Archives

  • Multidisciplinary Advance Sciences and Technology
    Vol. 1 No. 1 (2022)

    Science and technology is an interdisciplinary topic encompassing science, technology, and their interactions. Science is a systematic enterprise that builds and organizes knowledge in the form of explanations and predictions. It is the study of nature and behavior of the physical and natural world through the scientific method. It is also defined as the observation, identification, description, experimental, investigation, and theoretical explanation of natural phenomena. Technology is the collection of techniques and processes used in the production of goods or services or the accomplishment of objectives such as scientific investigation. It includes methods, systems, and devices that are the result of scientific knowledge being used for practical purposes. 

    In volume 1, Issue 1, we received more than 50 papers. After a regress review, we only selected 5 papers. This journal is working on quality, not quantity. 

  • Vol. 2 No. 3 (2023): Multidisciplinary Advance Sciences and Technology
    Vol. 2 No. 3 (2023)

    Wireless networks, cloud computing, and artificial intelligence (AI) have emerged as transformative technologies with the potential to contribute significantly to the creation of a sustainable environment. This issue provides an overview of the role and impact of these technologies in achieving sustainability goals.

    Wireless networks enable ubiquitous connectivity and communication, allowing for real-time data exchange and information sharing. This connectivity is instrumental in supporting sustainable practices such as smart grid systems, intelligent transportation networks, and environmental monitoring. By leveraging wireless networks, organizations and individuals can efficiently manage energy consumption, optimize resource allocation, and enhance overall operational efficiency, leading to reduced carbon footprint and environmental impact.

    Cloud computing offers scalable and on-demand access to computing resources, which is crucial for handling the massive amounts of data generated by sustainable initiatives. It enables the storage, processing, and analysis of data from diverse sources, including environmental sensors, renewable energy systems, and smart devices. The cloud infrastructure provides a platform for running AI algorithms and data analytics, enabling actionable insights and informed decision-making. This capability contributes to sustainable practices by facilitating predictive maintenance, optimizing energy consumption, and supporting resource allocation strategies based on real-time data analysis.

    AI, with its ability to learn from data and make intelligent predictions, plays a pivotal role in sustainability efforts. Machine learning algorithms can analyze complex environmental data patterns, helping identify areas for improvement and optimization. AI-powered systems can optimize energy distribution in smart grids, predict and prevent equipment failures, and automate energy management processes. Furthermore, AI algorithms can enhance the efficiency of waste management, optimize transportation routes, and promote sustainable agriculture practices. By harnessing AI technologies, organizations can make informed decisions, optimize resource utilization, and minimize environmental impact.

    The integration of wireless networks, cloud computing, and AI presents immense opportunities for sustainable development. These technologies enable real-time data collection, analysis, and decision-making, paving the way for more efficient and eco-friendly operations. They contribute to the achievement of sustainability goals by optimizing resource utilization, reducing energy consumption, and promoting environmentally conscious practices across various sectors. However, challenges related to data privacy, security, and the ethical implications of AI adoption must be carefully addressed to ensure that the potential benefits are realized while safeguarding individual rights and societal well-being.

     

  • Cyber Security and AI facilitators: challenges and Application
    Vol. 1 No. 2 (2023)

    In today's increasingly digital world, the intersection of cybersecurity and artificial intelligence (AI) has emerged as a critical frontier in safeguarding our digital infrastructure and data. This abstract provides a succinct overview of the challenges and applications of AI in the field of cybersecurity. Cybersecurity is a constantly evolving field, with cyber threats becoming more sophisticated and widespread. Traditional approaches to security are no longer sufficient to protect against these evolving threats. AI, with its capacity for rapid data analysis, pattern recognition, and adaptive decision-making, has become a crucial facilitator in the realm of cybersecurity.

    This issue discusses the challenges associated with the integration of AI into cybersecurity, including issues of data privacy, algorithmic bias, and the ongoing cat-and-mouse game between cyber attackers and defenders. Furthermore, it explores the ethical considerations surrounding AI-driven security solutions, such as the potential for misuse and the need for transparency. On the application front, this issue outlines the various use cases for AI in cybersecurity, including threat detection, anomaly detection, incident response, and predictive analysis. AI-driven technologies like machine learning and deep learning have shown promising results in identifying and mitigating cyber threats in real time.

