Search

Road traffic infrastructure management and exploitation is a material production industry with both service and technical attributes, directly related to human life, means of transport and other assets. Traffic safety criteria are expressed through the ability to smooth traffic and the safety when traveling. To meet the above requirements, the road traffic infrastructure system must ensure quality in all aspects such as: design - construction - operation management. The Trung Luong - My Thuan expressway through Tien Giang province, invested in BOT form, phase 1, does not meet traffic safety criteria. The route does not have an emergency stop lane for rescue activities in the event of an accident. This is due to the issues in investment project planning, surveying and designing. For that reason, the article evaluates the management and operation of roads invested in the form of public-private partnership (BOT) in Tien Giang province from the perspective of traffic safety and investment efficiency. The, the article points out the gaps of state management in the field of road exploitation, as a basis for proposing solutions to improve the efficiency of BOT road traffic operation in Tien Giang province, Vietnam.

The exponential increase in the number of diabetics around the world has led to an equally large increase in the number of diabetic retinopathy (DR) cases which is one of the major complications caused by diabetes. Left unattended, DR worsens the vision and would lead to partial or complete blindness. As the number of diabetics continue to increase exponentially in the coming years, the number of qualified ophthalmologists need to increase in tandem in order to meet the demand for screening of the growing number of diabetic patients. This makes it pertinent to develop ways to automate the detection process of DR. A computer aided diagnosis system has the potential to significantly reduce the burden currently placed on the ophthalmologists. Hence, this review paper is presented with the aim of summarizing, classifying, and analyzing all the recent development on automated DR detection using fundus images from 2015 up to this date. Such work offers an unprecedentedly thorough review of all the recent works on DR, which will potentially increase the understanding of all the recent studies on automated DR detection, particularly on those that deploys machine learning algorithms. Firstly, in this paper, a comprehensive state-of-the-art review of the methods that have been introduced in the detection of DR is presented, with a focus on machine learning models such as convolutional neural networks (CNN) and artificial neural networks (ANN) and various hybrid models. Each AI will then be classified according to its type (e.g. CNN, ANN, SVM), its specific task(s) in performing DR detection. In particular, the models that deploy CNN will be further analyzed and classified according to some important properties of the respective CNN architectures of each model. A total of 150 research articles related to the aforementioned areas that were published in the recent 5 years have been utilized in this review to provide a comprehensive overview of the latest developments in the detection of DR.The online version contains supplementary material available at 10.1007/s10462-022-10185-6.

Data clustering are applied in various fields such as document classification, dental X-ray image segmentation, medical image segmentation, etc. Especially, clustering algorithms are used in satellite image processing in many important application areas, including classification of vehicles participating in traffic, logistics, classification of satellite images to forecast droughts, floods, forest fire, etc. In the process of collecting satellite image data, there are a number of factors such as clouds, weather, ... that can affect to image quality. Images with low quality will make the performance of clustering algorithms decrease. Apart from that, the parameter of fuzzification in clustering algorithms also affects to clustering results. In the past, clustering methods often used the same fuzzification parameter, m = 2. But in practice, each element should have its own parameter m. Therefore, determining the parameters m is necessary to increase fuzzy clustering performance. In this research, an improvement algorithm for the data partition with confidence problem and multi fuzzifier named as TS3MFCM is introduced. The proposed method consists of three steps namely as “FCM for labeled data”, “Data transformation”, and “Semi-supervised fuzzy clustering with multiple point fuzzifiers”. The proposed TS3MFCM method is implemented and experimentally compared against with the Confidence-weighted Safe Semi-Supervised Clustering (CS3FCM). The performance of proposed method is better than selected methods in both computational time and clustering accuracy on the same datasets

Identification and repairing holes in ireless sensor networks (WSNs) are challenging with full and low complexity. A local healing approach based on distributed virtual forces will make the healing area of the hole possible. The construction of repairing design is a locally allocated and constructed methodology that will enhance the coverage area in WSNs. In this paper, an enhanced border and hole detection (EBHD) algorithm is proposed for the energy containment of WSN. The performance of the proposed EBHD methodology is compared with those of the existing methods through Network Simulator 2 (NS2). The simulation results prove that the proposed EBHD achieves a high packet reception ratio while improving the residual energy and reducing end-to-end delay.