bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and optimization of digitalization device of temperature control system using PID Ziegler-Nichols control in chemical engineering laboratory
Authors: Wiharya C , Sungkowo H , Suryandari A S , Kurniawan B I
Published: February 2021

The Temperature Control Module is a tool that is useful for adjusting the temperature of a liquid or solution so that the temperature of a liquid is as determined. A device that uses a heat exchanger as a place to combine temperature, so that the temperature of a liquid with another liquid will meet there so that the temperature of the liquid in the heat exchanger can be changed. From the temperature control practicum module prototype in the chemical laboratory, it is known that in the open loop test with hot water input temperature above 90 °C and cold water input temperature 28 °C an output value of 69 °C is obtained. This value is stable but slowly decreases. By providing feedback and the controller in the form of sensors and data will be processed by the PLC, then a value that corresponds to a set of values is obtained, namely 60 °C, but the output temperature is unstable because the output graph is still fluctuating and unstable. Based on experimental calculations and PID calculations using the Ziegler Nichols method, the Kp = 30, Ti = 77.95, and Td = 19.489 were obtained. By providing the PID algorithm calculated by the Ziegler Nichols method, it can increase the output temperature to 58 °C. This is due to the addition of the PID gain to be more substantial so that the system becomes unstable.
Microcontroller-based control system design using step counter method in drilling fixture
Authors: Faizin A , Wicaksono H , Sari N P
Published: February 2021

Automation in the manufacturing process in the industry today is absolutely needed. Advances in technology has changed the characteristics of the manufacturing industry. The development of robotics, visualization technology and collaborative automation has increased the development of the manufacturing industry, enabling automation to be applied not only in the mass production process but also in low volume production. This is done to pursue the benefits of investing in industrial automation machines: productivity, accuracy, safety, cost savings, quality improvement, and repeatability. Industrial automation is the main key in following the development of technology into industry 4.0. This study aims to propose a new pneumatic system using the microcontroller by implementing the step counter methods. In this research, it is used to control a drilling fixture that has 3 (three) DACs. The Arduino microcontroller system used in this study to perform the step counter methods. Arduino type Mega 2560 control system has 10 inputs and 10 outputs for the 5/2 directional control valve (5/2 directional control valve – DCV. Through the application of this method, the preparation of microcontroller-based control circuits can be done in a structured and easier way, both the steps of preparation, control, and troubleshooting.
The information security strategy of Bogor’s smart city to deal with threat in cyber space
Authors: Madjid M A , Legionosuko T , Samudro E G
Published: February 2021

The increasing quality and quantity of cyber-attacks and the demand for the information security revolution are also followed by the threat in developing Smart City of Bogor. In assuring these problems, it is crucial for the City Government of Bogor to develop and information security strategy which is capable to protect public privacy data and critical information of Smart City Bogor. The research was conducted with a qualitative method by using a grounded theory approach at the critical infrastructure of Smart City Bogor, which is the Department of Transportation, Department of Health, Department of Fire Rescue and Department of Communication and Information. The result showed that the information security as a core strategy which only created by the readiness of the legal ware as a causal factor in forming the knowledge and information security awareness in the preparation of priority program and development of the command, control, communications, computers, intelligence, surveillance and reconnaissance (C4ISR) systems of Smart City Bogor which became the basis of the formulation of Smart City Bogor’s information security theory.
Securing the electronic medical record by implementing Advanced Encryption Standard (AES) on the information system of a health service place
Authors: Rahmad C , Syulistyo A R , Sumari A D W
Published: February 2021

