A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

The vehicular adhoc network (VANET) is an emerging research topic in the intelligent transportation system that furnishes essential information to the vehicles in the network. Nearly 150 thousand people are affected by the road accidents that must be minimized, and improving safety is required in VANET. The prediction of traffic congestions plays a momentous role in minimizing accidents in roads and improving traffic management for people. However, the dynamic behavior of the vehicles in the network degrades the rendition of deep learning models in predicting the traffic congestion on roads. To overcome the congestion problem, this paper proposes a new hybrid boosted long short-term memory ensemble (BLSTME) and convolutional neural network (CNN) model that ensemble the powerful features of CNN with BLSTME to negotiate the dynamic behavior of the vehicle and to predict the congestion in traffic effectively on roads. The CNN extracts the features from traffic images, and the proposed BLSTME trains and strengthens the weak classifiers for the prediction of congestion. The proposed model is developed using Tensor flow python libraries and are tested in real traffic scenario simulated using SUMO and OMNeT++. The extensive experimentations are carried out, and the model is measured with the performance metrics likely prediction accuracy, precision, and recall. Thus, the experimental result shows 98% of accuracy, 96% of precision, and 94% of recall. The results complies that the proposed model clobbers the other existing algorithms by furnishing 10% higher than deep learning models in terms of stability and performance.

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An Investigation into the Application of Deep Learning in the Detection and Mitigation of DDOS Attack on SDN Controllers

Technologies ◽

10.3390/technologies9010014 ◽

2021 ◽

Vol 9 (1) ◽

pp. 14

Author(s):

James Dzisi Gadze ◽

Akua Acheampomaa Bamfo-Asante ◽

Justice Owusu Agyemang ◽

Henry Nunoo-Mensah ◽

Kwasi Adu-Boahen Opare

Keyword(s):

Deep Learning ◽

Network Architecture ◽

Learning Algorithm ◽

Single Point ◽

Denial Of Service ◽

Specific Work ◽

Learning Models ◽

Ddos Attack ◽

Deep Learning Algorithm ◽

Proposed Model

Software-Defined Networking (SDN) is a new paradigm that revolutionizes the idea of a software-driven network through the separation of control and data planes. It addresses the problems of traditional network architecture. Nevertheless, this brilliant architecture is exposed to several security threats, e.g., the distributed denial of service (DDoS) attack, which is hard to contain in such software-based networks. The concept of a centralized controller in SDN makes it a single point of attack as well as a single point of failure. In this paper, deep learning-based models, long-short term memory (LSTM) and convolutional neural network (CNN), are investigated. It illustrates their possibility and efficiency in being used in detecting and mitigating DDoS attack. The paper focuses on TCP, UDP, and ICMP flood attacks that target the controller. The performance of the models was evaluated based on the accuracy, recall, and true negative rate. We compared the performance of the deep learning models with classical machine learning models. We further provide details on the time taken to detect and mitigate the attack. Our results show that RNN LSTM is a viable deep learning algorithm that can be applied in the detection and mitigation of DDoS in the SDN controller. Our proposed model produced an accuracy of 89.63%, which outperformed linear-based models such as SVM (86.85%) and Naive Bayes (82.61%). Although KNN, which is a linear-based model, outperformed our proposed model (achieving an accuracy of 99.4%), our proposed model provides a good trade-off between precision and recall, which makes it suitable for DDoS classification. In addition, it was realized that the split ratio of the training and testing datasets can give different results in the performance of a deep learning algorithm used in a specific work. The model achieved the best performance when a split of 70/30 was used in comparison to 80/20 and 60/40 split ratios.

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A Convolutional Neural Network based System to Detect Plant Disease

Webology ◽

10.14704/web/v18si04/web18175 ◽

2021 ◽

Vol 18 (Special Issue 04) ◽

pp. 944-962

Author(s):

K. Niha ◽

Dr.S. Amutha ◽

Dr. Aisha Banu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Neural Network Model ◽

Plant Disease ◽

Performance Metrics ◽

Plant Diseases ◽

Detection Accuracy ◽

Learning Models ◽

Proposed Model

Disease in plants are a great challenge in the advancement of agriculture which affects farmers yield and the plants. In this modern research deep learning models got a spot light by increasing plant detection accuracy and classification. The proposed CNN (Convolutional Neural Network) model detect seven plant diseases out of healthy leaf, where the dataset considered in this work contain 8685 leaf images from Plant Village Dataset. The proposed modals performance are evaluated with respect to the performance metrics (F1 score, Precision and Recall) and are compared with SVM and ANN. Where the proposed CNN model outperforms the rest with the accuracy of 96.2% and the F1 score greater than 95%. The feasibility of the proposed model in plant detection and classification may provide a solution to the problem faced by farmers.

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Artificial Intelligence-Based Digital Image Steganalysis

Security and Communication Networks ◽

10.1155/2021/9923389 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Ahmed I. Iskanderani ◽

Ibrahim M. Mehedi ◽

Abdulah Jeza Aljohani ◽

Mohammad Shorfuzzaman ◽

Farzana Akther ◽

...

