bluuebunny/crossref_metadata_2025 huggingface dataset:
Live tracking of saline for betterment of patient
Authors: Zende Sayli Anand , Kulkarni Tanvi , Yadav Shubhada , Biradar Ajay , Devare Avinash
Published: November 2020

In hospitals, Saline is fed to patients to treat dehydration and use of saline improves their health. In current health care measures, whenever a saline is fed to any patient, the patient must be continuously monitored by nurse, doctor or caretaker. So basically, in all the hospitals nurse or caretaker is responsible for monitoring of saline. Due to the avoidance of nurses towards the saline level monitoring or lack of knowledge it can harm to the patients health. Therefore, to stop the patient from obtaining injured and shield their lives throughout saline feeding amount, the saline level observance system are developed. The planned system is made Internet web of Things (IoT) platform. The planned system includes of devices which can act as tier sensor for observance the crucial level of the saline within the saline bottle. Whenever the amount of the saline reaches to the predefined crucial level, then the nurses, caretaker, doctors are alerted through the alarm associate in Nursing an alert message are sent through the utilization of web to the involved nurses and doctors that there's a requirement for replacement of the saline bottle. This planned system may be utilized efficiently in homes as well as hospitals.
Quadcopter based emergency medikit delivery system for hill stations
Authors: Balamurugan C. R. , Revathy S. M. , Vijayakumar P.
Published: November 2020

Now-a-days UAV’s are more common for search, rescue and surveillance.  A quad copter UAV system is proposed to be used for aerial transportation of medicine. A Quadcopter has a drive chassis having a propeller, ESC, motor, frames and battery. Quadcopter is supported with GPS to know the exact position. To know the exact location and delivering medicine to accident area, an audio and video system is used. The quad frame built from quality materials, which are reinforced and much more stronger, this reduces arm breakage. A set of two plastic propellers, one normal and one pusher (reverse) to rotate and lift up the quadcopter. The brushless out-runner will provide more power with its high efficiency, long run times. The ESC includes programmable motor braking, soft start for helicopters and planes, timing, throttle input range and low-voltage cutoff. Lithium-Polymer (Li-Po) battery for very light weight, small size and durability without losing charging capacity. The whole quadcopter process can be monitored and controlled by a remote control system, quadcopter will capture the live video and current status can be seen visualized and provides information about all the other exact condition in real time.
Clinical Evaluation of the Polygenetic Background of Blood Pressure in the Population-Based Setting
Authors: Giontella Alice , Sjögren Marketa , Lotta Luca A. , Overton John D. , Baras Aris ,   on behalf of Regeneron Genetics Center, Minuz Pietro , Fava Cristiano , Melander Olle
Published: November 2020

The clinical value of the polygenetic component of blood pressure (BP) is commonly questioned. We evaluated a genetic risk score for BP (BP-GRS
858
), based on the most recently published genome-wide association studies variants that were significantly associated with either systolic BP or diastolic BP, for prediction of hypertension and cardiovascular end points. The genotyping was performed in 2 urban-based prospective cohorts: the Malmö Diet and Cancer (n=29 295) and the Malmö Preventive Project (n=9367) and a weighted BP-GRS
858
based on 858 SNPs was calculated. At baseline, we found a difference of 9.0 mm Hg (systolic BP) and 4.8 mm Hg (diastolic BP) between the top and the bottom quartile of BP-GRS
858
. In Malmö Preventive Project, the top versus bottom quartile of BP-GRS
858
was associated with a doubled risk of incident hypertension (odds ratio, 2.05 [95% CI, 1.75–2.39],
P
=1.4×10
−21
), a risk higher than that of body mass index, as evaluated in quartiles. In Malmö Diet and Cancer, significant association was found between the age and sex-adjusted BP-GRS
858
and the incidence of total cardiovascular events, stroke, coronary artery disease, heart failure, atrial fibrillation, and total mortality. Most of these associations remained significant after adjusting for traditional risk factors, including hypertension. BP-GRS
858
could contribute predictive information regarding future hypertension, with an effect size comparable to other well-known risk factors such as obesity, and predicts cardiovascular events. Given that the exposure to high polygenetic risk starts at birth, we suggest that the BP-GRS
858
might be useful to identify children or adolescents who would benefit from early hypertension screening and treatment.
Arterial Remodeling of the Intracranial Arteries in Patients With Hypertension and Controls
Authors: van Hespen Kees M. , Mackaaij Claire , Waas Ingeborg S.E. , de Bree Marloes P. , Zwanenburg Jaco J.M. , Kuijf Hugo J. , Daemen Mat J.A.P. , Hendrikse Jeroen , Hermkens Dorien M.A.
Published: November 2020

