scholarly journals Pear Defect Detection Method Based on ResNet and DCGAN

Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 397
Author(s):  
Yan Zhang ◽  
Shiyun Wa ◽  
Pengshuo Sun ◽  
Yaojun Wang
Keyword(s):  
Defect Detection ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Detection System ◽  
Implementation Process ◽  
Support Vector ◽  
Detection Accuracy ◽  
K Nearest Neighbor ◽  
Generation Network ◽  
Low Efficiency

To address the current situation, in which pear defect detection is still based on a workforce with low efficiency, we propose the use of the CNN model to detect pear defects. Since it is challenging to obtain defect images in the implementation process, a deep convolutional adversarial generation network was used to augment the defect images. As the experimental results indicated, the detection accuracy of the proposed method on the 3000 validation set was as high as 97.35%. Variant mainstream CNNs were compared to evaluate the model’s performance thoroughly, and the top performer was selected to conduct further comparative experiments with traditional machine learning methods, such as support vector machine algorithm, random forest algorithm, and k-nearest neighbor clustering algorithm. Moreover, the other two varieties of pears that have not been trained were chosen to validate the robustness and generalization capability of the model. The validation results illustrated that the proposed method is more accurate than the commonly used algorithms for pear defect detection. It is robust enough to be generalized well to other datasets. In order to allow the method proposed in this paper to be applied in agriculture, an intelligent pear defect detection system was built based on an iOS device.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

scholarly journals A Deep Learning Based Method for the Non-Destructive Measuring of Rock Strength through Hammering Sound

2019 ◽  
Vol 9 (17) ◽  
pp. 3484
Author(s):  
Shuai Han ◽  
Heng Li ◽  
Mingchao Li ◽  
Timothy Rose
Keyword(s):  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Rock Strength ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Strength Measurement ◽  
Regression Algorithms ◽  
Almost All ◽  
The Relationship ◽  
Non Destructive

Hammering rocks of different strengths can make different sounds. Geological engineers often use this method to approximate the strengths of rocks in geology surveys. This method is quick and convenient but subjective. Inspired by this problem, we present a new, non-destructive method for measuring the surface strengths of rocks based on deep neural network (DNN) and spectrogram analysis. All the hammering sounds are transformed into spectrograms firstly, and a clustering algorithm is presented to filter out the outliers of the spectrograms automatically. One of the most advanced image classification DNN, the Inception-ResNet-v2, is then re-trained with the spectrograms. The results show that the training accurate is up to 94.5%. Following this, three regression algorithms, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) are adopted to fit the relationship between the outputs of the DNN and the strength values. The tests show that KNN has the highest fitting accuracy, and SVM has the strongest generalization ability. The strengths (represented by rebound values) of almost all the samples can be predicted within an error of [−5, 5]. Overall, the proposed method has great potential in supporting the implementation of efficient rock strength measurement methods in the field.

scholarly journals Fatigue Detection on Face Image Using FaceNet Algorithm and K-Nearest Neighbor Classifier

2021 ◽  
Vol 7 (1) ◽  
pp. 22
Author(s):  
Faisal Dharma Adhinata ◽  
Diovianto Putra Rakhmadani ◽  
Danur Wijayanto
Keyword(s):  
Nearest Neighbor ◽  
Detection System ◽  
Well Being ◽  
Extraction Process ◽  
Polynomial Kernel ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Time Data ◽  
Multiclass Svm ◽  
Fatigue Detection

Background: The COVID-19 pandemic has made people spend more time on online meetings more than ever. The prolonged time looking at the monitor may cause fatigue, which can subsequently impact the mental and physical health. A fatigue detection system is needed to monitor the Internet users well-being. Previous research related to the fatigue detection system used a fuzzy system, but the accuracy was below 85%. In this research, machine learning is used to improve accuracy.Objective: This research examines the combination of the FaceNet algorithm with either k-nearest neighbor (K-NN) or multiclass support vector machine (SVM) to improve the accuracy.Methods: In this study, we used the UTA-RLDD dataset. The features used for fatigue detection come from the face, so the dataset is segmented using the Haar Cascades method, which is then resized. The feature extraction process uses FaceNet's pre-trained algorithm. The extracted features are classified into three classes—focused, unfocused, and fatigue—using the K-NN or multiclass SVM method.Results: The combination between the FaceNet algorithm and K-NN, with a value of  resulted in a better accuracy than the FaceNet algorithm with multiclass SVM with the polynomial kernel (at 94.68% and 89.87% respectively). The processing speed of both combinations of methods has allowed for real-time data processing.Conclusion: This research provides an overview of methods for early fatigue detection while working at the computer so that we can limit staring at the computer screen too long and switch places to maintain the health of our eyes. 

