Online ISSN: 2515-8260

Keywords : Sensors

Photonic Crystal Tweezers For Tumor Detection Using Artificial Intelligence

Sunil Sharma; Dr. Lokesh Tharani

European Journal of Molecular & Clinical Medicine, 2022, Volume 9, Issue 8, Pages 1009-1015

This paper is proposed for early tumor detection by photonic crystal tweezers with the use of a support vector machine (SVM) and artificial intelligence (AI)-based K-nearest neighbor (KNN) technique. Tumors are very serious and can cause more serious problems with human cells when infected. Previously, photonic crystal tweezers were used to detect tumor cells and proved very effective in many types of tumor detection. Among different AI techniques like     K- nearest neighbor (KNN), Adoptive Neuro Fuzzy Inference System (ANFIS), Fuzzy KNN (FKNN), Support Vector Machine (SVM) and probabilistic neural network (PNN); SVM and KNN observed accuracy of 96% and 92% respectively while the sensitivity is important observed by these two techniques are 32,358 nm/RIU and 11,258 nm/RIU was observed to be 1.251 and 1.337 for tumor cells, respectively. Majorly the research is supposed to offer advantages early detection of infected tumor cells by implication of tweezers with AI. 

Bayesian inference and Internet of Things based plant health care

Asha G Hagargund

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 3, Pages 293-302

Advancements in the Internet of things (IoT) enable us to monitor and maintain a variety of devices in various sectors. Based on the literature available, we have found that there are umpteen IoT based methods proposed for plant health care involving hardware interfacing like sensors, micro-controllers, and cameras. The existing solutions also make use of mobile applications, webservers, data-base for processing of information. This paper presents a novel and simple costeffective Bayesian theorem based plant-health-care using sensors interfaced with raspberry pi. Our solution also provides users to watch and water the plant remotely, using a smart-phone via webserver. We have considered rose plant’s favourable environmental factors for maintaining its health.

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.

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.

Study of IoT sensors for vehicle detection

Ashwini N; S. Mahalakshmi; Bhagya G

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 8, Pages 5349-5356

With the probability to develop road safety and grant economic benefits, intelligent vehicles have evoked a symbolic amount of interest from both academics and industry. A respectable vehicle detection and tracking system is one of the key modules for intelligent vehicles to perceive the surrounding environment. In this paper, we considered the features of different sensors to detect vehicles for having safe vehicular traffic across the expressway. A real-time monitoring has become a vital need for the today's intelligent traffic monitoring systems (ITS). Vehicle detection systems are widely used in practice for various purposes, e.g. monitoring traffic, improving the efficiency of traffic control systems, enhancing safety, or detecting intrusion to protected areas. Existing vehicle detector systems utilize several sensory technologies, the most popular ones being inductive
loop detectors, pneumatic road tubes, weight-in-motion system, microwave radars, ultrasonic sensors and video image processors.

An IoT enabled Convenient Vaccine Cold box for Biomedical Use

Dr Punit Fulzele; Amit Kumbhare; Abhiram Mangde; Dr. Abhay Gaidhane; Dr. Prachi Palsodkar; Prof. Alok Narkhede; Dr. Gaurav Mishra

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 7, Pages 1576-1585

Vaccines are very crucial for treating preventable diseases, but its need a constant environment like maintenance of temperature. Currently it is a barrier in healthcare scenario. In this paper, work is focused on the designing of the cold box which is capable to maintain a temperature range from 20-80 C constantly using Peltier-based thermoelectric chip, specific structural design obtain through calculation and IoT based solutions. Another crucial characteristic of this instrument is that it delivers an optimal power usage forecast to keep the ideal vaccine storage in precise temperature range in presence of limited power supply from environment. Due to a collective action of the phase change material and thermoelectric device, the temperature inside the box is will be maintained. The details of the location and the temperature settings will be accessible and visible on the Blink Application. This box is an efficient carrier as it will aid in tracing, automated settings, consistent temperature, carrying and packaging which will be an alternative to the currently available techniques in practice.

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.