Keyword(s):

Deep Learning ◽

Performance Metrics ◽

Fitness Function ◽

Model Performance ◽

Learning Models ◽

Image Steganalysis ◽

Proposed Model ◽

Performance Analyses ◽

Nondominated Sorting ◽

F Measure

Recently, deep learning-based models are being extensively utilized for steganalysis. However, deep learning models suffer from overfitting and hyperparameter tuning issues. Therefore, in this paper, an efficient θ -nondominated sorting genetic algorithm- ( θ NSGA-) III based densely connected convolutional neural network (DCNN) model is proposed for image steganalysis. θ NSGA-III is utilized to tune the initial parameters of DCNN model. It can control the accuracy and f-measure of the DCNN model by utilizing them as the multiobjective fitness function. Extensive experiments are drawn on STEGRT1 dataset. Comparison of the proposed model is also drawn with the competitive steganalysis model. Performance analyses reveal that the proposed model outperforms the existing steganalysis models in terms of various performance metrics.

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A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-2326 ◽

2021 ◽

pp. 182-189

Author(s):

S. Arokiaraj ◽

Dr. N. Viswanathan

Keyword(s):

Deep Learning ◽

Internet Of Things ◽

Short Term Memory ◽

The Other ◽

Learning Models ◽

Computational Overhead ◽

Temporal Features ◽

Human Movements ◽

Proposed Model ◽

Iot Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.

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Pansformers: Transformer-Based Self-Attention Network for Pansharpening

10.36227/techrxiv.17153228.v1 ◽

2021 ◽

Author(s):

Nithin G R ◽

Nitish Kumar M ◽

Venkateswaran Narasimhan ◽

Rajanikanth Kakani ◽

Ujjwal Gupta ◽

...

Keyword(s):

Deep Learning ◽

High Resolution ◽

Performance Metrics ◽

Satellite Image ◽

Image Data ◽

Landsat 8 ◽

Learning Models ◽

Attention Network ◽

Panchromatic Image ◽

Recent Success

Pansharpening is the task of creating a High-Resolution Multi-Spectral Image (HRMS) by extracting and infusing pixel details from the High-Resolution Panchromatic Image into the Low-Resolution Multi-Spectral (LRMS). With the boom in the amount of satellite image data, researchers have replaced traditional approaches with deep learning models. However, existing deep learning models are not built to capture intricate pixel-level relationships. Motivated by the recent success of self-attention mechanisms in computer vision tasks, we propose Pansformers, a transformer-based self-attention architecture, that computes band-wise attention. A further improvement is proposed in the attention network by introducing a Multi-Patch Attention mechanism, which operates on non-overlapping, local patches of the image. Our model is successful in infusing relevant local details from the Panchromatic image while preserving the spectral integrity of the MS image. We show that our Pansformer model significantly improves the performance metrics and the output image quality on imagery from two satellite distributions IKONOS and LANDSAT-8.

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Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

Electronics ◽

10.3390/electronics9030445 ◽

2020 ◽

Vol 9 (3) ◽

pp. 445 ◽

Cited By ~ 9

Author(s):

Laith Alzubaidi ◽

Omran Al-Shamma ◽

Mohammed A. Fadhel ◽

Laith Farhan ◽

Jinglan Zhang ◽

...

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Transfer Learning ◽

Cancer Diagnosis ◽

Classification Accuracy ◽

Cancer Classification ◽

Learning Models ◽

Proposed Model ◽

Domain Transfer ◽

Domain Transfer Learning

Breast cancer is a significant factor in female mortality. An early cancer diagnosis leads to a reduction in the breast cancer death rate. With the help of a computer-aided diagnosis system, the efficiency increased, and the cost was reduced for the cancer diagnosis. Traditional breast cancer classification techniques are based on handcrafted features techniques, and their performance relies upon the chosen features. They also are very sensitive to different sizes and complex shapes. However, histopathological breast cancer images are very complex in shape. Currently, deep learning models have become an alternative solution for diagnosis, and have overcome the drawbacks of classical classification techniques. Although deep learning has performed well in various tasks of computer vision and pattern recognition, it still has some challenges. One of the main challenges is the lack of training data. To address this challenge and optimize the performance, we have utilized a transfer learning technique which is where the deep learning models train on a task, and then fine-tune the models for another task. We have employed transfer learning in two ways: Training our proposed model first on the same domain dataset, then on the target dataset, and training our model on a different domain dataset, then on the target dataset. We have empirically proven that the same domain transfer learning optimized the performance. Our hybrid model of parallel convolutional layers and residual links is utilized to classify hematoxylin–eosin-stained breast biopsy images into four classes: invasive carcinoma, in-situ carcinoma, benign tumor and normal tissue. To reduce the effect of overfitting, we have augmented the images with different image processing techniques. The proposed model achieved state-of-the-art performance, and it outperformed the latest methods by achieving a patch-wise classification accuracy of 90.5%, and an image-wise classification accuracy of 97.4% on the validation set. Moreover, we have achieved an image-wise classification accuracy of 96.1% on the test set of the microscopy ICIAR-2018 dataset.

