In order to prevent air pollution and improve the living environment for residents, it is particularly important to carry out air quality forecasting. Air quality is affected by many factors, and showed significant no...
详细信息
In order to prevent air pollution and improve the living environment for residents, it is particularly important to carry out air quality forecasting. Air quality is affected by many factors, and showed significant nonlinear features. Output-input feedback Elman (OIF Elman) neural network can effectively solve non-linear problems. However, the disadvantages of OIF Elman neural network are easy to fall into local minimum, slow convergence and inflexibility. chickenswarmoptimization (CSO) algorithm has high operating efficiency and fast convergence speed. Therefore, this paper proposes an air pollution prediction model for OIF Elman neural network based on the CSO algorithm (CSO-OIF Elman neural network model). Evaluation indicators are absolute average error and accuracy rate. The efficacy of the proposed model is compared with other models such as traditional Elman neural network model, OIF Elman neural network model and Elman neural network model based on CSO algorithm (CSO-Elman neural network model). The experimental results show that CSO-OIF Elman neural network model has the best accuracy and the smallest absolute average error value, and has higher nonlinear fitting capabilities and generalization capabilities. The establishment of this model can provide useful reference value for atmospheric prediction research.
A high localization precision is obtained by the traditional chickenswarmoptimization (CSO) localization algorithms that have a good convergence and simple calculation operations. However, the ranging data between t...
详细信息
A high localization precision is obtained by the traditional chickenswarmoptimization (CSO) localization algorithms that have a good convergence and simple calculation operations. However, the ranging data between the location tags is underutilized, which results in a limited improvement of the localization precision. In order to enhance the localization precision, a novel CSO cooperative localization algorithm is proposed, and an objective function containing the ranging data between the location tags is developed. During the positioning procedure, conventional CSO positioning method uses the ranging data between the base station and the location tag to provide the initial location. On the basis of this initial location, the ranging data between the location tags is then applied for precise positioning. The simulation outcomes indicate that the novel algorithm could enhance efficiently the localization performance, and complete the synchronous positioning of all the location tags, compared to the conventional CSO algorithm.
Compressive Sensing (CS) has succeeded in presenting itself as an adequate method for Internet of Things (IoT) mainly because CS can reduce the size of raw data transmission and achieve traffic load balancing througho...
详细信息
Compressive Sensing (CS) has succeeded in presenting itself as an adequate method for Internet of Things (IoT) mainly because CS can reduce the size of raw data transmission and achieve traffic load balancing throughout networks. A recent work of CS discussed integration of CS with clustering techniques to achieve additional power saving during the transmission process. For data aggregation, CS method can be combined with cluster-based algorithms in plain CS or hybrid CS. However, total energy consumption for data aggregation using plain CS is still very large. In additional, hybrid CS data aggregation is efficient only if the cluster head (CH) receives a large enough data vector, i.e., greater than or equal to the CS measurement size. Otherwise use of CS leads to larger amount of transmissions by the CHs. In this paper, we propose a new Cluster Size Load Balancing for CS algorithm (CSLB-CS) which could achieve optimal utilization of CS method in an IoT-based sensor network. CSLB-CS includes a cluster load balancing technique that reduces total number of transmissions and improves the reconstruction process by optimizing the CS matrix. chicken swarm optimization algorithm that outperforms the other swarmalgorithms in terms of optimization accuracy and robustness is used to optimize the CS matrix. Detailed mathematical and comprehensive experimental analyses are provided to demonstrate efficiency of the proposed algorithm. Simulation results indicate that the proposed algorithm exceeds the performance of hybrid CS and plain CS in terms of network lifetime, overall energy consumption, total number of data transmitted, and reconstruction error.
Nowadays, computer networks and the Internet are unprotected from many security threats. Introducing adaptive and flexible security-related techniques is challenging because of the new types of frequently occurring at...
