Bio-medical waste management is critical for ensuring public health and environmental sustainability. However, due to the inherent ambiguities and complexities involved with waste characteristics and disposal techniques, measuring the efficiency of bio-medical waste management systems presents major hurdles. This study provides a Fuzzy TOPSIS-based (Technique for Order Preference by Similarity to Ideal Solution) strategy for thorough bio-medical waste management assessment. The suggested method combines the benefits of fuzzy logic and TOPSIS, allowing for the incorporation of subjective judgments and ambiguity in the evaluation procedure. Initially, a thorough set of criteria is constructed based on a review of current literature and recommendations from experts, comprising Environmental Impact, Compliance with Regulations, Health and Safety, Technological Feasibility, and Cost-effectiveness. To accurately represent the inherent ambiguity and imprecision in decision-making, each criterion is evaluated using linguistic variables. Furthermore, the Fuzzy TOPSIS approach is used to rate various bio-medical waste management systems depending on how well they perform in comparison to the identified criteria. The language judgments are represented as fuzzy numbers, and the idea of closeness coefficients is used for calculating the relative distance between each alternative and the ideal answer. An investigation in a healthcare facility is performed to demonstrate the feasibility and effectiveness of the suggested strategy. To assess numerous waste management approaches, the study uses real-world data on waste management practices, expert opinions, and linguistic analyses. The study’s findings emphasize the benefits of using a Fuzzy TOPSIS-based technique to evaluate bio-medical waste management. According to the findings of this research study, recycling is the best choice because it has the potential to reduce waste, recover resources, and preserve the environment. It assists decision-makers to account for uncertainties and subjectivity, increases transparency and consistency in decision-making, and aids in choosing of the best waste management system. The proposed approach advances sustainable waste management practices in the bio-medical area and provides a helpful tool for policymakers and practitioners looking to enhance waste management systems.

Neoteric biomedical, technological, and normative shifts have prompted care firms to establish clinical governance as a contrivance to assure high-quality service in an exceedingly intricate milieu. Web security is an epochal concern in the healthcare sector, although it has garnered scant attention since the inception of web applications. The necessity to provide adequate security for healthcare web applications (HWAs) cannot be exaggerated, as umpteen health agencies are contingent on them to carry out their operations. Every healthcare organization renders a humongous volume of data available online to practitioners, pharmacies, and patients. Researchers are continually endeavoring to ameliorate techniques to increase the security and longevity of HWAs. In this context, experts examined certain imperative security risks in HWAs to quantitatively evaluate them in the design phase and covered numerous facets of HWAs, along with their security attributes and risk factors. The authors have proposed a combined approach of fuzzy-based symmetric techniques, i.e., AHP-TOPSIS (Analytic Hierarchy Process–Technique for Order of Preference by Similarity to Ideal Solution), for the assessment of alternative HWAs, leveraging the multi-criteria decision-making (MCDM) approach. Ten consecutive HWAs from local hospitals in Uttar Pradesh, India, have been taken to estimate the security risk, incorporating this methodology to evaluate the priority of weightage and the impact of security attributes. Henceforth, the findings and methodology employed in this study can assist security practitioners in identifying and prioritizing the most influential risk factors to secure HWAs and encourage them to develop revamped or novel methods.

The highly transmissible COVID-19 virus has wreaked havoc on the global economy, health, and lives. The abrupt burst and exponential spread of this pandemic has shown the inadequacies of existing healthcare institutions in handling a public health emergency. As governments around the world strive to re-establish their economies, open workplaces, ensure safe journeys, and return to regular life, they require solutions to reduce losses. The proposed framework provides virtual assistance from various medical practitioners and physicians. Furthermore, it promotes the accuracy of information gathered from COVID-19 patients, which can aid in the launch of a variety of government decisions and public guidelines aimed at combating health exigencies. The authors present a revolutionary blockchain-based solution that builds trust between the medical professionals and patients while preventing accidental coronavisrus transmission. This solution also keeps track of COVID-19 patients and improves EHR management, which can be a viable solution for common EHR challenges such as lowering the risk of patient data loss, maintaining privacy and security, and obtaining immutable consensus on the maintenance of health records, gaps in hospital communication, and inefficient clinical data retrieval methods. This research work describes a COVID-19 patient-centric blockchain-based EHR employing JavaScript-based smart contracts for a decentralized healthcare management system. The proposed Hyperledger fabric and a Composer-based working prototype ensure the model’s security and the authenticity of the health records. The authors used the Hyperledger Caliper benchmarking tool, which measures latency, throughput, resource utilization, etc., under different conditions and control parameters. The findings highlight the importance of the proposed blockchain-enabled architecture in revolutionizing healthcare administration during and after the COVID-19 pandemic, promoting enhanced clinical outcomes and supporting patient-centered care.

With the rapid increase of cyberattacks that presently affect distributed software systems, cyberattacks and their consequences have become critical issues and have attracted the interest of research communities and companies to address them. Therefore, developing and improving attack detection techniques are prominent methods to defend against cyberattacks. One of the promising attack detection methods is behaviour-based attack detection methods. Practically, attack detection techniques are widely applied in distributed software systems that utilise network environments. However, there are some other challenges facing attack detection techniques, such as the immutability and reliability of the detection systems. These challenges can be overcome with promising technologies such as blockchain. Blockchain offers a concrete solution for ensuring data integrity against unauthorised modification. Hence, it improves the immutability for detection systems’ data and thus the reliability for the target systems. In this paper, we propose a design for standalone behaviour-based attack detection techniques that utilise blockchain’s functionalities to overcome the above-mentioned challenges. Additionally, we provide a validation experiment to prove our proposal in term of achieving its objectives. We argue that our proposal introduces a novel approach to develop and improve behaviour-based attack detection techniques to become more reliable for distributed software systems.

In the software development, high impact on diverse industries are expanding at a fast pace. Early defect fixation and adjustment according to needs are essential to increase the performance, accuracy, durability and reliability of the software. Machine Learning (ML) approaches have used to analyze the Software Requirement (SR) in software development offer a great way to accelerate symmetric development and defect correction, especially because software failures are unpredictable. This research utilizes classification and regression-based models like Random Forest (RF), Artificial Neural Network (ANN), and Adaptive Moment Estimation (AME) to forecast bug resolving times from applicable asymmetric data attributes. ML models are used to predict defect resolution and feature completion time. The data takes into consideration several stages including issue detection, testing, and validation. The outcome of ML models, K-Nearest Neighbors (KNN) algorithm has an accuracy of 66% but the proposed approach of RF, ANN, and ADE illustrates an astonishing 98% accuracy. The dataset with symmetric attributes has trends and strong correlations. The proposed model result is better than other available software failure time prediction in terms of precision and accuracy, providing a sound to defect solution and prediction in SR.