The intracranial arteries play a major role in cerebrovascular disease, but arterial remodeling due to hypertension has not been well described in humans. We aimed to quantify this remodeling for: the basilar artery, the vertebral, internal carotid, middle/anterior (inferior)/posterior cerebral, posterior communicating, and superior cerebellar arteries of the circle of Willis. Ex vivo circle of Willis specimens, selected from individuals with (n=24) and without (n=25) a history of hypertension, were imaged at 7T magnetic resonance imaging using a 3-dimensional gradient-echo sequence. Subsequently, histological analysis was performed. We validated the vessel wall thickness and area measurements from magnetic resonance imaging against histology. Next, we investigated potential differences in vessel wall thickness and area between both groups using both techniques. Finally, using histological analysis, we investigated potential differences in arterial wall stiffness and atherosclerotic plaque severity and load. All analyses were unadjusted. Magnetic resonance imaging and histology showed comparable vessel wall thickness (mean difference: 0.04 mm (limits of agreement:−0.12 to 0.19 mm) and area (0.43 mm
2
[−0.97 to 1.8 mm
2
]) measurements. We observed no statistically significant differences in vessel wall thickness and area between both groups using either technique. Histological analysis showed early and advanced atherosclerotic plaques in almost all arteries for both groups. The arterial wall stiffness was significantly higher for the internal carotid artery in the hypertensive group. Concluding, we did not observe vessel wall thickening in the circle of Willis arteries in individuals with a history of hypertension using either technique. Using histological analysis, we observed a difference in vessel wall composition for the internal carotid artery.
Low‐Density Lipoprotein Cholesterol Corrected for Lipoprotein(a) Cholesterol, Risk Thresholds, and Cardiovascular Events
Authors: Willeit Peter , Yeang Calvin , Moriarty Patrick M. , Tschiderer Lena , Varvel Stephen A. , McConnell Joseph P. , Tsimikas Sotirios
Published: November 2020

Background

Conventional "low‐density lipoprotein cholesterol (LDL‐C)" assays measure cholesterol content in both low‐density lipoprotein and lipoprotein(a) particles. To clarify the consequences of this methodological limitation for clinical care, our study aimed to compare associations of “LDL‐C” and corrected LDL‐C with risk of cardiovascular disease and to assess the impact of this correction on the classification of patients into guideline‐recommended LDL‐C categories.

Methods and Results

Lipoprotein(a) cholesterol content was estimated as 30% of lipoprotein(a) mass and subtracted from “LDL‐C” to obtain corrected LDL‐C values (LDL‐C
corr30
). Hazard ratios for cardiovascular disease (defined as coronary heart disease, stroke, or coronary revascularization) were quantified by individual‐patient‐data meta‐analysis of 5 statin landmark trials from the Lipoprotein(a) Studies Collaboration (18 043 patients; 5390 events; 4.7 years median follow‐up). When comparing top versus bottom quartiles, the multivariable‐adjusted hazard ratio for cardiovascular disease was significant for “LDL‐C” (1.17; 95% CI, 1.05–1.31;
P
=0.005) but not for LDL‐C
corr30
(1.07; 95% CI, 0.93–1.22;
P
=0.362). In a routine laboratory database involving 531 144 patients, reclassification of patients across guideline‐recommended LDL‐C categories when using LDL‐C
corr30
was assessed. In “LDL‐C” categories of 70 to <100, 100 to <130, 130 to <190, and ≥190 mg/dL, significant proportions (95% CI) of participants were reassigned to lower LDL‐C categories when LDL‐C
corr30
was used: 30.2% (30.0%–30.4%), 35.1% (34.9%–35.4%), 32.9% (32.6%–33.1%), and 41.1% (40.0%–42.2%), respectively.

Conclusions

“
LDL‐C” was associated with incident cardiovascular disease only when lipoprotein(a) cholesterol content was included in its measurement. Refinement in techniques to accurately measure LDL‐C, particularly in patients with elevated lipoprotein(a) levels, is warranted to assign risk to the responsible lipoproteins.
Recurrent Atherosclerotic Cardiovascular Event Rates Differ Among Patients Meeting the Very High Risk Definition According to Age, Sex, Race/Ethnicity, and Socioeconomic Status
Authors: An Jaejin , Zhang Yiyi , Muntner Paul , Moran Andrew E. , Hsu Jin‐Wen , Reynolds Kristi
Published: November 2020

Background

The risk for atherosclerotic cardiovascular disease (ASCVD) events may differ by sociodemographic factors among patients meeting the definition of very high risk according to the 2018 American Heart Association/American College of Cardiology cholesterol guideline, leading to treatment disparities. We estimated the risk for recurrent ASCVD events among adults meeting the definition of very high risk by age, sex, race/ethnicity, and socioeconomic status in a US integrated healthcare system.

Methods and Results

The study cohort included Kaiser Permanente Southern California members aged ≥21 years with a history of clinical ASCVD on September 30, 2009. Very high risk for recurrent ASCVD was defined by a history of ≥2 major ASCVD events or a history of 1 major event along with ≥2 high‐risk conditions. Patients were followed through 2015 for a first recurrent ASCVD event. Of 77 101 patients with ASCVD, 50.8% met the definition for very high risk. Among patients meeting the definition of very high risk, recurrent ASCVD rates were higher in older (>75 years) versus younger patients (21–40 years) (sex‐adjusted hazard ratio [HR] [95% CI] 1.85; 1.23–2.79), non‐Hispanic Black patients versus non‐Hispanic White patients (age‐, sex‐adjusted HR, 1.32; 1.23–1.41), those who lived in neighborhoods with lower (<$35k) versus higher annual household income (≥$80k) (HR, 1.20; 1.11–1.30), or with lower (≥31.2%) versus higher education levels (<8.8% high school or lower) (HR, 1.26; 1.19–1.34).

