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File fragment classification: A comprehensive survey of research advances
A crucial task in digital forensics is file fragment classification, which involves classifying file fragments into their respective types based on their content. It is integral to digital forensics and data recovery, where investigators reconstruct and analyze fragmented files to gather evidence in criminal cases, data breaches, or other cybercrimes. This comprehensive survey paper offers insights into the different methodologies used for file fragment classification, including but not restricted to specialized approaches, hierarchical classification, and neural networks. The paper also highlights the challenges in file fragment classification, such as the need for format standardization, limited training data, scalability, and noise and ambiguity. A research gap analysis of the existing literature was conducted, and it was identified that further research could be done to explore the effectiveness of different approaches for file fragment classification, including transfer learning, ensemble methods, and so on. 2025 Scrivener Publishing LLC. All rights reserved. -
Demystifying the Metaverse Era: The Enabling Technologies and Industry Use Cases
Metaverse can be called a 3D shared virtual space that is hyper realistic, immer-sive, instinctive, and interactive. Through metaverse, people try to visualize life in the manner that do not exist in the real world. The potential and promising digital technologies turn out to be a huge enabler of the metaverse dream. This chapter is to delineate the various versatile metaverse applications, implementation technol-ogies, and use cases (individual as well as industrial). 2025 Scrivener Publishing LLC. -
Future Trends of Roadmap to Metaverse Technology
The term metaverse is used to describe the interconnected network of technologies such as the Internet of Things (IoT), blockchain, artificial intelligence (AI), and other fields of technology, such as the medical field. Similar to how the Internet of Things and the Metaverse are digital twins, the latter makes extensive use of the former in its simulated office. In the blockchain-based Metaverse, this data serves as a means of tracing the provenance of various pieces of information. Such information is becoming useful in the Metaverse, which is used to train AI. With the help of AI and blockchain technology, Metaverse creates a digital virtual world where people may securely and freely participate in social and economic activities that go beyond the bounds of the actual world. In this article, we will discuss the technology used by the metaverse and the possibilities that exist for the metaverse in the healthcare arena. 2025 Scrivener Publishing LLC. -
Amalgamation of IoT, Blockchain, Artificial Intelligence for Metaverse
The metaverse is a set of technologies that uses a computer to create a virtual world of reality and human connections. Some of the most significant enablers of this evolution are the Internet of Things (IoT), blockchain, and artificial intelligence (AI). These technologies not only provide a better experience but also introduce a new way of how security, efficiency, and inter-activeness should be done within the Metaverse. This chapter discusses the intersection between IoT, blockchain, and AI and its relevance in the framework of Metaverse. It discusses how IoT devices generate environments, the blockchain maintains high levels of security and provides digital ownership, and AI facilitates interactions. The rise of these technologies guarantees that the use of the virtual worlds will be consistent and also enhance the user experience, yet the blend of these technologies brings a number of difficulties like interoperability problems, data privacy problems, and also the concern of combining such a lot of various systems. The Metaverse has been explored in these challenges to achieve its full potential. The objective of this chapter is to paint a picture of how IoT, blockchain, and AI should be utilized to improve the Metaverse. This chapter presents an analysis of technical and ethical issues, offers potential solutions to the current problems, and outlines the possible directions of further development. Our study thus points out that the application of these technologies together offers an enormous opportunity to propel the development of the Metaverse in its quest to deliver virtual spaces that are secure, intelligent, and interactive. The final part of the chapter outlines the long-term effect on the society as well as the future prospects for development and the potential ethical challenges in this popping field of study. 2025 Scrivener Publishing LLC. -
Exploring the Blockchain-Enabled Metaverse: A Comparative Study of Leading Platforms
The integration of metaverse and secure-based blockchain is transforming several domains, including the area of virtual employment fairs. This chapter comprehensively examined technologies and covers the areas and platform that is both immersive and secure for job searchers and recruiters. It provides a novel case study of a virtual job fair, focusing on its system architecture with metaverse and blockchain. The Decentraland platform is focused and comprises essential elements for metaverse environment and blockchain network. This will help through analyzing as well as interactions between attendees, recruiters, and system administrators the operational process, with an improved security, transparency, and user engagement. The study recognizes promising advancements, yet it accentuates important obstacles and unsolved issues, such as expansion, data protection, and portability. These concerns must be addressed in order to fully exploit the promise of the metaverse and blockchain in revolutionizing virtual interactions. 2025 Scrivener Publishing LLC. -
