bluuebunny/crossref_metadata_2025 huggingface dataset:
BMP9 and BMP10 Act Directly on Vascular Smooth Muscle Cells for Generation and Maintenance of the Contractile State
Authors: Wang Lei , Rice Megan , Swist Sandra , Kubin Thomas , Wu Fan , Wang Shengpeng , Kraut Simone , Weissmann Norbert , Böttger Thomas , Wheeler Matthew , Schneider Andre , Braun Thomas
Published: December 2020

Background:

Vascular smooth muscle cells (VSMCs) show a remarkable phenotypic plasticity, allowing acquisition of contractile or synthetic states, but critical information is missing about the physiologic signals, promoting formation, and maintenance of contractile VSMCs in vivo. BMP9 and BMP10 (bone morphogenetic protein) are known to regulate endothelial quiescence after secretion from the liver and right atrium, whereas a direct role in the regulation of VSMCs was not investigated. We studied the role of BMP9 and BMP10 for controlling formation of contractile VSMCs.

Methods:

We generated several cell type–specific loss- and gain-of-function transgenic mouse models to investigate the physiologic role of BMP9, BMP10, ALK1 (activin receptor-like kinase 1), and SMAD7 in vivo. Morphometric assessments, expression analysis, blood pressure measurements, and single molecule fluorescence in situ hybridization were performed together with analysis of isolated pulmonary VSMCs to unravel phenotypic and transcriptomic changes in response to absence or presence of BMP9 and BMP10.

Results:

Concomitant genetic inactivation of
Bmp9
in the germ line and
Bmp10
in the right atrium led to dramatic changes in vascular tone and diminution of the VSMC layer with attenuated contractility and decreased systemic as well as right ventricular systolic pressure. On the contrary, overexpression of
Bmp10
in endothelial cells of adult mice dramatically enhanced formation of contractile VSMCs and increased systemic blood pressure as well as right ventricular systolic pressure. Likewise, BMP9/10 treatment induced an ALK1-dependent phenotypic switch from synthetic to contractile in pulmonary VSMCs. Smooth muscle cell–specific overexpression of
Smad7
completely suppressed differentiation and proliferation of VSMCs and reiterated defects observed in adult
Bmp9/10
double mutants. Deletion of
Alk1
in VSMCs recapitulated the
Bmp9/10
phenotype in pulmonary but not in aortic and coronary arteries. Bulk expression analysis and single molecule RNA–fluorescence in situ hybridization uncovered vessel bed–specific, heterogeneous expression of BMP type 1 receptors, explaining phenotypic differences in different
Alk1
mutant vessel beds.

Conclusions:

Our study demonstrates that BMP9 and BMP10 act directly on VSMCs for induction and maintenance of their contractile state. The effects of BMP9/10 in VSMCs are mediated by different combinations of BMP type 1 receptors in a vessel bed–specific manner, offering new opportunities to manipulate blood pressure in the pulmonary circulation.
Evaluation of a new equation for estimating low-density lipoprotein cholesterol through the comparison with various recommended methods
Authors: Martínez-Morillo Eduardo , García-García María , Concha María Angeles Luengo , Varas Luis Rello
Published: December 2020

Introduction: The accurate estimation of low-density lipoprotein cholesterol (LDL) is crucial for management of patients at risk of cardiovascular
events due to dyslipidemia. The LDL is typically calculated using the Friedewald equation and/or direct homogeneous assays. However, both methods
have their own limitations, so other equations have been proposed, including a new equation developed by Sampson. The aim of this study
was to evaluate Sampson equation by comparing with the Friedewald and Martin-Hopkins equations, and with a direct LDL method.
Materials and methods: Results of standard lipid profile (total cholesterol (CHOL), high-density lipoprotein cholesterol (HDL) and triglycerides
(TG)) were obtained from two anonymized data sets collected at two laboratories, using assays from different manufacturers (Beckman Coulter and
Roche Diagnostics). The second data set also included LDL results from a direct assay (Roche Diagnostics). Passing-Bablok and Bland-Altman analysis
for method comparison was performed.
Results: A total of 64,345 and 37,783 results for CHOL, HDL and TG were used, including 3116 results from the direct LDL assay. The Sampson and
Friedewald equations provided similar LDL results (difference ≤ 0.06 mmol/L, on average) at TG ≤ 2.0 mmol/L. At TG between 2.0 and 4.5 mmol/L,
the Sampson-calculated LDL showed a constant bias (- 0.18 mmol/L) when compared with the Martin-Hopkins equation. Similarly, at TG between
4.5 and 9.0 mmol/L, the Sampson equation showed a negative bias when compared with the direct assay, which was proportional (- 16%) to the LDL
concentration.
Conclusions: The Sampson equation may represent a cost-efficient alternative for calculating LDL in clinical laboratories.
Women in sports: the applicability of common national reference intervals for inflammatory and coagulation biomarkers (HemSter Study)
Authors: Radišić Biljak Vanja , Vidranski Valentina , Ružić Lana , Simundic Ana-Maria , Vidranski Tihomir
Published: December 2020

