bluuebunny/crossref_metadata_2025 huggingface dataset:
Adverse Trends in Premature Cardiometabolic Mortality in the United States, 1999 to 2018
Authors: Shah Nilay S. , Lloyd‐Jones Donald M. , Kandula Namratha R. , Huffman Mark D. , Capewell Simon , O’Flaherty Martin , Kershaw Kiarri N. , Carnethon Mercedes R. , Khan Sadiya S.
Published: November 2020

Background

Life expectancy in the United States has recently declined, in part attributable to premature cardiometabolic mortality. We characterized national trends in premature cardiometabolic mortality, overall, and by race‐sex groups.

Methods and Results

Using death certificates from the Centers for Disease Control and Prevention's Wide‐Ranging Online Data for Epidemiologic Research, we quantified premature deaths (<65 years of age) from heart disease, cerebrovascular disease, and diabetes mellitus from 1999 to 2018. We calculated age‐adjusted mortality rates (AAMRs) and years of potential life lost (YPLL) from each cardiometabolic cause occurring at <65 years of age. We used Joinpoint regression to identify an inflection point in overall cardiometabolic AAMR trends. Average annual percent change in AAMRs and YPLL was quantified before and after the identified inflection point. From 1999 to 2018, annual premature deaths from heart disease (117 880 to 128 832), cerebrovascular disease (18 765 to 20 565), and diabetes mellitus (16 553 to 24 758) as an underlying cause of death increased. By 2018, 19.7% of all heart disease deaths, 13.9% of all cerebrovascular disease deaths, and 29.1% of all diabetes mellitus deaths were premature. AAMRs and YPLL from heart disease and cerebrovascular disease declined until the inflection point identified in 2011, then remained unchanged through 2018. Conversely, AAMRs and YPLL from diabetes mellitus did not change through 2011, then increased through 2018. Black men and women had higher AAMRs and greater YPLL for each cardiometabolic cause compared with White men and women, respectively.

Conclusions

Over one‐fifth of cardiometabolic deaths occurred at <65 years of age. Recent stagnation in cardiometabolic AAMRs and YPLL are compounded by persistent racial disparities.
Blood Pressure Changes in 1610 Subjects With and Without Antihypertensive Medication During Long‐Term Fasting
Authors: Grundler Franziska , Mesnage Robin , Michalsen Andreas , Wilhelmi de Toledo Françoise
Published: November 2020

Background

We investigated daily blood pressure (BP) changes during fasting periods ranging from 4 to 41 (10.0±3.8) days in a cohort of 1610 subjects, including 920 normotensive, 313 hypertensive nonmedicated, and 377 hypertensive medicated individuals.

Methods and Results

Subjects underwent a multidisciplinary fasting program with a daily intake of ≈250 kcal. Weight and stress scores decreased during fasting, and the well‐being index increased, documenting a good tolerability. BP mean values decreased from 126.2±18.6/81.4±11.0 to 119.7±15.9/77.6±9.8 mm Hg (mean change, −6.5/3.8 mm Hg). BP changes were larger for hypertensive nonmedicated subjects (>140/90 mm Hg) and reduced by 16.7/8.8 mm Hg. This reduction reached 24.7/13.1 mm Hg for hypertensive nonmedicated subjects (n=76) with the highest BP (>160/100 mm Hg). In the normotensive group, BP decreased moderately by 3.0/1.9 mm Hg. Interestingly, we documented an increase of 6.3/2.2 mm Hg in a subgroup of 69 female subjects with BP <100/60 mm Hg. In the hypertensive medicated group, although BP decreased from 134.6/86.0 to 127.3/81.3 mm Hg, medication was stopped in 23.6% of the subjects, whereas dosage was reduced in 43.5% and remained unchanged in 19.4%. The decrease in BP was larger in subjects fasting longer. Baseline metabolic parameters, such as body mass index and glucose levels, as well as age, can be used to predict the amplitude of the BP decrease during fasting with a machine learning model.

Conclusions

Long‐term fasting tends to decrease BP in subjects with elevated BP values. This effect persisted during the 4 days of stepwise food reintroduction, even when subjects stopped their antihypertensive medication.

Registration

URL:
https://www.drks.de/drks_web/
; Unique identifier: DRKS00010111.
Causal Prefrontal Contributions to Stop-Signal Task Performance in Humans
Authors: Yeung Michael K. , Tsuchida Ami , Fellows Lesley K.
Published: November 2020

The frontal lobes have long been implicated in inhibitory control, but a full understanding of the underlying mechanisms remains elusive. The stop-signal task has been widely used to probe instructed response inhibition in cognitive neuroscience. The processes involved have been modeled and related to putative brain substrates. However, there has been surprisingly little human lesion research using this task, with the few existing studies implicating different prefrontal regions. Here, we tested the effects of focal prefrontal damage on stop-signal task performance in a large sample of people with chronic focal damage affecting the frontal lobes (n = 42) and demographically matched healthy individuals (n = 60). Patients with damage to the left lateral, right lateral, dorsomedial, or ventromedial frontal lobe had slower stop-signal RT compared to healthy controls. There were systematic differences in the patterns of impairment across frontal subgroups: Those with damage to the left or right lateral and dorsomedial frontal lobes, but not those with ventromedial frontal damage, were slower than controls to “go” as well as to stop. These findings suggest that multiple prefrontal regions make necessary but distinct contributions to stop-signal task performance. As a consequence, stop-signal RT slowing is not strongly localizing within the frontal lobes.
Whatever His Arguments, Whatever-Relatives Are Not Free Relatives: A Reply to Caponigro’s Reply
Authors: Donati Caterina , Foppolo Francesca , Konrad Ingrid , Cecchetto Carlo
Published: November 2020

