Journal of Image Processing and Artificial Intelligence

AI-driven Multi-Hazard Prediction and Decision Support System for Flood and Fire Disaster Management

2026-03-23T12:12:57+00:00

Accurate risk assessment and coordinated response strategies are necessary for effective disaster management to reduce loss and damage. An AI-powered multi-hazard decision support system for managing forest fires and floods is presented in this paper. While real-time environmental parameters temperature, humidity, rainfall, wind speed, and atmospheric pressure are continuously gathered through a weather API to facilitate live risk assessment, a predictive model that can recognise risk patterns is trained using historical disaster datasets. Based on predetermined thresholds, the system classifies risk into low, medium, and high levels and calculates the likelihood of disasters. Automated alarms are produced, safe shelter evacuation routes are calculated, and vital resources are allocated as efficiently as possible in high-risk scenarios. In order to facilitate organised communication and coordinated response, the platform also includes role-based access for individuals, government officials, and non-governmental organisations. The suggested approach improves situational awareness and facilitates data-driven disaster preparedness and response by combining prediction, early warning, evacuation planning, and resource management into a single framework.

Ethical and Responsible Artificial Intelligence for Sustainability

2026-02-18T10:46:23+00:00

Artificial Intelligence (AI) has surfaced as a revolutionary technology that holds the promise of making substantial contributions to worldwide sustainability initiatives. However, the ethical and responsible development and deployment of AI are crucial to ensure that its benefits are realized without causing unintended harm. This paper explores the role of ethical and responsible AI in promoting sustainability across environmental, economic, and social dimensions. The integration of AI into sustainability practices offers a range of opportunities, including enhanced energy efficiency, optimized resource management, and more accurate climate predictions. However, these advancements come with challenges, especially in guaranteeing that AI systems are equitable, clear, and responsible. A significant issue is the ecological effect of AI, especially the energy usage linked to the training of extensive models. To address this, the idea of "Green AI" has surfaced, concentrating on minimizing the carbon emissions of AI technologies. Additionally, the fairness and inclusivity of AI systems are critical to avoid perpetuating biases that may lead to discriminatory outcomes, particularly in resource allocation and decision-making processes. Ensuring that the accessibility and advantages of AI for marginalized communities are crucial for promoting social equity in sustainability initiatives. Furthermore, it is imperative to have transparency and accountability measures in place to guarantee that AI-generated decisions, especially those concerning climate action and resource management, are comprehensible and can be contested when necessary. This paper also discusses the importance of global collaboration and governance in establishing ethical standards for AI in sustainability. Regulatory frameworks must be developed to ensure that AI technologies align with principles of fairness, transparency, and inclusivity while minimizing risks to the environment and society. In conclusion, ethical and responsible AI is vital for advancing sustainability goals. By addressing key concerns around fairness, transparency, energy efficiency, and accountability, AI can become a powerful tool in promoting sustainable development and fostering a more equitable future.

Real-Time Smart Class Management and Surveillance System Using Computer Vision

2026-01-19T04:21:46+00:00

Automated classroom attendance systems based on facial recognition offer a contactless, reliable, and time-efficient alternative to conventional manual attendance procedures. This work proposes a real-time smart classroom management system that integrates classical computer vision methods with advanced deep learning–based facial recognition models to improve identification accuracy and system robustness. The proposed framework is specifically designed to operate effectively under realistic classroom environments, addressing practical challenges such as variations in illumination, facial pose, occlusions, and high student density. The system performs automated face detection, feature extraction, and recognition to accurately identify enrolled students and record attendance without human intervention. By minimizing manual effort, the proposed approach significantly reduces administrative workload and limits the possibility of proxy attendance or human error. Furthermore, the architecture supports real-time processing and is scalable, making it suitable for deployment across multiple classrooms and institutional settings. The integration of intelligent analytics within the classroom environment also opens opportunities for future extensions, such as student engagement monitoring and performance analysis, contributing to the development of smart and digitally enabled educational ecosystems.

An Overview of Explainable Artificial Intelligence (XAI) and Its Application

2026-03-10T10:33:59+00:00

This assessment paper emphasise about newb technology of Explainable Artificial Intelligence (XAI) is an emerging and vital field of research that addresses the "black box" problem prevalent in modern machine learning. As AI systems become more complex and integrated into high-stakes domains such as healthcare, finance, and criminal justice, their inherent opacity raises critical concerns regarding transparency, trust, and accountability. The primary goal of XAI is to provide methods and techniques that enable human users to understand, interpret, and appropriately trust the decisions and predictions made by AI algorithms. While XAI provides a powerful framework for responsible AI development, challenges such as the performance-interpretability trade-off, lack of standardized evaluation metrics, and potential for human misinterpretation remain areas of active research. Ultimately, XAI is a critical step toward creating a symbiotic relationship between humans and AI, where intelligent systems operate not just with high performance but with ethical and transparent reasoning.

Smart Vision: CNN-Based Attendance System

2026-01-31T11:59:53+00:00

Attendance management plays a vital role in academic institutions and organizations; however, traditional approaches such as manual registers, identity cards, and fingerprint-based systems suffer from inefficiency, lack of scalability, and vulnerability to proxy attendance. To overcome these challenges, this paper proposes Smart Vision, a Convolutional Neural Network (CNN)–based automated attendance system that employs computer vision and deep learning for real-time face detection and recognition. The proposed system captures facial images with a standard camera and processes them with a trained CNN to extract distinctive facial features for accurate identification. Recognized faces are matched against a stored database, and attendance is automatically recorded along with date and time, ensuring minimal human intervention. The system is designed to operate effectively under varying illumination conditions and facial orientations, enhancing robustness and reliability. By eliminating physical contact and manual processes, the proposed solution improves operational efficiency while preventing impersonation and data inconsistencies. Experimental evaluation demonstrates that the system achieves high recognition accuracy with reduced processing time when compared to conventional attendance methods. The architecture is scalable, cost-effective, and adaptable to different environments such as classrooms, offices, and large-scale organizations. The proposed Smart Vision system highlights the practical application of CNN-based facial recognition in intelligent automation and provides a secure, contactless, and efficient solution for modern attendance management systems.