Implementation of Random Forest Algorithm in Order to Use Big Data to Improve Real-Time Traffic Monitoring and Safety

Nowadays the active traffic management is enabled for better performance due to the nature of the real-time large data in transportation system. With the advancement of large data, monitoring and improving the traffic safety transformed into necessity in the form of actively and appropriately. Per-formance efficiency and traffic safety are considered as an im-portant element in measuring the performance of the system. Although the productivity can be evaluated in terms of traffic congestion, safety can be obtained through analysis of incidents. Exposure effects have been done to identify the Factors and solutions of traffic congestion and accidents.In this study, the goal is reducing traffic congestion and im-proving the safety with reduced risk of accident in freeways to improve the utilization of the system. Suggested method Man-ages and controls traffic with use of prediction the accidents and congestion traffic in freeways. In fact, the design of the real-time monitoring system accomplished using Big Data on the traffic flow and classified using the algorithm of random-ized forest and analysis of Big Data Defined needs. Output category is extracted with attention to the specified characteristics that is considered necessary and then by Alarms and signboards are announced which are located in different parts of the freeways and roads. All of these processes are evaluated by the Colored Petri Nets using the Cpn Tools tool

Optimization of Brain Tumor MR Image Classification Accuracy Using Optimal Threshold, PCA and Training ANFIS with Different Repetitions.

BACKGROUND: One of the leading causes of death is brain tumors. Accurate tumor classification leads to appropriate decision making and providing the most efficient treatment to the patients. This study aims to optimize brain tumor MR images classification accuracy using optimal threshold, PCA and training Adaptive Neuro Fuzzy Inference System (ANFIS) with different repetitions. MATERIAL AND METHODS: The procedure used in this study consists of five steps: (1) T1, T2 weighted images collection, (2) tumor separation with different threshold levels, (3) feature extraction, (4) presence and absence of feature reduction applying principal component analysis (PCA) and (5) ANFIS classification with 0, 20 and 200 training repetitions. RESULTS: ANFIS accuracy was 40%, 80% and 97% for all features and 97%, 98.5% and 100% for the 6 selected features by PCA in 0, 20 and 200 training repetitions, respectively. CONCLUSION: The findings of the present study demonstrated that accuracy can be raised up to 100% by using an optimized threshold method, PCA and increasing training repetitions. KEYWORDS: ANFIS ; Brain Tumor Detection ; PCA ; Training Repetition; MR

Iris recognition system based on canny and LoG edge detection methods

Iris recognition has obtained an incredible consideration in a variety of fields such as border areas, industrial areas, security susceptible areas and so on. In the eye, sclera and iris are utilized since the prior inputs employing to identify the eye with various systems such as segmentation incorporating with various versions. The internal edge in the eye isn't an ordinary circle that might produce difficulty in exact recognition. The image has a smaller amount texture after that it causes iris legacy in segmentation step. In order to develop a good iris authentication algorithm for individual identification, the presented paper recognize iris images by utilizing two edge detection approaches like Canny and Laplacian of Gaussian (LoG) to reduce the noisy data and detect the edges. The experimental results demonstrate that Canny edge detector can better detect the edges than LoG

Osdes_net: oil spill detection based on efficient_shuffle network using synthetic aperture radar imagery

Synthetic Aperture Radar (SAR) imagery can be beneficial for segmenting oil spills, which are a common environmental hazard. Oil spill detection in SAR imagery faces several challenges, including speckle noise, heterogeneous backgrounds, blurred edges, and a lack of comprehensive datasets with multiple images. ShuffleNet is one of the deep networks, which has never been used for oil spill segmentation. In this article, ShuffleNet blocks are used to detect oil spills in SAR images, which is more effective than other methods. Besides, the main network design, six other blocks were evaluated, and the most valuable one was selected. We use group convolutions, shuffle channels, and atrous convolutions in this model with a minimum number of layers of ReLU. The methods are evaluated based on the Intersection Over Union (IoU) parameter so that the proposed method improved the mIoU by 7.1% over the best results of some previous methods

Ultra-fast 1-bit comparator using nonlinear photonic crystalbased ring resonators

In this paper, a photonic crystal structure for comparing two bits has beenproposed. This structure includes four resonant rings and some nonlinear rods. Thenonlinear rods used inside the resonant rings were made of a doped glass whose linearand nonlinear refractive indices are 1.4 and 10-14 m2/W, respectively. Using Kerr effect,optical waves are guided toward the correct output ports. In this study, plane waveexpansion and finite difference time domain methods were used for calculation ofphotonic bandgap and simulation of optical wave propagation, respectively. The size ofthe proposed structure is 1585 μm2 which is more compact than the previous works.Furthermore, the obtained maximum delay time is about 2 ps that is proper to highspeedprocessing. The normalized output power margins for logic 0 and 1 are calculatedas 25% and 71%, respectively. According to the obtained results, this structure can beused for optical integrated circuits

Unsupervised feature learning based on sparse coding and spectral clustering for segmentation of synthetic aperture radar images

Synthetic aperture radar (SAR) image segmentation is fundamental for the interpretation and understanding of these images. In this process, the representation of SAR image features plays an important role. Spectral clustering is an image segmentation method making it possible to combine features and cues. This study presents a new spectral clustering method using unsupervised feature learning (UFL). In this method, the SAR image is primarily processed by the non‐negative matrix factorisation (NMF) algorithm and then non‐negative features containing spatial structure information are extracted. Afterwards, the extracted features are learned using a sparse coding algorithm to increase the discrimination power of the features. Sparse coding is an unsupervised learning algorithm which finds the patterns or high‐level semantics of the data. Ultimately, the SAR image segmentation operation is performed by applying spectral clustering on learned features. In this method, sparse coding learns features and simultaneously creates the similarity function required in spectral clustering through the production of sparse coefficients. Therefore this method avoids the Gaussian similarity function, which has a problem with scale parameter adjustment that is one of the drawbacks of spectral clustering methods. The results demonstrate that, compared with wavelet and GLCM features, NMF features manage to obtain more meaningful information and provide a better SAR image segmentation result. The results have also demonstrated that SAR image segmentation using learned features is significantly improved compared with segmentation by unlearned features. The experimental results indicate the effect of UFL on SAR image segmentation