Medical records are very crucial in health services all over the world. This is due to not only to record individual health conditions but also for getting the most appropriate medical services at a local health facility when in mobile conditions. By having a look at the medical records, the local health workers can know the medical history and give their best service in treating the patient. Conventional medical records commonly are written manually and are not flexible for the mobile environment. Therefore, the electronic medical record (EMR) has become mandatory to be implemented to cope with such problems. Another important thing that requires attention is the patient has the right to know the contents of the medical record, especially when having health services at a different hospital or other health services when at mobile conditions. Accessing the EMR from the mobile environment brings a crucial matter, namely, how to secure the contents of EMR and ensure that the access can only be given to the legitimate owner fit as well as other legitimated actors such as the related doctors and the EMR officials. Based on the arisen problem, the contribution of this research is to make the implementation of encryption algorithms that will be used on Secure Electronic Medical Record (SEMR) systems using secure and fast encryption techniques by using Advanced Encryption Standard (AES) on the information system of a health service place.
Public service innovation through the application of mobile-based smart city concepts in Sukun sub-district, Malang City
Authors: Aji D K P , Ariyanto R , Rahutomo F , Ismail A , Batubulan K S
Published: February 2021

Public service provides services to the public carried out by the government whose services can be in the form of goods, services, or administration. The sub-district is a public service provider from the application stage to the publication phase of the document. Where people need not go to several agencies to take care of licensing documents, but enough to come to the district in their respective regions. Districts as the spearhead of government administrators must carry out their functions as the embodiment of excellent public service. However, in reality, public services at the sub-district level are of poor quality. Less responsive, less informative, and convoluted bureaucracy is often a major problem in service. Even though in the current technological era, the sub-districts should adopt technology for their activities. Innovation in using smart city concept technology in sub-districts is the key to solving existing problems because integrating entities in the smart city concept can facilitate the exchange of information, speed of data exchange, and rapid decision-making by service officers the public. So that the public services organized by the sub-district become more quality.
Detecting body parts from natural disaster victims using You Only Look Once (YOLO)
Authors: Moechammad S , Cahya R , Berkah A N A
Published: February 2021

Natural disasters are unpredictable events in both the location and the time of occurrence. Natural disasters can cause property loss and can even be claimed by life. To reduce the amount of losses, the handling of rapid evacuation should be conducted by the SAR team to help victims of natural disasters. But in fact, there are a lot of obstacles in the evacuation process. Starting from the difficulty of searching the victim’s body, the difficulty of the terrain reached until limited equipment needed. In this study designed the body detection system of natural disaster victims using image processing where the shooting of victims was carried out using drones aiming to help find victims in a difficult or prone location when reached directly by humans. Background of the problem, in this research proposed a development method for the detection of victims of natural disaster that aims to help the SAR team as well as natural disaster volunteers in the search for victims who are in a difficult to reach place. The method used by You Only Look Once (YOLO) uses the Python programming language associated with image processing. From the research has been obtained accuracy detection object disaster victims with good accuracy. Based on the experiments that have been done obtained a good accuracy value of 95.49% with epoch of 12000.
Evaluation of educational media of private body parts for down syndrome children
Authors: Pradibta H , Sukmana S E , Ikawati D S E , Sabila W I , Hikmah A M , Ramdani M A I
Published: February 2021

Educational media of private body parts for Down syndrome children is an educational media in the form of audio-video animation containing information about private body parts. This application developed as part of the supportive learning for Down syndrome children who have entered puberty so that they can avoid sexual abuse. An evaluation is carried out to determine whether the educational media created are according to the needs. The evaluation process is done by interviewing the respondent after conducting a trial on the educational media. The selected respondents were teachers, parents and students. Testing is held at SLB Tk. National Bagian C Malang. The interview questions based on several criteria for assessing multimedia applications, including 1) Ease of use of navigation, 2) Cognition content, 3) Presentation of information, 4) Media integration, 5) Artistic and aesthetics, 6) Learning function. The results of evaluations show that the educational media developed have met the criteria of multimedia assessment. Application of private body parts can provide understanding to Down syndrome children about personal body changes and social contact.
Optimal multi-layer perceptron parameters for early stage diabetes risk prediction
Authors: Wijayaningrum V N , Saragih T H , Putriwijaya N N
Published: February 2021

