Optimization design of cross border intelligent marketing management model based on multi layer perceptron-grey wolf optimization convolutional neural network

Graph convolutional network – Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation

Emotion recognition plays a pivotal role in advancing Human-Computer Interaction (HCI) by enabling systems to understand and respond to human emotional states. Among various modalities, Speech Emotion Recognition (SER) stands out as a non-invasive, cost-effective, and temporally efficient approach for detecting emotions. This study leverages the RAVDESS dataset, which provides high-quality audio recordings for emotion classification. The proposed methodology involves preprocessing audio signals to remove noise, extracting temporal and spectral features in both time and frequency domains, and implementing machine learning models for multi-class emotion classification. The study evaluates and compares the performance of several classification models, including Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Decision Trees (DT). Experimental results demonstrate promising accuracy levels, highlighting the potential of these machine learning techniques in SER. This research contributes to the integration of SER within Brain-Computer Interfaces (BCI) and other emotion-aware applications, paving the way for enhanced interactive systems. Keywords – Speech Emotion Recognition -Machine Learning - Random Forest -Multilayer Perceptron - Support Vector Machine - Convolutional Neural Networks - Decision Tree - RAVDESS Dataset

Machine learning methods such as multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this context, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as “genomic images.” In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP. Also, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding.

MLP, CNN, LSTM and Hybrid SVM for Stock Index Forecasting Task to INDU and FTSE100

Exploring the optimization of communication strategies for animation films in the context of cross-cultural communication, this research integrates the Internet of Things (IoT) and convolutional networks. The research constructs a collaborative filtering (CF) movie recommendation model based on a graph convolutional neural network (GCN) and investigates its application in cross-cultural communication. The fusion of IoT and convolutional networks in movie communication is also analyzed, and the effectiveness of the proposed GCN-CF model is validated through comparative experiments. The results indicate that, compared to other models, the GCN-CF model achieves the lowest Root Mean Square Error (RMSE) on the MovieLens 100 K and MovieLens 1 M datasets, with values of 0.8762 and 0.8275, respectively. Compared to traditional models, the GCN-CF model exhibits significantly superior performance in terms of RMSE, with reductions ranging from 0.6 to 5.2%, highlighting its heightened detection accuracy and overall performance. Moreover, the performance of the GCN-CF model is enhanced after introducing attention mechanisms and auxiliary information on both datasets, showing an improvement of 0.4% compared to the scenario without these additions. This data demonstrates the effectiveness of attention mechanisms and auxiliary information. Finally, the research presents an animation film communication strategy based on IoT and convolutional networks, offering novel insights for film production and communication, along with positive implications for cultural exchange and the advancement of the global media industry.

To reconstruct magnetic resonance (MR) images from undersampled Cartesian k-space data, we propose an algorithm based on two deep-learning architectures: (1) a multi-layer perceptron (MLP) that estimates a target image from 1D inverse Fourier transform (IFT) of k-space; and (2) a convolutional neural network (CNN) that estimates the target image from the estimated image of the MLP. The MLP learns the relationship between 1D IFT of undersampled k-space which is transformed along the frequency-encoding direction and the target fully-sampled image. The MLP is trained line by line rather than by a whole image, because each frequency-encoding line of the 1D IFT of k-space is not correlated with each other. It can dramatically decrease the number of parameters to be learned because the number of input/output pixels decrease from N2 to N. The next CNN learns the relationship between an estimated image of the MLP and the target fully-sampled image to reduce remaining artifacts in the image domain. The proposed deep-learning algorithm (i.e., the combination of the MLP and the CNN) exhibited superior performance over a single MLP and a single CNN. And it outperformed the comparison algorithms including CS-MRI, DL-MRI, a CNN-based algorithm (denoted as Wang’s algorithm), PANO, and FDLCP in both qualitative and quantitative evaluation. Consequently, the proposed algorithm is applicable up to a sampling ratio of 25% in Cartesian k-space.

