scholarly journals Traffic Flow Anomaly Detection Based on Robust Ridge Regression with Particle Swarm Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Mingzhu Tang ◽  
Xiangwan Fu ◽  
Huawei Wu ◽  
Qi Huang ◽  
Qi Zhao
Keyword(s):  
Anomaly Detection ◽  
Traffic Flow ◽  
Ridge Regression ◽  
Cross Validation ◽  
Sliding Window ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Feature Sets ◽  
Proposed Model ◽  
Fold Cross Validation

Traffic flow anomaly detection is helpful to improve the efficiency and reliability of detecting fault behavior and the overall effectiveness of the traffic operation. The data detected by the traffic flow sensor contains a lot of noise due to equipment failure, environmental interference, and other factors. In the case of large traffic flow data noises, a traffic flow anomaly detection method based on robust ridge regression with particle swarm optimization (PSO) algorithm is proposed. Feature sets containing historical characteristics with a strong linear correlation and statistical characteristics using the optimal sliding window are constructed. Then by providing the feature sets inputs to the PSO-Huber-Ridge model and the model outputs the traffic flow. The Huber loss function is recommended to reduce noise interference in the traffic flow. The L2 regular term of the ridge regression is employed to reduce the degree of overfitting of the model training. A fitness function is constructed, which can balance the relative size between the k-fold cross-validation root mean square error and the k-fold cross-validation average absolute error with the control parameter η to improve the optimization efficiency of the optimization algorithm and the generalization ability of the proposed model. The hyperparameters of the robust ridge regression forecast model are optimized by the PSO algorithm to obtain the optimal hyperparameters. The traffic flow data set is used to train and validate the proposed model. Compared with other optimization methods, the proposed model has the lowest RMSE, MAE, and MAPE. Finally, the traffic flow that forecasted by the proposed model is used to perform anomaly detection. The abnormality of the error between the forecasted value and the actual value is detected by the abnormal traffic flow threshold based on the sliding window. The experimental results verify the validity of the proposed anomaly detection model.

Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

2021 ◽  
pp. 1-17
Author(s):  
J. Shobana ◽  
M. Murali
Keyword(s):  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Contextual Information ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Proposed Model

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

scholarly journals Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5609 ◽  
Author(s):  
Shahab S. Band ◽  
Saeid Janizadeh ◽  
Subodh Chandra Pal ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Gully Erosion ◽  
Slope Aspect ◽  
Swarm Optimization ◽  
Proposed Model ◽  
Deep Learning Neural Network

This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.

An Adaptive Chaos Quantum-Behaved Particle Swarm Optimization Algorithm for Real-Time Dispatch in Power System with Wind Generation

2014 ◽  
Vol 1070-1072 ◽  
pp. 297-302
Author(s):  
Zhi Kui Wu ◽  
Chang Hong Deng ◽  
Yong Xiao ◽  
Wei Xing Zhao ◽  
Qiu Shi Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Real Time ◽  
Wind Power ◽  
Wind Farm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Good Convergence ◽  
Proposed Model

A real-time dispatch (RTD) model for wind power incorporated power system aimed at maximizing wind power utilization and minimizing fuel cost is proposed in this paper. To cope with the prematurity and local convergence of conventional particle swarm optimization (PSO) algorithm, a novel adaptive chaos quantum-behaved particle swarm optimization (ACQPSO) algorithm is put forward. The adaptive inertia weight and chaotic perturbation mechanism are employed to improve the particle’s search efficiency. Numerical simulation on a 10 unit system with a wind farm demonstrates that the proposed model can maximize wind power utilization while ensuring the safe and economic operation of the power system. The proposed ACQPSO algorithm is of good convergence quality and the computation speed can meet the requirement of RTD.