scholarly journals Received Signal Strength-Based Indoor Localization Using Hierarchical Classification

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1067 ◽  
Author(s):  
Chenbin Zhang ◽  
Ningning Qin ◽  
Yanbo Xue ◽  
Le Yang
Keyword(s):  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Hierarchical Classification ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Position Information ◽  
Area Of Interest

Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. Wi-Fi received signal strength (RSS)-based indoor localization techniques have attracted extensive attentions because Wi-Fi access points (APs) are widely deployed and we can obtain the Wi-Fi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classification-based method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved K-Means clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the K-Nearest Neighbor (KNN) algorithm and support vector machine (SVM) with the one-versus-one strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in real-world environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods.

Snow Detection using In-Vehicle Video Camera with Texture-Based Image Features Utilizing K-Nearest Neighbor, Support Vector Machine, and Random Forest

2019 ◽  
Vol 2673 (8) ◽  
pp. 221-232 ◽  
Author(s):  
Md Nasim Khan ◽  
Mohamed M. Ahmed
Keyword(s):  
Real Time ◽  
Prediction Accuracy ◽  
Nearest Neighbor ◽  
Detection System ◽  
Weather Conditions ◽  
Video Camera ◽  
Image Features ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Weather Information

Snowfall negatively affects pavement and visibility conditions, making it one of the major causes of motor vehicle crashes in winter weather. Therefore, providing drivers with real-time roadway weather information during adverse weather is crucial for safe driving. Although road weather stations can provide weather information, these stations are expensive and often do not represent real-time trajectory-level weather information. The main motivation of this study was to develop an affordable in-vehicle snow detection system which can provide trajectory-level weather information in real time. The system utilized SHRP2 Naturalistic Driving Study video data and was based on machine learning techniques. To train the snow detection models, two texture-based image features including gray level co-occurrence matrix (GLCM) and local binary pattern (LBP), and three classification algorithms: support vector machine (SVM), k-nearest neighbor (K-NN), and random forest (RF) were used. The analysis was done on an image dataset consisting of three weather conditions: clear, light snow, and heavy snow. While the highest overall prediction accuracy of the models based on the GLCM features was found to be around 86%, the models considering the LBP based features provided a much higher prediction accuracy of 96%. The snow detection system proposed in this study is cost effective, does not require a lot of technical support, and only needs a single video camera. With the advances in smartphone cameras, simple mobile apps with proper data connectivity can effectively be used to detect roadway weather conditions in real time with reasonable accuracy.

Continuous wide spectrum odor sensing for electronic nose system

Sensor Review ◽  
2018 ◽  
Vol 38 (2) ◽  
pp. 223-230
Author(s):  
Wenli Zhang ◽  
Fengchun Tian ◽  
An Song ◽  
Zhenzhen Zhao ◽  
Youwen Hu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Wide Spectrum ◽  
Least Square ◽  
Support Vector ◽  
Detection Accuracy ◽  
K Nearest Neighbor ◽  
Sensing Element ◽  
Content Type ◽  
System Errors

Purpose This paper aims to propose an odor sensing system based on wide spectrum for e-nose, based on comprehensive analysis on the merits and drawbacks of current e-nose. Design/methodology/approach The wide spectral light is used as the sensing medium in the e-nose system based on continuous wide spectrum (CWS) odor sensing, and the sensing response of each sensing element is the change of light intensity distribution. Findings Experimental results not only verify the feasibility and effectiveness of the proposed system but also show the effectiveness of least square support vector machine (LSSVM) in eliminating system errors. Practical implications Theoretical model of the system was constructed, and experimental tests were carried out by using NO2 and SO2. System errors in the test data were eliminated using the LSSVM, and the preprocessed data were classified by euclidean distance to centroids (EDC), k-nearest neighbor (KNN), support vector machine (SVM), LSSVM, respectively. Originality/value The system not only has the advantages of current e-nose but also realizes expansion of sensing array by means of light source and the spectrometer with their wide spectrum, high resolution characteristics which improve the detection accuracy and realize real-time detection.

scholarly journals Classification model for accuracy and intrusion detection using machine learning approach

