An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information

As the COVID-19 pandemic rapidly spreads across the world, regrettably, misinformation and fake news related to COVID-19 have also spread remarkably. Such misinformation has confused people. To be able to detect such COVID-19 misinformation, an effective detection method should be applied to obtain more accurate information. This will help people and researchers easily differentiate between true and fake news. The objective of this research was to introduce an enhanced evolutionary detection approach to obtain better results compared with the previous approaches. The proposed approach aimed to reduce the number of symmetrical features and obtain a high accuracy after implementing three wrapper feature selections for evolutionary classifications using particle swarm optimization (PSO), the genetic algorithm (GA), and the salp swarm algorithm (SSA). The experiments were conducted on one of the popular datasets called the Koirala dataset. Based on the obtained prediction results, the proposed model revealed an optimistic and superior predictability performance with a high accuracy (75.4%) and reduced the number of features to 303. In addition, by comparison with other state-of-the-art classifiers, our results showed that the proposed detection method with the genetic algorithm model outperformed other classifiers in the accuracy.

Download Full-text

Extremal-Micro Genetic Algorithm Model for Time-Cost Optimization with Optimal Labour Productivity

Strojniški vestnik – Journal of Mechanical Engineering ◽

10.5545/sv-jme.2021.7406 ◽

2021 ◽

Vol 67 (12) ◽

pp. 682-691

Author(s):

Sivakumar A ◽

Bagath Singh N ◽

Sathiamurthi P ◽

Karthi Vinith K.S.

Keyword(s):

Genetic Algorithm ◽

Labour Productivity ◽

Cost Optimization ◽

Model Performance ◽

Time Cost ◽

Production Time ◽

Proposed Model ◽

Bus Body ◽

Micro Genetic Algorithm ◽

Genetic Algorithm Model

In a highly competitive manufacturing environment, it is critical to balance production time and cost simultaneously. Numerous attempts have been made to provide various solutions to strike a balance between these factors. However, more effort is still required to address these challenges in terms of labour productivity. This study proposes an integrated substitution and management improvement technique for enhancing the effectiveness of labour resources and equipment. Furthermore, in the context of time-cost optimization with optimal labour productivity, an extremal-micro genetic algorithm (Ex-μGA) model has been proposed. A real-world case from the labour-intensive medium-scale bus body fabricating industry is used to validate the proposed model performance. According to the results, the proposed model can optimize production time and cost by 34 % and 19 %, respectively, while maintaining optimal labour productivity. In addition, this study provides an alternative method for dealing with production parameter imbalances and assisting production managers in developing labour schedules more effectively.

Download Full-text

Prediction of Aero Engine Fault by Relative Vector Machine and Genetic Algorithm Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.998-999.1033 ◽

2014 ◽

Vol 998-999 ◽

pp. 1033-1036

Author(s):

Jian Wang ◽

Xiao Hu Duan ◽

Yan Li ◽

Peng Bai

Keyword(s):

Genetic Algorithm ◽

Effective Properties ◽

High Accuracy ◽

Experimental Results ◽

New Method ◽

Oil Analysis ◽

Maintenance Costs ◽

Vector Machines ◽

Aero Engine ◽

Genetic Algorithm Model

Diagnosis of engine fault is critical in reducing maintenance costs. A new method which incorporates hybrid relative vector machines and genetic algorithm (RVM-GA) was proposed to predict aero engine fault based on data of the spectrometric oil analysis. Experimental results show that it has a high accuracy and effective properties.

Download Full-text

Genetic Algorithm based Predictive model for COVID 19 – Theoretical Model based on an evolutionary approach

10.21203/rs.3.rs-107714/v2 ◽

2021 ◽

Author(s):

Srikanth Rangarajan ◽

Srikanth Poranki ◽

Bahgat Sammakia

Keyword(s):

Genetic Algorithm ◽

Real Time ◽

State Of The Art ◽

Contact Tracing ◽

Reproductive Number ◽

Time Data ◽

Proposed Model ◽

Fitness Value ◽

Real Time Data ◽

Novel Method

Abstract In this manuscript we propose a novel method that models the evolution, spread and transmission of COVID 19 pandemic. The proposed model is inspired partly from the evolutionary based state of the art genetic algorithm. The rate of virus evolution, spread and transmission of the COVID 19 and its associated recovery and death rate are modeled using the principle inspired from evolutionary algorithm. Furthermore, the interaction within a community and interaction outside the community is modeled. Using this model, the maximum healthcare threshold is fixed as a constraint. Our evolutionary based model distinguishes between individuals in the population depending on the severity of their symptoms/infection based on the fitness value of the individuals. There is a need to differentiate between virus infected diagnosed (Self isolated) and virus infected non-diagnosed (Highly interacting) sub populations/group. In this study the model results does not compare the number outcomes with any actual real time data based curves. However, the results from the model demonstrates that a strict lockdown, social-distancing measures in conjunction with more number of testing and contact tracing is required to flatten the ongoing COVID-19 pandemic curve. A reproductive number of 2.4 during the initial spread of virus is predicted from the model for the randomly considered population. The proposed model has the potential to be further fine-tuned and matched accurately against real time data.