Introduction: Intensive physical activity causes functional and metabolic changes in the athlete’s organism. The study aimed to verify the common
national available reference intervals (RIs) for common inflammatory and screening coagulation tests in a population of healthy young female athletes.
Materials and methods: One hundred and twenty-one female athletes (age range: 16–34), from various sports disciplines (water polo, handball,
volleyball, football, basketball), were included in the study. All participants completed the international physical activity short-form questionnaire.
Blood samples were collected between 8–10 am, after an overnight fast, before any physical activity. Reference intervals were determined according
to Clinical & Laboratory Standards Institute EP28-A3C Guidelines.
Results: Calculated RIs for white blood cell count (WBC), prothrombin time (PT), and activated partial thromboplastin time (APTT) ratio were in
accordance with the common national RIs. Calculated RI for C-reactive protein (CRP) was lower (< 2.9 mg/L) than the proposed cut-off for a healthy
population (< 5.0 mg/L). Reference interval for fibrinogen was higher (1.9–4.4 g/L), than the available RIs (1.8–3.5 g/L). D-dimer cut-off value was
set at 852 μg/L fibrinogen equivalent units (FEU), higher than the proposed 500 μg/L FEU for venous thromboembolism (VTE) exclusion.
Conclusions: The applicability of the available RIs for WBC count, PT, and APTT-ratio was confirmed. However, RIs for CRP and fibrinogen differed
significantly than the available common national RIs for the healthy non-athletes’ population. A higher cut-off for D-dimers should be extensively
verified before implementation for VTE diagnosis exclusion in a group of healthy young female athletes.
NATO Enlargement and the Problem of Value Complexity
Authors: Goldgeier James
Published: December 2020

Some of the recent literature on negotiations at the end of the Cold War regarding German reunification and the North Atlantic Treaty Organization (NATO) has deflected attention from an important policy problem that arose during subsequent deliberations about whether to enlarge the membership of NATO. Newly released U.S. and Russian archival materials highlight this problem very clearly, namely, how leaders manage tradeoffs and uncertainty. Pursuing one set of interests can harm the achievement of other interests. At times, policies take a while to form, adding to uncertainty in relations between countries. This article highlights the ways U.S. President Bill Clinton and his top advisers convinced themselves that they could both enlarge NATO and keep Russia on a Western-oriented track, despite Russian President Boris Yeltsin's repeated warnings to the contrary.
The High Tide of Anticolonial Legalism
Authors: Moyn Samuel
Published: December 2020

For a time in the 1960s it seemed as if one domain in which the global south’s enthusiastic struggle to arrogate the mantle of universalism as an exercise in “worldmaking” was the transformation of international law. Though this struggle was ultimately circumvented by great power politics and newer forms of international law and organization, it was a crucial moment. The introductory prosopographical survey that follows seeks to recapture the consensus of a set of northern and southern international lawyers in the 1960s who saw potential in the project of transforming their field to register the aims of a new epoch – the aims of postcolonial states.
Building the Nation: Sovereignty and International Law in the Decolonisation of South Asia
Authors: Saksena Priyasha
Published: December 2020

The position of the territorially sovereign nation-state as the fundamental building block of the contemporary world order has come under increasing challenge. Historians have long focused on social, cultural, economic, and technological factors to examine the constructed nature of the nation-state. In this article, I explore the role of law, and specifically the concept of sovereignty, in the creation of the unified spatial entity constituting the nation-state. I focus in particular on the decolonisation of South Asia and analyse legal arguments made in two international disputes (over Hyderabad and the river Indus) to understand the process through which the Indian nation-state came into being.
“Paved with Good Intentions:” Best Practices in the Ethics of Track Two Interventions
Authors: Jones Peter
Published: December 2020

This article unpacks the development of key ideas and debates which surround the ethical issues of Track Two. It defines what is meant by ‘Track Two’ and discusses how ethics might best be applied in practice to these dialogues. The ethical dimensions of four key issues are explored: accountability; the basis on which third parties feel they are entitled to intervene; the problem of dealing with actors who have committed atrocities; and ethical questions surrounding secrecy or confidentiality which is often required. The article suggests several ways forward in terms of creating a mechanism to enable practitioners to assist each other with the challenges they face. The article takes the view that a ‘hard and fast’ set of ethics may not be appropriate for the field, as each intervention is quite different, but rather that a set of ‘reflective questions’ should be developed to help practitioners confront ethical issues.
Towards an Inclusive Conception of Best Practices in Peace and Conflict Initiatives: The Case of the South Caucasus
Authors: Gamaghelyan Philip
Published: December 2020

