bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Concept Proof Approach for Conversion of Leaf Litter into Humus with The Help of Self-Aerated Composting Units
Authors: Banupriya D 
Published: July 2021

Abstract
About 68% of leaf litter conversion into humus was attained within 30 days using composting technology, which was made possible by employing the perforated composting unit for self-aeration developed by the author Saveetha School of Engineering campus. It can be related to conventional composting system CHs, but comparatively, it takes longer than these self-generated units to attain the same output say 60days. The self-generated composting units once achieved a sustainable environment and did not show any sign of stress on them since moisture maintenance was done regularly. Within the first 10 days of service of the composting machine, efficiency was under 17%, which might be because the micro-organisms needed time to become acclimatised to feed, which was more texture-hard and nutrient-leaner than the feed (cow dung) the organisms used to feed. In comparison, cow dung has to be supplemented even higher (50% or more). As the days passed by, there was a dramatic rise in humus conversion efficiency, which reached near the ultimate of 100% by the end of 70 days. Overall, the humus production increased with time in a linear fashion in Figure 1; the regression-correlation being significant at r2 = 0.99.
Exploring the Research Trends in COVID-19 in Collaboration with Industry 4.0 Technology as an Indispensable Effective Tool to promote Global Health through Bibliometric Analysis
Authors: Divekar Neha , Itankar Nilisha , Malge Shilpa 
Published: July 2021

Abstract
Objective: Currently, the entire world is dealing with a lethal crisis caused by COVID-19. SARS-CoV-2 is the root cause of the new disease Coronavirus. Novel Coronavirus has gripped more than 215 countries and territories worldwide. This study provides the bibliographic analysis of data on the epidemiological research conducted on COVID-19 and technology-based tools and applications extensively used to study and interpret the Coronavirus to tackle the pandemic in every way possible. Method: We investigated the data from the Scopus search engine, Google Scholar, and World Health Organization (WHO) using associated terms like Coronavirus, COVID-19, Artificial Intelligence (A.I.), Machine Learning (ML), Big data, Internet of Things (IoT). The data stretches from December 2019 up to 9 May 2020.Result: A.I., an ML-supported platform, is used by many agencies worldwide. Out of 217 publications from 57 countries, China and United States contributed maximum technology-based research articles in correlation with COVID 19. Conclusion: The world is in the middle of a pandemic due to COVID-19. Globally human health has been impacted. This paper attempts to understand the intellectual pattern of COVID-19 and 4.0 industry tools in research using the Scopus database and conducting the bibliometric analysis. This bibliometric analysis would also facilitate future researchers’ pathway to identify research carried out in COVID-19, focusing on A.I., ML, and advanced digital technologies.It would develop the existing knowledge potential and help future researchers collaborate and facilitate interdisciplinary research to tackle the pandemic better.
An Efficient Scheme for Reliable File Transfer using Residual Link Lifetime Estimation based on Sparse Vehicular Routes
Authors: Pasha Mohammad , Farooq Mohd. Umar , Ramsha Alfia Ameena 
Published: July 2021

Abstract
Abstract. Location based networking protocols in Vehicle Ad-Hoc Networks (VANETs) employ the hopping strategy to acquire the route to a destination node. One of the commonly used methods is to select the vehicle that is nearest to their destination. This greedy transmission approach leads to packet drop in disruptive networks like VANETs, since it doesn’t take account of the quality of the newly-established links. In this article, a new method is proposed for identifying the next-hop node from a potential set which takes account of the remaining life of the contact connections combined with the vehicular movement. Compared with the existing idea of predicting the new location using Kalman filters, the proposed method is based on vehicular density in the path to destination.
A Conceptual Framework for Secure Cloud Migration
Authors: Ahuja Srishti , Nisha T N 
Published: July 2021

Abstract
Cloud computing is a budding web-based technology that tends to prevail in our environment, especially in the areas of computing and information technology that require large-scale network computing. Cloud computing is a shared set of services gaining popularity due to its profitability, availability, and high production. Just on the security grounds, cloud computing’ immense potential cannot be overlooked: ongoing research and the investigation of consistent, robust, and integrated cloud computing security models may be the solitary motivational pathway. There are three service and four deployment models in cloud computing. Cloud computing, together with its several advantages, also brings a much more difficult condition concerning privacy, authenticated access, data protection, etc. Because of these problems, the acceptance of cloud computing is becoming difficult in the current era. In this study, several security concerns related to reliability and data privacy, main aspects impacting cloud computing, were tackled, and recommendations on specific areas were also discussed.
Blockchain Technology Empirical Studies on the Demand of Distributed Network
Authors: Mishra Manish Kumar , Balamurugan M , Reena Roy R , Amanullah M , Shantha Shalini K 
Published: July 2021

