bluuebunny/crossref_metadata_2025_split huggingface dataset:
Web innovation in horizontal wind pipe turbine propeler
Authors: Wahyudi B , Sudarmadji S , Sarjiyana S , Maskuri M 
Published: February 2021

Abstract
The efficiency limitations of wind turbines provided by Betz’s Theorem prompted researchers to develop new geometric shapes of wind turbines propeller that split pipe spiral slice by placing the web between turbine blades. The idea of developing the shape of the turbine was inspired by the shape of a duck’s or swan’s leg which has a web between its fingers that functions to obtain the thrust when swimming in water. The purpose of this study is to obtain the strength of material with optimal geometry and aerodynamic parameters in producing wind turbine efficiency. The method of obtaining research data is using solid work software for design, Ansys software for CFD simulations and Wind Tunnel laboratory equipment for experiments and also using Minitab 19 software for data processing. The results of the CFD simulation and experiment based research show that the turbine propeller turbine with using the web has higher efficiency than without the web.
CBCA: Consignment based communal authentication and encryption scheme for Internet of things using Digital Signature Algorithm
Authors: M Arun. , S PraveenKumar. , S Rajakumar.P. , P Thamizhikkavi. 
Published: February 2021

Abstract
IoT (Internet of things) gives a dream that communicate the extent of web by incorporating physical articles to distinguish themselves among the participating people. This contemporary observation encourages physical objects to represent them in the advanced world. There are heaps of hypotheses and future conjecture about different physical substances associated with the web, programs, Most of the devices needs security features from the wide range of attacks that are present in the environment. The computationally complex and asset expending methods have a restricted support over these installed physical objects and miniature sensor hubs. In this paper we propose a CBCA (Consignment based communal authentication) conspire for real objects that are moderately associated with the IoT condition. Client server correspondence model is used by present reality substances to communicate with each other. In our proposed framework we use lightweight features of CoAP(Constrained Application Protocol) to screen servers, regardless of whether the assets from the server are empowered on client in a vitality proficient way. Digital signature algorithm (DSA) with a key length of 512 bits is used to set up a protected meeting for the resource assessment. Our plan is assessed for real world situation utilizing NetDuino Plus 2 sheets. Our proposed framework gives an overwhelming obstruction against various attacks and its computationally proficient, get less association overhead simultaneously.
Cancer Classification using Ensemble Feature Selection and Random Forest Classifier
Authors: Koul Nimrita , Manvi Sunilkumar S 
Published: February 2021

Abstract
High volumes of genomic data made available by high through put gene expression sequencing technologies like next generation sequencing, microarray gene expression data have made it possible to develop models to computationally analyse this data and infer meaningful insights like presence of a disease, nature of disease, place of localization of the tumour in cancers etc. Since gene expression data is very high dimensional, each gene stands for one dimension, and has very small number of observations, it is imperative to apply feature selection on the data before using it for classification task. In this paper, we have proposed a method for classification of human cancer types by analysis of microarray gene expression data. We have used an ensemble feature selection algorithm for selecting subsets of 5, 10, 20 and 30 genes and applied random forest classifiers to obtain the classification accuracy and other performance parameters for comparison with existing solutions. We have been able to obtain 100% classification accuracy with just 5 genes on colon cancer data set with our algorithm.
Advanced Convolutional Neural Network Classification for Automatic Seizure Epilepsy Detection in EEG Signal
Authors: Mancha Venkata Ramana , Reddy E Srinivasa , Ch Satyanarayana 
Published: February 2021

Abstract
Epilepsy is one of the irregular electro-physiological disorder appeared in human brain, which is characterized by tonic recurrent seizures, Electroencephalogram (EEG) is a sufficient test measure to maintain records with respects to electrical activity of brain and it is widely used in analysis and detection of electro epileptic seizures. Manual inspection of EEG signal extraction will take more time to process and it puts heavy complex on neurologists affects their performance. It is often difficult in identification of brain subtle but emergency changes in EEG wave forms by visual inspection based on research area for bio- engineers implement different types of methodologies for identification of such type of subtle. But all these algorithms/methodologies don’t perform efficient accuracy in classification of normal, ictal class instances. So that in this paper, we propose a novel system based on machine learning, which is single dimensional pyramidal ensemble convolutional neural network (1D-PECNN). Here ensemble means different parts of the signal are assigned to different models for efficient analysis of data. We also propose mathematical augmented approach for learning features. In 1D-PECNN model, system consist high amount of desirable and learnable parameters, in all cases proposed approach 1D-PECNN gives maximum accuracy (Approximately from 92%-99%) when compare to state-of-the methods.
An Effective Analytics using Machine Learning Integrated Approaches for Diagnosis, Severity Estimation andPrediction of Heart Disease
Authors: Satyanandam N , Satyanarayana Ch 
Published: February 2021

