bluuebunny/crossref_metadata_2025_split huggingface dataset:
Confidentiality Guaranteed E-Health Treatment for Cloud Cover Enhanced Systems Based on IoT
Authors: Kiran Kumar Reddy P , Narasima Venkatesh A , Arumugam S S , Jegan Amarnath J 
Published: July 2021

Abstract
The advances made with Internet-of- Things (IoTs) on networking technologies and hardware has made for an at the moment more relaxed human life. In addition to the so -called “intelligent” settings, e-healthcare services, as well as real-time diagnoses and diagnostic consulting, have increasingly been integrated into IoT. Cloud cover layers have also been hired, and have demonstrated their utility by offering quick response time and low latency to increase the capability of IoT based health systems. Such a development, however, retains the privacy of users as a serious obstacle, to some degree solving security/privacy problems. Such technology still had less privacy controls when it was at a childhood stage. Therefore, our latest work is on an eHealth System for the security of privacy problems in electronic medical reports (EMR). In addition, we have interacted with and compared work on the proposed work in terms of reaction time and delay. The findings demonstrate the complete work in conjunction with normal network parameters is effective to provide privacy.
An Enhanced Approach for Decision Support System Using Fuzzy Soft Set
Authors: Jayagowri P , Arockiaraj S , Karthik R 
Published: July 2021

Abstract
The support group issue of choice has been vague in recent years and has played a major role. There was an additional method for the approval of articles from infinite multi-spectator results. This approach includes the preparation of a parametric comparison table for a decision support device for a floating soft array. Real issues change in the fields of engineering, municipal and medical science, economics, and how they can be resolved depending on the sophistication and inexactness of the concepts of mathematics. In recent years, a new form of inference was designed to deal with those systems effectively. All these include fuse theory, probability theory, contradictory sets, intuitive fuse sets, rough sets theory, statistical interval theory, etc. that can be used to solve different forms of system-integrated complexity and inaccuracy. Both of these proposals are linked to an inherent disadvantage, though, which is that the parameterization process of both hypotheses is inadequate.
CNN and Computational Configuration Mathematical Chromosomal Defect Analysis in Medical Images
Authors: SureshAnand M , Reddy Gangireddy Prabhakar , Jaanaa Rubavathy S , Kalaiselvi Geetha M 
Published: July 2021

Abstract
Chromosomal defect analysis plays an important role in current medical care and diagnosis as one of the principal methods in cytogenetics via the processing of a medical picture. There are two main elements of human karyotype analysis: first, chromosomes are separated by digital images of the chromosome metaphase under the microscope. Chromatids are then closely analyzed, compared, organized and categorized. The segmentation and classification operation is tedious, where conventional geometric or mathematical approaches have only limited impact due to low precision, according to this technique. In most cases however, the workflow is still highly supervised and errors are still required by humans. This paper provides an optimised workflow to isolate and automatically identify chromosomes by a combination of many CNN and mathematical optimizations called mCNN GO. Mask R-CNN is investigated to separate the chromosome from chromosome metaphase images and train mCNN GO to identify the sub-images. We apply a new functional approach to synthesize images on the labelled data in order to enhance the efficiency of the segmentation network. Moreover, to ensure accuracy of the results, we create computational algorithms to straighten the genomes before registration. Experimental findings indicate that our methods for automated karyotype analysis are greatly superior to state-of-the-art.
An Educated Peer Discovery Expanding Blockchain Framework
Authors: Singuluri Prem Kumar , Basha Shaik Lal John , Ahamed Shaik Khaleel , Nithya M 
Published: July 2021

