bluuebunny/crossref_metadata_2025_split huggingface dataset:
Measuring Accuracy of Stock Price Prediction Using Machine Learning Based Classifiers
Authors: Kaur Ranjeet , Sharma Dr. Yogesh Kumar , Bhatt Devershi Pallavi 
Published: March 2021

Abstract
Stock market prediction means to decide the future development of the stock estimation of a budgetary trade. The exact forecast of offer value development will prompt more benefit speculators can make. The investigation dependent on the past gathered enormous information with the utilization of the AI strategies is appropriate for different fields. The basic aim is to generate the analysis for driving good information which will be useful for the purpose of decision making. The quality of the decisions will definitely be enhanced. There are various machine learning techniques lies with different accuracies. The selection of the best technique such that the highest level of accuracy can be achieved. In the current research there are three techniques with different variant are tested for showing the relative accuracy of the specific technique. The all these techniques are based on supervised learning will requires training for the better accuracy. In current research paper all the techniques with different variants are trained with the different sizes of the training sets. These training sizes are 70:30,50:50 and 30:70. The best variant is the 70:30 for the KNN. The given variant shows the highest accuracy in terms of the prediction.
New Paradigm in Healthcare Industry Using Big Data Analytics
Authors: Luthra Hiya , Arun Sai Nihith T. , Sri Sai Pravallika V. , Raghuram Shree R , Chaurasia Ankur , Bansal Hina 
Published: March 2021

Abstract
New scientific methods, such as genome sequencing for the next decade, produce enormous amounts of biological data that leads us to scientific breakthroughs through careful study and interpretation, However, scholars fail to keep up with all the enormous data blocks. As the generation of salient Information is rising with each day, we can use it in a productive way. The medical data can be useful in fields of personalized medicine whose demand is growing exponentially. The term emerged in 2012, along with the “Industry 4.0” concept, Famous, and refers to big data, in the marketing of IT, in which industrial equipment-generated data could hold more Potential benefit for sector. Large Data Industrials refers to many Diverse produced time series by industrial equipment at a high speed. Preventive medicine for individual patients with chronic conditions, Big Data and healthcare are critical to address the risk of hospitalization. The current review discusses about application of big data in gene sequencing, healthcare, electronic health reports (EHRs), medical scans, genomic sequencing, reports of payers, pharmacy studies, wearables, and medical devices, gathered ample health data, to name a few. This paper mainly talks about the ways in which big data can be used and the tools for analyzing big data. Big data analytics aims to provide innovations that optimize patient care and generate value for healthcare institutions with improvements in medicine, infrastructure and funding.
A Comparative Study for Efficient Covid-19 Detecting Machine Learning Models on CT Images
Authors: Chaturvedi Ankur , Mishra Divyansh , Vikram Rajpoot Dr. , Gupta Janvi , Sharma Aditi 
Published: March 2021

Abstract
Covid19 has impacted the whole world drastically, Lakhs of people are getting infected every day and thus it’s getting difficult to detect Covid infection in huge population. Main cause of death due to Covid19 is Covid infection in lungs. Infection in lungs is detected by CT-Scans of Lungs, but since doctors need time analyze CT-Scan and also, they can’t do it 24 into 7. Hence, many people who need extra medical care for Covid infection in lungs, need to wait for analysis of CT-Scan by doctors that whether their lungs are infected or not. Here in the paper, it is tried to make an efficient Machine Learning Model which could detect Covid19 infection in Lungs, in order to reduce human resource time (time taken by doctors to analyze lung CT-Scan) so that needy patients would be getting started with corresponding medical treatments more quickly. It may save many lives. Here in the paper, it’s tried to detect Covid19 infection in lungs analyzing CT-scans using SVM classifier, Logistic regressions classifier and Machine learning perceptron with two approaches of preprocessing of pixel values and then results have been compared. Dataset used for the purpose, is kaggle dataset (Covid-19 Lung CT Scan.
Advanced Glaucoma Detection Using Hybrid Approach and Singular Value Decomposition from Fundus Images
Authors: Kirar B S , Ahmed G , Sharma S , Agrawal D K 
Published: March 2021

