bluuebunny/crossref_metadata_2025_split huggingface dataset:
Breast Cancer Prediction using Decision Tree
Authors: Ravi Shankar J S , Nithish S , Nithish Babu M , Karthik R , Shahid Afridi A 
Published: May 2021

Abstract
Cancer is one of the most difficult types of illnesses today, diagnosing the disease in the early stage is still tough for doctors. Genetic profiling plays a fundamental role in discovering environmental risk factors for cancer. a special mixed layered strategy of option tree and cluster is applied to create a most cancers risk assessment scheme. The proposed gadget is an invaluable device that forecasts certain types of cancers and it’s worth your serious considerations. This research uses statistical tools such as grouping, clustering and estimation to discover the aptitude of cancer patients. We have suggested this cancer prediction method in our paper based on the information-mining strategy. This computer reports the number of human breast cancers in the immediate future. This gadget is checked to be completed with previous clinical knowledge. The aim of this model is to protect the customers and to make it affordable to the person using it. and a predictive model will aid in physical injury prevention and diagnosis. This research helps detect a person’s chances of developing cancer at an early stage of treatment.
A Review on Wearable Epileptic Seizure Prediction System
Authors: Seethalakshmi V , Naveenkumar P , Prabu G Kavin , Kumaar S Praveen 
Published: May 2021

Abstract
Epileptic is a neurological condition that affects approximately 50 million people worldwide. Epileptic seizure prediction lowers the risk of a patient’s life being endangered by a seizure that occurs unexpectedly. The latest seizure prediction methods are computationally intensive due to the complicated hand-crafted features they extract, and they take a lot of memory to store their parameters, which makes them Inappropriate for IoT and connected systems with limited capabilities. In this paper, a deep learning-based IoT framework for accurate epileptic seizure prediction is presented. The proposed method combines the feature extraction and classification stages into a single integrated system in which raw data heartbeat and temperature signals are implemented without any pre-processing, reducing computing complexity even further. A machine learning based prediction model is proposed that extracts the relevant information from the temperature, heartbeat and haemoglobin value using of machine learning algorithm The health condition of patient or person can be found and give some analysis result like normal or abnormal condition. If abnormal condition is observed then the system predicts some medicine or dosage based on health condition and also send alert message using of GSM. In this work, a location tracking of patient is also included and alert is sent to authorized person when the patient fall down or patient get panic or abnormal health
Intelligent Stroke Subtyping Using Recursive Elimination
Authors: Lavanya G , Pradeep S , Prakash J , Prince S Selva 
Published: May 2021

Abstract
Ischemic stroke subtyping is essential for the forecast of ischemic stroke apart from its usage in effective design and treatment of the same. The manual assessment of affliction grouping procedure is time-consuming, having limitation on dataset and is prone to error. This work considers feature selection and forecast problems in medical datasets. Shapiro-Wilk algorithm has been used to rank the features and Pearson correlations between features have been analyzed. Additionally, the proposed work uses the Recursive Feature Elimination with Cross-Validation (RFECV) using linear SVC, Random-Forest-Classifier, Extra-Trees-Classifier, AdaBoost-Classifier and multinomial - Naïve-Bayes-Classifier to select the important features. Then a simple deep learning model has been exploited to classify the ischemic stroke subtype on the International Stroke Trial (IST) dataset. The proposed method classifies the ischemic stroke subtype exactly and the results also proved that the machine learning approach performed well than the human professionals.
IoT Based Automatic Monitoring and Control System
Authors: Chandra Prabha S , Ganishka G , Anto Mamman Shane S , Gayathri S 
Published: May 2021

Abstract
This paper is of systematic review is to analyze this research done on the period of time activity or trailing of the number of intravenous fluid (IV) level during a saline bag and additionally to watch the heart rate and temperature level of the patient. This technology provides health service for patients, particularly who are in intensive care Unit(ICU). Among the varied treatments, the saline medical aid, heart rate and temperature watching is that the most significant treatment that a lot of patients receive from the hospitals. According to this paper, an IoT based mostly system has been enforced which will monitor the heartbeat and temperature and live or track the number of IV fluid level from the output given by a hardware system consisting of a NodeMCU and pulse sensor and temperature sensor. Further, an alert system is value-added that is executed if the IV fluid level goes below the permissible level given within the devised algorithmic rule. The alert message is received by the doctor through associate degree alarm sound by using buzzer and additionally value-added to it, if the heart rate or temperature level goes above or below the permissible level the light can blink on the monitor to point the seriousness level of the patients. By this example the doctors will access the heartbeat rate, temperature and IV fluid level information of the patient from any location. The nurses or the duty doctor obtainable at the hospital will monitor the on top of mentioned information of the patient within the serial monitor through the period of time watching system. The period of time watching is completed via Google platform, this platform is safer to store the main points. The on top of mentioned information and different personal details of the patient area unit keep within the cloud, this may be utilised for future studies on the health condition of the patient. The example is realised using NodeMCU, pulse sensor, load cell, temperature sensor, and Google Platform.
Vehicle Speed Control and Accident Prevention System in Smog Zone
Authors: Kathirvelu M , shree A C Nithia , Manasa M , Naveen L , Karthi N 
Published: May 2021

