bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigation of the Interaction Between Magnetosheath Reconnection and Magnetopause Reconnection Driven by Oblique Interplanetary Tangential Discontinuity Using Three‐Dimensional Global Hybrid Simulation
Authors: Guo Zhifang , Lin Yu , Wang Xueyi 
Published: December 2020

AbstractMagnetosheath reconnection due to the interaction of an interplanetary directional discontinuity with the bow shock and Earth's magnetosphere under an initially northward interplanetary magnetic field (IMF) has been investigated in previous simulations (e.g., Guo et al., 2018, https://doi.org/10.1029/2018ja025679). Under an initially southward IMF, the magnetosheath reconnection could interact with reconnection at the magnetopause. In this study, using three‐dimensional (3D) global‐scale hybrid simulations, we present cases with incoming tangential discontinuities (TDs) in an initially southward IMF, which possess various magnetic field rotation angles (ΔΦ) and half‐width (w), to study the effects of pre‐existing magnetopause reconnection on the formation of magnetosheath flux ropes, as well as the subsequent interaction between the magnetopause reconnection and magnetosheath reconnection, with downstreams of both Quasi‐perpendicular (Q‐) and Quasi‐parallel (Q‐||) shock examined. The initial IMF is assumed to be oblique, with a finite Bx and Bz, similar to that in Guo et al. (2018), https://doi.org/10.1029/2018ja025679 but with a southward Bz < 0. Compared with the cases with an initially northward IMF, magnetopause reconnection weakens the compression processes of the TD and leads to less frequent reconnection in the magnetosheath. The existence and the structure of magnetosheath reconnection are found to strongly depend on the parameters w and of the TD. When interacting with the magnetopause reconnection, the magnetosheath flux ropes can re‐reconnect with the geomagnetic dipole field lines, forming new structures of magnetopause flux ropes. The resulting evolution of flux rope configuration is illustrated.
Interaction of Oppositely Traveling Medium‐Scale Traveling Ionospheric Disturbances Observed in Low Latitudes During Geomagnetically Quiet Nighttime
Authors: Wu Kun , Xu Jiyao , Wang Wenbin , Sun Longchang , Yuan Wei 
Published: December 2020

AbstractIn this study, an unusual event of medium‐scale traveling ionospheric disturbances (MSTIDs) that propagated in opposite directions was observed by multi‐instruments (all‐sky imager, global positioning system (GPS), and digisonde) in low latitudes during geomagnetically quiet nighttime. These MSTIDs had wavefronts aligned from northwest to southeast. One MSTID propagated southwestward and another propagated northeastward. They encountered and interacted with each other. Some MSTIDs structures merged into one and disappeared after the interaction. Furthermore, an eastward moving plasma bubble also encountered the MSTIDs and then gradually dissipated during the propagation process. The MSTIDs traveling in opposite directions should be related to ionospheric plasma motion and their trigger mechanisms. The interactive process of MSTIDS involved in plasma drifts and polarization electric fields. Meanwhile, the interaction with an ionospheric irregularity structure can change some of the MSTID characteristics.
Direct and Indirect Pathways of Convected Water Masses and Their impacts on the Overturning Dynamics of the Labrador Sea
Authors: Georgiou Sotiria , Ypma Stefanie L. , Brüggemann Nils , Sayol Juan‐Manuel , van der Boog Carine G. , Spence Paul , Pietrzak Julie D. , Katsman Caroline A. 
Published: December 2020

