bluuebunny/crossref_metadata_2025_split huggingface dataset:
Collaborative Filtering and Regression Techniques based location Travel Recommender System based on social media reviews data due to the COVID-19 Pandemic
Authors: Daya Sagar K. V. , Arunasri P. S. G. , Sakamuri Sridevi , Kavitha J. , Kamesh DBK 
Published: December 2020

Abstract
Nowadays, Dynamic industries like tourism is a enhance to boost many countries economy in recent years. The hotel sector leads to a significant role among all aspects of the tourism industry. Online travel platforms, in association with hotel management, are a part of hotel E-tourism that helps users to make travel plans online, suggest precise recommendations in consideration with the earlier feedbacks upon hotel stay. From the past few months, the user roaming ratio falls rapidly due to the COVID-19 Pandemic across the globe. In a business concern, this Pandemic affects all tourism sectors, specifically in the hotels’ occupancy. To retain business positions and attract the users in these problematic scenarios, hotel and travel platform management concerns about past preferences of users, which have a significant impact on gratification for a comfortable stay. The current market trends show that user reviews are playing a vital role to select a hotel based on a safe location. Our work tries to find and recommend the dimensions that contribute more towards higher satisfaction levels of users in Asian continental top tourism city hotels listed by the Master and VISA Inc survey with item collaborative filtering and regression techniques based on Tripadvisor reviews from social media in recent times. The outcome of the work is helpful for Asian continental hotel management to concentrate more on the dimensions for obtaining better reviews. From an online travel platform perspective, this work suggests the contexts for the travel recommender system to gain things effectively from users.
An overview of an automated essay grading systems on content and non content based
Authors: Dadi Ramesh , Pasha Syed Nawaz , Sallauddin Mohammad , Sidhardha Chintoju , Harshavardhan A. 
Published: December 2020

Abstract
Assessment is considered to play an essential function inside the Educational System. The interest of using automatic tools by human beings has been increased. So in the same way student response evaluation in education system with automatic assessment systems has grown exponentially in the last couple of years. Due to the increasing number of students and the use of online MOOC courses and lack of time and lack of consistency, assessment is shifted to automatic assessment. In this regard many of the researchers worked on it to make the assessment process easy. And they succeeded in assessment objective-type questions: i.e. multiple choices. Now here comes an interesting part is to assess the essays with automated tools. In this area, more number of researches worked and invented some tools for grading the essays but not up to the mark. Most of the assessment tools were assigning grades based on the style that is the number of sentences, the number of words, parts of speech, length of an essay, and grammar but not on the content of the essay. But few of the tools grading the essays based on the content by using traditional methods, and very few tools are using natural language processing methods.
Glımpse On Blockchaın Technology And Applıcatıons
Authors: Rao Dr. V. Chandra Shekhar , Kolluri Johnson , Phridviraj M. S. B. , Moeed Syed Abdul , Srinivas Dr. C. 
Published: December 2020

Abstract
The term blockchain pasteurizes the chain of blocks like linked lists which will be linked to the previous block. A blockchain is a distribted ledger of digital transactions which is decentralized and no individual, team or company has its controllability over it. However, with time, the use of blockchain has come into existence. It has wide range of applications including risk management, credit money, Internet of Things ( IoT), social networks, and financial resources. Blockchain has many benefits like decentralization, persistency, anonymity, audit ability. Blockchain is trustable one and it is very efficient. In particular, this paper gives us the detailed information about the blockchain and its applications.
RETRACTED: Hybrid Learning Method to Detect the Malicious Transactions in Network Data
Authors: Edamadaka Gayathri , Chowdary Ch. Smitha , Kumar M. Jogendra , Sai N. Raghavendra 
Published: December 2020

