In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.
Maulticollinearity is a problem that always occurs when two or more predictor variables are correlated with each other. consist of the breach of one basic assumptions of the ordinary least squares method with biased estimates results, There are several methods which are proposed to handle this problem including the method To address a problem and method To address a problem , In this research a comparisons are employed between the biased method and unbiased method with Bayesian using Gamma distribution method addition to Ordinary Least Square metho
... Show MoreMelanoidins can be diagnosed using the Fourier transform infrared (FTIR) technique. UV/Vis is an effective tool for both qualitative and quantitative analysis of chemical components in melanoidin polymers. The structural and vibrational features of melanoidin synthesized from D-glucose and D-fructose are identical, according to FTIR spectra, with the only difference being the intensity of bands. Using FTIR spectra, the skeleton of melanoidin is divided into seven major regions. The existence of the C=C, C=N, and C=O groups in all melanoidins formed from fructose and glucose with ammonia is confirmed by the areas ranging from 1600 to 1690 cm-1, and the band is largely evident as a broad shoulder. Both melan
... Show MoreCloud computing provides huge amount of area for storage of the data, but with an increase of number of users and size of their data, cloud storage environment faces earnest problem such as saving storage space, managing this large data, security and privacy of data. To save space in cloud storage one of the important methods is data deduplication, it is one of the compression technique that allows only one copy of the data to be saved and eliminate the extra copies. To offer security and privacy of the sensitive data while supporting the deduplication, In this work attacks that exploit the hybrid cloud deduplication have been identified, allowing an attacker to gain access to the files of other users based on very small hash signatures of
... Show MoreInformation systems and data exchange between government institutions are growing rapidly around the world, and with it, the threats to information within government departments are growing. In recent years, research into the development and construction of secure information systems in government institutions seems to be very effective. Based on information system principles, this study proposes a model for providing and evaluating security for all of the departments of government institutions. The requirements of any information system begin with the organization's surroundings and objectives. Most prior techniques did not take into account the organizational component on which the information system runs, despite the relevance of
... Show MoreEnergy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the
... Show MoreImitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co
... Show MoreWhen a vehicle is left parked in the sun for an extended period, the gathered heat causes damage to several interiors within the cabin and causes discomfort for people and animals left inside the car. In the present work, the effect of the orientation of a parked white minibus on temperature distribution and cooling load calculation is studied experimentally in an open environment. Two different cases were studied facing south and facing east. For several hours, the temperature inside the car cabin had been monitored and measured at five separate locations. The cooling load calculations are carried out based on the experimental measurements. The results show that the overheating of parked cars always happens as a result
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