AbstractThe dense waters formed by wintertime convection in the Labrador Sea play a key role in setting the properties of the deep Atlantic Ocean. To understand how variability in their production might affect the Atlantic Meridional Overturning Circulation (AMOC) variability, it is essential to determine pathways and associated timescales of their export. In this study, we analyze the trajectories of Argo floats and of Lagrangian particles launched at 53°N in the boundary current and traced backward in time in a high‐resolution model, to identify and quantify the importance of upstream pathways. We find that 85% of the transport carried by the particles at 53°N originates from Cape Farewell, and it is split between a direct route that follows the boundary current and an indirect route involving boundary‐interior exchanges. Although both routes contribute roughly equally to the maximum overturning, the indirect route governs its signal in denser layers. This indirect route has two branches: part of the convected water is exported rapidly on the Labrador side of the basin and part follows a longer route toward Greenland and is then carried with the boundary current. Export timescales of these two branches typically differ by 2.5 years. This study thus shows that boundary‐interior exchanges are important for the pathways and the properties of water masses arriving at 53°N. It reveals a complex three‐dimensional view of the convected water export, with implications for the arrival time of signals of variability therein at 53°N and thus for our understanding of the AMOC.
User Authentication: A Three Level Password Authentication Mechanism
Authors: Mishra Gouri Sankar , Mishra Pradeep Kumar , Nand Parma , Astya Rani , Amrita  
Published: December 2020

Abstract
User authentication is one of the cutting edge research to provide access to legitimate uses. A number of mechanisms like biometric, image based, graphical based have been implemented. In this research work a three level password authentication is proposed and reported experimental results. From the result analysis it is found that the three level authentication provides reliable security level in comparison to the existing mechanisms.
IoMT: A Review of Pacemaker Vulnerabilities and Security Strategy
Authors: Puat Haidhar Athir Mohd , Rahman Nor Azlina Abd 
Published: December 2020

Abstract
Internet of Medical Things or IoMT is known as the most wanted technology in the healthcare sector. Almost 420 million connected device have been deployed globally to all healthcare facilities and with around 70 million more devices expected to be deployment by early of 2020 [4]. This types of technology and connection of medical devices to healthcare IT framework is not only beneficial to the patients but facilitates the structure of the traditional healthcare system of delivery information from department to department in healthcare environment. As a result to this budget for healthcare management and handling can be reduce and use for better improvement. However, with the advantages of IoMT, all the security behind every IoMT devices need to be considered. This paper is reviewing on several IoMT devices such as smart pen, implantable cardiac devices (pacemaker), wireless vital monitors and several other devices in terms of its functionality and vulnerabilities that might expose the devices to the attacker. More detail discussion is focusing into Pacemaker which is one of the IoMT devices. Several security strategy and solutions to minimize the vulnerabilities of IoMT devices are also being discussed.
Plant Leaf Disease Classification and Detection System Using Machine Learning
Authors: Geetha G. , Samundeswari S. , Saranya G. , Meenakshi K. , Nithya M. 
Published: December 2020

Abstract
In a developing country like India agriculture plays a noteworthy role. Agricultural intervention in the livelihood of rural India indulges by about 58%. Among the agricultural products, tomato is one of the most used crops. Thus, preventing significant loss in quantity and yield of tomato is majorly dependent on recognition and classification of diseases a tomato plant might possess. Latest and fostering technologies like Image processing is used to rectify such issues using different types of techniques and algorithms. Initially, the leaves of a tomato plant get affected, when plant develops a particular type of disease. In this project, four consecutive stages are used to discover the type of disease. The four stages include pre-processing, leaf segmentation, feature extraction and classification. To remove the noise we are doing the pre-processing and to part the affected or damages area of the leaf, image segmentation is used. The k-nearest neighbors (KNN) algorithm, which is a guided, supervised and advance machine learning algorithm, is implemented to find solutions for both the problems related to classification and regression. During the terminal stage, user is recommended with the treatment. Mostly live plants are adversely affected by the diseases. This paper imparts representation of leaf disease detection employing image processing that can identify drawbacks in tomato plant from images, based on color, bound and texture to give the brisk and reliable results to the farmer.
Named Entity Recognition in English Text
Authors: Jain Rachna , Sharma Abhishek , Mishra Gouri Sankar , Nand Parma , Chakraborty Sudeshna 
Published: December 2020

