Systems and Soft Computing最新文献

With the development of the internet, the number of short video platform users has increased quickly. People's social entertainment mode has gradually changed from text to short video, generating many multimodal data. Therefore, traditional single-modal sentiment analysis can no longer fully adapt to multimodal data. To address this issue, this study proposes a short video sentiment analysis model based on multimodal feature fusion. This model analyzes the text, speech, and visual content in the video. Meanwhile, the information of the three modalities is integrated through a multi-head attention mechanism to analyze and classify emotions. The experimental results showed that when the training set size was 500, the recognition accuracy of the multimodal sentiment analysis model based on modal contribution recognition and multi-task learning was 0.96. The F1 score was 98, and the average absolute error value was 0.21. When the validation set size was 400, the recognition time of the multimodal sentiment analysis model based on modal contribution recognition and multi-task learning was 2.1 s. When the iterations were 60, the recognition time of the multimodal sentiment analysis model based on modal contribution recognition and multi-task learning was 0.9 s. The experimental results show that the proposed multimodal sentiment analysis model based on modal contribution recognition and multi-task learning has good model performance and can accurately identify emotions in short videos.

This work presents a semi-supervised multilayer neural network (MLNN) with an Autoencoder to develop a classification model for recognizing hand gestures from electromyographic (EMG) signals. Using a Myo armband equipped with eight non-invasive surface-mounted biosensors, raw surface EMG (sEMG) sensor data were captured corresponding to five hand gestures: Fist, Open hand, Wave in, Wave out, and Double tap. The sensor collected data underwent preprocessing, feature extraction, label assignment, and dataset organization for classification tasks. The model implementation, validation, and testing demonstrated its efficacy after incorporating synthetic sEMG data generated by an Autoencoder. In comparison to the state-of-the-art techniques from the literature, the proposed model exhibited strong performance, achieving accuracy of 99.68%, 100%, and 99.26% during training, validation, and testing, respectively. Comparatively, the proposed MLNN with Autoencoder model outperformed a K-Nearest Neighbors model established for comparative evaluation.

Cross media knowledge information retrieval provides strong support for information processing and utilization in the information society, but there are problems such as heterogeneity in cross media knowledge information. Therefore, a cross media knowledge information retrieval model using D-S evidence theory is proposed, which involves using approximate calculation methods to improve this theory for information fusion, reducing computational complexity, and using deep networks for fine-grained information retrieval to improve retrieval accuracy. The results showed that the improved theory enhanced computational efficiency by about 27.23 %. The memory usage was <60 %, and the average accuracy of information fusion reached 93.14 %. It also exhibited high recall and low false alarm rates. The cross media knowledge information retrieval model proposed in the study achieved accuracy of 92.64 %, 96.49 %, and 97.46 % on the three datasets used in the experiment, respectively. The study provides an effective, computationally efficient, and highly accurate model for cross media knowledge information retrieval, which is expected to promote research and application in this field. The combination of improved D-S evidence theory and deep networks provides a powerful approach to solving the problem of cross media heterogeneous information retrieval, which has a positive promoting effect on the processing and utilization of information in the information society.

In the fierce competition of the electricity market, how to consolidate and develop customers is particularly important. Aiming to analyze the electricity consumption characteristics of customer groups, this paper used a k-means algorithm and optimized it. The number of clusters was determined by the Davies-Bouldin index (DBI). An improved Harris Hawks optimization (IHHO) algorithm was designed to realize the initial cluster center selection. Based on data such as electricity purchase and average electricity price, electricity customer groups were clustered using the IHHO-k-means algorithm. The IHHO-k-means algorithm achieved the best clustering effect on Iris, Wine, and Glass datasets compared with the traditional k-means and PSO-k-means algorithms. Taking Iris as an example, the optimal value of the IHHO-k-means algorithm was 96.538, with an accuracy rate of 0.932, precision and recall rates of 0.941 and 0.793, respectively, an F-measure of 0.861, and an area under the curve (AUC) value of 0.851. In the customer dataset, the number of clusters determined by DBI was 4. The power customers were divided into four groups with different characteristics of electricity consumption, and their electricity consumption behaviors were analyzed. The results prove the reliability of the IHHO-k-means algorithm in analyzing electricity consumption characteristics of customer groups, and it can be applied in practice.

The need for more accurate GDP predictions in Nigeria has necessitated the exploration of additional indicators that reflect economic activities and socio-economic factors. This research pioneers a comprehensive approach to predicting Nigeria's Gross Domestic Product (GDP) by integrating a wide array of indicators beyond traditional economic metrics. The primary objective is to enhance the prediction accuracy of Nigeria's GDP using a diverse range of socio-economic indicators. Drawing from data spanning 2000 to 2021, the study incorporates variables like healthcare expenditure, net migration rates, population demographics, life expectancy, access to electricity, and internet usage. Utilising machine learning techniques such as Random Forest Regressor, XGBoost Regressor, and Linear Regression, the study rigorously evaluates the efficacy of these algorithms in forecasting GDP. The analysis reveals that all selected indicators have a strong correlation with GDP. Significantly, the Random Forest Regressor emerges as the most robust model, boasting an R² score of 0.96 and a Mean Absolute Error (MAE) of 24.29. The study underscores that optimising factors like healthcare, internet access, and electricity availability could serve as pivotal levers for accelerating Nigeria's economic growth.

In response to the missing discrete variables in current wireless network topology optimization, an improved particle swarm optimization algorithm based on fusion of discrete variables is proposed to obtain network discrete variables. A wireless network topology optimization model is constructed. The research results indicate that it has better anti-interference performance in complex situations, which contributes to improving network load balancing. The topology obtained by this method has independence and predictability. When optimizing network topology, it has high network node coverage. When the network nodes are 50, 100, 150, and 200, the connectivity is 99.85 %, 93.64 %, 91.25 %, and 90.18 %, respectively. The testing time is 19s, 34s, 54s, and 64s respectively, which has the best optimization performance. The method can effectively improve the missing discrete variables in wireless network topology optimization, which has good performance.