bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison of various machine learning approaches uses in heart ailments prediction
Authors: Gupta Gunjan , Adarsh U , Subba Reddy N V , Ashwath Rao B 
Published: January 2022

Abstract
Heart disease has been the leading cause of a huge number of deaths in recent years. As a result, an accurate and feasible system is required to diagnose this disease early to provide better treatment. Advances in machine learning have the potential to enhance healthcare access. Given the importance of a crucial organ like the heart, medical professionals and physicians have made it a priority to forecast heart failure-related events in clinical practice, nevertheless, forecasting heart failure-related events in clinical practice has generally failed to achieve high accuracy. The objective here is to demonstrate how machine learning may be used to solve the problem. By analyzing hundreds of healthcare data and other semantics, machine learning algorithms can analyze related cases with diseases and health conditions. Here a demonstration of how to load the data, generate predictions through different models from patient data is shown. The metrics are then compared for a better understanding of their function and what impact can be inferred from them.
Design of a Flexible Microstrip Antenna for BAN Applications
Authors: Sharma Avi , Kumar Pramod 
Published: January 2022

Abstract
A flexible microstrip antenna is a compact antenna that can be coupled with the skin. However, such antennas require to be coupled with an intermediate matching liquid medium which makes the antenna bulky, complicated, and expensive. Body area network devices are wearable wireless devices/sensors that are used to get the information of a patient’s health in terms of physiological changes irrespective of location. A flexible layer made of Polyethylene is chosen as the substrate and a copper patch is levied upon it. This substrate layer lies in between two adhesive layers (GIL GML 1000).In this paper, flexible antennas are designed and simulated for Body area networks (BANs). The S11 parameter, VSWR value, Gain, and the radiation pattern of the antennas are compared. The polyethylene substrate is highly flexible and lightweight; therefore it would be an ideal material to be used as the substrate of the required antenna.
An prediction of Healthy Diet required to Ease the recovery from Covid-19 using the approach of Machine Learning.
Authors: Maria Colaco Rencita , Shreya  , Subba Reddy N V , Acharya U Dinesh 
Published: January 2022

Abstract
Global terror that has shaken the world named, COVID-19 virus has taken away huge number of lives. According to the research there are lot of recovery cases also. Most important thing to survive from this disease is having good immunity. Everyone does not have same level of immunity. One main factor on which immunity depends is having a healthy diet. If the routine of having healthy diet is maintained, then the immunity to fight against this virus increases. It is much required that people need to be informed about having an healthy diet. Using the dataset of healthy dietary and using various machine learning algorithms we can determine what type of diet one person needs to have. By using algorithms like Random Forest, KNN, logistic regression and Support Vector Machines we can determine the type of diet and probability of recovery. The dataset required for analysis needs to have all the information regarding the diet. Based on the dataset the prediction is taken place by using Decision Tree algorithm. This method of finding the appropriate diet of a particular person based on amount of Sugar level, Blood Pressure and BMI can be the most useful research in this pandemic time.
Predicting Mental Health Illness using Machine Learning Algorithms
Authors: Vaishnavi Konda , Nikhitha Kamath U , Ashwath Rao B , Subba Reddy N V 
Published: January 2022

Abstract
Early detection of mental health issues allows specialists to treat them more effectively and it improves patient’s quality of life. Mental health is about one’s psychological, emotional, and social well-being. It affects the way how one thinks, feels, and acts. Mental health is very important at every stage of life, from childhood and adolescence through adulthood. This study identified five machine learning techniques and assessed their accuracy in identifying mental health issues using several accuracy criteria. The five machine learning techniques are Logistic Regression, K-NN Classifier, Decision Tree Classifier, Random Forest, and Stacking. We have compared these techniques and implemented them and also obtained the most accurate one in Stacking technique based with an accuracy of prediction 81.75%.
Modeling of Supervised Machine Learning using Mechanism of Quantum Computing.
Authors: Nivelkar Mukta , Bhirud S. G. 
Published: January 2022

