MATRIX Academic International Online Journal Of Engineering And Technology

A Helping Hand: Gestures Recognition using Android Mediapipe

2025-05-09T03:44:02+00:00

Attendance System using Face Clusters

2025-05-09T03:50:21+00:00

Abstract. In cases of schools & institutions, staff monitoring is regarded as
important but usually a cumbersome process. This paper describes a system, Face
recognition based attendance system that tracks near real-time video and clusters
faces for automatic attendance. The system is developed using python language
and uses OpenCV library for face detection and recognition and MySQL for
database. The face data is captured for processing and recognition and depends
on Haar cascade classifier in order to detect the face with subsistence and
reliability. This collected data is used to train an LBPH (Local Binary Patterns
Histogram) face recognizer which can detect the face of different people. The
system is tested and evaluated for the existent of accuracy as all the proposed
system simulated in the actual environment the specifications are reflecting
optimum performance. Besides easing out the process of attendance tracking this
approach also helps in improving accuracy to a larger extent since manual errors
often occur with such tracking methods.

Optimized Channel Allocation Algorithm for Performance Enhancement in Heterogeneous Networks

2025-05-09T04:00:01+00:00

Abstract. Heterogeneous networks (HetNets) enhance wireless communication
by integrating various access technologies. However, efficient channel allocation
remains a key challenge due to interference and diverse traffic demands. This
paper introduces an Adaptive Multi-Metric Channel Allocation (AMMCA)
algorithm that utilizes real-time parameters such as SINR, interference, and
user priority to dynamically allocate channels. Simulation in NS-3 demonstrates
improvements in throughput, delay, and packet loss over existing methods,
making AMMCA suitable for 5G and future networks.

Enhancing Privacy Protection on Android Devices through Federated Learning and Differential Privacy

2025-05-09T04:04:07+00:00

Abstract. The rapid expansion of Android devices has brought about significant
convenience and connectivity improvements, but it has also escalated the risk of
data breaches and unauthorized access to sensitive information. This research
presents a thorough investigation into the sources of data leakage within the
Android ecosystem, evaluates existing detection methodologies. Through the
analysis of real-world data breaches, critical vulnerabilities are identified for
android devices.
The study evaluates the effectiveness of current detection tools and their
limitations, revealing that while static and dynamic analysis techniques are
valuable, they are often resource-intensive and prone to false positives. To
address these challenges, a novel hybrid model integrating Federated Learning
(FL) and Differential Privacy (DP) is proposed. This DP+FL model ensures
data remains on user devices and adds noise to local model updates to maintain
privacy.
Experimental results demonstrate that the DP+FL model significantly
outperforms traditional FL and DP methods, achieving an accuracy of 95%,
precision of 94%, recall of 95%, and F1 score of 94%. These findings indicate
that the DP+FL model not only enhances privacy protection but also maintains
high model performance.

Smart Farming using Machine Learning

2025-05-09T04:09:20+00:00

Abstract. The traditional farming sector is undergoing a revolution with the
integration of smart technologies like machine learning and IoT. This research
paper discusses a smart farming model that enables farmers to diagnose crop
health issues by analyzing images of affected crops and relevant soil data, such
as pH and moisture. The system leverages machine learning algorithms to detect
crop diseases and provide actionable recommendations, including the type and
quantity of fertilizers, insecticides, or pesticides needed. This model promises
to enhance agricultural productivity, reduce chemical misuse, and increase
sustainability