bluuebunny/crossref_metadata_2025_split huggingface dataset:
Truth discovery in healthcare prediction using machine learning
Authors: Priyanka G , Mahalakshmi M , Nivedha G , Roshini J 
Published: May 2021

Abstract
Healthcare is one of the core areas of the medical field in today’s digital world. By analyzing large amounts of patient data, healthcare systems are needed to gain insights and help disease prediction. The truth discovery of the Health Recommendation System (TDHRS) is becoming an important platform for medical services. The proposed use of Boltzmann Machine (RBM)-FP-Growth Health Pattern Recognition (RBMG) smart HRS can provide insights into how big data analysis can be used to implement an effective health recommendation engine, and will be used in Transition from a standard solution to a more personalized paradigm in the telemedicine environment. By considering the Root Mean Square Error (RSME) and Mean Absolute Error (MAE) values, the proposed deep learning method (RBM-FP-Growth) exhibits fewer errors than other methods.
Automatic Voting System Using Convolutional Neural Network
Authors: Sruthi M S , Shanjai K 
Published: May 2021

Abstract
Electoral fraud or election fraud is an illegal intervention in the process of election, in which voters vote repeatedly for a favored party thereby increasing vote share. In Order to conduct elections ethically, this election fraud should be eliminated. Hence, the automatic voting system using Convolutional Neural Network (CNN) has been proposed. The Existing systems are time-consuming and difficult-to-maintain since they include manual processes. Presently different kinds of biometric processes available, among that face detection is the most effective. The proposed system develops an automatic voting system without any human intervence. This system includes a camera for capturing the images of electors, captured images will be stored in the database for analysis. data analysis will be performed on the database. In this method, all the label images are trained through the convolutional neural network to predict the output by classifying the images. This classification system produces an accuracy of approximately 90%.
A Secure Remote Clinical Sensor Network Approach for Privacy Enhancement
Authors: Sophia B , Jeril L , Kavin Harnesh M , Lalith Kumar V 
Published: May 2021

Abstract
Health being the biggest concern of the patients all around the globe, e-healthcare Remote Clinical Sensor Network assists in gathering the crucial body information of individual boundaries using sensors. There are chances forlots of issues like absence of vigor, diminished framework’s adequacy, bogus caution, since it is remote in nature. The security and protection assurance of the information gathered has been a major concern factor to worry about. Hence, we presented fused secure and fuzzy combination framework focusing on these issues to accomplish productive secure transmission and information combination. The proposed technique is on thought of providing a secured key and encoded by AES i.e., Advanced Encryption Standard mechanism. It handles loose information for less utilization of energy and therefore increasing the lifetime of the network. And thus the experimental results ensure the energy utilization, security and the proficiency of the put forth strategy.
Securing Connected & Autonomous Vehicles: Challenges posed by Adversarial Machine Learning and the way forward
Authors: Kiruthika S , Aashin A , Gopinath K , Gowtham A 
Published: May 2021

Abstract
Associated and self-ruling automobiles (CAVs) will shape a foundation for the future cutting edge canny transport frameworks (ITS) giving comfortable travelling, street wellbeing, alongside various worth added administrations. Such a change—which would be fuelled by attendant advances on advances for AI (Machine Learning) and remote interchanges—will empower a future vehicular biological system that is better included and more effective. Notwithstanding and are sneaking security issues identified with the utilization of Machine Learning in a particularly basic setting where a mistaken Machine Learning choice may not exclusively be an aggravation yet can prompt loss of valuable lives. Here it’s present a top to bottom outline of the different difficulties related with the utilization of Machine Learning In vehicular organizations. Moreover, we form the Machine Learning Pipeline of CAVs and present different potential security issues related with the selection of Machine Learning Strategies. Specifically, it’s center around the point of view of antagonistic Machine Learning Assaults on CAVs and framework an answer for protecting against ill-disposed assaults in different settings.
Machine Learning based Fraud Analysis and Detection System
Authors: Kousika N , Vishali G , Sunandhana S , Vijay M Arvind 
Published: May 2021

