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Cross-industry applications of cyber security frameworks
\"This book posits that rather than cybersecurity being simply an issue of securing systems and networks, corporate leaders need to think in terms of assuring the integrity and durability of the interconnected business and social structures that sit on top of an increasingly complex technology landscape\"-- Provided by publisher.
Book
Massive Point Cloud Processing for Efficient Construction Quality Inspection and Control
The construction of large-scale civil infrastructures requires massive spatiotemporal data to support the management and control of scheduling, quality control, and safety monitoring. Existing artificial-intelligence-based data processing algorithms rely heavily on experienced engineers to adjust the parameters of data processing, which is inefficient and time-consuming when dealing with huge datasets. Limited studies have compared the performance of different algorithms on a unified dataset. This study proposes a framework and evaluation system for comparing different data processing policies for processing huge spatiotemporal data in construction quality control. The proposed method compares the combination of multiple types of algorithms involved in the processing of massive point cloud data. The performance of data processing strategies is evaluated through this framework, and the optimal point cloud processing strategies are explored based on registration accuracy and data fidelity. Results show that a reasonable choice of combinations of point cloud sampling, filtering, and registration algorithms can significantly improve the efficiency of point cloud data processing and satisfy engineering demands for data accuracy and completeness. The proposed method can be applied to the civil engineering problem of processing a large amount of point cloud data and selecting the optimal processing method.
Journal Article
CompTIA Security+ Certification Guide
CompTIA Security+ Certification Guide makes the most complex Security+ concepts easy to understand despite having no prior knowledge. It offers exam tips in every chapter along with access to practical exercises and exam checklist that map to the exam objectives and it is the perfect study guide to help you pass CompTIA Security+ SY0-501 exam.
eBook
Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability
Industrial IoT (IIoT) has revolutionized production by making data available to stakeholders at many levels much faster, with much greater granularity than ever before. When it comes to smart production, the aim of analyzing the collected data is usually to achieve greater efficiency in general, which includes increasing production but decreasing waste and using less energy. Furthermore, the boost in communication provided by IIoT requires special attention to increased levels of safety and security. The growth in machine learning (ML) capabilities in the last few years has affected smart production in many ways. The current paper provides an overview of applying various machine learning techniques for IIoT, smart production, and maintenance, especially in terms of safety, security, asset localization, quality assurance and sustainability aspects. The approach of the paper is to provide a comprehensive overview on the ML methods from an application point of view, hence each domain—namely security and safety, asset localization, quality control, maintenance—has a dedicated chapter, with a concluding table on the typical ML techniques and the related references. The paper summarizes lessons learned, and identifies research gaps and directions for future work.
Journal Article
Applying IoT Sensors and Big Data to Improve Precision Crop Production: A Review
The potential benefits of applying information and communication technology (ICT) in precision agriculture to enhance sustainable agricultural growth were discussed in this review article. The current technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), as well as their applications, must be integrated into the agricultural sector to ensure long-term agricultural productivity. These technologies have the potential to improve global food security by reducing crop output gaps, decreasing food waste, and minimizing resource use inefficiencies. The importance of collecting and analyzing big data from multiple sources, particularly in situ and on-the-go sensors, is also highlighted as an important component of achieving predictive decision making capabilities in precision agriculture and forecasting yields using advanced yield prediction models developed through machine learning. Finally, we cover the replacement of wired-based, complicated systems in infield monitoring with wireless sensor networks (WSN), particularly in the agricultural sector, and emphasize the necessity of knowing the radio frequency (RF) contributing aspects that influence signal intensity, interference, system model, bandwidth, and transmission range when creating a successful Agricultural Internet of Thing Ag-IoT system. The relevance of communication protocols and interfaces for presenting agricultural data acquired from sensors in various formats is also emphasized in the paper, as is the function of 4G, 3G, and 5G technologies in IoT-based smart farming. Overall, these research sheds light on the significance of wireless sensor networks and big data in the future of precision crop production
Journal Article
Toward Efficient Health Data Identification and Classification in IoMT-Based Systems
The Internet of Medical Things (IoMT) is a rapidly expanding network of medical devices, sensors, and software that exchange patient health data. While IoMT supports personalized care and operational efficiency, it also introduces significant privacy risks, especially when handling sensitive health information. Data Identification and Classification (DIC) are therefore critical for distinguishing which data attributes require stronger safeguards. Effective DIC contributes to privacy preservation, regulatory compliance, and more efficient data management. This study introduces SDAIPA (SDAIA-HIPAA), a standardized hybrid IoMT data classification framework that integrates principles from HIPAA and SDAIA with a dual risk perspective—uniqueness and harm potential—to systematically classify IoMT health data. The framework’s contribution lies in aligning regulatory guidance with a structured classification process, validated by domain experts, to provide a practical reference for sensitivity-aware IoMT data management. In practice, SDAIPA can assist healthcare providers in allocating encryption resources more effectively, ensuring stronger protection for high-risk attributes such as genomic or location data while minimizing overhead for lower-risk information. Policymakers may use the standardized IoMT data list as a reference point for refining privacy regulations and compliance requirements. Likewise, AI developers can leverage the framework to guide privacy-preserving training, selecting encryption parameters that balance security with performance. Collectively, these applications demonstrate how SDAIPA can support proportionate and regulation-aligned protection of health data in smart healthcare systems.