Abstract
Network has carried comfort to the world by permitting adaptable change of information; however it likewise uncovered a high number of vulnerabilities. A Network Intrusion Detection System (NIDS) supports system to system executives to identify arrange security breaks in their associations. Distinguishing past and new attacks is one of the primary difficulties in IDSs inquiries about. Deep learning, which is a sub field of AI (1980‘s), is worried about classification that depends on the structure and capability of attention so-called neural structures. The progression on such learning and classification may improve the usefulness of IDS particularly in Industrial network Control Systems to build its location rate on obscure attacks. This work tells a deep learning method to deal with actualize a viable and also improved IDS for verifying modern system with the help of RNN.
Fundamental Perception of EDGE Computing
Authors: Mannanuddin Khaja , Ranjith Kumar M. , Aluvala Srinivas , Nagender Y. , Vishali S. 
Published: December 2020

Abstract
Edge computing is considered as a scattered computing process module where the end user data can be executed at the end corner or at the last node of the network which is indirectly represented as “Edge”. It creates an sensory impact where the end user data is executed very closely to the original data center Alex Reznik has defined edge computing as “anything that’s not a traditional data center” will be an edge to the client who is the end user. Edge computing is closely connected to equipments of IoT devices that are mostly operated by mobile user which helps in the reaching to the expected constraints based on the response time for the real world applications. Edge computing can be implemented on any hardware IoT equipment using any type of software tools. The major aim of Edge computing is to reduce the latency levels and perform the task from the nearest possible data source. Edge computing performs on instant data which is real time data processed by the sensors or end user clients whereas the cloud computing works on BIG DATA generated from different sources and locations. In this paper, We try to represent few basics of edge computing along with its pros and cons and how it is related to machine learning and IoT.
Deep learning time series to forecast COVID-19 active cases in INDIA: a comparative study
Authors: Shaik Mohammed Ali , Verma Dhanraj 
Published: December 2020

Abstract
In the present situation of “COVID-19 pandemic” which devastated worldwide socioeconomic implications that led by Indian government to initiate and to perform intense procedures to control the spread and impact by dispensing the capability to predict the outbreak, which hits the crest to decrease the impact of disease and guiding the government to update its policies as required for implementing protective steps needed for “Public Health System (PHS)”. These methodologies tend to be transforming among people for improvisation of vigor system. In this paper, we investigate thoroughly the precision of various “Time series” modeling techniques for detecting “corona virus outbreak” in topmost ten different states with the maximum number of “confirmed cases” as on 31 August 2020. We implemented six different deep learning approaches on time series for comparing the values associated in datasets that relates to the progression of the virus in each state based on the population as the attained results represent the scaling of time series methods accurately and predict various affected aspects in near future.
RETRACTED: Diabeties Predection Using Biosensors
Authors: Ravinder N. , Rajesh N. , Kantha Rao V. , Praveen T. , Sridhar M. 
Published: December 2020

