The inherent challenges of financial time series forecasting demand advanced modeling techniques for reliable predictions. Effective financial time series forecasting is crucial for financial risk management and the formulation of investment decisions. The accurate prediction of stock prices is a subject of study in the domains of investing and national policy. This problem appears to be challenging due to the presence of multi-noise, nonlinearity, volatility, and the chaotic nature of stocks. This paper proposes a novel financial time series forecasting model based on the deep learning ensemble model LSTM-mTrans-MLP, which integrates the long short-term memory (LSTM) network, a modified Transformer network, and a multilayered perception (MLP). By integrating LSTM, the modified Transformer, and the MLP, the suggested model demonstrates exceptional performance in terms of forecasting capabilities, robustness, and enhanced sensitivity. Extensive experiments are conducted on multiple financial datasets, such as Bitcoin, the Shanghai Composite Index, China Unicom, CSI 300, Google, and the Amazon Stock Market. The experimental results verify the effectiveness and robustness of the proposed LSTM-mTrans-MLP network model compared with the benchmark and SOTA models, providing important inferences for investors and decision-makers.

This paper introduces a new methodology for Alzheimer disease (AD) classification based on TensorFlow Convolu-tional Neural Network (TF-CNN). The network consists of three convolutional layers to extract AD features, a flatten-ing layer to reduce dimensionality, and two fully connected layers to classify the extracted features. The whole purpose of TensorFlow is to have a computational graph. To boost the classification performance, two main con-tributions have been done: data augmentation and multi-optimizers. The data augmentation helps to decrease over-fitting and increase the performance of the model. The training dataset images are augmented by normalizing, rotating, and cropping them. Four different optimizers are used with the TF-CNN, Adagrad, ProximalAdagrad, Adam, and RMSProp to achieve accurate classification. The re-sult demonstrates that the loss value of the Adam and RMSProp optimizers was lower than the Adagrad and ProximalAdagrad optimizers. The classification accuracy using Adam optimizer is 95.8%, while it reaches 100% when using RMSProp optimizer.

Audio classification using deep learning models, which is essential for applications like voice assistants and music analysis, faces challenges when deployed on edge devices due to their limited computational resources and memory. Achieving a balance between performance, efficiency, and accuracy is a significant obstacle to optimizing these models for such constrained environments. In this investigation, we evaluate diverse deep learning architectures, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), for audio classification tasks on the ESC 50, UrbanSound8k, and Audio Set datasets. Our empirical findings indicate that Mel spectrograms outperform raw audio data, attributing this enhancement to their synergistic alignment with advanced image classification algorithms and their congruence with human auditory perception. To address the constraints of model size, we apply model-compression techniques, notably magnitude pruning, Taylor pruning, and 8-bit quantization. The research demonstrates that a hybrid pruned model achieves a commendable accuracy rate of 89 percent, which, although marginally lower than the 92 percent accuracy of the uncompressed CNN, strikingly illustrates an equilibrium between efficiency and performance. Subsequently, we deploy the optimized model on the Raspberry Pi 4 and NVIDIA Jetson Nano platforms for audio classification tasks. These findings highlight the significant potential of model-compression strategies in enabling effective deep learning applications on resource-limited devices, with minimal compromise on accuracy.

Eye-gaze writing technology holds significant promise but faces several limitations. Existing eye-gaze-based systems often suffer from slow performance, particularly under challenging conditions such as low-light environments, user fatigue, or excessive head movement and blinking. These factors negatively impact the accuracy and reliability of eye-tracking technology, limiting the user’s ability to control the cursor or make selections. To address these challenges and enhance accessibility, we created a comprehensive dataset by integrating multiple publicly available datasets, including the Eyes Dataset, Dataset-Pupil, Pupil Detection Computer Vision Project, Pupils Computer Vision Project, and MPIIGaze dataset. This combined dataset provides diverse training data for eye images under various conditions, including open and closed eyes and diverse lighting environments. Using this dataset, we evaluated the performance of several computer vision algorithms across three key areas. For object detection, we implemented YOLOv8, SSD, and Faster R-CNN. For image segmentation, we employed DeepLab and U-Net. Finally, for self-supervised learning, we utilized the SimCLR algorithm. Our results indicate that the Haar classifier achieves the highest accuracy (0.85) with a model size of 97.358 KB, while YOLOv8 demonstrates competitive accuracy (0.83) alongside an exceptional processing speed and the smallest model size (6.083 KB), making it particularly suitable for cost-effective real-time eye-gaze applications.

The objective of this study is to explore the effectiveness of three digital shopping platforms (Plain Interactive, Marker-based Augmented Reality and Markerless Augmented Reality), on the impressions and purchase intentions of consumers. The study is mainly interested in analysing whether intelligent shopping platforms with AR elements provide any added advantage to an advertised product in the form of favourable attitude or a stronger purchase impulse. During the tests with the three shopping platforms, quantitative data was collected via computerised questionnaire. High and Low class users were statistically extracted, corresponding to the high or low probability to buy or recommend the advertised brand. The results show that Markerless AR system clearly outperforms the Marker-based AR and the Plain Interactive in terms of positive attitude from the users. The second better performing system is the Marker-based AR, which closely follows the Markerless AR, while the Plain Interactive system obtains least approval.