Reliable and accurate brain tumor segmentation is a challenging task even with the appropriate acquisition of brain images. Tumor grading and segmentation utilizing Magnetic Resonance Imaging (MRI) are necessary steps...
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Reliable and accurate brain tumor segmentation is a challenging task even with the appropriate acquisition of brain images. Tumor grading and segmentation utilizing Magnetic Resonance Imaging (MRI) are necessary steps for correct diagnosis and treatment planning. There are different MRI sequence images (T1, Flair, T1ce, T2, etc.) for identifying different parts of the tumor. Due to the diversity in the illumination of each brain imaging modality, different information and details can be obtained from each input modality. Therefore, by using various MRI modalities, the diagnosis system is capable of finding more unique details that lead to a better segmentation result, especially in fuzzy borders. In this study, to achieve an automatic and robust brain tumor segmentation framework using four MRI sequence images, an optimized Convolutional Neural Network (CNN) is proposed. All weight and bias values of the CNN model are adjusted using an improved chimp optimization algorithm (IChOA). In the first step, all four input images are normalized to find some potential areas of the existing tumor. Next, by employing the IChOA, the best features are selected using a Support Vector Machine (SVM) classifier. Finally, the best-extracted features are fed to the optimized CNN model to classify each object for brain tumor segmentation. Accordingly, the proposed IChOA is utilized for feature selection and optimizing Hyperparameters in the CNN model. The experimental outcomes conducted on the BRATS 2018 dataset demonstrate superior performance (Precision of 97.41 %, Recall of 95.78 %, and Dice Score of 97.04 %) compared to the existing frameworks.
Water damage accidents occur frequently in mines in China, and accurate prediction of incoming water has become an important guarantee for the safe and efficient mining of coal resources. To improve the accuracy of mi...
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Water damage accidents occur frequently in mines in China, and accurate prediction of incoming water has become an important guarantee for the safe and efficient mining of coal resources. To improve the accuracy of mine water prediction, this paper proposes the VMD-iCHOA-GRU mine water prediction model by selecting and improving it according to the previous research results in decomposition method, time series prediction model and optimizationalgorithm. After processing the raw data and setting the model parameters, MAE, RMSE, MAPE and R2 are selected as the evaluation indexes of prediction accuracy, and VMD-GRU model, iCHOA-GRU model, CHOA-GRU model and GRU model are selected as the comparison models to validate the advantages of the VMD-iCHOA-GRU model in the prediction of mine inrush water. The results show that the VMD-iCHOA-GRU model has the best prediction effect on the trend of water inflow, with the evaluation index values of 0.00862, 0.01059, 0.02189%, 0.87079, respectively, and with the smallest MAE, RMSE, MAPE, and the largest R2, and the highest prediction accuracy of the VMD-iCHOA-GRU model.
PM2.5 pollution in the atmosphere not only contaminates the environment but also seriously affects human health. Therefore, studying how to accurately predict future PM2.5 concentrations holds significant importance a...
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PM2.5 pollution in the atmosphere not only contaminates the environment but also seriously affects human health. Therefore, studying how to accurately predict future PM2.5 concentrations holds significant importance and practical value. This paper innovatively PM25proposes a high-accuracy prediction model: RF-ICHOACNN-LSTM-Attention. First, the Random Forest (RF) model is utilized to evaluate the importance of air pollution and meteorological features and select more suitable input features. Subsequently, a one-dimensional convolutional neural network (1DCNN) with efficient feature extraction capability is used to extract dynamic features from sequences. The extracted feature vector sequences are then fed into a Long Short-Term Memory Network (LSTM). After the LSTM, an Attention Mechanism is incorporated to assign different weights to the input features, emphasizing the role of the important features. Additionally, the improved chimp optimization algorithm (IChOA) is employed to optimize the number of neurons in the two hidden layers of LSTM, the learning rate, and the number of training epochs. The experimental results on 12 test functions demonstrate that the optimization performance of IChOA is better than that of ChOA and the representative swarm optimizationalgorithms used for comparison. In the case of PM2.5 predictions in Yining and Beijing, experimental results show that the proposed model achieved the best performance in terms of RMSE, MAE, and R2 This indicates its excellent prediction accuracy and generalization capability, Thus proving its effectiveness in predicting PM2.5 concentration in the real world.
