A combination of quantum mechanics principles and metaheuristic algorithm frameworks has given birth to quantum-inspired soft computing techniques. These methods augment population diversity, which is vital for effective global exploration, through the probabilistic nature of quantum bits. This paper also describes recent developments in quantum-inspired soft computing techniques, highlighting the merits of linking optimization techniques with the fundamentals of quantum mechanics in a wide variety of practical and industrial domains. Additionally, we discuss these algorithms’ enhancements and modifications and recognize the problems within this area. We collated algorithms presented from 2017 to 2023 and grouped them according to their motivating basis. Quantum-Inspired Soft Computing mainly uses Genetic and Evolutionary Algorithms as its primary methods. Following these, Swarm-Based Techniques are the next most widely applied approaches. Quantum-Inspired Neural Networks also play a role in this field, though they are used less frequently than the first two methods. Some areas where these methods have been applied include image processing and network optimization, as well as interdisciplinary areas like aviation management and civil engineering. The positive outcomes obtained from quantum-inspired soft computing imply that traditional algorithms can be enriched in the future by using the concepts of quantum to address optimization challenges across various domains.

This chapter presents an innovative approach to tackle the pressing need for early and precise detection of skin cancer, a disease with increasing global prevalence and potentially life-threatening consequences. The challenge of accurately identifying skin lesions is compounded by the diverse appearance of human skin and the presence of hair, demanding sophisticated machine learning (ML) techniques. Our proposed model addresses this challenge by harnessing a comprehensive dataset covering a wide spectrum of skin tones, ensuring robust representation in the training data. Leveraging state-of-the-art ML algorithms, especially convolutional neural networks (CNNs) and transfer learning, the system proficiently recognizes patterns indicative of skin cancer lesions. To enhance adaptability to skin diversity, meticulous calibration using data augmentation techniques is employed, significantly contributing to efforts to mitigate health disparities in dermatological diagnoses. With skin cancer incidence escalating globally, timely detection is paramount for effective treatment. To address this urgency, our chapter presents a deep learning framework for skin lesion classification in images, seamlessly integrating with the Internet of Medical Things (IoMT). This integration empowers the system to remotely assist medical specialists in diagnosing and treating skin cancer, extending its impact beyond traditional clinical settings. Performance evaluation metrics underscore the superiority of our framework over other pretrained architectures in terms of precision, recall, and accuracy, emphasizing the imperative of developing inclusive ML models to advance skin cancer detection across diverse populations.

Cutting-edge technologies to find diseases in plants are important for farming. Crop diseases threaten the world's food security. Thus, quick and precise disease diagnosis minimizes output losses and promotes sustainable farming methods. This work presents a new idea on how agricultural disease diagnosis can be transformed using IoT and ML with UAVs. Hubs with strategically placed Internet of Things (IoT) sensors capture the real-time picture of the environmental conditions prevailing in farms and high-definition pictures of the crops are taken using cameras installed in the farm and through drones. This data is used by machine learning (ML) models, namely Convolutional Neural Networks (CNNs), to reliably and early identify illnesses. We can improve decisions by associating environmental parameters with outbreaks of diseases by integrating IoT capabilities. The outcomes demonstrate promise for lowering the requirement for chemical treatments and continuously displaying excellent accuracy in illness identification, implementation achieved a reduction in chemical pesticide use by 40%, and an increase in crop yields by 25% coupled with a decrease in crop diseases by 30%. This research makes a very impressive contribution to the efforts of advocating for sustainable farming practices and the global fight against food insecurity. The study concludes with a range of comprehensive usability assessments, including exploratory evaluations. Additionally, it presents reflections on potential future developments and acknowledges current limitations in the sector of agriculture.