2021 ◽  
Vol 7 ◽  
pp. e437
Author(s):  
Arushi Agarwal ◽  
Purushottam Sharma ◽  
Mohammed Alshehri ◽  
Ahmed A. Mohamed ◽  
Osama Alfarraj
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Nearest Neighbor ◽  
Performance Metrics ◽  
Detection System ◽  
Confusion Matrix ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
K Nearest Neighbor

In today’s cyber world, the demand for the internet is increasing day by day, increasing the concern of network security. The aim of an Intrusion Detection System (IDS) is to provide approaches against many fast-growing network attacks (e.g., DDoS attack, Ransomware attack, Botnet attack, etc.), as it blocks the harmful activities occurring in the network system. In this work, three different classification machine learning algorithms—Naïve Bayes (NB), Support Vector Machine (SVM), and K-nearest neighbor (KNN)—were used to detect the accuracy and reducing the processing time of an algorithm on the UNSW-NB15 dataset and to find the best-suited algorithm which can efficiently learn the pattern of the suspicious network activities. The data gathered from the feature set comparison was then applied as input to IDS as data feeds to train the system for future intrusion behavior prediction and analysis using the best-fit algorithm chosen from the above three algorithms based on the performance metrics found. Also, the classification reports (Precision, Recall, and F1-score) and confusion matrix were generated and compared to finalize the support-validation status found throughout the testing phase of the model used in this approach.

scholarly journals Intrusion detection system based on machine learning techniques

2021 ◽  
Vol 23 (2) ◽  
pp. 953
Author(s):  
Musaab Riyadh ◽  
Dina Riadh Alshibani
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Special Kind ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Detection Accuracy ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Malicious Activity

Recently, the data flow over the internet has exponentially increased due to the massive growth of computer networks connected to it. Some of these data can be classified as a malicious activity which cannot be captured by firewalls and anti-malwares. Due to this, the intrusion detection systems are urgent need in order to recognize malicious activity to keep data integrity and availability. In this study, an intrusion detection system based on cluster feature concepts and KNN classifier has been suggested to handle the various challenges issues in data such as in complete data, mixed-type and noise data. To streng then the proposed system a special kind of patterns similarity measures are supported to deal with these types of challenges. The experimental results show that the classification accuracy of the suggested system is better than K-nearest neighbor (KNN) and support vector machine classifiers when processing incomplete data set, inspite of droping down the overall detection accuracy.

scholarly journals An Effective Heart Disease Prediction Model based on Machine Learning Techniques

2020 ◽  
Author(s):  
Rony Chowdhury Ripan ◽  
Iqbal H. Sarker ◽  
Md. Hasan Furhad ◽  
Md Musfique Anwar ◽  
Mohammed Moshiul Hoque
Keyword(s):  
Heart Disease ◽  
Prediction Model ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Healthcare Data ◽  
Learning Techniques

This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an \textit{optimal} value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

scholarly journals Detecting Unfavorable Driving States in Electroencephalography Based on a PCA Sample Entropy Feature and Multiple Classification Algorithms

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1248 ◽  
Author(s):  
Tao Zhang ◽  
Hong Wang ◽  
Jichi Chen ◽  
Enqiu He
Keyword(s):  
Traffic Accidents ◽  
Nearest Neighbor ◽  
Detection System ◽  
Principal Component ◽  
Sample Entropy ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Feature Sets ◽  
Multiple Classification

Unfavorable driving states can cause a large number of vehicle crashes and are significant factors in leading to traffic accidents. Hence, the aim of this research is to design a robust system to detect unfavorable driving states based on sample entropy feature analysis and multiple classification algorithms. Multi-channel Electroencephalography (EEG) signals are recorded from 16 participants while performing two types of driving tasks. For the purpose of selecting optimal feature sets for classification, principal component analysis (PCA) is adopted for reducing dimensionality of feature sets. Multiple classification algorithms, namely, K nearest neighbor (KNN), decision tree (DT), support vector machine (SVM) and logistic regression (LR) are employed to improve the accuracy of unfavorable driving state detection. We use 10-fold cross-validation to assess the performance of the proposed systems. It is found that the proposed detection system, based on PCA features and the cubic SVM classification algorithm, shows robustness as it obtains the highest accuracy of 97.81%, sensitivity of 96.93%, specificity of 98.73% and precision of 98.75%. Experimental results show that the system we designed can effectively monitor unfavorable driving states.

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