In recent times, the several cyber attacks are occurred on the network and thus, essential tools are needed for detecting intrusion over the network. Moreover, the network intrusion detection systems become an importa...
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In recent times, the several cyber attacks are occurred on the network and thus, essential tools are needed for detecting intrusion over the network. Moreover, the network intrusion detection systems become an important tool thus, it has the ability to safeguard the source data from all malicious activities or threats as well as protect the insecurity among individual privacy. Moreover, many existing research works are explored to detect the network intrusion model but it fails to protect the target network efficiently based on the statistical features. A major issue in the designed model is regarded as the robustness or generalization that has the capability to control the working performance when the data is attained from various distributions. To handle all the difficulties, a new meta-heuristic hybrid-based deep learning model is introduced to detect the intrusion. Initially, the input data is garnered from the standard data sources. It is then undergone the pre-processing phase, which is accomplished through duplicate removal, replacing the NAN values, and normalization. With the resultant of pre-processed data, the auto encoder is utilized for extracting the significant features. To further improve the performance, it requires choosing the optimal features with the help of an improved chimp optimization algorithm known as IChOA. Subsequently, the optimal features are subjected to the newly developed hybrid deep learning model. The hybrid model is built by incorporating the deep temporal convolution network and gated recurrent unit, and it is termed as DINet, in which the hyper parameters are tuned by an improved IChOA algorithm for attaining optimal solutions. Finally, the proposed detection model is evaluated and compared with the former detection approaches. The analysis shows the developed model is suggested to provide 97% in terms of accuracy and precision. Thus, the enhanced model elucidates that to effectively detect malware, which tends to improve data
In the evolving landscape of the smart grid(SG),the integration of non-organic multiple access(NOMA)technology has emerged as a pivotal strategy for enhancing spectral efficiency and energy ***,the open nature of wire...
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In the evolving landscape of the smart grid(SG),the integration of non-organic multiple access(NOMA)technology has emerged as a pivotal strategy for enhancing spectral efficiency and energy ***,the open nature of wireless channels in SG raises significant concerns regarding the confidentiality of critical control messages,especially when broadcasted from a neighborhood gateway(NG)to smart meters(SMs).This paper introduces a novel approach based on reinforcement learning(RL)to fortify the performance of *** by the need for efficient and effective training of the fully connected layers in the RL network,we employ an improved chimp optimization algorithm(IChOA)to update the parameters of the *** integrating the IChOA into the training process,the RL agent is expected to learn more robust policies faster and with better convergence properties compared to standard optimization *** can lead to improved performance in complex SG environments,where the agent must make decisions that enhance the security and efficiency of the *** compared the performance of our proposed method(IChOA-RL)with several state-of-the-art machine learning(ML)algorithms,including recurrent neural network(RNN),long short-term memory(LSTM),K-nearest neighbors(KNN),support vector machine(SVM),improved crow search algorithm(I-CSA),and grey wolf optimizer(GWO).Extensive simulations demonstrate the efficacy of our approach compared to the related works,showcasing significant improvements in secrecy capacity rates under various network *** proposed IChOA-RL exhibits superior performance compared to other algorithms in various aspects,including the scalability of the NOMA communication system,accuracy,coefficient of determination(R2),root mean square error(RMSE),and convergence *** our dataset,the IChOA-RL architecture achieved coefficient of determination of 95.77%and accuracy of 97.41%in validation *** was accompanied by the lowes
Removing redundant features and improving classifier performance necessitates the use of meta-heuristic and deep learning (DL) algorithms in feature selection and classification problems. With the maturity of DL tools...