Abstract
The spectacular surge in the proportion of credit card transactions, web based purchases, has led to a surge in fraudulent activities recently. For any business establishment, credit card security is a major concern. In this respect, credit card fraud is hard to identify. Thus it became imperative to implement effectual fraud detection systems for all credit card issuing banks to mitigate their losses. Betrayed transactions with real transactions in actuality are often dispersed and simple methods of matching are not enough to detect them accurately. The paper proposes an algorithm based on Machine Learning credit card fraud detection to solve the issue of a fraudulent transaction. This framework nominally increases the probability of card fraud by exponential activity. The results show that the accuracy of Random Forest, Support Vector Machine and KNN classifiers achieves respectively 94.84%, 89.46%. Random Forest could even predict new fraud cases very quickly.
Dual Axis Solar Tracking System using LDR Senors
Authors: Krishna Kumar J. , Nivas J. , Sankar S. , Uthirasamy R. 
Published: May 2021

Abstract
At present, the dual axis solar system is not being used properly. A single axis of solar energy system is more compact and more widely used than a double axis. However, a single axis tracking system is less effective but more efficient due to its economic viability. Therefore, we are analysing a double axis tracking system preferred tosingle axis tracking system. We use an independent method using a dummy solar tracker to check whether the sun’s rays reflected on the solar panel at a specific point at any given time based on the elevation calculations and azimuth angles of sunrays. To simulate the processes a simulation using the Borland C ++ builder is created. Here, azimuth-elevation angles are calculated accurately and precisely in any location and time.
Ultrasonic sensor enabled smart automated railway gate control system
Authors: Senthil Kumar R , Prabu M , Rani K S , Prabaakaran K , Chowdary Penekalapati Soniya , Rithika K 
Published: May 2021

Abstract
Railways is the most preferred modes of transport system for freights as well as passengers. Among the accidents occurring in railways, the accident due to unmanned railway crossing are predominant. This article focuses in providing a smart automatic solution for vigilant monitoring in avoiding the accidents in railway crossing, system is provided with extra features to perform superior to raw conventional automatic railway gate, instead of blind closing of gates, gates are provided with the sensor which detect any persons or vehicle beneath it when it is operated. Along gate, pop up projection are recommended in this system which will be effectively restricting the movement across railway crossing during the operation of gates. Additionally, a monitoring system is provided which gives signal to train drivers if any vehicle/living being got struck in between the railway gate, well in advance which aids train drivers to act accordingly. Along with all these additional features conventional alarm feature is also available to alert. The prototype designed with the above feature effectively gives a solution in avoiding accidents by strongly enforce the restriction in movement across the gates when train crossing the unmanned railway crossing, reaction time of the monitoring system in giving alert to train driver, if any vehicle/living being got locked between the gates is very low.
Deep Learning based Voice assistance in hospitals using Face Recognition
Authors: Jenifa G , Yuvaraj N , Karthikeyan B , Preethaa K R Sri 
Published: May 2021

Abstract
After listening to a wake word or order, voice assistants come in quite compact packages and can accomplish a range of acts. They are able to switch on the lights, answering basic questions, playing music, place online orders, etc. As voice assistants become more robust, their usefulness can also be extended in both the personal and business areas. Various descriptive variables are fused in speech signals, leading to considerable difficulties interpreting any of the variables. There are various effective algorithms for speech and face recognition for the real - world applications. When a human try to communicate with a bot or vice-versa, there occurs so many difficulties in information sharing. This paper deals with one such application of bot assistance in hospitals, where a patient communicates with the bot assistant. Though the task seems to be quite easy, it involves a main threat: surrounding noise, word error rate, accuracy of the speech. An obvious impression is to factorize the frame of audio into various enlightening variables, but it turns out to be extremely difficult. The face of the patient who enters the healthcare premise is captured and analysed through the Deep Neural Network (DNN) based face recognition algorithm. Once identifying the user, the information regarding the patient is gathered from the database and given as a voice over output. Now, it is necessary to measure the accuracy of the word recognition and the word error rate. For this Cascade Deep Factorization (CDF) and Iterative Signal Enhancement (ISE) algorithm are used and discussed in this paper. For several speech processing functions like speaker recognition, this factorization and reconstruction method offers possible values.
Full Fledged Automated Human Resource Management Software Suite
Authors: Sivasankaran M Sri , Suganya T 
Published: May 2021

