Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Sustainable Engineering Innovations Journal on Informatics Visualization and Social Computing
Sameer Ali
Australian National University
Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Forest Fire Alert System Using Satellite Imagery, Machine Learning, and GPS-Based Early Warning Mechanism Anwar Ali Sathio; Doulat Ram; Abdul Aziz Khan; Sameer Ali
International Journal of Sustainable Engineering Innovations Vol. 2 No. 1 (2026): International Journal of Sustainable Engineering Innovations (February 2026)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijsei.v2i1.152

Abstract

Background of study: Wildfires pose a critical threat to global ecosystems, biodiversity, and human safety, with climate change intensifying fire frequency, scale, and unpredictability. Traditional wildfire detection approaches often suffer from delayed response, limited coverage, and insufficient automation, which restrict effective mitigation and early intervention. Aims and scope of paper: This paper aims to design and evaluate an intelligent Forest Fire Alert System that integrates satellite remote sensing, machine learning models, Internet of Things based environmental sensing, and real time alert communication to enable early wildfire detection and proactive risk assessment. Methods: The proposed system employs a Convolutional Neural Network to detect active fire regions from multispectral satellite imagery, while a Random Forest classifier estimates wildfire risk levels based on meteorological variables and IoT sensor data. Geospatial positioning through GPS supports precise location mapping, and a web-based dashboard disseminates real time alerts to forestry authorities for rapid response. Result: Experimental evaluation demonstrates strong performance of the proposed framework. The CNN model achieved an accuracy of 94.7 percent, precision of 92.3 percent, recall of 96.1 percent, and an F1 score of 94.1 percent. The Random Forest model obtained an accuracy of 88.2 percent with a ROC AUC value of 0.91, indicating reliable fire risk prediction capability. Conclusion: The integrated Forest Fire Alert System outperforms conventional detection methods in terms of accuracy, detection speed, and automation. The proposed approach provides a scalable, IoT enabled, and proactive solution for intelligent wildfire monitoring and management under evolving climatic conditions.
Forest Fire Alert System Using Satellite Imagery, Machine Learning, and GPS-Based Early Warning Mechanism Anwar Ali Sathio; Doulat Ram; Abdul Aziz Khan; Sameer Ali
International Journal of Sustainable Engineering Innovations Vol. 2 No. 1 (2026): International Journal of Sustainable Engineering Innovations (February 2026)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijsei.v2i1.152

Abstract

Background of study: Wildfires pose a critical threat to global ecosystems, biodiversity, and human safety, with climate change intensifying fire frequency, scale, and unpredictability. Traditional wildfire detection approaches often suffer from delayed response, limited coverage, and insufficient automation, which restrict effective mitigation and early intervention. Aims and scope of paper: This paper aims to design and evaluate an intelligent Forest Fire Alert System that integrates satellite remote sensing, machine learning models, Internet of Things based environmental sensing, and real time alert communication to enable early wildfire detection and proactive risk assessment. Methods: The proposed system employs a Convolutional Neural Network to detect active fire regions from multispectral satellite imagery, while a Random Forest classifier estimates wildfire risk levels based on meteorological variables and IoT sensor data. Geospatial positioning through GPS supports precise location mapping, and a web-based dashboard disseminates real time alerts to forestry authorities for rapid response. Result: Experimental evaluation demonstrates strong performance of the proposed framework. The CNN model achieved an accuracy of 94.7 percent, precision of 92.3 percent, recall of 96.1 percent, and an F1 score of 94.1 percent. The Random Forest model obtained an accuracy of 88.2 percent with a ROC AUC value of 0.91, indicating reliable fire risk prediction capability. Conclusion: The integrated Forest Fire Alert System outperforms conventional detection methods in terms of accuracy, detection speed, and automation. The proposed approach provides a scalable, IoT enabled, and proactive solution for intelligent wildfire monitoring and management under evolving climatic conditions.
Next-Generation Digital Twin UX: IoT-Driven Smart and Interactive Design ANWAR ALI SATHIO; MUHAMMAD MALOOK RIND; SAMEER ALI
Journal on Informatics Visualization and Social Computing Vol. 1 No. 1 (2025): Journal on Informatics Visualization and Social Computing (JIVSC)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jivsc.v1i1.106

Abstract

Background of study: The rapid development of IoT-enabled systems has transformed user interaction by enabling intelligent, responsive, and interconnected digital environments. However, existing studies often emphasize traditional usability factors while overlooking emerging interaction attributes essential for next-generation Digital Twin and IoT-based interfaces. Aims and scope of paper: This study aims to investigate next-generation Digital Twin user experience (UX) by exploring interactive IoT design attributes, including gesture-based interaction, gaze tracking, multimodal interfaces, and AR-assisted usability. The research also develops an enhanced usability framework that integrates efficiency, cognitive load, and user satisfaction metrics. Methods: Using a mixed-method approach, the study integrates quantitative evaluations (task completion time, error rates) and qualitative assessments (NASA-TLX, SUS). Data were collected from open-source IoT usability datasets and supported by prototype testing, including touch-based, voice-assisted, gesture-controlled, and AR-enhanced interfaces. Result: Findings show that AR-enhanced and touch-based interfaces significantly improve task efficiency, reduce cognitive load, and increase user satisfaction. Gesture-based systems, while offering immersive interaction, exhibit higher error rates and cognitive strain. Users also expressed concerns regarding data security and interface complexity in IoT-enabled environments. Conclusion: IoT-enabled Digital Twin interaction offers substantial improvements in usability and engagement, particularly through AR and touch-based designs. However, challenges persist in gesture accuracy, voice recognition consistency, and privacy risks. This research establishes a structured framework for future IoT-UX development, emphasizing adaptive, intuitive, and user-centered design principles.
Co-Authors Abdul Aziz Khan Anwar Ali Sathio Anwar Ali Sathio Doulat Ram MUHAMMAD MALOOK RIND