Abstract
The key benefit of blockchain Bitcoin-style networks is their immutability and autonomous power. Bitcoin is suffering from scalability problems in spite of its performance. Unless it is rendered more scalable, the promise of blockchain technology cannot be achieved completely. We broaden an earlier study on a fundamental balance between the blockchain system’s efficiency and forking rate. We also suggest a system to improve blockchain device efficiency by reducing block spread time. Informed neighbor selection is adopted by the proposed system. Miners detach from neighbors with restricted bandwidth and choose higher bandwidth nodes. To test the efficiency of the proposed system, we construct a Blockchain Interactions Emulator. In standard operations proposed system increases the performance by 20% by ∼40%.
The methodology for communication problem in telecom sector using fuzzy sub-monoid and group theory
Authors: Jayagowri P , Karpagavalli K , Karthik R 
Published: July 2021

Abstract
In order to address the ‘channel’ problem for telecommunications operators, this study is a pioneer in the application of Fuzzy Sub monoid and Group Theory to the communication channel system. The channel entity community model was developed for the transmission of information in the channel system to analyses the operation of channel entities. The channel system network traffic equations were developed in accordance with the theory of stream conservation and the channel network matrix. The matrix dimensionality was reduced on the basis of the channel entity Fuzzy Sub monoid and group model. A solution to the flow-state transfer relationship of the channel system is obtained, which will be very useful for telecommunications operators to create a high-level mobile e-commerce application model and architecture.
Detection of Motorcycle Theft Using Force Sensing Resistor and Ultrasonic Sensor
Authors: Balamurugan S , Govind K , Kamalesh M , Jeevaharan M J , Natarajan V 
Published: July 2021

Abstract
This essay provides a technique for continued motorcycle surveillance to avoid theft of the bike at the moment. If someone attempts to steal the vehicle, the buzzer will alarm the surrounding and with the use of FSR and ultrasonic sensor theft is detected and the system will send a message/phone call and the car owner will be given the current location of the vehicle. GPS stands as a back-up so that we can have a complete track of the vehicle during the theft.
Wind Energy Generation Based for Grid Connected System Using Zeta Converter and Terminal Sliding Mode Control
Authors: Pradip C , Saju N , Tresa Sangeetha S V , Raj Murugananth Gopal 
Published: July 2021

Abstract
In this paper, the wind energy system (WES) is interfaced with the grid system using a permanent magnet synchronous generator (PMSG) with robust control of terminal sliding mode controller (SMC) is proposed. In this system, voltage source inverter (VSI) with three phases has connected in grid side using less resistive losses of LCL filter but without using grid connection transformer. The ZETA converter is utilized to develop the voltage of DC link wind system connected rectifier. The high voltage gain is achieved by using the PWM switching signals for the converter. The grid side VSI is used to provide the reliable, efficient and supplying secure with the power, and it is controlled using the control strategy of terminal sliding mode controller (TSM). The proposed system results are verified, and the TSM control’s achievement is based on VSI, which is connected in the grid side validated using MATLAB/Simulink.
Grid Interfaced Windfarm Using Dynamic Voltage Restorer for Power Compensation with Pv Fed Sepic Converter
Authors: Senthil Nayagam V , Pattanaik Balachandra , Sekar K , Manikandan S 
Published: July 2021

Abstract
In this paper, the wind energy generation farm is connected with the grid system. The dynamic voltage restorer (DVR) is installed to control the grid side’s under and overvoltage. The photovoltaic (PV) system is installed in the DVR system, and the voltage of the grid over and under voltages is also addressed to achieve the proposed system’s high efficiency. The grid voltage compensation is harvested from PV and injected through the DVR. The boost converter is used in the existing systems to improve the available power from PV. In this system, the SEPIC converter is used to achieve high voltage gain from the PV plant to improve DC link capacitor voltage which is the inverter input source. The DVR based wind energy farm using PV with SEPIC system results are achieved in MATLAB/Simulink.
A Design Space Exploration of VLSI Extreme Machine Learning
Authors: Gopi B , Arulkumaran G , Parameshwaran R , Khan Vijitha 
Published: July 2021