The field of peace and conflict studies has been maturing over the past few decades, not least thanks to the continual epistemological contestation between its philosophy and methodology. As a consequence, the methods of conflict resolution practice have been evolving. Dominated by realist approaches of conflict management during the Cold War, the field in the 1990s relied heavily on neo-liberal theories of economic interdependence, democracy building, and interest-based negotiations that can bring win-win outcomes. By the late 2000s, as the constructivist paradigm and critical theory started gaining ground in academia, the conceptual conversation shifted toward the possibilities of building inclusive societies and achieving structural and cultural peace via conflict transformation, rather than resolution, as the respective methodology.
Incorporating Best Practices into Design and Facilitation of Track Two Initiatives
Authors: d’Estrée Tamra Pearson , Fox Benjamin B.
Published: December 2020

Track Two processes were developed to facilitate movement in difficult international conflicts. Since Montville first coined the term, Track Two processes have considered deliberate and strategic ways to bring together adversaries in unofficial, private face-to-face interactions that allow for joint analysis and mutual learning. Such processes create the conditions for more nuanced problem definition and solution exploration. Ideally, these insights are then transferred into official peace processes, policymaking and decision-making. Transfer acknowledges a strategic dimension to planning for change; Track Two approaches abridge and accelerate the long-term accumulation approach by the strategic choice of participants, agenda and goals. This article reviews and summarizes our knowledge to date about how best to encourage Track Two inputs into negotiation and other Track One diplomatic efforts. It then offers a checklist for designing initiatives to best promote both intergroup learning and timely transfer to generate effective change.
Constructing Ethnic Diversity as a Security Threat: What it Means to Russia’s Minorities
Authors: Prina Federica
Published: December 2020

This article analyses the Russian government’s securitisation of inter-ethnic relations, and national minorities’ responses to such processes. While Russia’s securitising dynamics have been linked to threats associated with ethnic groups (perceived as) culturally distant from the Russian majority (such as non-Slavic and Muslim minorities), this article argues that securitisation can affect all of Russia’s national minorities (including Slavic and well-integrated communities). Through the analysis of the securitisation of three, partly converging, spheres of domestic politics (civil society, migration, and minority issues) the article highlights forms of (in)security impacting upon national minorities with reference to their experience of securitisation and format of their civic engagement. The article contributes to research exploring the relationship between security and minority studies, through a bottom-up perspective focusing on national minorities’ experience of securitisation. It employs empirical data based on semi-structured interviews with minority representatives held in 2015–2016 in six locations in the Russian Federation.
Family Life for Children in State Care
Authors: Breen Clarie , Krutzinna Jenny , Luhamaa Katre , Skivenes Marit
Published: December 2020

This paper examines what set of familial circumstances allow for the justifiable interference with the right to respect for family life under Article 8, echr. We analyse all the Courts’ judgments on adoptions from care to find out what the Court means by a “family unit” and the “child´s best interest”. Our analysis show that the status and respect of the child’s de facto family life is changing. This resonates with a view that children do not only have formal rights, but that they are recognised as individuals within the family unit that states and courts must address directly. Family is both biological parents and child relationships, as well between children and foster parents, and to a more limited extent between siblings themselves. The Court’s understanding of family is in line with the theoretical literature, wherein the concept of family reflects the bonds created by personal, caring relationships and activities.
Child Support and the Government’s Children’s Socio-economic Rights Obligations
Authors: Walker Lara
Published: December 2020

This article argues that the recovery of child support is a vital aspect of ensuring children’s socio-economic rights. The UK Government has a legal responsibility to assist parents to meet the needs of their children, whether by providing specialist support or through welfare payments. The Government cannot fulfil this responsibility by requiring parents to reach private agreements and failing to provide suitable additional support. The current law and policy focus purely on the duty to maintain. It is assumed that each parent has the responsibility to provide for their children, and there is no back up support under the Child Support Act where the parents are unable to do this. This fails to acknowledge and resolve wider social issues which are crucial to ensuring that child support is received, and children are not living in unnecessary poverty. The article argues that by taking this approach the Government is failing to fulfil its responsibility to protect children’s socio-economic rights. This article sets out a socio-economic rights framework for amending child support legislation and policy, highlighting the bare minimum Government obligations.
Barriers to Effective, Equitable and Quality Education
Authors: MacKenzie Alison , Bower Christine , Owaineh Mohammed
Published: December 2020

The Israeli occupation has had a considerable negative impact on the lives of Palestinians, such that achieving an effective, equitable, quality education for all children is far from being realised. Palestinian children are not only adversely affected by the occupation, but also by an educational system that fails fully and systematically to accord them their rights. Using rights-based participatory methods informed by human rights protocols, we explored the experiences of children with disabilities’ inclusion in schools. Our findings show that there is continuing failure to understand or implement the provisions of the uncrc or uncrpd, and that these children are systematically excluded or marginalised from education. We suggest that children’s rights to educational inclusion can be achieved by means of a whole system, whole educational and whole person approach, along with a consideration of a rights-based policy framework.
Reclaiming Progressive Realisation: A Children’s Rights Analysis
Authors: Byrne Seamus
Published: December 2020

