Issue 1 (June)

Cognitive computing represents a cutting-edge field that blends artificial intelligence (AI), machine learning (ML), and data analytics to mimic human thought processes. This paper explores the potential of cognitive computing in enhancing decision support systems (DSS). The integration of cognitive computing techniques allows DSS to process vast amounts of data, recognize patterns, and provide insights that can aid in making more informed decisions. This abstract discusses the key aspects of cognitive computing, its implementation in DSS, and the benefits it offers over traditional decision-making processes. We delve into how cognitive computing systems can analyze unstructured data, learn from historical information, and adapt to new inputs, thereby improving the accuracy and reliability of decisions. Furthermore, we examine the challenges faced in the development and deployment of cognitive computing for DSS and propose strategies to overcome them. The paper concludes by emphasizing the significance of cognitive computing in driving innovation in decision-making and the potential for its widespread adoption across various industries.

This paper explores the concept of cognitive radio networks (CRNs) with a focus on spectrum sharing and optimization. Cognitive radio technology enables wireless communication systems to adapt to their environment, enhancing their efficiency and flexibility by dynamically adjusting to available frequencies. The primary objective of CRNs is to utilize the radio spectrum more efficiently, mitigating the increasing demand for wireless bandwidth. This abstract delves into the principles of cognitive radio networks, their architecture, and their potential to revolutionize spectrum management. It discusses various spectrum-sharing strategies, such as underlay, overlay, and interweave, and analyzes their impact on network performance. Furthermore, the paper examines optimization algorithms and techniques that enable CRNs to maximize spectral efficiency, reduce interference, and enhance overall network throughput. The findings reveal that cognitive radio networks have the potential to address current and future spectrum challenges, promoting a more sustainable and efficient wireless communication landscape.

This paper focuses on the application of computational intelligence techniques for optimizing energy consumption in smart buildings. With the increasing demand for sustainable and efficient energy management, computational intelligence offers innovative solutions to address the complexities involved in energy optimization. The study explores various computational intelligence approaches, including machine learning, evolutionary algorithms, and neural networks, which are employed to analyze and predict energy consumption patterns. The integration of these techniques allows for the development of intelligent systems capable of real-time energy monitoring and control, ultimately leading to reduced energy consumption and improved sustainability. The paper further discusses the challenges and limitations associated with the implementation of computational intelligence in smart buildings and proposes potential future research directions to enhance the effectiveness and efficiency of energy optimization strategies.

This paper delves into the realm of computational methods specifically tailored for biomedical signal processing. Biomedical signal processing is a critical field with applications in diverse areas, such as medical diagnostics, patient monitoring, and physiological research. The abstract outlines the challenges faced in analyzing complex and noisy biomedical signals and how computational techniques can address these issues. The paper discusses various computational methods, including filtering, feature extraction, and machine learning algorithms, which have been successfully applied to enhance the quality and reliability of biomedical signals. It provides a comprehensive overview of the algorithms, their implementation, and the advantages they offer over traditional methods. Furthermore, the paper explores the integration of these computational approaches into real-world applications, emphasizing their potential to revolutionize healthcare. The findings highlight the importance of interdisciplinary collaboration and continuous innovation in the development of advanced computational methods for biomedical signal processing.

The advent of cyber-physical systems (CPS) has revolutionized the manufacturing industry, enabling the creation of smart manufacturing environments. This paper explores the challenges and solutions associated with integrating CPS into smart manufacturing processes. The challenges discussed include the complexity of data integration, ensuring cybersecurity, managing real-time operations, and adapting to a rapidly evolving technological landscape. Solutions proposed involve the development of robust data analytics tools, enhanced security protocols, advanced control algorithms, and flexible system architectures. By addressing these challenges, the paper emphasizes the potential of CPS to increase efficiency, reduce costs, and improve the quality of manufacturing operations.