bluuebunny/crossref_metadata_2025 huggingface dataset:
Successful management of symptomatic hydrocephalus using a temporary external ventricular drain with or without endoscopic third ventriculostomy in pediatric patients with germinoma
Authors: Ronsley Rebecca , Bouffet Eric , Dirks Peter , Drake James , Kulkarni Abhaya , Bartels Ute
Published: December 2021

OBJECTIVE

The objective of this study was to describe the management of hydrocephalus in a cohort of pediatric patients with germinoma.

METHODS

The authors conducted a retrospective chart review of patients with germinoma and symptomatic hydrocephalus treated at the Hospital for Sick Children between 2002 and 2020. Descriptive data included tumor location, CSF diversion procedure (external ventricular drain [EVD], endoscopic third ventriculostomy [ETV], ventriculoperitoneal [VP] shunt) and outcomes. The frontooccipital horn ratio (FOR) method was used to determine the presence of ventriculomegaly.

RESULTS

Of 39 patients with germinoma, 22 (73% male) had symptomatic hydrocephalus at diagnosis (11 pineal, 4 suprasellar, and 7 bifocal). Management of hydrocephalus included EVD (n = 5, 22.7%), ETV (n = 5, 22.7%), and combination ETV and EVD (n = 7, 31.8%), whereas 5 patients (22.7%) did not undergo surgical intervention. The median FOR at diagnosis was 0.42 (range 0.38–0.58), which correlated with moderate to severe ventriculomegaly. Carboplatin and etoposide–based chemotherapy induced fast tumor shrinkage, avoiding CSF diversion (n = 5) and resolving hydrocephalus with a transient EVD (n = 5). The median duration until EVD removal was 7 days (range 2–10 days). Two of 12 patients with EVD ultimately required a VP shunt. Kaplan-Meier overall survival was 100% and progression-free survival was 96.4% at a median follow-up of 10.4 years.

CONCLUSIONS

Timely initiation of chemotherapy is imperative to rapidly reduce tumor bulk in children with germinoma and limits the need for VP shunt insertions. In children in whom CSF diversion is required, hydrocephalus may be successfully managed with a temporary EVD ± ETV.
Repeated intravenous infusion of mesenchymal stem cells for enhanced functional recovery in a rat model of chronic cerebral ischemia
Authors: Takemura Mitsuhiro , Sasaki Masanori , Kataoka-Sasaki Yuko , Kiyose Ryo , Nagahama Hiroshi , Oka Shinichi , Ukai Ryo , Yokoyama Takahiro , Kocsis Jeffery D. , Ueba Tetsuya , Honmou Osamu
Published: December 2021

OBJECTIVE

Stroke is a major cause of long-term disability, and there are few effective treatments that improve function in patients during the chronic phase of stroke. Previous research has shown that single systemic infusion of mesenchymal stem cells (MSCs) improves motor function in acute and chronic cerebral ischemia models in rats. A possible mechanism that could explain such an event includes the enhanced neural connections between cerebral hemispheres that contribute to therapeutic effects. In the present study, repeated infusions (3 times at weekly intervals) of MSCs were administered in a rat model of chronic stroke to determine if multiple dosing facilitated plasticity in neural connections.

METHODS

The authors induced middle cerebral artery occlusion (MCAO) in rats and, 8 weeks thereafter, used them as a chronic stroke model. The rats with MCAO were randomized and intravenously infused with vehicle only (vehicle group); with MSCs at week 8 (single administration: MSC-1 group); or with MSCs at weeks 8, 9, and 10 (3 times, repeated administration: MSC-3 group) via femoral veins. Ischemic lesion volume and behavioral performance were examined. Fifteen weeks after induction of MCAO, the thickness of the corpus callosum (CC) was determined using Nissl staining. Immunohistochemical analysis of the CC was performed using anti-neurofilament antibody. Interhemispheric connections through the CC were assessed ex vivo by diffusion tensor imaging.