Abstract
Mechanism of quantum computing helps to propose several task of machine learning in quantum technology. Quantum computing is enriched with quantum mechanics such as superposition and entanglement for making new standard of computation which will be far different than classical computer. Qubit is sole of quantum technology and help to use quantum mechanism for several tasks. Tasks which are non-computable by classical machine can be solved by quantum technology and these tasks are classically hard to compute and categorised as complex computations. Machine learning on classical models is very well set but it has more computational requirements based on complex and high-volume data processing. Supervised machine learning modelling using quantum computing deals with feature selection, parameter encoding and parameterized circuit formation. This paper highlights on integration of quantum computation and machine learning which will make sense on quantum machine learning modeling. Modelling of quantum parameterized circuit, Quantum feature set design and implementation for sample data is discussed. Supervised machine learning using quantum mechanism such as superposition and entanglement are articulated. Quantum machine learning helps to enhance the various classical machine learning methods for better analysis and prediction using complex measurement.
Next frame prediction using ConvLSTM
Authors: Desai Padmashree , Sujatha C , Chakraborty Saumyajit , Ansuman Saurav , Bhandari Sanika , Kardiguddi Sharan 
Published: January 2022

Abstract
Intelligent decision-making systems require the potential for forecasting, foreseeing, and reasoning about future events. The issue of video frame prediction has aroused a lot of attention due to its usefulness in many computer vision applications such as autonomous vehicles and robots. Recent deep learning advances have significantly improved video prediction performance. Nevertheless, as top-performing systems attempt to foresee even more future frames, their predictions become increasingly foggy. We developed a method for predicting a future frame based on a series of prior frames that services the Convolutional Long-Short Term Memory (ConvLSTM) model. The input video is segmented into frames, fed to the ConvLSTM model to extract the features and forecast a future frame which can be beneficial in a variety of applications. We have used two metrics to measure the quality of the predicted frame: structural similarity index (SSIM) and perceptual distance, which help in understanding the difference between the actual frame and the predicted frame. The UCF101 data set is used for testing and training in the project. It is a data collection of realistic action videos taken from YouTube with 101 action categories for action detection. The ConvLSTM model is trained and tested for 24 categories from this dataset and a future frame is predicted which yields satisfactory results. We obtained SSIM as 0.95 and perceptual similarity as 24.28 for our system. The suggested work’s results are also compared to those of state-of-the-art approaches, which are shown to be superior.
Fake News Detection from Online media using Machine learning Classifiers
Authors: Pandey Shalini , Prabhakaran Sankeerthi , Subba Reddy N V , Acharya Dinesh 
Published: January 2022

Abstract
With the advancement in technology, the consumption of news has shifted from Print media to social media. The convenience and accessibility are major factors that have contributed to this shift in consumption of the news. However, this change has bought upon a new challenge in the form of “Fake news” being spread with not much supervision available on the net. In this paper, this challenge has been addressed through a Machine learning concept. The algorithms such as K-Nearest Neighbor, Support Vector Machine, Decision Tree, Naïve Bayes and Logistic regression Classifiers to identify the fake news from real ones in a given dataset and also have increased the efficiency of these algorithms by pre-processing the data to handle the imbalanced data more appropriately. Additionally, comparison of the working of these classifiers is presented along with the results. The model proposed has achieved an accuracy of 89.98% for KNN, 90.46% for Logistic Regression, 86.89% for Naïve Bayes, 73.33% for Decision Tree and 89.33% for SVM in our experiment.
Model Checking M2M and Centralised IOT authentication Protocols.
Authors: Rekha H , Siddappa M. 
Published: January 2022