Conclusions

Disparities in the risk for recurrent ASCVD events were present across sociodemographic factors among very high risk patients. The addition of sociodemographic factors to current definitions of very high risk could reduce health disparities.
Spironolactone Use and Improved Outcomes in Patients With Heart Failure With Preserved Ejection Fraction With Resistant Hypertension
Authors: Tsujimoto Tetsuro , Kajio Hiroshi
Published: November 2020

Background

Resistant hypertension is a salt‐retaining condition possibly attributable to inappropriate aldosterone secretion.

Methods and Results

This study was a secondary analysis of the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist) trial. Patients with heart failure with preserved ejection fraction (HFpEF) with (n=1004) and without (n=2437) resistant hypertension were included. Resistant hypertension was defined as systolic blood pressure ≥130 mm Hg and/or diastolic blood pressure ≥80 mm Hg in a patient with hypertension, despite the concurrent use of a renin‐angiotensin system blocker (angiotensin‐converting enzyme inhibitor/angiotensin receptor blocker), a calcium channel blocker, and a diuretic; or as those patients using ≥4 classes of antihypertensive medication. The primary outcome was a composite of cardiovascular death, aborted cardiac arrest, or heart failure hospitalization. We analyzed hazard ratios (HRs) for outcomes with 95% CIs in the spironolactone group and compared them with the placebo group using Cox proportional hazard models. The risk of primary outcome events in patients with HFpEF with resistant hypertension was significantly lower in the spironolactone group than in the placebo group (HR, 0.70; 95% CI, 0.53–0.91;
P
=0.009), whereas the risk of primary outcome events in patients with HFpEF without resistant hypertension was not significantly different between the 2 groups (HR, 1.00; 95% CI, 0.83–1.20;
P
=0.97). There was a significant interaction between spironolactone use and resistant hypertension (
P
=0.03). Similar associations were also observed in patients with HFpEF from the Americas (United States, Canada, Brazil, and Argentina) only.

Conclusions

Spironolactone may be an effective add‐on medication for patients with HFpEF with resistant hypertension taking angiotensin‐converting enzyme inhibitors/angiotensin receptor blockers, calcium channel blockers, and diuretics.
Direct Oral Anticoagulants in Patients With Inherited Thrombophilia and Venous Thromboembolism: A Prospective Cohort Study
Authors: Campello Elena , Spiezia Luca , Simion Chiara , Tormene Daniela , Camporese Giuseppe , Dalla Valle Fabio , Poretto Anna , Bulato Cristiana , Gavasso Sabrina , Radu Claudia Maria , Simioni Paolo
Published: November 2020

Background

In this prospective cohort study, we aimed to evaluate the efficacy and safety of direct oral anticoagulants (DOACs) versus heparin/vitamin K antagonists for the treatment of venous thromboembolism (VTE) in patients with inherited thrombophilia.

Methods and Results

We enrolled consecutive patients with acute VTE and inherited thrombophilia treated with DOACs (cases) or heparin/vitamin K antagonists (controls), matched for age, sex, ethnicity, and thrombophilia type. End points were VTE recurrence and bleeding complications; residual vein thrombosis and post‐thrombotic syndrome; VTE recurrence after anticoagulant discontinuation. Two hundred fifty‐five cases (age 52.4±17.3 years, Female 44.3%, severe thrombophilia 33.1%) and 322 controls (age 49.7±18.1 years, Female 50.3%, severe thrombophilia 35.1%) were included. The cumulative incidence of VTE recurrence during anticoagulation was 1.09% in cases versus 1.83%, adjusted hazard ratio (HR) 0.67 (95% CI, 0.16–2.77). The cumulative incidence of bleeding was 10.2% in cases versus 4.97%, HR 2.24 (95% CI 1.10–4.58). No major bleedings occurred in cases (versus 3 in controls). No significant differences regarding residual vein thrombosis and post‐thrombotic syndrome. After anticoagulant discontinuation, DOACs yielded a significantly lower 2‐year VTE recurrence risk versus traditional anticoagulants (HR, 0.61 [95% CI, 0.47–0.82]).

Conclusions

DOACs and heparin/vitamin K antagonists showed a similar efficacy in treating VTE in patients with thrombophilia. Although major bleeding episodes were recorded solely with heparin/vitamin K antagonists, we noted an overall increased bleeding rate with DOACs. The use of DOACs was associated with a lower 2‐year risk of VTE recurrence after anticoagulant discontinuation.
The Potential and Limits of Environmental Disclosure Regulation: A
Global Value Chain Perspective Applied to Tanker Shipping
Authors: Poulsen René Taudal , Ponte Stefano , van Leeuwen Judith , Rehmatulla Nishatabbas
Published: November 2020