This is a reply to Caponigro 2019, which argues that the phrase structure theory proposed in Donati and Cecchetto 2011 and Cecchetto and Donati 2015 falls short of accounting for the attested patterns of free relative clauses. Caponigro questions the reliability of the data supporting D&C’s hypothesis that ever-relatives introduced by a phrase (ever+NP relatives) should not be assimilated to free relatives. This reply reports the findings of four controlled experiments in English and Italian and discusses five properties that set free relatives apart from full relatives (occurrence with a complementizer, occurrence with a relative pronoun, infinitival use, absolute use, adverbial use). Crucially, ever+NP relatives do not pattern like free relatives in any of these five domains, either in Italian or in English. This clearly shows that ever-relatives are not a counterexample to D&C’s phrase structure theory. Another potential counterexample, Romanian free relatives, is also discussed. As for ever+NP relatives, in Italian they are shown to be garden-variety headed relatives, while in English they are headed relatives that involve a D-to-D movement that is responsible for the syntactic formation of the complex determiner what+ever.
Key to Mexican and Central American genera of Acanthocinini (Coleoptera, Cerambycidae, Lamiinae) with erect setae on elytral surface, excluding the Caribbean Islands
Authors: MONNÉ MIGUEL A. , SANTOS-SILVA ANTONIO , MONNÉ MARCELA L.
Published: November 2020

A key for identification of the 34 genera of Acanthocinini with erect setae on the elytra occurring in Mexico and Central America (excluding the Caribbean Islands) is provided. Leptrichillus Gilmour, 1960 is synonymized with Lepturgotrichona Gilmour, 1957, and Leptrichillus minutus Gilmour, 1960 is synonymized with Lepturges stigmaticus Bates, 1881 (currently, Lepturgotrichona stigmatica).
Integrative taxonomic review of the genus Synopsia Hübner, 1825 in the Middle East (Lepidoptera: Geometridae: Ennominae)
Authors: WANKE DOMINIC , HAUSMANN AXEL , SIHVONEN PASI , KROGMANN LARS , RAJAEI HOSSEIN
Published: November 2020

The geometrid genera Synopsia Hübner, 1825 and Synopsidia Djakonov, 1935 are revised, both being earlier validated at genus rank. Type specimens, original descriptions and additional specimens from different localities were examined. The revision is based on morphological characters, molecular data and distribution records. As a result, Synopsidia syn. nov. is regarded as junior synonym of the genus Synopsia. The synonymies of the species Scodonia tekkearia Christoph, 1883 and Synopsia znojkoi Djakonov, 1935 with Synopsia phasidaria phasidaria (Rogenhofer, 1873) comb. nov. are confirmed. Furthermore, Synopsidia phasidaria alvandi Wiltshire, 1966 syn. nov., Synopsidia phasidaria ardschira Brandt, 1938 syn. nov., Synopsidia phasidaria chiraza Brandt, 1938 syn. nov., Hashtaresia jodes Wehrli, 1936 syn. nov. and Synopsidia phasidaria mirabica Wehrli, 1941 syn. nov. are regarded as synonyms of Synopsia phasidaria phasidaria (Rogenhofer, 1873) comb. nov.. Synopsia phasidaria afghana (Wiltshire, 1966) comb. nov. is tentatively validated at subspecific rank. Synopsia centralis (Wiltshire, 1966) comb. nov., bona sp. is upgraded from subspecies to species level. Wing pattern, as well as male and female genitalia and diagnostic characters of examined genera and species are illustrated and analyzed. The distribution patterns of Synopsia phasidaria comb. nov. and Synopsia centralis bona sp., as well as the type localities of all discussed taxa, are provided. A complete checklist of the genus is given.
A new species of Chimarra Stephens 1829 (Trichoptera: Philopotamidae) and new caddisfly records from Rio Grande do Norte State, Brazil
Authors: QUEIROZ LARISSA L. , DIAS EVERTON S. , CALOR ADOLFO R.
Published: November 2020

