Brain tumors occur due to the expansion of abnormal cell tissues and can be malignant (cancerous) or benign (not cancerous). Numerous factors such as the position, size, and progression rate are considered while detecting and diagnosing brain tumors. Detecting brain tumors in their initial phases is vital for diagnosis where MRI (magnetic resonance imaging) scans play an important role. Over the years, deep learning models have been extensively used for medical image processing. The current study primarily investigates the novel Fine-Tuned Vision Transformer models (FTVTs)—FTVT-b16, FTVT-b32, FTVT-l16, FTVT-l32—for brain tumor classification, while also comparing them with other established deep learning models such as ResNet50, MobileNet-V2, and EfficientNet - B0. A dataset with 7,023 images (MRI scans) categorized into four different classes, namely, glioma, meningioma, pituitary, and no tumor are used for classification. Further, the study presents a comparative analysis of these models including their accuracies and other evaluation metrics including recall, precision, and F1-score across each class. The deep learning models ResNet-50, EfficientNet-B0, and MobileNet-V2 obtained an accuracy of 96.5%, 95.1%, and 94.9%, respectively. Among all the FTVT models, FTVT-l16 model achieved a remarkable accuracy of 98.70% whereas other FTVT models FTVT-b16, FTVT-b32, and FTVT-132 achieved an accuracy of 98.09%, 96.87%, 98.62%, respectively, hence proving the efficacy and robustness of FTVT’s in medical image processing.

Blockchain-based framework for secure and reliable land registry system

The Internet of Things (IoT) has radically transformed how patient information and healthcare monitoring are monitored and recorded and has revolutionized the area by ensuring regular 24 × 7 tracking without costly and restricted human resources and with a low mistake probability. The Internet of Medical Things (IoMT) is a subsection of the Internet of things (IoT) that uses medical equipment as things or nodes to enable cost-effective and efficient patient monitoring and recording. The IoMT can cope with a wide range of problems, including observing patients in hospitals, monitoring patients in their homes, and assisting consulting physicians and nurses in monitoring health conditions at regular intervals and issuing warning signals if emergency care is necessary. EEG signals, electrocardiograms (ECGs), blood sugar levels, blood pressure levels, and other conditions can be examined. In crucial situations, quick and real-time analysis is essential, and failure to provide careful attention can be fatal. A cloud-based IoT platform cannot handle these latency-sensitive conditions. Fog computing (FC) is a novel paradigm for assigning, processing, and storing resources to IoT devices with limited resources. Where substantial processing power or storage is required, all nodes in a fog computing scheme can delegate their jobs to local fog nodes rather than forwarding them to the cloud module at a greater distance. Identifying potential security risks and putting in place adequate security measures are critical. This work aims to examine a blockchain (BC) as a potential tool for mitigating the impact of these difficulties in conjunction with fog computing. This research shows that blockchain can overcome fog computing’s privacy and security concerns. It also discusses blockchain’s issues and limitations from the perspective of fog computing (FC) and the IoMT.

The internet of medical things (IoMT) is a smart medical device structure that includes apps, health services, and systems. These medical equipment and applications are linked to healthcare systems via the internet. Because IoT devices lack computational power, the collected data can be processed and analyzed in the cloud by more computationally intensive tools. Cloud computing in IoMT is also used to store IoT data as part of a collaborative effort. Cloud computing has provided new avenues for providing services to users with better user experience, scalability, and proper resource utilization compared to traditional platforms. However, these cloud platforms are susceptible to several security breaches evident from recent and past incidents. Trust management is a crucial feature required for providing secure and reliable service to users. The traditional trust management protocols in the cloud computing situation are centralized and result in single-point failure. Blockchain has emerged as the possible use case for the domain that requires trust and reliability in several aspects. Different researchers have presented various blockchain-based trust management approaches. This study reviews the trust challenges in cloud computing and analyzes how blockchain technology addresses these challenges using blockchain-based trust management frameworks. There are ten (10) solutions under two broad categories of decentralization and security. These challenges are centralization, huge overhead, trust evidence, less adaptive, and inaccuracy. This systematic review has been performed in six stages: identifying the research question, research methods, screening the related articles, abstract and keyword examination, data retrieval, and mapping processing. Atlas.ti software is used to analyze the relevant articles based on keywords. A total of 70 codes and 262 quotations are compiled, and furthermore, these quotations are categorized using manual coding. Finally, 20 solutions under two main categories of decentralization and security were retrieved. Out of these ten (10) solutions, three (03) fell in the security category, and the rest seven (07) came under the decentralization category.