Exploring how transnational environmental governance and the operation of global value chains (GVCs) intersect is key in explaining the circumstances under which mandatory disclosure can improve the environmental footprint of business operations. We investigate how the governance dynamics of the tanker shipping value chain (a major emitter of greenhouse gases) limits the effectiveness of the European Union (EU) monitoring, reporting, and verification (MRV) regulation, which mandates the disclosure of greenhouse gas emissions for ships calling at EU ports. Although MRV seeks to help shipowners and ship managers save fuel and reduce emissions, it does not address the complexity of power relations along the tanker shipping value chain and currently cannot disentangle how different actors influence the design, operational, commercial, and ocean/weather factors that together determine fuel consumption. In particular, the EU MRV neglects to reflect on how oil majors exert their power and impose their commercial priorities on other actors, and thus co-determine fuel use levels. We conclude that, in its current form, the EU MRV is unlikely to lead to significant environmental upgrading in tanker shipping. More generally, we argue that regulators seeking to facilitate environmental upgrading need to expand their focus beyond the unwanted behaviors of producers of goods and providers of services to also address the incentive structures and demands placed on them by global buyers.
Acanthostachys calcicola (Bromeliaceae, Bromelioideae), a new species from a limestone outcrop in Tocantins State, Brazil

Authors: MARCUSSO GABRIEL MENDES , MONRO ALEXANDRE K. , MELO PABLO HENDRIGO ALVES DE , LOMBARDI JULIO ANTONIO
Published: November 2020

A new species of bromeliad, Acanthostachys calcicola, a third taxon for the genus, is here described. A. calcicola was discovered during field work in the limestone outcrops of south-eastern Tocantins, central Brazil. It is most similar to A. strobilacea, but differs from it in petal colour and length, and exserted stamens and pistil. A conservation assessment classifies A. calcicola as endangered (EN) with extinction.
Brazilian Diplosmittia Sæther: D. trifida sp. n. and new records of D. plaumanni Pinho, Mendes & Andersen, 2009 (Diptera: Chironomidae)
Authors: SHIMABUKURO ERIKA MAYUMI , LAMAS CARLOS JOSÉ EINICKER , PINHO LUIZ CARLOS
Published: November 2020

Sæther (1981) erected the genus Diplosmittia based on a species from Saint Lucia and Saint Vincent in the British West Indies. The genus was revised by Pinho et al. (2009). To date, the genus comprises ten species: Diplosmittia harrisoni Sæther, 1981; D. carinata Sæther, 1985; D. recisus Sæther, 1988; D. beluina Andersen, 1996; D. forficata Andersen, 1996; D. plaumanni Pinho, Mendes & Andersen, 2009; D. boraceia Pinho, Mendes & Andersen, 2009; D. aragua Pinho, Mendes & Andersen, 2009; D. cerayma Pinho, Mendes & Andersen, 2009; and D. caribensis Wiedenbrug & Silva, 2016. Diplosmittia sasai Makarchenko & Makarchenko, 2005 was placed as a synonym of Pseudosmittia mathildae Albu, 1968 by Makarchenko & Makarchenko (2008). Except for D. carinata from Michigan, U.S.A., all species are Neotropical and mostly recorded from the Caribbean and northern part of South America. During field work in a remote mountainous region in the Brazilian Amazon, a unique new species with the gonostylus split into three parts was collected and is described and figured below. In addition, new records of D. plaumanni are provided.
Status changes in some species of Leptohyphes Eaton (Leptohyphidae, Ephemeroptera)
Authors: MOLINERI CARLOS
Published: November 2020

Leptohyphes Eaton is a Panamerican genus with stout nymphs inhabiting mainly mountain streams. In the present work two Leptohyphes species are revalidated: Leptohyphes comatus Allen and Leptohyphes myllonotus Allen & Roback. Additionally, a new diagnosis for the nymphal stage of Leptohyphes petersi Allen is presented, and its records from Brazil are removed.
An Improvement of Contention Window for IEEE 802.15.6 CSMA/CA
Authors: Kouka Neji , Guesmi Tarek , Ashokkumar K. , Korbaa Ouajdi
Published: November 2020

The IEEE 802.15.6 standard is become an essential wireless access technology for the WBAN networks, with an objective to adapt wireless communications to the recent applications. This paper proposes an improvement of the well known CSMA/CA procedure with an idea to give a new size of
its contention window length. Simulation results show that, compared with standard IEEE 802.15.6, the delay time, packet loss rate of the improved CSMA/CA are be significantly reduced.
Road Fault Detection by Using Convolutional Neural Networks
Authors: Ashok Kumar K. , Pulikonda Vamsi , Sai Narendarnath
Published: November 2020

Bad conditions of road due to the potholes are one of the major cause of road damage and accidents to vehicles. Recently, with the increase in pollution and vehicular traffic, most of roads are being filled with many small and large potholes in most of places in the country. Detecting
potholes manually is a time-consuming task and labour-intensive task, automating this process which saves a lot of time and money. Hence, Many different methodologies have been implemented that is from reporting to authorities manually to the use of laser imaging. Though all of these techniques
have some disadvantages like risk while detection, high setup cost. By using the concept of Convolu-tional neural networks (CNN), a computer vision-based method we easily can identify the limitations, using the concept of Neural network by processing the image and detecting the potholes saves
a lot of time and money. Inputting the data to the model with the camera regularly to identify properly potholes, cracks. This is best options for automating bad-road identification problem.
An Integrated Approach to Encompass Big Data Cloud Adoption Using Data Mining
Authors: Nandhini S. , Khanna Prangad , Jain Sachit , Pal Raunak
Published: November 2020

