The landscape of healthcare data collaboration heralds an era of profound transformation, underscoring an exceptional potential to elevate the quality of patient care and expedite the advancement of medical research. The formidable challenge, however, lies in the safeguarding of sensitive information’s privacy and security - a monumental task that creates significant obstacles. This paper presents an innovative approach designed to address these challenges through the implementation of privacy-preserving federated learning models, effectively pioneering a novel path in this intricate field of research. Our proposed solution enables healthcare institutions to collectively train machine learning models on decentralized data, concurrently preserving the confidentiality of individual patient data. During the model aggregation phase, the proposed mechanism enforces the protection of sensitive data by integrating cutting-edge privacy-preserving methodologies, including secure multi-party computation and differential privacy. To substantiate the efficacy of the proposed solution, we conduct an array of comprehensive simulations and evaluations with a concentrated focus on accuracy, computational efficiency, and privacy preservation. The results obtained corroborate that our methodology surpasses competing approaches in providing superior utility and ensuring robust privacy guarantees. The proposed approach encapsulates the feasibility of secure and privacy-preserving collaboration on healthcare data, serving as a compelling testament to its practicality and effectiveness. Through our work, we underscore the potential of harnessing collective intelligence in healthcare while maintaining paramount privacy protection, thereby affirming the promise of a new horizon in collaborative healthcare informatics.

The Internet of Medical Things (IoMT) heralds a transformative era in healthcare, with the potential to revolutionize patient care, healthcare services, and medical research. As with all technological progressions, IoMT introduces a suite of complex challenges, predominantly centered on security. In particular, ensuring the integrity, confidentiality, and availability of health data in real-time communication stands paramount, given the sensitivity of the information and the ramifications of potential breaches or misuse. In light of these challenges, existing security frameworks, while commendable, exhibit limitations. Specifically, they often grapple with comprehensive anomaly detection, effective resistance to replay attacks, and robust protection against threats like man-in-the-middle attacks, eavesdropping, data tampering, and identity spoofing. The proposed framework integrates state-of-the-art encryption techniques, cutting-edge pattern recognition modules, and adaptive learning mechanisms. These components collaboratively ensure data integrity during transmission, provide robust resistance against conventional and novel attack vectors, and adapt to evolving threats through continuous learning. Moreover, the framework incorporates sophisticated checksum techniques and advanced behavioral analysis, further enhancing its protective capabilities. Our system demonstrated significant improvements in anomaly detection and attack resistance metrics, consistently outperforming benchmark solutions like MRMS and BACKM-EHA.

In this article, a multiobjective multiproduct production planning (MOMPP) problem discussed for a hardware firm. The hardware firm produces various types of hardware locks and other items in a production run. The firm manager's objectives are minimizing the production cost and inventory holding cost while maximizing the net profit subject to some system constraints. The multiproduct production planning is solved with the last production run information precisely known to the decision-maker, and finally, the model is solved using the intuitionistic and neutrosophic programming approaches, respectively. Also, the multiproduct production planning problem is discussed for situations when the product information is vague. The interval-valued trapezoidal neutrosophic numbers used to define this Vagueness. The multiobjective multiproduct production planning problem under fuzziness is solved using the neutrosophic compromise programming. The stepwise solution procedures are discussed using the case study.

The demand for cost-efficient and clean power energy cannot be overemphasised, especially in a developing nation like India. COVID-19 has adversely affected many nations, power sector inclusive, and resiliency is imperative via flexible and sustainable power generation sources. Renewable energy sources are the primary focus of electricity production in the world. This study examined and assessed the optimal cost system of electricity generation for the socio-economic sustainability of India. A sustainable and flexible electricity generation model is developed using the concept of flexible fuzzy goal programming. This study is carried out with the aim of achieving the government’s intended nationally determined contribution goals of reducing emission levels, increasing the capacity of renewable sources and the must-run status of hydro and nuclear, and technical and financial parameters. The result shows an optimal cost solution and flexibility in how increased electricity demand would be achieved and sustained via shifting to renewable sources such as solar, wind and hydro.

Artificial intelligence (AI) systems have emerged as a powerful tool for management decision-making. However, there is a lack of a comprehensive framework that aims to bridge the gap between AI systems and human decision-makers. The proposed approach provides a novel framework, stepping towards the enhancement of AI-human communication with real-time feedback, iterative refinements, and user-centric interface designs. The framework is based on design principles of modularity, scalability, user-centricity, and adaptability, which were conceptualized to make it robust, flexible, and highly effective. Finally, it is expected that the indicated case studies and application scenarios will show the applicability and effectiveness of the framework in different contexts of industry, and therefore, provide concrete examples of how the benefits of the framework might be in practice. Simulation results show that the proposed mechanism has been significantly adopted, demonstrating a 10-20% increase in efficiency, user satisfaction, and feedback responsiveness compared to the existing mechanisms such as EHIDM and HCADMR. These results underscore the potential of the proposed framework to significantly enhance interaction dynamics between AI systems and human users.