Download Full-text

A Lightweight YOLOv4-Based Forestry Pest Detection Method Using Coordinate Attention and Feature Fusion

Entropy ◽

10.3390/e23121587 ◽

2021 ◽

Vol 23 (12) ◽

pp. 1587

Author(s):

Mingfeng Zha ◽

Wenbin Qian ◽

Wenlong Yi ◽

Jing Hua

Keyword(s):

Detection Method ◽

Feature Fusion ◽

State Of The Art ◽

Detection Methods ◽

Model Parameters ◽

Symmetric Structure ◽

Proposed Model ◽

Pest Detection ◽

Feature Information ◽

Small Targets

Traditional pest detection methods are challenging to use in complex forestry environments due to their low accuracy and speed. To address this issue, this paper proposes the YOLOv4_MF model. The YOLOv4_MF model utilizes MobileNetv2 as the feature extraction block and replaces the traditional convolution with depth-wise separated convolution to reduce the model parameters. In addition, the coordinate attention mechanism was embedded in MobileNetv2 to enhance feature information. A symmetric structure consisting of a three-layer spatial pyramid pool is presented, and an improved feature fusion structure was designed to fuse the target information. For the loss function, focal loss was used instead of cross-entropy loss to enhance the network’s learning of small targets. The experimental results showed that the YOLOv4_MF model has 4.24% higher mAP, 4.37% higher precision, and 6.68% higher recall than the YOLOv4 model. The size of the proposed model was reduced to 1/6 of that of YOLOv4. Moreover, the proposed algorithm achieved 38.62% mAP with respect to some state-of-the-art algorithms on the COCO dataset.

Download Full-text

POPPONENT: Highly accurate, individually and socially efficient opponent preference model in bilateral multi issue negotiations (Extended Abstract)

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/730 ◽

2017 ◽

Cited By ~ 3

Author(s):

Farhad Zafari ◽

Faria Nassiri-Mofakham

Keyword(s):

Gradient Descent ◽

State Of The Art ◽

Search Algorithm ◽

High Accuracy ◽

The State ◽

Preference Model ◽

Proposed Model ◽

And Performance ◽

Negotiation Outcome

In automated bilateral multi issue negotiations, two intelligent automated agents negotiate on behalf of their owners over many issues in order to reach an agreement. Modeling the opponent can excessively boost the performance of the agents and increase the quality of the negotiation outcome. State of the art models accomplish this by considering some assumptions about the opponent which restricts their applicability in real scenarios. In this paper, a less restricted technique where perceptron units (POPPONENT) are applied in modelling the preferences of the opponent is proposed. This model adopts a Multi Bipartite version of the Standard Gradient Descent search algorithm (MBGD) to find the best hypothesis, which is the best preference profile. In order to evaluate the accuracy and performance of this proposed opponent model, it is compared with the state of the art models available in the Genius repository. This results in the devised setting which approves the higher accuracy of POPPONENT compared to the most accurate state of the art model. Evaluating the model in the real world negotiation scenarios in the Genius framework also confirms its high accuracy in relation to the state of the art models in estimating the utility of offers. The findings here indicate that the proposed model is individually and socially efficient. This proposed MBGD method could also be adopted in similar practical areas of Artificial Intelligence.

Download Full-text

A New ANN-Particle Swarm Optimization with Center of Gravity (ANN-PSOCoG) Prediction Model for the Stock Market under the Effect of COVID-19

Scientific Programming ◽

10.1155/2021/6656150 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Razan Jamous ◽