Abstract
The Human Resource Management System is essentially worried about dealing with the Administrator of HUMAN RESOURCE Department in an organization. A Human Resource Management System alludes to the frameworks and cycles at the crossing point between human asset the executives and data innovation. It blends HRM as an order and specifically its essential HR exercises and cycles with the data innovation field, though the programming of information preparing frameworks developed into full-fledged HR SUITE. The fundamental target of this paper is to diminish the exertion of Administrator to keep the day-by-day occasions, for example, participation, projects, works, arrangements, and so forth This paper manages the way toward distinguishing the representatives, recording their participation hourly and ascertaining their successful payable hours or days. This paper FULL fledged HR management suite ought to keep up the records of every single worker and their time spend in to organization, which can be utilized for execution evaluation. In view of that move, expulsion, advancement should be possible.
Phishing website detection using machine learning and deep learning techniques
Authors: Selvakumari M , Sowjanya M , Das Sneha , Padmavathi S 
Published: May 2021

Abstract
Phishing has become more damaging nowadays because of the rapid growth of internet users. The phishing attack is now a big threat to people’s daily life and to the internet environment. In these attacks, the attacker impersonates a trusted entity intending to steal sensitive information or the digital identity of the user, e.g., account credentials, credit card numbers and other user details. A phishing website is a website which is similar in name and appearance to an official website otherwise known as a spoofed website which is created to fool an individual and steal their personal credentials. So, to identify the websites which are fraud, this paper will discuss the machine learning and deep learning algorithms and apply all these algorithms on our dataset and the best algorithm having the best precision and accuracy is selected for the phishing website detection. This work can provide more effective defenses for phishing attacks of the future.
Machine Learning based Classification for Heart Disease Identification
Authors: Siamala Devi S , Harini Karthika G , Deepika M 
Published: May 2021

Abstract
Cardiac illness is one of the unpredictable infections and around the world numerous individuals experienced this sickness. On schedule and effective recognizable proof of coronary illness assumes a critical part in medical care, especially in the arena of cardiology. A productive and precise framework is proposed to finding coronary illness and the framework depends on AI procedures. Supervised learning algorithms such as Multi-Layer Perceptron (MLP), Multinomial Logistic Regression (MLR), Fuzzy Unordered Rule Induction Algorithm (FURIA) and C4.5 are then used to model CAD cases. This approach is tested on medical data that has 26 features and 335. MLR accomplishes most noteworthy expectation precision of 88.4 %. This methodology is benchmarked on Cleveland heart coronary illness information also. For this situation additionally, MLR, beats different methods. Projected hybridized model increases the exactness of arrangement calculations from 8.3 % to 11.4 % for the Cleaveland information. The proposed technique is, along these lines, a promising tool for finding CAD patients with improved forecast precision.
Analyzing the Condition of Gear Module Using Internet of Things
Authors: Venkatesh T , Jamuna R , Bharath G , Arunprasath M , Nishaji A 
Published: May 2021

Abstract
This project is focused on the use of the Internet of Things Vibration Detection. The assessment of passing motions in a system is vibration control. When unusual vibrations are detected, this project involves developing an SMS alert. It requires a laboratory-based vibration detection experiment when applying the different magnitude of the force. This force causes the sensor’s values to change from their initial values. Acceleration values can be seen on the webpage and, if necessary, analysis can be done. The warning signal will be produced considering the maximum magnitude at which destruction is high. Using GSM technology, this message will be sent to various mobile phones. Properly performed, vibration monitoring can be extremely useful in identifying the extent of the damage and assessing the origin in order to avoid damage to structures. We are currently using our key parts, the Arduino microcontroller, the accelerometer sensor, and the GSM module. The Arduino Uno microcontroller’s code is written in C++.
Directivity Enhancement in UWB Microstrip Patch Antenna
Authors: Balamurali S , Asvitha R , Deenadhayalan S , Ishwarya S , Kaavya Varshiny A 
Published: May 2021