RESULTS

Motor recovery was better in the MSC-3 group than in the MSC-1 group. In each group, there was no change in the ischemic volume before and after infusion. However, both thickness and optical density of neurofilament staining in the CC were greater in the MSC-3 group, followed by the MSC-1 group, and then the vehicle group. The increased thickness and optical density of neurofilament in the CC correlated with motor function at 15 weeks following induction of MCAO. Preserved neural tracts that ran through interhemispheric connections via the CC were also more extensive in the MSC-3 group, followed by the MSC-1 group and then the vehicle group, as observed ex vivo using diffusion tensor imaging.

CONCLUSIONS

These results indicate that repeated systemic administration of MSCs over 3 weeks resulted in greater functional improvement as compared to single administration and/or vehicle infusion. In addition, administration of MSCs is associated with promotion of interhemispheric connectivity through the CC in the chronic phase of cerebral infarction.
Outcome of endoscopic transcortical intraventricular biopsy of isolated thickened pituitary stalk lesions in children
Authors: Jeon Chiman , Choi Jung Won , Kong Doo-Sik , Shin Hyung Jin
Published: December 2021

OBJECTIVE

The purpose of this study was to investigate the efficacy and safety of endoscopic transcortical intraventricular biopsy in pediatric patients with isolated thickened pituitary stalk lesions.

METHODS

From 1994 to 2018, 16 pediatric patients (8 males and 8 females) underwent endoscopic transcortical intraventricular biopsy at a single institution to establish an accurate pathological diagnosis for appropriate treatment strategies.

RESULTS

Preoperatively, the median diameter of the pituitary stalks was 5.31 ± 1.67 mm (range 3.86–9.17 mm). Overall, the diagnostic yield at endoscopic intraventricular biopsy was 93.8%. Histopathological diagnosis included germinoma (n = 10, 62.5%), Langerhans cell histiocytosis (n = 4, 25%), pilocytic astrocytoma (n = 1, 6.3%), and pituicytoma (n = 1, 6.3%). Two patients developed new diabetes insipidus after the procedure, and 3 patients had new postoperative hypothyroidism. There were no postoperative neurological deficits in this series.

CONCLUSIONS

Neuroendoscopic biopsy via the transcortical intraventricular corridor was shown to be safe and to aid in a reliable histopathological diagnosis in the management of isolated pituitary stalk lesions in pediatric patients. It can be considered a minimally invasive alternative to open biopsy via transcranial or endonasal corridors.
Traditions of Russian Orthodox Culture in Children’s Literary and Art Publications in Russia at the End of the 20th - Beginning of the 21st Century
Authors: Горбатенко Анжелика Геннадьевна
Published: December 2021

В статье рассматриваются издания детских православных журналов кон. XX - нач. XXI в. через призму традиций православной публицистики, зародившейся в XVII-XVIII вв. Принимаются во внимание журналы, выходящие для детей младшего и старшего возраста, особенности рубрик, авторство статей, направление публикаций. Уточняются преемственность и новаторские отличия рассматриваемых журналов в сравнении со светскими и более ранними православными публицистическими изданиями, преимущественно ориентированными на детскую аудиторию.

The article examines the publication of Orthodox children’s magazines of the late 20th - early 21st century, through the prism of the traditions of Orthodox journalism that originated in the 17th-18th centuries. The author takes into account the journals published for young and older children, the features of the headings, the authorship of articles, the direction of publications. The continuity and innovative differences of the reviewed journals are clarified in comparison with secular and earlier Orthodox journalistic publications, mainly aimed at children.
Orthodox Christianity Church in the diary M. M. Prishvin’s (1905-1954)
Authors: Красников Сергей Васильевич
Published: December 2021

Статья посвящена теме Церкви в дневниках М. М. Пришвина. Акцент делается на эволюции взглядов писателя, а также выявляются те факторы, которыми она обусловлена. Автор отмечает, что впервые о природе Церкви М. М. Пришвин задумался во время своего участия в религиозно-философском кружке Д. Мережковского. Не понимая истинного назначения Церкви, он предпринимает попытки объяснить ее природу и цель существования через призму пантеистического идеала. В довоенный период писатель считает, что причастность человека к церковной жизни вовсе не обязательна, однако с началом Первой мировой войны и революции 1917 г. его взгляды претерпевают важное изменение: он открывает в ней значительную пользу не только для отдельного человека, но и для всей страны. Решающим в деле приятия писателем Церкви и доверия к ней сыграла Великая Отечественная война. В последние годы его жизни мы не находим у М. М. Пришвина критики Православной Церкви, но, напротив, встречаем многочисленные записи о её важном сотериологическом значении для человека. Автор приходит к выводу о сложном эклектичном взгляде М. М. Пришвина на Церковь, что обусловлено его юношеским увлечением марксизмом, пантеистическими воззрениями на мир, а также огромном влиянии Валерии Лиорко. Однако, несмотря на это, писатель не переставал осмыслять данную тему на протяжении всей своей жизни, что и привело его в итоге к сознательному позиционированию себя членом Православной Церкви.

