The digital transformation of the healthcare sector has revolutionized operational efficiency and patient care, yet concurrently exposed healthcare organizations to unprecedented cybersecurity risks, jeopardizing patient confidentiality and organizational integrity. This study undertakes a comprehensive investigation into contemporary cybersecurity strategies and emerging trends within the healthcare industry. Through a meticulous examination of published literature from reputable databases, including PubMed/MEDLINE, CINAHL, and Web of Science, critical patterns and vulnerabilities are discerned, underlining the escalating frequency and severity of cyber threats such as ransomware and phishing attacks. Emphasizing the pivotal role of organizational cyber resilience governance and policies, the study identifies a notable gap in standardized cybersecurity risk assessment methodologies, signaling the urgent need for innovative approaches. In response to identified challenges, the research proposes the development of novel methodologies to fortify cybersecurity defenses and protect patient data. Leveraging cutting-edge technologies such as blockchain and artificial intelligence, the study advocates for proactive measures to mitigate emerging threats and ensure data security and patient privacy in healthcare environments. Moreover, the integration of end-to-end security measures and the adoption of DevOps methodologies are highlighted as promising avenues for enhancing cybersecurity resilience. Results from a systematic literature review underscore the imperative for ongoing research and collaboration to address cybersecurity challenges in healthcare effectively. By offering insights into key cybersecurity features, technologies, and responsibilities within the healthcare sector, this study aims to inform stakeholders and policymakers, facilitating the implementation of robust cybersecurity measures. Furthermore, the study presents key findings regarding the current state of cybersecurity in healthcare, including challenges faced and potential solutions identified through the research process. Ultimately, through concerted efforts and the utilization of innovative strategies, healthcare organizations can navigate the evolving cybersecurity landscape, safeguarding patient information and upholding the integrity of healthcare systems.

Blockchain technology can revolutionize product authenticity verification by utilizing decentralized networks to collect and retain product data. This generates an irrevocable record of a product's path from manufacture to sale, making it possible to detect phony or counterfeit goods, a problem that plagues many different industries.
A common problem that compromises consumer confidence and harms brand integrity in a variety of businesses is counterfeiting. Our strategy involves establishing a blockchain-based product registry, allowing various supply chain nodes, including production and store shipping, to update information such as origin, materials, and certificates. Smart contracts also provide various applications for detecting fake goods, thus preventing the introduction of counterfeit goods into the supply chain. Based on predefined standards, these self-executing contracts validate the product's legitimacy. Blockchain technology makes it possible to verify things accurately, ensuring that customers receive the real goods they pay for. Our system enables accurate verification, ensuring that customers receive genuine goods. by scanning a QR code that connects to the blockchain record, customers can instantly authenticate the legitimacy and history of a product, ensuring its authenticity. By offering a clear picture of the product's route, this approach boosts consumer trust by ensuring a high level of security and traceability. This method offers a safe and effective solution to the age-old issue of product authenticity because of its decentralized structure and immutable record-keeping. This is a breakthrough in fighting counterfeiting and maintaining product integrity by empowering customers, defending brands, and encouraging a more trustworthy marketplace.

This research aims to obtain the best accuracy in classifying stunting children's data using K-Nearst Neighbor (KNN) by combining Particle Swarm Optimization (PSO). The K-NN algorithm is an algorithm which is an unsupervised algorithm, and is proven to be good in data mining while Particle Swarm Optimization (PSO) show. Better optimization performance compared to other methods. The methodology in this research is data collection, data pre-processing, classification of stunted children, data sharing, searching for the optimal k value to the classification process and performance testing or Particle Swarm Optimization. This dataset has an abnormal data structure where certain attribute values  have quite wide ranges.The results of the K-NN classification, the average accuracy of each fold, shows that the highest accuracy was obtained at a value of k = 10, namely 86.08% and the lowest was in the last experiment with a value of k = 7500 of 72.67%. It can be concluded that the higher the k value, the resulting accuracy will increase. Meanwhile, the results of K-NN classification with PSO can be concluded that the higher the w value, the greater the possibility of getting better fitness. This result is also in accordance with research where the best w value is above 0.5 and less than 1. This is because if the w value is more than 1 it can cause the particles in the PSO to become unstable because the resulting speed is not controlled. It is proven from the test results that the range This value produces better average accuracy and starts to decrease again when entering the value w = 1. Then the test results also show that a small value of w can result in the role of particle speed becoming insignificant and can increase the possibility of early convergence. It can be seen from the results of testing the number of PSO popsizes that the highest average accuracy was 93.2% at a value of w = 0.9. From the description above, KNN shows an accuracy of 86.08%, while KNN with PSO increases to 93.9%, so this shows that KNN with PSO is more accurate in classifying stunted children.

Employing Computerized Adaptive Testing (CAT) to evaluate verbal ability symptoms proves advantageous over traditional tests by delivering heightened measurement precision and reducing the testing burden. The CAT-Verbal Ability, developed from a large sample of 2689 participants in Gulf countries, underwent meticulous item bank development, ensuring unidimensionality, local independence, and investigating differential item functioning (DIF). The CAT-Verbal Ability item bank has high content validity, is unidimensional, locally independent, and does not have DIF; these outstanding psychometric qualities were confirmed by CAT simulations that were based on real data. With just 14 items needed, CAT simulations showed a high degree of measurement accuracy (r=0.73). In addition to being a psychometrically sound instrument, the proposed CAT-Verbal Ability demonstrated acceptable marginal reliability, criterion-related validity, sensitivity, and specificity. This makes it an efficient assessment method that reduces testing burden while maintaining information integrity, and it also saves time.

The COVID-19 pandemic has necessitated a shift to online assessments, posing significant challenges for teachers in fairly evaluating student performance. The absence of invigilation has led to widespread cheating, with students copying answers from the Internet or top-ranked peers. This paper addresses these issues by proposing guidelines and techniques for fair student assessment without invigilation. The research begins with an analysis of traditional assessment methods and their limitations in the context of unmonitored online exams. It then explores various online examination frameworks, including multiple-choice questions, short-answer questions, and interactive simulations. The study identifies key weaknesses in current online assessment practices and highlights the potential of advanced online examination frameworks. By implementing the suggested techniques, educators can improve the reliability and fairness of online assessments, ensuring a more accurate evaluation of students' knowledge. This article serves as a valuable resource for educators, instructional designers, and e-learning professionals seeking to enhance the efficacy of online assessments.