs a quality detection method of business expansion Digital Archives based on artificial intelligence technology. The business characteristics of business development are extracted, the minimum business data unit is described, the digital archive catalogue database is established, the digital archive evaluation standard is defined, the text similarity is calculated, the user model is established, and the quality inspection mode is established by using artificial intelligence technology. Experimental results: the average classification accuracy of the designed method based on artificial intelligence technology and the other two quality detection methods is 55.763, 43.560 and 42.605, which proves that the quality detection method based on artificial intelligence technology has higher use value.

scholarly journals An Enhanced Classification Model for Likelihood of Zero-Day Attack Detection and Estimation

2021 ◽  
Vol 5 (4) ◽  
pp. 69-75
Author(s):  
Victor T. Emmah ◽  
Chidiebere Ugwu ◽  
Laeticia N. Onyejegbu
Keyword(s):  
Classification Accuracy ◽  
Daily Basis ◽  
Attack Detection ◽  
Classification Model ◽  
Reward Learning ◽  
Sensitive Information ◽  
Detection Systems ◽  
Vulnerability Model ◽  
Benchmark Datasets ◽  
And Training

The growing threat to sensitive information stored in computer systems and devices is becoming alarming. This is as a result of the proliferation of different malware created on a daily basis to cause zero-day attacks. Most of the malware whose signatures are known can easily be detected and blocked, however, the unknown malwares are the most dangerous. In this paper a zero-day vulnerability model based on deep-reinforcement learning is presented. The technique employs a Monte Carlo Based Pareto Rule (Deep-RL-MCB-PR) approach that exploits a reward learning and training feature with sparse feature generation and adaptive multi-layered recurrent prediction for the detection and subsequent mitigation of zero-day threats. The new model has been applied to the Kyoto benchmark datasets for intrusion detection systems, and compared to an existing system, that uses a multi-layer protection and a rule-based ranking (RBK) approach to detect a zero-day attack likelihood. Experiments were performed using the dataset, and simulation results show that the Deep-RL-MCB-PR technique when measured with the classification accuracy metrics, produced about 67.77%. The dataset was further magnified, and the result of classification accuracy showed about 75.84%. These results account for a better error response when compared to the RBK technique.

scholarly journals Research on GA-SVM Based Head-Motion Classification via Mechanomyography Feature Analysis

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1986 ◽  
Author(s):  
Yue Zhang ◽  
Jing Yu ◽  
Chunming Xia ◽  
Ke Yang ◽  
Heng Cao ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Kernel Functions ◽  
Support Vector ◽  
Motion Classification ◽  
Time Frequency ◽  
Feature Sets ◽  
Average Accuracy ◽  
Training Samples ◽  
And Training

This study investigated classification of six types of head motions using mechanomyography (MMG) signals. An unequal segmenting algorithm was adopted to segment the MMG signals generated by head motions. Three types of features (time domain, time-frequency domain and nonlinear dynamics) were extracted to construct five feature sets as candidate datasets for classification analysis. Genetic algorithm optimized support vector machine (GA-SVM) was used to classify the MMG signals. Three different kernel functions, different combinations of feature sets, different number of signal channels and training samples were selected for comparative analysis to evaluate the classification accuracy. Experimental results showed that the classifier had the best overall classification accuracy when using the radial basis function (RBF). Any combination of three different types of feature sets guaranteed an average accuracy of over 80%. In the case of the best combination (feature set 2 + 3 + 5), the classification accuracy was up to 88.2%. Using four channels to acquire MMG signal and no less than 60 training samples can assure a satisfactory classification accuracy.

scholarly journals COMPARISON OF BACKGROUND SUBTRACTION, SOBEL, ADAPTIVE MOTION DETECTION, FRAME DIFFERENCES, AND ACCUMULATIVE DIFFERENCES IMAGES ON MOTION DETECTION

SINERGI ◽  
2018 ◽  
Vol 22 (1) ◽  
pp. 51
Author(s):  
Dara Incam Ramadhan ◽  
Indah Permata Sari ◽  
Linna Oktaviana Sari
Keyword(s):  
Image Processing ◽  
Object Recognition ◽  
Motion Detection ◽  
Background Subtraction ◽  
Detection Method ◽  
Detection Methods ◽  
Lighting Conditions ◽  
Frame Difference ◽  
Best Value ◽  
Adaptive Motion

