VOLUME 13, ISSUE 12, DECEMBER 2025 | IJIREEICE

Renewable Energy: Integrated Smart Photobioreactor (PBR) For Mircoalgae Culture Application

admin — Fri, 05 Dec 2025 12:18:13 +0000

Abstract: In this study, the focus will be on the design and development of a smart photobioreactor (PBR) for microalgae culture with integrated harvesting components meant for renewable energy application. While there are lots of PBR designs have been reported, but the harvesting unit is not integrated in the PBR or built separately/stand-alone. Therefore, the extraction process is not straight-forward and will be a bit complicated during harvesting. Having a stand-alone harvesting unit means it involves more manpower and need to be manually done which is not error proof. In this paper, the idea is to have a PBR that is smartly monitor or control the growth or cultivation of the microalgae at its most optimum growth parameters together with a built-in harvesting system which will collect the output from microalgae automatically and in a closed-loop manner. For this purpose a prototype of a PBR was built to mimic a relatively real-case growth conditions.

Keywords: Photobioreactor; Microalgae culture; Smart; Integrated; Harvesting; Renewable energy.

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Design, Analysis, and Optimization of the Radiation Characteristics of a Circular Loop Antenna Using a Genetic Algorithm

admin — Fri, 05 Dec 2025 15:46:07 +0000

Abstract: This study presents the design, analysis, and optimization of the radiation characteristics of a circular loop antenna employing a Genetic Algorithm (GA) to achieve enhanced performance across desired frequency bands. The circular loop antenna was modeled and simulated using the GA tool box in MATLAB, and key parameters such as loop radius, conductor radius, feed position, and substrate properties were optimized using GA to improve gain, bandwidth, and radiation efficiency. The optimization process utilized a fitness function that minimized return loss and maximized radiation resistance, leading to an optimal configuration that outperformed conventional designs. Simulation results demonstrate that the GA-optimized antenna exhibits improved impedance matching and stable radiation patterns over a wide frequency range. The proposed method confirms the effectiveness of evolutionary algorithms in antenna design, providing a robust framework for the automated optimization of microwave and wireless communication antennas.

Keywords: Circular loop antenna, Optimization, Genetic Algorithm, MATLAB, Radiation resistance, Radiation pattern, Gain.

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Smart IOT Based Solar Robotic Grass Cutter and Water Sprinkler

admin — Sat, 06 Dec 2025 14:52:29 +0000

Abstract: Traditional grass-cutting machines depend on manual operation and the use of fossil fuels, which leads to environmental pollution, increased operational costs, and greater physical effort. To overcome these limitations, this project proposes the design and implementation of a solar powered grass cutter integrated with a water sprinkler system. The robot operates wirelessly through a Bluetooth-based mobile application that enables remote control of its movement and functionality. The core of the system is the Arduino UNO microcontroller, which coordinates motor control, blade operation, and sprinkler activation. A solar panel is used to charge the onboard rechargeable battery, ensuring continuous operation using renewable energy. The drive mechanism employs DC motors for mobility, and the cutting blade is powered through a high-torque motor for efficient grass trimming. The water sprinkler system is interfaced with a mini water pump that can be activated as required. This system minimizes human intervention, promotes energy efficiency, and eliminates dependency on fossil fuels. The portable design and eco-friendly operation make the robot suitable for home gardens, institutional lawns, and small-scale agricultural fields. The integration of solar energy and wireless control enhances both sustainability and ease of use, contributing to smart automation in agricultural and domestic maintenance applications.

Keywords: Solar-powered, Bluetooth based, portable design, high-torque.

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CYBER-PHYSICAL SYSTEM FOR ENVIRONMENTAL MONITORING

admin — Sat, 06 Dec 2025 16:49:35 +0000

Abstract: Cyber-Physical System for Environmental Monitoring addresses the growing challenges posed by rapid environmental pollution, urbanisation, and industrialisation by providing an intelligent and automated monitoring solution. Traditional environmental data collection methods are slow, geographically limited, and lack real-time analytical capabilities. This project presents the design and implementation of an IoT-integrated Cyber-Physical System (CPS) that enables continuous, real-time environmental monitoring and analysis.

The system uses a Raspberry Pi 4 as the central processing unit, interfaced with low-cost sensors including the DHT11 for temperature and humidity, MQ135 for air quality, BMP180 for atmospheric pressure, and an LDR for light intensity measurement. These sensors collect real-time environmental data that are processed locally and transmitted wirelessly via the Raspberry Pi’s built-in Wi-Fi to the ThingSpeak cloud platform using HTTP/MQTT protocols. The cloud layer supports data storage, visualization, and remote analysis through interactive dashboards, enabling timely decision-making.

The integrated CPS architecture successfully combines sensing, computation, and communication, while automated alerts are triggered when environmental parameters exceed predefined thresholds, enabling proactive responses to hazardous conditions. The system ensures accuracy, scalability, low power consumption, and cost-effectiveness, making it suitable for deployment in smart cities, industrial zones, and agricultural environments.

Moreover, this project establishes a foundation for future advancements such as AI-driven predictive analytics and edge computing to support autonomous environmental control. Overall, the developed system contributes to sustainable environmental management and aligns with UN Sustainable Development Goal 13 (Climate Action) by promoting intelligent, data-driven monitoring for a safer and cleaner environment.