Abstract
It is very difficult to develop a perfect security protocol for communication over the IoT network and developing a reliable authentication protocol requires a detailed understanding of cryptography. To ensure the reliability of security protocols of IoT, the validation method is not a good choice because of its several disadvantages and limitations. To prove the high reliability of Cryptographic Security Protocols(CSP) for IoT networks, the functional correctness of security protocols must be proved secure mathematically. Using the Formal Verification technique we can prove the functional correctness of IoT security protocols by providing the proofs mathematically. In this work, The CoAP Machine to Machine authentication protocol and centralied IoT network Authentication Protocol RADIUS is formally verified using the well-known verification technique known as model checking technique and we have used the Scyther model checker for the verification of security properties of the respective protocols. The abstract protocol models of the IoT authentication protocols were specified in the security protocol description language and the security requirements of the authentication protocols were specified as claim events.
Design and Simulation of Waveguide Bragg Grating based Temperature Sensor in COMSOL
Authors: Dhavamani Vigneshwar , Chakraborty Srijani , Ramya S , Nandi Somesh 
Published: January 2022

Abstract
With the advancements in the domain of photonics and optical sensors, Fibre Bragg Grating (FBG) sensors, owing to their increased advantages, have been researched widely and have proved to be useful in sensing applications. Moreover, the advent of Photonic Integrated Circuits (PICs) demands the incorporation of optical sensing in waveguides, which can be integrated on silicon photonic chips. In this paper, the design of a sub-micron range Waveguide Bragg Grating (WBG) based temperature sensor with high peak reflectivity and thermal sensitivity is proposed. The flexibility of COMSOL Multiphysics software is explored to simulate the sensor and the results are verified with the analytical values calculated using MATLAB. The simulation is carried out for the proposed design having 16000 gratings and a corresponding peak reflectivity of 0.953 is obtained. A thermal sensitivity of 80 pm/K is achieved, which is approximately eight times better than that of FBG based sensor.
An evaluation of approaches for enhancing inductive learning with a transductive view
Authors: Lokesh Kumar T N , Das Bhaskarjyoti 
Published: January 2022

AbstractAvailability of enough labeled data is a challenge for most inductive learners who try to generalize based on limited labeled dataset. A traditional semi-supervised approach for the same problem attempts to approach it by methods such as wrapping multiple inductive learners on derived pseudo-labels, unsupervised feature extraction or suitable modification of the objective function. In this work, a simple approach is adopted whereby an inductive learner is enhanced by suitably enabling it with a transductive view of the data. The experiments, though conducted on a small dataset, successfully provide few insights i.e. transductive view benefits an inductive learner, a transductive view that considers both attribute and relations is more effective than one that considers either attributes or relations and graph convolution based embedding algorithms effectively captures the information from transductive views compared to popular knowledge embedding approaches.
Design and Implementation of Power-Efficient FSM based UART
Authors: Kamath Akshatha , Mendez Tanya , Ramya S , Nayak Subramanya G 
Published: January 2022

Abstract
The remarkable innovations in technology are driven mainly by the high-speed data communication requirements of the modern generation. The Universal Asynchronous Receiver Transmitter (UART) is one of the most sought-after communication protocols. This work mainly focuses on implementing and analysing the UART for data communication. The Finite State Machine (FSM) implements the baud rate generator, transmitter, and receiver modules. Cadence NCSIM was utilized for simulation, and Cadence RTL Compiler was used during synthesis using the 45 nm and 90 nm General Process Design Kit (GPDK) library files. The baud rate of 9600 bps and 50 MHz clock frequency was used to design UART. The increased speed and complexity of the VLSI chip designs has resulted in a significant increase in power consumption. The comparative analysis of power and delay for different clock periods shows an improvement in the total power and the Power Delay Product (PDP) with increasing clock periods. Better results were observed using 45 nm in comparison to the 90 nm library.
Comparative analysis of multiple machine learning algorithms for epileptic seizure prediction
Authors: Lekshmy H O , Panickar Dhanyalaxmi , Harikumar Sandhya 
Published: January 2022