Any discussion on children’s socio-economic rights should not overlook their means of fulfilment; namely, the principle of progressive realisation and the legal duties arising therefrom. This article argues that from a children’s rights perspective, this principle has remained legally and operationally underdeveloped by the Committee on the Rights of the Child. This can be seen from their failure sufficiently to engage with, and address, the obligations imposed by progressive realisation in their General Comments and in their Concluding Observations on state practice issued under the Convention’s reporting process. This article argues that the cumulative consequence of these failings is that the Committee has peripheralised this important legal obligation within children’s rights scholarship and has ultimately undermined its ability to achieve its full legal potential in enhancing children’s socio-economic rights. In advocating for the promulgation of a revised General Comment on the General Measures of Implementation, it demonstrates how children’s rights can meaningfully reconnect with the principle in a more consequential way.
A Reflection on “Divinatory Conventions in Late Shang China” and the Grammar of the Oracle-Bone Inscriptions
Authors: Takashima Ken-ichi
Published: December 2020

This paper questions Keightley’s claim that the divinatory conventions followed in the Diviner Li 歷-Group charges were different from those followed in the Diviner Bin 賓-Group charges. The paper relies in part on what may be shown as an objective guideline, i.e., what is commonly referred to as “presupposition”, a supposition made prior to any utterance or sentence. The paper also addresses the problem of qi 其 and the negatives used with or without it.
Three new enchytraeid species (Enchytraeidae, Annelida) from mountain soils of Korea and ten species new for the country
Authors: FELFÖLDI TAMÁS , DÓZSA-FARKAS KLÁRA , NAGY HAJNALKA , HONG YONG
Published: December 2020

In this paper, three new enchytraeid species (Enchytraeidae, Clitellata) are described from different mountains of Korea: Cognettia koreana sp. n., Henlea bidiverticulata sp. n. and Mesenchytraeus longidiverticulatus sp. n. Validity of these new species was confirmed by comparative morphological and molecular taxonomic analyses (based on ITS, CO1 and H3 sequences). In addition, ten enchytraeid species never recorded before from Korea were found in the studied soil samples: Bryodrilus diverticulatus Černosvitov, 1929, Cernosvitoviella atrata Bretscher, 1903, Cernosvitoviella minor Dózsa-Farkas, 1990, Cognettia lapponica Nurminen, 1965, Cognettia varisetosa (Martinsson, Rota & Erséus, 2015), Fridericia cf. sohlenii Rota, Healy & Erséus, 1998, Henlea nasuta Eisen, 1878, Marionina clavata Nielsen & Christensen, 1961, Marionina communis Nielsen & Christensen, 1959, Marionina seminuda Xie & Rota, 2001. Morphological observations and new reference DNA sequences are also provided for the above-mentioned species.
Description of the male of Tanyxiphium harriet (Hymenoptera: Mymaridae), with new distribution records and synonymy
Authors: TRIAPITSYN SERGUEI V. , AISHAN ZHULIDEZI , HUBER JOHN T.
Published: December 2020

Tanyxiphium longissimum Huber, syn. n. (Hymenoptera: Mymaridae) is synonymized under T. harriet (Zeya) based on examination of specimens from Hainan Island and Yunnan Province in the Oriental part of China, Sulawesi Island in Indonesia, Peninsular Malaysia, and Thailand. These are new country distribution records except for Thailand. The previously unknown male of T. harriet is described from Sulawesi Island, and the female is redescribed and illustrated based on non-type material examined.
Unsupervised Multi-Document Abstractive Summarization Using Recursive Neural Network with Attention Mechanism
Authors: Chakravarty Aniv , Kallimani Jagadish S.
Published: December 2020

Text summarization is an active field of research with a goal to provide short and meaningful gists from large amount of text documents. Extractive text summarization methods have been extensively studied where text is extracted from the documents to build summaries. There are various
type of multi document ranging from different formats to domains and topics. With the recent advancement in technology and use of neural networks for text generation, interest for research in abstractive text summarization has increased significantly. The use of graph based methods which handle
semantic information has shown significant results. When given a set of documents of English text files, we make use of abstractive method and predicate argument structures to retrieve necessary text information and pass it through a neural network for text generation. Recurrent neural networks
are a subtype of recursive neural networks which try to predict the next sequence based on the current state and considering the information from previous states. The use of neural networks allows generation of summaries for long text sentences as well. This paper implements a semantic based
filtering approach using a similarity matrix while keeping all stop-words. The similarity is calculated using semantic concepts and Jiang–Conrath similarity and making use of a recurrent neural network with an attention mechanism to generate summary. ROUGE score is used for measuring
accuracy, precision and recall scores.
Deep Learning Autoencoder for Single Specimen Face Remembrance
Authors: Tyagi Nitin , Gupta Sandeep , Singh Shekhar , Saraswat Krishan Kumar
Published: December 2020