AbstractNatural Language processing has been one of the challenging field of computational linguistics. Language processing occurs in several steps in which Named Entity Recognition is one of the prominent phase. NER frameworks have been contemplated and created generally for a considerable length of time, however precise frameworks utilizing profound neural systems (NN) have just been presented over the most recent couple of years. We present a far reaching review of profound neural system models for NER, and balance them with past ways to deal with NER dependent on highlight designing and other regulated or semi-administered learning calculations.
Image Captioning Using Deep Convolutional Neural Networks (CNNs)
Authors: Geetha G. , Kirthigadevi T. , Ponsam G.Godwin , Karthik T. , Safa M. 
Published: December 2020

Abstract
Earth is challenging to label satellite image clips with atmospheric conditions and various classes of land cover and land use. We proposed an algorithms to help the global community for a better understanding that where, how, and why deforestation take place all over the world. Upcoming development in satellite imaging technology have set grow to new opportunities for more precise investigation of both broad and minute changes occurring on Earth, including deforestation. Since 40 years, almost a fifth of the Amazon rain forest has been cut down. To estimate and analysis the forest this application is developed. Satellite images are trained on deep convolutional neural networks (CNNs) to learn image features and used multiple classification frameworks including gate recurrent unit label captioning and sparse_cross_entropy to predict multiclass, multi-label images. By fine-tuning an architecture consisting of the encoder of pre-trained VGG-19 parameters trained on ImageNet data together with the GRU decoder.
A Survey on Image Segmentation Using Deep Learning
Authors: Mary S. Prince , Ankayarkanni  , Nandini Usha , Sathyabama  , Aravindhan S. 
Published: December 2020

Abstract
Image segmentation is a critical process in computer vision. It involves dividing a visible input into segments to simplify image analysis. Segments represent objects or parts of objects and comprise sets of super-pixels. Image segmentation sorts pixels into larger components, eliminating the need to believe individual pixels as units of observation. Brain tumour segmentation is a crucial task in medical image segmentation. Early diagnosis of brain tumours plays a crucial role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumours for cancer diagnosis, from great deal of MRI images generated in clinical routine, may be a difficult and time-consuming task. There is a requirement for automatic brain tumour image segmentation. The method is proposed to segment normal tissues like substantial alba, grey matter, spinal fluid, and abnormal tissue like tumour part from a resonance Imaging (MRI) automatically. The system also uses to segment the tumour cells along the morphological filtering are going to be wont to remove background noises for smoothening of region. The project results will be presented as segmented tissues and classification using, Convolutional Neural Network (CNN) classifier.
A Customized Data Recovery Tool
Authors: Angamutu Kausalyani A/P , Rahman Nor Azlina Abd , Suki Nik Nurul Ain Nik 
Published: December 2020

Abstract
Data recovery is the process of salvaging deleted, formatted, corrupted, damaged or inaccessible data from the storage media as it cannot be accessed through normal way. There are two types of data recovery which is physical recovery and logical recovery. Physical recovery needs special equipment to repair the damaged parts of storage device like scratched plates, stalled spindle, fried chip, etc. and this process need to be undertaken in clean and controlled environment to extract data from storage device. Logical recovery is a software-based solution that recovers data from storage device which faced logical error in operating system or accidental user deletion. This paper will discuss on logical recovery and it is divided into few sections which is public awareness on data recovery, literature review on existing data recovery tool, analysis on user requirements for data recovery tool, proposed data recovery tool functionality and proposed data recovery tool framework.
A Review on IoMT device Vulnerabilities and Countermeasures
Authors: Jegatheswaran Reshiwaran A/L , Sakira Isaac Joshua , Rahman Nor Azlina Abd 
Published: December 2020