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“Design and Implementation of a Smart Irrigation System Using Soil Moisture Sensor and IoT”

admin — Wed, 10 Dec 2025 12:30:46 +0000

Abstract: This paper presents an soil moisture sensor to detect the moisture content in the soil and an NodeMCU (ESP32) Microcontroller Board to control a water pump. The system uses a IoT-based smart irrigation system designed to automatically control water flow according to soil moisture levels. When the soil is dry, the motor is switched ON to provide water, and it automatically switches OFF when the required moisture level is reached. The project is further enhanced using IoT, allowing users to monitor and control the irrigation process remotely via the internet. This approach ensures efficient water usage, reduces manual effort, and is especially beneficial for agricultural and home gardening applications.

Keywords: IoT, NodeMCU (ESP32) Microcontroller, Soil Moisture Sensor, Smart Irrigation, Automated Watering System

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Water Quality Prediction Using Machine Learning Technique

admin — Sat, 13 Dec 2025 16:45:20 +0000

Abstract: Water quality assessment is essential for ensuring public health, environmental safety, and sustainable water resource management. Traditional methods of water monitoring rely on manual sampling and laboratory analysis, which are often time-consuming, expensive, and incapable of providing real-time insights. This study proposes a machine learning (ML)-based framework for accurate and timely prediction of water quality parameters such as pH, turbidity, dissolved oxygen, nitrates, and phosphates. Historical and sensor-based datasets are utilized to train and evaluate supervised ML models, including Random Forest (RF), Support Vector Machine (SVM), and XGBoost. Data preprocessing, feature selection, and model evaluation are incorporated to enhance prediction accuracy. Experimental results demonstrate that the proposed ML models can reliably forecast water quality metrics, providing early warnings of potential contamination events. This approach not only reduces dependence on manual testing but also supports real-time water management and pollution mitigation strategies, making it suitable for smart city and industrial applications.

Keywords: Water Quality Prediction, Machine Learning, Random Forest, XGBoost, Support Vector Machine, Environmental Monitoring, Predictive Analytics

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BATTERY MANAGEMENT SYSTEM OF EV WITH HYBRID CHARGING USING IOT AND AI

admin — Tue, 16 Dec 2025 14:54:16 +0000

Abstract: Future Electrical vehicles (EV) are widely praised for their environmental benefits, superior efficiency, and enhanced driving experience compare to conventional internal combustion engine (IC) vehicles. They represent a significant stepping sustainable transportation, though they still phase challenges related to initial cost and infrastructure. BMS systems will not only extend the battery life and promote safe operation, but also incorporate two new functionalities: the capability to optimize scheduling and utilization lifetime, and the ability to detect and diagnose anomalies early to enable predictive maintenance and minimize downtime. Furthermore, BMS development and deployment for hybrid energy storage and end-of-life equipment repurposing are key enablers for achieving the broad adoption of electric vehicles and the accelerated integration of renewable energy into the electrical grid.

Keywords Battery Management System, Electric Vehicles, Hybrid Charging, Artificial Intelligence, Internet of Things, Machine Learning, Datasets.

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LoRa BASED HYBRID POWER GENERATION SYSTEM

admin — Tue, 16 Dec 2025 15:05:47 +0000

Abstract: The project develops a solar–wind hybrid power generation system with battery storage to ensure a reliable and sustainable energy supply. It uses an energy management system, MPPT charge controllers, inverter, and real-time monitoring to optimize power generation, storage, and usage while protecting system components. Designed for residential and small commercial applications, the system provides continuous power, improves efficiency, and supports energy independence with reduced environmental impact.

Keyword: Wind Turbines, system, power, MPPT

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Fingerprint Based Exam Hall Authentication System

admin — Fri, 19 Dec 2025 05:44:57 +0000

Abstract: In educational institutions, ensuring the integrity of exams and preventing malpractice is a growing concern. Traditional methods of student authentication during exams, such as student ID cards or roll call, are prone to human errors and fraud. This paper proposes a fingerprint-based exam hall authentication system to improve security and ensure accurate identity verification. By leveraging biometric fingerprint recognition, the system provides a more secure, efficient, and automated solution for confirming student identity before and during exams.

The proposed system captures the student’s fingerprint using a fingerprint scanner, which is then matched with a pre-enrolled fingerprint database to authenticate the student. This approach eliminates the possibility of impersonation and helps to prevent cheating. Additionally, the system records each authentication event, creating a reliable log that can be used for audit purposes.

The implementation of the fingerprint-based system not only enhances security but also streamlines the exam process by reducing the need for manual checks and enhancing the overall exam experience. Furthermore, the system can be integrated with existing exam management software to facilitate seamless operations.

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Skin Cancer (Melanoma) Detection Using Deep Learning

admin — Sat, 20 Dec 2025 16:45:59 +0000

Abstract: Melanoma is one of the deadliest forms of skin cancer, and early diagnosis is critical for improving patient survival rates. This paper presents a deep learning-based melanoma detection system that classifies dermoscopic skin images into benign and malignant categories. The proposed system employs a Convolutional Neural Network (CNN) with EfficientNet architecture for accurate feature extraction and classification. A Flask-based web application is developed to enable users to upload images and receive real-time predictions. The experimental results demonstrate that the proposed approach achieves reliable accuracy and can assist dermatologists in clinical decision-making The proposed system employs a Convolutional Neural Network (CNN) using EfficientNet architecture to classify dermoscopic skin lesion images into benign and malignant categories. Image preprocessing techniques including resizing, normalization, and data augmentation are applied to enhance model robustness and reduce overfitting. The trained model is integrated into a web-based application using the Flask framework, enabling users to upload skin lesion images and receive real-time prediction results.

Keywords: Melanoma Detection, Deep Learning, CNN, EfficientNet, Medical Image Analysis

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