The rapid advancement and incorporation of Artificial Intelligence (AI) and the Internet of Things (IoT) have created exceptional opportunities to revolutionize healthcare and treatment methods and offer significant potential for broader industrial information integration. Nevertheless, the growth of intelligent healthcare systems faces challenges such as data confidentiality concerns and the safety of AI algorithms. The need for local datasets is the main problem in applying traditional AI to the development of a personalized model for health care. Thus, to tackle these issues, a novel healthcare system based on blockchain powered by federated matrix meta-learning supported by IoT. In this system, IoT devices function as light nodes, uploading local, shareable information to an edge server for model training, while non-tampered models downloaded through smart contracts handle local private data. This framework comprises four key modules: a hierarchical feature extraction module, a graph topology formulation unit, a dynamic prototype optimization algorithm, and a predictive query integration system. Blockchain technology ensures the healthcare model remains consistent and protects private data from leaks. Also, it has offered a federated matrix meta-learning model known as the federated Matrix-prototype Graph Network (MGN) to handle heterogeneous healthcare data efficiently. This model, based on metrics and graph networks, excels at capturing data distributions even with limited labeled data. To validate the efficacy of the proposed framework, we conducted extensive evaluations using two widely recognized datasets: CheXpert only for medical imaging and CIFAR 100 for general image classification. These experiments increased the performance of up to 85 percent of existing healthcare systems, demonstrating the potential of the proposed integrated approach to solve the industry’s main problems. Thus, this study advances the current discourses on the development of strong, privacy-oriented, and context-aware AI solutions for health systems, which, together with intelligent health technology, will help to raise the efficiency and effectiveness of patient care in the future.

This research in industrial engineering emphasizes data security and confidential capacity. It involves a careful assessment of mutual information, using professional terminology to enhance communication resilience. The study employs deep learning techniques to increase secret capacity during communication, comparing the calculated transmit power with a set threshold for maximization. The master node arranges node pairs based on power requirements, facilitating cluster formation through logical operations. This method optimizes power usage, ensuring dynamic cluster arrangement based on node pair outputs. The system's confidential capacity is evaluated and enhanced systematically. Mutual information between primary and eavesdropping channels aids in calculating confidential capacity. The research focuses on optimizing transmit power using advanced methodologies, aiming to increase secret capacity within power constraints. At decision points, the research checks if the derived transmit power maximizes confidential capacity. If so, the master node organizes nodes in ascending order based on power demands, establishing node pairs and using an OR gate to determine logical values. Nodes with the same logical value form distinct clusters, demonstrating a systematic process for enhancing confidential capacity. This professional approach contributes to the system's robustness and security, aligning with best practices in confidential information management.

Smart cities require effective and sustainable intelligent solutions in transport, economy, fuel, and government affairs to keep up with the fast rise of urbanization. Smart city infrastructure is one of the most successful options; it blends the Internet of Things (IoT), large-scale data, and the green internet. It has several issues, including unstable IoT security, apparatus preservation, safety difficulties, excessive production charges of massive data center building projects, bad impact confrontation, struggle to create green internet users' confidence, personal space easily leaked, and so on. The blockchain is a circulated peer-to-peer (P2P) system and standard intelligence secretarial know-how. This work presents a blockchain-based infrastructure for the sustainable Internet of Everything (IoE)-enabled distribution economy in super smart cities that sustenance safety and secrecy-oriented spatial-temporal decentralized secure services. The infrastructure uses cognitive fog nodes. It uses artificial intelligence (AI) to develop and quote important factual information, produces semantic analytics, and protects results in blockchain and decentralized sources to simplify distribution economy facilities. The proposed model provides a long-term incentive tool that might support secure smart city facilities, including a distribution economy, smart connections, and cyber-physical communication using blockchain and IoE.