The article is devoted to the topic of the Church in the diaries of M. M. Prishvin. The emphasis is placed on the evolution of the writer’s views, as well as the factors that determine it. The author notes that for the first time M. M. Prishvin thought about the nature of the Church during his participation in the religious and philosophical circle of D. Merezhkovsky. Not understanding the true purpose of the Church, he attempts to explain its nature and purpose of existence through the prism of the pantheistic ideal. In the pre-war period, M. M. Prishvin believes that a person’s involvement in the Church is not at all mandatory, but with the beginning of the First World War and the revolution of 1917, his views undergo an important change: he discovers in it a significant benefit not only for the individual, but also for the whole country. The Second World War played a decisive role in M. M. Prishvin’s acceptance of the Church and trust in it. In the last years of M. M. Prishvin’s life, we do not find him criticizing the Church, but, on the contrary, we findnumerous records about hits important soteriological significance for man. The author comes to the conclusion about M. M. Prishvin’s complex eclectic view of the Church, which is due to his youthful fascination with Marxism, pantheistic views on the world, as well as the huge influence of Valeria Liorko. In spite of this, however, the writer continued to think about the topic throughout his life, which eventually led him to consciously position himself as a member of the Orthodox Church.
Analisis Faktor-Faktor Resistensi Terhadap Teknologi Smartwatch (Studi Pada Perilaku Konsumen Di Indonesia)
Authors: Insani Salman Faris , Wijayanti Ariyani Wahyu , Irawati Desti
Published: December 2021

Pertumbuhan Internet of Thing (IOT) di abad ke-21 ini sangat pesat. Meskipun IoT menawarkan peluang baru, namun memunculkan tantangan pada resistensi. Resistensi adalah suatu reaksi konsumen terhadap inovasi produk, reaksi dapat berupa penundaan pembelian ataupun penolakan. Tujuan dari penelitian ini adalah untuk mengetahui faktor-faktor yang mendasari resistensi konsumen terhadap produk smartwatch. Sampel yang digunakan adalah konsumen di Indonesia yang mengetahui namun tidak berminat untuk memiliki produk smartwatch sebanyak 163 responden. Hasil studi ini  mengindikasi bahwa variabel gangguan berpengaruh terhadap resitensi konsumen. Selain itu masalah privasi memiliki pengaruh terhadap gangguan dan ketergantungan memiliki pengaruh terhadap kekhawatiran privasi. Selain itu persepsi ketidakbermanfaatan, persepsi harga, persepsi kebaruan dan efikasi diri tidak memiliki pengaruh terhadap resistensi.
ТРАНСГУМАНІЗМ НЕ ПОСТГУМАНІЗМ: ЕКСПЛІКАЦІЯ АКТУАЛЬНОЇ СЕМАНТИКИ ПОНЯТЬ
Authors: Гоц Людмила Сергіївна
Published: December 2021