Abstract
According to a report by Allied Market Research they have said that by 2021 the IOMT would most likely reach about $136.8 billion worldwide [12]. This indicates that sooner almost all medical Centre would implement IOMT devices in their Centre and also in their patients’ body to get better result. Having a good growth on this it may indicate on the development of medical. It also proves on the movement from traditional medical to technology era medical whereby for checkups patients does not need to turn up as the device of IOMT would present the data. IOMT devices would allow the Medical Centre to cut cost on certain equipment as they could be done by using small IOMT device. This device is designed to generate data and provide indication for the users. IoMT also saves on the patients waiting time since analysis and prescription can be done on the go and there is no need for queuing up at medical facilities. Appointments can also be scheduled remotely depending on availability of the medical personnel. Because of the dependence on the internet for the transfer and storage or medical data, there is a big risk regarding data privacy and security. The aim of this research is to explore the vulnerabilities associated with IoMT devices that focusing on Mobile Cardiac Telemetry and Respiro Smart Inhaler system and suggest suitable protection mechanisms against the possible attacks to the IoMT devices.
Performance Evaluation of Analytics Models for Trends Analysis of News
Authors: Heng Lau Yee , Logeswaran Rajasvaran , Marikannan Booma Poolan 
Published: December 2020

Abstract
Microblogging services, especially Twitter, allow the user to share their most recent thoughts, feelings or news freely and almost immediately. Hence, the number of news tweets generated by the news media is increasing exponentially. Mining the valuable data from the large volume of tweets can help increase the revenue of organisations by allowing them to engage with the public faster and better by responding to the latest topics of interest. In this work, mining the hot keywords and being able in classifying the news tweets, trending topic and keywords in the news tweets. Both supervised and unsupervised machine learning models are used. Several machine learning algorithms are being used to compare the accuracy in classifying the tweets.
Performing Experimentation with Physics Model to Predict Statistical Weather Condition
Authors: Jayanthiladevi A. , Nagaraju V. , Devi L. , Padmavathi N. , Sailaja P. , Minu M.S , Tapas Bapu B R 
Published: December 2020

Abstract
Ice in wind turbines may cause a tremendous reduction in energy conservation. As, ice over turbines are not considered to be a traditional weather prediction data, prediction towards power can leads to higher error. This work anticipates a statistical approach dependent on Niave bayes regression to identify production loss has to be analyzed. It measures input of regional weather condition and various other conditions, and identify power production loss for 48 hours to enhance prediction of next generation energy loss. This can be trained with various prediction measurements and drastically enhances other conventional approaches for longer period. It may diminish absolute production error by ∼100kW and it computes its skill with other models. Prediction of weather data is considered to be one of the effectual data for diverse statistical prediction and some calculations are not so absolute. This method can be computational less cost and may be trained again for next prediction.
MathFun: A Mobile App For Dyscalculia Children
Authors: Rohizan Rizawati , Soon Lim Hean , Mubin Siti Azreena 
Published: December 2020

Abstract
Learning disability can vary from dyslexia (reading), dyscalculia (math) and dysgraphia (writing) where it focuses on certain learning disability that is being faced by the children. Dyscalculia is one of the common learning disabilities where in this category children are lack in ability of studying the math. However, with the enhancement of technologies, variety of application can be created in aiding the children learning process such as mobile learning by using mobile app. This paper focuses on the findings from the result of the MathFun app on the effectiveness towards enhancing the learning process for the learning disability children. MathFun is a mobile app created by following the Calculic Model approach for Malaysia Dyscalculia children. Usability was performed in order to assess the usability and verifying the effectiveness of MathFun. This study involved 3 teachers and three children. Descriptive analysis was performed from the collected data. Based on the outcome, it’s shows that the MathFun app able to help and improve the learning and has the potential to be use in the classrooms.
Coordinate Access System for Live Video Acquisition
Authors: Annapurna B. , Rama Reddy T , Raghavendran Ch. V. , Singh Raushan Kumar , Prasad Vedurai Veera 
Published: December 2020

