Traditional sport was based on the ability of the players and less science and knowledge was considered. However, sport has become a profession and an industry. Therefore, the use of technology and analysis on data in order to achieve goals is very important. Classification is one of technologies to classify new incoming samples. Furthermore, sports produce considerable information about each season, teams, matches and players. Classification on sport data helps managers and coaches in order to predict the match result, evaluate the player performance, predict the player injury, identify the sports talent and evaluate the match strategy. There are many algorithms to predict the basketball results, track the health of players and determine the strategy of the match against different opponents, which help coaches a lot. Further, preprocessing procedure makes better dataset. In this paper, we use classification methods on sport dataset using preprocessing procedure and without preprocessing. The results show an improvement was obtained results using preprocessing. Manuscript profile

Radio Frequency Identification Systems (RFID) are wireless identification technology that provide communication and identification of tagged objects through wireless communications. In some applications, a single reader is not enough to cover a specific identification area, so readers are placed very close to each other for optimal connectivity and coverage. This environment is called Dense Reader Environments (DREs). Such networks are susceptible to reader collision problems that cause decreases the performance of network. The reader-to-reader collision is an important challenge in dense reader RFID networks, that lead to the reading throughput barrier and degrade the system performance. In this paper we propose a reader anti-collision protocol based on the density of each reader in order to improve the network's throughput in dense RFID systems. Proposed algorithm develops based on GDRA but in this new approach each reader choice a time slot based on its density. Simulation results show that proposed algorithm provide higher throughput in compression with GDRA. Manuscript profile

Wireless sensor network includes hundreds or even thousands of sensor nodes, which are devices with low energy and often impossible to be replaced or recharged. For this reason, efficient use of energy is one of the major challenges in these networks. One way for increasing energy efficiency in such networks is clustering. Clustering protocols such as LEACH, TEEN, SEP and DEEC randomly select the cluster heads for the clustering process. This causes the cluster heads to be close together, decreasing the efficiency of protocols. For this reason, we proposed a new approach called EBDCH. In this method, when cluster heads are close together, we distribute them based on the energy situation. The proposed approach runs on the clustering protocols. The simulation results show that with the implementation of the proposed method, throughput is increased and network lifetime is improved compared to protocols LEACH, DEEC, SEP and EEHC respectively at 26%,28%,37% and 36%. Manuscript profile