Online ISSN: 2515-8260

Keywords : Sensors


Surveillance of Road Traffic by Predicting the Rapidity using ITS System

Anjani Rai; Ashish Sharma

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 69-75

Road traffic rapidity forecasting may be a testing problem done intelligent transport system (ITS) and need picked up expanding attentions. Existing meets expectations would principally In light of crude rapidity sensing data gotten from framework sensors or explored vehicles that are restricted Toward unreasonable cosset for sensor sending And upkeep. With meagre pace observations, accepted routines depended main on pace sensing data need aid insufficient, particularly the point when emergencies such as traffic mishaps happen. On location the problem, this paper plans on enhance those way traffic rapidity forecasting Toward fusing universal pace sensing data for new-type “sensing” data from cross area sources, for example, tweet sensors from Online networking and path sensors from guide And traffic administration platforms. Mutually displaying majority of the data starting with different datasets acquires huge numbers challenges, including area questionable matter of low-determination data, dialect vagueness of traffic portrayal in writings Also heterogeneity of cross-domain data. Because of the opposition on this disputes, we exhibit a bound together probabilistic system, known as Topic-Enhanced Gaussian procedure amassed representation (TEGPAM), comprising about apparatus, i. E. Area disaggregation representation, traffic subject representation Also traffic rapidity Gaussian transform representation, that coordinate new-type data with customary data. Investigations looking into true data from two expansive urban areas On America accept the adequacy and effectiveness by our representation.

Prediction of the Crop Cultivating using Resembling and IoT Techniques in Agricultural Fields for Increasing Productivity

Anant Ram; Rakesh Kumar

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 50-53

The agriculture plays a prevailing job in the development of the nation's economy. Atmosphere and other natural changes has become a significant danger in the agribusiness field. AI is a fundamental methodology for accomplishing viable and viable answers for this issue. Harvest Prediction includes anticipating the best output from accessible authentic information like climate parameters and soil parameters. This recommender system uses real time data as input to the machine learning. The sensors collect data from the soil and send that data to the cloud (firebase). Then the machine learning model retrieves that data and predicts the best crop and sends that crop to the cloud. We develop an android application which retrieves the sensor values from the cloud and displays them. This forecasting facilitates the farmer to forecast the best crop earlier than cultivating onto the agriculture field, which in turn increases the productivity.

Toxic gas detection using IOT Sensors: A Comprehensive study

S. Sindhu; Dr.M. Saravanan; S. Srividhya

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1840-1845

Atmospheric pollution is the massive issue faced by the people worldwide. The primary sources of hazardous gases include ignition of coal, oil for electricity and transport, as well as emissions from industries and refineries. Volatile organic compounds are common in air pollutants which includes different kind of chemicals cause’s adverse health effects. In last few years sensors which has high sensitive to VOCs had been used; this paper summarizes the latest advances in sensors for detection of pernicious gases. In addition analytical information revised here shows the efficacy of the existing approaches in toxic gas prediction and an improvement in terms of data validation techniques to improvise the accuracy.