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Removing redundant features and improving classifier performance necessitates the use of meta-heuristic and deep learning (DL) algorithms in feature selection and classification problems. With the maturity of DL tools, many data-driven polarimetric synthetic aperture radar (POLSAR) representation models have been suggested, most of which are based on deep convolutional neural networks (DCNNs). In this paper, we propose a hybrid approach of a new multi-objective binary chimpoptimizationalgorithm (MOBChOA) and DCNN for optimal feature selection. We implemented the proposed method to classify POLSAR images from San Francisco, USA. To do so, we first performed the necessary preprocessing, including speckle reduction, radiometric calibration, and feature extraction. After that, we implemented the proposed MOBChOA for optimal feature selection. Finally, we trained the fully connected DCNN to classify the pixels into specific land-cover labels. We evaluated the performance of the proposed MOBChOA-DCNN in comparison with nine competitive methods. Our experimental results with the POLSAR image datasets show that the proposed architecture had a great performance for different important optimization parameters. The proposed MOBChOA-DCNN provided fewer features (27) and the highest overall accuracy. The overall accuracy values of MOBChOA-DCNN on the training and validation datasets were 96.89% and 96.13%, respectively, which were the best results. The overall accuracy of SVM was 89.30%, which was the worst result. The results of the proposed MOBChOA on two real-world benchmark problems were also better than the results with the other methods. Furthermore, it was shown that the MOBChOA-DCNN performed better than methods from previous studies.
Internet of Medical Things (IoMT) is a promising field that is widely used in healthcare applications nowadays. The IoMT is an extension of the Internet of Things (IoT) that is utilized for the processing, generation,...
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Internet of Medical Things (IoMT) is a promising field that is widely used in healthcare applications nowadays. The IoMT is an extension of the Internet of Things (IoT) that is utilized for the processing, generation, collection, and evaluation of medical data. However, the most concerning issue in IoMT is regarding the privacy and protection of the IoMT data. Because privacy preservation of information is the major concern in IoT-based healthcare systems. Hence, there is a need for a trustworthy end-to-end system, which can tolerate even insider attacks. The core aim of this research work is to promote the latest secure technique for transmitting the data from sink nodes to the server and also to ensure the security level of communication between them. Here, the gathered sink node is authenticated by deep hybrid methods of combining the Auto Encoder (AE) and Bidirectional Long Short-Term Memory (BiLSTM). This hybrid model is named as Auto Encoder-Bidirectional Long Short-Term Memory (AE-Bi-LSTM), in which the parameters are tuned by the improved chimp optimization algorithm (IChoA). Hence, data privacy preservation makes to secure communication in the networks. The proposed privacy-prevention model is utilized for maintaining communication between sink nodes and servers. The communication is done through three different steps (a) Sanitation, (b) Optimal key generation, and (c) Restoration. Moreover, from the overall result analysis, the accuracy and precision rate of the designed IChoA-AE-Bi-LSTM approach are 95.58 and 91.93%. The experimental analysis shows that the designed model offers a better authentication scheme and guarantees data privacy than traditional models. Moreover, applying a high amount of energy will reduce the lifetime of the sensor nodes. Here, privacy preservation is a challenging task due to fraudulent attacks from third-party service providers. In the future, advanced techniques will be implemented with lightweight encoded mechanisms.
Detecting and recognizing marine mammals have found serious attention recently, given the inhomogeneous underwater sound propagation environment. Meanwhile, the emphasis on applying machine learning and swarm-based in...
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Detecting and recognizing marine mammals have found serious attention recently, given the inhomogeneous underwater sound propagation environment. Meanwhile, the emphasis on applying machine learning and swarm-based intelligence algorithms has become stronger. This paper developed an accurate dolphin recognizer and proposed a multi-layer perceptron neural network (MLPNN). Despite many capabilities, MLPNNs suffer from severe built-in defects tackling real-world problems, including convergence rate, entrapment in local minima, and sensitivity to initialization. Therefore, the chimpoptimizationalgorithm (ChOA) is first utilized to optimize the MLPNN parameters. Furthermore, the modified-chimp concept is introduced and applied to improve ChOA efficiency. Finally, a common benchmark dataset is used, followed by developing an experimental dolphin vocalization dataset to evaluate the performance of the designed model. The results are verified by a comparative study with Slime Mould algorithm (SMA), Harris Hawks optimization (HHO), Henry Gas Solubility optimization (HGSO), and Kalman Filter (KF) approaches, as well as classic ChOA, based on recognition accuracy, convergence speed, and entrapment in local minima. The simulation results indicated that the weighted-chimp optimizer and the proposed recognizer outperform other benchmark recognition methods significantly.
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