bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dimensionally improved residual neural network to detect driver distraction in real time
Authors: Balamurugan M , Kalaiarasi R 
Published: July 2021

Abstract
Bountiful reasons are there for crop up of road accidents. Predominantly most of the road accidents are turn out by the humans. In compare with other sources of road accidents, distracted driving is the foremost one. Driver gets distracted ascribed to many factors such as texting using phone, seeing outside of the car. The regime can grasp the road accidents by enforcing the traffic regulations moreover the regime seeks the assistance from the technology to reduce the car accidents due to driver distraction. There are numerous research undergoing to reduce the chance of the driver to get distracted by monitoring the physical activities of the driver while driving the four wheeler. Automatic detection of driver distraction can support to develop an apprise system by observing the activities of the driver that provide better result and evading the car accidents. Deep learning is a leading technology in automated industries to detect the traffic light signs and pedestrians. The novel ResNext
101
is the proposed work and inherited from its parent model called Residual Network (ResNet) to train and test the model. The “Next” in the word ResNext represents next dimension of ResNet called as “cardinality”. In this paper, with the help of Convolutional Neural Network (CNN), the system detects and captures the distracted moments of the driver from proper driving gestures automatically in the form of 2D image. The test results of the model outperforms and proved its significance over the existing driver detection algorithms by bagging higher efficacy with 97.6% accuracy in classifying the live driver posture.
Geospatial Analysis of the Dynamics of Climate in Kolkata Metropolitan Area
Authors: Talapatra Adrita , Majumder Arijit , Das Sandipan 
Published: July 2021

Abstract
The Kolkata Metropolitan Area region comprises urban towns that have witnessed major changes and have been exposed to urban agglomerations, population outbursts, and have faced difficulties in regional management. The growing cities comprising these areas thus faced various kinds of natural changes. One of the major significant changes included the change in climaticconditions. Climateis the weather experienced by any region for a long period, particularly extending to 30-35 years. Therefore, the climate is a long-term study of any region where any slight change shows a significant impact on the lives and balance of the region’s habitats. This paper tries to deal with the analysis of climate change in the KMA region. Metropolitan cities in India are currently considered prime concerns due to various climatic distortion and global warming effects. Thus, the paper tries to show the factors and effects of climate change. The study period has been taken for 35 years in the paper. The analysis is carried out using various GIS techniques using the ArcMap 10.1.1 software and statistical techniques to generate graphs. From 1989 to 2006 ranged high as the population showed a rising trend in Kolkata, Howrah, and Kalyani.However, this trend changed for some time during 2014 and 2015 as various plantation activities, programs, and projects were taken up by the government to safeguard climate change’s harmful effect. It shows a rise in the temperature and fall in the rainfall quantities from 2015 to 2019. It also shows the disturbed rainfall pattern as a characteristic feature in the KMA cities as sudden droughts or urban flooding rise as a common phenomenon. The area is highly exposed to constant modifications. The paper concludes in a nutshell how the changes have affected the region and are still having asignificant impact on influencing lives and living.
A Modified Hybrid Blockchain Framework for Secured Data Transaction
Authors: Bhanupriya P , Gauni Sabitha , Kalimuthu K , Manimegalai C T 
Published: July 2021

Abstract
Blockchain has recently become an essential tool which enables sensitive cloud services without the need for central confidence. For example, several different cryptocurrencies were permitted with public blockchains. Unfortunately, confidential details may be exposed on current public blockchain and smart contracts implemented there. Whilst some continuous work is under way to resolve these insecure knowledge leakage problems using advanced cryptography, they need major improvements on current and common Blockchain technology such as Ethereum and are typically costly in computing. On the other hand, blockchain applications were proposed to allow the data exchange among the pre accepted nodes/participants to be more efficient and privacy-preserving. While private blockchains respond to certain challenges of privacy by allowing only the particular community of participants to view sensitive data, they do not allow public transparency for communications because businesses are accepted by a known number of users also cannot be freely viewed. One natural problem is whether we should use public and private Blockchain networks in order to allow effective, improve privacy and accountable applications in view of these findings? In this work, we try in connection with digital auctions to face this challenge. In specific, we provide a newly designed blockchain architecture combined with private and open blockchains which enables sensitive offers to be opened up on a secluded blockchain so solitary the merchant can study the offers, and none of others. We also use shared blockchains to report the public sale winner and to make transfers responsible. Moreover, we demonstrate how we can promote sincere activity among auction participants by using intelligent contracts on public blockchains. Our detailed analytical findings suggest that it’s more cost effective compared to pure public auction implementations based on blockchain.
Automated Vision Defect Detection Supported Deep Convolutional Neural Networks
Authors: Lalitha S , Shanthi N , Gopinath S 
Published: July 2021

