Traffic congestion is defined as the state on the transport which is characterized by slow speeds, longer trips duration, and more number of vehicular queuing. It is more in urban areas than compared with rural areas....
Traffic congestion is defined as the state on the transport which is characterized by slow speeds, longer trips duration, and more number of vehicular queuing. It is more in urban areas than compared with rural areas. Traffic congestion occurs when the vehicles occupy greater space than the capacity of roads. Prediction of the traffic flow is useful for common people and vehicle users. Machine learning-based prediction systems are mainly used in various real-time application areas. This research work introduces machine learning concepts to predict traffic flow in an earlier manner. Here Deep Autoencoder (DAN), Deep Belief Network (DBN), and Random Forest (RF) applied on the online dataset for traffic flow prediction. Among the three techniques, RF produces a better outcome in terms of accuracy, precision, recall, RMSE, and MSE values. RF technique produces a 92.7% accuracy value.
The presence of large quantities of Prosopis juliflora (PJ) fibers in natural habitats presents substantial threats to the environment and economies of numerous developing countries. Utilizing natural fibers in polyme...
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Static random access memory is used by most conventional processors as cache storage. To store information in the caches, other technologies have been employed, such as integrated dynamic-random access memory and Sync...
Static random access memory is used by most conventional processors as cache storage. To store information in the caches, other technologies have been employed, such as integrated dynamic-random access memory and Synchronous Dynamic Random Access Memory (SDRAM). SDRAM has a higher transmission rate than asynchronous Random Access Memory (DRAM). A memory controller is needed to control the data flow. Additionally, a high-speed storage controller consumes a lot of dynamic power. This necessitates the use of a memory controller that is optimized to reduce the power consumption of the memory controller. Due to increased power consumption and the requirement for more expensive packaging, multiple memories aren't feasible from a financial standpoint. Thus, memory sharing and power consumption are reduced, resulting in lower cost per bit. The study suggests reducing the memory controller's dynamic power by reducing switching operations and boosting refresh. These studies seek to implement a controller architecture based on the refresh method switching power optimization in an ASIC (ASIC). There is a review of the performance of the Xilinx Simulator for ISE.
作者：
C. VijayanR. ManimaranDepartment of Mathematics
Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Campus No.1 Jawaharlal Nehru Salai Vadapalani Chennai-600026 Tamilnadu India
In this paper, we are going to deal with the pricing formulae and an exchange option for the asset pricing process, which is represented by stochastic delay differential equations; we are into introducing a recently c...
In this paper, we are going to deal with the pricing formulae and an exchange option for the asset pricing process, which is represented by stochastic delay differential equations; we are into introducing a recently come into existence model in which Poisson jumps are occurring as an increase in stochastic delay differential equation using the Black Scholes Model. This method would help the investors to speculate on the market trends, control their level of risk and grow their market value most efficiently.
Live streaming using HTTP Adaptive Streaming (HAS) concentrates on an appropriate quality level of video even during the dynamic change in network conditions. The concept of utilizing super-short segments aims to mini...
Live streaming using HTTP Adaptive Streaming (HAS) concentrates on an appropriate quality level of video even during the dynamic change in network conditions. The concept of utilizing super-short segments aims to minimize the delay between the server and display. However, implementing the super-short segments streaming approach incurs substantial encoding overhead, as each segment necessitates an Instantaneous Decoder Refresh (IDR) frame and a larger quantity of HTTP GET requests to retrieve the segments. In this paper, a novel methodology of adapting the segment size of the stream dynamically concerning the buffer occupancy status at the client-side based on the Backward Q-Learning SARSA approach. The ITU-T P.1203.1 recommendation incorporates Pv model for video quality assessment within its learning process to determine an appropriate segment size for estimation. The implementation of the proposed BQSA method deals with segment size adaptation on the top of HTTP/2.0 in a cloud environment, where a 4G wireless network is used as the last mile connectivity in supporting a MPEG–DASH video streaming. The experimental results show that the proposed method provides a significantly lower server to display delay during a live streaming session.
Number based Non-Subsampled Contourlet Transform (NSCT) diminishes the difficulty of the transformation operation kernel in biomedical video compression by dispensing the requirement for floating number mathematics. Y...
Number based Non-Subsampled Contourlet Transform (NSCT) diminishes the difficulty of the transformation operation kernel in biomedical video compression by dispensing the requirement for floating number mathematics. Yet, the dynamic scope of INSCT is huge and in this manner, equipment cost is more. In this manuscript, an innovative transformation operation kernel for biomedical video compression is projected which utilizes a novel set of complex and real- esteemed -NSCT coefficients. The projected NSCT decreases the equipment cost, area utilized and propagation time by diminishing the complication with intermediary information duration. Proposed transformation can be implemented by digit parallel multiplications and serial shift operations. Thus the itinerant fault can be decreased by standardize this numeral information and shift quantity for Biomedical video compression. Intermediary information duration and area, power consumption of the proposed method is fewer.
The first mangrove forests appeared during the late cretaceous- Early tertiary period along Thethys Sea coasts. Since then, mangrove forests have faced and survived several catastrophic climate change events. The sea ...
The first mangrove forests appeared during the late cretaceous- Early tertiary period along Thethys Sea coasts. Since then, mangrove forests have faced and survived several catastrophic climate change events. The sea level is not and has not been constant. Studies carried out on mangrove peat prove mangroves extinction in various parts of the world due to abrupt sea-level rise. The response of mangrove in different geographic regions will depend on factors such as local rate of sea-level rise, coastal topography, and sediment supply rates. It is vital to understand mangrove floor dynamics to understand how mangroves will respond to rising sea-level. Rod surface elevation table is used worldwide to understand surface elevation or loss in wetlands. This paper deals with studying the work done using Rod Surface Elevation Table (RSET) technique to learn mangrove floor level changes worldwide. This literature review also reveals the gap in organised research studying mangrove surface elevation gain rates using RSET technique in India.
This paper analyzes the well-known Engset loss system in fuzzy environments, wherein all data are intrinsically imprecise, as in the real world. A solution procedure is devised to fuzzify various measures that quantif...
This paper analyzes the well-known Engset loss system in fuzzy environments, wherein all data are intrinsically imprecise, as in the real world. A solution procedure is devised to fuzzify various measures that quantify the performance of the system. It is shown that it suffices to solve a pair of nonlinear parametric problems in order to arrive at the fuzzified output. For illustration, a numerical example is also presented.
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