Real-Time Connected Car Services

In recent years, the patterns ofconnected car are tied in with giving driversmore answers for making the journeyconsistent. Vehicles today are outfitted withhigh innovation highlights and in-vehicleavailability. The Integrated Connected VehicleServices is produced to convey an incorporateddriving experience to all vehicle owners, tomake a communication stage for drivers toimpart and share data between vehicles. Thesystem allows to discover nearby vehicles insiderange, giving the driver early notice caution ofcrisis vehicles inside certain range. Moreover,the system likewise enables the driver to sharebasic data which later plots into the maps foralternate drivers to view and plan the journey.The information of transmission between thevehicles are incorporated through firebase cloudservices. Firebase is known as an effective clouddatabase and ready to screen the applicationdevelopment

A Comparability Study on Driver Fatigue Using C#, C++ and Python

Accidents on road are very commonthese days. Most of them are caused by driverfatigueness. Some common causes and symptomshave been identified. One of the main solutionto detect driver fatigue is by analyzing the facialfeatures of the drivers. This paper discusses aboutthe facial features that can be used to detect driverfatigue. Further examples on existing vehiclesafety technology is also discussed. Primarily, thiswork emphasizes on the study of three differentprogramming languages and its compatibilitywhich works best to be integrated with theproposed hardware. Based on the study, theresult is discussed and the suitable programminglanguage is suggested

Performance Analysis of First Order Optimizers for Plant Pest Detection Using Deep Learning

Several of the major issues affecting food productivity are a pest. The timely and precise detection of plant pests is crucial for avoiding the loss of agricultural productivity. Only by detecting the pest at an early stage can it be controlled. Due to the cyclical nature of agriculture, pest accumulation and variety might vary from season to season, rendering standard approaches for pest classification and detection ineffective. Methods based on machine learning can be utilized to resolve such issues. Deep Learning, which has become extremely popular in image processing, has recently opened up a plethora of new applications for smart agriculture. Optimizers are primarily responsible for the process of strengthening the deep learning model’s pest detection capabilities. In order to detect pests on tomato plants, this study compares the performance of a few gradient-based optimizers, including stochastic gradient descent, root means square propagation, adaptive gradient, and adaptive moment estimation, on a proposed deep convolution neural network architecture with augmented data. In comparison to other optimizers, the evaluation findings demonstrate that the Adam optimizer performs better with an accuracy of 93% for pest identification

An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms

In recent times, digitization is gaining importance in different domains of knowledge such as agriculture, medicine, recommendation platforms, the Internet of Things (IoT), and weather forecasting. In agriculture, crop yield estimation is essential for improving productivity and decision-making processes such as financial market forecasting, and addressing food security issues. The main objective of the article is to predict and improve the accuracy of crop yield forecasting using hybrid machine learning (ML) algorithms. Methods: This article proposes hybrid ML algorithms that use specialized ensembling methods such as stacked generalization, gradient boosting, random forest, and least absolute shrinkage and selection operator (LASSO) regression. Stacked generalization is a new model which learns how to best combine the predictions from two or more models trained on the dataset. To demonstrate the applications of the proposed algorithm, aerial-intel datasets from the github data science repository are used