2024-07

Journal of Information Technology and Informatics (القضية : 02) (الحجم : 03)

The purpose of this study is to investigate the integration of ethical Artificial Intelligence (AI) with Internet of Things (IoT) and corporate systems, with a particular focus on the significant functions that responsible AI plays in the development of environmentally responsible business practices. The synthesis of research places an emphasis on the integration of AI technology with robust ethical standards, principles of corporate social responsibility, and concerns for the environment. This integration enhances both the operational efficiency of the organization as well as the overall sustainable business philosophy. The evaluation places a strong emphasis on the significant part that public administrations play in the process of building ethical governance frameworks for AI, which ensure that AI is in accordance with social and environmental objectives. In addition to this, it studies the potential for AI, the IoT, and big data to work together to solve challenges related to sustainability. According to the findings of the study, it is essential to make continuous improvements to AI systems in order to guarantee their scientific growth while also taking into consideration the concerns of society and the environment. It has been recommended that in the future, research should place a higher priority on performing empirical validations and adopting these integrative technologies in specific businesses. It would be beneficial to bridge the gap between theoretical notions and practical implementations, which would ultimately result in a business climate that is more environmentally sensitive and socially conscientious.

2024-06

Indonesian Journal of Computer Science (القضية : 03) (الحجم : 13)

In the rapidly evolving field of medical diagnostics, the integration of deep learning (DL) and machine learning (ML) technologies has dramatically advanced the accuracy and efficiency of brain cancer and tumor diagnosis using magnetic resonance imaging (MRI). This review explores the transformative impact of these technologies, highlighting their role in enhancing tumor detection, classification, and early diagnosis interventions. DL and ML algorithms have significantly improved the analysis of complex imaging data, enabling more precise and faster diagnostic decisions, which are crucial for effective patient management and treatment planning. This review encompasses a broad spectrum of studies that illustrate the capabilities of these computational techniques in handling large datasets, learning intricate patterns, and achieving a high diagnostic performance. By delving into various algorithmic approaches and their clinical implications, this study underscores the importance of continued advancements and the integration of AI technologies in the field of oncology, aiming to foster better patient outcomes through innovative diagnostic tools.