Accurate detection and diagnosis of brain tumors at early stages is significant for effective treatment. While numerous methods have been developed for tumor detection and classification, several rely on traditional techniques, often resulting in suboptimal performance. In contrast, AI-based deep learning techniques have shown promising results, consistently achieving high accuracy across various tumor types while maintaining model interpretability. Inspired by these advancements, this paper introduces an improved variant of EfficientNet for multi-grade brain tumor detection and classification, addressing the gap between performance and explainability. Our approach extends the capabilities of EfficientNet to classify four tumor types: glioma, meningioma, pituitary tumor, and non-tumor. For enhanced explainability, we incorporate gradient-weighted class activation mapping (Grad-CAM) to improve model interpretability. The input MRI images undergo data augmentation before being passed through the feature extraction phase, where the underlying tumor patterns are learned. Our model achieves an average accuracy of 98.6%, surpassing other state-of-the-art methods on standard datasets while maintaining a substantially reduced parameter count. Furthermore, the explainable AI (XAI) analysis demonstrates the model’s ability to focus on relevant tumor regions, enhancing its interpretability. This accurate and interpretable model for brain tumor classification has the potential to significantly aid clinical decision-making in neuro-oncology.

Conventional deduplication systems face critical challenges such as excessive write amplification, high read/write latency, and sub-optimal storage utilization. These limitations often undermine the performance benefits of deduplication by slowing down I/O acknowledgements due to amplified deduplication I/Os, excessive data chunk replication, and strict consistency requirements. To address these issues, we present Speed-Dedup, a novel deduplication framework that employs a deduplicated primary–semi-deduplicated replica object approach. This strategy reduces write amplification by restricting deduplication to the primary object while maintaining a semi-deduplicated replica object used for immediate read/write acknowledgements, thus enhancing I/O latency and storage efficiency. Speed-Dedup also replaces traditional strong consistency models with eventual consistency, allowing for non-blocking read operations and improving overall system throughput. Experimental results demonstrate that Speed-Dedup significantly outperforms traditional methods like GRATE and CAO, showing up to 21% improvement in I/O performance under low deduplication ratios and maintaining 14% or more gains under higher ratios. Additionally, write amplification is substantially reduced and latency improves by over 100% with faster recovery times during system failures. These findings highlight the effectiveness of Speed-Dedup as a scalable and efficient solution.

In recent years, significant advancements have been made in spatiotemporal sequence prediction, with PredRNN++ emerging as a powerful model due to its superior ability to capture complex temporal dependencies. However, the current unidirectional nature of PredRNN++ limits its ability to fully exploit the temporal information inherent in many real-world sequences. In this research, we propose an enhancement to the PredRNN++ model by incorporating a bidirectional network, enabling the model to consider both past and future contexts during prediction. This bidirectional extension enhances the model’s ability to predict sequences accurately and reliably, especially for data with intricate temporal patterns. Our experimental results demonstrate that the Bidirectional PredRNN++ outperforms the original model across several benchmark datasets, highlighting its potential for a wide range of applications in spatiotemporal data analysis.