Abstract
Biosensors are little investigative gadgets that join a component of natural acknowledgment and a physio-compound transducer to change an organic sign into an electrical perusing. Their specialized allure these days lies in their solid proficiency, high affectability, and consistent limit with regards to estimation. A biosensor embedded in contact central focuses to decide the degree of tear glucose and to hand-off the information to the patient’s body siphon. The siphon can mix insulin, the missing hormone, the necessary degree. This machine evades the distinctive blood checks and implantations for interesting sorts of diabetes that are really basic consistently. Diabetes characteristics are calculated by the sensor and the algorithms of machine learning are used to obtain the solution. Glucose measurements in blood plasma are positive, suggesting that the sensor is designed to estimate physiological blood glucose levels with negligible atomic effects. We are aimed at contributing to the growing biotechnology sector, with a focus on Glucose-Oxidise Biosensor (GOB) modelling from a regression perspective through quantitative learning methods. Blood glucose monitoring was developed as an effective tool for diabetes administration. Since it is suggested to maintain typical levels of blood glucose, a progression of appropriate biosensors of glucose has been developed. The technology of glucose biosensors, including treatment tools, reliable glucose monitoring systems and non-invasive glucose detection frameworks, has been fully developed over the last 50 years. In any case, a few steps are still associated with the achievement of accurate and reliable measurement of glucose. Utilizing a few AI calculations to demonstrate the amperometric reaction of a GOB with subordinate factors under various conditions, for example, temperature, benzoquinone, pH and glucose levels. In particular, kernel-based regression techniques, such as support vector machines, are being used today as one of the best machine learning techniques. Since a GOB reaction’s affectability is emphatically connected with these needy factors, their cooperations ought to be advanced to boost the yield signal for which a hereditary calculation and mimicked tempering is utilized. This dataset is modelled by a nonlinear regression approach which uses a rather simple model of the biosensor performance to allow a very low prediction error. This demonstrates the brief history, basic principles, analytical results, and the current status in clinical practice of glucose biosensors. We report a model that is consistent with the optimization and shows a good generalization error. The results obtained show that the sensor created is a contender for persistent blood glucose checking.
Machine learning intersections and challenges in deep learning
Authors: Jamalpur Bhavana , Korra Seena Naik , Rajanala Vijaya Prakash , Sudarshan E , Yadav Bonthala Prabhanjan 
Published: December 2020

Abstract
Deep learning is certainly not a limited finding tactic, however, it follows several procedures and also topographies which could be associated with a huge speculum of complex problems. The strategy knows the illustrator in addition to differential attributes in a stratified way. Deep learning strategies have created a notable innovation along with sizable effectiveness in an assortment of applications with useful protection units. It is taken into account to become the greatest choice for revealing the architecture in high-dimensional relevant information by using a backpropagation process and concepts analysis. As deep learning has assisted help make significant advancements and also significant performance in many therapies, the wide-spread domain names of deep learning are service, scientific study, cancer cells diagnosis, natural language processing, medical diagnosis, handwriting recognition, trumpet call recognition, stock market study. This paper provides the intersections towards machine learning and also the challenges of deep learning.
Evaluation and scrutiny of XML files, XML empowered records and native XML records
Authors: Tharun Reddy S. , Mothe Rajesh , Ghate Sukhaveerji , Sunil G. 
Published: December 2020

Abstract
With the expanding prevalence of XML information and a extraordinary requirement for an information base administration framework ready to store, recover and control XML-based information in an effective way, information base exploration networks and programming businesses have attempted to react to this necessity. XML-empowered information base and local XML information base are two methodologies that have been proposed to address this test. These two methodologies are an inheritance information base framework which are stretched out to store, recover and control XML-based information. The significant goal of this paper is to investigate and look at between the two methodologies and reach to a few measures to have a reasonable rule to choose the best approach in every situation. When all is said in done, local XML information base frameworks have greater capacity in correlation with XML-empowered information base framework for overseeing XML-based information.
Findings on detection of fraud smartphone apps by ranking using mining leading sessions
Authors: Sai Hanuman Prof A , Sujan Mohan P. , Chythanya Kanegonda Ravi 
Published: December 2020

Abstract
The world as we know now is full of mobile users everywhere and the majority leads with smartphone users which are filled with android and iOS applications. This has produced huge development in smartphone technology at a lightning rate because the apps have become popular and a common concept. For this we are dwelling into data mining in which the concept of mining data from a specific application is tough and pivotal job. These are the methodologies we are using in this paper. We are combining these two methods of mining data and ranking fraud in app store which will be a formidable task. According to report at google play store and apple store there are 2.4 million apps as of May 2019 so that shows quick surge in the past few years. Furthermore, above 500k independent app developers competing to for all the end users. Primarily finding fraud apps is done by scanning the top apps ranked apps which will be around 30-40 of them but they are on top for short period of time i.e. few days for verifying their authenticity. However, this can’t be applied when dealing with thousands of apps for each day. Hence, we have to go for an extensive approach in which we can apply a methodology to validate each app. As the importance of mining is established so we need to mine the critical data of app which includes reviews, ratings and rankings of an app that get integrated into an algorithm that can determine the fraud. So, in this paper of discovering the ranking fraud of smartphone app, we need to develop a precise and faultless methodology that gives result of trusted apps.
Performance of Vehicle Arrival Traffic Data at Fuel Station using Queuing System
Authors: Sarla Pushpalatha , Reddy Doodipala Mallikarjuna , Endla Purushotham 
Published: December 2020