Trichoptera is the largest order of exclusively aquatic insects, comprising more than 16,000 described species with cosmopolitan distribution. There are about 800 species recorded from Brazil so far, mostly from the North, Southeast, and South regions. In Northeastern Brazil, the state of Rio Grande do Norte has only one Trichoptera species recorded so far (Oecetis excisa). Here, Chimarra (Chimarra) potiguar n. sp. is described and illustrated. The new species can be easily distinguished from its congeners by the following features: Segment X has its mesal lobe elongate, sub-rectangular; lateral lobes long and rounded apically, dorsal margin with subapical invagination; the apex of each inferior appendage has a pronounced apicodorsal acute projection, and the phallotremal sclerite complex is curved, with small spines dorsally. Chimarra (C.) potiguar is morphologically similar to Chimarra (Chimarra) bidens, but the new species differs from it by the length and shape of the mesal lobe and lateral lobes of segment X, and by general shape of the basal regions of inferior appendages. Five additional caddisfly species are recorded for the first time from Rio Grande do Norte state: three in Hydropsychidae (Leptonema sparsum, Macrostemum hyalinum, and Smicridea (Smicridea) palifera) and two in Polycentropodidae (Cyrnellus fraternus and Cernotina bibrachiata).
Sentiment Analysis of Twitter Data to Examine the Movement of Exchange Rate and Sensex
Authors: Chethan N. , Sangeetha R.
Published: November 2020

In this paper tweets available on social media about USD/INR exchange rate, BSE Sensex, NSE Nifty have been collected and Sentiment Analysis using R programming has been performed. A sentiment score has been obtained for each of the sentences and also word cloud plot have been obtained.
In this paper twitter feeds are collected using the keywords: USD/INR, #USD/INR, #BSE, #Sensex, #NSE. For the purpose of obtaining the tweets, R programming is used. In this study to obtain the word cloud plot, the sentiment has been classified across 8 categories viz Anticipation, anger,
trust, surprise, sadness, joy, fear and disgust. On a day to day basis, Sentiment Analysis gives the overall sentiment on a given day stating if the sentiment for a given day is either Positive or Negative or whether it is Neutral. It also breaks down the tweets into various categories which
help in identifying the moods of the investors not only by the sentiment but also by the number of tweets. Further, the word cloud plot offers a simple and effective way of capturing the key events or news which was discussed on Twitter. Sentiment analysis can be used effectively by investors
to make a prediction of what direction the stock price movements will happen based on the sentiment prevailing in the market. This study also shows how R programming can be used to perform sentiment analysis on the stock price movement based on twitter feeds. Word cloud can be used to visualize
text data in which the size of each word cloud denotes its significance.
An Enhanced Web Based Attendance Application Using Global Positioning System and Face Recognition
Authors: Jabez J. , Keerthanaa V. , Kaviya V. , Gowri S.
Published: November 2020

The fashion of sustaining faculty attendance each day is one significant fundamental task in all institutions. The likelihood of having a fallacy, being tampered amidst for parsimonious reasons transpires further great while handling mythical ways of managing attendance. That embeds
the institution at the hazard, comprising abrupt efficient, furthermore even restricting the individual interruption in data. Besides, using a biometric mechanism engrosses more extended time plus subsistence of the design is too unusual. To complete with this wearisome process, we purpose
to design a Web-based application that includes locating GPS coordinates also facial id based upon front-facing cameras concerning registering the attendance of the student which performs the method simple plus more productive. Google GPS API and camera API that exists in this application
to permit the application increased flawless. The user’s attendance is verified based upon the GPS location and camera API. Also, the permission is done with facial ID via face recognition. This system necessity is sensitive to the campus location, i.e., except authorized admin, that
should not operate in areas outside the institution plant. A database is used to hold track of all the user’s attendance data. The application is a user-friendly UI in which the admin can analyze the detailed reports of all the users. A monthly report of a user can be downloaded by them
during the next day’s entry of their attendance. The application is capable of generating daily, monthly and yearly reports of all users. That, we intend to create alternative attendance system for organizations.
Classification and Prediction of Hypoglyceamia
Authors: Roobini M. S. , Kosaraju Veena Praneeth , Vemparala Akhil Babu , Lakshmi M. , Vignesh R.
Published: November 2020

As per the literature survey done from different publications, we came to a conclusion that there is a disease called hypoglycemia which is a similar type of disease polygenic disorder. This disease is having many levels and each level will be having its own values numerically, if it
crosses the limit, gets to fall into next level. The continuous glucose monitoring system is an effective tool which enables the user to monitor their blood glucose level. This is a Eupepsia Flu involving a significant number of people world-wide. If untreated Hypoglycemia related infirmities
in most target organs of the body may consecutively cataclysmic. Here many parameters will be taken to conclude the disease summary, among them the major parameters are Insulin level, BP, Urine Urea, Kidney functionality, liver functionality, gender and age. This project comprises most of
the diabetes survey as it is a beginning module of getting into hypoglycemia. To implement this project the algorithms using are Random Forest, Kth Nearest Neighbour (KNN), Advanced SVM, Convolution Neural Network (CNN), etc. From the result of all the algorithms we will find which
algorithm is best to predict and which parameter is responsible for the getting the disease whether due to excess produced in body or deficiency.
Prediction of Association Among Factors in a Disease Using Machine Learning
Authors: Roobini M. S. , Vaishali V. , Sangeethai T. , Lakshmi M. , Ponraj Anitha
Published: November 2020

