Shared transportation is widely used in current urban traffic. As a representative mode of transport, shared bikes have strong mobility and timeliness, so it is particularly critical to accurately predict the number of bikes used in an area every hour. In this paper, London bike-sharing data are selected as a data set to primarily analyze the impact of meteorological elements and time factors on bike-sharing demand. At the same time, it is important to use LSTM neural network models and popular machine learning models to predict demand for shared bikes at an hourly level. Through data analysis and visualization, the major elements affecting the bike-sharing demand are found to include humidity, peak hours, temperature, and other elements. The root mean squared error of the LSTM model is 314.17, the $R^2$ score is as high as 0.922, and the error is small in comparison to other machine learning models. Through the evaluation indicators, it can be seen that the LSTM model has the smallest error between the prediction results and the true values of the compared machine learning methods, and the change trend of the model prediction result curve is basically consistent with the actual result curve.

Emotion recognition, as an important part of human-computer interaction, is of great research significance and has already played a role in the fields of artificial intelligence, healthcare, and distance education. In recent times, there has been a growing trend in using deep learning techniques for EEG emotion recognition. These methods have shown higher accuracy in recognizing emotions when compared with traditional machine learning methods. However, most of the current EEG emotion recognition performs multi-category single-label prediction, and is a binary classification problem based on the dimensional model. This simplifies the fact that human emotions are mixed and complex. In order to adapt to real-world applications, fine-grained emotion recognition is necessary. We propose a new method for building emotion classification labels using linguistic resource and density-based spatial clustering of applications with noise (DBSCAN). Additionally, we integrate the frequency domain and spatial features of emotional EEG signals and feed these features into a serial network that combines a convolutional neural network (CNN) and a long short-term memory (LSTM) recurrent neural network (RNN) for EEG emotion feature learning and classification. We conduct emotion classification experiments on the DEAP dataset, and the results show that our method has an average emotion classification accuracy of 92.98% per subject, validating the effectiveness of the improvements we have made to our emotion classification method. Our method for emotion classification holds potential for future use in the domain of affective computing, such as mental health care, education, social media, and so on. By constructing an automatic emotion analysis system using our method to enable the machine to understand the emotional implications conveyed by the subjects’ EEG signals, it can provide healthcare professionals with valuable information for effective treatment outcomes.

A virtual machine placement optimization model based on optimized ant colony algorithm is proposed. The model is able to determine the physical machines suitable for hosting migrated virtual machines. Thus, it solves the problem of redundant power consumption resulting from idle resource waste of physical machines. First, based on the utilization parameters of the virtual machine, idle resources and energy consumption models are proposed. The models are dedicated to quantifying the features of virtual resource utilization and energy consumption of physical machines. Next, a multi-objective optimization strategy is derived for virtual machine placement in cloud environments. Finally, an optimal virtual machines placement scheme is determined based on feature metrics, multi-objective optimization, and the ant colony algorithm. Experimental results indicate that compared with the traditional genetic algorithms-based MGGA model, the convergence rate is increased by 16%, and the optimized highest average energy consumption is reduced by 18%. The model exhibits advantages in terms of algorithm efficiency and efficacy.

Outlier detection is an important task in the field of data mining and a highly active area of research in machine learning. In industrial automation, datasets are often high-dimensional, meaning an effort to study all dimensions directly leads to data sparsity, thus causing outliers to be masked by noise effects in high-dimensional spaces. The “curse of dimensionality” phenomenon renders many conventional outlier detection methods ineffective. This paper proposes a new outlier detection algorithm called EOEH (Ensemble Outlier Detection Method Based on Information Entropy-Weighted Subspaces for High-Dimensional Data). First, random secondary subsampling is performed on the data, and detectors are run on various small-scale sub-samples to provide diverse detection results. Results are then aggregated to reduce the global variance and enhance the robustness of the algorithm. Subsequently, information entropy is utilized to construct a dimension-space weighting method that can discern the influential factors within different dimensional spaces. This method generates weighted subspaces and dimensions for data objects, reducing the impact of noise created by high-dimensional data and improving high-dimensional data detection performance. Finally, this study offers a design for a new high-precision local outlier factor (HPLOF) detector that amplifies the differentiation between normal and outlier data, thereby improving the detection performance of the algorithm. The feasibility of this algorithm is validated through experiments that used both simulated and UCI datasets. In comparison to popular outlier detection algorithms, our algorithm demonstrates a superior detection performance and runtime efficiency. Compared with the current popular, common algorithms, the EOEH algorithm improves the detection performance by 6% on average. In terms of running time for high-dimensional data, EOEH is 20% faster than the current popular algorithms.

Recent studies demonstrate that algorithmic music attracted global attention not only because of its amusement but also its considerable potential in the industry. Thus, the yield increased academic numbers spinning around on topics of algorithm music generation. The balance between mathematical logic and aesthetic value is important in music generation. To maintain this balance, we propose a research method based on a three-dimensional temporal convolutional attention neural network. This method uses a self-collected traditional Chinese pentatonic symbolic music dataset. It combines clustering algorithms and deep learning-related algorithms to construct a three-dimensional sequential convolutional generation model 3D-SCN, a three-dimensional temporal convolutional attention model BoYaTCN. We trained both of them to generate traditional Chinese pentatonic scale music that considers both overall temporal creativity and local musical semantics. Then, we conducted quantitative and qualitative evaluations of the generated music. The experiment demonstrates that BoYaTCN achieves the best results, with a prediction accuracy of 99.12%, followed by 3D-SCN with a prediction accuracy of 99.04%. We have proven that the proposed model can generate folk music with a beautiful melody, harmonious coherence, and distinctive traditional Chinese pentatonic features, and it also conforms to certain musical grammatical characteristics.