Brain tumor classification is essential for clinical diagnosis and treatment planning. Deep learning models have shown great promise in this task, but they are often challenged by the complex and diverse nature of brain tumors. To address this challenge, we propose a novel deep residual and region-based convolutional neural network (CNN) architecture, called Res-BRNet, for brain tumor classification using magnetic resonance imaging (MRI) scans. Res-BRNet employs a systematic combination of regional and boundary-based operations within modified spatial and residual blocks. The spatial blocks extract homogeneity, heterogeneity, and boundary-related features of brain tumors, while the residual blocks significantly capture local and global texture variations. We evaluated the performance of Res-BRNet on a challenging dataset collected from Kaggle repositories, Br35H, and figshare, containing various tumor categories, including meningioma, glioma, pituitary, and healthy images. Res-BRNet outperformed standard CNN models, achieving excellent accuracy (98.22%), sensitivity (0.9811), F1-score (0.9841), and precision (0.9822). Our results suggest that Res-BRNet is a promising tool for brain tumor classification, with the potential to improve the accuracy and efficiency of clinical diagnosis and treatment planning.

Smart devices in smart cities face the dual challenge of requiring real-time data processing and storing that data permanently on the cloud for future use. This creates a significant conflict between the need for immediate responsiveness and the demands of long-term storage. While Cloud Computing (CC) offers a viable platform for processing and storing this data, it introduces delays, longer response times, and network congestion issues. Internet of Things (IoT) devices require real-time responses, but their data must also be available for subsequent analysis and decision-making. Although edge and fog computing paradigms have been developed to address these issues, they suffer from limited resources and scalability challenges, making them inadequate for the above needs. A hybrid paradigm is often proposed to address the limitations of both cloud and edge computing. However, synchronization between cloud and edge can introduce delays, negatively affecting overall performance. The proposed framework, Regional Computing (RC), addresses big data challenges in smart cities by providing an intermediate solution between edge (limited power) and cloud (distant) computing. rc manages data during peak hours, alleviating congestion on the public network. The collected data is subsequently offloaded to cloud servers for long-term storage and detailed analysis. Preliminary findings are very encouraging, indicating substantial enhancements in reducing network congestion, minimizing delays, and lowering costs.

This paper conducts a thorough comparative analysis of optimization algorithms for an unconstrained convex optimization problem. It contrasts traditional methods like Gradient Descent (GD) and Nesterov Accelerated Gradient (NAG) with modern techniques such as Adaptive Moment Estimation (Adam), Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP). Through empirical experiments, convergence speed, solution accuracy and robustness, is evaluated providing insights to aid algorithm selection. The convergence dynamics of convex optimization, is explored analyzing classical algorithms and contemporary neural network (NN) methodologies. The study concludes with a comparative assessment of these algorithms performance metrics and their respective strengths and weaknesses.

COVID-19 poses a global health crisis, necessitating precise diagnostic methods for timely containment. However, accurately delineating COVID-19-affected regions in lung CT scans is challenging due to contrast variations and significant texture diversity. In this regard, this study introduces a novel two-stage classification and segmentation CNN approach for COVID-19 lung radiological pattern analysis. A novel Residual-BRNet is developed to integrate boundary and regional operations with residual learning, capturing key COVID-19 radiological homogeneous regions, texture variations, and structural contrast patterns in the classification stage. Subsequently, infectious CT images undergo lesion segmentation using the newly proposed RESeg segmentation CNN in the second stage. The RESeg leverages both average and max-pooling implementations to simultaneously learn region homogeneity and boundary-related patterns. Furthermore, novel pixel attention (PA) blocks are integrated into RESeg to effectively address mildly COVID-19-infected regions. The evaluation of the proposed Residual-BRNet CNN in the classification stage demonstrates promising performance metrics, achieving an accuracy of 97.97%, F1-score of 98.01%, sensitivity of 98.42%, and MCC of 96.81%. Meanwhile, PA-RESeg in the segmentation phase achieves an optimal segmentation performance with an IoU score of 98.43% and a dice similarity score of 95.96% of the lesion region. The framework’s effectiveness in detecting and segmenting COVID-19 lesions highlights its potential for clinical applications.

The emergence of online enterprises spread across continents has given rise to the need for managing the tacit knowledge and expertise of employees. Scenarios that includes the intention of the employer to find tacit expertise and knowledge of an employee that is not documented or self-disclosed have been addressed in this article. In today’s world management of tacit knowledge has become important for the organizations. Recent studies have also proposed hosting tacit knowledge management module over cloud for large global enterprises. There are many conceptual frameworks for the acquisition and processing of the tacit knowledge. This article has proposed a reputation based approach utilizing social interactions of employees to identify the expert based on the tacit knowledge. The existing reputation-based approaches towards expertise ranking in enterprises utilize PageRank, Normal distribution, and the Hidden Markov model for expertise ranking. These models suffer negative referral, collusion, reputation inflation, and dynamism. However, the authors have proposed a Bayesian-based approach utilizing the beta probability distribution reputation model for employee ranking in enterprises that can be hosted as a cloud service for the employees of the enterprise. The experimental results reveal improved performance compared to previous techniques in terms of mean average error (MAE) for the three data sets. The proposed scheme is able to differentiate categories of interactions in a dynamic context. The results reveal that the technique is independent of the rating pattern and density of data.

We evaluated whether predictive alerts improve recovery in DriftGuard-SLA, a bounded controller for the Kubernetes-based DeathStarBench Hotel Reservation application. A logistic model trained on 35 monitoring runs was frozen before evaluation in 350 runs comprising 70 matched blocks, five policies, six faults, and a no-fault condition. For new sustained service-level objective breaches within 10 s, monitoring-run predictions achieved a Matthews correlation coefficient of 0.285, tie-aware average precision of 0.174, precision of 0.725, and recall of 0.119. Compared with monitoring alone, the complete controller reduced violation time by 7.70 s (95% confidence interval, 0.00–21.40; Holm-adjusted p = 0.031). Compared with fixed rules sharing its action mapping, the reduction was inconclusive at 1.08 s (−1.07–5.18; Holm-adjusted p = 0.453). Excluding four pre-injection actions yielded a descriptive sensitivity estimate of 0.02 s (−1.21–1.40). Of 38 faulted run actions, 4 preceded injection, 14 occurred during an active breach, 14 preceded a new onset, 5 had neither relation during complete observation, and one was censored. These findings support the feasibility of bounded intervention and paired evaluation, but do not establish superior predictive triggering, cost savings, or generalization beyond this benchmark.