Advanced technology is being adopted by the medical industry to enhance people’s overall health and make life easier. The introduction of 6G communication technology alongside the Internet of Things (IoT) possesses the capacity to revolutionize medical services, making them more advanced, and improving the infrastructure with the use of smart wearables and devices. However, this also brings some challenges, especially when maintaining the privacy and safety of the patient’s medical information. Blockchain technology can tackle these problems by providing a distributed and secure framework. When blockchain technology and the IoT are combined, the healthcare industry will be transformed. The tackling of issues like data security, personalized patient data management, and maintenance of data integrity can be performed in a better way. Healthcare practices become more reliable due to the distributed structure of blockchain, which guarantees the immutability of data. This review focuses on how combining blockchain technology with the IoT can help revolutionize the medical industry. It gives a fundamental introduction to blockchain technology, its architecture, the IoT, decentralizing IoT with blockchain, and its applications.

Brain tumors are life-threatening conditions that require early and accurate detection for effective treatment planning. Traditional diagnostic methods, such as MRI scans and biopsies, depend heavily on radiologist interpretation, making the process time-consuming and susceptible to errors. Deep learning has revolutionized medical imaging by automating tumor detection and classification. Although Convolutional Neural Networks (CNNs) have been widely used, their limited capacity to capture long-range dependencies in images can hinder performance. Vision Transformers (ViT-B16) offer a promising alternative by processing images as sequences of patches, allowing for better analysis of global contextual information. This paper presents a deep learning-based framework for brain tumor classification using ViT-B16. The model leverages transfer learning and pretraining on large-scale datasets to enhance feature extraction and improve classification accuracy. A comparative analysis with CNN-based models, including VGG-16, ResNet-50, and YOLOV10, shows that ViT-B16 achieves superior accuracy and reliability in brain tumor detection. This study contributes to improving early tumor diagnosis, streamlining the medical imaging workflow, and advancing AI-driven healthcare solutions.

It is becoming harder to manage the growing amounts of waste generated daily at an increasing rate. These problems require an efficient solution that guarantees effectiveness and transparency and maintains trust within the community. To improve the process of traditional waste management, we proposed a unique solution, “GREENLINK”, which uses a combination of blockchain technology with the concept of zero-knowledge proofs (ZKPs), non-fungible tokens (NFTs), and Walrasian equilibrium. Zero-knowledge proofs (cryptographic protocols) are used to verify organizations and prove compliance (e.g., certification, recycling capacity) without disclosing sensitive information. Through an iterative bidding process, the proposed framework employs Walrasian equilibrium, a technique to balance supply and demand, guaranteeing equitable pricing and effective resource distribution among participants. The transactions and waste management activities are securely recorded on an immutable ledger, ensuring accountability, traceability, and transparency. The performance of the proposed model is evaluated. Parameters like average latency, TPS, and memory consumption are calculated using Hyperledger Caliper (a blockchain performance benchmark framework).

The rapid expansion of digital art markets has highlighted challenges in ensuring efficient, fair, and profitable auctions for non-fungible tokens (NFTs). Existing NFT auction platforms often face issues with optimizing auction parameters to maximize revenue while maintaining transparency and security. This work addresses these challenges by proposing a blockchain-based NFT auction framework that leverages an English auction model optimized through a Modified Dynamic Time-Extension Queueing Model (MDTEQM). To account for uncertainty, Monte Carlo simulations are used to evaluate auction performance, estimate expected revenue, assess the precision of these estimates, and verify the effectiveness and stability of the MDTEQM approach. Smart contracts following the ERC-721 standard were developed in Solidity, with ANKR facilitating transaction processing and IPFS via Pinata managing decentralized digital asset storage. Auction processes are algorithmically defined within smart contracts to optimize parameters for revenue maximization. Cost analysis demonstrates the solution’s economic feasibility. Performance evaluation using Hyperledger Caliper assessed latency, throughput, CPU, and memory usage across key token operations—createToken, buyToken, and resellToken. createToken exhibited the highest latency (up to 11.95 s) and lowest throughput, while resellToken showed the best performance with latency as low as 8.51 s and highest throughput. CPU utilization ranged from 70–80%, with memory usage averaging 675–755 MB. Monte Carlo simulations modeled dynamic bid arrivals and time extensions, demonstrating that increasing simulation sizes reduces variability and narrows confidence intervals in expected revenue estimation. The expected revenue stabilizes near $450 with higher simulations, balancing computational cost and reliability. This research offers a scientifically rigorous and practically scalable solution for next-generation NFT auction ecosystems

