scholarly journals Two-Step Real-Time Night-Time Fire Detection in an Urban Environment Using Static ELASTIC-YOLOv3 and Temporal Fire-Tube

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2202 ◽  
Author(s):  
MinJi Park ◽  
Byoung Chul Ko
Keyword(s):  
Urban Areas ◽  
State Of The Art ◽  
Fire Detection ◽  
Classification Performance ◽  
Performance Comparison ◽  
Detection Methods ◽  
Night Time ◽  
Detection Systems ◽  
Ambient Lighting ◽  
Fast Classification

While the number of casualties and amount of property damage caused by fires in urban areas are increasing each year, studies on their automatic detection have not maintained pace with the scale of such fire damage. Camera-based fire detection systems have numerous advantages over conventional sensor-based methods, but most research in this area has been limited to daytime use. However, night-time fire detection in urban areas is more difficult to achieve than daytime detection owing to the presence of ambient lighting such as headlights, neon signs, and streetlights. Therefore, in this study, we propose an algorithm that can quickly detect a fire at night in urban areas by reflecting its night-time characteristics. It is termed ELASTIC-YOLOv3 (which is an improvement over the existing YOLOv3) to detect fire candidate areas quickly and accurately, regardless of the size of the fire during the pre-processing stage. To reflect the dynamic characteristics of a night-time flame, N frames are accumulated to create a temporal fire-tube, and a histogram of the optical flow of the flame is extracted from the fire-tube and converted into a bag-of-features (BoF) histogram. The BoF is then applied to a random forest classifier, which achieves a fast classification and high classification performance of the tabular features to verify a fire candidate. Based on a performance comparison against a few other state-of-the-art fire detection methods, the proposed method can increase the fire detection at night compared to deep neural network (DNN)-based methods and achieves a reduced processing time without any loss in accuracy.

scholarly journals Automatic Fire and Smoke Detection Method for Surveillance Systems Based on Dilated CNNs

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1241
Author(s):  
Yakhyokhuja Valikhujaev ◽  
Akmalbek Abdusalomov ◽  
Young Im Cho
Keyword(s):  
Cost Effective ◽  
Classification Performance ◽  
Detection Methods ◽  
False Alarms ◽  
Surveillance Systems ◽  
Smoke Detection ◽  
Detection Systems ◽  
Number Of False Alarms ◽  
Unseen Data ◽  
Vision Detection

The technologies underlying fire and smoke detection systems play a crucial role in ensuring and delivering optimal performance in modern surveillance environments. In fact, fire can cause significant damage to lives and properties. Considering that the majority of cities have already installed camera-monitoring systems, this encouraged us to take advantage of the availability of these systems to develop cost-effective vision detection methods. However, this is a complex vision detection task from the perspective of deformations, unusual camera angles and viewpoints, and seasonal changes. To overcome these limitations, we propose a new method based on a deep learning approach, which uses a convolutional neural network that employs dilated convolutions. We evaluated our method by training and testing it on our custom-built dataset, which consists of images of fire and smoke that we collected from the internet and labeled manually. The performance of our method was compared with that of methods based on well-known state-of-the-art architectures. Our experimental results indicate that the classification performance and complexity of our method are superior. In addition, our method is designed to be well generalized for unseen data, which offers effective generalization and reduces the number of false alarms.

scholarly journals State-of-the-art in detecting academic plagiarism

2013 ◽  
Vol 9 (1) ◽  
Author(s):  
Norman Meuschke ◽  
Bela Gipp
Keyword(s):  
Information Technology ◽  
State Of The Art ◽  
Detection Methods ◽  
The Internet ◽  
Plagiarism Detection ◽  
Detection Systems ◽  
Highly Efficient ◽  
Cross Lingual ◽  
Widespread Dissemination ◽  
Academic Plagiarism

The problem of academic plagiarism has been present for centuries. Yet, the widespread dissemination of information technology, including the internet, made plagiarising much easier. Consequently, methods and systems aiding in the detection of plagiarism have attracted much research within the last two decades. Researchers proposed a variety of solutions, which we will review comprehensively in this article. Available detection systems use sophisticated and highly efficient character-based text comparisons, which can reliably identify verbatim and moderately disguised copies. Automatically detecting more strongly disguised plagiarism, such as paraphrases, translations or idea plagiarism, is the focus of current research. Proposed approaches for this task include intrinsic, cross-lingual and citation-based plagiarism detection. Each method offers unique strengths and weaknesses; however, none is currently mature enough for practical use. In the future, plagiarism detection systems may benefit from combining traditional character-based detection methods with these emerging detection approaches.

scholarly journals An Improvement of the Fire Detection and Classification Method Using YOLOv3 for Surveillance Systems

