While capturing underwater image there are lot of imposed due to low light, light variation, poor visibility. Photography is about light, but since water has an a lot more prominent density than air — around 800 times more noteworthy not all wavelengths of light travel similarly well inside it. This implies as we go down into deep water, we lose the shades of the range one by one. This is the reason submerged photographs lose all the red and orange hues even at a genuinely shallow profundity and appear to be increasingly more blue as we go deep in water, henceforth captured image need enhancement. It’s a vital research area, in this paper we will review different techniques of underwater image enhancement.
Adverse effects can be seen in the entire body due to the major disorders known as Diabetes. The risk of dangers like diabetic nephropathy, cardiac stroke and other disorders can increase severally because of the undiagnosed diabetes. Around the globe the people are suffering from this disease. For a healthy life early detection of this disease is very curtail. As the causes of the diabetes is increasing rapidly this disease might turn up as a reason for worldwide concern. Increasing the chances for a more accurate predictions and form experiences automatic learning by computational method may be provided by Machine Learning (ML). With the help of R data manipulation tool for trends development and with risk factor patterns detection in Pima Indian diabetes technique of machine learning is been used in the current researches. With the use of R data manipulation tool analysis and development five different predictive models is done for the categorization of patients into diabetic and non- diabetic. supervised machine learning algorithms namely multifactor dimensionality reduction (MDR), k-nearest neighbor (k-NN), artificial neural network (ANN) radial basis function (RBF) kernel support vector machine and linear kernel support vector machine (SVM-linear) are used for this purpose.
Over the past few years we can observe the WSNs or Wireless Sensor Network applications in various fields increasing immensely. The energy efficiency, network lifetime and clustering process prime goal is the working network's optimization is the focus of many of the routing algorithm. Keeping in mind the network homogeneity, for network performance reinforcement we suggest instead of single path to use multiple paths. For WSNs, Reinforcement Intelligence Routing Protocol (RIRP)[1]. In the multihop wireless sensor networks an efficient and effective method for security improvements local monitoring has worked well [2]. But taking in consideration the power consumption in the current practice of local monitoring is costly. For ensuring long-lived operations in the sensor network reinforcement intelligent routing protocol is critical [3]. For ensuring both the aspects improvement in security and long-lived operations, the development of mechanism that is effective and incorporated with the Reinforcement protocol is an open problem. With the help of local monitoring to solve this issue, section of the traffic going in and out of its neighbors is supervised by each node to keep a check on any suspicious behavior like unlikely long delays in packet forwarding [4]. To integrate the existing reinforcement protocol of the network and without any niggardly in the consumption of energy in the local monitoring with the help of a protocol [5]. In comparison to other protocols in this protocol the region of instability starts later. At a constant rate the nodes of the RIRP or Reinforcement Intelligent Routing Protocol dies. Few problems such as cluster head selection process, network lifetime and network stability are evaluated and worked in the technique proposed here [6]. To reduce the overload consumption as much as possible the nodes switches in between the active and sleep mode.
Clustering is effective method to increase network lifetime, energy efficiency, and connectivity of Sensor nodes in wireless sensor network. An energy efficient clustering algorithm has been proposed in this paper. Sensor nodes are clustered using K-means algorithm which dynamically forms number of clusters in accordance with number of alive nodes. Selection of suitable CH is done by fuzzy inference system by choosing three fuzzy input variable such as residual energy of Sensor node, its distance from cluster center and base station. Amount of data transmitted by member nodes to CH is reduced by machine learning that classify similar data at regular interval. The simulation results show that proposed algorithm outperforms other cluster based algorithms in terms of data received by base station, number of alive node per round, time of first node, middle node and last node to die for various density of sensor nodes and scalable conditions.
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