In today's modern age, communication can be challenging when individuals or groups are not physically nearby. Hence, digital communication has become indispensable in our daily lives, facilitating interactions through video calls, phone calls, or chats across various platforms. The proposed model aims to enhance digital communication among local individuals using a local meeting framework. Its key features encompass video and audio calls, screen sharing, chat functionality, and the ability to schedule or host meetings. The envisioned application focuses on video conferencing, leveraging WebRTC, a free and open-source project that empowers web browsers and mobile apps with real-time communication capabilities via APIs. Through Local Meet, browsers can directly exchange real-time media in a peer-to-peer manner, ensuring heightened security compared to other streaming systems, all without the need for third-party software. Additionally, the proposed system includes mechanisms to alert groups about network connectivity issues, particularly when the network fails to support the required bandwidth. It is primarily designed for online classes and discussions on various subjects. Additionally, Local Meet assists organizers in tracking attendance and saving the information discussed during sessions.

Scene identification in Very High-Resolution (VHR) photography presents a formidable challenge. Although Convolutional Neural Networks (CNNs) have enhanced accuracy in feature learning, their deep layers often struggle to accurately depict object relationships within images. To address this limitation, the paper introduces an advanced Multilayer Perceptron (MLP) acting as a deep classifier, utilizing RMSprop and Adadelta optimizers for classification. Our proposed model, CNN-MLP, merges the strengths of MLP and CNN methods. It utilizes a pre-trained CNN, devoid of fully-connected layers, for feature generation, supplemented by data augmentation (DA) techniques to enrich the training dataset. The resulting feature maps undergo classification using an MLP, achieving an outstanding classification performance. The model excels in identifying barren and farm land, even within the same image, showcasing its efficacy in scene classification. This success is demonstrated using three publicly available VHR image datasets UC-Merced, Aerial Image (AID), RSI CB 128, NWPURESISC45 combined to and also create a blended dataset with the overall 96.5 % percentage of the accuracy

Distributed Ledger Technology (DLT) is a decentralized database system where transactions are recorded and verified across multiple nodes. Its key features include immutability, time-stamping, and consensus-based validation. Numerous DLT applications are in supply chain management, intellectual property, cross border payments, energy trading, real estate, and online donations. DeFi, a combination of cryptocurrency and blockchain technology, offers financial services without intermediaries. Hence transactions with various digital assets based on cryptocurrency price feeds needs an automated framework. Smart contracts, self-executing contracts with terms directly written into code, automate processes and reduce manual intervention. This paper proposes a decentralized financial trading model using the AAVE protocol. AAVE is a decentralized platform that allows for transactions with various digital assets based on cryptocurrency price feeds. Proposed model uses Chainlink, a decentralized oracle network, to provide accurate and reliable price feeds. IPFS is used for data storage, while Graph is employed for indexing and querying blockchain data. The paper presents an example of a DeFi protocol to simulate banking operations, showcasing the potential of DeFi and DLT in revolutionizing traditional banking processes.