Text Mining is one of the most censorious techniques for analysis of data. It processes the unstructured data which has been found to hold nearly 80% of the world’s data. In the present time a majority of industries and big firms use and store massive amount of data sets. The
information gets stored in the data Warehouses, and cloud platforms respectively. The data getting stored shows an exponential growth each time since multiple amount of new data is said to get stored every minute from distinct sources. Due to heavy presence of information stored in the platforms
it becomes difficult to gather and extract the most relevant part from the data. Thus, text mining and data mining techniques are applied with algorithms such as CNN and RNN supporting it. The algorithm performs deep analysis of data and presents the most relevant piece of information, removing
the less important ones. The relevance of the project comes into existence when it’s seen to be integrated into industries such as software markets, social media analysis and formulating of market reports. Thus, providing an extremely comprehensive information which would enhance scope
and application of the data getting stored.
A Robust False Spam Review Detection Using Deep Long Short-Term Memory (LSTM) Based Recurrent Neural Network
Authors: Deva Hema D. , Tharun J. , Arun Dev G. , Sateesh N.
Published: November 2020

Our day-to-day activity is highly influenced by development of Internet. One of the rapid growing area in Internet is E-commerce. People are eager to buy products from online sites like Amazon, embay, Flipkart etc. Customers can write reviews about the products purchased online. The
purchasing of good through online has been increasing exponentially since last few years. As there is no physical contact with goods before purchasing through online, people totally rely on reviews about the product before purchasing it. Hence review plays an important role in deciding the
quality of the product. There are many customers who give online reviews about the product after using it. Hence the quality of the product is decided by the reviews of the customers. Thus, detection of fake reviews has become one of the important task. The proposed system will help in finding
such fake reviews about the product, so that the fake reviews can be eliminated. Therefore, the purchasing of the products will be totally based on the genuine reviews. The proposed system uses Deep Recurrent Neural Network (DRNN) to predict the fake reviews and the performance of the proposed
method has compared with Naïve Bayes Algorithm. The proposed model shows good accuracy and can handle huge amount of data over the existing system.
Machine Learning Based Malicious Android Application Detection
Authors: Jany Shabu S. L. , Reddy Rohan Loganathan , Anu V. Maria , Gladence L. Mary , Refonaa J.
Published: November 2020

The ultimate aim of the project is to improve permission for detecting the malicious android mobile application using machine learning algorithms. In recent years, the usages of smartphones are increasing steadily and also growth of Android application users are increasing. Due to growth of Android application users, some intruders are creating malicious android applications as a tool to steal the sensitive data and identity theft/fraud mobile bank, mobile wallets. There are so many malicious applications detection tools and software are available. But an effectiveness of malicious applications detection tools is the need for the hour. They are needed to tackle and handle new complex malicious apps created by intruder or hackers.
Predicting Physico-Chemical Characteristics of Brain Tumour
Authors: Roobini M. S. , Bharathi T. V. L. , Sailaja T. Aishwaya , Lakshmi M. , Ponraj Anitha , Deepa D.
Published: November 2020

This research proposes a series of algorithms that work for improved Brain Tumor identification and classification. The Brain Tumor study based on the MRI image will effectively resolve the classification method for diagnosis of brain tumors. There are three stages: Extraction of features,
Reduction of features and classification. Extraction function and reduction of functionality used for two algorithms. The extracted characteristics are Mean, Standard deviation, Curtosis, Skewness, Entropy Contrast, Variance, Smoothness, Correlation and Power. The result is then supplied to
Support Vector Machine (SVM) for the Benign or Malignant classification of tumours.
Block-Chain Based Incentive in Vehicular Ad Hoc Networks Using Credit Coin
Authors: Refonaa J. , Teja Y. Pavan , Manjunath Y. , Dhamodaran S. , Jany Shabu S. L. , Anu Maria , Lakshmi M.
Published: November 2020

Vehicular ad hoc networks (VANET) are otherwise called wise transportation frameworks. VANET guarantees auspicious and exact correspondences between vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) to improve street wellbeing and upgrade the productivity of traffic stream. Because of its open remote limit and high versatility, VANET is powerless against pernicious hubs that could get entrance into the system and do genuine medium access control (MAC) layer dangers, for example, denial of service attacks, information alteration attacks, pantomime attacks, Sybil attacks, and replay attacks. With the prevalence of Block Chain comes grave security related concerns. Accomplishing security and discernibility at the same time stays an open inquiry. Endeavors have been made to address the issues, while they may expose to explicit situations. We propose a vehicular declaration convention Echo-Announcement in Credit Coin. It accomplishes productivity and protection safeguarding for the useful use in sending declarations. We plan an impetus component dependent on Block chain in Credit Coin. Clients oversee notoriety focuses while they acquire or spend coins as motivating forces. In the interim, Credit Coin despite everything jam security and accomplishes obscurity. Besides, in view of Block chain, Credit Coin forestalls numerous security attacks and accomplishes contingent protection since Trace administrator will follow malignant hubs when a startling occasion happens.
A Comparative Analysis of Rainfall Prediction Using Support Vector Machine and Random Forest
Authors: Dhamodaran S. , KipsonRoy G. , Kishor A. , Refonaa J. , JanyShabu S. L.
Published: November 2020