     

  • Beyond Boundaries: AI and Blockchain Transformations
    Vol. 3 No. 1 (2024)

    The special issue titled "Beyond Boundaries: AI and Blockchain Transformations" delves into the dynamic interplay between two cutting-edge technologies, artificial intelligence (AI) and blockchain. As these technologies continue to evolve independently, their convergence holds immense promise for reshaping industries, fostering innovation, and transcending traditional boundaries. The overarching goal of this special issue is to explore the transformative potential that arises from the synergy between AI and blockchain. Contributions within this edition aim to shed light on the novel applications, challenges, and opportunities that emerge when these two technologies converge. Researchers and practitioners are invited to present their insights, case studies, and theoretical frameworks to foster a deeper understanding of this rapidly evolving landscape.

  • Vol. 2 No. 2 (2023): Multidisciplinary Advance Sciences and Technology
    Vol. 2 No. 2 (2023)

    Science and technology is an interdisciplinary topic encompassing science, technology, and their interactions. Science is a systematic enterprise that builds and organizes knowledge in the form of explanations and predictions. It is the study of nature and behavior of the physical and natural world through the scientific method. It is also defined as the observation, identification, description, experimental, investigation, and theoretical explanation of natural phenomena. Technology is the collection of techniques and processes used in the production of goods or services or the accomplishment of objectives such as scientific investigation. It includes methods, systems, and devices that are the result of scientific knowledge being used for practical purposes. 

    In volume 2, Issue 2, we received more than 80 papers. After a regress review, we only selected 5 papers. This journal is working on quality, not quantity. 

     

     

  • AI facilitators In the IoT Era
    Vol. 1 No. 1 (2023)

    AI facilitators are vital in the IoT era, streamlining processes and enhancing IoT applications. They process and analyze vast data, enabling real-time insights and predictive analytics. Automation and adaptability optimize operations, while personalized user experiences improve interactions. Facilitators also bolster security by detecting anomalies and threats. Their edge deployment reduces latency, and they manage resources efficiently, crucial for resource-constrained IoT devices. In healthcare, they enable remote monitoring, and in environmental monitoring, they aid in data analysis for better decision-making. Overall, AI facilitators maximize IoT potential, ensuring smart, secure, and efficient interconnected systems. Prof. DUX is one of the example of AI hub. He is an AI education facilitator developed by the Institute of Artificial Intelligence and Robotics at Near East University. It individualizes education for each student by leveraging AI-driven algorithms to analyze learning patterns, pace, and strengths. Prof. DUX fosters engagement and communication through real-time participation in online discussions and chats, promoting active learning. It employs AI-driven assessment tools that evaluate not only factual recall but also critical thinking and problem-solving skills.

  • Computing Techniques and AI Facilitators: Present and Future Challenges
    Vol. 2 No. 4 (2023)

    This issue delves into the contemporary landscape of computing techniques and AI facilitators, offering an in-depth analysis of their current applications and the challenges they present. It scrutinizes the intricate interplay between artificial intelligence and evolving computing technologies, exploring synergies and complexities inherent in their integration. This issue further investigates the transformative impact of these technologies on diverse industries, highlighting the implications for societal, ethical, and technological paradigms. Additionally, it sheds light on the current state of research and development in these domains, emphasizing key breakthroughs and their implications. Looking forward, the issue outlines anticipated future challenges, considering the rapid pace of advancements in AI and computing and their potential societal, economic, and ethical ramifications. Through this exploration, the issue contributes valuable insights into the dynamic landscape of computing techniques and AI, offering a nuanced understanding of both present scenarios and future trajectories.

  • Emerging Technologies in Intelligent Networks and Cybersecurity for Healthcare, Smart Grids, and Communication Systems
    Vol. 4 No. 2 (2024)

    This issue explores the dynamic convergence of advanced technologies such as blockchain, convolutional neural networks, and ensemble machine learning models to address pressing challenges across healthcare, communication, and smart grid applications. In an era marked by rapid digital transformation, ensuring secure, efficient, and scalable solutions is crucial. The papers in this issue present innovative approaches that redefine technological integration in areas like 6G-enabled healthcare systems, fortified encryption methods, and resilient smart grids.

    Key highlights include a blockchain-empowered 6G framework for intelligent medical applications that enhances data security and responsiveness, as well as a study on convolutional neural networks that bolsters encryption and decryption mechanisms for secure data exchanges. Also featured is an analysis of detecting cyber-physical attacks in smart grids and IoT environments, emphasizing the need for robust defense mechanisms. Further reviews on long-distance optical fiber communication systems, machine learning models for breast cancer prediction, and cloud computing services for distributed mobile devices add depth to the discourse, each contributing unique insights into scalability and resilience for future networks.

    This collection aims to serve as a valuable resource for researchers, engineers, and healthcare professionals who seek to harness the power of AI, blockchain, and IoT to drive advancements in intelligent and secure networks across various fields.