Real Time Traffic Sign Detection and Recognition using Adaptive Neuro Fuzzy Inference System

Billah, Mustain; Waheed, Sajjad; Ahmed, Kawsar; Hanifa, Abu

doi:10.5120/cae2015651877

Cited by 5 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the neural network can be used to classify protein defective molecular sequences, which can achieve a better classification effect in theory (32). Furthermore, support vector machine and others classification techniques are also verified for different purposes (33)(34)(35).…”

Section: Classification Model Of Protein Defect Molecular Sequence-based On Neural Networkmentioning

confidence: 99%

Protein molecular defect detection method based on a neural network algorithm

Zheng

Kahrizi²

2020

Cell Mol Biol (Noisy-le-grand)

View full text Add to dashboard Cite

Proteins, as the largest macromolecules in the body, are among the most important components of the body and play very vital and important roles.These substances are made up of a series of amino acid chains that, depending on the type of protein, the number of these amino acids can reach several thousand.Proteins function differently depending on the type and location of their presence, including enzymatic activity to catalyze the process, identify microbes and cancer cells, transport substances such as respiratory gases, and signalize. In the biochemical experiment, the problem of optimizing the detection of protein molecular defects, because of the randomness of the information, parameters, selection and setting, limits the detection accuracy of protein molecular defects. Based on the characteristics of fast learning speed and a robust network of neural network algorithm, a protein molecular defect detection method based on a neural network algorithm was proposed. Firstly, the protein secondary structure was predicted by the method of protein secondary structure prediction based on the generalized regression neural network to obtain the protein structural features; secondly, the protein defective molecular sequence classification model based on the neural network was used to classify the protein defective molecular sequence to achieve the protein molecular defect detection. The results showed that the detection accuracy of the proposed method was very high, which meets the needs of protein molecular defect detection, and has some application advantages compared with similar detection methods.

show abstract

Section: Classification Model Of Protein Defect Molecular Sequence-based On Neural Networkmentioning

confidence: 99%

Protein molecular defect detection method based on a neural network algorithm

Zheng

Kahrizi²

2020

Cell Mol Biol (Noisy-le-grand)

View full text Add to dashboard Cite

show abstract

“…By comparing the results with other models, it can be concluded that the ANFIS in this case also shows better performance.mTraffic sign detection is an important part of the Driver Assistance System because it allows automatic adjustment to the conditions prescribed for them. Billah et al (2015) propose an ANFIS model for the recognition of circular signs based on the data obtained by image processing and video processing. The recognition accuracy is more than 98%, which sufficiently highlights the model's capabilities.…”

Section: Safetymentioning

confidence: 99%

Application of the ANFIS model in road traffic and transportation: A literature review from 1993 to 2018

Stojčić

2019

Oper. Res. Eng. Sci. Theor. Appl.

View full text Add to dashboard Cite

The paper's focus is on researching the application of the ANFIS (Adaptive Neuro Fuzzy Inference System) model in traffic and transport through a review of relevant papers. The ANFIS, as an element of artificial intelligence, is widely used in intelligent transport systems. All collected papers are divided into 7 sub-areas, namely: 1) vehicle routing, 2) traffic control at intersections with light signaling, 3) vehicle steering and control, 4) safety, 5) modeling of fuel consumption, engine performance and exhaust emissions, 6) traffic congestion prediction, and 7) other applications. For each sub-area, the analysis of the proposed models is performed with a tabular overview of respective input and output variables, while in the third section the discussion of the results is given. It is found that the steering and control of vehicles represent a sub-area with the highest percentage in the total number of examined papers, while the security applications take second place.

show abstract

“…An important feature is that ANFIS can effectively model nonlinear connections of inputs and outputs [43]. ANFIS training is based on the application of an algorithm of error propagation backward, either alone or in combination with the method of least squared error, i.e., hybrid algorithm [44]. ANFIS uses the Takagi-Sugeno method of inference, and a typical fuzzy rule, assuming two inputs (x and y) and a logical AND operation, can be written as follows:…”

Section: Adaptive Neuro-fuzzy Inference Modelmentioning

confidence: 99%

“…This layer is often called a fuzzification layer, because it determines the membership degree of the value of a variable to a particular fuzzy set [45]. The nodes of this layer are adaptive, which means that their parameters are adjusted during a training period [44]. The first-layer nodes that represent the membership functions of the input variable X can be defined as µ A j (x), where j (j = 1 ,..., 2) denotes the number of membership functions [46].…”

Section: Adaptive Neuro-fuzzy Inference Modelmentioning

confidence: 99%

Development of an ANFIS Model for the Optimization of a Queuing System in Warehouses

Stojčić

Pamučar

Mahmutagić³

et al. 2018

Information

View full text Add to dashboard Cite

Queuing systems (QS) represent everyday life in all business and economic systems. On the one hand, and there is a tendency for their time and cost optimization, but on the other hand, they have not been sufficiently explored. This especially applies to logistics systems, where a large number of transportation and storage units appear. Therefore, the aim of this paper is to develop an ANFIS (Adaptive neuro-fuzzy inference system) model in a warehouse system with two servers for defining QS optimization parameters. The research was conducted in a company for the manufacturing of brown paper located in the territory of Bosnia and Herzegovina, which represents a significant share of the total export production of the country. In this paper, the optimization criterion is the time spent in the system, which is important both from the aspect of all customers of the system, and from that of the owner of the company. The time criterion directly affects the efficiency of the system, but also the overall costs that this system causes. The developed ANFIS model was compared with a mathematical model through a sensitivity analysis. The mathematical model showed outstanding results, which justifies its development and application.

show abstract

Real Time Traffic Sign Detection and Recognition using Adaptive Neuro Fuzzy Inference System

Cited by 5 publications

References 12 publications

Protein molecular defect detection method based on a neural network algorithm

Protein molecular defect detection method based on a neural network algorithm

Application of the ANFIS model in road traffic and transportation: A literature review from 1993 to 2018

Development of an ANFIS Model for the Optimization of a Queuing System in Warehouses

Contact Info

Product

Resources

About