Abstract
Radiation acceptance in FPGAs is an increasing priority, especially for dependable data processing in electronic equipment used during engineering and geostationary operations. A loss in FPGA board’s durability based on a single impact made by radioactive contaminants is the impetus behind the whole study. Inefficiency is a widely used method to increase the potential of radioactivity systems for data integrity. In excess usage of the zone, delay, and transmission range, durability brings with an overload. Also, with replication induction methods or even the quantity of backup stages, the defective system designs differ in configuration and resources use. The electromagnetic background varies based on the atmosphere and space climatic conditions and during the project’s operational time cycle. About the particular radiation level can also minimize the overhead costs attributable to maintenance at a run. In this article, we design a Dynamic Reliability Management scheme that uses, perceives, determines any appropriate durability degree, and implements operated reorganization using the radiation details, thereby varying the system reliability of the target computing modules. DRM specially developed flow produces a catalogue of similar compatible circuit applications with different output factor amplitudes. Spin software flow picks a necessary redundancy level using the radioactivity data and reconstructs the computational module with the relevant compatible module. The consequences of defects, errors and deficiencies caused by planetary protons emanating from solar radiation on ambient electronic structures in many sectors will begin with a simple but detailed analysis. Prevention steps against particle adverse effects, looking at different levels of both the architecture system from material to the device level, are applied. Challenges are also addressed to maintain effectiveness in the future advancement of technology. For random - access records, switch, GPU and operating networks, such problems in control measures are raised.
Design of Low Power and Failure Free SRAM Cell Using Read Assist Circuits
Authors: Ramamohan Reddy K , Aruna Priya P 
Published: July 2021

Abstract
Static Random Access Memory (SRAM) is one of the main peripherals in all the Very-large-scale Integrated (VLSI) chips, which enact a vital part in all executable applications. The write ability and read stability are prime factors with low power supply and improve operation speed with temperature, process, and voltage variations. In VLSI design, the System on Chip (SoC) performance is improved by applying low power supply and change in few Pico seconds of cycle times or memory access. The read/write operation is not accurate in lower supply voltage because it will not flip to the desired voltage levels. For improving performance, proposed an SRAM cell to flip to the desired voltage levels to reduce readability failures in low supply voltages. The additional requirement is needed to increase the chip’s performance with lower supply voltages, and continuously additional circuit techniques are needed for obtaining the SRAM memory’s readability and writing ability. In this paper, different performance improvement methods of SRAM memory cells are analyzed. The reduced word line voltage read assist circuit is designed for the SRAM memory cell.
Performance analysis of Cooperative underlay CRN in NOMA aided Energy harvesting for multiple secondary users
Authors: Kalaimagal G , Vasanti M S 
Published: July 2021

Abstract
This paper proposes a relaying scheme in an underlay cognitive radio network(CRN), where a source node desires to transmit the information simultaneously to more than two desired destinations. The power required for the relay operation is based on applying energy harvesting from the base station (BS). The proposed paper also includes Non-orthogonal multiple access (NOMA) with fixed power allocation to the different users to perform the decoding process irrespective of the channel condition. We have proposed a mathematical expression for the analysis of outage probability for the different users. In this respect, the linear model is discussed for perfect successive interference cancellation (SIC) at the receiver. Using the fixed power allocation process, we have simulated different output parameters such as Bit Error Rate (BER), NOMA capability, and the outage probability.
Analysis of Ultrasound Transducer Probe and Alternate Material for Its Foot Print
Authors: Joseph Raj P L , Kalimuthu K 
Published: July 2021

AbstractA 128-element color ultrasound transducer probe (Sonoray – DS50PLUS-C352UB) compatible with any machine is analyzed. Various coating methods were discussed; the footprint is coated with TiO2 by the dip coating method, and characterization is done simultaneously. Curvilinear ultrasound transducer probe is most used to scan the abdomen part of the human body. Its characteristics were analyzed to find an appropriate alternate nanomaterial to be coated over its footprint to make the device a cost-effective one with even better output. Before and after the nanomaterial is coated, Fesem, EDS, and XRD tests were done over the transducer footprint. The dip coating method is easier, quicker, and cheaper but less effective among various coating methods.
A Multilayer Neural Networks Supervised Learning Algorithm Based Energy-Efficient VLSI Processor Design
Authors: Mohanapriya R. , Vijendra Babu D. , SathishKumar S. , Sarala C. , Anjali E. , Elumalai J. 
Published: July 2021