Abstract
Vehicle arrival is a main criterion in the delay assessment of different fuel stations. In this paper, pragmatic studies are carried out to investigate the congestion traffic flow at fuel stations. The complications at fueling points have been measured and also found the length of the queue, waiting time of the customers. The results confirmed that the vehicle arrival pattern of arterial road near the fuel station exhibit traffic congestion this finding is helpful to model the fuel station in terms of planning and designing.
Characterization Of Size Dependent Thermal Properties In Strained Nanocrystalline Powder Using Williamson – Hall Method
Authors: Endla Purushotham , Reddy Ch Krishna 
Published: December 2020

Abstract
In this present investigation Williamson–Hall technique has been used for the size dependent thermal properties are crystallite size, effect of strain on the Debye Waller factors, root mean square amplitudes of vibrations and Debye temperatures of nickel nanoparticles. The ball milled nanopowders has been characterized by X-ray diffraction method. The values of the crystallite size and strain obtained from the Williamson-Hall method to the experimental data were reasonable. The ball milling time increased with decreasing particle size, Debye temperature, vacancy formation energy and increasing strain, mean square amplitudes of vibrations and Debye-Waller factor for the nickel sample. The change in lattice strain particle size of the samples was explained by Williamson-Hall method. All the thermal properties of nickel nanoparticles were explained more precisely using Williamson-Hall plots, and also milling time versus thermal properties plots. The results calculated were approximately similar with the standard values.
Mathematical Modelling Of Extraction Of The Underground Fluids: Application To Peristaltic Transportation Through A Vertical Conduit Occupied With Porous Material
Authors: Ravi Kiran G , Shamshuddin Md , Balarama Krishna C , Rajesh Chary K 
Published: December 2020

Abstract
This paper gives a theoretical idea for extracting the underground fluids through peristalsis. Due to the similarity in aforesaid problem, peristaltic transportation of a viscous Newtonian fluid over a vertical porous conduit is studied by considering the impact of heat transfer. Presuming the long-wavelength approximation, explicit solutions are found as asymptotic expansions with reference to free convection and porosity parameters. Mathematical expressions for coefficient of heat transfer, temperature and mean flux are derived. It is experiential that for few precise values of dissimilar parameters under contemplation, the coefficient of heat transfer increases significantly as Grashof number and Eckert number increases. This narrates to optimization of heat transfer in some processes. Further, it has been observed that mean flow enhances with amplitude ratio, porosity in addition to pressure drop. This authorizes auxiliary research on the impacts of peristalsis on the flow features in vertical channels.
How the collaborative learning can strengthen the Teaching and learning process in Engineering Education
Authors: Praveena D. , Purushotham E. , Sunil Kumar J. , Vijaya Lakshmi P. 
Published: December 2020

Abstract
Teaching is making a difference on the lifetime of the learner, making them to be a productive member of the society. The method of teaching is completed during a well organized manner then it can improves the learners constructive, innovative thoughts, inter personal skills and provides ability to guide to future learners. collaborative learning involves the pedagogies of learner in interactive mode and involves the co operation, interaction between the learners. By making the discussion as group wise, by sharing their ideas openly learners present their subject. Collaborative learning enhances co operative nature, communication skills, builds confidence about the topic and therefore the most vital thing is it provides good idea about time management. Students. were participated within the activity in agood interactive way.
Ion Conductance, Walden Product Pressure Coefficient And Hydrodynamic Radii Of Sodium Salt Of Methane Sulphonic Acid In Water-Methanol And Water-Dmf Compositions
Authors: Radhika V. , Sunil Kumar J. , Srivani N 
Published: December 2020