Classification and prediction of various data have become one of the most interesting area of research work. Numerous researchers are working on various data samples for predicting and classifying into various categories. Weather prediction is not an exception. Rainfall prediction is
one of the most widely predicted data sample as it needs to be predicted in advance in order to take preventive measures. Heavy rainfall can endanger day to day activities and also could lead to various other disasters such as floods. Though numerous research works have been carried out on
this particular area, we have made an comparative analyses of predicting rainfall using SVM classifier and Random Forest classifier. A web portal is built which runs SVM and Random Forest algorithms, that are designed for predicting the rainfall. The comparison results show that SVM classifiers
are able to predict better than the Random Forests algorithms by classifying the data sample for about 92%.
Plant Disease Detection and Classification Using Bacteria Foraging Optimization Algorithm Through Convolution Neural Network
Authors: Teja Venigalla Sai , Srinivas Chilakapati , Radhika P.
Published: November 2020

Humans can recognize the plants infected by diseases but separated from our visual perception it is hard to recognize plant diseases. In croplands without taking the right care and prompt action, the entire field may become a region afflicted by diseases. So we identify the plant diseases
ahead of time with the assistance of present-day computer technologies. An advanced model was introduced to accurately recognize and classification plant diseases. Here we proposed an approach that can use the Convolutional Neural Network (CNN) based on BFOA for distinguishing diseases in
plants. The input picture for the extraction of features is divided into 3 clusters by the Euclidean distance measurement metric of the k-mean algorithm and from the ROI, parameters of the GLCM matrix are calculated in the same cluster prior to BFOA. Assigning matrix parameters as BFOA input
improves the network’s accuracy and efficiency in determining. In classification, we proposed a Convolutional Neural Network (CNN) using ResNet50 as a pre-trained network in deep learning toolbox which classifies from a given dataset. The approach is more reliable as the detection and
classification of plant diseases are more precise.
Detection of Fake Online Reviews Using Semi-Supervised and Supervised Learning
Authors: Roobini M. S. , Chowdary B. Nikhil , Chowdary J. Madhav , Aruna J. , Ponraj Anitha
Published: November 2020

Online reviews have an incredible effect on the present business and trade. The development of web-based business organizations has pulled in numerous buyers since they provide a scope of items on aggressive costs. The main aspect most buyers depends on while doing online shopping is
the review of items for closing the choice of object. Basic leadership for the acquisition of online items generally relies upon reviews given by the clients. Henceforth, deft people or gatherings attempt to control item surveys for their advantages. In perspective on the impacts of these
phony surveys, various systems to recognize these were proposed in the research. Because of reviews and its nature, this is hard to group these utilizing only one classifier. Henceforth, the present research discusses a classifier for dealing with identifying such phony reviews. The study
also presents the text mining techniques both supervised and semi-supervised to identify counterfeit online reviews just as looks at the effectiveness of the two strategies on the datasets with hotel surveys.
A Probe into the Parallel Elements of Diabetes Mellitus and Alzheimer’s Disease
Authors: Roobini M. S. , Lakshmi M.
Published: November 2020

Alzheimer’s Disease (AD) is a standout amongst the most familiar types of memory loss influencing a huge number of senior individuals around the world which is the main source of dementia and memory misfortune. AD causes shrinkage in hippocampus and cerebral cortex and it grows
the ventricles in the mind Enhancing home and network based composed consideration is basic to alleviating Alzheimer’s impacts on people and families and to decreasing mounting medicinal services costs. Distinguishing early morphological changes in the mind and making early determination
are vital for Alzheimer’s ailment (AD). A few machine learning techniques, for example, Support vector machines have been utilized and a portion of these strategies have been appeared to be extremely compelling in diagnosing AD from neuroimages, some of the time significantly more viable
than human radiologists. MRI uncover the data of AD however decay districts are diverse for various individuals which makes the finding somewhat trickier. By utilizing Convolutional Neural Networks, the issue can be settled with insignificant mistake rate. This paper proposes a profound Convolutional
Neural Network (CNN) for Alzheimer’s Disease finding utilizing mind MRI information examination. The calculation was prepared and tried utilizing the MRI information from Alzheimer’s Disease Neuroimaging Initiative.
Powerful Internet of Things Connectivity Enabled Intelligent Building Using Machine Learning
Authors: Agarwal Rishi , Iyer Rahul , Vinnarasi A. , Gooda Hari Chandan , Aggarwal Vibhor
Published: November 2020

In the ever-so-growing technological world, the Internet of things is one of the most prevalent technologies that is being utilized in offices, hospitals, and even at homes. However, IoT, when integrated with other technologies, becomes more powerful and user interactive. Our system
utilizes microcontroller (NodeMCU) as a Wifi based gateway which would connect different sensors with cloud-based servers and unlike conventional automation, face recognition would be introduced in IoT frameworks for room personalization, along with communication among the appliances, which
will be beyond the interaction with the internet. The focus is to minimize human intervention and personalize it as accurately as possible. People accessing a room will have different settings for an archetype. For each person, the settings of the room will be altered using facial recognition.
The machine learning would consist of two classes, i.e., known and unknown. For an unknown person approaching a room, a notification will be sent to the administrator. The collective data received from sensors (temperature, pressure, gas, humidity, current, and potentiometer) will be monitored
and controlled using relays connected with Arduino which transfers data to the cloud through NodeMCU.
Online Auction System
Authors: Pandian Asha , Are Ranadheer , Sandeep Podile Sai , Reddy Kasu Karunakar
Published: November 2020