Diabetes is an alarming threat to people around the world because the number of diabetics is increasing every year. Diabetics with other complications have a very high risk of death. Therefore, the use of technology to predict the risk of early diabetes is needed. Neural Network as one part of artificial intelligence plays a role in solving prediction problems with satisfying results. In this study, a multi-layer perceptron neural network is used to predict the risk of early stage diabetes with optimal parameters from the optimization results using Improved Crow Search Algorithm. The test results prove that the multi-layer perceptron with optimal parameters is able to provide better accuracy compared to other algorithms such as J48, PART, Decision Table, Naïve Bayes, AIRS1, AIRS2, and Single Layer Perceptron with the highest accuracy values of 97.69% and 96.92% for one and two hidden layers, respectively. This proves that the proposed solution can be used to predict the early stage diabetes risk.
Control position of the double nozzles on the Y (+) and Y (-) axis of 3D symmetric bilateral printing using G-Code
Authors: Audiana V U , Setiawan B , Sumari A D W , Wibowo S
Published: February 2021

The most widely used universal Cartesian 3-Dimensional (3D) Printing today is not fast enough. However, by applying double nozzles, the process for making symmetric bilateral product can be done in half of the time. A symmetric bilateral product is an object that has the same size on the right and left sides. So, the method can be implemented by having double actuators that are mounted by a nozzle on each side. Both nozzles, called as Y and -Y nozzle, move on the same time and distance but in different direction. Besides that, an appropriate mechanical support is needed to be prepared, so that the motors can move in different direction at the same time. Therefore, a design simulation for two Y actuators was carried out. The simulation and the data taking were using actuator simulation software. The simulation results shows that the G-code can control two Y actuators with 99.64% accuracy with average error of 0.216% for revolution speed, and 100% accuracy with average error 0% for distance. The motor can move in different directions with the same type of screw.
HDPE plastic extruder design and control for 3D printing with plastic pellet feeding method
Authors: Setiawan B , Permatasari D C , Sumari A D W , Winarno T , Audiana V U , Damayanti A M
Published: February 2021

3-Dimensional (3D) Printing Technology (3D printer) has become a Standard Acceleration Tool for Industry 4.0. Until now, 3D Printing system material melting technology has been using Fused Deposition Modelling (FDM) method. FDM is the melting of plastic materials through melting temperature and the formation of 3D objects in layer by layer manner. Meanwhile, the feeding for melting material is in the form of rolling filament. Currently, in this research FDM uses plastic pellete feeding method. Its exstrusion is called extruder, that is, plastic pellet processed through heater zone and pushed out by screw to the nozzle where the nozzle is a Computer Numerical Control (CNC) drives 3-Degree-of-Freedom (3DOF) applying cartesian axes. In this research, the printing uses high density polyethylene plastic pellete (HDPE). Temperature control in melting heater using proportional-integral-derivative (PID) method is controlled by microntroller and programmed through marlin firmware. The design and implementation result showed that the extruder system is capable to melt HDPE pellet at 200°C with heater power 300Watt and the screw can rotate to push out melted plastic at 2mm of nozzle.
The role of the blades number and pitch angles to the capability of self-start the Darrieus wind turbine
Authors: Adiwidodo S , Yudiyanto E , Wahyudi B , Susilo S H
Published: February 2021

Darrieus is classified as a vertical axis wind turbine that has a simple construction. It is widely used for small-scale domestic needs in rural or aquaculture areas. As with other types of vertical axis wind turbines, Darrieus have low efficiency. There is resistance on one side of the blade which aggravates the shaft rotation. Another problem is the low ability to self-starting. Some even need help from an external source to start. This is certainly detrimental in terms of energy conversion. The purpose of this study is to obtain information on the wind speed needed for a Darrieus to self-starting on variations in the number of blades and pitch angles. The blade profile used is NACA 6412. Variations in the number of blades are 2,3,4 and 5 and pitch angles of -10°, -5°, 0°, 5°, and 10°. Data retrieval was carried out experimentally using the wind tunnel. It is shown that the self-start wind speed was strongly influenced by the number of turbine blades. The number of blades 5 requires the lowest average self-start wind speed. The pitch angle has a different effect on the wind self-starting speed depending on the number of blades.
The effect of variation of extra fan condenser and engine speed to COP of mobile air conditioners
Authors: Nurhadi N , Listiyono L , Maskuri M , Prasetyo B R
Published: February 2021