Abstract
Ion-conductance, prfencial solvation by Walden product pressure coefficient pressure coefficient and Hydrodynamic Radii correlated with different electrolytes with in solvents is formed by diverse aspects like cohesive energy density, relative viscosity, solvent dielectric constant values of intermediate, with in duel mixture associations of its solvent adesive solvent measures. Sodium saltion solvent interactions of solute ion with solvent core. Conductance data and physical properties of ion viscosity electrolytes are used by change of the sodium salt ion conductance values, Walden product pressure coefficient and Hydrodynamic radii behavior of the ions and these are measured for derived Sodium salt of Methane Sulphonic acid electrolytein water-MeOH and water-DMF of dissimilar composition in the heat choice of 283K to 313K. A
o values KA of electrolyle sodium saltion pair, Association constant values figured out with Shedlovsky Extrapolation method. A°rise with composition of pure water solvent in organic aqueous duel mixture composition in binary sheeth. KA value is more in 80% water-MeOH composition signifying sodium saltion of sodium solvention interactions are more at this binary composition of solvention and thermodynamic properties for the function of specified sodium saltion solvent interaction in composition for structural effects.
Analysis of Self-similar Nature Vehicle Arrival Data Pattern on Arterial Roads
Authors: Sarla Pushpalatha , Reddy Mallikarjuna , Reddy Archana 
Published: December 2020

Abstract
Traffic congestion is a main criterion of increasing the disturbance of the movements of traffic on roads. In various metropolitan areas managing the traffic congestion is a difficult task, it was investigated that busy traffic reveals self-similarity, influenced by this aspect, realtime road traffic arrival data pattern proven as self-similar paradigm by means of disparate Hurst index methods. The current article furnishes various proficiency used to measure Hurst index which is popular demarcate of self-similar behavior traffic. In this paper, real time data is used to investigate the pattern of vehicular arrival data traffic flow at Mancherial Chowrasta intersection of Karimnagar City. Traffic arrival data patterns are recorded from location is processed and analyzed using Hurst index method named as correlogram. Quantitative results finally reveal that analysis shown in this study is supportive to manage the traffic signals at that junction, and to discover methods to avoid congestion traffic streams.
A Prediction based Encryption Approach for Telemedicine Applications
Authors: Vallathan G. , Rajani T. , Kumaraswamy Eelandula , Srinivas C. 
Published: December 2020

Abstract
The rapid progression of health care technologies systems and transmission strategies makes it reliable to gain, dispense and manage data over medical devices and as well improves conventional hospital information systems (HIS) to deliver effective health care services. When the medical information is communicated through wireless network, there exists a high chance of modifying the information. Before examining the patient, the physician has to check for the integrity of received medical image. A futuristic tele healthcare framework has been proposed to ensure the security, for offering complete healthcare services at reduced cost. In this paper, the proposed framework encompasses the integration of three modules viz. Prediction, Padding and Chaotic map encryption. This work offers an enhanced security for both patient medical data as well as medical scan images to a great extent. Two processes are involved in the padding sequence namely: forward and backward snail tour. After the snail tour process, the adjacent pair of pixels in the original image is XORing in order to make it ready for further process using the chaotic map encryption algorithm. Three operations have been carried out in chaotic approach namely: permutation, diffusion and substitution. This encryption method arises, a valid confusion and diffusion in the image pixels such that, the security of the images/data is enhanced. The performance of the proposed approach is assessed and compared with existing schemes such as ECC and Chaotic map encryption on a set of MRI/CT medical images. Experimental outcomes demonstrate that the proposed framework offers robustness in terms of security, quality and reliability which alleviate misdiagnosis at the physician end in telemedicine applications.