Abstract
Blockchain technology has become a new model for encryption, unsustainability and confidence in both consumers and suppliers (SPs) in order to increase the quality of services. A game theory framework is used to simulate a competitive market with the existing platform controlled by a centralizer as a confidant third party to provide an economic overview of such a Blockchain-based platform sector. The portals serve as a mediator in this industry and provide consumers with services delivered by SPs. The key reasons for blockchain based network market performance are I how engagement by SPs represents its service efficiency (QoS) and (ii) how SPs are empowered to contribute their services, such as the virtual environment. A non-cooperative two-stage competitive game is used in our game formulation, whereby the first stage model how to promote SPs on a blockchain basis and the second stage modeleves rivalry between platforms to draw consumers. We therefore have an equilibrium analysis that offers a valuable description of the effect on competition among platforms and SPs’ equilibrium reward policy of the service quality of the blockchain network. Our numerical analysis reveals that the incentive to balance is increasing proportionately to the Fault tolerance of a blockchain-based network when the incentive is unfavourable if the number of participating SPs has a non-increasing QoS.
Augmentation of SECaaS model with eCISO in cloud-based security services: A Comprehensive study
Authors: Saxena Siddharth , Nisha T N 
Published: July 2021

Abstract
In today’s competitive world and the time of business discontinuity and falling economies due to many reasons, enterprises worldwide are trying to process business data securely and minimize IT systems’ operational cost with technologies in cloud computing. With the benefit of cost-efficiency in cloud services, there are some concerns regarding IT security, IT governance, and compliance, which plays a vital role in the business as usual. Security as a service model is a cloud-based security service model to provide secure environments, mediums, and systems to enterprises. In this paper, we are attempting to introduce a new service model to cloud-based security platform and perform a comprehensive study with augmenting security as a service (SECaaS) model with eCISO services for better governance of risks and decision making related to IT security to optimize the service model to help out CEOs and CIOs to ensure the security of their IT systems and data.
Edge Computer-Enabled Internet of Vehicle Applications with Secure Computing and Load Balancing
Authors: Majumder Darpan , Mohan Kumar S , Ashoka D V , Shajin Naragunam A 
Published: July 2021

Abstract
In recent years, the Network of Automobiles has attracted great interests. Several IoV applications have been developed to improve road safety, performance and comfort. A virtualization is a common tool for disruption streaming video. However, cloud computing can cause undue delays in applications sensitive to progressive delays, such as auto/assisted driving and emergency error handling. The beneficial technology is edge computed, which expands processing and storage capacities to the network’s periphery. Throughout this report, we merge mobile agent networks or fixed-point processing domains into defined road networks to establish collaboration for all these machine and intensity implementations in IoVs. IoV is designed to optimize the use of these stratified boundary computation strengths in the idea of technology communication and communication technologies. Moreover, the loss of all nodes and communications, which can have life-threatening implications, is possible in an IoV system that is complicated and dynamic. We are integrating both partial computing offloading and consistent job assignments with a reprocessing system for EC-SDIoV to ensure the IoV services’ fulfilment with high reliability. Since the problem of optimization is built to optimize latency durability by the faulty tolerant particulate swarm protocol. The performance appraisal findings validate that both latency reduction and stability are still feasible under the current scheme.
Challenges in Single Sign-On
Authors: Pandey Prashant , Nisha T N 
Published: July 2021

Abstract
Single sign-on uses a centralized session concept, and it is a service that authenticates one and one-on-one designated platform. Later, with this, we can use various services and products without logging in every time. However, there are sure demanding situations in implementing SSO. Business enterprise no longer is most effective and needs to integrate user network logon with a nearby application and software program (SaaS) cloud offering. Simultaneously, in an enterprise, SSO’s centralized solution does not include all the users, applications, devices, and equipment. Another undertaking from the records angle is that the user can log on outside SSO answers. Its user identification may also exist in exceptional forms. Simultaneously, the enterprise desires to cope with bequest desktop programs that do not coordinate with users’ listing logins which may be the frail hyperlink that reasons SSO to fail in the company. With the efficiently applied SSO and designing framework keeping in mind the challenges, we can enable and disable users to enter more than one system, platform, apps, and different resources. Also, it may effectively resolve and dispose of the problem of password-related downtime and reset expenses. Simultaneously, it removes the danger of threat from an insider, improves authentication processes and user experience, delivers authority to the company, and simultaneously provides them with the right of entry to firmly on top of the user’s things.
Usability of Blockchain Technology in Higher Education: A systematic review identifying the current issues in the education system
Authors: RushabhBalpande  , Patil Kanchan 
Published: July 2021