Abstract
Neural networks are abstract structures modeled by the brain to store evidence in the form of spikes. When introduced in VLSI circuits, neural networks are supposed to have new computer processing methods and economically viable computer simulations. We suggest a novel set of training examples for neural nets spatial and temporal coding in this article. In just this procedure, going through the roof neuronal is programmed to promote analogue VLSI applications with resistor analogue memory, from which incredible energy consumption can be accomplished. Can also suggest many strategies to boost efficiency on a model training and prove that the proposed method’s SVM classifier is as high as it was for the retrained dataset’s province temporal coding Computational Intelligence algorithms. Incorporating the developed framework can even recommend very massive circuit boards. The frequency analogue circuits utilize intermittent processing to reimburse capacitance processes, unlike the traditional analogue voltage and current type circuitry being used compute-in-memory circuits. Even though connectors lacking operating amps can still be constructed, it can also be controlled with incredibly low energy consumption. Finally, the preservation of the designed highlights algorithms toward alterations from the system’s production phase and is inevitable in analogue VLSI deployment.
Performance Evaluation of LDCMAC for Wireless Sensor Networks
Authors: Amru M , Pothalaiah S , Sowmya T 
Published: July 2021

Abstract
Wherever sensor nodes in each other’s contact range (CR) exist, the data packets may be transmitted in the same loop when a wireless sensor network (WSN) occurs. On a present contentious sequential MA protocol, so few nodes can forward their data packets through multiple layers in a single loop. Consequently, there has been a notable improvement in propagation time (E2ETD) with increasing nodes. The article suggests supporting event-driven WSNs with a low-delay containment-based contending sync MAC (LDCMAC) protocol. LDCMAC doubles the level of nodes that may send their databases within the same interval while nodes has data packets in one CR.LDCMAC is assessed and its performance is compared with CL-MAC protocol using NS-3 simulations. The findings show that the LDCMAC exceeds substantially the E2ETD and packet delivery ratio of CL-MAC.
Vehicle Theft Identification and License Authentication Using IoT
Authors: Verma Aryan , Seth Shikhar , Kumar Abhit , Sarada V 
Published: July 2021

Abstract
In this 21st century, it is difficult to trace the device on real-time platforms. To overcome this complex situation leads to developing a fingerprint cum GPS/GSM tracking prototype. This prototype model proposes that the system will get the location of the vehicle, i.e., latitude and longitude of the vehicle, and the tracing is done with the SMS sent on the receiver’s phone by sending SMS via GSM module to make the vehicle security feature to work online that is stored and can be viewed real-time by sending data by ESP module serving as a wireless fidelity module. The theft vehicle is then detected by sending the message from the tracker to the owner’s phone. On the receiver’s phone, the owner will get the emergency message, where the owner can send back the SMS to the GSM module to stop the vehicle motor using a microcontroller.
Effective Parametric Image Sequencing Technology with Aggregate Space Profound Training
Authors: Rajeshkannan S , Mishra Suman , Ganesh Babu T R , Mohankumar N 
Published: July 2021