Abstract
The air quality index (AQI) is a proportion of what air contamination means for one’s wellbeing inside a brief timeframe. The higher the AQI esteem, the more noteworthy the degree of air contamination. In a smog zone, the air pollutants can be detected with an air quality detector that will provide information about invisible toxic pollutant matters present in the air. This paper is concentrated on vehicle collision avoidance in smog places. For instance in the road, the driver suffers from unclear vision because PM (Particulate Matter) enters his/her eyes which leads to vehicle collision. A vehicle collision avoidance system with a microcontroller and onboard sensors in the smog zone is developed. It mainly focuses on the digital driver-vehicle interface implementation by using CAN protocol. The framework of CAN lessens wiring by and large. In this system, information like humidity, temperature, and distance is obtained. The humidity module measures both the temperature and humidity content and an the ultrasonic sensor is adjusted to gauge the distance between the object and vehicle, if a hindrance is identified inside 300cm from the vehicle, the alarm gives a signal to the driver. Once an individual vehicle enters the smog zone, it lowers the speed.
Monitoring and Intelligent Prediction Method Using IoT
Authors: Saravanan K , Nivedhaa M , Kavya shree V , Manikandan S , Kumar Raj 
Published: May 2021

Abstract
Clinical methods for checking domesticated animals’ wellbeing are lacking, as they give just irregular data and required a lot of asset interest as far as time and veterinary aptitude. The creature wellbeing observing framework that is assigning equipment which will mount on the creature body, at present there are no such frameworks which will give on current status of the creature. At present to distinguish the wellbeing status of creature we needed to sit tight for veterinary mastery which set aside long effort for its appearance. The framework won’t just improve singular creature wellbeing, yet it likewise distinguishes and forestalls far and wide infections, regardless of whether it started from common causes or from natural assaults. Such a framework would help in early conclusion of sicknesses. The framework comprises of various sensors for example Temperature sensor, Heart rate sensor. The gadget is vital just as supportive for the medical care of creatures.
Solar Based Charging Station for E-Vehicle
Authors: Gayathri A , Prasanna C , Priyanka M , Rahul M , Abdullah K Mohamed 
Published: May 2021

Abstract
An E-vehicle charging station, otherwise known as an EV charging station, an electric re-energize point, a charging point, an electronic charging station (ECS), or an electric vehicle supply hardware (EVSE), is a piece of a framework that gives electric energy to the re-energizing of module electric vehicles, like electric vehicles, local area electric vehicles, and module mixtures. The main objective of this paper “Solar Based Charging Station for E-Vehicle” is to generate maximum power from the solar panel by tilting its angle based on the intensity of the light that falls on the solar panel. Also, the amount of power available in the charge station is continually monitored locally and from the remote area by using the concepts of Internet of things
Power factor correction based EV battery charger using a bridgeless isolated SEPIC converter
Authors: Prabaakaran K , Senthil Kumar R , Katta Pradeep , Balaji A , Dilipan P , Kaniyappan M 
Published: May 2021

Abstract
Electric Vehicle (EV) battery chargers which uses the conventional PFC (power factor correction) circuits have the limitation of efficiency because of its high conduction loss which occur at the input of DBR (diode bridge rectifier). To solve this issue, a bridgeless isolated single ended primary inductance converter (SEPIC) is used. Throughout the charging process, the input current operates with a power factor of one. Conduction losses are drastically reduced by the removal of the DBR and current conduction due to fewer devices. The overall performance of proposed system is shown by means of various modes of operation, simulation and experimental process. EV batteries are typically charged in constant current or voltage mode, which increases the charger’s overall efficiency.
Solar PV Based Super Lift LUO Converter for BLDC Motor Drive
Authors: Mohammed Ovaiz A , Katta Pradeep , Senthil Kumar R , Yugin M , Dhiwakar M , Chandru S , Manikandan P 
Published: May 2021

AbstractNowadays, in electronic circuit design we are mostly using Voltage lift technique. In an arithmetic progression, it depicts the rise of voltage stage by stage. It gives voltage rising stage by stage in a geometric progression using a Super-lift converter, which is often used in solar PV applications. In power series, it enhances the transfer gain effectively. For providing good statistics and dynamic performance, Developed Cuckoo algorithm based on MPPT is used. MATLAB software is used to investigate the dynamic characteristics and analyse the closed loop performance of these converters with resistive loads under supply and load. Boosted power is supplied to BLDC motor.
Detection of Online Fake Reviews Based on Text Emotion
Authors: Kiruthika S , Priyan V Vishnu 
Published: May 2021