This paper aims learning powerful picture portrayal for single preparing test per individual face acknowledgment. Propelled at achievement of deep learning (DL) within picture portrayal, this article proposes a regulated autoencoder (AE), which is another sort of edifice obstruct for
deep structures. There are 2 highlights particular our managed AE from standard AE. In the initial place, we uphold the countenances by variations to be delineated using the standard substance of the diacritic, as example, frontispiece having unbiased appearance and typical enlightenment,
further, we uphold features comparing to an analogous diacritic to be comparable. Therefore, our administered autoencoder separates the highlights which are strong to fluctuations in light, articulation, impediment, and present, and encourages the face acknowledgment. We heap such regulated
AEs to achieve the deep design and utilize it for separating highlights in picture portrayal. Exploratory corollary on the Extended Yale B (Athinodoros Georghiades, et al., 2001. From few to many: Illumination cone models for face recognition under variable lighting and pose. PAMI, 23(6),
pp.643–660), informational collections show that by coupling with the normally utilized inadequate portrayal based grouping, our stacked managed AEs-based face portrayal essentially beats the usually utilized picture portrayals in single example per individual face acknowledgment, and
it attains lofty cognizance precision conflicted and alternative DL prototypes, which includes the deep Lambertian organize, incite of appreciably fewer preparing detail and without area data. Additionally, regulated AE can correspondingly be utilized for face check that additionally shows
its acceptability for frontispiece rendering.
Food Crisis-How Artificial Intelligence and Machine Learning Are Solving Humanity’s Greatest Challenge—A Review
Authors: Kiran Anirudh , Narayana Raj G. , Talawar Manjunath B.
Published: December 2020

Humanity is heading towards a crisis. The mammoth task of providing food for 2 billion more people by 2050 is being deliberated by governments, scientists and agriculturists alike. The consequence of climate change has led to erratic and non-uniform crop growth. Floods are inundating
agriculture land, drought is making crop cultivation impossible and pests and insects are wiping out entire crop fields. Just as the situation seems to be going out of hand, technology is proving itself to be the guardian angel yet again. With the power of Machine Learning and Artificial Intelligence,
scientists are able to understand and predict intolerable growing conditions, identify various weather and pest infestation patterns and provide sustainable solutions, which helps accomplish our ultimate goal—increasing crop production by two-folds in the next thirty years. This paper
gives an insight about ways in which artificial intelligence and machine learning are helping humanity overcome one of its biggest challenge.
Polarity Identification for Handwritten Text in Multilingual Documents Using Open Source Optical Character Recognition Tools
Authors: Chandrika C. P. , Kallimani Jagadish S.
Published: December 2020

Sentimental analysis is a prerequisite for many applications. We propose a model which scans handwritten text in English and Kannada languages by a CamScanner and then translated into editable text by using various Open Source Optical Character Recognition tools. The performances of
different OCRs are analyzed and tabulated. Sentimental analysis is performed on the statements written in both English and Kannada languages using Wordnet, Algorithmia Rest API and local dictionaries and we have obtained the satisfied results. The same sentimental analysis module is also applied
on customer reviews for the mobile product and reviews are taken from Amazon Web Services. The opinion of the customer about the product can be identified correctly.
Predicting Job Satisfaction and Employee Turnover Using Machine Learning
Authors: Yogesh Inchara , Suresh Kumar K. R. , Candrashekaran Niveditha , Reddy Dhrithi , Sampath Harshitha
Published: December 2020

Employee turn_over inflicts costs on the company. The employee must be supplanted, and the new employee trained. These quits may likewise make critical and exorbitant interruptions the production process. This gives lucid motivation to the firm to forestall stops or, in any event, to
have the option to anticipate when and where stops can be anticipated. On the off chance that employees are approached to assess their superiors and the appropriate responses will be made accessible to the superior, it is most obvious that only positive feedbacks will be provided. Along these
lines, the point is to utilize Machine Learning techniques to foresee employee turn_over. Appropriate predictions cause companies to take necessary decisions on employee retention or succession planning. Algorithms: One-Sample T-Test (T-Test), Decision Tree (DT), AdaBoost (AB),
Logistic Regression (LR), Random Forest Classifier (RFC).
A Methodology to Handle Heterogeneous Data Generated in Online Social Networks
Authors: Sailaja Kumar K. , Evangelin Geetha D. , Sahoo Pratap Rudra
Published: December 2020

Analyzing the heterogeneous data generated by social networking sites is a research challenge. Twitter is a massive social networking site. In this paper, for processing the heterogeneous data, a methodology is devised, which helps in categorizing the data obtained from Twitter into
different directories and understanding the text data explicitly. The methodology is implemented using Python programming language. Python’s tweepy package is used to download the Twitter stream data which includes images, videos and text data. Python’s Aylien API is used for analyzing
the Twitter text data. Using this API, sentiment analysis report is generated. Using Python’s matplotlib package, a pie chart is generated to visualize the sentiment analysis results. Further an algorithm is proposed for sentiment analysis, which not only categorizes the tweets into
positive, negative and neutral (as Aylien API does), but also categorizes the tweets into strongly and weakly, positive and negative based on the polarity and subjectivity. Django platform and Python’s TextBlob package are used for implementing this algorithm. For this experiment, data
is collected from Twitter using the hash tags related to different events/topics like IPL2018, World Cup2018, Modi, and Delete Facebook etc. during the period Monday Jan 22, 2018 to Monday May 28, 2018. Moreover, the data is collected and processed using Python TextBlob. Also conducted the
Sentiment analysis on text data using TextBlob and visual reports are generated using Google chart. The results obtained from both the above-mentioned approaches are compared and it is observed that the proposed algorithm gives better sentiment analysis of the tweets.
Medicine Image Data Abstraction Using Image Processing
Authors: Ashwini C. P. , Divya B. M.
Published: December 2020