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Generative Adversarial Networks for Visible to Infrared Video Conversion

Recent Advances in Image Restoration with Applications to Real World Problems ◽

10.5772/intechopen.93866 ◽

2020 ◽

Author(s):

Mohammad Shahab Uddin ◽

Jiang Li

Keyword(s):

Deep Learning ◽

Performance Metrics ◽

Infrared Image ◽

Image Databases ◽

Learning Models ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Optical Images ◽

Infrared Video ◽

Image Pairs

Deep learning models are data driven. For example, the most popular convolutional neural network (CNN) model used for image classification or object detection requires large labeled databases for training to achieve competitive performances. This requirement is not difficult to be satisfied in the visible domain since there are lots of labeled video and image databases available nowadays. However, given the less popularity of infrared (IR) camera, the availability of labeled infrared videos or image databases is limited. Therefore, training deep learning models in infrared domain is still challenging. In this chapter, we applied the pix2pix generative adversarial network (Pix2Pix GAN) and cycle-consistent GAN (Cycle GAN) models to convert visible videos to infrared videos. The Pix2Pix GAN model requires visible-infrared image pairs for training while the Cycle GAN relaxes this constraint and requires only unpaired images from both domains. We applied the two models to an open-source database where visible and infrared videos provided by the signal multimedia and telecommunications laboratory at the Federal University of Rio de Janeiro. We evaluated conversion results by performance metrics including Inception Score (IS), Frechet Inception Distance (FID) and Kernel Inception Distance (KID). Our experiments suggest that cycle-consistent GAN is more effective than pix2pix GAN for generating IR images from optical images.

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A Novel Deep Learning Based Sentiment Analysis of Movie using Hybrid CNN_SVM Algorithm

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d7115.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 12391-12394

Keyword(s):

Machine Learning ◽

Decision Making ◽

Deep Learning ◽

Random Forest ◽

Sentiment Analysis ◽

Business Strategy ◽

Performance Metrics ◽

Classification Models ◽

Learning Models ◽

Meaningful Information

Data flow in web is becoming high and vast, extracting useful and meaningful information from the same is especially significant. The extracted information can be utilized for enhanced decision making. The information provided by the end-users is normally in the form of comments with respect to different products and services. Sentiment analysis is effectively carried out in these kinds of compact review to give away the people’s opinion of any products. This analyzed data will be efficient to improve the business strategy. In our work the collected online movie reviews are analyzed by using machine learning sentiment classification models like Random Forest, Naive Bayes, KNN and SVM. The work has been extended with CNN and hybrid CNN-SVM deep learning models to achieve higher performance. Comparing the workings of all the above classification models for sentiment analysis based upon various performance metrics is the main objective of the paper.

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Metrics for Automatic Evaluation of Text from NLP Models for Text to Scene Generation

European Journal of Electrical Engineering and Computer Science ◽

10.24018/ejece.2021.5.4.341 ◽

2021 ◽

Vol 5 (4) ◽

pp. 20-25

Author(s):

S. Yashaswini ◽

S. S. Shylaja

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Cost Function ◽

Performance Metrics ◽

Evaluation Criteria ◽

Ground Truth ◽

Automatic Evaluation ◽

Mean Square ◽

Learning Models ◽

Classification Tasks

Performance metrics give us an indication of which model is better for which task. Researchers attempt to apply machine learning and deep learning models to measure the performance of models through cost function or evaluation criteria like Mean square error (MSE) for regression, accuracy, and f1-score for classification tasks Whereas in NLP performance measurement is a complex due variation of ground truth and results obta.

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A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique

Information ◽

10.3390/info12090374 ◽

2021 ◽

Vol 12 (9) ◽

pp. 374

Author(s):

Babacar Gaye ◽

Dezheng Zhang ◽

Aziguli Wulamu

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Deep Learning ◽

Sentiment Analysis ◽

Language Processing ◽

Short Term Memory ◽

State Of The Art ◽

Accuracy Score ◽

Learning Models ◽

Proposed Model

With the extensive availability of social media platforms, Twitter has become a significant tool for the acquisition of peoples’ views, opinions, attitudes, and emotions towards certain entities. Within this frame of reference, sentiment analysis of tweets has become one of the most fascinating research areas in the field of natural language processing. A variety of techniques have been devised for sentiment analysis, but there is still room for improvement where the accuracy and efficacy of the system are concerned. This study proposes a novel approach that exploits the advantages of the lexical dictionary, machine learning, and deep learning classifiers. We classified the tweets based on the sentiments extracted by TextBlob using a stacked ensemble of three long short-term memory (LSTM) as base classifiers and logistic regression (LR) as a meta classifier. The proposed model proved to be effective and time-saving since it does not require feature extraction, as LSTM extracts features without any human intervention. We also compared our proposed approach with conventional machine learning models such as logistic regression, AdaBoost, and random forest. We also included state-of-the-art deep learning models in comparison with the proposed model. Experiments were conducted on the sentiment140 dataset and were evaluated in terms of accuracy, precision, recall, and F1 Score. Empirical results showed that our proposed approach manifested state-of-the-art results by achieving an accuracy score of 99%.

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