Abstract
Heart Disease is one of the precarious issues in the medial domain whereby the number of cases is huge in the global scenario. The instances of heart transplants increase a lot every year even in the developed countries. Heart disorder identifies a number of heart disease disorders with the cardiac disorders including illnesses of the blood stream, coronary artery disease, cardiovascular rhythms and related cardiac defects. Cardiovascular diseasing commonly implies disorders requiring restricted or blocked blood pathways that can lead to cardiac attack, chest pressure (angina) or a stroke. Some cardiac disorders including key causes of heart disease are often regarded as influencing the cardiac muscle, valves and rhythms. Many cases of cardiac illness can be reversed or managed with a safe lifestyle. Heart Failure (HF) is not a disorder but a complex health illness. The rise in costs in health care, the increasing occurrence, the decreased quality of life, frequent hospitalizations and early mortality have turned HF into a global and Indian crisis and have emphasized the need to diagnose HF and determine its magnitude and efficacy. The principal goal of this research manuscript is to find a machine-based learning approach to diagnoses, intensity predictions and heart disease prediction. In this manuscript, we present the regression as well as ensemble learning based analysis on the benchmark dataset of Cleveland from UCI repository for the estimation of cardiac diseases. A further aspect is that 303 records are scheduled for this review. The knowledge from Cleveland consists of approximately 13 features and we have intended to group it in five groups in this report. The presented outcomes on the assorted algorithmic approaches are quite effective and ensemble-based approach is quite performance aware.
Detection of intruder using the improved dragonfly optimization algorithm
Authors: Devarakonda Nagaraju , Anandarao S. , Kamarajugadda Raviteja 
Published: February 2021

Abstract
Now a days, we are completely dependent on the systems and also on the internet. Most of our valuable information is stored in the system or in the cloud. So, it’s very necessary to protect our data and also our system from the outside intruders and also from the intruders already entered into our system. To protect our system, intrusion detection systems are constructed using many traditional techniques. Some of them are decision tree, KDD, data mining techniques, Artificial Neural Network. But there are many flaws in these systems. In this paper, we have used the improvised dragonfly optimization algorithm. Here the traditional dragonfly optimization algorithm is improved by adding the convergence and fitness function. This usage of improvised dragonfly optimization algorithm in detecting the intruders has given the best performance than the other methods. To justify the statement, we have shown the experimental study and also comparative study with other two optimization algorithm. Here first the detection rate is computed on the darknet 2020 dataset by using the traditional dragonfly algorithm, then the detection rate is again computed on the same dataset but with the improved the dragonfly optimization algorithm. Finally, in the comparative study it’s very clear that the improved dragonfly optimization algorithm produced the accurate result than the other optimization algorithm.
Automatic Vehicle Number Plate Recognition System Using Machine Learning
Authors: Ravi Kumar J M S V , Sujatha B , Leelavathi N 
Published: February 2021

Abstract
The number plate recognition (NPR) system is one of the categories of smart transportation and detection mechanism (STDM). This is a combination of the technology in which the application enables the system to detect and automatically read the license id of number plate of vehicle from digitally captured images. Automatically capturing the license plate is the process of detecting and transforming the pixels data of a digital image into the plain text data or ASCII text of the number plate. Our project contains a method for the vehicle number plate recognition from the image using mathematical morphological operations (erosion, dilation). The main objective is to use and combine different morphological operations in such a way that the license plate of the certain vehicle can be detected and translatedeffectively. This is based on various operation such as image improvement, Gray scale transformation, Bilateral Filtering edge detection and getting the number plate from the picture of vehicle. After the completion of the above-mentioned steps, now the process of segmentation is being applied to detect the text present on number plate by making use ofmatching of template and OCR. This system is able to detect the license number accurately as well as quickly from the vehicle’s picture.
Context-aware Multi-data Fusion to Achieve Finest Ride-share Schedule Using Big Data
Authors: Potluri Avinash , Narendra Kumar N.V. 
Published: February 2021

Abstract
Enabling the taxi rides to share empty seats in their vehicles, to riders, is a win-win situation for both riders and drivers. Existing methods mainly target on minimizing the cost of travel for vehicles. In this work we focus on assigning a new rider to a vehicle considering the real-time context of both riders and drivers. The crux of the problem lies in analysing massive amounts of data in an instance of time to obtain the optimal ride schedule. We further study the scenario, to accommodate best compatible riders with minimal change in the route, to maximize the satisfaction of all possible entities in the business model in several aspects. Our system takes advantage from route recommendation and utility maximization approaches. We also inherit the benefits of collaborative filtering to provide best ride experience. The effectiveness of our approach is shown with experimentation on various datasets. Our work has significant importance, as it redefines the personalization preferences based on realtime context.
Analyzing and Predicting Covid-19 epidemic using Machine Learning Techniques
Authors: Lakshmanarao A. , Seshadri Rao Ch. , Gadde Sridevi 
Published: February 2021