Abstract
Biometric systems are the most advanced access technology developed so far in the 21st century. It does not even require to carry key cards or passwords in mind. Today most of the commercial and private entries are protected by biometric recognition systems like fingerprint scans facial recognition, retina scans, voice matching, etc. Even our phones, laptops, and daily access devices are equipped with biometric systems. In banks, the PCs are secured by the combination of passwords and fingerprint scans. Biometric scans are considered the most secure access technology so far. Our paper is to examine whether they are secure? Should we rely on them with our hard-earned money and social identity? Is there any way we can use these services without actually compromising our data and security? Our observation is on our digital identity. Promoting digitization in every department brings our topic in the picture. All our information is saved in our phones, our daily routine, whom we talk, what we purchase, whom we chat, where we travel, etc. Almost every smartphone has biometric fingerprint locks which means our phones have our fingerprint scans in database and with internet blend it’s tethered worldwide. Our fingerprints are connected to our bank accounts, PAN Cards, Passport, and SIM Cards using Aadhar Cards. If someone has our fingerprint they can easily reach our Aadhar Card and through that to all our personal information. Most of the phone companies are Chinese, Korean, German, and American. As per their country policies, they must share their data with the governing authorities. We aim to create a security system without actually using the biometric scans. The system is an advancement of the biometric system but with better accuracy and intelligence. We interface image acquisition tools to live track the red color things. The web camera or inbuilt system lens can be used as the acquisition system. When the red color object is moved in front of the lens it shows the corresponding coordinate of the object shown. We use these x and y coordinate of the objects as the authentication points. If the correct value grant access is 120 ⩽ x ⩽ 122 means the system grants permission only if the value of x=120,121 or 122 is obtained. Now, this is tricky. Even if you know the correct value also, it is very difficult to bring the correct point. Think about if you don’t know the point and it is also possible to make it much difficult by adding y coordinate so if the desired point is x=10, y=12 (10, 12) it is way more difficult. Each point is a possible password candidate and the screen of any device have megapixels where 1 Megapixel=106 pixels. Each pixel is a possible key or password entry. It can keep all our information safe and secure. We use a microcontroller and motor driver connected gate to demonstrate the result.
An Effectual Data Management Over Healthcare Systems for Improving Privacy in Iot Environment
Authors: Prasad K. Krishna , Aithal P. S. , Jayanthiladevi A. 
Published: December 2020

Abstract
The advancements in hardware and networking technology are merged with Internet of Things (IoT) which provides flexibility for monitoring the health condition of individuals. This is a smart environment that includes city, homes, and agriculture; as well, it recently includes healthcare systems for disease prediction and for medical advices. To improve the ability of IoT with healthcare system, the layers of data transmission has to be analyzed. This is to validate the faster response of physicians during the time of communication and lower latency. Moreover, this technology encounters various challenges in providing privacy towards the users and to address this privacy issue to certain extent. In the initial stage, the technology runs with a lesser privacy. Hence, this work deals with the modeling of e-healthcare framework that deals with preserving electronic medical records and attempts to eliminate privacy issue. However, the experimentation is done with MATLAB environment that concentrates on the metrics like response time and delay. The anticipated model provides better outcome in contrary to other models. The outcomes of this work are effectual while offering privacy with standardized network factors.
An Experimental Study on Partial Replacement of Cement and Fine Aggregate with Industrial Waste in Concrete Paver Blocks
Authors: Venkateswara Rao J , Rajesh S , Shankar Gopi 
Published: December 2020

Abstract
The present experimental investigation reports the relative results of flexural and compressive strength of conventional uni-paver concrete blocks of M-40 grade with the paver blocks produced by the combined partial replacement of the cement and fine aggregate with fly ash and pond ash respectively. Incorporation of these industrial products resulted in the saving of cement subsequently an overall saving of about 10% in the economy per each paver block production, while not compromising with its performance in respect to its strength aspect. The paver block considered in this experimental work is of zig- zag shape specimen of size 201 mm × 100 mm × 80 mm. Initially, fine aggregate and cement were replaced in percentages of 5, 10, 15 and 20 separately with pond ash and fly ash respectively. Later on, both fine aggregate and cement were replaced and the performance of paver block is evaluated in terms of its mechanical properties.