Efforts to increase coefficient of performance (COP) of Mobile Air Conditioners (MAC) still continue to be done, including by increasing the extra fan condenser speed. The problem is whether it can improve the COP, it needs to be tested further. This study focuses to examine the effect of extra fan condenser and engine speed variations to COP. This study was conducted on a MAC engine stand which able to measure the pressure and temperature of MAC system. The data measured at inlet compressor (P1, T1), outlet compressor (P2, T2), outlet condenser (P3, T3), and outlet expansion valve (P4, T4), at engine speed of 1.000, 1.500, 2.000, 2.500 and 3.000 rotation per minute (RPM), respectively with variations extra fan condenser speed of 0.15, 1.74, and 2.22 m/sec. The data was plotted on p-h diagram to obtain enthalpy value, so that obtained COP value by calculation. The major result obtained are the variations of extra fan condenser and engine speed had an effect on the COP. The highest COP (4.48) was achieved at an engine speed of 2,000 RPM with extra fan condenser speed 2.22 m/sec. Increasing extra fan condenser speed causes better cooling at condenser and cooling effect on the evaporator.
The effect of strain hardening on the limit forming ratio on the single-step incremental hole flanging process on aluminium plates
Authors: Mashudi I , Fakhruddin M , Hardjito A
Published: February 2021

Hole-flanging is the process of forming a plate to produce a collar around the hole. This formed collar has several important functions to increase the bonding surface area, as a supporting medium for other components, as a guide, increase construction rigidity, etc. This collar is generally formed through conventional hole flanging which involves making the initial hole in the plate, then punch with a certain profile and a larger diameter pressing the plate in one step of the forming process. This forming process will cause the deformation of the plate so that a collar is formed around the hole. In recent studies, it can be found that the collar can be formed not only with one stage of the conventional hole flanging process, but can also be achieved by a multi-stage incremental hole flanging process using a punch with a multi-purpose general profile. In this research, an experimental study was carried out on the effect of ‘strain hardening’ on the limit forming ratio (LFR) in the single-step incremental hole flanging process on aluminium plates using conical punch. The research hypothesis shows the influence of ‘strain hardening’ on the limit forming ratio (LFR) in the single-step incremental hole flanging process.
Web innovation in horizontal wind pipe turbine propeler
Authors: Wahyudi B , Sudarmadji S , Sarjiyana S , Maskuri M
Published: February 2021

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

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

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

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

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

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

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

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

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

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

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

Classification 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

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

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

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

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

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.
Investigation of the tangential discontinuities formation in the glycerol under shear load
Authors: Efremov D V , Chudinov V V , Uvarov S V
Published: February 2021

The work is devoted to the study of the formation of tangential discontinuities of glycerol parameters under shear load. The studies were carried out using a modified coaxial cylinder viscometer. The experiment was inspired by the work of Ya. I. Frenkel, in which it was noted that “… liquids, being condensed media, should exhibit effects that are more characteristic of solids than gases …”, while the thermodynamic and kinetic properties should be determined by structural “variables”, traditional for both “liquid” and solid state. These studies will further help in explaining the anomalous momentum transfer regimes caused by the mechanisms of instability that are realized under the conditions of the “decay” of multiple metastable states, usually observed under intense (dynamic and shock-wave) loading in liquids and solids.
Numerical simulation of slow deformation perturbations in fault zones
Authors: Kazakbaeva A A , Smolin I Yu
Published: February 2021

Over the past half a century, the concept of slow deformation waves of the Earth has been developed and widely discussed in the Earth sciences. The velocity of slow waves is considered to be 5–6 orders of magnitude less than the velocity of sound and 7–8 orders of magnitude greater than tectonic flows. Analyzing and classifying various manifestations of slow deformation waves in the geomedium accumulated over forty years V. G. Bykov identifies two types of autowaves in his review—inter-fault and intra-fault. Here, the process of generation and propagation of slow deformation disturbances between two faults in an elastoplastic medium is studied numerically. The faults were defined as narrow elongated soft areas inclined to the axis of the load application. Just in these faults plastic deformation was permitted to generate. The simulations proved that the fronts of deformation waves head towards each other at approximately the same velocities, their shapes being close to planar but slightly curved.
Influence of grain size on the nucleation and development of plasticity in nanocrystalline FeNi films
Authors: Korchuganov A V , Kryzhevich D S , Chumakov Y A , Grigoriev A S
Published: February 2021