Abstract
The entire world is suffering from a novel disease called covid-19 caused by a coronavirus since 2019. The main reason for the seriousness of the disease is the lack of efficient legitimate medication or vaccine. The World Health Organization (WHO) suggested several precautions to regulate the spread of disease and to reduce the contamination thereby reducing deaths. In this paper, we analysed the covid-19 dataset available in Kaggle. The previous contributions from several authors of similar work focused on covid-19 datasets having a limited number of samples. Our paper used the dataset updated till November 15th 2020. Three different aspects are considered mainly in this paper, namely the number of confirmed cases, number of recovered cases, and number of death cases. All the aspects are analysed in a daily and weekly manner. We applied linear regression, polynomial regression, and holt’s method to predict the future number of confirmed, recovered, and death cases. This analysis is useful for the health sectors and frontline workers to help reduce the contamination caused by this disease.
An approach for suggestion mining based on deep learning techniques
Authors: Raghunadha Reddy T , Vijayapal Reddy P , Murali Mohan T , Dara Raju 
Published: February 2021

Abstract
An organization often uses forums and social media channels for getting feedback from costumers or users. The ratings of products on rating platforms are a useful feedback to make a product better. The feedback from a customer is in the form of a suggestion which appears in a rating text or is directly asked from the customer. Suggestion mining is a binary classification problem that labels sentences as Suggestion or Non-suggestion. The suggestion mining is similar to sentiment analysis which is associated with common linguistic properties and challenges irrespective of the domain and application. Most of the previous works of suggestion mining proposed rule based methods and a very few developed statistical classifiers by using manually identified features. Recently, several researchers paid attention on deep learning technique based solutions to suggestion mining where features are automatically learned. In this work, various deep learning techniques like RNN, LSTM, Attention based LSTM and GRU are used in the experimentation of suggestion mining. The experiment carried out on the dataset provided in SemEval 2019 suggestion mining competition. The Attention based LSTM achieved best accuracies for suggestion mining when compared with other deep learning techniques.
Prediction and Analysis of Corona Virus Disease (COVID-19) using Cubist and OneR
Authors: Lalitha R.V.S. , Divya Lalitha J. , Kavitha K. , RamaReddy. T , Srinivas Rayudu , Sujana Challapalli 
Published: February 2021

Abstract
Inflammatory syndrome usually occurs with the adults who have already affected with corona virus. Multi system syndrome in children may affect different parts of the body like lungs, liver, kidneys and brain. The level of current infection can be known by proceeding with two kinds of tests, namely viral tests and anti body tests. Viral tests determine the level of current infection and antibody tests are about past infection. Though decision on test will be provided by health care units, there will be an uncertainty in evaluation of results. The variation in potential of antibodies will influence the resulting parameters. The effect of inaccurate test results is to be traced, because people may suffer from reinfection in their bodies over a period of time. In this paper the uncertainty analysis is carried out using Cubist and OneR algorithms. In the cubist algorithm leaf nodes can be analyzed using regression models. Accuracy of the predictions are analyzed using contingency table and false positives and true negatives are tracked using confusion matrix. This analysis assists in determining the trans conditional state of the disease with dynamism. With the analysis carried out cubist proves to be the best algorithm.
CANONICAL VARIATE FEATURE SELECTION BASED ADAPTIVE ENHANCED WINNOW MAP REDUCE CLASSIFICATION FOR PREDICTIVE ANALYTICS
Authors: Sivakkolundu R. , Kavitha V. 
Published: February 2021

AbstractClassification is a key issue to be resolved in data mining. Few research works have been designed for performing predictive analysis through classifying the information on data warehouse. But, classification accuracy (CA) of conventional works was lower when considering a big size of data as input. In order to addresses this drawback, a Canonical Variate Feature Selection based Adaptive Enhanced Winnow Map Reduce Classification (CVFS-AEWMRC) Method is proposed. The CVFS-AEWMRC Method is designed for organizing and classifying the collected and stored data for decision making. Initially, Canonical Variate Feature Selection (CVFS) is carried out in CVFS-AEWMRC Method to select the relevant features for performing the classification. Canonical Variates analysis is a machine learning technique used to find linear combinations of features which have maximum correlation with each other. The features with maximum correlation are selected for performing the classification. Then, Adaptive Enhanced Winnow Map Reduce Classification (AEWMRC) Process is carried out in CVFS-AEWMRC Method to classify the stored data for taking decision. Adaptive Enhanced Winnow technique learns the linear classifier from labeled data samples. Winnow employs the multiplicative scheme for performing the classification process. Winnow learns the hyperplane to classify the data points for decision making. By this way, the data classification is carried out in accurate manner for decision making during the predictive analytics process. Experimental analysis of CVFS-AEWMRC Method is performed on metrics namely feature selection rate (FSR), CA, classification time (CT) and False positive rate (FPR) with number of features and data points.
Design of implantable cardioverter defibrillator using low power subthreshold digital circuit
Authors: Vasudeva Reddy T , Nareshkumar B , Anirudh Reddy R , Kavitha Reddy P 
Published: February 2021