Abstract
That containing the full workflow of evaluation, patient stratification, treatment preparation, operation and follow-up is assisted by robot and quick solutions for target identification and segmentation. Present state-of-the-art describing approaches are generally established on computer teaching approaches that use supervised learning image databases. The reliability in processing large volumetric images and the need for powerful, representative image characteristics reflect two major challenges to be tackled. In high-dimensional spaces where the object of interest is parametric, traditional volume scanning methods do not scale to the vast spectrum of possible possibilities. The representativeness of the picture is subject to considerable manual engineering efforts. We suggest a process in the sense of volumetric image parsing for object identification and segmentation to solve a two-step study problem: structural position approximation and boundaries differentiation. To this end, we implement a new paradigm, MSDL, which uses the advantages of efficient object approximation in marginalised centralised environments and the automatic functional nature of the network architecture of Deep Learning (DL). Intelligent systems immediately define and detach explicative properties immediately from close to zero image data, but just the approximation’s high difficulty restricts their use in the volume context.
Cardiac Arrest Prediction using Machine Learning Algorithms
Authors: Karthikeyan R , Vijendra Babu D. , EKarthik  , Suresh R. , Nalathambi M , Dinakaran S. 
Published: July 2021

Abstract
Cardiac arrest and other cardiovascular problems are the most prevalent issue among millions of men, and there are numerous causes that function as the basis of this crisis, such as people’s wellbeing, mainly because of job stress, exhaustion, bad food quality, and an elevated cholesterol level as a consequence of the lack of technology cardiac disease. Many scientific and medical support programs change every day, yet every program has its own special features, advantages and disadvantages. The goal of this article is to research the probability of cardiac arrest based on various regulated or unregulated variables in specific data set machine learning algorithms.
Convolutional Neural Network Architecture Based on FPGA with Reduced Requirements for Parameters
Authors: Muthu Krishnammal P , Padmavathy T V , Shakunthala M , Vimal Kumar M N 
Published: July 2021

Abstract
The progress of deep learning has rapidly accelerated development at an exponential rate of existing technologies. Deep convolutionary algorithms have received much popularity due to exceptional success in various technology application areas. Although the quality was outstanding, their modernising has, therefore, always posed a challenge, particularly for resource-restricted hardware devices, due to their memory and computational access intensive nature of CNNs. Research article suggests a new lowered Cnn that is used for implementations for image recognition, resulting in a major decrease in the number of the communication network. Influenced by Squeezed Network, our process of reductions substitutes convolution kernel clusters with larger particles but eliminates entirely minimum duration apart from the last classification level. If implemented in software, the building model means lower computation time. By placing all qualified neural network thought-provoking Xilinx modules, we decided to utilise structure. The system design requires 2x lower complexity and a 1.2x Delay Informational processing relative to Cnns, culminating in a successful hardware design.
RETRACTED: FPGA-based adaptive linear-line approximation and implementation for optimum photovoltaic device power control
Authors: Mohandas R , Omkumar S , Rubesh Kumar T , Rukmani Devi D 
Published: July 2021

Abstract
Photovoltaic technology is one of the greatest attractions among the various renewable energy sources, coupled with the availability of atmospheric temperature in quantity and without emissions of greenhouse gases due to its poor PV power. Therefore, networking opportunities lie in applying electrical power generation and transmission methods to produce voltage level with favorable rates for effective application. The photovoltaic panel’s suitable methodology is important for analyzing, controlling, and monitoring a PV generator’s design. The photovoltaic system is the basic component of a PV device, so the PV array needs to be run at the highest power point to optimize the system’s energy production. In the system, a solar tracking system is needed to function at the maximum power point. The characteristics of a PV cell’s output power and energy are semi, and therefore its output fluctuates in compliance with the difference in solar irradiation and temperature. An amount of studies has been aimed at designing successful MPPT schemes over the last decade. Research projects are still increasing interesting; however, discovering building FACTS devices and resolving their digital quality issues. Besides, the architecture of MPPTs that could cope with irradiance and temperature is difficult. The establishment of innovative strategies and transmitted source inverter control techniques for a PV device with different pulse widths. To build a filter that remove the solar array PV panel’s using current instantaneous reactive power. The voltage regulation filter based mostly on PV array is then configured with adjusted adaptive threshold control with sequence analysis size. The MATLAB simulator and FPGA and legitimate digital numerical simulations are expected to show strong harmonic adjustment for the existing PV-based SAPF.