Abstract
A portable Ultra Wideband (UWB) antenna for wireless applications. The ground plane of the intended antenna seems to have an extra open-ended inverted slot. The antenna has a good radiation pattern, propagation pattern, impedance bandwidth, and gain, and it can operate in the frequency range of 3.1-10.6 GHz, which is perfect for UWB applications. The antenna is small, measuring 38.87 × 24.00 mm2. The proposed antenna is ideal for wireless applications because of these features.
Smart Traffic Control Scheduling in Smart City Signal Control
Authors: Kirubakaran S , Santhosh S , Tamilselvan S , Varunika G , Vishnu K 
Published: May 2021

Abstract
The most critical concerns in urban life are increasing population growth and an increase in the count of vehicles on road, tends to traffic congestion. Traffic signal control devices are commonly used to track and monitor the vehicles. Traffic congestion leads to air and noise pollution and many health issues. Therefore, this project reduces time and fuel consumption. In this project, a video processing technique is used to present a new approach for controlling traffic light. The developed model evaluates the traffic light scheduling based on the count of vehicles. Raspberry pi is used to execute it.
Safety Monitoring System in Coal Mine Using IoT
Authors: Sathishkumar N , Manoj A M , Muniraj K , Naveenkumar M , Praveen C 
Published: May 2021

Abstract
The key abstract is to use IoT to incorporate a coal mine safety monitoring system. The extraction of coal from the field is known as coal mining. Coal is used as a fuel in the steel and cement industry to extract iron from ore and to manufacture cement. Every parameter in the underground mining industry must be controlled on a regular basis, including methane gas, high temperatures, fire incidents, and so on. The level of safety in coal mines is still poor, resulting in fatalities. A coal mineshaft salvage action is profoundly perilous because of various elements. It is particularly risky for rescuers to enter a coal mineshaft burrow in a debacle without earlier attention to the climate on the grounds that ensuing blasts are probably going to happen at any second. It is along these lines basic to recognize unstable climate data like toxic gases and high temperatures, just as to direct a visual review of excavators caught in a fell passage through the imploded burrow. These information would aid rescuers in devising a strategy and equipping themselves to carry out the rescue operation defensively. This paper proposes a design for coal mines that will reduce the damage caused by a coal mine accident and allow for a more effective rescue operation.
Speaking system for speechless people using Flex sensors
Authors: Singaram M , Aravin P , Dharshine NA , Ganesh P , Gokul E 
Published: May 2021

Abstract
The most critical capacity of human beings is to interact (i.e., express their emotions, opinions and feelings) with each other. We cannot overstate the use of communications. In particular, it is very difficult for unspeakable people to communicate their meaning to ordinary people, since there are more than 300 sign languages, and it is difficult for an average person to learn and use these languages. As a result, unspeakable people face numerous irritations and frustrations that limit their ability to fulfill their regular tasks. Different psychiatric services are available to the muted population in order to get their speech back, but the expense of these treatments is costly enough that no one can afford them. ere we suggest a smart speech device that allows voiceless people to communicate their message to ordinary people using hand movements and gestures. The system uses hand motion reading with the aid of flex sensors along with a microcontroller (Aurdino) unit.
The estimated of 18.1 kDa class IV small heat shock protein (sHsp) from Hevea brasiliensis using of PHYRE2 and SWISS-MODEL software
Authors: Pasaribu S A , Basyuni M , Purba E , Hasanah Y 
Published: May 2021

Abstract
The objective of research is Hevea and Ricinus which are two of the latex-producing plant species. The modelling of protein was obtained from Hevea brasiliensis and Ricinus communis. Identification of small heat shock heat protein 18.1kDa very different between hevea and ricinus to coverage 92% and 69% analyzed by SWISS-MODEL. In addition, the data from similarity, identity, range, and Q-mean were distinct. According to the PHYRE2 analysis, we observed the residue presenting in Hevea and Ricinus not much different (Hevea: 12-158; Ricinus: 11-156). The 3D-ligand site prediction confirms is not found in Hevea as well as Ricinus. Appropriately, the small heat shock protein involved in different species will be helpful in the development of stress tolerance in plant crops, the different homology images and templates were identified as different binding site predictions. These models can be used for supporting the Hevea and Ricinus proteins modeling, mainly in the molecular study.
The Use of Dual and Surge Irrigation to Improve The Efficiency of Surface Irrigation
Authors: Aljumily A.M.H. , Al-Hadeethi Isam K. 
Published: May 2021

