Water management is one of the crucial topics discussed in most of the international forums. Water harvesting and recycling are the major requirements to meet the global upcoming demand of the water crisis, which is prevalent. To achieve this, we need more emphasis on water management techniques that are applied across various categories of the applications. Keeping in mind the population density index, there is a dire need to implement intelligent water management mechanisms for effective distribution, conservation and to maintain the water quality standards for various purposes. The prescribed work discusses about few major areas of applications that are required for efficient water management. Those are recent trends in wastewater recycle, water distribution, rainwater harvesting and irrigation management using various Artificial Intelligence (AI) models. The data acquired for these applications are purely unique and also differs by type. Hence, there is a dire need to use a model or algorithm that can be applied to provide solutions across all these applications. Artificial Intelligence (AI) and Deep Learning (DL) techniques along with the Internet of things (IoT) framework can facilitate in designing a smart water management system for sustainable water usage from natural resources. This work surveys various water management techniques and the use of AI/DL along with the IoT network and case studies, sample statistical analysis to develop an efficient water management framework.

Recent trends have shown a widespread increase in the landscape of digital healthcare (i.e., Healthcare 4.0) services, such as personalized healthcare, intelligent rehabilitation, telemedicine, and smart diet management, among others. These healthcare services are based on a variety of diverse requirements. Fulfilling these requirements require proactive intelligent analytics and self-sustainability of networks. Self-sustainability enables the operation of a network with minimum possible interaction from the end-users/network operators, whereas proactive intelligent analytics enables efficient management of resources in response to users’ requests. To enable healthcare 4.0 with proactive online analytics and self-sustainability, one can leverage digital twins. In this article, we present an overview and recent advances of digital twins for healthcare 4.0. An architecture of digital twins for healthcare is also proposed. Furthermore, we present several use cases of digital twins. Finally, we present open research challenges with possible solutions.

Industry 5.0 is one of the emerging stages of industrialization in which humans collaborate with cutting-edge technologies to enhance various workplace processes. The primary objective is to emphasize meeting the needs of people and provide enhanced resilience and understanding of sustainability. Industry 5.0 enables cooperation with such advanced technologies and the stakeholders in the education sector to ensure efficiency and effectiveness in the teaching-learning process. The present study provides an exhaustive review of the role of Industry 5.0 in smart education. At the outset, a brief overview of the present scenario in the education sector and its associated challenges is presented. This sets the stage for establishing the need for Education 1.0 and its progressive transition to Education 4.0. Further, the motivation to integrate Industry 5.0 with Education 4.0 and the related enabling technologies that support are discussed. The paper extensively provides a description of the application of Industry 5.0 in various educational sectors namely in medical education, further learning, distance education, engineering education, and shop floor training. The study also presents seven case studies highlighting the successful implementation of Industry 5.0 in versatile sectors and regions. Finally, the challenges of Industry 5.0 in education are discussed pointing to potential future directions of research.

Security, data privacy and decentralization are significant challenges in the Internet of Things (IoT) domain. These challenges are inherited attribute of another emerging technology, Blockchain. This enforced convergence of IoT and Blockchain, attracting researchers to study on the effective use of Blockchain’s strength to solve the challenges of IoT. Rapid IoT adoption requires standardization and mature solution on security, data protection for compliance and performance for commercialization. These demands made a surge in variant blockchain flavours and combinations catering to different problems, and one such is Lightweight Scalable Blockchain (LSB). LSB had considerable caveats that require improvement for better adoption in the IoT domain. This paper focuses on encrypting transaction transmission, improving transaction flow, block validation, hash quality, hash rate and storage cost to improvise security and performance. The experimental evaluation is demonstrated on data from the temperature sensor to showcase superior applicability of the proposed work in the IoT domain. Implementation and result comparison with conventional LSB proves, the following achievements 1) An additional layer of transaction encryption using hybrid Elliptic Curve ElGamal (EC-ElGamal) method increases the security of the transmitted transaction for security enhancement. 2) Obtained 20% reduction in transaction processing time, 22% reduction on block validation processing time, 53% improvement on the hash operation and quality with an overall 7% saving on the storage cost thereby increased the overall performance.

In recent decades, the mortality rate of breast cancer in females is rapidly increasing because of unawareness and failed to detect in earlier stages. In existing, several studies are attempted to develop a robust mechanism for detecting breast cancers from the given input samples. However, they are not as much effective because of several limitations and the secured sharing of sensitive medical images is still a challenging problem faced by medical sector. Thus, the proposed study aims to introduce an automated disease diagnosis system using federated learning and deep learning which automates and speed up the process efficiently. The five crucial steps that involved in the proposed study are image acquisition, encryption, optimal key generation, secured data storing and disease classification. Initially, the required input medical images are gathered in the image acquisition stage. Then, to afford more confidentiality, the gathered medical samples are encrypted through an Extended ElGamal Image Encryption (E-EIE) method. Here, the efficiency of encryption process is enhanced by generating the suitable keys in optimal manner with the help of Improved Sand Cat Swarm Optimization (I-SCSO) algorithm. Next, the security of encrypted images are improvised by utilizing federated learning flower (FLF) framework for storage purpose. This framework has the ability to transmit the medical images with higher security. Finally, the stored images are decrypted and performs disease classification by using convolutional capsule twin attention tuna optimal network (C2T2Net) model. The available loss in the proposed classifier is reduced by fine-tuning the parameters using chaotic tuna swarm optimization (CTSO) algorithm. For simulation analysis, the proposed study used Python software and the experimental analysis is carried out by using BreakHis Database. The simulation results shows that the proposed study obtained higher performance in terms of accuracy (95.68%), recall (95.6%), precision (95.66%), F-measure (95.63%), specificity (95.6%) and kappa coefficient (95.26%).