Journal of Image Processing and Intelligent Remote Sensing(JIPIRS) ISSN 2815-0953

Wired Fingerprint-Based Classroom Attendance System for Secured Student Attendance Archiving Using Arduino UNO Microcontroller

2024-03-21T05:34:40+00:00

This study, which successfully addresses the shortcomings of traditional attendance-checking methods, such as human error, that are inevitable in manual attendance systems given the fact that it is time-consuming. Paper-based systems can be susceptible to forgery, as students may attempt to sign in on behalf of absent classmates. This undermines the integrity of attendance records. Introduces a fingerprint-based classroom attendance system designed using the Arduino Uno microcontroller. The research explores the feasibility of fingerprint biometrics for identity verification in educational settings. Using Arduino Uno, Fingerprint Sensor, RTC Module, and the LCD Monitor the researchers successfully developed a working prototype for the Wired Fingerprint-Based Classroom Attendance. 600 tests were applied to collect the (1.0) lowest and (2.0) highest time of the fingerprint sensor and calculate its average (1.7). The developed system operates offline, storing data securely on an SD card, making it particularly suitable for institutions in areas with restricted internet access. Comparative performance evaluations against conventional pen-and-paper methods highlight the fingerprint-based system's notable capacity, accuracy, positioning it as a transformative tool to enhance attendance tracking procedures and eliminates attendance-related issues to improve overall classroom operations.

Solar Based Floor Cleaning Robot Using IOT

2024-03-21T10:05:38+00:00

This project introduces a new type of robot, Solar-Powered Autonomous Floor Cleaning Robot in response to the increasing demand of sustainable and energy-efficient solutions in robotics. The robot is designed to move on its own indoors, outdoors and clean different types of floors while getting power from solar panels that are integrated into its body. By using this method, it tries to reduce the impact on the environment that comes with conventional cleaning methods as well as increase the efficiency of floor maintenance generally.The primary objective of the proposed robot is to address the challenge of maintaining optimal efficiency in solar energy harvesting by ensuring the cleanliness of solar panels in industrial environments. The robot incorporates autonomous navigation and cleaning capabilities, employing advanced sensors and artificial intelligence algorithms to detect and navigate around obstacles while efficiently cleaning the solar panel surfaces.The robot's design emphasizes modularity and scalability, allowing it to adapt to diverse industrial environments and solar panel configurations. Equipped with cleaning brushes and a water-efficient system, the robot ensures thorough cleaning without causing damage to the solar panels. Additionally, the integration of real-time monitoring capabilities enables remote tracking of cleaning operations and assessment of overall system performance.

Face Emotional Detection Using Neural Network

2024-03-26T10:25:41+00:00

The face reaction detection using neural network is a very much popular topic in the artificial intelligence and compute vision field. The main goal of this research is to develop a system that can accurately detect the emotional system of a person based on these facial expression. The proposed system consists of so many stages, including face detection, feature extraction, and classification. In the first stage, the face is detected and cropped from the input image using a pre-trained face detection model. Then, the relevant facial features are, like, extracted, such as the position and shape of the eyebrows, mouth, and eyes, and other stuff. The extracted features are then fed into a neural network model, which is trained on a large dataset of labeled facial expressions to learn the relationship between the facial features and emotional states. To evaluate the performance of the proposed system, several metrics are used, like, accuracy, precision, recall, and F1-score. The system is tested on a large dataset of images with labeled emotional states, and the results show that, woah, the system achieves high accuracy and precision in detecting emotions. In conclusion, the proposed system is, you know, an effective approach for face emotional detection using neural networks. The system can be used in a variety of applications, such as human computer interaction and social robotics and emotion-based marketing, all those cool things.

Robust Parking Space Allocation System Using Open CV and Scikit-learn

2024-03-28T08:32:39+00:00

The proliferation of urbanisation has led to an increased demand for efficient parking management systems. In response, this project presents a Smart Parking Assistant system aimed at providing real-time space availability notifications to users through their smartphones. Leveraging computer vision techniques implemented via Open CV and machine learning algorithms from the Scikit-learn library, the system captures video feed from a webcam to detect and quantify the number of empty parking spaces in a designated area. Upon receiving a request from the user, the system processes the video feed to analyse the occupancy status of parking slots, utilising advanced image processing techniques to accurately identify empty spaces along with mask image of the parking lot. The model built using Scikit-learn efficiently categorises the available slots, enabling the system to relay the precise number of open spaces to the user's smartphone. Furthermore, the Smart Parking Assistant incorporates geo-spatial functionalities to enhance user experience. By integrating the Haversine distance formula, the system calculates the distances between the user's location and nearby parking areas. This information is then displayed to the user, allowing them to conveniently locate and navigate to the nearest available parking facility. The proposed system offers a comprehensive solution to address the challenges associated with parking management in urban environments. By harnessing the power of computer vision, machine learning, and geo-spatial technologies, it provides users with timely and accurate information regarding parking space availability, ultimately improving efficiency and convenience in urban parking scenarios.

Deepfake Detection Based on Temporal Analysis of Facial Dynamics Using LSTM and ResNeXt Architectures

2024-03-28T08:39:22+00:00

The proliferation of deepfake technology presents a critical challenge to the authenticity and trustworthiness of digital media. To address this issue, we propose an innovative deepfake detection framework that combines the power of Long Short-Term Memory (LSTM) and ResNeXt architectures. By integrating spatial and temporal analysis methods, our approach aims to accurately identify manipulated videos amidst the vast sea of online content. Through rigorous experimentation and evaluation using diverse datasets, our framework demonstrates promising results in effectively distinguishing between genuine and fake videos. This research contributes to the ongoing efforts to combat deepfake misinformation and uphold the integrity of digital media platforms.