Abstract
Glaucoma is a known to be an of eye disease that is accountable for impaired vision worldwide. For medical images, image decomposition has high impact for diagnosis of diseases. This work proposes an intelligent hybrid approach-based image decomposition method for advanced glaucoma detection. The proposed approach employs Discrete Wavelet Transform (DWT) hybridisation with Empirical Wavelet Transform (EWT). Texture features are used and extracted from decomposed image components as these captures subtle variation. To discover effective features, extracted features are added to Singular Value Decomposition (SVD). In addition, efficient features are added to the Support Vector Machine (SVM) algorithm and 95.83 percent accuracy, 91.67 percent sensitivity and 100 percent specificity has been achieved for twelve-fold cross validation. Hence, the approach proposed demonstrates superior efficiency of decomposition.
Design and Analysis of an Efficient Multi-Relational Decision Tree Learning Algorithm
Authors: Yadav Chhutten Singh , Kumar Abhishek , Kumar Ankit , Dadheech Pankaj 
Published: March 2021

Abstract
In today’s day of modern era when the data handling objectives are getting bigger and bigger with respect to volume, learning and inferring knowledge from complex data becomes the utmost problem. The research in Knowledge Discovery in Databases has been primarily directed to attribute-value learning in which one is described through a fixed set tuple given with their values. Database or dataset is seen in the form of table relation in which every row corresponds to an instance and column represents an attribute respectively. In this paper a New framework is introduced a much more sophisticated and deserving approach i.e., Hybrid Multi-Relational Decision Tree Learning Algorithm which overcomes with Exiting technology drawbacks and other anomalies. Result show that Hybrid Multi- Relational Decision Tree Learning Algorithm provides certain methods which reduces its execution time. Experimental results on different datasets provide a clear indication that Hybrid Multi-Relational Decision Tree Learning Algorithm is comprehensively a better approach.
A Secure Image Watermarking Scheme Based on DWT, SVD and Arnold Transform
Authors: Kumar Lalan , Singh Kamred Udham 
Published: March 2021

Abstract
The rapid advancement and extensive growth in communication technology is creating a critical need to develop novel methods and techniques to protect copyright, ownership, authentication and content integrity of digital media. An efficient solution to this kind of problem is digital watermarking of multimedia. A watermark refers to a visible or invisible digital data to be embedded in a cover data so as to provide copyright protection at time when need arises. Numerous watermarking methods have been presented in the literature and several watermarking software packages have been developed in past decades. Still robustness and security of watermark against a variety of attacks is one of the most important issues to be solved. In this paper, techniques are presented for watermarking of 8-bit images. The watermark is subjected to Arnold Transform to introduce randomness, thus enhancing security and improving PSNR. Embedding is done in low frequency LL band of original image. Inverse Discrete wavelet Transform is then computed to obtain the watermarked image. The results are evaluated by applying number of attacks on the watermarked image. The experimental results show that the proposed watermarking technique is robust and efficient.
Classification of Imbalanced Data:Review of Methods and Applications
Authors: Kumar Pradeep , Bhatnagar Roheet , Gaur Kuntal , Bhatnagar Anurag 
Published: March 2021

Abstract
Imbalance in dataset enforces numerous challenges to implement data analytic in all existing real world applications using machine learning. Data imbalance occurs when sample size from a class is very small or large then another class. Performance of predicted models is greatly affected when dataset is highly imbalanced and sample size increases. Overall, Imbalanced training data have a major negative impact on performance. Leading machine learning technique combat with imbalanced dataset by focusing on avoiding the minority class and reducing the inaccuracy for the majority class. This article presents a review of different approaches to classify imbalanced dataset and their application areas.
A Secure and Efficient Data Migration Over Cloud Computing
Authors: Madhukar Rao G. , Srinivas K. , Samee Sayyad , Venkatesh K , Dadheech Pankaj , Raja Linesh , Yagnik Garvit 
Published: March 2021