Hosam ALRahhal ◽

Mohamed El-Darieby

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Stock Market ◽

Stock Markets ◽

Prediction Accuracy ◽

Processing Time ◽

High Accuracy ◽

Swarm Optimization ◽

Proposed Model ◽

Closing Price

Since the declaration of COVID-19 as a pandemic, the world stock markets have suffered huge losses prompting investors to limit or avoid these losses. The stock market was one of the businesses that were affected the most. At the same time, artificial neural networks (ANNs) have already been used for the prediction of the closing prices in stock markets. However, standalone ANN has several limitations, resulting in the lower accuracy of the prediction results. Such limitation is resolved using hybrid models. Therefore, a combination of artificial intelligence networks and particle swarm optimization for efficient stock market prediction was reported in the literature. This method predicted the closing prices of the shares traded on the stock market, allowing for the largest profit with the minimum risk. Nevertheless, the results were not that satisfactory. In order to achieve prediction with a high degree of accuracy in a short time, a new improved method called PSOCoG has been proposed in this paper. To design the neural network to minimize processing time and search time and maximize the accuracy of prediction, it is necessary to identify hyperparameter values with precision. PSOCoG has been employed to select the best hyperparameters in order to construct the best neural network. The created network was able to predict the closing price with high accuracy, and the proposed model ANN-PSOCoG showed that it could predict closing price values with an infinitesimal error, outperforming existing models in terms of error ratio and processing time. Using S&P 500 dataset, ANN-PSOCoG outperformed ANN-SPSO in terms of prediction accuracy by approximately 13%, SPSOCOG by approximately 17%, SPSO by approximately 20%, and ANN by approximately 25%. While using DJIA dataset, ANN-PSOCoG outperformed ANN-SPSO in terms of prediction accuracy by approximately 18%, SPSOCOG by approximately 24%, SPSO by approximately 33%, and ANN by approximately 42%. Besides, the proposed model is evaluated under the effect of COVID-19. The results proved the ability of the proposed model to predict the closing price with high accuracy where the values of MAPE, MAE, and RE were very small for S&P 500, GOLD, NASDAQ-100, and CANUSD datasets.

Download Full-text

SingleChannelNet: A model for Automatic Sleep Stage Classification with Raw Single-Channel EEG

10.1101/2020.09.21.306597 ◽

2020 ◽

Author(s):

Dongdong Zhou ◽

Guoqiang Hu ◽

Jiacheng Zhang ◽

Rui Yan ◽

Fan Li ◽

...

Keyword(s):

Single Channel ◽

State Of The Art ◽

Sleep Stage ◽

High Accuracy ◽

Sleep Staging ◽

Feature Representations ◽

Proposed Model ◽

Stage Classification ◽

Sleep Stage Classification ◽

Electroencephalogram Eeg

AbstractIn diagnosing sleep disorders, sleep stage classification is a very essential yet time-consuming process. Most of the existing state-of-the-art approaches rely on hand-crafted features and multi-modality polysomnography (PSG) data, where prior knowledge is compulsory and high computation cost can be expected. Besides, few studies are able to obtain high accuracy sleep staging using raw single-channel electroencephalogram (EEG). To overcome these shortcomings, this paper proposes an end-to-end framework with a deep neural network, namely SingleChannelNet, for automatic sleep stage classification based on raw single-channel EEG. The proposed model utilizes a 90s epoch as the textual input and employs two multi-convolution blocks and several max-average pooling layers to learn different scales of feature representations. To demonstrate the efficiency of the proposed model, we evaluate our model using different raw single-channel EEGs (C4/A1 and Fpz-Cz) on two different datasets (SHHS and Sleep-EDF datasets). Experimental results show that the proposed architecture can achieve better overall accuracy and Cohen’s kappa (SHHS: 89.2%-84.8%, Sleep-EDF: 89.4%-85%) compared with state-of-the-art approaches. Additionally, the proposed model can learn features automatically for sleep stage classification using different single-channel EEGs with distinct sampling rates from different datasets without using any hand-engineered features.

Download Full-text

Stance detection using improved whale optimization algorithm

Complex & Intelligent Systems ◽

10.1007/s40747-021-00294-0 ◽

2021 ◽

Author(s):

Avinash Chandra Pandey ◽

Vinay Anand Tikkiwal

Keyword(s):

Optimization Algorithm ◽

Multilayer Perceptron ◽

Detection Method ◽

Whale Optimization Algorithm ◽

Detection Methods ◽

Fake News ◽

Proposed Model ◽

Whale Optimization ◽

Textual Data ◽

Social Media Platforms

AbstractNews is a medium that notifies people about the events that had happened worldwide. The menace of fake news on online platforms is on the rise which may lead to unwanted events. The majority of fake news is spread through social media platforms, since these platforms have a great reach. To identify the credibility of the news, various spam detection methods are generally used. In this work, a new stance detection method has been proposed for identifying the stance of fake news. The proposed stance detection method is based on the capabilities of an improved whale optimization algorithm and a multilayer perceptron. In the proposed model, weights and biases of the multilayer perceptron are updated using an improved whale optimization algorithm. The efficacy of the proposed optimized neural network has been tested on five benchmark stance detection datasets. The proposed model shows better results over all the considered datasets. The proposed approach has theoretical implications for further studies to examine the textual data. Besides, the proposed method also has practical implications for developing systems that can result conclusive reviews on any social problems.

Download Full-text

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset207430 ◽

2020 ◽

pp. 129-139

Author(s):

Weihai Sun ◽

Lemei Han

Keyword(s):

Fault Detection ◽

Signal Analysis ◽

Energy Analysis ◽

Detection Method ◽

Sound Signal ◽

Practical Significance ◽

Time Frequency ◽

Detection Approach ◽

Machine Fault ◽

Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

Download Full-text