A molecular dynamics study of the features of the nucleation and evolution of plastic deformation in nanosized nanocrystalline FeNi films under uniform uniaxial tension is carried out. The dependences of the strength properties of the films on the grain size are obtained. It is found that the dependences of stresses at which defects begin to nucleate, maximum strength is achieved and plastic flow is realized, on the grain size have a pronounced maximum. The optimal grain structure for which the nanocrystalline film has the maximum strength at uniaxial stretching is determined. The calculated dependences allow determining the critical grain size at which the Hall-Petch relationship is reversed. It is shown that the nucleation and initial development of plasticity in nanosized films with grain size above the critical one has a dislocation nature. The twinning contribution to the film plasticity increases at strains corresponding to plastic flow.
Peculiarities of the development of plastic deformation in a textured FeNi alloy with a gradient grain structure
Authors: Korchuganov A V , Kryzhevich D S , Chumakov Y A , Grigoriev A S
Published: February 2021

The mechanisms of nucleation and development of plastic deformation in the FeNi alloy with a gradient grain structure with a pronounced axial texture were revealed. The study was carried out on the basis of a molecular dynamics method. The simulated sample contained four layers, each of which consisted of grains of approximately the same size. The sample was subjected to uniform uniaxial tension at a constant rate in the direction of the grain misorientation axis. It was found that initially plastic deformation develops only in the layer with the largest grains and represents an abrupt growth of stacking faults. This process ends with the hardening of the deformed sample. At higher deformations, smaller grains contribute to the development of plasticity. At the same time, an almost ideal crystal structure can be restored in large grains, and twinning occurs in medium-sized ones.
Comparative Analysis of Mechanical Properties of Laminated Carbon Fiber Reinforced Composites Based on PEEK and PI Matrices
Authors: Kosmachev P V , Alexenko V O , Panin S V
Published: February 2021

The mechanical properties of laminated carbon fiber reinforced composites based on PEEK and PI matrices have been studied. Various schemes of carbon fiber lay-outing were employed. Flexural stress-strain curves, optical images of fracture surfaces and SEM micrographs of composite structure were analyzed. It has been shown that laminated composites reinforced with continuous unidirectional fibers according to layout scheme [0°/0°] possessed the maximum values of mechanical properties (flexural modulus and bending strength) under three-point bending. PEEK based composites exhibited 2 times higher flexural strength in contrast to PI based ones (0.4 GPa and 0.2 GPa, respectively), while the modulus of elasticity in bending was 4 times larger (61 GPa and 15 GPa, respectively).
Some approaches for residual porosity estimating
Authors: Kosyanov D Yu , Zavjalov A P , Vornovskikh A A , Zakharenko A M , Liu X , Li J
Published: February 2021

Several approaches for materials residual porosity assessment are presented in this paper. Importance of development of accurate model is shown based on Al2O3–Ce:YAG and Al2O3–Ce:(Y,Gd)AG ceramic phosphors taken as model objects. The evaluated difference in the outcomes of different methods applied for estimating porosity has been shown to be as high as ∼40%. The obtained results become applicable to another types of inclusions within the studied volume of a material as in the models under consideration the main role is given to a geometrical perspective of the problem.
Development of methodological approaches for non-destructive testing of welded joints of steel finned panels by the active thermography method
Authors: Kotovshchikov I O , Yakovlev Ju O , Prohorovich V E
Published: February 2021

This paper examines methodical approaches to non-destructive testing (NDT) of thin steel T-welded joints made by ultrasound welding. The choice of the active thermography method is justified. Numerical parameters were developed for characterize the operational properties of the plate-fin heat exchanger and the results of finned panel inspection by the active thermography method. These parameters were compared and the correlation was found. The developed methodological approaches will be useful in the future for developing an NDT technique that allows detecting internal defects in welded seams which decrease breaking load and worsen the operational properties of products made by finned panels.