Abstract
Migrations are complex and depend on the project and its requirements, so security is essential to ensure a safe migration to the cloud. There are many things to consider, such as compliance with industry regulations, performance, or internal security policies. When migrating to a cloud environment, security is a shared responsibility. The cloud is based on a shared responsibility model in which the Internet service provider is responsible for cloud security and the customer is responsible for cloud security. In this paper we proposed a high-security for transferring large scale data files to the cloud database management system and an inter-cloud knowledge migration process that provides quick intervals. Measuring the effectiveness of the proposed work we have compare the performance with exiting cloud migration services. In our proposed work communication plays an essential role in its success. It is necessary to ensure clear and efficient communication between the parties involved in the migration, from decision makers to IT experts, legal teams and security officials. By sharing the needs, goals, and threats among the parties involved, our prosed work creates a successful migration strategy for the business, thereby avoiding disruptions, data loss, and other potential risks.
Plasma-assisted Co/Zr-metal organic framework catalysis of CO2 hydrogenation: influence of Co precursors
Authors: LI Yanqin , ZHAO Jing , BU Decai , ZHANG Xulei , PENG Teng , DI Lanbo , ZHANG Xiuling 
Published: March 2021

Abstract
In this study, Co/Zr-metal organic framework (MOF) precursors were obtained by a room-temperature liquid-phase precipitation method and the equivalent-volume impregnation method, respectively, using a Zr-MOF as the support, and Co/Zr-MOF-M and Co/Zr-MOF-N catalysts were prepared after calcination in a hydrogen–argon mixture gases (

V

Ar

:

V

H

2

=
9
:
1

) at 350 °C for 2 h. The catalytic activities of the prepared samples for CO2 methanation under atmospheric-pressure cold plasma were studied. The results showed that Co/Zr-MOF-M had a good synergistic effect with cold plasma. At a discharge power of 13.0 W,

V

H

2

:

V

CO

2

=
4
:
1

and a gas flow rate of 30 ml·min−1, the CO2 conversion was 58.9% and the CH4 selectivity reached 94.7%, which was higher than for Co/Zr-MOF-N under plasma (CO2 conversion 24.8%, CH4 selectivity 9.8%). X-ray diffraction, scanning electron microscopy, transmission electron microscopy, N2 adsorption and desorption (Brunauer–Emmett–Teller) and x-ray photoelectron spectroscopy analysis results showed that Co/Zr-MOF-M and Co/Zr-MOF-N retained a good Zr-MOF framework structure, and the Co oxide was uniformly dispersed on the surface of the Zr-MOF. Compared with Co/Zr-MOF-N, the Co/Zr-MOF-M catalyst has a larger specific surface area and higher Co2+/Cototal and Co/Zr ratios. Additionally, the Co oxide in Co/Zr-MOF-M is distributed on the surface of the Zr-MOF in the form of porous particles, which may be the main reason why the catalytic activity of Co/Zr-MOF-M is higher than that of Co/Zr-MOF-N.
Motional heating of spatially extended ion crystals
Authors: Kalincev D , Dreissen L S , Kulosa A P , Yeh C-H , Fürst H A , Mehlstäubler T E 
Published: March 2021

Abstract
We study heating of motional modes of a single ion and of extended ion crystals trapped in a linear radio frequency (rf) Paul trap with a precision of

Δ

n

̄

̇

≈
0.1

phonons s−1. Single-ion axial and radial heating rates are consistent and electric field noise has been stable over the course of four years. At a secular frequency of ω
sec = 2π × 620 kHz, we measure

n

̄

̇

=
0.56

(

6

)

phonons s−1 per ion for the center-of-mass (com) mode of linear chains of up to 11 ions and observe no significant heating of the out-of-phase (oop) modes. By displacing the ions away from the nodal line, inducing excess micromotion, rf noise heats the com mode quadratically as a function of radial displacement r by

n

̄

̇

(

r

)

