Forest fires present significant threats to both natural ecosystems and human communities, highlighting the need for advanced detection systems for early intervention and mitigation. This paper aims to develop a novel forest fire detection system by integrating Internet of Things technology, machine learning algorithms, and real-time data from weather APIs. The proposed system utilizes IoT sensors to gather environmental parameters and weather conditions, enhancing the accuracy of fire detection. A machine learning model trained on this data distinguishes between normal environmental fluctuations and signs of fire. Additionally, an image processing algorithm is employed to analyze images for the presence of smoke or flames. Integration and testing of the system demonstrate its promising results in terms of accuracy and efficiency compared to traditional methods. This paper contributes to technology-driven solutions for forest fire management, with significant implications for environmental conservation and public safety.
The Daily Border Returns System is a web-based system designed to automate the current manual documentation of border daily returns. The system facilitates efficient data management for day-to-day border activities, such as compiling daily travelers' reports, monthly reports, and situation reports. It utilizes a centralized database to store and manage relevant information about travelers, their immigration status, and any specific conditions or restrictions associated with their cases. The system enables immigration officers to easily access, update, and send information to headquarters, and it can print daily and monthly returns, aiding in decision-making and ensuring accurate and up-todate records. The system will be developed using Python, which is suitable due to its excellent support for working with data and generating reports. JavaScript will be used for web development, as it integrates well with HTML and CSS and has a vast ecosystem of libraries and frameworks, making it a good choice for the web-based interface of the Immigration Daily Border Returns System.
The rapid urbanization and industrialization have caused a significant increase in environmental pollution, especially in air quality. This paper introduces an IoT-based environmental monitoring system to address these issues. Using state-ofthe- art IoT technology, the system deploys sensors in key locations to continuously collect data on air quality indicators like Particulate Matter (PM), Carbon Monoxide (CO), and Ozone (O3). The collected data is wirelessly transmitted to a central server for processing and analysis. The system provides real-time and historical data through a user-friendly interface, enabling users to monitor current conditions, identify trends, and identify potential pollution sources. The key features include scalability, allowing for the addition of new sensors, and proactive decision-making through alerts when environmental parameters exceed predefined thresholds. This paper aims to empower communities with the knowledge needed to address and mitigate air pollution, creating healthier and more liveable urban environments in the face of rapid global urbanization.
The escalating biodiversity loss demands a paradigm shift in wildlife conservation. This paper proposes an innovative AI system for holistic wildlife management. The deep learning algorithms identify individual animals through biometrics in camera traps, drone footage, and bioacoustics. This surpasses traditional methods, enabling tracking across vast landscapes. The real-time animal tracking data, analyzed by machine learning, allows for early detection of poaching, habitat disturbances, and animal distress. Furthermore, the system integrates environmental sensors to provide a holistic understanding of ecological conditions. The correlating animal movement with environmental data helps identify crucial habitats and predict climate threats. This unified platform empowers proactive wildlife management, transitioning conservation from reactive to evidence-based practices for long-term biodiversity preservation.
The tobacco industry is crucial to Malawi's economy, accounting for over 70% of the country's foreign exchange earnings and supporting the livelihoods of a significant portion of the population. Despite its vital role, the current auction management system used by Auction Holdings Limited (AHL) encounters several issues. These include labor-intensive manual processes, a lack of transaction transparency, and inefficiencies that result in delayed payments and disputes over lot allocations. This paper proposes developing an online tobacco auction management system to address these challenges. The new system aims to convert existing manual processes into automated workflows, minimizing human error and speeding up processing times. By utilizing digital technologies, the system will offer real-time updates on auction activities, enhancing transparency for all stakeholders, including farmers, buyers, and regulatory bodies. Additionally, it will streamline the lot allocation process and ensure prompt and secure payment transactions, thereby reducing disputes and improving participant satisfaction. This paper provides a thorough analysis of the proposed system's design and functionality, including user interface specifications, data management protocols, and security features. A feasibility study covering operational, economic, legal, and technical aspects confirms the system's viability. This paper also examines the system's potential impact on the tobacco industry, highlighting benefits like increased operational efficiency, enhanced stakeholder trust, and overall economic improvements.