Abstract
They should more truly detect abnormalities in the refractive error, used to prevent the children of amblyopia, which can lead to permanent visual impairment, but at first it was detected. A variety of tools have been adopted to protect the area of amblyopia more easily. Amblyopia is a lifetime serious eye disease. Amblyopia contributes to forecasting and treatment and rapid public treatment. They are also useful for completely automatic detection of amblyopia deep neural networks. A tele amblyopia dataset is used for detection. Then proposed deep convolutional neural networks are used for automated amblyopia detection on tele amblyopia dataset. The proposed algorithm comprises of 2 phases. In the first phase, the Enhanced Firefly Algorithm (EFA) is used to segment the eye region. In second phase, a DCNN is designed and trained to classify the segmented eye areas as amblyopia or normal. The test comes about appear that the proposed strategy can work well with the programmed discovery of amblyopia.
Moving from Detection Centric to Prevention Centric Security Using Automation: A Survey
Authors: Sravanthi RVS , Nisha T N 
Published: July 2021

Abstract
In the present time, cybersecurity plays a crucial role in every aspect. Protecting data, hardware, software alone is not sufficient as the technology is growing exponentially and information is easily available. Regarding artificial intelligence data, Sentinel One platform, endpoint security shows that from February 20, 2020, to March 17, 2020, the trend was in an upward motion, as the attacks attempted has peaked to 145 threats per 1,000 endpoints, in comparison to 31 or 38 at the start of that period. According to the Q2 2018 Threat Study, the quarterly report by Nexus guard, the average denial-of-service (DDoS) attack grew to more than 26Gbps, up 500 percent in scale. Regardless of the complexities of the modern cyber climate, stereotypic defense solutions do not fulfill the information security requirements. Earlier detection techniques were sufficient to protect the cyber environment, but due to growing cyber threats such as Ransomware, malware, DDoS, we need strong protection techniques to restrict the threat’s entry. Given this context, this study explains the evolution of the Preventive techniques and detective techniques; both the approaches are complementary, which would be discussed in this paper by considering Automation Technology. It introduces the survey of various Automation Technologies implemented for the advancement of prevention-centric security. Based on the evaluation metrics, the selection of better technology is concluded.
Fingerprint Presentation Attack Detection Approaches in Open-Set and Closed-Set Scenario
Authors: Gupta Anshdha , Verma Akhilesh 
Published: July 2021

Abstract
In the biometric system, fingerprints are widely used to recognise an individual’s identity and are vulnerable to presentation attacks such as spoofing. Spoof fingerprints are fake fingerprints created using artificial materials like play-do, silicone, etc., which fool the biometric identification system. Spoof handling is an urgent need nowadays, as these attacks’ success rate is more than 70%. Much work has been done on fingerprint spoof detection, but mostly involved closed-set problems, i.e. spoof materials used are the same for training and testing set, but they face poor generalisation problem. Hence to overcome closed-set problems, Open-set solution came into existence in which some novel spoof materials were introduced in the testing set, which was not seen during training. This paper will give a brief review of various closed-set and open-set solution techniques along with the tabular view of their Average Classification Error (ACE) scores.
Applying data mining technique to predict trends in air pollution in Mumbai
Authors: Sadhasivam Jayakumar , Muthukumaran V , Thimmia Raja J , Vinothkumar V , Deepa R , Nivedita V 
Published: July 2021

Abstract
Prediction of air quality is a topic of great interest in air quality research due to direct association with health effect. The prediction provides pre-information to the overall population of the area about the status of pollution on which they can take precautionary measures and can protect their health. The problem arises when the level of SO2, NO2 and residual suspended particulate matters in the air increases than that of theirs restricted level. In this paper, the Prophet Algorithm, open source software, is applied to predict the trend of air pollution in the city of Mumbai, Maharashtra. The Prophet is machine learning algorithm to forecast and also to predict time series data. It is based on additive model where non-linear trends are fit with yearly and weekly seasonality. The graphical results are generated after using this algorithm which shows the trending pattern of the pollutants in the air of Mumbai.
An efficient method for high performance prediction mechanism for diabetes using enhanced Firefly algorithm and Map-Reduce
Authors: Sujatha R , Gomathi B , Padma T 
Published: July 2021

AbstractAlgorithms in Data mining are utilized to predict unrefined data into useful conventional information. This conservative information plays a vital role in the Health care industry. In this study we focus on the functionalities of diabetes prediction. In diabetes data we have the problem of data imbalance in predicting the accuracy. The Proposed tailored Firefly Algorithm along with Map reduce is used to augment the efficacy and precision of prediction. Comparison of Different bench mark algorithms with our new Extended Fire Fly is done and variety of classification methods are used with moto to increase the effectiveness. The new method helps to maximize the prediction of accuracy and reduces the time. The PIMA Indian Diabetic Dataset from UCI machine learning repository is utilized for our experiment results. Different metrics are used in order to prove the effectiveness.
Secure Cloud Backup for Data Sources Based on Blockchain
Authors: Mouleeswaran S K , Aruna R , Visumathi J , Gurusubramani S 
Published: July 2021