scholarly journals An allotment of H1B work visa in USA using machine learning

2018 ◽  
Vol 7 (2.27) ◽  
pp. 93
Author(s):  
Pooja Thakur ◽  
Mandeep Singh ◽  
Harpreet Singh ◽  
Prashant Singh Rana
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Foreign Workers ◽  
Classification Models ◽  
Single Model ◽  
Proposed Model ◽  
The Status ◽  
Accuracy Parameter ◽  
Lottery System ◽  
Fold Cross Validation

H1B work visas are utilized to contract profoundly talented outside specialists at low wages in America which help firms and impact U.S economy unfavorably. In excess of 100,000 individuals for every year apply tight clamp for higher examinations and also to work and number builds each year. Selections of foreigners are done by lottery system which doesn’t follow any full proofed method and so results cause a loophole between US-based and foreign workers. We endeavor to examine petitions filled from 2015 to 2017 with the goal that a superior prediction model need to develop using machine learning which helps to foresee the aftereffect of the request of ahead of time which shows whether an appeal to is commendable or not. In this work, we use seven classification models Decision tree, C5.0, Random Forest, Naïve Bayes, Neural Network and SVM which predict the status of a petition as certified, denied, withdrawal or certified with-drawls. The predictions of these models are checked on accuracy parameter. It is found that C5.0 outperform with the best accuracy of 94.62 as a single model but proposed model gives better results of 95.4 accuracies which is built by machine ensemble method and this is validated by 10 fold cross-validation. 

A weighted coefficient model for estimation of Australian daily soil temperature at depths of 5cm to 100cm based on air temperature and rainfall

Soil Research ◽  
2011 ◽  
Vol 49 (4) ◽  
pp. 305 ◽  
Author(s):  
Brian Horton ◽  
Ross Corkrey
Keyword(s):  
Soil Temperature ◽  
Air Temperature ◽  
Cross Validation ◽  
Soil Depth ◽  
Mean Square ◽  
Air Temperatures ◽  
Proposed Model ◽  
Soil Temperatures ◽  
Weather Stations ◽  
Fold Cross Validation

Soil temperatures are related to air temperature and rainfall on the current day and preceding days, and this can be expressed in a non-linear relationship to provide a weighted value for the effect of air temperature or rainfall based on days lag and soil depth. The weighted minimum and maximum air temperatures and weighted rainfall can then be combined with latitude and a seasonal function to estimate soil temperature at any depth in the range 5–100 cm. The model had a root mean square deviation of 1.21–1.85°C for minimum, average, and maximum soil temperature for all weather stations in Australia (mainland and Tasmania), except for maximum soil temperature at 5 and 10 cm, where the model was less precise (3.39° and 2.52°, respectively). Data for this analysis were obtained from 32–40 Bureau of Meteorology weather stations throughout Australia and the proposed model was validated using 5-fold cross-validation.

scholarly journals Analisis Sentimen Review Restoran menggunakan Algoritma Naive Bayes berbasis Particle Swarm Optimization

2019 ◽  
Vol 6 (1) ◽  
pp. 23-28
Author(s):  
Retno Sari
Keyword(s):  
Particle Swarm Optimization ◽  
Cross Validation ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Swarm Optimization ◽  
Fold Cross Validation

Terdapatnya aplikasi yang memudahkan untuk mengetahui ulasan dari suatu tempat atau makanan membuat pembaca dengan mudah menentukan tempat untuk mereka berwisata kuliner. Ulasan yang diberikan terdiri dari ulasan positif dan ulasan negatif. Algoritma Naive Bayes berbasis Particle Swarm optimization dilakukan untuk mengetahui apakah terdapat peningkatan akurasi. Dataset yang digunakan berupa review restoran yang dibagi menjadi 2 class yaitu class positif dan class negatif, data diujikan menggunakan 10 Fold Cross Validation. Analisis sentimen review restoran menggunakan Algoritma Naive Bayes berbasis Particle Swarm Optimization menghasilkan akurasi sebesar 82.45%. Hasil ini lebih baik dibandingkan dengan menggunakan algoritma Naive Bayes saja yang menghasilkan akurasi sebesar 74.34%.