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6519
Author(s):  
Akmalbek Abdusalomov ◽  
Nodirbek Baratov ◽  
Alpamis Kutlimuratov ◽  
Taeg Keun Whangbo
Keyword(s):  
Real Time ◽  
High Speed ◽  
Fire Detection ◽  
Classification Performance ◽  
Surveillance Systems ◽  
Detection Systems ◽  
Fire Detector ◽  
Board Level ◽  
In Fire ◽  
Shape And Size

Currently, sensor-based systems for fire detection are widely used worldwide. Further research has shown that camera-based fire detection systems achieve much better results than sensor-based methods. In this study, we present a method for real-time high-speed fire detection using deep learning. A new special convolutional neural network was developed to detect fire regions using the existing YOLOv3 algorithm. Due to the fact that our real-time fire detector cameras were built on a Banana Pi M3 board, we adapted the YOLOv3 network to the board level. Firstly, we tested the latest versions of YOLO algorithms to select the appropriate algorithm and used it in our study for fire detection. The default versions of the YOLO approach have very low accuracy after training and testing in fire detection cases. We selected the YOLOv3 network to improve and use it for the successful detection and warning of fire disasters. By modifying the algorithm, we recorded the results of a rapid and high-precision detection of fire, during both day and night, irrespective of the shape and size. Another advantage is that the algorithm is capable of detecting fires that are 1 m long and 0.3 m wide at a distance of 50 m. Experimental results showed that the proposed method successfully detected fire candidate areas and achieved a seamless classification performance compared to other conventional fire detection frameworks.

Toward Constructing a Balanced Intrusion Detection Dataset

2021 ◽  
Vol 2 (3) ◽  
pp. 132-142
Author(s):  
Amer Abulmajeed Abdulrahman Alsameraee ◽  
Mahmood Khalel Ibrahem
Keyword(s):  
Intrusion Detection ◽  
Large Scale ◽  
State Of The Art ◽  
Class Imbalance ◽  
Sampling Technique ◽  
Machine Learning Algorithms ◽  
Intrusion Detection Systems ◽  
Detection Methods ◽  
Effective Solution ◽  
Detection Systems

Several Intrusion Detection Systems (IDS) have been proposed in the current decade. Most datasets which associate with intrusion detection dataset suffer from an imbalance class problem. This problem limits the performance of classifier for minority classes. This paper has presented a novel class imbalance processing technology for large scale multiclass dataset, referred to as BMCD. Our algorithm is based on adapting the Synthetic Minority Over-Sampling Technique (SMOTE) with multiclass dataset to improve the detection rate of minority classes while ensuring efficiency. In this work we have been combined five individual CICIDS2017 dataset to create one multiclass dataset which contains several types of attacks. To prove the efficiency of our algorithm, several machine learning algorithms have been applied on combined dataset with and without using BMCD algorithm. The experimental results have concluded that BMCD provides an effective solution to imbalanced intrusion detection and outperforms the state-of-the-art intrusion detection methods.

scholarly journals TweepFake: About detecting deepfake tweets

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251415
Author(s):  
Tiziano Fagni ◽  
Fabrizio Falchi ◽  
Margherita Gambini ◽  
Antonio Martella ◽  
Maurizio Tesconi
Keyword(s):  
Social Media ◽  
State Of The Art ◽  
Language Model ◽  
Generative Models ◽  
Language Modeling ◽  
Detection Methods ◽  
Neural Models ◽  
Detection Techniques ◽  
Detection Systems ◽  
Media Messages

The recent advances in language modeling significantly improved the generative capabilities of deep neural models: in 2019 OpenAI released GPT-2, a pre-trained language model that can autonomously generate coherent, non-trivial and human-like text samples. Since then, ever more powerful text generative models have been developed. Adversaries can exploit these tremendous generative capabilities to enhance social bots that will have the ability to write plausible deepfake messages, hoping to contaminate public debate. To prevent this, it is crucial to develop deepfake social media messages detection systems. However, to the best of our knowledge no one has ever addressed the detection of machine-generated texts on social networks like Twitter or Facebook. With the aim of helping the research in this detection field, we collected the first dataset of real deepfake tweets, TweepFake. It is real in the sense that each deepfake tweet was actually posted on Twitter. We collected tweets from a total of 23 bots, imitating 17 human accounts. The bots are based on various generation techniques, i.e., Markov Chains, RNN, RNN+Markov, LSTM, GPT-2. We also randomly selected tweets from the humans imitated by the bots to have an overall balanced dataset of 25,572 tweets (half human and half bots generated). The dataset is publicly available on Kaggle. Lastly, we evaluated 13 deepfake text detection methods (based on various state-of-the-art approaches) to both demonstrate the challenges that Tweepfake poses and create a solid baseline of detection techniques. We hope that TweepFake can offer the opportunity to tackle the deepfake detection on social media messages as well.

