bluuebunny/crossref_metadata_2025_split huggingface dataset:
Recommender System: A bibliometric analysis
Authors: Sharma Sheetal , Gupta Kamali , Gupta Deepali 
Published: January 2021

Abstract
The exponential growth in the online share of businesses has to lead to a gigantic wave of options available to the active user. Recommender systems, therefore assist the users to go through the tailored list of products to match their preferences. A range of recommender systems is available to serve the purpose. This article will navigate through the basic of recommender systems, and its classifications types viz. collaborative filtering, content-based filtering, demographic, hybrid, and knowledge-based recommender system. It aims to analyze publications of the Scopus database using biblioshiny tool of RStudio software. A bibliometric analysis is conducted on 556 papers to analyze the recent research trends in recommendation systems. Further, challenges have also been discussed that need to be dealt with the recommender system.
Analytical Comparison of Cloud Data Centre Services and Cost
Authors: Rayjada H , Shukla P 
Published: January 2021

Abstract
Comparing with the traditional data centre, cloud computing makes it easier for enterprises to scale their services and lowers the cost of access for smaller companies. The cost comparison between cloud computing and the old-style data centre is an important issue of concern. In this paper, the cost roles traditional cloud data centre and server room are settled up, the performance of the data centre and cloud computing were tested based on the compared with the data centre with low workload intensity strength. More than a few data centre have a primary backup of data and servers. The cloud data centre provider like Amazon, Google Facebook data centre are called Availability zones. The availability zones are a within the same region, distinct colocation centre is connected to Virtual Private Cloud network. This research discovering new network architectures for the data centre. In this paper, we considerate the among different data centre charges and network architectures. the comparison on cost using current and predicted trends in data centre cost and power consumption.
Analysis and Comparison of Different Blockchain Algorithms in IoT Security
Authors: Parmar Monika , Kumar Neeraj , Kaur Harsimran Jit , Sharma Abha , Sharma Sandhya , Sandhu Mamatha 
Published: January 2021

Abstract
Internet of things (IoT) and Blockchain are two advancements that are picking up notoriety since the hour of their creation. Sooner rather than later, IoT is going to impact everything we use in our daily lives. As the utilization of this innovation expands, the danger to misuse it likewise increments. Existing advancements are insufficient to manage this. Along these lines, Blockchain has risen as a successful answer for understanding the security issues identified with IoT. Information put away in Blockchain is permanent and can’t be changed very effectively. Likewise, the information is added to the block only after it is affirmed by everybody in the system, and in this manner, it is permitting secure exchanges. The individuals who approve the exchanges or transactions and include them in a block are known as miners or excavators. In this article, a detailed overview of the different Blockchain algorithms that are being used for IoT security has been explored. Also, an attempt has been made to provide a detailed overview regarding Blockchain which can act as a reference for the researchers who wish to pursue research in the Blockchain domain.
IoT in Building Smart Cities and Smart Environment: Your Way to Success
Authors: Kumar Mukesh , Mehta Gaurav , Nayar Nandini , Gupta Manik 
Published: January 2021

Abstract
Internet of Things has given us a new epoch of computing technology. As we know that Internet, a rebellion innovation has transformed everything, Internet of Things has made a hope for excellent future of Internet with Machine-Machine type of communication in addition. Thoughts of creation that correlates a different means of considering the events with invented concepts that avail the possibilities proposed by new approaches in intelligent ways. Framing an intelligent environment depends on smart education system, smart traffic management system, smart vehicle management system, smart waste management system, industrial control, smart agriculture etc. Today, safety on roads, cleanliness in residential areas, security at home has become an essential component in our daily life. So there is a necessity to avail new approach of innovative technologies based on Internet of Things to solve the environmental problems. In this paper we surveyed some of these problems and suggested various executable plans also. The actualization of smart sensing and transferring of data has offered a significant assurance for building the blueprints. This review paper has also shown that it is possible and affordable to construct these smart systems based on Internet of Things (IoT). We have studied various blueprints which can sense unforeseen happenings such as landslides, traffic jam, accidents, etc. using different sensors and demonstrate the facts accumulated on LED display. Observational outcomes have shown good agreement with the hypothetical statements.
Secured Communication in Vehicular Adhoc Networks (VANETs) using Blockchain
Authors: More Shivaprasad , Sonkamble Rahul , Naik Udaykumar , Phansalkar Shraddha , More Priyanka , Saini Baljit Singh 
Published: January 2021

