TechTrends: Navigating the Frontier of Emerging Technologies

Medical images bear sensitive patient information, making their transmission a security concern. The privacy and security of such graphical representations and incidental patient information in transit via public networks must be preserved. Medical images contain sensitive information, which sets them apart from ordinary images. Medical images are more sensitive and contain crucial information. This leads to a reliance on more secure techniques than conventional methods like cryptography and data hiding, which normally take more time and security. In this chapter, we propose implementing two innovative techniques to enhance the security of medical data sharing: Prescription information concealed in medical images and secure and share-prescription using a multi-secret sharing blockchain. Prescription data hiding, on the other hand, refers to the encryption of prescription details within normal-looking images like X-ray or MRI scans, among others. Incorporating sensitive data into the images makes it difficult for unauthorized persons to access them. Additionally, we build on the potential of using a blockchain, an immovable and distributed database, to share crucial clinical information safely. Medical data is kept through a blockchain database, which spreads the data around a network. It becomes harder for attackers to tamper or alter the data using traditional methods. Smart contracts also add security to data sharing by enabling data to be available only to the relevant parties, which gives security an extra layer. As a novel solution to solve the serious security issues of medical data sharing, the proposed scheme involves prescription data hiding in medical images, multi-secret sharing-based encryption, and the security properties of the blockchain. Our proposed techniques ensure the privacy and integrity of patients' data when transmitting medical images. 

IoT devices call for optimum resource management solutions regarding edge computing reliability and performance. This chapter presents an intelligent and fuzzybased adaptive resource management solution for IoT devices in the context of edge computing. In this specific method, fuzzy support is applied to enable a resource dynamic allocation according to the devices' IoT needs and requirements. This is because the resource allocation process is intelligent and adaptive; hence, the system can modify its past resource allocation decisions to provide better functions. The proposed approach also acknowledges exploiting the particularities of the edge computing environment. It entails factors like the availability of the resources at the edge and the associated limitations, for example, in terms of power consumption and the processors' capacity. To consider the feasibility of the proposed approach, extensive simulations and experiments were conducted, and the performance of resource management by utilizing the proposed approach is compared with those of existing approaches. From the results obtained, the proposed method is superior to the current methods concerning resource usage, energy consumption, and system performance. In general, the presented intelligent and fuzzy-based adaptive resource management strategy presents great potential in increasing the efficiency and effectiveness of resource management of IoT devices in the context of edge computing, which would help establish more effective IoT systems shortly.

Finding well-fitting clothing that aligns with personal style preferences remains a significant challenge in the fashion industry. Existing solutions, such as online shopping platforms, lack accurate prediction capabilities for how garments would fit individuals. GarbGenius proposes an innovative approach integrating Artificial Intelligence (AI) powered recommendation systems and Augmented Reality (AR) enhanced virtual try-on experiences to address this gap. GarbGenius will deliver personalized recommendations based on current fashion trends by leveraging userimage input submission and comprehensive clothing datasets. Implementation involves developing user-friendly interfaces for user image input submission, clothing datasets, and algorithmic analysis of Vision Transformer (ViT) with recommendations, ensuring a seamless and immersive fashion wardrobe experience. Through iterative testing and maintenance, GarbGenius aims to empower users to confidently explore and embrace the latest trends tailored to their unique preferences.

One of the intriguing subfields of Artificial Intelligence (AI) is Machine Learning (ML). The ability to learn without explicit programming has been referred to as machine learning. Over the past few years, machine learning has emerged as an important research topic in several business verticals. Big data's technological developments have made accessing vast amounts of diverse data simple. With the help of new hardware capabilities, this enormous volume of data may be processed quickly and efficiently in a manageable amount of time. Therefore, Machine Learning algorithms have proven to be the most successful at using big data to solve difficult business challenges in almost real-time. This chapter briefly overviews some popular machine learning approaches and their uses in mechatronics, particularly in the tool wear prediction process for milling.

