Cancer accounts for a huge mortality rate due to its aggressiveness, colossal potential of metastasis, and heterogeneity (causing resistance against chemotherapy). Lung and colon cancers are among the most prevalent types of cancer around the globe that can occur in both males and females. Early and accurate diagnosis of these cancers can substantially improve the quality of treatment as well as the survival rate of cancer patients. We propose a highly accurate and computationally efficient model for the swift and accurate diagnosis of lung and colon cancers as an alternative to current cancer detection methods. In this study, a large dataset of lung and colon histopathology images was employed for training and the validation process. The dataset is comprised of 25000 histopathology images of lung and colon tissues equally divided into 5 classes. A pretrained neural network (AlexNet) was tuned by modifying the four of its layers before training it on the dataset. Initial classification results were promising for all classes of images except for one class with an overall accuracy of 89%. To improve the overall accuracy and keep the model computationally efficient, instead of implementing image enhancement techniques on the entire dataset, the quality of images of the underperforming class was improved by applying a contrast enhancement technique which is fairly simple and efficient. The implementation of the proposed methodology has not only improved the overall accuracy from 89% to 98.4% but has also proved computationally efficient.

This rapidly changing digital world is always sensitive to improving security and resilience to protect the inhabitants of this ecosystem in terms of data, processes, repositories, communication, and functions. The transformation of this digital ecosystem is heavily dependent on cloud computing, as it is becoming the global platform for individuals, corporates, and even governments. Therefore, the concerns related to security are now linked closely with cloud computing. In this paper, a multi-cloud security framework takes a view on the development of security mechanisms to provide a diversion to the attacker. The purpose is to gain more time to analyze the attack and mitigate the intrusion without compromises. This mechanism is designed using the honeypot technology that has been around for some time but has not been used in cloud computing and other technologies. The proposed framework provides modules related to managing the multi-cloud platform, the intrusion detection and prevention system, and honeypots. The results show significant improvement in the accuracy of detecting attacks. These results are generated in a two-phase scenario, and the first phase has been analyzed without the engagement of the honeypot module presented in the framework. The second phase has been executed with same parameters and conditions by engaging the honeypot module. It includes a comparison taxonomy of both results and an in-depth study of existing honeypots, as well as critical design elements for current honeypot research and outstanding concerns for future honeypots in IoT, multi-cloud contexts.

Kubernetes is an open-source container management tool which automates container deployment, container load balancing and container(de)scaling, including Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA). HPA enables flawless operation, interactively scaling the number of resource units, or pods, without downtime. Default Resource Metrics, such as CPU and memory use of host machines and pods, are monitored by Kubernetes. Cloud Computing has emerged as a platform for individuals beside the corporate sector. It provides cost-effective infrastructure, platform and software services in a shared environment. On the other hand, the emergence of industry 4.0 brought new challenges for the adaptability and infusion of cloud computing. As the global work environment is adapting constituents of industry 4.0 in terms of robotics, artificial intelligence and IoT devices, it is becoming eminent that one emerging challenge is collaborative schematics. Provision of such autonomous mechanism that can develop, manage and operationalize digital resources like CoBots to perform tasks in a distributed and collaborative cloud environment for optimized utilization of resources, ensuring schedule completion. Collaborative schematics are also linked with Bigdata management produced by large scale industry 4.0 setups. Different use cases and simulation results showed a significant improvement in Pod CPU utilization, latency, and throughput over Kubernetes environment.

True-Context (TContext) is a gamified VR platform for fire hazard recognition; empirical evidence shows it outperforms non-contextual VR delivery, but its theoretical positioning relative to established learning frameworks remains unarticulated. This study examined TContext against thirteen learning frameworks to: (1) assess the degree of theoretical alignment between TContext’s design and each framework’s prescriptions; (2) identify significant gaps; and (3) derive design recommendations to strengthen TContext’s theoretical completeness and future development. Using qualitative document analysis of the published TContext corpus and the canonical literature on thirteen learning frameworks, the study examined alignment, divergence, and potential integration. We coded each framework using a directed content analysis approach, then analyzed six cross-framework themes (social/collaborative mediation, individualization/adaptivity, reflective/metacognitive processing, learner agency, transfer/longitudinal validation, and design-inferred versus measured-outcome evidence) to identify recurring patterns. The analysis produced 52 documented strengths and 44 limitations. TContext aligned most strongly with ELT, CLT, CTML, LM-GM, and Constructivism; moderately with FT, Gamification of Learning, LBD, and DT; partially with TPACK and LPS; and least with SDL and Social Constructivism. Four persistent cross-framework gaps emerged: the absence of collaborative learning affordances, the lack of adaptive personalization, the lack of longitudinal retention measurement, and constrained learner agency within pre-scripted scenarios, along with seven targeted design recommendations to address each gap. TContext emerges as a novel multi-framework integrative model whose three core constructs, Contextual Embeddedness, Temporal Stratification, and Distributed Awareness, are not collectively anticipated by any existing framework. Future research should test the recommendations, measure longitudinal retention, and validate the model cross-culturally beyond the UAE.