В останню третину ХХ століття в Європі та Америці інституюються такі інтелектуальні течії й суспільні рухи, як «трансгуманізм» та «постгуманізм», що наразі набувають ознак глобального поширення. Актуальні закордонні дослідження доволі чітко диференціюють поняття «транс-» та «постгуманізм», водночас дослідники з України та ближнього зарубіжжя часто не відрізняють ці явища одне від одного. Проте дослідження пост- та трансгуманістичної проблематики в українській гуманітаристиці потребують узгодження термінологічного апарату з поняттями світової науки. Мету дослідження становить експлікація актуального семантичного наповнення понять «трансгуманізм», «постгуманізм», їх диференціація та концептуалізація задля коректної інтеграції у вітчизняну гуманітаристику. Методологія. З метою експлікації актуального сенсу термінів «транс-», «постгуманізм» у науковій літературі та здійснення їх семантичної диференціації були застосовані методи семантичного, термінологічного і компаративного аналізу, семіотичний та герменевтичний підходи. Головні результати та висновки дослідження. Виявлення сенсових патернів понять «транс-» і «постгуманізм» у закордонній науковій літературі та першоджерелах дає змогу здійснити їх семантичну диференціацію. На відміну від попередніх етапів їх існування, на сьогодні рухи транс- та постгуманізму є досить сформованими; вони різняться метою і мають різні цілі. Трансгуманізм є антропоцентричним рухом, який прагне подолати природну обмеженість людини, акцентує її вищість і необхідність вдосконалення людства шляхом застосування високих технологій. На відміну від нього, постгуманізм відкидає антропоцентричну перспективу і переглядає панівне ієрархічне положення людини у всесвіті в напрямку спростування стратегій видового шовінізму (видізму) — присвоєння життєвих привілеїв істотам певного виду.
ТВОРЧІСТЬ ІЛЛІ РЄПІНА У КОНТЕКСТІ УКРАЇНСЬКОЇ ТА СВІТОВОЇ ХУДОЖНЬОЇ КУЛЬТУРИ
Authors: Турчак Леся Іванівна
Published: December 2021

Мета статті — виявити внесок художника-реаліста, педагога І. Рєпіна в українське та світове образотворче мистецтво. Методологія дослідження ґрунтується на принципах системності та історизму. Використано методи наукового дослідження: дослідницько- пошуковий, джерелознавчий, системно-аналітичний та біографічний. Наукова новизна. Досліджено творчість І. Рєпіна як представника української культури, оскільки ідеологія Російської імперії розглядала діяльність митця у контексті російського, а згодом радянського мистецтва. Висновки. Досліджено та проаналізовано творчий шлях І. Рєпіна і доведено, що попри те, що художник жив і працював у Санкт-Петербурзі та Москві, його діяльність тісно пов’язана з Україною. Тематика багатьох творів художника відображає історію, традиції українського народу. Митець ґрунтовно готувався до написання робіт — консультувався з істориками, досліджував життя, побут, українські національні костюми. Герої його картин передають внутрішні переживання людей, силу характеру, адже художник довго працював над створенням картин, детально продумував сюжет і ідею. Твори І. Рєпіна є зразком високої професійної підготовки митця, вирізняються національною тематикою та ідейним змістом. Визначено, що в останні десятиліття життя і творчості І. Рєпіна художні методи та засоби, тематика робіт дещо змінилися, однак це лише підтверджує нові творчі погляди митця. Наголошено, що хоча І. Рєпін був не лише вихідцем з України, а й багато своїх шедеврів присвятив українським мотивам, красі рідної природи, побуту, мові, одягу та пісням, однак Росія його презентує як відомого представника російської школи мистецтв. Сталося це тому, що Російська імперія, а згодом СРСР, сприймала діяльність українських митців як невід’ємну складову російської культури. З’ясовано, що творчість українського художника І. Рєпіна є вагомим внеском у розвиток вітчизняного, російського і світового образотворчого мистецтва.
Indonesia: Train journalists to write about reproductive health
Authors:   Frontiersin Reproductive Health
Published: December 2021

After government control over the media was liberalized in 1998, Indonesian journalists had a new mandate to explore new issues and foster public debate. Several studies had found evidence that women’s health worsened from 1997–99, yet media coverage of this topic was limited. To increase press coverage of reproductive health (RH) topics, the Population Council conducted an 18-month media project in collaboration with the State Ministry for Women’s Empowerment and other key agencies. The project focused on improving RH knowledge and reporting skills among 22 print journalists. Project staff monitored RH coverage in 22 major newspapers, magazines, and tabloids. After Indonesian journalists received background information and training on RH issues, the quality of their coverage improved. However, as noted in this brief, continued efforts are needed to provide media representatives with ideas for news stories and feature articles and training in use of research findings.
Molecular Investigation of the Unfolded Protein Response in Select Human Tauopathies
Authors: Pitera Aleksandra P. , Hartnell Iain J. , Scullard Lucy , Williamson Kirsten L. , Boche Delphine , O’Connor Vincent , Deinhardt Katrin
Published: December 2021

Background:

Tauopathies are a group of neurodegenerative diseases associated with the accumulation of misfolded tau protein. The mechanisms underpinning tau-dependent proteinopathy remain to be elucidated. A protein quality control pathway within the endoplasmic reticulum, the unfolded protein response (UPR), has been suggested as a possible pathway modulating cellular responses in a range of neurodegenerative diseases, including those associated with misfolded cytosolic tau.