In order to show signs of improvement recuperation and maintain a strategic distance from any reactions it is significant that the patient should take right drug for the affliction is they are enduring with. Medication data on names is a noteworthy wellspring of learning for patients.
This is the significant part of prescription however we are discovering numerous cases wherein the incorrect data about the medication may make terrible effects on the individuals and will in general broaden numerous issues as opposed to decreasing the ailment. The fundamental explanation
behind this, is absence of data about the medication to the patient and some being ignorant or mind conviction on the medications. In order to defeat this issue medicine data abstraction using image processing is an application which causes you to see the medication dependent on the manifestations
to which the prescription is to be utilized at anyplace, whenever, through your cell phone. It encourages you to get the data of the drug by simply sitting at your home and snapping the photo of the medication.
A Comparative Analysis and Critical Review of Summarization Techniques
Authors: Goswami Mausumi , Niranjanamurthy M. , Assisi Anlin , Pillai Sanjana , Sunny Christy , Rakshitha B. S. , Purkayastha B. S.
Published: December 2020

Unstructured data is outcome of various major business applications. Summarizing unstructured data is considered as a significant task in this era of smart digital evolution. Text summarization is basically done using the common approaches on the basis of certain predefined rules. Text
summarization is a branch of text mining which helps in bringing together the main content of the text from the original text or document. This work was an attempt to understand the existing literature and make a critical review on the pros and cons of the methods used. A gap analysis is also
included after inspecting various methods. A comparative study of intrinsic and extrinsic evaluation techniques is included. A methodology is proposed for text summarization using machine learning.
Emotion Analysis from Human Voice Using Various Prosodic Features and Text Analysis
Authors: Jain Vybhav , Rajeshwari S. B. , Kallimani Jagadish S.
Published: December 2020

Emotion Analysis is a dynamic field of research with the aim to provide a method to recognize the emotions of a person only from their voice. It is more famously recognized as the Speech Emotion Recognition (SER) problem. This problem has been studied upon from more than a decade with
results coming from either Voice Analysis or Text Analysis. Individually, both these methods have shown a good accuracy up till now. But, the use of both of these methods in unison has showed a much more better result than either one of those parts considered individually. When different people
of different age groups are talking, it is important to understand their emotions behind what they say as this will in turn help us in reacting better. To try and achieve this, the paper implements a model which performs Emotion Analysis based on both Tone and Text Analysis. The prosodic features
of the tone are analyzed and then the speech is converted to text. Once the text has been extracted from the speech, Sentiment Analysis is done on the extracted text to further improve the accuracy of the Emotion Recognition.
Authorship Identification Using Supervised Learning and n-Grams for Hindi Language
Authors: Kallimani Jagadish S. , Chandrika C. P. , Singh Aniket , Khan Zaifa
Published: December 2020

Authorship Identification pertains to establishing the author of a particular document, currently unknown, based on the documents previously available. The field of authorship identification has been explored so far primarily in the English language, using several supervised and unsupervised
machine learning models along with usage of NLP techniques, but work on regional languages is highly limited. This may be due to the lack of collection of proper datasets and preprocessing techniques attributed to the rich morphological and stylistic features in these languages. In this paper
we apply some supervised machine learning models, namely SVM and Naïve Bayes to Hindi literature to perform authorship analysis by picking four Hindi authors. We compare and analyze the accuracy which is so obtained using different models and bag of words approach.
Machine Learning Techniques in Healthcare—A Survey
Authors: Aiswariya Milan K. , Kumar Niharika P.
Published: December 2020

The development of science and technology has led to a very busy lifestyle among urban people across the globe. Due to the advent of cutting-edge technologies, connectivity and networking is a boon to the people living in urban areas. Thus, a vast amount of patient data from admission,
treatment and discharge is collected across the clinical community. These rich data being available online has been under-utilized and the question arises on how best the data can be utilized. With the centralized data and powerful data analytical algorithms are running in powerful machines,
until recent past, the machine learning is yet to be used for improving the diagnosis, prediction and secure data access process in healthcare. In this proposal, machine learning algorithms are used for enhanced medical diagnosis, personalized healthcare, predicting disease outbreaks in certain
regions and measures for securing healthcare data from malicious attacks. The work focuses on 3 major chronic diseases such as Heart Attack, Stroke and Diabetics. Enhanced medical diagnosis involves the methods for predicting readmissions to hospital after X days of their discharge.
Personalized healthcare involves methods for disease diagnosis and building treatment plan. The predictions are based upon on the patient’s medical reports and living habits. Disease outbreaks in an area involves methods for monitoring and predicting epidemic outbreaks in an area, during
certain period of time based on information from social media.
Performance of Lightweight Aggregate Concrete Containing Expanded Polystyrene, Cinder and Ground-Granulated Blast-Furnace Slag
Authors: Harish M. L. , Narendra H. , Azam Afzal Md.
Published: December 2020