Abstract
online groups offer clients with ways to overpower a few data hurdles and limitations, like the challenge to get self-governing data about movies and for the co-occurrence of positive and negative reactions within reviews. False reviews will disturb such choices because of misleading data, causing commercial disadvantages for the customers. Recognition of false opinions has thus expected very large concentration now days. But, many websites have only concentrated on handling the problematical comments and reviews. Our work tends to categorize people opinions into groups of true orfalse polarization by employing text feature analysis. In our work, our team analyze people opinions by implementing Sentiment Analysis techniques to recognize false opinions. Sentiment Analysis and text feature categorization techniques were used to a database containing people opinions. Furthermore, the estimation reviews acquired from reviewers could be categorized into good or bad opinions, that could be utilized by a customer to choose a movie. Further the proposed technique will graph based on the classification of true and fake reviews as the analysis of good and bad reviews for a product (movie). It will help us to predict the ratio of fake reviews to true reviews easily. To estimate the accomplishment of SA technique, this work has employed accurateness, exactness, recollection and F-degree as a performance rate.
Daily stress classification using functional near infrared spectroscopy
Authors: Devi S Siamala , Elamparithi  , Gowtham V , Sharon B Julian 
Published: May 2021

Abstract
Any mental or physical pressure can be termed as a stress. Stress usually originates from any action or feelings that make a person to feel irritated or panic. An external inducement, action or an atmosphere that pressurizes an individual person is known as a stressor. Several studies related to mental stress have pointed on the insight of psychological state in the presence or absence of investigational stressor. Anyhow, considering psychological state without investigational stressor will not precisely signify the no stress state as people naturally experience extensive pressure in their day-to-day life. It is high time to improvise the stress detection in an exact manner. In this paper, functional near-infrared spectroscopy (fNIRS) was evaluated in 41 healthy members to enumerate their prefrontal cortical oxygenation while executing a reasoning task as an investigational stressor, by considering distinct strain level. The primary six attributes are mined which includes slope, mean, standard deviation, peak, skewness, and kurtosis of the oxygenated hemoglobin concentration. With the help of Support Vector machines (SVM), by considering the several attribute combinations and its time frame, the daily stress and mental stress were properly classified. Our discoveries show that the higher the use of web-based media, the higher the danger of sadness, with young ladies being exposed to the most elevated danger. An early misery locator is proposed to track and control this danger factor of web-based media usage. We similarly misuse multi-source learning in SNMDD and suggest another SNMD-based Tensor Model (STM) to advance the exactness. To extend the flexibility of STM and to additionally improve the capability with execution guarantee. Our structure is assessed through a customer concentrate with 3126 online casual network customers. A segment examination is applied, and besides applies SNMDD for huge extension datasets and analyses the features of the three SNMD types. The experimental results show that SNMDD is hopeful for recognizing on the web relational association.
Noval Approach For Chronic Kidney Disease Using Machine Learning Methodology
Authors: Abinaya U , Devi S Anitha , Haritha B , Raghunathan T 
Published: May 2021

Abstract
Chronic kidney disease (CKD) is a health-related problem in the global context which has high mortality and mobility rate. It in-turn which induces other health diseases. Patients regularly neglect the illness, since there are no conspicuous side effects during the beginning phases of CKD. Discovering CKD at the earliest empowers opportune treatment to the patients and enhances the movement of the infection. Machine learning models helps therapist accomplish this objective because of their rapid and precise acknowledgment execution. Here, we proffer an KNN and Logistic regression, system for detecting CKD. From the reputed University of California Irvine (UCI) AI store, the CKD data set was collected, which contains tremendous set off non existing characteristics K Nearest Neighbour attribution isutilized in the place of non-existing qualities that chooses a few examples with most of the comparative estimations that handles missing information for each fragmented example. The qualities that are missing generally found, all things considered, clinical circumstances since patients may miss a few estimations for different reasons. After adequately rounding out the fragmented informational index, six AI calculations (strategic relapse, irregular backwoods, uphold vector machine, k-closest neighbour, feed forward neural organization and credulous Bayes classifier) were utilised to set up the models. Among these AI models, irregular woodland accomplished the best execution with 99.75% conclusion precision. We proposed an incorporated model, by breaking down the misjudgments produced by the set up models. Utilising perceptron, which consolidates calculated relapse and irregular woods, which could accomplish a normal exactness of 99.83% after multiple times of re-enactment.
Defect Detection of Industrial Products Using Image Segmentation and Saliency
Authors: Prabha P Anantha , Bharathwaj M , Dinesh K , Prashath G Hari 
Published: May 2021