/

r

2

=
0.89

(

4

)

phonons s−1
μm−2 per ion, while the oop modes are protected from rf-noise induced heating in linear chains. By changing the quality factor of the resonant rf circuit from Q = 542 to Q = 204, we observe an increase of rf noise by a factor of up to 3. We show that the rf-noise induced heating of motional modes of extended crystals also depends on the symmetry of the crystal and of the mode itself. As an example, we consider several 2D and 3D crystal configurations. Heating rates of up to 500 ph s−1 are observed for individual modes, giving rise to a total kinetic energy increase and thus a fractional time dilation shift of up to −0.3 × 10−18 s−1 of the total system. In addition, we detail how the excitation probability of the individual ions is reduced and decoherence is increased due to the Debye–Waller effect.
Tumor IsomiR Encyclopedia (TIE): a pan-cancer database of miRNA isoforms
Authors: Bofill-De Ros Xavier , Luke Brian , Guthridge Robert , Mudunuri Uma , Loss Michael , Gu Shuo 
Published: March 2021

Abstract

Summary
MicroRNAs (miRNAs) are master regulators of gene expression in cancers. Their sequence variants or isoforms (isomiRs) are highly abundant and possess unique functions. Given their short sequence length and high heterogeneity, mapping isomiRs can be challenging; without adequate depth and data aggregation, low frequency events are often disregarded. To address these challenges, we present the Tumor IsomiR Encyclopedia (TIE): a dynamic database of isomiRs from over 10 000 adult and pediatric tumor samples in The Cancer Genome Atlas (TCGA) and The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) projects. A key novelty of TIE is its ability to annotate heterogeneous isomiR sequences and aggregate the variants obtained across all datasets. Results can be browsed online or downloaded as spreadsheets. Here, we show analysis of isomiRs of miR-21 and miR-30a to demonstrate the utility of TIE.

Availability and implementation
TIE search engine and data are freely available to use at https://isomir.ccr.cancer.gov/.

Supplementary information
Supplementary data are available at Bioinformatics online.
Mild traumatic brain injury is associated with dysregulated neural network functioning in children and adolescents
Authors: Safar Kristina , Zhang Jing , Emami Zahra , Gharehgazlou Avideh , Ibrahim George , Dunkley Benjamin T 
Published: March 2021

Abstract
Mild traumatic brain injury is highly prevalent in paediatric populations, and can result in chronic physical, cognitive and emotional impairment, known as persistent post-concussive symptoms. Magnetoencephalography has been used to investigate neurophysiological dysregulation in mild traumatic brain injury in adults; however, whether neural dysrhythmia persists in chronic mild traumatic brain injury in children and adolescents is largely unknown. We predicted that children and adolescents would show similar dysfunction as adults, including pathological slow-wave oscillations and maladaptive, frequency-specific, alterations to neural connectivity. Using magnetoencephalography, we investigated regional oscillatory power and distributed brain-wide networks in a cross-sectional sample of children and adolescents in the chronic stages of mild traumatic brain injury. Additionally, we used a machine learning pipeline to identify the most relevant magnetoencephalography features for classifying mild traumatic brain injury and to test the relative classification performance of regional power versus functional coupling. Results revealed that the majority of participants with chronic mild traumatic brain injury reported persistent post-concussive symptoms. For neurophysiological imaging, we found increased regional power in the delta band in chronic mild traumatic brain injury, predominantly in bilateral occipital cortices and in the right inferior temporal gyrus. Those with chronic mild traumatic brain injury also showed dysregulated neuronal coupling, including decreased connectivity in the delta range, as well as hyper-connectivity in the theta, low gamma and high gamma bands, primarily involving frontal, temporal and occipital brain areas. Furthermore, our multivariate classification approach combined with functional connectivity data outperformed regional power in terms of between-group classification accuracy. For the first time, we establish that local and large-scale neural activity are altered in youth in the chronic phase of mild traumatic brain injury, with the majority presenting persistent post-concussive symptoms, and that dysregulated interregional neural communication is a reliable marker of lingering paediatric ‘mild’ traumatic brain injury.
Leg or antenna injury inCataglyphisants impairs survival but does not hinder searching for food
Authors: Gilad Tomer , Dorfman Arik , Subach Aziz , Scharf Inon 
Published: March 2021