Lightweight aggregate concrete is developed by substituting normal weight aggregate either fully or partially based on strength and density required. Expanded polystyrene (EPS) bead is a type of low density material, which also has good energy-absorbing characteristics and can be used
as light weight aggregate in concrete. In the present study, Structural lightweight aggregate concrete (SLWAC) was produced by fully replacing normal weight aggregate with combinations of EPS beads to Cinder by the ratio 20:80, 40:60, 60:40, 80:20 respectively. And GGBS was used as supplementary
cementitious material. The resulting concrete had strength variation between 29.5 to 11.6 MPa, and the density variation of 2192 to 1701 kg/m3. Considering strength and density criteria 40:60 ratios was observed as the optimal mix. The Compressive strength acquired by concrete was inversely
proportional to the volume of EPS beads. Effect of fibers on mechanical properties such as flexural strength, compressive strength, and split-tensile strength was studied on the optimal mix by using polypropylene fibers, it was observed that an 8.78% increase in flexural strength at 1% fibers,
16.5% increase in Compressive strength at 0.5% fibers, and 35.4% increase in split-tensile strength at 1% fibers. Along with this, durability tests such as water absorption and permeability tests were performed, the performance of this concrete in water absorption test is well within the limits
but in permeability, it underperformed which confirms that as the EPS ratio in the concrete increased, the absorption and depth of penetration values increased considerably. Microscopic observations were also made to study the interface amongst the cement paste and aggregates. It was revealed
that GGBS did not influence significantly on the bonding with EPS beads.
A Review on the Importance of Machine Learning and Artificial Intelligence in Real Life Problem Solving
Authors: Suma Priya D. S. V. , Esther Rani D. , Pavan Shankar Sai A. , Konda Babu A. , Nandan Durgesh
Published: December 2020

This paper clearly explains the concept, importance and main aim of machine learning and construction of the machine learning system. There are several ideas regarding this machine learning which are formed by a number of strategies. This effort leads to introduce many machine learning
methods such as learning by commands, concept, learning by comparison, and learning by some algorithms. This article provides information about the main purpose of machine learning and its development. Machine learning is the primary aspect that promotes any system to have intelligence. One
of its main applications is artificial intelligence. Machine learning is highly suited for complex level system representation. There are a number of machine learning concepts that leads to the integration of number of networks.
A Fuzzy Based Approach for Empirical Analysis of Unstructured Data
Authors: Goswami Mausumi , Purkayastha B. S.
Published: December 2020

Computational intelligence and soft computing has many promising technologies such as Text Mining. Document Classification using soft computing techniques like fuzzy logic helps to find a more practical solution due to ambiguity and uncertainty present in the text data. Uncertainty
and information may be reflected as the part and parcel of any industrial or engineering problem to be solved. Information refers to the facts required to solve it and uncertainty refers to the non-random lack of certainty (‘non-random uncertainty’), ambiguity, haziness in the
system. It is very important to ponder on the nature of uncertainty involved in a problem. Father of fuzzy logic, Lofti Zadeh (1965) suggested that decision-making using set membership is the key when it is required to deal with uncertainty. Fuzzy clustering helps to identify patterns which
are difficult to be discovered using crisp clustering. Natural languages contain non-random uncertainty. To deal with non-random uncertainty or different degrees of truth or partial truth Fuzzy logic may be used. This work focuses on fuzzy logic based approaches being utilized for identification
of coherent patterns. Empirical Analysis are conducted to realize and evaluate the effect of the methodology proposed.
Analysis of Rice Ageing Process Using Fuzzy and Graph Cut
Authors: Nigam Bhawna , Sharma Anvika , Basavaprasad B. , Niranjanamurthy M.
Published: December 2020

Rice botanically belongs to Oryza sativa L. of Gramineae family. Rice is the significant principal foods for almost majority of the world’s population and impacts the livelihood and economy of many billion people. Though there are many well established techniques such as electronic
devices, sensors, biosensors or high end instruments to study the different chemical components, sample preparation, specificity, sensitivity, accuracy and reusable issues. In order to overcome such issues, an alternate technique or method need to be developed which can replace the human intervention
to avoid the experimental errors through analyzing the surface structure rather than the chemical method. Among the different changes that occurred during the ageing process, internal structure is also an important phenomenon because of the starch modification with several factors like temperature,
moisture content and storage period. Hence, the objective of the study concentrates on the analyzing the structural changes to assess the age of rice through fuzzy and graph cut segmentation technique.
Changing Landscape of Recruitment Industry: A Study on the Impact of Artificial Intelligence on Eliminating Hiring Bias from Recruitment and Selection Process
Authors: Raveendra P. V. , Satish Y. M. , Singh Padmalini
Published: December 2020