Deep learning is being broadly used for various problems faced in medical field reports. There are various symptoms and early stimulation signs that leads to a dangerous effect in the human system. In today’s world, opinions and analysis accessible to us are one of the most critical
factors in formulating our prediction and similarities and its treatment must be done with great care some tests such as habits, neuro psychological and laboratory tests, neuro imaging and electroencephalography (EEG) are taken prior the treatment. By the usage of SVM algorithm find similar
symptoms these include with great accuracy. Various factors are taken into account and a thorough analysing is done with different types of classifier to find the most accurate one. SVM is a productive algorithm to be used but this paper mainly focuses on providing various analysing algorithm
for acquiring accurate result.
Prediction of Extreme Atmospheric Temperature with Meteorological Tower Observations Using Fuzzy Logic Technique
Authors: Sardar Maran P. , Ashok Kumar K. , Refonaa J. , Shabu Jany , Jesudoss A. , Lakshmanan L.
Published: November 2020

In recent years, weather prediction has drawn much interest in science. Temperature prediction is an important criterion in various fields such as weather prediction, electricity, aviation, and pollution, etc. The fuzzy logic model has a strong capability to express meaning and can
communicate data that is unreliable and unclear. A fuzzy knowledge rule-based methodology is analyzed in this paper for the prediction of extreme temperature on the southeast coast of Chennai. In order to estimate temperature, the present study uses different parameters of meteorological tower
observations in the fuzzy rule. Time-series observations of atmospheric temperature, relative humidity, atmospheric pressure, wind velocity and rainfall for 2010–2018 are analyzed to model the extreme temperature.
Lung Nodule Classification on Computed Tomography Using Neural Networks
Authors: Sivasangari A. , Deepa D. , Lakshmanan L. , Jesudoss A. , Roobini M. S.
Published: November 2020

Lung cancer is a leading health issue and the major cause of death among all types of cancers. CT scanning is the popular method for lung cancer diagnosis detection. Manual processing of tomograms take long time for diagnosis. It is not an easy task. This complex work can also reduce
the quality of diagnosis. Machine learning and neural network algorithm can be used to automatically process X-ray pictures, tomograms and PET images to detect diseases. The goal of the proposed work is to find any abnormal thing in lungs. Convolutional neural network is trained to classify
abnormal area from the normal cells. The detection algorithm is designed to determine the existence of cancer in tomography images and validation, training and testing using CT images. The proposed work investigates the performance of classifier by training algorithm with morphological feature
extraction. The performance results shows that proposed method achieves higher accuracy than existing methods.
An End-to-End Novel Forecasting Model for Crime Prediction Based on Big Data
Authors: Potlapally Sachit , Reddy Reddygari Manoj Kumar , Kamalesh Murari D.
Published: November 2020

Representation strategies are a decent extra investigation device for investigating high dimensional showcasing information by advertising specialists. Other at that point frequently utilized procedures they give a superior understanding into the first information. By utilizing the
picture design acknowledgment conceivable outcomes of the human minds, perception gives a ground-breaking extra investigating apparatus for information mining and unique model making in showcasing research. Utilizing the relationship scene the showcasing specialists could recognize significant
connection designs in their information. By choosing factors with fascinating connections with regards to an extra second representation obscure connections between groups of factors are found. This brought about new models to foresee client conduct.
Predictive Analysis of Diabetes Mellitus Using Machine Learning Techniques
Authors: Roobini M. S. , Sai Satwick Y. , Reddy A. Anil Kumar , Lakshmi M. , Deepa D. , Ponraj Anitha
Published: November 2020

In today’s world diabetes is the major health challenges in India. It is a group of a syndrome that results in too much sugar in the blood. It is a protracted condition that affects the way the body mechanizes the blood sugar. Prevention and prediction of diabetes mellitus is
increasingly gaining interest in medical sciences. The aim is how to predict at an early stage of diabetes using different machine learning techniques. In this paper basically, we use well-known classification that are Decision tree, K-Nearest Neighbors, Support Vector Machine, and
Random forest. These classification techniques used with Pima Indians diabetes dataset. Therefore, we predict diabetes at different stage and analyze the performance of different classification techniques. We Also proposed a conceptual model for the prediction of diabetes mellitus using different
machine learning techniques. In this paper we also compare the accuracy of the different machine learning techniques to finding the diabetes mellitus at early stage.
E-Learning Recommended System
Authors: Kumar M. Vijay , Reddy M. Venkata Pramid , Jany Shabu S. L. , Refonaa J. , Dhamodaran S.
Published: November 2020

In Hybrid e-learning recommender frameworks, to share the data between researcher is deficient, which makes it difficult to apply cross breed filtering (HF) proposal framework. We propose a communitarian filtering (CF) methods utilizing memory based separating to beat the issues which we experienced on crossover sifting strategies. Memory-based separating procedures utilize the whole client thing dataset to make various neighborhoods of clients. Neighborhood-based calculation decide the likeness between two clients or things, and produces an expectation for the client by taking the weighted normal of the considerable number of appraisals. Influence engendering implies that a student can advance toward dynamic students, and such practices can invigorate the moving practices of his neighbors. This self-association based suggestion approach accomplishes a steady structure dependent on circulated and base up practices of people. The trial results exhibit that memory based sifting strategies to give customized and diversified suggestions in e-learning situations.
Paddy Leaf Disease Analysis with Hybrid Algorithms Using Deep Learning Techniques
Authors: Roobini M. S. , Sai Talluri Pramod , Kamal Thatavarthi Sri Krishna , Ponraj Anitha , Sivasangari A. , Vignesh R.
Published: November 2020

