In the age of security around data and machine learning, it is important to protect that data while enabling means to effectively analyze it.This paper proposes a secure framework that combines cryptographic data protection with deep learning techniques, allowing meaningful knowledge extraction without sacrificing data security. A CMAP authentication protocol is proposed for securing data exchange on federated cloud servers and brings together cryptography, machine learning, and statistical models to contextualize data. The cryptographic algorithm selected for data exchange was the DES (Data Encryption Standard) algorithm for encryption. Data preparation for neural network and deep learning processing included techniques such as one-hot encoding, tokenization, stemming, stop-word removal, and normalization. The various available deep learning models (CNN, LSTM, and GRU) were evaluated for predictive analysis and data classification; the data was defined as binary to ensure compatibility with numerous machine learning algorithms.This paper highlights that merging cryptographic algorithms with machine learning is achievable while positively affecting the relationship between data privacy and analysis.

Healthcare data continues to face challenges related to centralized storage vulnerabilities, unauthorized access, and a lack of transparency. Blockchain technology offers a decentralized and tamper-resistant solution; however, ensuring secure identity verification and privacy-preserving access control remains a key requirement in healthcare environments. This paper presents NBBZVF, a blockchain-driven identity verification framework that integrates Non-Fungible Tokens (NFTs) for data ownership and Zero-Knowledge Proofs (ZKPs) for confidential authentication. The proposed architecture utilizes Solidity-based smart contracts, wallet-based identity mechanisms, and IPFS for secure data storage and retrieval. Experimental results demonstrate enhanced protection against identity fraud, selective and controlled access to sensitive medical records, and efficient data integrity validation. This approach strengthens patient-centric security while supporting scalable and interoperable healthcare applications.

In the context of smart cities, Non-Fungible Tokens (NFTs) are transforming digital art markets by enabling secure, decentralized transactions. As NFT trading grows, incorporating intelligence and adaptability becomes crucial—making Machine Learning (ML) integration essential. However, existing models, particularly Cooperative Game Theoretic Trading (CoGTT) frameworks, underutilize ML across all trading phases. Key gaps include limited real-time adaptability, suboptimal negotiation strategies, and inadequate buyer–seller matchmaking. This research addresses these gaps by integrating ML into a three-phase CoGTT framework—ML-augmented Naive Trading, Min–Max Price Negotiation, and Equilibrium-Based Trading—to enhance decision-making and pricing. The methodology applies ML algorithms such as decision trees, clustering, and reinforcement learning (Q-learning) within a public blockchain–based simulation environment using smart contracts. The simulation uses a customized dataset reflecting both market dynamics and artist credibility. The dataset is synthetically generated to emulate an NFT marketplace while maintaining controlled experimental conditions, which may limit direct applicability to volatile real-world markets. Zero-knowledge proofs (ZKPs) are employed to preserve privacy. ZKPs are employed to preserve privacy. A comparative analysis of ML models for NFT price estimation and strategic bidding demonstrates the effectiveness of combining predictive algorithms with reinforcement learning. Linear Regression and Random Forest models both accurately estimate NFT prices, with Random Forest achieving higher real-time prediction accuracy (R2 = 0.9920). K-Means clustering effectively segments market participants to support targeted negotiation, achieving a silhouette score of 0.8178. Integrating Q-learning with Random Forest enables dynamic bidding strategies that minimize the gap between recommended and actual prices. The discrete action set (decrease, stay, increase) supports interpretable, real-time bid adjustments. These findings highlight the potential for ML-driven NFT trading systems to support scalable, privacy-compliant digital marketplaces in smart cities, aligning trading behavior with market demands through automated, data-driven processes.

Healthcare systems are essential for patient care and data management but face persistent challenges with data security, interoperability, and transparency. Traditional centralized storage models increase data breach risks and limit patients’ control over personal health information, complicating seamless data sharing across providers. However, centralized storage systems still lack the security and interoperability required for robust data sharing. To address this, our study proposes a ZKP-based verification system integrated with NFT blockchain technology, aimed at enhancing identity verification while ensuring data privacy and patient control. In our method, NFTs serve as unique digital identifiers linked to patient records, while zero-knowledge proofs (ZKPs) confirm data ownership without disclosing sensitive information. Our findings indicate that this approach mitigates identity fraud risks, strengthens data security, and offers a patient-centered system for selective data access. Beyond patient records, our solution supports transparency in clinical trials and pharmaceutical supply chains, combating counterfeiting and improving quality control. In our paper, we proposed a ZKP-based verification system integrated with NFT blockchain technology to improve identity verification in healthcare, offering a robust, patient-centered approach to data security and integrity.

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).

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.

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.

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

The need for secure and efficient methods for managing and verifying Verifiable Credentials (VCs) is becoming increasingly important. The proposed system aims to revolutionize the processes of credential issuance and sharing by facilitating seamless interactions between issuers, credential holders, and verifiers. It ensures secure issuance, storage, and verification of credentials through QR-based mechanisms while adhering to the ELMO framework standards, which are currently focused on academic applications. The platform is built on the OpenID Connect Authentication Protocol, integrated with the Ed25519 encryption algorithm and blockchain technology, prioritizing user privacy, usability, and standardization across multiple domains. This approach allows users to retain ownership of their data while sharing only the necessary information without exposing sensitive details, particularly in academic and professional fields. The objective is to create a user-friendly, secure, and reliable credentialing ecosystem with significant implications for employment, education, and other sectors, ultimately benefiting society.

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.

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.

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

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.

An NFT (non-fungible token) marketplace is a platform where people can buy and sell one-of-a-kind digital items such as art work, music, and videos. These items are represented by NFTs, which are not exchangeable for other tokens or assets and are stored on a blockchain. The growing use of blockchain technology and the desire for individuals to own and trade rare digital items have fueled the growth of NFT marketplaces. The purpose of this paper is to demonstrate the creation and working of an NFT marketplace named “TokenFuse” and discuss its features as well as the background technologies that keep it running. Our NFT marketplace uses the power of Inter Planetary File System (IPFS) Technology to provide artists with a unique and secure way to sell their original works as NFTs. In contrast to traditional marketplaces that rely on centralized servers and databases, our platform stores and distributes NFTs in a decentralized and peer-to-peer network, ensuring security and redundancy. It also provides a unique and cost-effective way for authors to monetize their works by taking advantage of blockchain technology. Our platform has extremely low fees, making it one of the most affordable options on the market.

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.