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Limited device resources and an ever-changing cybersecurity landscape compound the challenges faced by the network protection infrastructure for Internet of medical things (IoMT) applications, which include different device ecosystems, privacy concerns, and problems with interoperability. Protecting private medical information in IoMT apps is challenging; a comprehensive strategy that provides user education, standard protocols, and robust security mechanisms is necessary to overcome these obstacles. With the advancement of IoMT, the network of clinical systems, gadgets, and sensors is integrated with the Internet of things (IoT) to enable intelligent healthcare solutions. However, the sensitive data sharing and the substantial connections in the IoMT systems raise security and privacy concerns in the network. Therefore, network security is critical in IoMT applications due to data breaches, vulnerabilities, and distributed denial of service attacks on medical data. This study reviews the network security techniques implemented in the existing studies for IoMT applications using machine learning and blockchain technology. This study presents an overview of IoMT healthcare applications by highlighting the security challenges encountered and the necessity of adopting advanced techniques to deal with complex threats. The research is mainly about how deep reinforcement learning (DRL), commonly used for intrusion detection, access control, and anomaly detection, works over time and how it can be used in IoMT applications. With the notion of providing robust security in IoMT applications, this study appraises the benefits of blockchain technology, such as data integrity, accountability, and confidentiality. Besides, this study addresses the limitations and challenges of various security techniques that IoMT systems employ. This work assesses the findings, research gaps, and future advancements for enhancing network security in IoMT applications. With an extensive analysis of existing research, this survey guides researchers, medical practitioners, and decision-makers to scale up the DRL and blockchain in IoMT systems more efficiently in the future.

The emergence of 6G networks promises ultra-high data rates and unprecedented connectivity. However, the effective utilization of the millimeter-wave (mmWave) as a critical enabler of foreseen potential in 6G, poses significant challenges due to its unique propagation characteristics and security concerns. Deep learning (DL)/machine learning (ML) based approaches emerged as potential solutions; however, DL/ML contains centralization and data privacy issues. Therefore, federated learning (FL), an innovative decentralized DL/ML paradigm, offers a promising avenue to tackle these challenges by enabling collaborative model training across distributed devices while preserving data privacy. After a comprehensive exploration of FL enabled 6G networks, this review identifies the specific applications of mmWave communications in the context of FL enabled 6G networks. Thereby, this article discusses particular challenges faced in the adaption of FL enabled mmWave communication in 6G; including bandwidth consumption, power consumption and synchronization requirements. In view of the identified challenges, this study proposed a way forward called Federated Energy-Aware Dynamic Synchronization with Bandwidth-Optimization (FEADSBO). Moreover, this review highlights pertinent open research issues by synthesizing current advancements and research efforts. Through this review, we provide a roadmap to harness the synergies between FL and mmWave, offering insights to reshape the landscape of 6G networks. [ABSTRACT FROM AUTHOR]

An idea management system is where ideas are stored and then can be evaluated and analyzed. It provides the structure and the platform for users to contribute ideas for innovation and creativity. Designing and developing an idea management system is a complex task because it involves many users and lot of ideas. Some of the critical features for an idea management system are supports for the ideas generation process, stimulate users to actively participate, facilitates reuse of ideas and encourages open collaboration among the users. This paper discusses a conceptual architecture design of an idea management system called "Kacang Cerdik" based on three novel concepts; actor, object and workflow. In the system, an idea is an object or an entity that can be manipulated by the users. An idea as an object can be used to generate other ideas. It can also derive its features from other ideas. In this system, an idea is linked with other ideas in the system. The idea will flows from one state to another state in a single workflow. A single workflow is a process or a pipeline that consists of many states. At each of the states, an idea (an object) can manipulated by a user (an actor) based on his current role. The single workflow consists of five different states which are editing, collaborating, reviewing, publishing and evaluating. In the system, a user is an actor who has multiple roles such as a submitter, a reviewer or an evaluator. Based on this concept, a user can submit a new idea or can review an existing idea or can vote on an idea. The system also uses the concept of crowd wisdom to evaluate the ideas. Finally, the potentially viable ideas at the end of the workflow or the pipeline can be further evaluated in a new product development cycle. This conceptual design encourages open collaborations among the users, reuse of previous ideas and use of crowd wisdom in ideas evaluation. A working prototype called "Kacang Cerdik" has been developed using these concepts as a proof-of-concept.

Business intelligence is becoming more essential for supply chain administrators to make good decisions. The globalization of supply chains makes their management and control more challenging. Blockchain is a distributed digital ledger technology that guarantees traceability, transparency, and security and promises to ease global supply chain management issues. This paper proposes the Blockchain-assisted Secure Data Management Framework (BSDMF) for financial data handling for supply chain integrated business intelligence models. Analyzing, collecting, and demonstrating data could be important to a business, its supply chain performance, and sustainability. The blockchain can interrupt supply chain processes for improved finance handling, distributed management, and process automation. The study's experimental result will help organizations deploy blockchain applications with intelligent business strategies to support supply chain management effectively. The simulation outcome has been implemented, and the recommended method achieves a computation time of fewer than 2 hours, an efficiency ratio of 97.4%, an error ratio of 94.1%, data authentication of 92.1%, and a data management ratio of 98.7%. [ABSTRACT FROM AUTHOR]