详细信息
Nowadays, computer networks and the Internet are unprotected from many security threats. Introducing adaptive and flexible security-related techniques is challenging because of the new types of frequently occurring attacks. An intrusion detection system (IDS) is a security device similar to other measures, including firewalls, antivirus software, and access control models devised to strengthen communication and information security. Network intrusion detection system (NIDS) plays a vital function in defending computer networks and systems. However, several issues concerning the sustainability and feasibility of existing techniques are faced with recent networks. These concerns are directly related to the rising levels of necessary human interactions and reducing the level of detection accuracy. Several approaches are designed to detect and manage various security threats in a network. This study uses Chimp chickenswarmoptimization-based Deep Long Short-Term Memory (ChCSO-driven Deep LSTM) for the intrusion detection process. A CNN feature extraction process is necessary for effective intrusion detection. Here, the Deep LSTM is applied for detecting network intrusion, and the Deep LSTM is trained using a designed optimization technique to enhance the detection performance.
order to accurately predict the changes in the throughput of port petrochemical products and facilitate the formulation of relative decisions, this paper analyzes the factors affecting the throughput of port petrochem...
详细信息
order to accurately predict the changes in the throughput of port petrochemical products and facilitate the formulation of relative decisions, this paper analyzes the factors affecting the throughput of port petrochemical products in a city through the GRA method. After sorting and selection, PCA method is used for pretreatment. In the SVM algorithm, ICSO is used to obtain the best parameters and improve the prediction accuracy and efficiency. In view of the variability of future development, three development scenarios are set up to prepare for the throughput forecast of petrochemical products in a city's port. The results show that the optimization speed of ICSO algorithm is very fast. When the training iteration is 20, the best fitness value is obtained, which is 0.0572. The training effect of ICSO-SVM algorithm is good, the gap between it and the original data is small, and the overall trend is close to the original data. In the test prediction, ICSO-SVM algorithm has the best prediction effect, and its MAE, RMSE and MAPE are the smallest. The minimum MAE is 762.2, 477.0 smaller than CSO-SVM algorithm, and the latter's MAE is 1239.2. The minimum MAPE of the proposed algorithm is 1.05%, while that of CSO-SVM algorithm is 1.71%. In general, the prediction error of ICSO-SVM algorithm is smaller. After the prediction of different development scenarios, the throughput of petrochemical products in a port of a city shows an increasing trend in the next five years. This method can be applied to the development forecast of port petrochemical products and provide reference for decision-making.
Anaemia occurs when the haemoglobin (Hgb) value falls below a certain reference range. It requires many blood tests, radiological images, and tests for diagnosis and treatment. By processing medical data from patients...
详细信息
Anaemia occurs when the haemoglobin (Hgb) value falls below a certain reference range. It requires many blood tests, radiological images, and tests for diagnosis and treatment. By processing medical data from patients with artificial intelligence and machine learning methods, disease predictions can be made for newly ill individuals and decision-support mechanisms can be created for physicians with these predictions. Thanks to these methods, which are very important in reducing the margin of error in the diagnoses made by doctors, the evaluation of data records in health institutions is also important for patients and hospitals. In this study, six hybrid models are proposed to classify non-anaemia records, Hgb-anaemia, folate deficiency anaemia (FDA), iron deficiency anaemia (IDA), and B12 deficiency anaemia by combining artificial intelligence and machine learning methods TreeBagger, Crow Search algorithm (CSA), chicken swarm optimization algorithm (CSO) and JAYA methods. The proposed hybrid models are analysed with two different approaches, with/without applying the SMOTE technique to achieve high performance by better emphasizing the importance of parameters. To solve the multiclass anaemia classification problem, fuzzy logic-based parameter optimization is applied to improve the class-based accuracy as well as the overall accuracy in the dataset. The proposed methods are evaluated using ROC criteria to build a prediction model to determine the anaemia type of anaemic patients. As a result of the study on the dataset taken from the Kaggle database, it is observed that the six proposed hybrid methods outperformed other studies using the same dataset and similar studies in the literature.
暂无评论