Abstract
Users can upload, save and download their data with cloud storage. The cloud does however handle the user’s generation of data, as well as devices created by the fast Internet development of stuff, including such webcam, and IoT and edges cameras. This growth in outsourced data volume has posed two main problems in cloud infrastructure cloud management: optimum data storage and data protection. Cloud storage employs a deductibility strategy to prevent redundant data to solve the former problem and uses cryptography to solve the latter problem. Both of these questions however are orthogonal. The convergent encryption is concurrently tackles the two problems. The conventional systems rely on web server, however which put some confidence in a private entity that may malfunction in one stage. In this paper we propose a new, transparent, authenticated online storage framework, which removes data replication from the security of the file and enhances the credibility of blockchain software. Two different approaches to data deduplication secrecy are discussed, in particular: double hazing is used and symmetrical encryption is used. We have implemented the suggested architecture in order to show stability and accessibility in spite of the increase request throughput requirements for cloud data processing.\
Impact Analysis of Security and Charging Infrastructure on the Adoption of Electric Vehicles
Authors: Mohanty Abhishek , Hallur Giri Gundu 
Published: July 2021

Abstract
In the Global innovation index report, 2019, India ranked 52 among the 129 nations, thus strengthening its standing in terms of technological adoption. Developed countries for long have adopted electric vehicles, and India too has initiated this. EVs have proven to be better than fossil-based traditional vehicles in terms of convenience, efficiency in operations, price, and performance. This research paper focuses on understanding the consumer perception regarding the adoption of EVs across factors like price, performance, ease of availability, security issues, efficiency, charging Infrastructure, and user experience. This paper focuses on two factors, namely security issues and charging Infrastructure, and draws an inference of how important these factors were in the respondents’ eyes. The results showed these two factors contributed to about 68% of the concerns of the Indian consumers.
Smart Autonomous Car Parking For the Modern Vehicles
Authors: Baburaj E , Tapas Bapu B R , Tamilselvi M , Dappuri Bhasker 
Published: July 2021

Abstract
The objective of intelligent car park management is to use advanced sensing & monitoring technology to maximize parking areas within large car parks and make car parking easy for passengers by transferring these to accessible parking spaces. An effective and insightful way to simplify parking infrastructure operations by utilizing the Internet for parking technologies. The IoT gives wireless connectivity to the device, which helps the customer control the parking area’s capacity. With the rising in automobile numbers in metropolitan areas, the main issue confronting road congestion is. This paper aims to solve the question. Typically the customer expends his time and energy to pursue the limited room accessible in a particular car park. The user is told of the parking detail. Therefore, it is reduced the waiting period of the customer searching for a car park. RFID technology is used to prevent car fraud.
A Symmetrically Diminished Interconnected Database Segmentation Framework Using Data Mining
Authors: Mohan Kumar S , Majumder Darpan , Shajin Naragunam A , Ashoka D V 
Published: July 2021

Abstract
Clustering is a series of mathematical learning methods for the exploration of heterogeneous partition structures grouping homogeneous data known as clusters. Clustering has successfully been implemented in many areas, such as medicine, genetics, economics, industry, and so on. We propose the notion of clustering for problems of multifactorial data processing in this article. The aim of a case study is to examine trends in 813 individuals for an issue in occupational medicine. To minimise the dimensionality of the data set, we use the key component analysis as the most widely used statistical method in factor analysis. The natural problems, particularly in the field of medicine, are mostly focused on performance criteria of a stratified kind, while PCA processes only quantitative. In comparison, consistency data are typically binary-coded, initially unnoticeable, quantitative replies. We are therefore introducing a new approach that enables theoretical and practical data to be analysed simultaneously. The idea of this approach is to project important variables on the quantitative feature space. The corresponding Clustering algorithm subspaces are then given an ideal model.
Prioritizing and Ranking the Taxonomy of Factors Critical to GDPR Compliance
Authors: Koley Shreya , Vijayakumar Bharathi S 
Published: July 2021