At present, most of the short-term wind speed forecasting researches directly use the original data as the input or break them down, and take the decomposed series as the input for forecasting model. There is a lack of feature analysis of the original data and the decomposed series. In this paper, from the perspective of feature analysis of wind speed, Ensemble Empirical Mode Decomposition (EEMD) and Convolutional Neural Networks (CNN) are used to decompose the sequence and extract features, and Multilayer Perceptron (MLP) is used to predict the wind speed. Firstly, EEMD is used to decompose the wind speed into a series of subsequences; Secondly, CNN is used to extract the features of each decomposition layer, and the input variables of each decomposition layer are constructed; Finally, MLP is used to predict each decomposition layer; At the same time, Adam is used to optimize the parameters of CNN and MLP. The results of case study and comparison show that EEMD-CNN-MLP-Adam has high prediction and good generalization, which can provide reference for wind speed prediction in different regions and periods.

Traditional geological mapping methods, which rely on field observations and rock sample analysis, are inefficient for continuous spatial mapping of geological features such as alteration zones. Deep learning models such as convolutional neural networks (CNNs) have ushered in a transformative era in remote sensing data analysis. CNNs excel in automatically extracting features from image data for classification and regression problems. CNNs have the ability to pinpoint specific mineralogical changes attributed to mineralisation processes by discerning subtle features within remote sensing data. In this study, we deploy CNNs with three sets of remote sensing data, namely Landsat 8, Landsat 9, and ASTER, to delineate diverse alteration zones within a mineral-rich region north of Broken Hill in western New South Wales, Australia. Our methodology involves model training using two distinct sets of training samples generated through ground truth data and a fully automated approach through selective principal component analysis (PCA). We also compare CNNs with conventional machine learning models, including k‑nearest neighbours, support vector machines, and multilayer perceptron. Our findings indicate that training with a ground truth-based dataset produces more reliable alteration maps. Additionally, we find that CNNs perform slightly better when compared to conventional machine learning models, which further demonstrates the ability of CNNs to capture spatial patterns in remote sensing data effectively. We find that Landsat 9 surpasses Landsat 8 in mapping iron oxide areas when employing the CNNs model trained with ground truth data obtained by field surveys. We also observe that using ASTER data with the CNNs model trained on the ground truth-based dataset produces the most accurate maps for two other important types of alteration zones, argillic and propylitic. This underscores the utility of CNNs in enhancing the efficiency and precision of geological mapping, particularly in discerning subtle alterations indicative of mineralisation processes, especially those associated with critical metal resources.

A new multi-data-driven spatiotemporal PM2.5 forecasting model based on an ensemble graph reinforcement learning convolutional network

This paper demonstrates exceptional performance of approximately 10.0 dB learning-based gain using the Deep Convolutional Neural Network (DCNN) for demodulation of a Rayleigh-faded wireless data signal. We simulate FSK demodulation over an AWGN Rayleigh fading channel with average signal to noise ratios (SNR) from 10 dB to 20 dB. The most recent and accurate classifier is the Deep Convolutional Neural Network (DCNN) which resulted in the lowest error bit probabilities between 0.00128 to 0.00019 for the range of SNRs. A comparative study has been applied between DCNN and other machine learning classifiers such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Multi-Layer Perceptron (MLP) which give bit error probabilities between 0.021 to 0.002, and Quadratic Discriminant Analysis (QDA) which gives bit error probabilities between 0.027 to 0.003. Frequency-shift keying (FSK) demodulation using matched filtering showed bit error probabilities between 0.025 to 0.0025. We also discuss the complexity issues with the DCNN regarding decoding rates and training set sizes. This work shows how much the DCNN would provide substantial benefit as the demodulator.

Due to the benefits of convolutional neural networks (CNNs) in image classification, they have been extensively used in the computerized classification and focus of crop pests. The intention of the current find out about is to advance a deep convolutional neural network to mechanically identify 14 species of tea pests that possess symmetry properties. (1) As there are not enough tea pests images in the network to train the deep convolutional neural network, we proposes to classify tea pests images by fine-tuning the VGGNET-16 deep convolutional neural network. (2) Through comparison with traditional machine learning algorithms Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP), the performance of our method is evaluated (3) The three methods can identify tea tree pests well: the proposed convolutional neural network classification has accuracy up to 97.75%, while MLP and SVM have accuracies of 76.07% and 68.81%, respectively. Our proposed method performs the best of the assessed recognition algorithms. The experimental results also show that the fine-tuning method is a very powerful and efficient tool for small datasets in practical problems.