Journal Article
Integrating Artificial Intelligence and Machine Learning with Blockchain Security
Due to its transparency and dependability in secure online transactions, blockchain technology has grown in prominence in recent years. Several industries, including those of finance, healthcare, energy and utilities, manufacturing, retail marketing, entertainment and media, supply chains, e-commerce, and e-business, among others, use blockchain technology.In order to enable intelligent decision-making to prevent security assaults, particularly in permission-less blockchain platforms, artificial intelligence (AI) techniques and machine learning (ML) algorithms are used. By exploring the numerous use cases and security methods used in each of them, this book offers insight on the application of AI and ML in blockchain security principles. The book argues that it is crucial to include artificial intelligence and machine learning techniques in blockchain technology in order to increase security.
eBook
Genomic analysis of Listeria monocytogenes from US food processing environments reveals a high prevalence of QAC efflux genes but limited evidence of their contribution to environmental persistence
Background
Quaternary ammonium compound (QAC) efflux genes increase the minimum inhibitory concentration of
Listeria monocytogenes
(
Lm
) to benzalkonium chloride sanitizer, but the contribution of these genes to persistence in food processing environments is unclear. The goal of this study was to leverage genomic data and associated metadata for 4969
Lm
isolates collected between 1999 and 2019 to: (1) evaluate the prevalence of QAC efflux genes among
Lm
isolates from diverse US food processors, (2) use comparative genomic analyses to assess confounding factors, such as clonal complex identity and stress tolerance genotypes, and (3) identify patterns in QAC efflux gene gain and loss among persistent clones within specific facilities over time.
Results
The QAC efflux gene cassette
bcrABC
was present in nearly half (46%) of all isolates. QAC efflux gene prevalence among isolates was associated with clonal complex (𝛘
2
< 0.001) and clonal complex was associated with the facility type (𝛘
2
< 0.001). Consequently, changes in the prevalence of QAC efflux genes within individual facilities were generally attributable to changes in the prevalence of specific clonal complexes. Additionally, a GWAS and targeted BLAST search revealed that clonal complexes with a high prevalence of QAC efflux genes commonly possessed other stress tolerance genes. For example, a high prevalence of
bcrABC
in a clonal complex was significantly associated with the presence of the SSI-1 gene cluster (
p
< 0.05). QAC efflux gene gain and loss were both observed among persistent populations of
Lm
in individual facilities, suggesting a limited direct role for QAC efflux genes as predictors of persistence.
Conclusion
This study suggests that although there is evidence that QAC efflux genes are part of a suite of adaptations common among
Lm
isolated from some food production environments, these genes may be neither sufficient nor necessary to enhance persistence. This is a crucial distinction for decision making in the food industry. For example, changes to sanitizer regimen targeting QAC tolerance would not address other contributing genetic or non-genetic factors, such as equipment hygienic design which physically mediates sanitizer exposure.
Journal Article
Machine Learning Algorithms and Fundamentals as Emerging Safety Tools in Preservation of Fruits and Vegetables: A Review
Machine learning assists with food process optimization techniques by developing a model to predict the optimal solution for given input data. Machine learning includes unsupervised and supervised learning, data pre-processing, feature engineering, model selection, assessment, and optimization methods. Various problems with food processing optimization could be resolved using these techniques. Machine learning is increasingly being used in the food industry to improve production efficiency, reduce waste, and create personalized customer experiences. Machine learning may be used to improve ingredient utilization and save costs, automate operations such as packing and labeling, and even forecast consumer preferences to develop personalized products. Machine learning is also being used to identify food safety hazards before they reach the consumer, such as contaminants or spoiled food. The usage of machine learning in the food sector is predicted to rise in the near future as more businesses understand the potential of this technology to enhance customer experience and boost productivity. Machine learning may be utilized to enhance nano-technological operations and fruit and vegetable preservation. Machine learning algorithms may find trends regarding various factors that impact the quality of the product being preserved by examining data from prior tests. Furthermore, machine learning may be utilized to determine optimal parameter combinations that result in maximal produce preservation. The review discusses the relevance of machine learning in ready-to-eat foods and its use as a safety tool for preservation were highlighted. The application of machine learning in agriculture, food packaging, food processing, and food safety is reviewed. The working principle and methodology, as well as the principles of machine learning, were discussed.
Journal Article
GEN-RWD Sandbox: bridging the gap between hospital data privacy and external research insights with distributed analytics
Background
Artificial intelligence (AI) has become a pivotal tool in advancing contemporary personalised medicine, with the goal of tailoring treatments to individual patient conditions. This has heightened the demand for access to diverse data from clinical practice and daily life for research, posing challenges due to the sensitive nature of medical information, including genetics and health conditions. Regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and the General Data Protection Regulation (GDPR) in Europe aim to strike a balance between data security, privacy, and the imperative for access.
Results
We present the Gemelli Generator - Real World Data (GEN-RWD) Sandbox, a modular multi-agent platform designed for distributed analytics in healthcare. Its primary objective is to empower external researchers to leverage hospital data while upholding privacy and ownership, obviating the need for direct data sharing. Docker compatibility adds an extra layer of flexibility, and scalability is assured through modular design, facilitating combinations of Proxy and Processor modules with various graphical interfaces. Security and reliability are reinforced through components like Identity and Access Management (IAM) agent, and a Blockchain-based notarisation module. Certification processes verify the identities of information senders and receivers.
Conclusions
The GEN-RWD Sandbox architecture achieves a good level of usability while ensuring a blend of flexibility, scalability, and security. Featuring a user-friendly graphical interface catering to diverse technical expertise, its external accessibility enables personnel outside the hospital to use the platform. Overall, the GEN-RWD Sandbox emerges as a comprehensive solution for healthcare distributed analytics, maintaining a delicate equilibrium between accessibility, scalability, and security.
Journal Article