Abstract
Blockchain facilitates the creation of a shared document of a digital transaction in a distributed way where no third-party regulates any data or related activity. Earlier blockchain was used to transmit money, but today it has a huge scope of utilization in various domains like banking, health care, the Internet of Things (IoT), and many more. Inside the educational domain, blockchain offers many circumstances for disperse administration of records in an interoperable way in higher educational institutions. The primary purpose of the study is to address the current issues relevant to educational institutions and to identify the appropriate blockchain characteristics that can solve these issues. To recognize and extract significant knowledge from the selected research studies, a systematic literature review approach has been followed by us. Our research study represents the current problems in three forms, i.e., financial, digital, and physical. The consequences of the study show that the main problems faced by education institutions are the risk of manipulation, the tough of authenticating, and difficulty in interchanging records among institutions. This research analyzes the blockchain characteristics such as fragmentation, traceability, and consensus system, focusing on a few problems relevant in this domain.
Data Communication through Reliable Sensor using Huffman Coding System
Authors: Padmanaban K , Senthil Kumar A M , Velmurugan A K , Madhan E S 
Published: July 2021

Abstract
Wireless sensor networks (WSNs) contain amount of sensor nodes that can forecast the atmosphere to transmit the information in unfavourablesituations. Though, restricted efficient also security problems in WSN. To solve these problems this paper Data Communication through Reliable Sensor using Huffman Coding system (DCRS) is introduced. In this strategy, the node with highest remaining energy will getdivision in the information transmit procedure. Security foreachhop node is accessiblethrough binary hop count (BHC) also verification is specified utilizing Huffmancoding to avoid the malicious as well as other attacks in the network. The DCRS strategy is formalized through a network simulator.
Deep Analysis of Covid-19 Receptors Recognition and Locating In Pulmonary Ultrasound
Authors: Sivagami S , Divya J , Sailaja P , Jagadish Kumar N 
Published: July 2021

Abstract
In the course of the latest COVID-19 flu epidemic, several projects have been carried out to test LD-based strategies for the helping diagnosis of lung diseases. Deeper learning (DL) has proven its effectiveness in radiography. Although the present study relies on CT scans, DL strategies for interpreting pulmonary ultrasound (LUS) images are being used in this article. In specific, we present a new, completely annotated LUS data collection obtained from multiple Italian institutions with labels showing the level of disease intensity in a shot, photo, and digit optimization mask. By using these data, we implement numerous profound models that deal with the related tasks of automated LUS image analysis. We introduce a new deeper network derived from Space Converter Networks, that continuously estimates the extreme disease score for an input frame and weakly controlled the location of pathological machines. We implement also a new approach for efficient video-level averaging of frames based on uninorms. Finally, we benchmark deep state-of-the-art models for estimating COVID-19 biomarker pixel classification. Experiment was conducted on the planned dataset show satisfactory results for all the tasks considered which will pave the way for potential DL studies for the diagnosis of LUS-based COVID-19.
Artificial Neural Network for Rainfall Analysis Using Deep Learning Techniques
Authors: Nandakumar S D , Valarmathi R , Sudha Juliet P , Brindha G 
Published: July 2021

Abstract
The estimation of rainfall is one of the most critical and daunting challenges in today’s environment. Weather and rainfall are typically extremely nonlinear and dynamic, needing sophisticated machine models and simulation for forecasting accurately. The economy of India is agriculture and is focused primarily on crop production and precipitation. Predictions of rainfall are important for all farmers to assess crop productivity. Rainfall forecast involves the application of science and technology to determine weather conditions. In order to utilize water supplies efficiently, the crop productivity and the pre-program of water systems, it is necessary to determine the precipitation in detail. The actions of such nonlinear processes can be modeled using an Artificial Neural Network (ANN). Most researchers in this area have been effectively utilizing ANN for the past 25 years. This article offers you an summary of some of the methodologies valid for using ANN for rainfall prediction by numerous researchers.The survey also states that forecasts of rainfall using ANN technologies are more accurate than conventional mathematical and numerical approaches.
RETRACTED: Development of a Prototype Power Distribution Misuse Monitoring System
Authors: Prabahar Godwin James T , Ganesan R , Kumar Pardeep , Perumal P 
Published: July 2021