An Optimized Approach for Breast Cancer Classification for Histopathological Images Based on Hybrid Feature Set

2020 ◽  
Vol 16 ◽  
Author(s):  
Inzamam Mashood Nasir ◽  
Muhammad Rashid ◽  
Jamal Hussain Shah ◽  
Muhammad Sharif ◽  
Muhammad Yahiya Haider Awan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Classification Performance ◽  
Diagnose Breast Cancer ◽  
Histopathological Images ◽  
And Performance ◽  
Learned Features ◽  
Intelligent Healthcare

Background: Breast cancer is considered as the most perilous sickness among females worldwide and the ratio of new cases is expanding yearly. Many researchers have proposed efficient algorithms to diagnose breast cancer at early stages, which have increased the efficiency and performance by utilizing the learned features of gold standard histopathological images. Objective: Most of these systems have either used traditional handcrafted features or deep features which had a lot of noise and redundancy, which ultimately decrease the performance of the system. Methods: A hybrid approach is proposed by fusing and optimizing the properties of handcrafted and deep features to classify the breast cancer images. HOG and LBP features are serially fused with pretrained models VGG19 and InceptionV3. PCR and ICR are used to evaluate the classification performance of proposed method. Results: The method concentrates on histopathological images to classify the breast cancer. The performance is compared with state-of-the-art techniques, where an overall patient-level accuracy of 97.2% and image-level accuracy of 96.7% is recorded. Conclusion: The proposed hybrid method achieves the best performance as compared to previous methods and it can be used for the intelligent healthcare systems and early breast cancer detection.

scholarly journals Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

2019 ◽  
Vol 11 (16) ◽  
pp. 1933 ◽  
Author(s):  
Yangyang Li ◽  
Ruoting Xing ◽  
Licheng Jiao ◽  
Yanqiao Chen ◽  
Yingte Chai ◽  
...  
Keyword(s):  
Image Classification ◽  
State Of The Art ◽  
Sample Selection ◽  
Spatial Relations ◽  
Speckle Noise ◽  
Classification Performance ◽  
Selection Strategy ◽  
Training Set ◽  
Polarimetric Synthetic Aperture Radar ◽  
Unlabeled Sample

Polarimetric synthetic aperture radar (PolSAR) image classification is a recent technology with great practical value in the field of remote sensing. However, due to the time-consuming and labor-intensive data collection, there are few labeled datasets available. Furthermore, most available state-of-the-art classification methods heavily suffer from the speckle noise. To solve these problems, in this paper, a novel semi-supervised algorithm based on self-training and superpixels is proposed. First, the Pauli-RGB image is over-segmented into superpixels to obtain a large number of homogeneous areas. Then, features that can mitigate the effects of the speckle noise are obtained using spatial weighting in the same superpixel. Next, the training set is expanded iteratively utilizing a semi-supervised unlabeled sample selection strategy that elaborately makes use of spatial relations provided by superpixels. In addition, a stacked sparse auto-encoder is self-trained using the expanded training set to obtain classification results. Experiments on two typical PolSAR datasets verified its capability of suppressing the speckle noise and showed excellent classification performance with limited labeled data.

Affective labour and class distinction in the night-time economy

2021 ◽  
pp. 003802612110063
Author(s):  
Steven Threadgold ◽  
David Farrugia ◽  
Julia Coffey
Keyword(s):  
Symbolic Dynamics ◽  
Urban Areas ◽  
Service Work ◽  
Night Time ◽  
Feminist Analysis ◽  
Consumption Experience ◽  
Gendered Politics ◽  
The Aesthetic ◽  
The Relationship ◽  
Class Distinction

This article contributes to recent debates about the relationship between affective labour and class by exploring the classed distinctions enacted through affective labour in the urban night-time economy. Bringing theories of affective labour into a dialogue with Bourdieusian feminist analysis, the article explores the affective and symbolic dynamics of hospitality labour in a gentrified inner-urban neighbourhood of Melbourne, Australia. It shows how the practice of hospitality labour enacts classed distinctions and tensions emerging from the gentrification of inner-urban areas, and how the aesthetic and symbolic dimensions of class contribute to the valorisation of affect in hospitality venues. The valorisation of affect are processes in which the value attributed to an atmosphere or consumption experience is based on the forms of distinction practised within the venue, enacted in aesthetics, tastes and modes of embodiment. The article also shows how practices of class distinction – both ‘punching up’ and ‘managing down’ – are connected to the gendered politics of service work in the way that workers manage the threat of violence or sexual harassment in venues. In general, the article shows how the classed dynamics of gentrification are enacted in affective economies, and therefore how Bourdieusian analysis of class can be usefully deployed in theoretical debates about affective labour.

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