AbstractInjury is common in nature resulting, for example, from fighting, partial predation, or the wear of body parts. Injury is costly, expressed in impaired performance, failure in competition, and a shorter life span. A survey of the literature revealed the frequent occurrence of injury in ants and its various causes. We examined whether leg or antenna injury impacts food-discovery time and reduces the likelihood of reaching food in workers of the desert ant Cataglyphis niger. We examined the search-related consequences of injury in groups of either 4 or 8 workers searching for food in a short arena, a long arena, and a maze. We conducted a small field survey to evaluate the prevalence of injury in the studied population. Finally, we compared the survival rates of injured versus uninjured workers in the laboratory. Injury was common in the field, with almost 9% of the workers collected out of the nest, found to be injured. Injured workers survived shorter than uninjured ones and there was a positive link between injury severity and survival. However, we could not detect an effect of injury on any of the searching-related response variables, neither in the arenas nor in the mazes tested. We suggest that workers that survive such injury are only moderately affected by it.
Results of the Seventh Scientific Workshop of ECCO: Precision Medicine in IBD—Prediction and Prevention of Inflammatory Bowel Disease
Authors: Torres Joana , Halfvarson Jonas , Rodríguez-Lago Iago , Hedin Charlotte R H , Jess Tine , Dubinsky Marla , Croitoru Kenneth , Colombel Jean-Frédéric 
Published: March 2021

Abstract
Inflammatory bowel disease [IBD] is a complex chronic disorder with no clear aetiology and no known cure. Despite recent advances in overall disease management and improved therapeutics, patients with IBD still experience a substantial burden. Furthermore, as the incidence continues to increase in developing areas of the world, it is expected that the burden of IBD to society will increase and exert tremendous pressure on health care systems worldwide. Therefore, new strategies to prevent the global increase of IBD are urgently required. Data are being progressively acquired on the period preceding disease diagnosis, which support the concept that IBD has a preclinical period that may reveal the triggers of disease and may be amenable to early intervention. Having a better knowledge of this preclinical period will increase the potential not only for improved understanding of disease pathogenesis and improved therapeutics, but also for disease prediction and prevention.
Iranian and Iraqi torture survivors in Finland and Sweden: findings from two population-based studies
Authors: Garoff Ferdinand , Tinghög Petter , Suvisaari Jaana , Lilja Eero , Castaneda Anu E 
Published: March 2021

Abstract

Background
Most refugees and other forced migrants have experienced potentially traumatic events (PTEs). Torture and other traumatic experiences, as well as various daily stressors, impact the mental health and psychosocial well-being of war-affected populations.

Methods
The study includes two population-based samples of Iranian and Iraqi men living in Finland and Sweden. The Finnish Migrant Health and Well-being Study (Maamu) was conducted in 2010–2012. The Linköping study was conducted in Sweden in 2005. In both samples, health and well-being measures, social and economic outcomes as well as health service utilization were reported.

Results
The final sample for analysis consisted of two groups of males of Iranian or Iraqi origin: 278 residents in Finland and 267 residents in Sweden. Both groups were subdivided according to the reported PTEs: Torture survivors; Other PTEs; No PTEs. Migrants that reported PTEs, torture survivors in particular, had significantly poorer social and health outcomes. Torture survivors also reported lower trust and confidence in authorities and public service providers, as well as more loneliness, social isolation and experiences of discrimination.

Conclusions
Torture and other PTEs prevalent in refugee and migrant populations create a wide-ranging and long-term impact in terms of increased risk of various types of adverse social and health conditions. Early identification through systematic and effective screening should be the first step in guiding migrants and refugees suffering from experiences of torture and other PTEs to flexible, multidisciplinary services.