Abstract
Epilepsy is a common neurological disease that affects more than 2 percent of the population globally. An imbalance in brain electrical activities causes unpredictable seizures, which eventually leads to epilepsy. Neurostimulators have the power to intervene in advance and avoid the occurrence of seizures. Its efficiency can be increased with the help of heuristics like advanced seizure prediction. Early identification of preictal state will help easy activation of neurostimulator on time. This research concentrates on the performance analysis of various machine learning algorithms on recorded EEG data. Through this study, we aim to find the best model, which can be used to create an ensemble model for better learning. This involves modeling and simulation of classical machine learning technique like Logistic regression, Naive Bayes model, K nearest neighbors Random Forest, and deep learning techniques like an Artificial neural network, Convolutional neural networks, Long short term memory, and Autoencoders. In this analysis, Random Forest and Long Short-Term Memory performed well among all models in terms of sensitivity and specificity.
Optimization of Load Forecasting in Smartgrid using Artificial Neural Network based NFTOOL and NNTOOL
Authors: Mishra Sthitprajna , Prasad Ganthia Bibhu , Sridharan Abel , Rajakumar P , Padmapriya D. , Kaliappan S. 
Published: January 2022

AbstractThe motivation behind the research is the requirement of error-free load prediction for the power industries in India to assist the planners for making important decisions on unit commitments, energy trading, system security & reliability and optimal reserve capacity. The objective is to produce a desktop version of personal computer based complete expert system which can be used to forecast the future load of a smart grid. Using MATLAB, we can provide adequate user interfaces in graphical user interfaces. This paper devotes study of load forecasting in smart grid, detailed study of architecture and configuration of Artificial Neural Network(ANN), Mathematical modeling and implementation of ANN using MATLAB and Detailed study of load forecasting using back propagation algorithm.
Sensorless Speed Contorl of Doubly-Fed Induction Machine Using Reactive Power Based MRAS
Authors: Kumar Samal Sidhartha , Jena Smrutisikha , Ganthia Bibhu Prasad , Kaliappan S. , Sudhakar M. , Sriram Kalyan S. K. 
Published: January 2022

AbstractA sensorless speed control method for doubly-fed induction machine (DFIM) operating with constant frequency but in variable speed mode is presented in this project work. The control method is based on rotor speed estimation technique by a reactive power model reference adaptive system (MRAS) observer. The presented technique does not depend on any kind of flux evaluation and also independent to the resistance variation of either stator or rotor. The MRAS observer has a capacity for speed catching operation. PI controller is designed and also optimized using algorithm for better dynamic behaviour of the machine. MATLAB Simulink model and the simulation results are shown to check the effectiveness of the observer and also of the controller.
Comparison of Classification Models Based on Deep learning on COVID-19 Chest X-Rays
Authors: Mane Pallavi R , Shenoy Rajat , Prabhu Ghanashyama 
Published: January 2022

Abstract
COVID -19, is a deadly, dangerous and contagious disease caused by the novel corona virus. It is very important to detect COVID-19 infection accurately as quickly as possible to avoid the spreading. Deep learning methods can significantly improve the efficiency and accuracy of reading Chest X-Rays (CXRs). The existing Deep learning models with further fine tune provide cost effective, rapid, and better classification results. This paper tries to deploy well studied AI tools with modification on X-ray images to classify COVID 19. This research performs five experiments to classify COVID-19 CXRs from Normal and Viral Pneumonia CXRs using Convolutional Neural Networks (CNN). Four experiments were performed on state-of-the-art pre-trained models using transfer learning and one experiment was performed using a CNN designed from scratch. Dataset used for the experiments consists of chest X-Ray images from the Kaggle dataset and other publicly accessible sources. The data was split into three parts while 90% retained for training the models, 5% each was used in validation and testing of the constructed models. The four transfer learning models used were Inception, Xception, ResNet, and VGG19, that resulted in the test accuracies of 93.07%, 94.8%, 67.5%, and 91.1% respectively and our CNN model resulted in 94.6%.
RF and microwave diagnostics for compact plasma traps and possible perspectives for fusion devices
Authors: Torrisi G. , Naselli E. , Di Donato L. , Mauro G.S. , Mazzaglia M. , Mishra B. , Pidatella A. , Sorbello G. , Mascali D. 
Published: January 2022

