Metaverse is the new buzzword in today's digital world. It is a new internet application and a new form of the social world that integrates new technologies such as; Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), Extended Reality (XR), Blockchain (BC),etc. The metaverse can be seen as a virtual world that mimics the real world. Most things that can be done in the real world, such as shopping, making new friends, attending concerts, playing games, and so on, can be done differently in Metaverse. While a person who represents himself with his presence in the real world realizes this representation in the digital world with his personal data. Hence, the Metaverse exploits personal data to create and maintain this virtual world. At this point, privacy concerns of users in Metaverse occur, and Metaverse Service Providers (MSP) should take these concerns into account. This paper focuses on privacy concerns in Metaverse, presents some measures in order to minimize these concerns, and provides a comprehensive list of personal data collected and processed in Metaverse.

This paper introduces a new privacy-preserving transfer learning framework for the classification of kidney diseases. In the proposed framework, transfer learning is employed for feature extraction, and differential privacy is used to obtain noisy gradients. A variety of CNN architectures, including Xception, ResNet50, InceptionResNetV2, MobileNet, DenseNet201, InceptionV3, and VGG19 are utilized to evaluate the proposed framework. Analysis of a large dataset of 12,400 labeled kidney CT images shows that transfer learning architectures based on the proposed framework achieve excellent accuracy ratios in privacy-preserving classification. These results demonstrate the effectiveness of the proposed framework in enabling transfer learning models to classify kidney diseases while ensuring privacy. The MobileNet architecture stands out for its exceptional performance, with an impressive accuracy of 99.83% in privacy-preserving classification. Considering the findings of this study, it is evident that the proposed framework is appropriate for the early and private diagnosis of kidney diseases and promotes the achievement of promising results in this field.

This study utilizes a robust dataset provided by Energy Exchange Istanbul (EXIST), a leading authority in energy data, which contains hourly energy consumption and production data from 01/01/2018 to 31/12/2023 across Turkey. Various machine learning and deep learning methods such as linear regression (LR), random forest (RF), support vector machines (SVR), convolutional neural networks (CNN), long short-term memory networks (LSTM), and the proposed hybrid CNN-LSTM model are applied to predict energy consumption and production more accurately. This study transforms time series data into a regression problem using the sliding window method. The experimental results show that the hybrid CNN-LSTM model outperforms the other models in forecasting total energy consumption and natural gas, hydro dam, lignite, hydro river, wind, and fuel oil production. The CNN-LSTM model achieved the lowest RMSE and MAE values and the highest R² scores. The success of the proposed hybrid approach is due to the combination of CNN's ability to identify local patterns and LSTM's ability to learn long-term dependencies. This study demonstrates the hybrid CNN-LSTM model's effectiveness in accurately forecasting energy consumption and production. It makes an important contribution to more efficient use of energy resources.

This study proposes a novel hybrid convolution and vision transformer model (ConvViT) designed to detect harmful insect species that adversely affect agricultural production and play a critical role in global food security. By utilizing a dataset comprising images of 15 distinct insect species, the suggested approach combines the strengths of traditional convolutional neural networks (CNNs) with vision transformer (ViT) architectures. This integration aims to capture local-level morphological features effectively while analyzing global spatial relationships more comprehensively. While the CNN structure excels at discerning fine morphological details of insects, the ViT’s self-attention mechanism enables a holistic evaluation of their overall configurations. Several data preprocessing steps were implemented to enhance the model’s performance, including data augmentation techniques and strategies to ensure class balance. In addition, hyperparameter optimization contributed to more stable and robust model training. Experimental results indicate that the ConvViT model outperforms commonly used benchmark architectures such as EfficientNetB0, DenseNet201, ResNet-50, VGG-16, and standalone ViT, achieving a classification accuracy of 93.61%. This hybrid approach improves accuracy and strengthens generalization capabilities, delivering steady performance during training and testing phases, thereby increasing its reliability for field applications. The findings highlight that the ConvViT model achieves high efficiency in pest detection by integrating local and global feature learning. Consequently, this scalable artificial intelligence solution can support sustainable agricultural practices by enabling the early and accurate identification of pests and reducing the need for intensive pesticide use.

Intelligent transportation systems (ITS) involve collecting, storing, and delivering real-time traffic data to optimize efficiency, enhance transportation safety, and reduce energy consumption by integrating advanced electronics, information systems, and telecommunication technologies into roads, vehicles, and related infrastructure. These systems typically use some indicators such as speed, flow, and density to predict traffic information, which are measured by geographically fixed-position sensors located on the roads. Privacy leakage is a major problem since traffic information derived from users’ or vehicles’ mobility data contains sensitive information. Consequently, existing approaches are constrained by geographical dependencies, restrictions and privacy vulnerabilities. This study introduces a state-of-the-art, privacy-preserving, feature engineering-based, geo-independent traffic speed prediction framework to address these challenges for the first time. The framework, evaluated using performance metrics such as mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE), and root-mean-square error (RMSE) on a real-world dataset, was implemented using long short-term memory, bidirectional long short-term memory, gated recurrent unit (GRU), linear regression, support vector regression, multilayer perceptron and random forest regression, and differential privacy with varying epsilon values. According to the results, GRU yielded the most favorable outcome with MAE of 7.7, MAPE of 28.5%, MSE of 132.7, and RMSE of 11.5 for Istanbul, MAE of 9.7, MAPE of 17.2%, MSE of 177.4, and RMSE of 13.2 for Eskisehir, and MAE of 3.1, MAPE of 5.4%, MSE of 12.4, and RMSE of 3.4 for Konya Roads with the epsilon value of 200, respectively. These findings highlight the framework’s potential to advance privacy-aware traffic management and urban mobility solutions, offering a transformative approach for ITS applications.