Deep Learning in Waste Management and Recycling in Digital Smart City
For waste management and recycling in smart cities, the fast growth of urban populations and the subsequent rise in garbage creation have posed considerable issues. For cities to be sustainable and ecologically friendly, good waste management and the promotion of recycling practises are crucial. Deep learning techniques have become a potent tool for solving complicated issues and streamlining numerous procedures in a variety of fields in recent years. In the framework of smart cities, this chapter proposes improved Deep learning model with IOT Architecture for recycling and garbage management. 2025 Scrivener Publishing LLC. -
Leveraging ResNeXt50 and LSTM for Enhanced Plant Disease Detection: A Hybrid Model Proposal
This chapter explores the implementations of deep learning algorithms along with remote sensing technologies for precise identification and categorization of plant diseases, focusing on enhancing accuracy and efficiency in agricultural practices. This research study intends to succeed in building a hybrid model for the classification and forecasting of diseased plants with high accuracy. Plant disease detection and classification is a critical field of study within agricultural science and technology. It involves identifying and categorizing diseases affecting plants to ensure timely and effective management practices. Early and accurate identification of plant diseases is crucial to minimize crop loss, maintain food security, and reduce the use of pesticides, which can have adverse environmental and health effects. In any country, both the yield and the quality of agricultural products are essential for the success of agriculture. Plant disease (i.e. abnormal growth or functionality) detection is tough work, which has prompted numerous investigators to apply image processing, machine learning (ML), computer vision, and big data analytics, etc., techniques, which make the challenging assignment easier. The proposed approach integrates the deep convolutional neural network ResNeXt50 with long short-term memory (LSTM) networks to tackle the dual tasks of plant leaf disease classification and segmentation. The ResNeXt50 backbone extracts intricate spatial features from plant leaf images, while the LSTM component models the temporal dynamics of disease progression. This hybrid model exploits the hierarchical feature representation of ResNeXt50 and the sequential learning capabilities of LSTM to enhance accuracy and contextual understanding of plant leaf diseases. The model's training accuracy was enhanced to a maximum of 99.74% and a validation accuracy of 95.44%, scoring 94% in F1, 96% in recall, and 96% in accuracy. Comparative analysis reveals that the ResNeXt50 + LSTM model outperforms other classifiers, including Inception V3, AlexNet, ResNet50, and VGG16, addressing overfitting and vanishing gradient issues. The model demonstrates superior performance in handling imbalanced data and excels in plant disease prediction, validated through various benchmarks and datasets. This study confirms the hybrid model's robustness and potential for practical application in plant pathology. 2025 by The Institute of Electrical and Electronics Engineers, Inc. -
Accelerated Reliability Sampling Plan Based on Transformed Lindley Distribution
This study presents the development of Accelerated Reliability Sampling Plans (ARSPs) in the form of a Lindley distribution, considering the risks to both the producer and consumer. Sampling plan tables with varying values for both the risks WERE formulated. By leveraging a known Acceleration Factor (AF) as a foundation, ARSPs were systematically evaluated for their sensitivity to AF fluctuations, ensuring robustness under diverse testing scenarios. An example was used to illustrate the practical application of the formulated sampling plan tables. The proposed ARSPs facilitate efficient product reliability assessment under accelerated conditions, potentially reducing testing time and cost while achieving the desired reliability targets. 2025 Scrivener Publishing LLC. -
Fake News Detection in Healthcare Using Machine Learning
The internet has revolutionary power in todays society, acting as an unmatched catalyst for technical innovation, worldwide connectedness, and information dissemination. It has transformed communication and made knowledge more accessible to all, and given people, companies, and society the tools they need to prosper in the connected digital world. However, this power is responsible for navigating issues, such as the proliferation of fake news and safeguarding information integrity. As peoples health comes first, false information about it might have disastrous consequences. Even for the most knowledgeable professionals in the field, identifying false information about health can be difficult because of the variety of factors that must be considered. New advances in machine learning have enabled automatic classification of bogus news. For the detection of fake news correctly, we must train the automation in such a way that it captures the bogus correctly, and for that the data we input is of at most importance and, in fact, the most important as well. To enhance the models capacity to discern between authentic and fake news, this study investigates the extraction of structural and semantic information from text using a combination of named entity recognition and syntactic parsing. Utilizing these characteristics, we trained a variety of machine learning algorithms, assessed their effectiveness, and found that the Random Forest classifier outperformed the others in classification. 2025 Scrivener Publishing LLC. -
