<p class="Default">The distributed object decision (DOD) was applied to choose a single solution for problem among many complexes solutions. Most of DOD systems depend on traditional technique like small form factor optical (SFFO) method and scalable and oriented fast-based local features (SOFF) method. These two methods were statistically complex and depended to an initial value. In this paper proposed new optimal technical called gray wolf optimization (GWO) which is used to determine threshold of sensor decision rules from fusion center. The new algorithm gave better performance for fusion rule than numerical results. The results are providing to demonstrate of fusion system reduced of bayes risk by a high rate of 15%-20%. This algorithm also does not depend on the initial values and shows the degree of complexity is better than other algorithms.</p>
The prediction is most important goals in economic quantitative studies, it basis in design and plan future economic policies properly process over forecasting accuracy. This paper is aiming at the problem salp swarm algorithm (SSA) for predicting grain yield is prone to fall into the local optimal problem. An improved SSA is proposed with combine with back propagation neural network. Using the different advantages of SSA algorithm in global search capabilities, combining the two for further optimize the weight, improve the accuracy and robustness of the grain yield prediction model. The specific implementation is selected from 1963 to 2013. These methods are used to define agricultural datasets that supports crop growth decision for grain product and its influencing factors were tested as a data set. The results show that, the improved salp swarm optimization can be classified as a good predict tool for the domestic food production trend in recent years compared with the SSA. This paper briefly introduces three artificial methods BP neural networks, SSA and improved SSA optimization algorithm. The natural behavior of salp, barrel-shaped plankton that are mostly water by weight optimization and combined with mixed-group of intelligent algorithm are simulated. The simulation results of grain production prediction illustrate that the predict precision of the improved SSA is much higher than of both conventional BPNN and SSA techniques and it’s very efficient and practicable.
A new proposed method is presented, where multiple antennas have been applied into HIPERLAN/2 system in addition to employing space-time diversity technique, especially the Alamouti technique. The suggested approach is used to cancel or reduce the effect of the transmitted power using a feedback signal process within the transceiver unit, especially when the antennas are closely located and working in full-mode duplexing. Several parameters including the transmitted power, the received power, and the feedback accuracy have been considered for testing the performance of the system in term of the signal to noise ratio (SNR) versus bit error rate (BER). A software programme using MATLAB and Simulink is implemented to evaluate the proposed method. The results showed that the system performance is heavily dependent on the amount of the mismatch in the feedback, the received power, and the transmitted power. The performance of the system decreases as the feedback accuracy increases when the transmitted power and the received power are constant. At the same time, the performance of the system decreases as the transmitted power increases when the received power and the mismatch are constant. Finally, the increase in the received power enhances the system performance when the other parameters are constant.
In modern agriculture, the substrate industry prefers porous materials for plants to provide water and nutrients in soilless cultivation. Composted sawdust is such a substrate. The sawdust industry is interested in avoiding composting sawdust because it is time and labor-consuming. The study objective was to evaluate whether non-composted (fresh) Bombax ceiba (red cotton tree) sawdust with added nutrients could be an alternative to composted sawdust for okra production. The sawdust was mixed with nutrients in the form of banana peels (a potassium source), eggshells (a calcium source), and urea (a nitrogen source). We conducted two independent pot experiments. Treatments were viz.: T1: non-fertilized 100% sandy clay loam soil (control) (vol/vol); T2: non-composted 100% B. ceiba sawdust (vol/vol); T3: non-composted 80% B. ceiba sawdust + 20% banana peels (vol/vol); T4: non-composted 60% B. ceiba sawdust + 20% banana peels + 20% eggshells (vol/vol); T5: non-composted 60% B. ceiba sawdust + 20% banana peels + 20% eggshells (vol/vol) + urea (@ 91 kg N ha−1). In both experiments, the germination of okra seeds was unaffected by the sawdust mixtures. The phenological development of okra was significantly greater in non-fertilized clay loam soil than in any non-composted sawdust mixtures. Plant height, leaf relative water content, stability index of the membrane, root length, chlorophyll content index, root and shoot dry and fresh weight, stem diameter, and single leaf area of okra were lower in all non-composted B. ceiba sawdust mixtures compared to the control. In contrast to T2, T5 resulted in fewer days before the first flower developed, an increase in the number of pods plant−1, length of pod plant−1, the diameter of the pod, fresh and dry weight of pod plant−1, and the seed numbers pod−1. It is concluded that amending non-composted B. ceiba sawdust with banana peels, eggshells, and urea (T5) enhanced its perspective as a growth medium for okra. Nonetheless, the amendments were unlikely to establish an adequate yield of okra, as was the case with non-fertilized sandy clay loam soil.
With the advent of the data age, the continuous improvement and widespread application of medical information systems have led to an exponential growth of biomedical data, such as medical imaging, electronic medical records, biometric tags, and clinical records that have potential and essential research value. However, medical research based on statistical methods is limited by the class and size of the research community, so it cannot effectively perform data mining for large-scale medical information. At the same time, supervised machine learning techniques can effectively solve this problem. Heart attack is one of the most common diseases and one of the leading causes of death, so finding a system that can accurately and reliably predict early diagnosis is an essential and influential step in treating such diseases. Researchers have used various data mining and machine learning techniques to analyze medical data, helping professionals predict heart disease. This paper presents various features related to heart disease, and the model is based on ensemble learning. The proposed system involves preprocessing data, selecting attributes, and then using logistic regression algorithms as meta-classifiers to build the ensemble learning model. Furthermore, using machine learning algorithms (Support Vector Machines, Decision Tree, Random Forest, Extreme Gradient Boosting) for prediction on the Framingham Heart Study dataset and compared with the proposed methodology. The results show that the feasibility and effectiveness of the proposed prediction method based on group learning provide accuracy for medical recommendations and better accuracy than the single traditional machine learning algorithm.
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