Fuzzy Traffic Signal Light Intelligent Control System Based on Microcontroller

Vehicular travel is increasing throughout the world, particularly, in large urban areas. Since there are a lot of traffic congestions, the monitoring and control of city traffic is becoming a major problem in many countries. Due to this factor, traffic signals now become a common feature of cities controlling heavy traffic. Careful planning of these signals is important to increase the efficiency of traffic flow on road. Controlling traffic on oversaturated intersections is a big issue. This paper presents a microcontroller implementation of traffic light control system using fuzzy logic that is used to change the traffic signal cycles adaptively at a four-way intersection. Using the fuzzy logic rules, the system decides, whether to extend the current green signal or terminate it. The control system controls traffic signals for regulating traffic on oversaturated intersections with the integration of left and right turns. The control system also controls the safe and continuous flow of emergency vehicles

developed; Island Differential Evolution Neural Network

There exist many approaches to training neural network. In this system, training for feed forward neural network is introduced by using island model based differential evolution. Differential Evolution (DE) has been used to determine optimal value for ANN parameters such as learning rate and momentum rate and also for weight optimization. Island model used multiple subpopulations and exchanges the individual to boost the overall performance of the algorithm. In this paper, four programs hav

Parallel Differential Evolution Algorithm with Multiple Trial Vectors to Artificial Neural Network Training

In this paper, parallel differential evolutionalgorithm with multiple trial vectors for trainingartificial neural networks (ANNs) is presented. Theproposed method is PDEA, which is a DE-ANN+modified by adding island model. Within PDEA, anisland model is designed to cooperatively search forthe global optima in search space. By combining thestrengths of the differential evolution algorithm withmultiple trial vectors and island model, PDEA greatlyimproves the optimization performance. PDEAalgorithm is used for ANN training to classify theparity-p problem. Results obtained using proposedalgorithm has been compared to the results obtainedusing other evolutionary algorithms

Neural Network Learning Enhancement using Island Model based Differential Evolution Algorithm

Classification is a machine learning techniqueused to predict group membership for data instances.To simplify the problem of classification neuralnetworks are being introduced. In this paper, theadaptation of network weights using Island Modelbased Differential Evolution (IMDE) was proposedas a mechanism to improve the performance ofArtificial Neural Network (ANN). DifferentialEvolution (DE) has been used to determine optimalvalue for ANN parameters such as learning rate andmomentum rate and also for weight optimization.Island model used multiple subpopulations andexchanges the individual to boost the overallperformance of the algorithm. In this paper, fullyconnected topology is being used. This systemproposes an island model based differential evolutionalgorithm to enhance the learning speed of neuralnetwork training. The results have revealed thatIMDENN has given quite promising results in termsof convergence rate smaller errors compared to otheralgorithms

Melioidosis in Myanmar

Sporadic cases of melioidosis have been diagnosed in Myanmar since the disease was first described in Yangon in 1911. Published and unpublished cases are summarized here, along with results from environmental and serosurveys. A total of 298 cases have been reported from seven states or regions between 1911 and 2018, with the majority of these occurring before 1949. Findings from soil surveys confirm the presence of Burkholderia pseudomallei in the environment in all three regions examined. The true epidemiology of the disease in Myanmar is unknown. Important factors contributing to the current gaps in knowledge are lack of awareness among clinicians and insufficient laboratory diagnostic capacity in many parts of the country. This is likely to have led to substantial under-reporting