Bivariate Cointegrated Model with Gamma Innovations
The nature of time-bound data is its non-stationarity, that is, the constant presence of factors such as trend, seasonality, or both. Adopting mechanisms such as the method of differencing or ordinary least squares results in a loss of information or overestimation or underestimation of the parameters, respectively. A cointegration study reflects the notion of a long-run equilibrium, which is a concept of sensitivity in macroecometrics. Thus, cointegration can be defined as the onset of a longterm equilibrium between two or more time series that evolve under the influence of time, with the potential advantage of establishing a dynamic relationship using standard methods. Thus, this study explores the theoretical approach of estimating an error correction model for a cointegrated bivariate VAR (2) model with gamma innovation. To obtain the parameter estimates of the proposed model, we employ the conditional maximum likelihood estimation, implemented through the NewtonRaphson algorithm, because of the gamma distributions non-closed form nature. A theoretical study is strengthened by artificial simulations that support mathematical derivations. 2025 Scrivener Publishing LLC. -
Intrusion Detection Through Deep Learning: Emerging Trends and Challenges
The chapter begins with an introduction that sets the stage for a comprehensive journey into the world of deep learning. The chapter then delves into the critical components of deep learning, including neural network architectures, convolutional neural networks (CNNs), recurrent and recursive networks, and the application of deep learning. Moreover, it explains intrusion detection, its classification, and its methodology. By the end of the chapter, readers will have gained a solid understanding of the fundamental principles and tools necessary to delve deeper into the application of deep learning in intrusion detection, and challenges inherent in it. 2026 John Wiley & Sons, Inc. Published 2026 by John Wiley & Sons, Inc. -
Biocompatibility and toxicity of nanomaterials in the designing of tools for the diagnosis of infectious diseases
Nanomaterials have revolutionized the landscape of infectious disease diagnostics by offering unparalleled advantages in terms of sensitivity, specificity, and rapidity. However, their integration into diagnostic tools necessitates a profound understanding of their biocompatibility and toxicity profiles to ensure both diagnostic accuracy and patient life. The successful translation of these nanomaterials into practical diagnostic tools hinges on a thorough understanding of their biocompatibility and toxicity. Biocompatibility, a fundamental characteristic of nanomaterials refers to their ability to coexist with biological systems without causing harm or triggering immune responses. This chapter unfolds the pivotal role of biocompatibility assessment, examining the compatibility of nanomaterials with biological systems. In vitro and in vivo evaluation methods, and adherence to regulatory standards, are emphasized as essential components of biocompatibility analysis. Simultaneously, the exploration of nanotoxicity and its hazardous effect highlights the significance of establishing safe exposure limits. Toxicity is a pressing concern when dealing with nanomaterials. The chapter explores the factors that contribute to nanomaterials toxicity, including size-dependent effects, surface modifications, and the route of exposure. It also delves into the mechanisms by which nanomaterials can exert toxicity, such as reactive oxygen species, reaction with surface expanded group, and penetration into the cell. To mitigate the potential risk associated with nanomaterials, the chapter discusses strategies for improving biocompatibility. Finally, it gives a glance into the various tools prepared highlighting the successful integration of biocompatible nanotoxicity of nanomaterials into infectious disease diagnostics. Ultimately, this chapter emphasizes the need for comprehensive biocompatibility and toxicity evaluation as integral components of designing effective and safe diagnostic tools thereby contributing to the advancement of healthcare through cutting-edge nanotechnology. 2025 Scrivener Publishing LLC. All rights reserved. -
Role of nanomaterials in the development of nanobiosensors for infectious diseases