Recent food scandals in India have heightened concerns regarding food safety and trust in food establishments. As current distribution channels struggle to meet market demands, there’s a pressing need for a robust food traceability system. This paper proposes such a system, utilizing blockchain technology and QR codes to track food items throughout the supply chain. It examines the advantages and disadvantages of decentralized systems, highlighting their role in ensuring traceability, transparency, efficiency, reliability, and security. The proposed system aims to provide comprehensive traceability, leveraging features like distributed ledgers and decentralized systems, which offer immutability, transparency, consensus, disintermediation, and smart contracts. Food security, encompassing both availability and access to food, is vital. A key aspect of food security is ensuring the safety and quality of goods within the supply chain. Promoting organic and wholesome foods can enhance the supply chain. Despite regulatory approval of packaged goods, there’s a lack of regular testing and assessment of the available supply in the market. This paper underscores the urgency for a secure and reliable food traceability system to safeguard consumer health and restore trust in the food market emphasizing its capacity to mitigate risks, optimize operations, and bolster consumer confidence in the food supply chain.

Blockchain technology can potentially revolutionize several industries, including agriculture, by addressing agri-product deception, accountability, price tampering, and low consumer trust. Several significant challenges arise due to active human involvement in the agricultural supply chain, including monetary losses, food contamination, spoilage, lack of transparency, delays, poor traceability, security issues, and data vulnerability. The decentralised computing architecture and secure distributed public ledger technology of blockchain make it a suitable substitute to address these problems and attain profitability and confidence for all parties involved. By reviewing previous research and examining case studies of blockchain start-up businesses, this article seeks to illustrate the diverse ways in which blockchain technology can be applied in the agriculture sector, additionally, it will resolve current problems. Blockchain technology presents a viable means of promoting a more reliable, superior, safer, and environmentally sustainable agri-food system in the future. Agri-Chain aims to connect different stakeholders while addressing challenges prevalent in the agriculture sector through the use of Internet of Things (IoT) devices and by implementing Ethereum smart contracts. Although blockchain use in agriculture is currently in its initial stages and faces numerous challenges, including installation costs, privacy concerns, security concerns, scalability difficulties, performance issues, and immaturity, it can revolutionize the industry.

The rising incidence of melanoma, the most common form of skin cancer, is characterized by the economic burden of skin cancer, which includes high costs for healthcare and lost productivity due to sickness. A blend of artificial intelligence and blockchain technology has resulted in a significant step forward within modern healthcare diagnostics for skin cancer. This research brings forth a novel approach to Decentralized Homomorphic Encryption using Transfer Learning for Skin Cancer (DHETL) that combines blockchain with transfer learning techniques to improve the accuracy of Convolutional Neural Networks (CNNs) in analyzing skin cancer. The DHETL model is a distributed system anchored at the convergence point of cryptography, machine learning, and blockchain for secure processing and analysis of medical images. Hence, particle swarm optimization (PSO) enhanced CNNs combined with Adam Max feature extractors are used in combination with dermatological image-based transfer learning from pre-trained models for skin cancer detection as proposed by the DHETL model located at the intersection of cryptography, machine learning, and blockchain. It also deals with limited labeled data in medical imaging through domain adaptation. Furthermore, the system incorporates homomorphic encryption to ensure patient information privacy and data security in the healthcare sector.

The field of genomic data management is rapidly evolving, with the potential to revolutionize healthcare. It can provide insights into genetic predispositions, disease prevention, conservation of genetic data for genetic diversity, personalized treatments, and understanding of rare diseases. However, traditional methods of maintaining genomic data have data security and integrity issues. This research project addresses these challenges by utilizing the power of blockchain technology to develop decentralized, secure, and efficient management in healthcare. This study introduces a blockchain-centric framework for facilitating secure access to extensive genomic data, thereby empowering researchers to retrieve pertinent genetic information without compromising individual privacy. By implementing this system, researchers can elevate the medical sector by studying genetic data, developing personalized healthcare solutions, and enhancing patient outcomes while ensuring the privacy of personal information. The outcomes of our project indicate that proprietors of genetic data can assert control, deriving advantages from the immutability and security of data inherent in blockchain technology. Consequently, this approach diminishes reliance on external entities or genetic management organizations, fostering heightened transparency and averting monopolization of genetic data. Our distinctive methodology extends beyond technological considerations, underscored by a steadfast commitment to data security, ethical data utilization, robust support for researchers, and the unwavering commitment to positioning our platform as a vanguard in global healthcare advancements.