Artificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing flower pollination algorithm in the environmental field to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement. We estimate the regression function of the semi-parametric model by estimating the parametric model and estimating the non-parametric model, the parametric model is estimated by using an instrumental variables method (Wald method, Bartlett’s method, and Durbin’s method), The nonparametric model is estimated by using kernel smoothing (Nadaraya Watson), K-Nearest Neighbor smoothing and Median smoothing. The Flower Pollination algorithms were employed and structured in building the ecological model and estimating the semi-parametric regression function with measurement errors in the explanatory and dependent variables, then compare the models to choose the best model used in the environmental scope measurement errors, where the comparison between the models is done using the mean square error (MSE).
Objectives: To evaluate the families’ attitudes toward environment pollution, and determine the relationship
between families’ attitudes towards environment pollution and their demographic characteristics of age,
education, type of family, and socioeconomic status.
Methodology: A descriptive design is carried throughout the present study to evaluate families’ attitudes toward
environment pollution for the period of October 5th2013 to May 7th2014. A non-probability "purposive" sample of
(110) families’ is selected. The sample is comprised of two groups; (75) urban families’ and (35) rural ones. An
evaluation tool is designed and constructed for the purpose of the study. It is consisted of (4) main parts;
dem
Mercury is a heavy metal that is extremely toxic. There are three types of it: inorganic, organic, and elemental. Mercury in all its forms has been shown to have harmful effects on living things. It can multiply its concentration from lower to higher trophic levels and accumulate in the body's various tissues. Aquatic organisms bodies have been exposed to mercury mostly through various human activities. The largest source of mercury pollution in the air is thermal power plants that mostly use coal as fuel. It is carried to a body of water after being deposited on the ground surface from the air. The way it enters the food chain is through aquatic plants and animals. Mercury accumulations in the kidney, liver, gills, or gonadal tissu
... Show MoreMercury is a heavy metal that is extremely toxic. There are three types of it: inorganic, organic, and elemental. Mercury in all its forms has been shown to have harmful effects on living things. It can multiply its concentration from lower to higher trophic levels and accumulate in the body's various tissues. Aquatic organisms bodies have been exposed to mercury mostly through various human activities. The largest source of mercury pollution in the air is thermal power plants that mostly use coal as fuel. It is carried to a body of water after being deposited on the ground surface from the air. The way it enters the food chain is through aquatic plants and animals. Mercury accumulations in the kidney, liver, gills, or gonadal tissues of sp
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In this research provide theoretical aspects of one of the most important statistical distributions which it is Lomax, which has many applications in several areas, set of estimation methods was used(MLE,LSE,GWPM) and compare with (RRE) estimation method ,in order to find out best estimation method set of simulation experiment (36) with many replications in order to get mean square error and used it to make compare , simulation experiment contrast with (estimation method, sample size ,value of location and shape parameter) results show that estimation method effected by simulation experiment factors and ability of using other estimation methods such as(Shrinkage, jackknif
... Show MoreEnergy efficiency is a significant aspect in designing robust routing protocols for wireless sensor networks (WSNs). A reliable routing protocol has to be energy efficient and adaptive to the network size. To achieve high energy conservation and data aggregation, there are two major techniques, clusters and chains. In clustering technique, sensor networks are often divided into non-overlapping subsets called clusters. In chain technique, sensor nodes will be connected with the closest two neighbors, starting with the farthest node from the base station till the closest node to the base station. Each technique has its own advantages and disadvantages which motivate some researchers to come up with a hybrid routing algorit
... Show MoreIn networking communication systems like vehicular ad hoc networks, the high vehicular mobility leads to rapid shifts in vehicle densities, incoherence in inter-vehicle communications, and challenges for routing algorithms. It is necessary that the routing algorithm avoids transmitting the pockets via segments where the network density is low and the scale of network disconnections is high as this could lead to packet loss, interruptions and increased communication overhead in route recovery. Hence, attention needs to be paid to both segment status and traffic. The aim of this paper is to present an intersection-based segment aware algorithm for geographic routing in vehicular ad hoc networks. This algorithm makes available the best route f
... Show MoreAn intrusion detection system (IDS) is key to having a comprehensive cybersecurity solution against any attack, and artificial intelligence techniques have been combined with all the features of the IoT to improve security. In response to this, in this research, an IDS technique driven by a modified random forest algorithm has been formulated to improve the system for IoT. To this end, the target is made as one-hot encoding, bootstrapping with less redundancy, adding a hybrid features selection method into the random forest algorithm, and modifying the ranking stage in the random forest algorithm. Furthermore, three datasets have been used in this research, IoTID20, UNSW-NB15, and IoT-23. The results are compared with the three datasets men
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