Abstract
Due to tremendous Technological advancements researchers and scientists have proposed designs operating under subthreshold and near threshold regions. With recently, growing the need for low power consumption of devices, the low power circuits have become more eye-catching to the design engineers. But the act of subthreshold design has become more profound to the PVT variations due to exponential relationship of current in Subthreshold region depends on supply voltage(Vgs) and threshold voltage(Vth). The motivation for low power electronics has been derived from the subthreshold operation & its driving forces. In this paper the subthreshold design of source coupled logic circuit is described with its functionality under subthreshold region and performance in static and dynamic mode of operation and used as Implantable cardioverter defibrillator under 28nm technology.
Extracting Facial Features in Filter Modified Images using Neural Networks
Authors: Sumithra M , Shyamala Devi M , Poornima A , Sarkar Shatadru , Patel Harish , Mahankali Preethi , Navya Boggula 
Published: February 2021

Abstract
Facial identification plays a vital role in this digital era. Recent advancements in technology provide various filtering effects while capturing the images. Extracting the facial features from the filter modified image becomes challenging. To identify the important facial attribute values from those images, special features extraction techniques needs to be applied. Facial points are the key factors to recognize the face. When these factors are altered, facial detection becomes crucial. Initially apply the image pre-processing techniques to reduce the noise level in the images then perform feature selection. To overcome the curse of dimensionality, Occam’s razor approach is followed to limit the selection of attributes. In different stages the features are going to be filtered by replacing the weaker attributes. Later Pearson Correlation, a filter based correlation technique is applied to evaluate the deviated numerical value from the target. The deviated features can be treated with Lasso effect to suppress the poor weighted features. Finally through recursive feature elimination, highest influencing attribute factor can be identified. In this approach some of the facial feature which is used in training the neural network can be discarded based on the deviation rates. In terms of accuracy the proposed system can recognize the dataset with 70% of accuracy with filter effected images.
Design and Development of Adaptive filter for Real Time Signal Processing
Authors: Samalla Dr Krishna , SubbaRao S.P.V , Mallikarjuna Rao G , Jagadeesh B.N. 
Published: February 2021

Abstract
In Communication the signal is distorted due to noise and the notch filters are used for eliminating unwanted frequencies For filtering the signal in a specific band Notch filter is used. Notch filter can pass the signal below and above notch frequency. In most of the signal processing applications frequency estimation of narrow band signals in noisy environment are required For frequency detection and tracking Adaptive IIR filter or Adaptive FIR filters can be used. Adaptive FIR Filter is more stable than Adaptive IIR filter. In most practical applications Adaptive IIR Notch Filter (AIIRNF)is used The advantage of Adaptive IIR notch filter over FIR Filter is less number of filter coefficients and less complexity in design In this paper Adaptive IIR Notch filter is designed for real time applications. The performance of designed adaptive IIR Notch filter is evaluated based on Magnitude and Frequency response.
Numerical study of hard-metal powder compaction
Authors: Akhmetshin L R , Smolin I Yu , Buyakova S P 
Published: February 2021

Abstract
Powder compacting is a widely used process for both metallurgy and pharmaceutical production. The mathematical simulation of the powder compaction process is a promising tool for its study. The compacting of WC/Co powder by a punch in a cylindrical die is simulated in this work. The use of the Drucker-Prager Cap constitutive model is made. Results on stress distribution and volume plastic deformation during compaction are obtained. Relative density distribution in the powder compact can also be estimated from volume plastic strain. The severe influence of frictional contact between powder and die wall on the results is noted.
Formation of SERS-substrate based on Ag-Cu bimetallic nanoparticles for detection of ultra-small amounts of substance
Authors: Dubkov S , Savitskiy A , Tarasov A , Novikov D , Mineeva N , Bazarova M , Overchenko A , Gromov D 
Published: February 2021

Abstract
The aim of this work was to determine the sensitivity threshold and enhancement factor of a planar SERS substrate based on an array of composite Ag - Cu nanoparticles. The nanoparticles were deposited using the method of vacuum-thermal evaporation followed by annealing at 300 ° C. Methylene blue was chosen as the analyte. The possibility of detecting the micro- and nanomolar concentration of methylene blue using a SERS-substrate with an active Ag-Cu layer at a laser wavelength of 632.8 nm is shown. The use of such arrays of nanoparticles as an active layer makes it possible to achieve an analytical enhancement factor of the SERS substrate of the order of 6×105.