Abstract
Plasma diagnostics is a topic having a great impact on R&D in compact ion sources as well as in large fusion reactors. Towards this aim, non-invasive microwave diagnostics approaches, such as interferometric, polarimetric and microwave imaging profilometric techniques can allow obtaining volumetric, line-integrated or even space-resolved information about plasma electron density. Special probes can be also designed and implemented in order to characterize external and/or self-generated radio-waves in the plasmas. In particular, the design, construction and operation of a K-band microwave interferometry/polarimetry setup based on the Frequency-Modulated Continuous-Wave (FMCW) method at INFN-LNS will be described: this tool provides reliable measurements of the plasma density even in the extreme unfavorable wavelength-to-plasma scale ratio in plasma-based ion sources. A “frequency sweep” and a post-processing filtering method (for interferometry and polarimetry, respectively) were used to filter out the multipath contributions or cavity induced depolarizations in the detected signals. Besides this, the use of the aforementioned RF plasma-immersed probes will also be discussed, which allow measuring local E-fields and fast temporal response in order to characterize turbulent (through kinetic instabilities, cyclotron maser emission, etc.) vs. stable plasma regimes. An analysis based on wavelet transform applied to measurements of plasma radio self-emission in B-minimum and simple mirror traps will be presented. These tools and methods have the potential to be applied to plasma machines both in compact traps and large-size reactors with a proper scaling.
Fabrication of 3D scaffolds based on fully biobased unsaturated polyester resins by microstereo-lithography
Authors: Gonçalves Filipa A M M , Fonseca Ana C , Cordeiro Rosemeyre , Piedade Ana P , Faneca Henrique , Serra Arménio , Coelho Jorge F J 
Published: January 2022

AbstractAdditive Manufacturing (AM) technologies are an effective route to fabricate tailor made scaffolds for tissue engineering (TE) and regenerative medicine, with microstereo-lithography (µSLA) being one of the most promising techniques to produce high quality 3D structures. Here, we report the crosslinking studies of fully biobased unsaturated polyesters (UPs) with 2-hydroxyethyl methacrylate (HEMA) as the unsaturated monomer (UM), using thermal and µSLA crosslinking processes. The resulting resins were fully characterized in terms of their thermal and mechanical properties. Determination of gel content, water contact angle, topography and morphology analysis by atomic force microscopy and scanning electron microscopy were also performed. The results show that the developed UP resins (UPRs) have promising properties for µSLA.In vitrocytotoxicity assays performed with 3T3-L1 cell lines showed that the untreated scaffolds exhibited a maximum cellular viability around 60%, which was attributed to the acidic nature of the UPRs. The treatment of the UPRs and scaffolds with ethanol (EtOH) improved the cellular viability to 100%. The data presented in this manuscript contribute to improve the performance of biobased UPs in AM.
Mean exit time in irregularly-shaped annular and composite disc domains
Authors: Carr Elliot J , VandenHeuvel Daniel J , Wilson Joshua M , Simpson Matthew J 
Published: January 2022

Abstract
Calculating the mean exit time (MET) for models of diffusion is a classical problem in statistical physics, with various applications in biophysics, economics and heat and mass transfer. While many exact results for MET are known for diffusion in simple geometries involving homogeneous materials, calculating MET for diffusion in realistic geometries involving heterogeneous materials is typically limited to repeated stochastic simulations or numerical solutions of the associated boundary value problem (BVP). In this work we derive exact solutions for the MET in irregular annular domains, including some applications where diffusion occurs in heterogenous media. These solutions are obtained by taking the exact results for MET in an annulus, and then constructing various perturbation solutions to account for the irregular geometries involved. These solutions, with a range of boundary conditions, are implemented symbolically and compare very well with averaged data from repeated stochastic simulations and with numerical solutions of the associated BVP. Software to implement the exact solutions is available on GitHub.