Auction through site is done, which is an extremely monster potential commercial center for merchants and supporters. This undertaking, a web Auction System has two classes as customer interface and administrator interface. The venture includes the look and execution of a web sell off
framework. This strategy presents a web show of class insightful item they have to sell or offer. There’s Associate in nursing administrator board by that Associate in nursing administrator will the board the total offering framework. Administrator will endorse item by the classes and
can likewise the executives the enlisted clients. The examination organize is performed first for the contextual analysis. Furthermore, the expectation to figure out what goes on operationally inside the web house. It conjointly characterizes the format in humanoid application. Inside which
a few assortments of bidders offer the auction and last the sale won can store the consequences of the triumphant bidder subtleties and send through mail face to face and in the long run the load technique can applied.
Crime Records Management System
Authors: Pandian Asha , Harsha Ravuru Sai , Theja Porachenu Ravi , Krishna Tadavarthi Sai
Published: November 2020

The suggested work extends to all of the police cells around the whole country and tackles the problem of Crime Data Management in particular. Crime prevention, identification, and prosecution of offenders is well understood to rely on an extremely sensitive framework of data management.
Police service reliability and the potential with which it fights violence depends on the type of info they can obtain from their current databases and how easily they can gain it. The program will initially be applied across the complete cities and the towns and then interconnected so as
a police officer can gather data from all databases in the state itself, thus serving to solve investigations efficiently and effectively. The project was designed to take a view of distributed architecture, with unified database depot. The program was intended for the data storage. Using
the SQL server’s constructs and all the user interfaces were built utilizing DOT Net techniques.
Automated Toll Tax Collection System to Reduce Traffic Time and Energy
Authors: Narasimhan Kandavel , Senthilkumar A. , Keerthana M. D. , Priya L. Divya , Priya S. Devi
Published: November 2020

In the technological world most of the problems are solved by the use of remote technology. Here in our paper we are identified the toll plaza problem and make the solution for this problem. When the user using our application initially they are select the current place and the destination
place. After selecting the source and destination address the possible routes are displayed using Google maps. After selecting the particular route the number of toll plaza displayed to the user and the toll amount for each tollgate also displayed. These toll amounts for every tollgate was
updated by the admin panel. Then user can know about the expenditure particularly for tollgates while travelling to somewhere. In next when we comes to payment options here two types of payment done in our application. In the first user can make the total toll amount before starts the travel.
Here in this phase the toll amount for each tollgate was updated by the admin panel. In the second phase payment for the tollgate would be done by automatically at that time like when the user reaches particular distance before the tollgate amount will debited from the account. Finally through
our application we can previously know the number of tollgates and makes the payment before arriving. Then these are all reducing the waiting time of the user in the tollgates.
Underground Cable Fault Detection Using Internet of Things (IoT)
Authors: Murugan N. , Senthil Kumar J. S. , Thandapani T. , Jaganathan S. , Ameer N.
Published: November 2020

A fault in electrical equipment is defined as defect in the system where the current is diverted or blocked from its original path. Faults occur due to cheap materials and improper sheath in the cables. Also, whenever a fault occurs in underground cable it is very difficult to detect
the exact location of the fault and for repairing the same cable. The proposed system helps to find the exact location of the fault and determines the fault using an Arduino microcontroller. It also detects the leakage current using a relay driver to avoid faults occurring repeatedly. The
concept is based on ohm’s law, due to variation in current the voltage drop can vary on counting on the length of the fault in cable. The fault data are stored in cloud for future reference to the base station. The exact location is displayed in the LCD screen connected with the microcontroller.
Voice Controlled Wheel Chair with Intelligent Stability
Authors: Srinivasan Arumugam , Vinoth ThangaRaj , Ravinder R. , Mosses SamuelPriyan , Kumar Yagnesh
Published: November 2020

This paper is about a wheel chair controlled by voice command developed to help the physically challenged people. The physically challenged person as user can control the wheel chair by voice commands such as forward, reverse etc. ‘Julian’ a grammar based recognizer is used
in this system. The basic commands, the short motion command and the command of verification are the three commands given to the system. Julian has successful recognition rate of 98% of commands given to it based on the studies and experiments conducted. Patients and physically challenged
people are depending on others while moving on wheel chair hence they do not have freedom in motion. This voice command controlled wheel chair provides freedom of motion to patients and physically challenged to be independent on movement to the respective places. Arduino control the motor
by motor driver based on the commands processed by the voice recognition module. This voice command controlled wheel chair provides motion on the patient or physically challenged people voice. Gyroscopic sensor is used to maintain the level straight of the wheel chair when the person travelling
in the slope it prevents the person to feel off from the chair which is operated by servo motor.
Real-Time Suspicious Human Action Recognition from Surveillance Videos for Resource-Constrained Devices
Authors: Subha I. , Narmadha P. , Nivedha S. , Sethukarasi T.
Published: November 2020