Abstract
This study aims to unfold the myriad factors essential in the viewpoint of GDPR implementation. Additionally, this study will prioritize and rank the critical factors which would ensure the successful enablement of GDPR in any organization.The approach is to prioritize and rank the underlying factors for GDPR compliance by implementing an efficient multi-criteria decision-making technique, specifically the analytical hierarchy process. A well-defined taxonomy of twenty factors has been defined that spreads across the four broad categories of critical factors essential for the enablement of GDPR in an organization. Governance and People related factors get the highest priority and rank. Factors such as issuing privacy policy on the public platform, recruitment of GDPR experts have the highest priority. Additionally, with the help of Pareto analysis, it was discovered that the top eight factors account for 80% of the total weight age out of the twenty factors. This study focuses only on the pre-established components for GDPR implementation identified from previous notable works. The scope of the study is constricted to the insight of select GDPR experts also does not incorporate a broader spectrum of the Data Privacy ecosystem. This study contributes to the prioritization of factors for GDPR compliance in a two-fold manner. One, a complete classification of underlying factors is proposed, and two, they are prioritized and ranked to serve as a rulebook for GDPR practitioners while working towards the enablement of GDPR in their organization.
Service Oriented Virtual Machine for Maximising Quality of Service in Wireless Networks
Authors: Radhika K , Murali Mohan Babu Y , Periasamy J K , Saravanan T R 
Published: July 2021

Abstract
A traditional big data network is a wireless multimedia network, as multiple video/audio streaming account for 70 percent of mobile traffic and the fifth generation wireless networks are projected to rise 500 times. However, it does raise various new and transparent challenges for multimedia big data communication over 5G wireless systems because multimedia big data services provide timely, throughput broadcasts through time-sensitive communication networks with restricted wireless resources. In order to address the above issues, we suggest in this paper information-centric virtualization architectures for a statistical latency consistency of content delivery across 5G wireless large media platforms for technology. In particular, three successful nominee strategies to promise the mathematical time limit for interactive large-data communications are implemented in our proposed scheme: information-centered network which extracts an optimum in-network storage location for multimedia big data; a virtualization of the network feature which transforms PHY architecture into many virtualized channels. In our architectures we build the 3 primary computer-generated machine collection besides power distribution plans to collectively simplify the application of NFV and SDN techniques in the ICN architecture: to improve performance for single users, to integrate effective combining capability with equity allocation for all users besides under non-cooperative betting Via models and computational assesses, we prove that our suggested designs and applications greatly perform better other existing systems to enable QoS provisioning with statistical delays over 5G immersive Large Data Wireless communication.
Internet Connected Modern Fire Fighting Robot
Authors: Sabhanayagam T , Senthil Kumar T , Narendra M , Martin Sahayaraj J 
Published: July 2021

Abstract
When technological development progresses, human interference has become less common, and machines are being used for diverse activities and humans’ health. Currently, fire misfortunes still keep happenings running, place human life and properties at risk, and find fires impossible to save lives. In these situations, a robot is used to defend human lives, property and the world from the flames. This model is an IoT robot which detects fire. The robot may be told regarding its activity and disabled. An air pump or a fire style carbon dioxide pump small stretches. Medium lengths. The combination of fire and carbon monoxide is identified via the mounted sensors that generate a graph. The research is often valuable for supplying supplementary details health officials inhaled over the quantity of hazardous gas the inhabitants of the impacted region over a span such that adequate steps can be taken to repair the injury.
Aggregate Customer Products Reviews on machine learning techniques using ontology process
Authors: Suma Christal Mary S , Josphineleela R , Sundaramoorthy K , Thenmozhi E , Mamatha K. 
Published: July 2021

Abstract
Online customer reviews are the best advertisement and/or communication tools for product consistency. Consumers often prefer to do some research before agreeing to buy. Customer ratings are a significant source of knowledge for influencing consumer buying choices in the present. In this paper, we discussed SigmaCR’s aim to explore machine learning techniques and skills, natural language processing and information technology to create an intelligent device that analyses consumer product feedback via the Internet and provides comprehensive product summaries and consumer feelings. It would also be a very practical method for evaluating the power of many recent research projects, including ontology-based knowledge extraction and sentimental analysis.
Sliding Mode Controller for Electric Vehicles based on Switched Boost Converter
Authors: Hussain Syed Masood , Pattanaik Balachandra , Minda Wondimagegn , Addis Denekew , Chala Bakana , Abate Abibual 
Published: July 2021

Abstract
This paper proposes electric vehicle energy conversion of DC-DC by using switched capacitor step-up converter and the implemented system’s control techniques. In this system, the proposed switched boost capacitor converter is interfaced with a power source, attaining high power gain that could not be achieved by the existing systems like VSI and boost converter. In this proposed system, the large inductor is ignored and the capacitor that used for filtering purpose. Because of this, the system is directed to low cost and high density. The switched capacitor regulated converter is applicable for all the power conversion such as dc-ac, dc-dc. In this paper, the proposed Sliding mode controller (SMC) controls the BLDC motor in the vehicle’s inverter. The proposed system results are obtained and verified using MATLAB/Simulink.