Convolutional neural network (CNN) consists of a few pairs of both convolution layer and subsampling layer. Thus it has more hidden layers than multi-layer perceptron. With the increased layers, the size of convolution mask ultimately determines the total number of weights in CNN because the mask is shared among input images. It also is an important learning factor which makes or breaks CNNs learning. Therefore, this paper proposes the best method to choose the convolution size and the number of layers for learning CNN successfully. Through our face recognition with vast learning examples, we found that the best size of convolution mask is 5 by 5 and 7 by 7, regardless of the number of layers. In addition, the CNN with two pairs of both convolution and subsampling layer is found to make the best performance as if the multi-layer perceptron having two hidden layers does.

ObjectivesA subset of non-functioning pituitary macroadenomas (NFMAs) may exhibit early progression/recurrence (P/R) after tumor resection. The purpose of this study was to apply deep learning (DL) algorithms for prediction of P/R in NFMAs.MethodsFrom June 2009 to December 2019, 78 patients diagnosed with pathologically confirmed NFMAs, and who had undergone complete preoperative MRI and postoperative MRI follow-up for more than one year, were included. DL classifiers including multi-layer perceptron (MLP) and convolutional neural network (CNN) were used to build predictive models. Categorical and continuous clinical data were fed into the MLP model, and images of preoperative MRI (T2WI and contrast enhanced T1WI) were analyzed by the CNN model. MLP, CNN and multimodal CNN-MLP architectures were performed to predict P/R in NFMAs.ResultsForty-two (42/78, 53.8%) patients exhibited P/R after surgery. The median follow-up time was 42 months, and the median time to P/R was 25 months. As compared with CNN using MRI (accuracy 83%, precision 87%, and AUC 0.84) or MLP using clinical data (accuracy 73%, precision 73%, and AUC 0.73) alone, the multimodal CNN-MLP model using both clinical and MRI features showed the best performance for prediction of P/R in NFMAs, with accuracy 83%, precision 90%, and AUC 0.85.ConclusionsDL architecture incorporating clinical and MRI features performs well to predict P/R in NFMAs. Pending more studies to support the findings, the results of this study may provide valuable information for NFMAs treatment planning.

The increasing need for secure identification has led to advancements in biometric authentication. Traditional single-factor methods, such as passwords or tokens, are vulnerable to cyber threats, necessitating more robust alternatives. This study proposes a Dual Biometric Authentication System (DBAS) based on SCCM (SIFT-CNN-MLP), integrating fingerprint and iris recognition to enhance security, precision, and reliability. The system leverages state-of-the-art machine learning and computer vision techniques, including Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), and Scale-Invariant Feature Transform (SIFT). MLP and CNN models analyze intricate iris patterns, while SIFT and CNN extract distinguishing features from fingerprint ridge-valley structures. By intelligently combining authentication results from both biometrics, DBAS ensures seamless access for authorized users while effectively blocking imposters. The proposed SCCM framework demonstrates high performance in terms of accuracy. For iris authentication, MLP achieved 97.33% accuracy, while CNN outperformed with 97.92% accuracy. Similarly, for fingerprint authentication, SIFT yielded 91.30% accuracy, whereas CNN excelled with 99.23% accuracy. The SCCM-based DBAS significantly enhances authentication accuracy and robustness compared to traditional methods. It is highly effective for computer logins, mobile security, and critical infrastructure protection, making it a novel, future-proof solution for secure authentication.

It is observed that pneumonia is the largest cause of death in toddlers. Early detection would facilitate in early treatment, hence, saving many lives. This paper intend to develop a method for automating the detection of pneumonia using chest X-rays and comparing convolutional neural network and multilevel perceptron. Custom Convolutional neural network and multi-layer perceptron were implemented on a chest X-ray dataset by Kaggle. A GUI was created that accepts the chest X-ray and predicts the presence of pneumonia and gives the congestion percentage. The model accuracy was 92.63% and 77.56% for convolutional neural network and multilayer perceptron respectively. The GUI developed gives favourable outcomes. Convolutional neural network showed better results as compared to multi-layer perceptron. Thus, the custom convolutional neural network was used for the GUI.