Transmissible illnesses brought on by viruses, bacteria, fungi, and parasites are referred to as infectious diseases. These can escalate into undesirable pandemic circumstances that disrupt both regular life functions and the world's population. These in turn have an effect on the current global economy, lead to joblessness, induce stress on the body, mind, and emotions, and put human survival in jeopardy. Consequently, in order to avert worldwide life impairment, prompt discovery, treatment, isolation, and control of the spread of pandemic infectious diseases within the town of origin are essential. As of right now, the World Health Organization (WHO) lists 12 infectious diseases that can be fatal: COVID-19, severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), human immunodeficiency virus (HIV), human papilloma virus (HPV), influenza, hepatitis, herpes simplex virus (HSV), Zika virus, chikungunya, dengue, and rota virus. Biosensors are becoming more and more potent instruments for diagnosing infectious diseases. Analytical tools that may transform biochemical data into detectable signals such as optical, electrical, magnetic, or thermal signals are referred to as biosensors. The growing need for highly selective, low-concentration sensing of a wide variety of chemicals has spurred the creation of sophisticated instruments known as nanobiosensors, which combine biological components, advanced materials, and nanoscale materials. The design, principle, underlying reasoning, receptor, and molecular features of sensor systems with a focus on the recent COVID-19 pandemic are all covered in this chapter. For critical comparison, electrochemical biosensor systems which included a variety of sophisticated nanostructures like semiconductors, metal organic frameworks (MOFs), MXenes, polymeric nanocomposites, metal and metal oxide nanoparticles, and combinations of biomolecules reported recently were specifically divided into distinct sub-sections. This chapter focuses on the difficulties that exist today in converting lab research into practical device applications, as well as the potential for the future and the commercialization of electrochemical diagnostic devices for the detection of corona viruses. It is anticipated that the background information and overall advancements presented in this study will be instructive for sensor researchers and will make it easier to design and fabricate electrochemical sensors for viruses that pose a threat to human life, with a wider range of applications for any desired pathogen. 2025 Scrivener Publishing LLC. All rights reserved. -
Bibliometric Insights into the Nexus of Digital HR, Innovation, and Sustainability: Toward a Smart Workforce
HR professionals use AI, blockchain, cloud computing, big data analytics, and Metaverse to optimize the workforce as technology advances. These technologies boost corporate value, employee performance, and smart workforce development. Metaverse improves virtual reality training, 3D simulations, and wearable self-tracking technologies. Cloud computing simplifies simulations and collaborative mixed reality for employees. AI tools usage increases an organization's staff efficiency. Smart workforce tactics and workplace technologies improve success and human experience management, especially in virtual, remote, and collaborative work contexts. Many companies have failed to integrate Metaverse in the workplace despite advances in digital technologies. A Biblioshiny analysis- based systematic assessment of human capital management automation systems addresses this gap. This study examines smart workforce requirements and future automation trends at the organizational, managerial, and individual levels. Additionally, this study allows for the creation of a self-sustaining virtual HR system. 2025 Scrivener Publishing LLC. All rights reserved. -
Secure Equitability in Chemical Networks
Let G (V,E) be a simple connected graph with a set of nodes V(G) and a set of edges E(G). A secure equitable dominating set [Formula Presented] is a dominating set in which, for any vertex [Formula Presented], there exists at least one vertex [Formula Presented] such that the vertex u belongs to the equitable neighborhood of v and, if we swap the vertices u and v, then the equitable domination property of the graph does not change. A secure equitable dominating set of minimum number of nodes in G is named a [Formula Presented] -set and the cardinality of a [Formula Presented] -set is called the secure equitable domination number of G, denoted by [Formula Presented]. In this paper, we study the bounds of secure equitable domination number in certain chemical structures. Moreover, we give an application of the parameter on interconnection networks. 2026 Scrivener Publishing LLC. -
An Encryption and Decryption of Block Ciphers Using Multipartite Graphs
Cryptography is the technique that secures the process of sending and receiving messages. It involves designing the text to be communicated in a complex form, so that possible intruders might find it difficult to access the information conveyed. This approach comprises a lock that encodes or encrypts the information and a key that decodes or decrypts the ciphered text. This is essential to secure the flow of confidential information. Graph theory plays a crucial role in cryptography. A graph-based technique revolves around the maintenance of security in information flow by factoring the text into graphical models of all the cipher parameters and forming a reverse mechanism to decipher the ciphered model. In this study, we introduce a novel cryptographic graphbased model, aiming to enhance the effectiveness of the existing approaches, thereby advancing secure communication in this digital era. This approach specifically deals with block ciphers structured as a multipartite graph, ensuring a secure encryption, with symmetric key cryptography. 2026 Scrivener Publishing LLC. -