Abstract
In order to satisfy the rising need for efficient energy Synchro phasor systems have a vast data volume for wide-range tracking and power system operation. In the context of the big data problem, the design of conventional intrusion detection systems, using rules manually generated on the basis of expert expertise, is know-how intensive. This article introduces a clear and integrated approach to developing a hybrid IDS which learns temporary transmission line requirements including disruptions, showed normal processes and cyber-attacks. A machine learning algorithm called general trajectory mining is used to learn patterns for situations by fusing synchrophasor calculation data with power system audit logs automatically and reliably. An IDS prototype has been developed and validated as a proof of concept. For a multiple, seven power transmitting devices, the IDS prototype classifies disruptions, regular control operations and cyber assault correctly for distance security.
Content-Based Image Retrieval Techniques: A Survey
Authors: Sivakumar M , Saravana Kumar N M , Karthikeyan N 
Published: July 2021

Abstract
In these eras, digital day’s cell smartphones and cameras are gaining a titanic reputation, resulting in the speedy growth of virtual images that are available throughout the internet. With an exponential boom within the image sizes, databases in different media lie an extremely good project for massive scale image search. Image retrieval from databases or the internet wishes an unsuccessful and effective technique because of the explosive increase of digital snapshots. Image recovery is known to be a comprehensive research area, particularly in content-based image retrieval (CBIR). CBIR retrieves comparable photographs from a huge photograph database primarily based on photo features, which has been totally lively study vicinity recently. The content materials that may be derived from images which include color, shade, texture, shape, are used for retrieving an image from the database. This paper will provide a survey and discuss the contemporary literature of various sorts of image retrieval (IR) structures and comparisons among them.
Classification of Diseases in Paddy using Deep Convolutional Neural Network
Authors: Malathi V , Gopinath M P 
Published: July 2021

Abstract
Paddy disease detection is decisive in the field of automatic pathogens diagnosis machine. Currently, Deep Con- volutional neural network typically examined the state-of-the art results in image classification. In this work, we proposed a novel DCNN model to identify previously known bacteria leaf blight, brown spot, leaf blast, leaf smut and narrow diseases in prior knowledge. A unique repository of data holds 1260 images of different diseases, 80% of data carried out for training and 20% for testing the samples. To add advantages to our model, we built our model using ADAM optimizer and conducted comparative research over SVM (support vector machine), KNN (K-Nearest neighbor) and ANN (Artificial Neural Network). The dataset given to the novel DCNN model with keras framework and achieved testing accuracy of 0.940 with less training error rate of 0.013. The interpretation outcome demonstrates that high level image classification accuracy with less error rate was achieved by novel DCNN model than traditional methods. Therefore, our model performs best for recognizing 5 paddy diseases and can be possibly implemented in day to day life application.
Smart Door Lock to Avoid Robberies in ATM
Authors: Sidda Reddy V. , Kalli Sivanagireddy , Gebregziabher Haftom , Ravindra Babu B. 
Published: July 2021

AbstractIn the present situation, the ATM system is used by most of the population to withdraw currency. The number of ATM Centers is growing increasingly as social computerization and automation are dramatically increasing. Simultaneously, even though the CCTV cameras are installed in the ATM cabins, many ATM thefts have happened in many locations. It is also important to adjust the protection framework. We present a protection mechanism for ATM robbery by using intelligent & powerful technology to minimize these forms of robberies. This scheme also analyses different physical assaults on ATMs. We would use a Wireless Sensor Network in our proposed framework to track the theft and catch the criminal & hand them over to cops in a specific area. USB Camera for the recording & transmitting of the face of the person operating the ATM locker. Tilt and vibration sensors are used to identify the unusual behavior performed on the ATM system. Tilt and vibration sensors are used.
On To A Centralized Sponsorship of Blockchain Based IoT Operational Infrastructure
Authors: Josephine M S , Balasubadra K , Kevin Andrews S , Lakshmi Narayanan K 
Published: July 2021

Abstract
With the rapidly developed IT technology, large-scale networks can be designed to link many heterogeneous devices into the Internet. The majority of current infrastructures are clustered, easier to maintain, but cannot efficiently provide immutable and verifiable service between multiple parties. For large-scale infrastructures like decentralization, reliability, trackability, and immutability, Blockchain technology offers several desirable functions. This article introduces an infrastructure based on blockchain that facilitates unchanging and controllable services. However, the need for storage capacity presents a threat to resource-controlled architecture when adding blockchain technologies to the infrastructure. The paper attempts to tackle the storage dilemma with a centralized storage side chain. In fact, the proposed architecture has a distributed storage structure where most of the blockchain is kept at the cloud, although these current blocks are kept in the real network integration node. The framework architecture allows the local, blockchain and server infrastructures to be conveniently merged to create a distributed blockchain storage between two connectors, the database and the blockchain. The extended version blockchain connection builds block of the network info, and addresses network/cloud overlay sync problems in blockchain. In a practical case of professional IoT, we also have a test case to demonstrate the reliability of the general architecture processing blockchain.