scholarly journals Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2025 ◽  
Author(s):  
Jun Hong Park ◽  
Seunggi Lee ◽  
Seongjin Yun ◽  
Hanjin Kim ◽  
Won-Tae Kim
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Transfer ◽  
Detection System ◽  
Smart Cities ◽  
Fire Detection ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Detection Systems ◽  
Fire Detection System

A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

Smart Fire Detection System with Early Notifications Using Machine Learning

2017 ◽  
Vol 16 (02) ◽  
pp. 1750009 ◽  
Author(s):  
Mohammad Sultan Mahmud ◽  
Md. Shohidul Islam ◽  
Md. Ashiqur Rahman
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Fire Department ◽  
Fire Detection ◽  
Machine Learning Algorithms ◽  
False Alarms ◽  
Police Station ◽  
Local Police ◽  
Long Time ◽  
Fire Detection System

House fire is one of the major concerns for designers, builders, and residents of property. In the case of detecting fire, individual sensors have been used for a long time, but they cannot detect the level of fire and notify the emergency response units. To solve this problem, this study attempts to propose an intelligent early fire detection system that would not only detect the fire by using integrated sensors but also notify the appropriate authorities including fire department, ambulance services, and local police station simultaneously to protect valuable lives and properties. Signals from the integrated detectors e.g., heat, smoke, and flame go through the machine learning algorithms to check the potentiality of the fire as well as broadcast the predicted result to various parties using a GSM modem. To consolidate the predicted output, structured forest for fast edge detection has also been applied. The final outcome of this development also minimized false alarms, thus making this system more reliable.

scholarly journals Fire Detection System Using Machine Learning

2021 ◽  
Vol 1916 (1) ◽  
pp. 012209
Author(s):  
A Arul ◽  
R S Hari Prakaash ◽  
R Gokul Raja ◽  
V Nandhalal ◽  
N Sathish Kumar
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Fire Detection ◽  
Fire Detection System

scholarly journals A TBB-CUDA Implementation for Background Removal in a Video-Based Fire Detection System

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Fan Wang ◽  
Xiao Jiang ◽  
Xiao Peng Hu
Keyword(s):  
Data Transfer ◽  
Detection System ◽  
Gaussian Model ◽  
Fire Detection ◽  
Distribution Model ◽  
Memory Bandwidth ◽  
Limited Memory ◽  
Background Removal ◽  
Fire Detection System ◽  
Gaussian Distribution Model

This paper presents a parallel TBB-CUDA implementation for the acceleration of single-Gaussian distribution model, which is effective for background removal in the video-based fire detection system. In this framework, TBB mainly deals with initializing work of the estimated Gaussian model running on CPU, and CUDA performs background removal and adaption of the model running on GPU. This implementation can exploit the combined computation power of TBB-CUDA, which can be applied to the real-time environment. Over 220 video sequences are utilized in the experiments. The experimental results illustrate that TBB+CUDA can achieve a higher speedup than both TBB and CUDA. The proposed framework can effectively overcome the disadvantages of limited memory bandwidth and few execution units of CPU, and it reduces data transfer latency and memory latency between CPU and GPU.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

scholarly journals An Efficient Classifier for U2R, R2L, DoS Attack

2020 ◽  
Vol 9 (1) ◽  
pp. 644-647
Keyword(s):  
Machine Learning ◽  
Network Security ◽  
Learning Algorithms ◽  
Research Area ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Detection Accuracy ◽  
Cyber Attack ◽  
Detection Systems

The internet has become an irreplaceable communicating and informative tool in the current world. With the ever-growing importance and massive use of the internet today, there has been interesting from researchers to find the perfect Cyber Attack Detection Systems (CADSs) or rather referred to as Intrusion Detection Systems (IDSs) to protect against the vulnerabilities of network security. CADS presently exist in various variants but can be largely categorized into two broad classifications; signature-based detection and anomaly detection CADSs, based on their approaches to recognize attack packets.The signature-based CADS use the well-known signatures or fingerprints of the attack packets to signal the entry across the gateways of secured networks. Signature-based CADS can only recognize threats that use the known signature, new attacks with unknown signatures can, therefore, strike without notice. Alternatively, anomaly-based CADS are enabled to detect any abnormal traffic within the network and report. There are so many ways of identifying anomalies and different machine learning algorithms are introduced to counter such threats. Most systems, however, fall short of complete attack prevention in the real world due system administration and configuration, system complexity and abuse of authorized access. Several scholars and researchers have achieved a significant milestone in the development of CADS owing to the importance of computer and network security. This paper reviews the current trends of CADS analyzing the efficiency or level of detection accuracy of the machine learning algorithms for cyber-attack detection with an aim to point out to the best. CADS is a developing research area that continues to attract several researchers due to its critical objective.

Reducing False Alarms in Vision-Based Fire Detection

2017 ◽  
pp. 263-290
Author(s):  
Neethidevan Veerapathiran ◽  
Anand S.
Keyword(s):  
Video Processing ◽  
Detection System ◽  
Chemical Properties ◽  
Fire Detection ◽  
False Alarms ◽  
Video Signals ◽  
Detection Systems ◽  
Physical Sensors ◽  
Single Feature ◽  
Fire Detection System

Computer vision techniques are mainly used now a days to detect the fire. There are also many challenges in trying whether the region detected as fire is actually a fire this is perhaps mainly because the color of fire can range from red yellow to almost white. So fire region cannot be detected only by a single feature and many other features (i.e.) color have to be taken into consideration. Early warning and instantaneous responses are the preventing ideas to avoid losses affecting environment as well as human causalities. Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms. In order to reduce false alarms of conventional fire detection systems, system make use of vision based fire detection system. This chapter discuss about the fundamentals of videos, various issues in processing video signals, various algorithms for video processing using vision techniques.

scholarly journals Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques

2020 ◽  
Vol 17 (24) ◽  
pp. 9347 ◽  
Author(s):  
Md Mamunur Rashid ◽  
Joarder Kamruzzaman ◽  
Mohammad Mehedi Hassan ◽  
Tasadduq Imam ◽  
Steven Gordon
Keyword(s):  
Machine Learning ◽  
Smart City ◽  
Service Providers ◽  
Detection System ◽  
Smart Cities ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Increased Risk ◽  
Iot Devices

In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain.

scholarly journals Innovating Fire Detection System Fire using Artificial Intelligence by Image Processing

2020 ◽  
Vol 9 (11) ◽  
pp. 349-356
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Intelligent System ◽  
Detection System ◽  
Fire Detection ◽  
Modern Technology ◽  
Detection Accuracy ◽  
Data Sets ◽  
Industry Professionals ◽  
Review Current

with fires spreading increasingly around the world due to increasing global warming, it has become imperative to develop an intelligent system that detects fires early, using modern technology. Therefore, we used one of the artificial intelligence techniques, which is deep learning, which is one of the popular methods now. Professionals have done a lot of research, experiments, and coding software to detect fires using deep learning. Through this paper, we review current methods that are reached by industry professionals, as well as data sets and fire detection accuracy for each method.

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