Abstract
In the last decade, the vehicular ad-hoc network (VANET) field is growing drastically. In VANET the communication is the most important aspect through which the data transmission is taking place. Data transmission is maybe related to data in the form of multimedia messages, notifications, announcements, or some warning messages that are involved in VANETs. Data transmission among different vehicles would lead to an exchange of audio-video files in the formed network. These multimedia messages should be transmitted in the fraction of second as this network is built for temporary communication. Vehicles involved in this communication should be trustworthy otherwise other vehicles those who are part of this network would be misguided due to intruder present in the network. To achieve security and integrity in the constituted network, system has proposed the blockchain security environment as a part of this proposed system. Blockchain induces high-end communication in the ad-hoc network. It also enhances the overall security aspect of the constituted network. In this paper, we have proposed blockchain-based security system for vehicular communication that handles this communication securely in a well efficient manner. The performance of the proposed system will be measured based on parameters like end-to-end delay, reliability, and packet delivery ratio.
Cloud computing: a walk through in models, challenges, and energy solutions
Authors: Gupta N , Gupta K , Singh A , Kaur A , Gupta D 
Published: January 2021

Abstract
The paradigm of cloud computing in recent times has engrossed a lot of working populace in its service stack offerings. This considerable rise of human traffic landing onto cloud platform for service extraction has consequently led to exponential rise in energy consumption during dynamic and heterogeneous workload situations encompassing huge cloud infrastructure in form of CPU, Hard Disks, network devices etc. The energy released not only poses costs on its stakeholders but is affecting the environment due to rise in carbon footprints. As per available stats [1], the total power consumption of cloud has significantly arisen from 632 billion kilowatt hours in year 2007 to 163 billion kilowatt hours in 2020 which is adequate to leave a devastating effect on environment. Though research is being carried out in this direction, still efficacious measures are far away from its pragmatic implementations. Therefore, the research study conducted here discusses about existing solutions in the direction of energy management in cloud computing. The latter half of paper presents abrief discussion on gaps and future directions.
Heart disease prediction using machine learning algorithms
Authors: Jindal Harshit , Agrawal Sarthak , Khera Rishabh , Jain Rachna , Nagrath Preeti 
Published: January 2021

Abstract
Day by day the cases of heart diseases are increasing at a rapid rate and it’s very Important and concerning to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. The research paper mainly focuses on which patient is more likely to have a heart disease based on various medical attributes. We prepared a heart disease prediction system to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient. We used different algorithms of machine learning such as logistic regression and KNN to predict and classify the patient with heart disease. A quite Helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of Heart Attack in any individual. The strength of the proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using KNN and Logistic Regression which showed a good accuracy in comparison to the previously used classifier such as naive bayes etc. So a quiet significant amount of pressure has been lift off by using the given model in finding the probability of the classifier to correctly and accurately identify the heart disease. The Given heart disease prediction system enhances medical care and reduces the cost. This project gives us significant knowledge that can help us predict the patients with heart disease It is implemented on the.pynb format.
Detection of Prostate Cancer Using Deep Learning Framework
Authors: Patel Abhishek , Singh Sanjay Kumar , Khamparia Aditya 
Published: January 2021

Abstract
Recent studies in Prostate Cancer signifies as magnetic resonance imaging targets to biopsy shows more enhanced result. The systematic study of Medline, Embase, Scopus, Cochrane helps in meta-analysis. Prostate specific antigen is obtained from curative radiotherapy. Prostate-specific membrane antigen positron emission tomography helps to localize recurrence prostate cancer whether it has increased. Prostate specific antigen rising helps to identify and prompted the reason of Prostate-specific membrane antigen positron emission tomography imagining. angiogenesis play an important role for diagnosis noninvasive cancer with technique contrast-enhanced ultrasound. Prostate Cancer accuracy were determined by MRI-targeted biopsy and the transrectal ultrasound-guided biopsy.
Body Sensor Networks Architecture and security issues in Healthcare application
Authors: Kumar Vinod , Badal Neelendra , Mishra Rajesh 
Published: January 2021

Abstract
This paper exhibits the idea of Body Sensor Systems (BSNs) which can be broadly utilized in healthcare applications and all. It investigates systems and related applications for medical issues and discussed vital challenges and metrics of BSNs. Of late, miniaturized health observing gadgets have turned out to be essentially plausible with other current advances in the Wireless Sensor Networks (WSNs) and Embedded Designing technologies for computing. However, BSN provides the means to analyze the low cost which can sense the existing environment as well as their wireless nature for which it turns out to be suitable to develop the subject. Sooner or later, the reconciliation of a huge swath of Wireless Network into existing specific medical technology and so forth can be seen. These kinds of systems, by and large, comprise radio frequency-based correspondence abilities. Every sensor in a sensor network comprises of the three kinds of subsystems: first is the sensor subsystem which can sense the environment, the second subsystem helps to apply the calculations on the detected information as well as the third subsystem handles the exchanging of the messages with their neighboring sensors. Wireless Sensor Networks (WSNs) consist of small wireless computers that sense, process and impart environmental stimuli including temperature, etc.
Evaluation of Bernstein Polynomial as a Machine Learning technique
Authors: Wadhwa Deekshant , Singh Nitesh Kumar , Jatana Nishtha 
Published: January 2021