A smart city is a technologically modern urban area that uses different modes of electronic methods and sensors to collect specific data. The information collected can be used efficiently and effectively to improve the quality of operations across the city. Fifth generation (5G) technology for wireless mobile communication is best suited for smart city services, which provide higher data rates, increased traffic capacity, ultra-low latency, and high connection density. Rich healthcare sector (HCS) is one core foundation block for any smart city, which will benefit from a wide range of vital communication infrastructure provided by 5G. The purpose of this chapter is to analyze the effects and ramifications of 5G in HCS from a wide range of perspectives. The technological setting and the financial advantages of 5G are also covered in this chapter, keeping smart cities in mind. More information is provided on the model that is suggested for the HCS for the 5G-enabled smart city.

Many IOT in the healthcare industry offers improved medical facilities for patients, benefiting doctors' offices and hospitals as well. The Internet of Medical Things (IOMT) plays a crucial role in enhancing the accuracy, reliability, and efficiency of electronic devices within the healthcare sector. Researchers are advancing a digital healthcare system by connecting existing medical resources and healthcare services. While IOT is impacting various industries, our focus is on its research contributions to the healthcare sector. The proposed system integrates multiple medical equipment, such as sensors and web or mobile-based applications, enabling communication across a network. This system facilitates the monitoring and storage of patient health data and medical information during the pandemic situation and after math. Additionally, using machine learning and data analysis, the proposed system aids in predicting the severity of the pandemic in different zones, offering potential solutions. The system seeks to improve outcomes for post-COVID-19 patients by using a multimodal approach to track their health.

Currently, numerous applications in modern life incorporate IoT (Internet of Things). The fundamental principle of IoT involves connecting various devices to the internet to access information. Among these devices are sensor nodes (SN) that utilize Wireless Sensor Networks (WSN) for internet connectivity. The SN's battery life will steadily decrease as it is communicating with the BS (Base Station). Several methods are suggested to lengthen the lifespan of the SNs. By using a hybrid technique that separates the entire WSN network into clusters and leverages the cluster head (CH) to cut down on communication between the IOT devices. The suggested hybrid model focuses on managing the WSN-IOT energy efficiently. The system enhances its energy efficiency during communication, which includes inter-cluster communication, intra-cluster communication, and communication with the BS. The proposed model utilizes M-LEACH, M-CS, and M-PSO algorithms in distinct stages. The network is divided into three levels: The first level concentrates on inter-cluster formation and CH selection using the M-LEACH. In the second stage, M-CS facilitates communication between multiple CHs and the BS. Finally, the M-PSO algorithm is employed based on the threshold of CH nodes for improved outcomes.

In the realm of computer vision, image processing, and image retrieval is one of the most rapidly growing disciplines. The image can be retrieved using the annotation and the content-based. CBIR (Content-based image retrieval) is becoming more popular in a range of areas, as well as data mining, education, diagnostic imaging, preventing crime, weather prediction and remote sensing. We can browse, search and retrieve images using an image retrieval system. This paper presents the CBIR using an orthogonal combination (OC) the Local Binary pattern (LBP), center-symmetric local binary pattern (CS-LBP) and PCA (Principal Component Analysis) of LBP. By computing the Gray-level difference, this approach connects the referenced pixel to its surrounding neighbors. Choosing the most appropriate images that correspond to the search image from the stored database is the paper's primary objective. The proposed model has a combination of OC-LBP, CS-LBP, and PCA-LBP.

Cache oblivious algorithms are designed to get the good benefit from any of the underlying hierarchy of caches without the need to know about the exact structure of the cache. These algorithms are cache oblivious i.e., no variables are dependent on hardware parameters such as cache size and cache line length. Optimal utilization of cache memory has to be done in order to get the full performance potential of the hardware. We present here the miss rate comparison of cache oblivious matrix multiplication using the sequential access recursive technique and normal multiplication program. Varying the cache size the respective miss rates in the L1 cache are taken and then comparison is done. It is found that the miss rates in the L1 cache for the cache oblivious matrix multiplication program using the sequential access recursive technique is comparatively lesser than the naive matrix multiplication program. © 2013 IEEE.