Recent developments in computer vision are seen as a vital advancement in video surveillance. The goal of this research is to build a deep learning model that is capable of analyzing and classifying the video from running CCTV streams to detect criminal actions and identify suspects
on the scene. In particular, we focus on the detection of dangerous human behaviors in surveillance videos. This work provides a low cost embedded solution that can be integrated with the existing CCTV cameras. This integration can reduce the cost of transmitting the data to any centralized
server, which may have various privacy implications and takes much inference time. We also benchmark our models performance with the existing real-world dataset in terms of accuracy and resource constraints. Using the concept of Multiple Instance Learning on the histogram of the optical flow
of the videos combined with the pose estimation of the persons on scene, we provide a lightweight model which has 13 times lesser inference time than the existing very deep models. Focusing on one important thing, this research will expand to which state-of-the-art deep neural networks will
“see” violence in photographs and videos, and recognize criminal behavior using characteristics such as gestures, gait, and unethical behavior. This helps enforcement agencies to unravel crime cases faster and also to scale back crimes by identifying the suspects in the surveillance
videos.
Transient Aggregation of Convection: Observed Behavior and Underlying Processes
Authors: Masunaga Hirohiko , Holloway Christopher E. , Kanamori Hironari , Bony Sandrine , Stein Thorwald H. M.
Published: November 2020

Convective self-aggregation is among the most striking features emerging from radiative–convective equilibrium simulations, but its relevance to convective disturbances observed in the real atmosphere remains under debate. This work seeks the observational signals of convective aggregation intrinsic to the life cycle of cloud clusters. To this end, composite time series of the Simple Convective Aggregation Index (SCAI), a metric of aggregation, and other variables from satellite measurements are constructed around the temporal maxima of precipitation. All the parameters analyzed are large-scale means over 10° × 10° domains. The composite evolution for heavy precipitation regimes shows that cloud clusters are gathered into fewer members during a period of ±12 h as precipitation picks up. The high-cloud cover per cluster expands as the number of clusters drops, suggesting a transient occurrence of convective aggregation. The sign of the transient aggregation is less evident or entirely absent in light precipitation regimes. An energy budget analysis is performed in search of the physical processes underlying the transient aggregation. The column moist static energy (MSE) accumulates before the precipitation peak and dissipates after, accounted for primarily by the horizontal MSE advection. The domain-averaged column radiative cooling is greater in a more aggregated composite than in a less aggregated one, although the role of radiative–convective feedback behind this remains unclear.
Relationships between Wintertime Sea Ice Cover in the Barents Sea and Ocean Temperature Anomalies in the Era of Satellite Observations
Authors: Schlichtholz Pawel
Published: November 2020

Investigation of the predictability of sea ice cover in the Barents Sea is of paramount importance since sea ice changes in this part of the Arctic not only affect local marine ecosystems and human activities but may also influence weather and climate in northern midlatitudes. Here, observational data from the period 1981–2018 are used to identify statistical linkages of wintertime sea ice cover in the Barents Sea region to preceding sea surface temperature (SST) and Atlantic Water temperature anomalies in that region. We find that the ocean temperature anomalies formed by local air–sea interactions during the winter-to-spring season are a significant source of predictability for sea ice area (SIA) in the Barents Sea region the following winter. Optimal areas for constructing SST predictors of Barents Sea SIA and skill scores from retrospective statistical forecasts are shown to differ between the periods up to and since the onset of rapid sea ice decline in the region. In the EARLY period (1982–2003), springtime SSTs in the western Barents Sea predicted 44% of the variance of the following winter Barents Sea SIA. In the LATE period (2003–17), springtime SSTs in the southern Barents Sea predicted 70% of the variance of the following winter Barents Sea SIA. Regression analysis suggests that feedbacks from anomalous winds may be important for the predictability of wintertime sea ice cover in the Barents Sea region.
U.S. Tropical Cyclone Activity in the 2030s Based on Projected Changes in Tropical Sea Surface Temperature
Authors: Hall Timothy M. , Kossin James P. , Thompson Terence , McMahon James
Published: November 2020

We use a statistical tropical cyclone (TC) model, the North Atlantic Stochastic Hurricane Model (NASHM), in combination with sea surface temperature (SST) projections from climate models, to estimate regional changes in U.S. TC activity into the 2030s. NASHM is trained on historical variations in TC characteristics with two SST indices: global–tropical mean SST and the difference between tropical North Atlantic Ocean (NA) SST and the rest of the global tropics, often referred to as “relative SST.” Testing confirms the model’s ability to reproduce historical U.S. TC activity as well as to make skillful predictions. When NASHM is driven by SST projections into the 2030s, overall NA annual TC counts increase, and the fractional increase is the greatest at the highest wind intensities. However, an eastward anomaly in mean TC tracks and an eastward shift in TC formation region result in a geographically varied signal in U.S. coastal activity. Florida’s Gulf Coast is projected to see significant increases in TC activity relative to the long-term historical mean, and these increases are fractionally greatest at the highest intensities. By contrast, the northwestern U.S. Gulf Coast and the U.S. East Coast will see little change.