Abstract
The manufacturing industries have been searching and developing new solutions to increase the product quality and to decrease the time taken and costs of production. Defect detection methodologies consume much time in manufacturing and manual inspections for quality enhancement. The existing systems cannot handle the data other than the trained ones as they followed the comparison process with the dataset which is of more time consuming and lack of effective depth representation. In the proposed system, multi scale saliency defect detection algorithm is implemented to obtain the boundaries and range of defect in the surface of industrial products. The defect in the products can be detected using pre-processing defect image with color channels, detecting uneven illumination and post processing the defect image thereby splitting out the defect part from original image with edge detection and contours. Hence the output will be of more robust and accurate comparing to the existing systems.
Self-Powered Vehicle with Enhanced Battery Technology
Authors: Revathi R , Sasidharan S , Kumaran G , Mariesh M 
Published: May 2021

Abstract
The covid-19 pandemic has created a situation where public transportation turns out to be not advisable, and people start encouraging their vehicles for safety reasons. If this was the situation, then the number of vehicles on the road would be increased drastically which will lead to a lot of pollution problems and also demand for fuel. To balance the situation, an innovative vehicle with a self-powered facility using piezo transducer and regenerative braking technology with innovative battery pack technology has been created and it turns transportation into a sustainable one. The battery system is designed with new innovative and efficient techniques. All the innovations and designs lead to eco-friendly, sustainable, and more comfortable transport facilities with cheap prices that could be affordable by normal people. The application for the designed self-powered vehicle including Battery Management System (BMS) can be applied in various application of battery vehicles. This innovative technology move on the fact of renewable energy and the environmentally friendly process is a goal aiming to stop the the valuable natural resources which pretend to destroy. Self-powered source of energy plays a major part in the future of the nation. It is cent percent sure that the development of this prototype will indeed contribute to the technology development in electrical and electronics engineering, which make the pride of very own country, India.
Design and Fabrication Of Microstrip MIMO Antenna For 5G Smart Phones
Authors: Nithya S , Sandhiya M , Sathish Kumar R , Vaishnavi S , Vishnu Prasad S R 
Published: May 2021

Abstract
Now a days, extensive research on the 5G technology is on the rise. With the commercialization of the fifth generation (5G) mobile communication, high data rate communication and intelligent wireless services is approaching. In this project a wide band eight element MIMO (Multiple Input Multiple Output) array with high element isolation enrichment for 5G metal frame smart phones is presented. We built a rectangular patch antenna with 56 radiation and 2×4 elements. The MIMO microstrip antenna with 112 radiation using the microstrip feedline is designed for 5G wireless communication. The 2×4 MIMO microstrip antenna provides excellent results with a bandwidth impedance of 0.7GHZ and access to recovering 32dB loss to 7GHZ. The designed MIMO antenna array comprises of T shaped and inverted T shaped slots. To improve the bandwidth, the required resonances can be obtained by adjusting the E shaped slots. Furthermore, to improve the element isolation between two antennas, L and T shaped slot is introduced between each antenna element.
Arduino enabled IoT based Smart Shopping Trolley
Authors: Nithiavathy R , Asmitha Shree R , Praveen Kumar S , Raghul S 
Published: May 2021

Abstract
As technology evolves and sees new developments in various fields, including artificial intelligence, machine learning, so on, there are growing customer expectations in world wide web. With the rapidly changing lives, customers have absolutely no time to wait in long lines to do their jobs. We present a clever shopping method with RFID and arduino controller in this paper. The trolleys in the shopping centers are a protocol so that they can verify the items placed in them automatically and the last bill is forwarded to a web application, available on any phone or handheld computer. The system is also subject to anti-theft management, where the system allows no customer to take unchecked products.
Vehicular ADHOC Network Based Safety Improvement
Authors: Karpagam M. , Shyam Kumar M. , Monikavishnuvarthini A , Sowmiya M. 
Published: May 2021

Abstract
As the development of processing power and communication speed increasing the possibilities of many thing which where thought impossible things are possible like communication between vehicles often referred as VANET-Vehicular networking. The possibilities of intruders even like even they can bypass through normal firewalls in industries now we have a problem in our VANET as the rate of speed increases with increase in processing speed it gives a good way to manipulate and get away details very fast. So we want ensure the robustness of our system, starting from the dashboard to the server. I had used the guidelines for modelling a application that will be used in dashboard in way that it will not provide any access to unauthorised data. I had used many technologies from traditional to implemented modified method to get accurate results with less nodes and less bandwidth. As we are working on a real time network here speed of decision making should be high with accuracy. We are going to discuss about practical system setup.