An emerging trend of implementing Artificial Intelligence (AI) technologies can be seen in such domains that were solely dominated by humans. Today, AI is utilized extensively in HR department to assist and accelerate recruitment and selection process (Martin, F.R., 2019. Employers
Are Now Using Artificial Intelligence To Stop Bias In Hiring. Retrieved September 22, 2019, from analyticsindiamag. com: https://analyticsindiamag.com/employersare-using-ai-stop-bias-hiring/.). This paper attempts to present the impact of AI on recruitment and selection process, incorporation
of AI in eliminating unconscious biases during hiring. The study addresses the rising questions such as how AI has changed the landscape of recruitment industry, role of AI in recruitment and selection process, whether AI can help in eliminating the unconscious bias during recruitment and
selection process. In order to uncover the understanding and figure out the potential solutions that AI brings to the HR process, an extensive review of literature has been carried out. It is concluded by analyzing the past contributions that AI offers potential solution to recruitment managers
in optimizing the recruitment and selection process and is able to negate human biases prevalent during hiring. The future waits for augmented intelligence technologies offering better results taking over repetitive administrative jobs completely.
A Comprehensive Study on Commercial Applications of Cloud Computing
Authors: Jagannatha S. , Tulasimala B. N.
Published: December 2020

In the world of information communication technology (ICT) the term Cloud Computing has been the buzz word. Cloud computing is changing its definition the way technocrats are using it according to the environment. Cloud computing as a definition remains very contentious. Definition
is stated liable to a particular application with no unanimous definition, making it altogether elusive. In spite of this, it is this technology which is revolutionizing the traditional usage of computer hardware, software, data storage media, processing mechanism with more of benefits to
the stake holders. In the past, the use of autonomous computers and the nodes that were interconnected forming the computer networks with shared software resources had minimized the cost on hardware and also on the software to certain extent. Thus evolutionary changes in computing technology
over a few decades has brought in the platform and environment changes in machine architecture, operating system, network connectivity and application workload. This has made the commercial use of technology more predominant. Instead of centralized systems, parallel and distributed systems
will be more preferred to solve computational problems in the business domain. These hardware are ideal to solve large-scale problems over internet. This computing model is data-intensive and networkcentric. Most of the organizations with ICT used to feel storing of huge data, maintaining,
processing of the same and communication through internet for automating the entire process a challenge. In this paper we explore the growth of CC technology over several years. How high performance computing systems and high throughput computing systems enhance computational performance and
also how cloud computing technology according to various experts, scientific community and also the service providers is going to be more cost effective through different dimensions of business aspects.
Enhancing the Lifespan of Wireless Sensor Networks Using NeuroGenetic Algorithm
Authors: Vanitha G. , Yogish H. K.
Published: December 2020

In recent years, the growth of Wireless Sensor Networks (WSN) has been extended to various applications. Coverage is one of the major issues in the field to know about the patient’s activity. Sensors are used to monitor and track the patients in the hospitals. Wireless body sensors
are placed in the body to obtain the values of the targeted area like electroencephalography (EEG), blood pressure sugar level and electrocardiography (ECG). Sensor nodes require energy to perform the tasks like collecting the targeted patient’s data, processing and broadcasting the
data based on the application. The power consumption in the sensor nodes is considerably high which limits the lifetime of the surveillance systems. One of the prime factors to increase the lifetime of WSN is by splitting the deployed sensors into covers which are the group of sensors that
monitors all the targets. Once sensor power drains from one cover, another cover gets activated. The proposed NeuroGenetic Algorithm (NGA) minimizes the energy consumption of sensor nodes by improvising the number of covers. It is achieved by minimizing the number of sensors which has been
utilized at a particular time. Simulation is done in Matlab and the max number of covers obtained and analyzed with the existing algorithms in the literature. The comparison results shown in NGA are more efficient than the existing algorithm by discovering the number of covers which increases
the lifetime of WSN.
Automated Teller Machine Security with Image Processing and Machine Learning Techniques
Authors: Chayadevi M. L. , Madhyastha Sujith , Nisarga K. N. , Charitha H. , Susharan B.
Published: December 2020

There are many scenarios in society with thefts and crimes involved in Automated Teller Machines (ATM). These events are increasing day-by-day and which is also increasing the complexities on the crime investigation agencies. In order to deal with these situations, we have proposed
an automated security method inside ATMs using image processing techniques which can alert the concerned authorities immediately whenever these types of situations arise. Hybrid method with Viola-Jones algorithm has been used for face recognition along with the Haar-cascade features. In the
case of objects such as knife, gun etc. inside an ATM, combination of SVM and random forest algorithms are used for object detection. TensorFlow with machines learning algorithms have been used in the hybrid methodology. Android application has been developed to prevent and alert the crimes
and send alert messages to the concerned. Speech alert system is developed to assist blind and physically challenged people.