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The global interest in market prediction has driven the adoption of advanced technologies beyond traditional statistical models. This paper explores the use of machine learning and deep learning techniques for stock market forecasting. We propose a comprehensive approach that includes efficient feature selection, data preprocessing, and classification methodologies. The wavelet transform method is employed for data cleaning and noise reduction. Feature selection is optimized using the Dandelion Optimization Algorithm (DOA), identifying the most relevant input features. A novel hybrid model, 3D-CNN-GRU, integrating a 3D convolutional neural network with a gated recurrent unit, is developed for stock market data analysis. Hyperparameter tuning is facilitated by the Blood Coagulation Algorithm (BCA), enhancing model performance. Our methodology achieves a remarkable prediction accuracy of 99.14%, demonstrating robustness and efficacy in stock market forecasting applications. While our model shows significant promise, it is limited by the scope of the dataset, which includes only the Nifty 50 index. Broader implications of this work suggest that incorporating additional datasets and exploring different market scenarios could further validate and enhance the model's applicability. Future research could focus on implementing this approach in varied financial contexts to ensure robustness and generalizability. [ABSTRACT FROM AUTHOR]

In the context of complex structures and blurred cell boundaries present in breast cancer histopathological tissue images under a microscope, traditional thresholding methods struggle to accurately separate lesion areas in breast cancer image segmentation. To address this challenge, we propose a multi-threshold segmentation method for breast cancer images based on an improved Dandelion Optimization algorithm. This approach incorporates the concept of opposite-based learning and utilizes the improved Dandelion Optimization algorithm to calculate the maximum between-class variance as the optimization objective. Moreover, the method establishes fallback strategies and incorporates a memory matrix, while leveraging the golden jackal energy judgment mechanism to identify optimal thresholds. The experimental results show that compared with the Crow search algorithm, Harris Hawks optimization algorithm, artificial gorilla troop optimization algorithm, dandelion optimization algorithm, ocean predator algorithm, whale optimization algorithm, sparrow search algorithm, and sine cosine algorithm, and the improved Dandelion optimization algorithm achieves the highest fitness value and converges at the fastest speed when using the same threshold number, it also occupies an advantageous position in terms of peak signal-to-noise ratio, structural similarity index, feature similarity index, and mean square error. [ABSTRACT FROM AUTHOR]

To improve the tracking accuracy of moving targets in Non-Line-of-Sight (NLOS) environments and reduce positioning errors, a hybrid TDOA/AOA positioning method is proposed using the Time Difference of Arrival (TDOA) and Angle of Arrival (AOA) joint localization. The method is based on an improved multi-objective dandelion optimization algorithm. In the optimization process of the fitness function for the positioning model using the dandelion optimization algorithm, a fast non-dominated sorting method is used to determine the core dandelions, and an approximation ideal solution sorting method is employed to achieve global optimization, thereby enhancing the speed and accuracy of the model solution. By introducing a multi-objective mechanism, the optimal individual of one population guides another population, accelerating the convergence speed, balancing the exploration and exploitation abilities to a certain extent, and enhancing the optimization performance. Simulation results show that compared to TDOA/AOA, Taylor, Chan, PSO, and DOA algorithms, the proposed algorithm outperforms TDOA/AOA algorithm and other similar algorithms. The algorithm exhibits excellent performance in terms of node positioning accuracy, convergence speed, and robustness. [ABSTRACT FROM AUTHOR]