Existing CNN architectures have been developed to train digital image datasets obtained from hardware systems operating with classical bits, such as optical cameras. With the increase of quantum computing algorithms and quantum system providers, academic research is being conducted to combine the strengths of classical computing and quantum algorithms. This fusion allows for the development of hybrid quantum systems, with proposed methods specifically for the quantum representation of digital images. While methods for transforming digital images into quantum-compatible circuits have been proposed, no study has been found on the quantum transformation of entire datasets, especially for the use of fully classical CNN architectures. This article presents the quantum image dataset transform method, which utilizes quantum circuits to transform digital images and create a new dataset of the transformed images. Each of the 10,000 digital images of 28 $$\times $$ × 28 dimensions in the MNIST handwritten digits dataset is individually sub-parts, and the common weight values of each segment are determined as the phase value to be used in the quantum circuit. The quantum outputs of each sub-part are converted into classical equivalents by creating a quantum converter, and a new digital image is obtained by combining all the sub-parts. The newly generated digital images are labeled as $${\textbf {MNIST}} {\textbf {Q}}^{{\textbf {+}}}_{{{\textbf {image}}}}$$ MNIST Q image + and are publicly shared along with the original MNIST dataset. The paper evaluates both a custom 3-layer CNN architecture and several pre-trained models, including EfficientNetV2B3, ResNet-50, DenseNet-121, and ConvNeXt Tiny. After training for 30 epochs, the 3-layer CNN architecture achieved the highest accuracy of 99.23%, significantly outperforming the pre-trained models, with DenseNet-121 achieving 81.70%, EfficientNetV2B3 64.23%, ResNet-50 53.25%, and ConvNeXt Tiny 53.41%. The results highlight the superior performance of the 3-layer CNN in adapting to the quantum-transformed dataset and demonstrate the potential of quantum transformations to enhance the learning ability of classical CNN models. This foundational research aims to pave the way for further exploration into the integration of quantum-transformed datasets in classical deep learning frameworks.

In recent years, significant progress has been observed in cloud computing. As the number of companies using the cloud increases, so does the need to protect user data. Today, the major challenges facing cloud computing include the security, protection, and processing of data belonging to users. This study aims to investigate security issues and identify appropriate security techniques in the world of cloud computing. Also, the identification of security challenges that may arise in the field of cloud computing in the future has been evaluated. In this research, we used two different research methods: systematic literature review (SLR), and survey and interview with different security experts who work in the field of cloud computing. All the security challenges, most compromised attributes, and identified mitigation techniques from both SLR and survey were presented and discussed.

Through the recommendation of personalized information, recommendation systems are essential in improving the user experience on a variety of online websites. Some popular and conventional techniques have not been able to perform well in data volume and sparse data problems. In order to compete with them, this paper presents and assesses two algorithms: RBM-Q-Learning 1 and RBM-Q-Learning 2. By proposing novel mechanisms for state representation and action selection, these algorithms attempt to overcome the shortcomings of conventional methods, utilizing Restricted Boltzmann Machines (RBMs) as a major component. The performance of recommendation algorithms on two datasets from the MovieLens platform—MovieLens 100K and MovieLens 1M—are compared in-depth through an experimental research presented in this research. The experiments have been performed based on MAE, RMSE, HR, ARHR, Diversity and Novelty measurement indicators and the proposed algorithms have found proper stability.

Accurate short-term power forecasting is essential for enhancing the efficiency and reliability of energy systems. Nonetheless, conventional techniques for forecasting struggle to detect nonlinear patterns in power time series, as maintaining both stability and accuracy in predictions is tough. This research presents a unique prediction framework that integrates a Multilayer Perceptron (MLP) with survivor-based evolutionary selection strategies. The proposed neural network architecture comprises three hidden layers containing 32, 16, and 8 neurons, respectively. This enables the network to extract features while preserving essential information progressively. A Survivor selection process is employed to enhance the model’s efficacy. This approach retains the optimal training models for subsequent training phases. This technique enhances both predictive accuracy and training efficiency. The amalgamation of Survivor-based selection methodologies with MLP architectures for short-term power generation forecasting is overlooked in the existing literature, although it holds promise. Thus, the proposed model is evaluated against established baselines, including Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR). The results from 30 distinct trials indicate that the proposed MLP (32-16-8) combined with the Survivor approach exhibits the minimal prediction errors, with a mean absolute error (MAE) of 5.3588 and a root mean square error (RMSE) of 10.0216. This strategy is superior in minimizing errors compared to alternative methods. Furthermore, statistical analyses utilizing the Wilcoxon signed-rank test and paired t-test indicate that the proposed method significantly outperforms SVR and RF, while displaying performance comparable to LR. The findings indicate that including a Survivor-based selection mechanism in the MLP training process is an effective and reliable method for forecasting short-term generation power.

In the field of soft computing, the Adaptive Neuro-Fuzzy Inference System (ANFIS) has been more well-liked in recent years for its predictive capabilities. Appropriate ANFIS parameter adjusting is critical, which creates a gap in its predictive integration with traditional optimization techniques. Although some academics have concentrated on incorporating single-objective optimization, they frequently encounter issues with reliability and stability when striving to solve problems. In this work, an innovative multi-objective optimization technique that integrates ANFIS with MOPSO_HS is introduced. The model has consistency in problem solving and shows accurate predictions for both odd and even interval input models. In addition, three actual datasets are used to demonstrate the effectiveness of the suggested model's integration. A comparison is made between the suggested integrated model and established algorithms after 20 runs of analysis. The algorithm's accuracy, stability, and dependability in resolving integration problems are demonstrated by the results, which also show how superior it is to alternative approaches.