Nowadays, digital image processing is not only used to recognize motionless objects, but also used to recognize motions objects on video. One use of moving object recognition on video is to detect motion, which implementation can be used on security cameras. Various methods used to detect motion have been developed so that in this research compared some motion detection methods, namely Background Substraction, Adaptive Motion Detection, Sobel, Frame Differences and Accumulative Differences Images (ADI). Each method has a different level of accuracy. In the background substraction method, the result obtained 86.1% accuracy in the room and 88.3% outdoors. In the sobel method the result of motion detection depends on the lighting conditions of the room being supervised. When the room is in bright condition, the accuracy of the system decreases and when the room is dark, the accuracy of the system increases with an accuracy of 80%. In the adaptive motion detection method, motion can be detected with a condition in camera visibility there is no object that is easy to move. In the frame difference method, testing on RBG image using average computation with threshold of 35 gives the best value. In the ADI method, the result of accuracy in motion detection reached 95.12%.

scholarly journals Peningkatan Akurasi Klasifikasi Ketidaktepatan Waktu Kelulusan Mahasiswa Menggunakan Metode Boosting Neural Network

Jurnal Varian ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 95-102
Author(s):  
I Ketut Putu Suniantara ◽  
Gede Suwardika ◽  
Siti Soraya
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Feedforward Neural Network ◽  
Classification Model ◽  
Classification Problems ◽  
Accuracy Rate ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
And Training

Supervised learning in Machine learning is used to overcome classification problems with the Artificial Neural Network (ANN) approach. ANN has a few weaknesses in the operation and training process if the amount of data is large, resulting in poor classification accuracy. The results of the classification accuracy of Artificial Neural Networks will be better by using boosting. This study aims to develop a Boosting Feedforward Neural Network (FANN) classification model that can be implemented and used as a form of classification model that results in better accuracy, especially in the classification of the inaccuracy of Terbuka University students. The results showed the level of accuracy produced by the Feedforward Neural Network (FFNN) method had an accuracy rate of 72.93%. The application of boosting on FFN produces the best level of accuracy which is 74.44% at 500 iterations

Automatic accounting of Baikal diatomic algae: approaches and prospects

2019 ◽  
pp. 295-299
Author(s):  
Кonstantin А. Elshin ◽  
Еlena I. Molchanova ◽  
Мarina V. Usoltseva ◽  
Yelena V. Likhoshway
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
Classification Accuracy ◽  
Diatom Species ◽  
Bounding Box ◽  
Synedra Acus ◽  
And Training

Using the TensorFlow Object Detection API, an approach to identifying and registering Baikal diatom species Synedra acus subsp. radians has been tested. As a result, a set of images was formed and training was conducted. It is shown that аfter 15000 training iterations, the total value of the loss function was obtained equal to 0,04. At the same time, the classification accuracy is equal to 95%, and the accuracy of construction of the bounding box is also equal to 95%.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

scholarly journals Detection of Movement Events of Long-Track Speed Skating Using Wearable Inertial Sensors

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3649
Author(s):  
Yosuke Tomita ◽  
Tomoki Iizuka ◽  
Koichi Irisawa ◽  
Shigeyuki Imura
Keyword(s):  
Gold Standard ◽  
Detection Method ◽  
Standard Measure ◽  
Detection Methods ◽  
Phase Identification ◽  
Foot Pressure ◽  
Pressure System ◽  
Speed Skating ◽  
Stance Time ◽  
Integrated Detection

Inertial measurement units (IMUs) have been used increasingly to characterize long-track speed skating. We aimed to estimate the accuracy of IMUs for use in phase identification of long-track speed skating. Twelve healthy competitive athletes on a university long-track speed skating team participated in this study. Foot pressure, acceleration and knee joint angle were recorded during a 1000-m speed skating trial using the foot pressure system and IMUs. The foot contact and foot-off timing were identified using three methods (kinetic, acceleration and integrated detection) and the stance time was also calculated. Kinetic detection was used as the gold standard measure. Repeated analysis of variance, intra-class coefficients (ICCs) and Bland-Altman plots were used to estimate the extent of agreement between the detection methods. The stance time computed using the acceleration and integrated detection methods did not differ by more than 3.6% from the gold standard measure. The ICCs ranged between 0.657 and 0.927 for the acceleration detection method and 0.700 and 0.948 for the integrated detection method. The limits of agreement were between 90.1% and 96.1% for the average stance time. Phase identification using acceleration and integrated detection methods is valid for evaluating the kinematic characteristics during long-track speed skating.

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