Abstract
Regression algorithms are used to device a function to predict the output based on that function for new unseen inputs. We have used Bernstein polynomial as a type of approximation curve to fit a polynomial between all features of a dataset so as to perform regression on it. As approximation curves do not generally meet most of the input points, which are used to create it, hence the chances of it over-fitting to a model are low. In this paper, we have used Bernstein polynomial separately for each feature versus output graph, assuming that each feature in the dataset is independent of each other and combines the outcome of each curve to predict the final result.
Symptomatically Brain Tumor Detection Using Convolutional Neural Networks
Authors: Totakura Varun , Madhusudhana Reddy E. , Vuribindi Bhargava Reddy 
Published: January 2021

Abstract
In the human body, the most important and the complex organs work with billions of cells in the brain. The abnormal growth or uncontrolled division of cells around the brain will cause a brain tumor. These group of cells which affect the functioning of the brain and also destroys the human cells. In the olden days, the detection of brain tumors is way much harder than nowadays. The usage of modern computer vision techniques has made the detection to be more accurate and easy. In this paper, firstly the detection of tumor in the brain was performed using a Sequential Neural Network (SNN) model which classifies the symptoms, as the brain tumor and then Magnetic Resonance Images (MRI) Scans are used for the further confirmation. The SNN model has an accuracy of 99.36% whereas the Convolutional Neural Network (CNN) Model used in this paper is 99.89% accurate.
A Data Mining Inspired Methodology towards the Identification of Diabetic Retinopathy
Authors: Pradeep Kumar KG , Karunakara K , Thyagaraju GS , Dixit Sunanda 
Published: January 2021

AbstractThe Biomedical image analysis technique used in most of the clinical diagnosis activities, which is one of the explorative areas that appeal intense significance among scientists. The retinal fundus images are utilized in clinical diagnosis extensively for the treatment and to observe various eye diseases. Diabetic retinopathy is one of the foremost sources for blindness. The major diagnostic sign of diabetic retinopathy is the damage of blood vessels due to various reasons in the eye and then establishment of lesions in the retina. The screening and detection of Diabetic Retinopathy can be performed using retinal fundus images. The identification and analysis of diabetic retinopathy (DR) by means of color fundus images involves experienced practitioners to recognize the existence of many small topographies with a detailed grading system, makes this a complex and time-consuming mission. In this paper, a novel systematized method for the discovery of exudates in retinal images to diagnose diabetic retinopathy. The color fundus images are characterized and analyzed to find microaneurysms on the retina and provides the severity. The algorithm is tested on datasets provided by ophthalmologists and Messidor dataset, which gave excellent and promising results.
Automatic Plant Identification Using Transfer Learning
Authors: Sachar S , Kumar A 
Published: January 2021

AbstractPlant identification is a widely researched area in the field of computer vision. Many attempts have been made to automate the process of plant identification using an image of a part of plant including flower, leaf and bark. Leaf has proven be the most reliable source of information. After exhaustive experiments, we chose to apply transfer learning to compare the feature extraction capabilities of VGG-16, Xception, MobileNetV2 Convolutional Neural Network (CNN) and DenseNet121 architectures to freely available Swedish, Flavia and MalayaKew leaf image datasets. Random Forest is used as classifier to identify the species of given leaf. The evaluations and comparisons of the specified feature extractor models are provided. DenseNet121 achieved maximum accuracy of 100%, 99% and 92.4% respectively in the three datasets.
Optimization based internal model control design
Authors: Rajasekhara Reddy B , Tulasiram K , Akshay Naregalkar , Pranavanand S 
Published: January 2021

Abstract
Control of nonlinear and/or constrained processes is a challenging and important task, especially chemical processes that need special attention to maximize the product thereby the profit. In this work, an optimization-based internal model control design is presented for both linear and nonlinear systems. For controlling nonlinear systems, the process dynamics are linearized around the operating point. The use of an optimizer as an internal model controller will enable the control engineer to deal with the constraints posed by the various process parameters and the effect of various process parameter variations in the closed-loop response is reported. Simulation case studies are presented viz., a first-order LTI system, and Continuous Stirred Tank Reactor(CSTR) to prove the efficacy of the optimization based internal model controller in controlling linear and nonlinear constrained processes.
A Study on Classification Techniques based on Opinions
Authors: Pooja Bhukal , Jaswinder Singh 
Published: January 2021