scholarly journals UbiSites-SRF: Ubiquitination Sites Prediction Using Statistical Moment with Random Forest Approach

2021 ◽  
Author(s):  
Shazia Murad ◽  
Arwa Mashat ◽  
Alia Mahfooz ◽  
Sher Afzal Khan ◽  
Omar Barukab
Keyword(s):  
Random Forest ◽  
Computational Models ◽  
Cross Validation ◽  
Statistical Moment ◽  
Site Directed Mutagenesis ◽  
Classification Model ◽  
Jackknife Test ◽  
Proposed Model ◽  
Protein Transcription ◽  
Fold Cross Validation

Abstract Ubiquitination is the process that supports the growth and development of eukaryotic and prokaryotic organisms. It is helpful in regulating numerous functions such as the cell division cycle, caspase-mediated cell death, maintenance of protein transcription, signal transduction, and restoration of DNA damage. Because of these properties, its identification is essential to understand its molecular mechanism. Some traditional methods such as mass spectrometry and site-directed mutagenesis are used for this purpose, but they are tedious and time consuming. In order to overcome such limitations, interest in computational models of this type of identification is therefore being developed. In this study, an accurate and efficient classification model for identifying ubiquitination sites was constructed. The proposed model uses statistical moments for feature extraction along with random forest for classification. Three sets of ubiquitination are used to train and test the model. The model is assessed through 10-fold cross-validation and jackknife tests. We achieved a 10-fold accuracy of 100% for dataset-1, 99.88% for dataset-2 and 99.84% for the dataset-3, while with Jackknife test we got 100% for the dataset-1, 99.91% for dataset-2 and 99.99%. for the dataset-3. The results obtained are almost the maximum, which is far better as compared to the pre-existing models available in the literature.

scholarly journals A Hybrid Network Anomaly Detection system using Glowworm Swarm Optimization with Principal Component Analysis

2021 ◽  
Author(s):  
Rashmita Khilar ◽  
K. Mariyappan ◽  
Mary Subaja Christo ◽  
J Amutharaj ◽  
Anitha T ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Detection Rate ◽  
Detection System ◽  
Principal Component ◽  
Swarm Optimization ◽  
Detection Model ◽  
Glowworm Swarm Optimization ◽  
Proposed Model

Abstract The security of the network is a significant issue in any distributed system. For that intrusion detection system (IDS), have been proposed for securing the network from malicious activities. This research is proposed to design and develop an anomaly detection model for detecting attacks and unusual activities in IoT networks. The primary objective of this research is to design efficient IDS for IoT network. The intrusion detection plays an essential role in detecting different attacks on IoT and enhances the performance of the IoT. In this research, anomaly detection in IoT networks using glowworm swarm optimization (GSO) algorithm with principal component analysis (PCA) is proposed. However, the proposed model is metaheuristic algorithm-based anomaly detection model to identify attacks by using the NSL-KDD dataset. The GSO algorithm based on PCA is implemented to perform the anomaly detection. For feature extraction, the PCA is used, and for classification, the GSO algorithm is used. For performance analysis, various parameters like accuracy, precision, recall, detection rate and FAR are evaluated. For normal class the proposed model achieved 94.14% accuracy, for DoS 95.52%, for R2L 93.15%, for probe 93.50% and for U2R 88.62% accuracy. Overall the detection rate was 94.08% and FAR was 3.41%.

scholarly journals Recommender System for Term Deposit Likelihood Prediction using Cross-validated Neural Network

2021 ◽  
pp. 21-28
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Proposed Model ◽  
Tree Classifier ◽  
Customer Perspective ◽  
Fold Cross Validation

For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer details are influential while considering possibilities of taking term deposit subscription. An automated system is provided in this paper that approaches towards prediction of term deposit investment possibilities in advance. Neural network along with stratified 10-fold cross-validation methodology is proposed as predictive model which is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concluded that proposed model provides significant prediction results over other baseline models with an accuracy of 88.32% and MSE of 0.1168.

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