According to the standards of India the financial, political and social security depend legitimately just as in a roundabout way on the yearly creation of rice. Wellspring of pay of hundred a large number of individuals relies just upon rice creation and nothing else included according
to the report of International Rice Research Institute (IRRI), 37% of the rice yield misfortune is because of illnesses. In a general public the rancher can deal with crop on-time with suitable treatment. By a yield shrewd rice is the hugest humanoid sustenance on the planet, which can be
taken care of legitimately than some other gather. Sequentially, the fundamental target of cultivating is to yield and feed nourishment to the country. In this way, these leaf ailments in any structures in rice crop will in general reason decrease in quality, yield and financial movement separately.
This venture reports a novel methodology for recognition and distinguishing proof of rice leaf infections by utilizing different propelled calculations, for example, VGG16, Alex net, and so on to expand the precision to more prominent level. To apply misfortune minimization procedure, for
example, straight out cross entropy to build the precision. It expands precision with utilization of mixture calculations by performing tweaking. Just as proposes the kind of substance compost to be utilized when any sort of malady gets distinguished. Just as proposes the sort of compound
compost to be utilized when any kind of sickness gets recognized.
Golden Hour Emergency Communication Link Using Internet of Things (IOT), ZigBee and Global System for Mobile Communications (GSM)
Authors: Prabu Shankar K. C. , Asthana Dhairya , Chaturvedi Kunik Vidyarthi
Published: November 2020

The use of vehicles becomes important everywhere in the world with an increase in the use of vehicles. There has been a steady increase in accidents as well, vehicle manufacturers are attaining the security features of their products by introducing advanced automated technologies to
avoid accidents from occurring but no significant growth has been seen on technologies to implement proper measures to ensure the safety of a person after the accident has occurred. Our solution implements various layers of security measures to prevent an unauthorized person from using a vehicle
and at the same time creating a steady communication between accident victims and the emergency responders. Our device uses various sensors to analyze the data live and instantly create a communication between victim and responder which provides detailed information about the intensity of
the accident along with the GPS location to increase the possibility of survival in a dire situation.
An Internet of Things-Based Privacy-Preserving Pub/Sub Model Using Block Chains
Authors: Refonaa J. , Chandra BillaKoushik , Bhargav CH V. V. S. , JanyShabu S. L. , Dhamodaran S. , Anu Maria , Gladance Mary , Jamunarani D.
Published: November 2020

In IOT, cyber physical system has made an extraordinary progress in a wide scope of dispersed joining situations. In the digital physical system, interconnected detecting gadgets gather information in the general condition and send information to every single intrigued hub through the
system, in this manner sharing all hub information. This procedure can be actualized by utilizing a pub/subsystem. Distribute buy in (pub-sub) systems give proficient extensible data trade between data makers (distributers) and data customers (endorsers). The pub-sub worldview decouples data
makers from buyers. Distributers can concentrate on producing data and endorsers on utilizing it, as opposed to on monitoring one another. Data spread and conveyance depends on content as opposed to on the personality or address of the makers and purchasers. The exponential develop of web
of things applications prompts proposing haze registering structure that is geospatially dispersed and inertness delicate. Since off beat dispersal of data is the primary issue for run of the mill circulated applications, distribute buy in system is acquainted as a key innovation with give
nonconcurrent correspondences to disseminated systems. We propose a privacy preserving substance based distribute/buy in conspire with differential security to accomplish proficient privacy preserving validation, information uprightness, and distribute buy in classification. This system exhibits
the primary extensive and down to earth usage of an ongoing privacy preserving pub-sub system.
Efficient and Effective Duplicate Detection in Hierarchical Data
Authors: Roobini M. S. , Sumanth Kumar P. B. S. , Raviteja Reddy P. B. , Ponraj Anitha , Aruna J.
Published: November 2020

Although there is a long profession on distinguishing duplicates, just a handful in social data arrangements center around copy detection in ever more complex progressive systems, including XML data. Right now, present a novel technique for XML duplicate discovery, Renamed XMLDup. XMLDup
utilizes a Bayesian algorithm defining the chance of duplicating two XML components, taking into account the data within the components, but also how data is structured. Likewise, to improve the effectiveness of Unit Review, Novel technique for pruning, equipped for noteworthy increases over
the un-streamlined calculation rule, is introduced. We demonstrate through trials that our estimate is can accomplish high accuracy via trials we show our estimation is outflanking another cutting-edge duplicate discovery arrangement, both as far as proficiency and of adequacy.
Energy Meter Using Telegram Bot
Authors: Venkat M. , Kartik R. , Krishna P. Vamshi , Gautham  , Radhika P.
Published: November 2020