From Concept to Clinic
Virtual reality (VR) is an innovative technology with various applications in fields such as simulation, gaming, sports, and entertainment. VR technology has extended its reach into the medical industry, leveraging computer-generated information and visuals to simulate real-world sensory experiences. Augmented reality (AR) is an expertise that covers computer-generated virtual objects onto the real world when it is seen through mobile phones, tablets, or AR glasses. VR and AR have started popular in 2016. Autism spectrum disorder (ASD) is a many-sided neurodevelopmental condition and is caused because of difficulties in social interaction, communication, and iterative behavior. ASD is increasingly predominant globally, touching roughly 2 in 100. If there is timely diagnosis and intervention, then there will be fewer problems. In recent years, AR and VR technologies have become one best option and novel means in the analysis and treatment of autism. Controlled environments that can imitate real-life social situations, contribution clinician's critical insights into the perceptions, and interactions of individuals with ASD in relation to their surroundings will be provided by AR and VR. These technologies assist customized intermediations, rendering them extremely adaptable to the distinct requirements of each individual. 2026 Scrivener Publishing LLC. -
Meditating in VR
This chapter examines how immersion in virtual reality (VR) affects the psychological well-being, perceived interactivity, and positive thinking of young adults. Using a questionnaire adapted from Katy Tcha-Tokey's VR survey and established scales by Sally J. McMillan, Jang-Sun Hwang, and Diener, data was collected from 224 students at Christ University. Participants engaged in a VR meditation session using Meta Quest 2 headsets. Results indicated stronger correlations between immersion and psychological well-being (r = 0.511) and positive thinking (r = 0.485) compared to perceived interactivity (r = 0.319). Regression analyses confirmed immersion's predictive power for psychological well-being (? = 0.511, R2 = 0.261) and perceived interactivity (? = 0.485, R2 = 0.236), with lesser impact on positive thinking (R2 = 0.101). The study suggests that VR immersion notably influences psychological well-being and perceived interactivity among young adults. 2026 Scrivener Publishing LLC. -
Healthcare Metaverse
Discussions regarding metaverse technologies are happening all over the place, from universities to business tycoons. A lot of people are thinking about how to make their apps work better in the metaverse. To better serve their patients, more and more healthcare firms are embracing the metaverse. In this research, healthcare metaverses are examined. We show how to improve healthcare services in the metaverse and increase patient use cases by using safer approaches to managing chronic diseases, mental health, and fitness. With the advent of digital twins, artificial intelligence (AI), immersive technologies, the Internet of Things (IoT), and blockchain (BC), new possibilities in healthcare are emerging in the metaverse. These innovations have the potential to change the way people perceive healthcare, save costs, and enhance patient outcomes. Healthcare may be revolutionized by using AI and BC technology to sift through massive amounts of data and develop individualized treatment regimens. But IoT devices gather vital data for patient therapy instantly. The healthcare system and people's lives throughout the globe may both benefit from these concepts coming together. The recommendations made in this article should be adhered to ensure that digital procedures continue to benefit customers. 2026 Scrivener Publishing LLC. -
Leveraging Deep Learning for Early Detection of Autism Spectrum Disorder in Augmented and Virtual Reality Mental Healthcare Environments
The advent of digital health interventions offers new vistas for the early detection and management of autism spectrum disorder (ASD). This paper explores the innovative application of deep learning algorithms within augmented reality (AR) and virtual reality (VR) environments to enhance the early detection of ASD. Integrating AR and VR in mental healthcare provides immersive, controlled settings in which individuals behaviors and responses can be observed and analyzed in real time. We propose a novel framework that utilizes deep learning techniques to process and analyze the data collected from these digital environments. Our approach leverages facial expression analysis, eye tracking, and behavioral pattern recognition to identify early signs of ASD. By capturing subtle nuances in behavior often overlooked in traditional diagnostic settings, our method offers a non-invasive, engaging, and efficient alternative for early ASD detection. The potential of this technology extends beyond diagnosis, offering possibilities for personalized intervention strategies that can be adapted to each individual's unique needs. Our findings indicate that deep learning in AR and VR environments could significantly enhance the accuracy and timeliness of ASD diagnosis, paving the way for earlier and more effective interventions. This study underscores the importance of interdisciplinary collaboration in developing innovative healthcare solutions, combining the fields of artificial intelligence, psychology, and digital media to improve outcomes for individuals with ASD. 2026 Scrivener Publishing LLC.