Antennas are fundamental to every form of wireless communication system. Antenna placement is crucial for successful wireless communication, according to satellites and transmitters. So, to enable the use of IoT for remote antenna deployment, this paper provides an IoT-based antenna placement solution. In this case, this paper examines the transmitted orientation of each antenna over the Internet of Things using a sensor-based system that includes a motor on each antenna. When a satellite's or transmitting station's orientation changes, it is necessary to reposition the antenna. The receiving antennas might be located in different parts of the world, at great distances from each other. So, extremely long-distance antenna placement is within the realm of possibility because of modern technology. Online visibility of antenna sites is available to the operator in charge of the IoT. The antenna monitoring GUI system is utilized by the IoT. With IoT-based antenna placement solution technology, the antenna's orientation can be monitored and updated coordinates provided to the motor, allowing it to position the antenna correctly.
This proposed system introduces a dynamic coupon generation mechanism empowered by machine learning algorithms, particularly reinforcement learning, to enhance people's engagement and optimize coupon distribution in such a way that the coupon for the subsidy is given to farmers fairly by prioritizing those in need. This process involves key stages, including data gathering, preprocessing, feature enhancement, and autonomous decision-making logic development, taking into account diverse factors such as farmer segments and budget constraints. The system interacts with HTML, PHP, CSS, and JavaScript, with Python supporting the implementation. This paper aims to continuously adapt the model to meet evolving customer behaviors and preferences. This technological approach is essential for optimizing coupons effectively and staying responsive to real-time customer interactions and preferences.
Traditional methods of managing attendance records, particularly in educational institutions like schools, often rely on manual written records, which can be limited by issues such as lack of transparency and accuracy. This study introduces a web application that aims to overcome these limitations by automating tasks and improving transparency and accuracy through the use of geofencing technology. By creating virtual boundaries around predefined areas and requiring teachers to record their location coordinates during attendance, the system can accurately determine their presence or absence based on their location relative to these boundaries. Offering features like real-time tracking, automated reporting, and trend analysis, this system can provide valuable insights into attendance patterns not only for schools but also for industries like healthcare, logistics, and manufacturing. The attendance tracking system utilizing geofencing technology presents a solution for organizations to efficiently and precisely monitor attendance, enhancing operational efficiency and accountability.
This paper focuses on crime status prediction through an ensemble methodology applied to extensive datasets obtained from catalog.data.gov, specifically targeting Los Angeles crime incidents since 2020. The research methodology comprises meticulous data collection, rigorous preprocessing, exploratory data analysis, model selection, and comprehensive model evaluation. Initial challenges included data inaccuracies and privacy-preserving measures in location data, necessitating thorough cleaning and transformation processes. Exploratory data analysis revealed crucial insights, including the 'Status' attribute's limited correlation, crime code distributions, areawise crime counts, and temporal patterns. To address class imbalance within 'Status', the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the dataset. Model evaluation highlighted the superiority of random forest models employing 10 and 20 decision trees, alongside KNN, which demonstrated consistent high accuracy, balanced precision-recall trade-offs, and notable F1 scores in crime status prediction.
Frothy Disturbance Intrusion Detection Systems (FIDS) can help detect and prevent security attacks using the Support Vector Machine (SVM) algorithm. Recognizing the importance of FIDS in protecting various domains linked to the internet, focus lies on adapting traditional intrusion detection methods for the landscape, which faces challenges such as resource constraints and limited memory and battery capacity. This study entails the creation of a lightweight attack detection technique that utilizes a supervised machine learning-based FIDS using the SVM algorithm. Simulations are used to demonstrate the usefulness of the proposed SVM-based FIDS classifier, which employs a combination of two or three complex features and achieves satisfactory classification accuracy and detection time. This strategy has the ability to enhance application security by effectively addressing the particular.