An individual from the power office, whose obligation is to peruse the energy meter and handover the bills to the proprietors of that house each month. This is only meter readings, we need to take care of the tabs. The fundamental downside of this framework is that a person must go
zone and he must peruse the meter of every house and handover the bills. Ordinarily, mistakes like additional bill sum or notice from the electric board despite the fact that the bills are paid have regular blunders. To beat this disadvantage we have accompanied a thought which will dispense
with the outsider between the buyer and specialist co-op, even the mistakes will be survived. Right now, the thought of shrewd energy meter utilizing IoT and Arduino have been presented. Right now, we are utilizing Arduino on the grounds that it is energy effective for example it expends less
force. Right now, meters that are as of now introduced at our homes are not supplanted, however a little alteration on the as of now introduced meters can change the current meters into smart meters. Programmed ON and OFF of the meter. The threshold setting and sending of notice is an extra
errand that is performing.
Electrocardiogram Beat Classification Using Data Filtration Technique and Support Vector Machine
Authors: Jain Paras , Praneeth CH. N. V. S. , Kannan Iragavarapu , Sai Potluri Harsha , Abel Jaba Deva Krupa
Published: November 2020

This work addresses the automatic classification of arrhythmia beats into four generalized classes as described by the Association for the Advancement of Medical Instrumentation (AAMI) standard. We propose a method that includes time-series, statistical and frequency features of RR-interval,
DWT, and EMD analysis of QRS morphology. Also, a data filtration technique using support vector selection and under-sampling is applied to find those features as well as data points having significant prediction capabilities. While testing the above combination on MIT-BIH arrhythmia database,
adopting the inter-patient paradigm, we achieved 70%, 99.79%, 64.5%, and 80.55% Se and 61.76%, 94.64%, 83.22%, and 77.48% PPV for F, N, SVEB, and VEB classes respectively. Further, the proposed method reduced the classifier’s complexity through feature selection and computation time
by data reduction while maintaining the generalization capability of the model. Another finding includes the significant contribution that RR-interval, 180–360 Hz and 0–45 Hz band power, and non-linear statistical characteristics have in distinguishing the arrhythmia classes. The
feature and data selection criterion used is F -score and one-class classification by RBF-SVM respectively. The classifier used for building the final model is SVM with the cubic kernel.
Advanced Android Crime Tracker
Authors: Pandian Asha , Prakash Ravi , Prakash Swetabh , Rohith Kandula
Published: November 2020

We have proposed an advanced android crime tracker mobile application which will track the status of the investigation as well as it also tracks complaints reported by the public. The main purpose of the application is to help higher authorities like CBI and police to speed up their
investigation process and track status of multiple cases at a time. In this application, public will help them to get the crime details quickly and then they can take action immediately. It will help the police to know the crime ratio in the society. This application will show all the updated
crime post and once case is solved then admin can delete the post and can also update the status of investigation. Public can upload photos of crime spot and location of crime spot and photos of items found on scene. User can also post their missing person details by doing same procedure.
User can also check the status updated by the admin and incase of any query or emergency; the user can chat with the police with the help of chat box. For women safety, we have provided a SOS button which will call police by clicking on that button. This application is designed in such a way
that all the post which is uploaded by peoples can be seen by police also. After that they can take action immediately on it and they can write the status of the crime. This system will speed up the crime investigation process.
Brain Tumor Segmentation Using Deep Learning
Authors: Pandian Asha , Kumar Bala Gopi Sai , Revanth Venkata , Kiran Kanipakam Sai
Published: November 2020

Manual recognition of the cerebrum tumor for malignant growth determination from MRI pictures is a troublesome, repetitive and tedious assignment. The precision and the power of cerebrum Tumor discovery in this way, are significant for the determination, treatment arranging, and treatment
result assessment. Generally, the programmed cerebrum tumor location techniques use hand structured highlights. Correspondingly, customary strategies for profound learning, for example, ordinary neural systems require a lot of commented on information to learn from, which is frequently hard
to acquire in clinical area. Here, we portray another model two-pathway-bunch CNN (Convolutional Neural Network) design for cerebrum tumor recognition, which misuses neighborhood highlights and worldwide relevant highlights at the same time. This model implements equivariance in the two-pathway
CNN model to decrease dangers and over fitting parameter sharing. At last, we implant the course engineering into two-pathway-brunch CNN in which the yield of an essential CNN is treated as an extra source and connected at the last year. Approval of the model on BRATS2013 and BRATS2015 information
collections uncovered that inserting of a gathering CNN in to a two pathway engineering improved the general execution over the as of now distributed best in class while computational multifaceted nature stays alluring.
Decentralized Privacy Protection Framework Based on Attribute-Based Signcryption
Authors: Sellam V. , Bommana Neha Pranadika , Sruthi V. C.
Published: November 2020

In the last decade, the digital world has widely changed how we handle our daily actions on the internet. India has the second-largest internet users in the globe with about 370 million users. The Indian business market has understood the significance of the threats in managing digital
data. This has led to the increased allocation of budgets for the implementation of security to protect data from breaches. The data security has been constantly improvised and the existing systems include various schemes focusing on central authority which must be fully trusted and is responsible
for both attribute management and key generation. This system introduces multi-authority using cipher-text instead of key-policy ABSC scheme without destroying the attribute security. Since, the mechanism to verify does not require plaintext. It accepts any Monotone Boolean Function. Finally,a
partial decryption algorithm is used to outsource operations to the cloud to save computational resources.
Machine Learning Algorithm Based Bandwidth Prediction for Internet Usage
Authors: Rajaram J. , Nalini M. , Vadivelan N.
Published: November 2020