Abstract
A field experiment was conducted in a farm(5km northwest of Ramady City) during Autumn Season in a silty clay loam sedimentary soil. The ground water table depth is 120 cm. Field has divided into 10 strips(2.40x80)m with spaces 1m between strips and 2 m between replicates each strips has divided into 3 furrows (20 cm in depth, 80 m length,with 0.2% slop) with each others.Study factors included Contineous Irrigation (conventional), Dual Contineous irrigation, Contineous irrigation with two phases, Surge irrigation, Dual Surge irrigation Cycle ratio was 0.25 with time On-time 4 minutes and off-time 12 minutes for both dual surge irrigation and surge irrigation with discharge of 2 litter/sec. for both advance phase while the discharge after completing advance phase was 0.8 litter/sec. The plots were planted with corn(Zea mays L)and irrigated drainage water with salinity 5.8-6.2 ds/m, and Euphrates water (1.1-1.3 ds/m). Consumptive water use was calculated by Evaporation Pan method. Twenty test has been carried out for advance and recession of flow along furrow by using eight observation stations. Apportinity time and absorbed water depth in each middle furrow was calculated at each station. Practical and Theoretical relative yield of corn were calculated.Results showed that Dual Surge Irrigation system reduced of salty water used to complete advance phase with rate of 12.41% comparing with Dual Contineous irrigation system, The efficiency of Salt- water displacement proceeses downward increased by 11.56%. by the effect of fresh water under Dual surge irrigation system compared with Dual Contineous Irrigation system, The percentage fresh water saving reached to 44.94% and 51.30% in dual surge and dual Contineous irrigation system respectively, Insert Surge Irrigation technique on Dual Contineous Irrigation system leads to reduce salt- water advance phase time, late in recession time in Dual surge Irrigation and Surge Irrigation with the other treatments during advance and post- advance phase, distribution of Dual surge Irrigation system as compared with dual Contineous irrigation system in salt- water distribution efficiency during advance phase and uniformity of total absorption depth along the furrow for all testing months during the season, theoretical relative yield (RY%) has decreased gradually with increasing in depended depth for calculating the average soil salinity (ECe) with assumption of no mixing between fresh and saline water, practical relative yield exceeds the theoretical relative productivity when assuming the condition of mixing between fresh water and salt water, the practical relative yield has distinction on the theoretical yield at depth of 0-90-cm in Maas 1984 equation.
Analysis of Many Fatty acid and Volatile Oils in Some Ganoderma spp. Using Gas –Liquid Chromatography Technique
Authors: Saadoon H. F. , Khorsheed A.Ch. , Abdul-Hadi Sh. Y. 
Published: May 2021

Abstract
Natural compounds are regarded as one of the best important natural sources for getting effective compounds, especially indifferent medical fields due to the nature of their composition. One of these fungi is Ganoderma spp presented in Iraq. In this study, the fatty acids were isolated from the fruiting bodies of Ganoderma spp under study, calculating their concentration and identification with Gas–Liquid Chromatography technique. fatty acids compounds which were separated and including Butyric acid, Myristic acid, Palmitic acid, Heptadecanoic acid, Stearic acid, Oleic acid, Linoleic acid, Arachidic acid, Eicosenoic acid, Linolenic acid, Erucic acid, Arachidonic acid, Tricosanoic acid, docosadienoic acid, Nervonic acid. Also, Volatile oil compounds were separated by the light clevenger device and identified with GLC– technique, volatile oils were included Camphor, Sabine,Terpinen, Myrcene, Limonene, Cineole, Linalool, a-Pinene.