Magnesium alloys possess excellent lightweight properties but are limited by their low hardness, wear resistance, and susceptibility to corrosion. Laser cladding, a surface modification technique, offers a promising solution to enhance the performance and expand the application range of magnesium alloys. This chapter aims to provide an in-depth analysis of laser cladding on magnesium alloys, focusing on the benefits, challenges, and recent advancements in this field. The chapter discusses the enhancement of wear resistance, corrosion protection, resist against heat, and tailored properties achieved through laser cladding. Additionally, it addresses the challenges related to thermal concerns, material compatibility, and surface preparation. The chapter concludes by highlighting the potential of laser cladding as a valued technique for enhancing the performance and durability of magnesium alloy components. 

Plant diseases present a substantial challenge for farmers, causing significant crop losses and diminishing yields. Timely detection and effective treatment are crucial, yet traditional methods often fall short due to their reliance on human expertise and the potential for misdiagnosis. These conventional approaches can be slow and cumbersome, leading to delays in addressing plant health issues. Our proposed system addresses these challenges by employing Convolutional Neural Networks (CNNs) for precise and efficient leaf disease detection and targeted fertilizer recommendations. Using CNNs for image classification, the system can accurately identify plant diseases from leaf images and suggest appropriate fertilizers, thereby optimizing treatment strategies. This CNN-based approach was rigorously tested on a comprehensive dataset of plant leaf images, demonstrating impressive accuracy in disease detection and fertilizer recommendations. The system's advanced capabilities streamline the diagnostic process and enhance crop yield by providing farmers with timely and precise recommendations. Integrating CNN-based analysis with actionable insights reduces reliance on extensive human expertise and fosters more sustainable and efficient farming practices.

This chapter compares two MPPT algorithms—Conservative MPP and Fast Terminal Sliding Mode Controllers (FTSM)—under consistent partial shading conditions. Methods include Artificial Neural Networks (ANNs), Fuzzy Logic (FL), and FTSM MPPT. FTSM algorithms may be applied in partly shadowed situations on a global MPP. This study proposes an FTSM technique that, when employed, establishes the efficient FTSM controller settings such that the global maximum PV device, power point, is monitored while partly shaded. It is designed in MATLAB for dynamically changing shadow patterns, as evidenced in its setup and test performance. ANN, (FL), and FTSM are compared in the findings. A less accurate method for measuring the global MPP has been found. The following strategies are compared in a solar array under partial shade situations. Nonlinear experimental effects justify the model's control strategy and stability analysis. 

Social network analysis is a crucial aspect of data mining, requiring the encoding of network data into low-dimensional representations called network embeddings to preserve topology structure and attribute information. This enhances applications like classification, link prediction, anomaly detection, and clustering, with deep neural network techniques gaining interest. This survey explores the current literature on network representation learning, focusing on neural network models. It introduces basic models, extends them for complex scenarios, introduces embedding techniques, discusses network representation learning applications, and suggests future research directions.

Virtual Reality (VR) technology has opened new avenues for enhancing student engagement and learning outcomes in education. The special issue of the Journal of Virtual Reality in Education brings together cutting-edge research, case studies, and expert opinions on the potential of VR to transform the educational landscape. The articles in this issue explore how VR can be leveraged to create immersive, interactive, and personalized learning experiences that cater to diverse learning styles and needs. From anatomy education to language learning, and virtual field trips to STEM education, the contributions in this issue demonstrate the versatility and effectiveness of VR in enhancing student engagement, motivation, and academic achievement. Through a mix of quantitative and qualitative research methods, the authors investigate the impact of VR on learning outcomes, student attitudes, and teacher professional development. The findings suggest that VR has the potential to increase student participation, improve knowledge retention, and foster deeper understanding and empathy. This chapters aims to provide educators, researchers, and policymakers with a comprehensive understanding of the opportunities and challenges of integrating VR into educational practice. We aim to inspire further research, experimentation, and adoption of this promising technology by showcasing the latest developments and innovations in VR-based education.