The applicability of framework structure and affiliation arranging recognize a basic activity in the bandwidth prediction. The procedure for predicting the framework use is to see the basic transmission limit with respect to future periods. This prediction helps with utilizing the techniques
workplaces in the saint way. Thinking about the fundamental cost of bandwidth, at top hours of a framework traffic we can follow an amazing sort of plan to purchase. In this paper, the past use data of FWDR organize centers is at risk to univariate direct time plan ARIMA model after precise
change is used to calculate necessary bandwidth limit concerning future needs. The anticipated data is veered from the obvious data gained from a for all intents and purposes indistinguishable framework and the foreseen data has been viewed as inside ten percent MAPE. This design reduction
the MAPE by eleven point seventy-one percentage and fifteen point forty-two percent of self-rulingly when stood separated from the non-able changed ARIMA model at ninety-nine percent CI. The outcome show that the suitably changed ARIMA design has improved show when meandered from non-intentionally
changed ARIMA model. Increasingly significant dataset can be passed on with season alterations and thought of expanded length groupings, for dynamically unequivocal and longer term needs.
Intelligence Driven-Depression Identification of Facebook Users
Authors: Yamini B. , Glory J. Sherine , Aravindkumar S.
Published: November 2020

Depression is a severe dispute in wide-ranging personal health for all. Every year millions of populace endures depression and some few of them receive adequate treatment. Through their Face-book postings and statuses, whatsapp statuses and shares of post, expressive words handling
during they speak or post, emotional icons or pictures they post or from their browse histories, expressing user interests, feelings and daily routines can be measured. Many researchers proved that usage of User Generated Content in a proper mode helps to decide one’s depression level.
Analyzing the Generated user related contents helps in prediction of depression (Marcus, M., et al., 2012. Depression: A Global Public Health Concern. WHO Dataset, pp.6–8). Leveraging Social media like Facebook, Twitter and Instagram has valuable sign for designating depression in persons.
In the proposed work, potential use of Facebook to sense and identify main depressive chaos in individual is explored. Sharing or posting of images or text by an individual plays a vital role in identifying the victim with depression. Text based analysis done on posted or shared textual content,
whereas Chromatic analysis done on the images along with text based analysis for the images with textual contents. Emotion features and color features of these two analyses are used to identify the depression level of the individual by psychological classification using Support Vector Machine
(SVM). The performance of the SVM is compared with Naïve Bayes Classifier. The Findings and methods of the proposed work, presents a road map in developing new methodologies in identifying the major depression levels of those who suffer and to guide healthcare agencies.
Convolutional Recurrent Neural Networks Based Speech Emotion Recognition
Authors: Gayathri P. , Priya P. Gowri , Sravani L. , Johnson Sandra , Sampath Visanth
Published: November 2020

Recognition of emotions is the aspect of speech recognition that is gaining more attention and the need for it is growing enormously. Although there are methods to identify emotion using machine learning techniques, we assume in this paper that calculating deltas and delta-deltas for
customized features not only preserves effective emotional information, but also that the impact of irrelevant emotional factors, leading to a reduction in misclassification. Furthermore, Speech Emotion Recognition (SER) often suffers from the silent frames and irrelevant emotional frames.
Meanwhile, the process of attention has demonstrated exceptional performance in learning related feature representations for specific tasks. Inspired by this, propose a Convolutionary Recurrent Neural Networks (ACRNN) based on Attention to learn discriminative features for SER, where the Mel-spectrogram
with deltas and delta-deltas is used as input. Finally, experimental results show the feasibility of the proposed method and attain state-of-the-art performance in terms of unweighted average recall.
Scale-Similarity Subgrid-Scale Turbulence Closure for Supercell Simulations at Kilometer-Scale Resolutions: Comparison against a Large-Eddy Simulation
Authors: Sun Shiwei , Zhou Bowen , Xue Ming , Zhu Kefeng
Published: November 2020

In numerical simulations of deep convection at kilometer-scale horizontal resolutions, in-cloud subgrid-scale (SGS) turbulence plays an important role in the transport of heat, moisture, and other scalars. By coarse graining a 50 m high-resolution large-eddy simulation (LES) of an idealized supercell storm to kilometer-scale grid spacings ranging from 250 m to 4 km, the SGS fluxes of heat, moisture, cloud, and precipitating water contents are diagnosed a priori. The kilometer-scale simulations are shown to be within the “gray zone” as in-cloud SGS turbulent fluxes are comparable in magnitude to the resolved fluxes at 4 km spacing, and do not become negligible until ~500 m spacing. Vertical and horizontal SGS fluxes are of comparable magnitudes; both exhibit nonlocal characteristics associated with deep convection as opposed to local gradient-diffusion type of turbulent mixing. As such, they are poorly parameterized by eddy-diffusivity-based closures. To improve the SGS representation of turbulent fluxes in deep convective storms, a scale-similarity LES closure is adapted to kilometer-scale simulations. The model exhibits good correlations with LES-diagnosed SGS fluxes, and is capable of representing countergradient fluxes. In a posteriori tests, supercell storms simulated with the refined similarity closure model at kilometer-scale resolutions show better agreement with the LES benchmark in terms of SGS fluxes than those with a turbulent-kinetic-energy-based gradient-diffusion scheme. However, it underestimates the strength of updrafts, which is suggested to be a consequence of the model effective resolution being lower than the native grid resolution.
The Double-Peaked El Niño and Its Physical Processes
Authors: Shin Na-Yeon , Kug Jong-Seong , McCormack F. S. , Holbrook Neil J.
Published: November 2020