Abstract
Sentiment analysis or opinion mining is one of the considerable undertakings of Natural Language Processing. Sentiment analysis is a quick-growing investigation area in computer science. It includes a way of improvement for the collection and assessment of comments or opinions about legislation, laws, and opinions of products, and so forth, which are posted on the internet. Opinion mining analyzes the opinions given by people. In sentiment analysis, the customer feedback, review, shopping site review, online purchase reviews, politics opinions are considered for the analysis. Sentiment analysis is about detecting the emotions i.e. sad, happy, angry, excited, etc. The main objective of opinion mining is characterizing what the public think and comment. This review paper features certain discussions concerning the opinion-based classification and used approaches for the extraction of feeling assessment and opinion mining. Paper presents a comparison between different classification techniques. Applications and challenges of opinion mining are also discussed in this paper.
Improving Data Quality using Big Data Framework: A Proposed Approach
Authors: Soni Shikha , Singh Amritpal 
Published: January 2021

Abstract
The field of Big Data and Big Data Analytics is one of the most emerging fields in today’s technology. Data being the most important aspect of Big Data, it is important to select it in such a way that it provides maximum efficiency when analysed. Data quality is a concept which is taken in account while measuring the quality of the dataset. Data quality imposes certain defined characteristics on data which needs to be fulfilled up to a certain extent for the data to be considered reliable for analysis. In this paper, we have proposed an approach which introduces a ‘believability factor’ to measure the reliability or believability of the dataset by taking a sample of the unstructured dataset. This paper along with believability factor proposes the methodology to calculate execution time of the sampled dataset and Mean Absolute Error between the believability of the sampled and unstructured dataset.
Smart City: A new phase of sustainable development using fog computing and IoT
Authors: Malik Swati , Gupta Kamali 
Published: January 2021

Abstract
Fog computing is a distributed technology which performs computations at the edge of the network. IoT devices are generating large amount of data that too Heterogeneous in nature. In order to handle applications which, require time sensitive data and quick decision making, a reliable platform is required which can handle all the tasks in a robust manner considering fault tolerance and state migration. To process request originating from end user, an efficacious and pragmatic approach is to get the request served at the edge of the network rather than cloud envisioned with the aim to minimize the latency. Quick decision-making characteristic of fog computing makes it a smart choice for smoother processing and execution of smart city applications. As utilization of internet and IoT devices has boomed the industry, it is important to have reliable applications for day to day activities like payment of bills, traffic light management, health support system etc. The research study conducted explores multi-dimensional abstractions that can extend technological contributions in current epidemic situation.
Essence of Scalability in Wireless Sensor Network for Smart City Applications
Authors: Dogra Roopali , Rani Shalli , Sharma Bhisham , Verma Sandeep 
Published: January 2021

Abstract
Internet of Things (IoT) is the emerging research area. Along with many physical objects, Wireless Sensor Networks (WSNs) play an impeccable role in the smart city applications. It is learned from the retrospective study that the interaction between different types of objects, gathering of data from all the zones, their analyses etc., are very difficult to handle without energy conserving algorithms and protocols. Furthermore, applications of IoT specifically smart cities require a fault tolerant, scalable and reliable framework for real time processing of collected data. In this paper, we have proposed a cluster-based routing methodology which could be used at the sensing layer of smart city IoT. We select Cluster Head (CH) based on energy, distance, network energy and node density. The consideration of above parameters helps in acquiring the optimized performance of the protocol Zone-Stable Election Protocol (Z-SEP). We have performed simulations in MATLAB to emphasize the role of scalability of WSN in smart city application. We found that this protocol under performs when it is encountered for scalablity.
Identification of Disturbances in Power System and DDoS Attacks using Machine Learning
Authors: Panthi Manikant 
Published: January 2021

Abstract
Power system disturbances are the most common problem that often occurs due to human-made or natural events. The most challenging issue is finding the main cause of power disturbance and making appropriate decisions in response to it. The accuracy of the human judgement is not good in predicting the power disturbance; it is not easy for operators to monitor the system’s situation. To make human findings easier, we incorporate machine learning techniques to differentiate between the categories of power disruptions and focus more on DDoS attacks, which are its main cause. We evaluate various machine learning techniques to discriminate between power disturbances and to discuss the advantage of incorporating machine learning to enhance the productivity of the power system design.