Business During COVID: An IOT Based Automated Sand Truck Management Solution

As a result of the development in computing technologies have begun to believe the human expectations on these needs in the different sort of components. The eSand Transport System with IOT (eSTSI) is a sand transport system designed to provide secure and accurate data such as gross weight with the sand and the truck, viewing the details of the owner when the RFID card is detected, sending alerts through the mobile application from the Firebase by interconnecting with IOT device, viewing the schedule of the selected truck with the data and destination, and displaying the location once the truck is passed the checkpoints. The main functionalities of eSTSI are to identify the truck with the correct information via the RFID card that retrieves the data who has enrolled with the app and stores the data in the firebase. The expected services are aimed to provide by this system

Deep Learning Model Regression Based Object Detection for Adaptive Driving Beam Headlights

As the world move toward automated driving (AD) continues, the future of adaptive driving beam headlights (ADB), is quickly coming into focus. Engineers, developers and designers are researching hard to identify the most effective combination of components to meet driver requitements for safety and visibility. ADB is a technology used in automotive headlight systems that automatically adjusts the beam pattern of the headlights to provide the best visibility for the driver while also reducing glare for oncoming drivers. The system uses cameras, sensors, and algorithms to detect the presence of other vehicles on the road and adjust the headlight beams accordingly. This allows the driver to have the highest level of visibility while minimizing the risk of dazzling other drivers. ADB is available on many vehicles including Europe, Asia & Middle East. Adaptive capabilities help reveal critical objects such as lane markings, pedestrians, and oncoming cars while avoiding using full high beams that might temporarily blind an oncoming vehicle driver. However, designing and developing a solution for the real road conditions is time-intensive, expensive, and complex. Hence, there is a requirement for adaptive driving beam headlights to detect the oncoming vehicles to reduce the glare for oncoming vehicle drivers. The detection solution needs to be fast, accurate and easy to integrate with automotive vehicular system. This paper reviews various detection techniques that can be used in implementing adaptive headlamps and application of the Machine Learning technique to predict fast and accurate object detection

Machine learning-based predictive models for cardiovascular risk assessment in data analysis, model development, and clinical implications

Cardiovascular diseases (CVDs) remain a leading global cause of morbidity and mortality. Timely identification of individuals at risk is paramount for effective interventions and prevention. This study endeavors to develop machine learning approaches for predicting the initial cardiovascular risk level analyzing the dataset encompassing patient demographics, medical history, lifestyle factors, and clinical indicators. Patient characteristics, including age, gender, diabetes or hypertension presence, smoking status, and physical activity level, along with medical indicators such as blood pressure, cholesterol, and glucose levels, are considered. Diverse machine learning algorithms—logistic regression, decision tree classifier, random forests, linear SVC, naive bayes, and neural network—are employed to train and optimize predictive models. Evaluation metrics (accuracy, precision, recall, F1 score, and AUC-ROC) assess model performance. Accurate risk prediction models hold significance in aiding healthcare decisions, optimizing resource allocation, and enhancing patient outcomes. Identifying high-risk individuals early enables preventive strategies and personalized interventions, reducing the CVD burden. Study objectives encompass dataset preprocessing, exploratory analysis, feature selection and engineering, model training and optimization, and performance evaluation. Findings contribute to cardiovascular risk prediction, presenting a robust model for accurate risk assessment and improved patient outcomes

Web Data Scraping Technology using TF-IDF to Enhance the Big Data Quality on Sentiment Analysis

Tourism is a booming industry, with huge future potential for global wealth and employment. There are countless data generated over social media sites every day creating numerous opportunities to bring more insights to decision-makers. The integration of Big Data Technology into the tourism industry will allow companies to conclude where their customers have been and what they like. This information can then be used by businesses, such as those in charge of managing visitor centers or hotels, etc and the tourist can get a clear idea of places before visiting. The technical perspective of natural language is processed by analysing the sentiment features of online reviews from tourists, and we then supply an enhanced long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We have constructed a web review database using a crawler and web scraping technique for experimental validation to evaluate the effectiveness of our methodology. The text form of sentences first classified through Vader and Roberta model to get the polarity of the reviews. In this paper, we have conducted study methods for feature extraction, such as Count Vectorization, TFIDF Vectorization, and implemented Convolutional Neural Network (CNN) classifier algorithm for the sentiment analysis to decide the tourist’s attitude towards the destinations is positive, negative, or simply neutral based on the review text that they posted online. The results demonstrated that from the CNN algorithm after pre-processing and cleaning the dataset, we have received an accuracy of 96.12% for the positive and negative sentiment analysis

Comparison of Numerical Modelling Techniques for Multi-Layered Microstrip Patch antenna

Proc Computation in Electromagnetics (IEE Conf.) Bournemouth, UK