Recently, El Niño diversity has been paid much attention because of its different global impacts. However, most studies have focused on a single warm peak in sea surface temperature anomalies (SSTAs), either in the central Pacific or the eastern Pacific Ocean. Here, we demonstrate from observational analyses that several recent El Niño events show double warm peaks in SSTA—called “double-peaked (DP) El Niño”—that have only been observed since 2000. The DP El Niño has two warm centers, which grow concurrently but separately, in both the central and eastern Pacific. In general, the atmospheric and oceanic patterns of the DP El Niño are similar to those of the warm-pool (WP) El Niño from the development phase, such that the central Pacific peak is developed by the zonal advective feedback and reduced wind speed anomalies. However, a distinctive difference exists in the eastern Pacific where the DP El Niño has a second SSTA peak. In addition, the DP El Niño shows more distinctive anomalous precipitation along the Pacific intertropical convergence zone (ITCZ) when compared with the WP El Niño. We demonstrate that the peculiar precipitation anomalies along the Pacific ITCZ play a critical role in enhancing the equatorial westerly wind stress anomalies, which help to develop the eastern SSTA peak by deepening the thermocline in the eastern Pacific.
Emulation of Community Land Model Version 5 (CLM5) to Quantify Sensitivity of Soil Moisture to Uncertain Parameters
Authors: Gao Xiang , Avramov Alexander , Saikawa Eri , Schlosser C. Adam
Published: November 2020

Land surface models (LSMs) are limited in their ability to reproduce observed soil moisture partially due to uncertainties in model parameters. Here we conduct a variance-based sensitivity analysis to quantify the relative contribution of different model parameters and their interactions to the uncertainty in the surface and root-zone soil moisture in the Community Land Model 5.0 (CLM5). We focus on soil-texture-related parameters (porosity, saturated matric potential, saturated hydraulic conductivity, shape parameter of soil-water retention model) and organic matter fraction. A Gaussian process emulator is constructed based on CLM5 simulations and used to estimate soil moisture across the five-dimensional parameter space for sensitivity analysis. The procedure is demonstrated for four seasons across various U.S. sites of distinct soil and vegetation types. We find that the emulator captures well the CLM5 behavior across the parameter space for different soil textures and seasons. The uncertainties of surface and root-zone soil moisture are dominated by the uncertainties in porosity and shape parameter with negligible parametric interactions. However, relative importance of porosity versus shape parameter varies with soil textures (sites), depths (surface versus root zone), and seasons. At most of the sites, surface soil moisture uncertainty is attributed largely to shape parameter uncertainty, while porosity uncertainty is more important for the root-zone soil moisture uncertainty. All individual parameter and interaction effects demonstrate less variability across different soil textures and seasons for root zone than for surface soil moisture. These results provide scientific guidance to prioritize reducing the uncertainty of sensitive parameters for improving soil moisture modeling with CLM.
Tidal and Near-Inertial Internal Waves over the Reykjanes Ridge
Authors: Vic Clément , Ferron Bruno , Thierry Virginie , Mercier Herlé , Lherminier Pascale
Published: November 2020

Internal waves in the semidiurnal and near-inertial bands are investigated using an array of seven moorings located over the Reykjanes Ridge in a cross-ridge direction (57.6°–59.1°N, 28.5°–33.3°W). Continuous measurements of horizontal velocity and temperature for more than 2 years allow us to estimate the kinetic energy density and the energy fluxes of the waves. We found that there is a remarkable phase locking and linear relationship between the semidiurnal energy density and the tidal energy conversion at the spring–neap cycle. The energy-to-conversion ratio gives replenishment time scales of 4–5 days on the ridge top versus 7–9 days on the flanks. Altogether, these results demonstrate that the bulk of the tidal energy on the ridge comes from near-local sources, with a redistribution of energy from the top to the flanks, which is endorsed by the energy fluxes oriented in the cross-ridge direction. Implications for tidally driven energy dissipation are discussed. The time-averaged near-inertial kinetic energy is smaller than the semidiurnal kinetic energy by a factor of 2–3 but is much more variable in time. It features a strong seasonal cycle with a winter intensification and subseasonal peaks associated with local wind bursts. The ratio of energy to wind work gives replenishment time scales of 13–15 days, which is consistent with the short time scales of observed variability of near-inertial energy. In the upper ocean (1 km), the highest levels of near-inertial energy are preferentially found in anticyclonic structures, with a twofold increase relative to cyclonic structures, illustrating the funneling effect of anticyclones.