Objective:

In this study we investigated three different clinically defined tauopathies to establish whether these diseases are accompanied by the activation of UPR.

Methods:

We used PCR and western blotting to probe for the modulation of several reliable UPR markers in mRNA and proteins extracted from three distinct tauopathies: 20 brain samples from Alzheimer’s disease patients, 11 from Pick’s disease, and 10 from progressive supranuclear palsy. In each disease samples from these patients were compared with equal numbers of age-matched non-demented controls.

Results:

Our investigation showed that different markers of UPR are not changed at the late stage of any of the human tauopathies investigated. Interestingly, UPR signatures were often observed in non-demented controls.

Conclusion:

These data from late-stage human cortical tissue report an activation of UPR markers within the aged brain across all cohorts investigated and further support the emerging evidence that the accumulation of misfolded cytosolic tau does not drive a disease-associated activation of UPR.
UFO: Unified Foundational Ontology
Authors: Guizzardi Giancarlo , Botti Benevides Alessander , Fonseca Claudenir M. , Porello Daniele , Almeida João Paulo A. , Prince Sales Tiago
Published: December 2021

The Unified Foundational Ontology (UFO) was developed over the last two decades by consistently putting together theories from areas such as formal ontology in philosophy, cognitive science, linguistics, and philosophical logics. It comprises a number of micro-theories addressing fundamental conceptual modeling notions, including entity types and relationship types. The aim of this paper is to summarize the current state of UFO, presenting a formalization of the ontology, along with the analysis of a number of cases to illustrate the application of UFO and facilitate its comparison with other foundational ontologies in this special issue. (The cases originate from the First FOUST Workshop – the Foundational Stance, an international forum dedicated to Foundational Ontology research.)
Speech to Indian Sign Language Translator
Authors: Monga Hemang , Bhutani Jatin , Ahuja Muskan , Maid Nikita , Pande Himangi
Published: December 2021

Indian Sign Language is one of the most important and widely used forms of communication for people with speaking and hearing impairments. Many people or communities have attempted to create systems that read the sign language symbols and convert the same to text, but text or audio to sign language is still infrequent. This project mainly focuses on developing a translating system consisting of many modules that take English audio and convert the input to English text, which is further parsed to structure grammar representation on which grammar rules of Indian Sign Language are applied. Stop words are removed from the reordered sentence. Since the Indian Sign Language does not support conjugation in words, stemming and lemmatization will transform the provided word into its root or original word. Then all the individual words are checked in a dictionary holding videos of each word. If the system does not find words in the dictionary, then the most suitable synonym replaces them. The system proposed by us is inventive as the current systems are bound to direct conversion of words into Indian Sign Language on-the-other-hand our system aims to convert the sentences in Indian Sign Language grammar and effectively display it to the user.
Real Time Tracing and Alerting System for Vehicles and Children to Ensure Safety and Security Using Labview
Authors: Deepalakshmi R. , Vijayalakshmi R. , Lavanya S. , Rakshitha Rasmi T.K. , Sathiya S.B.
Published: December 2021

The Absolute time monitoring, detecting and Alerting System for vehicles and children is required to trace and transmit the collected information at regular intervals to ensure safety and security of children. The illustration of the Realtime detecting and warning System consists of two units: Tracing Unit that traces the location information, transfers to the monitoring area, records the data in the database and takes the help of these data to locate the exact point of area of the vehicle with Google/other maps. The second unit is Alerting Unit that tracks the students using active Radio Frequency Identification Devices (RFID)which will be placed on student ID card. radio-wave trans-receiver transmits a common radio wave which is received by the RFID in the ID card. This radio-wave is modified by the RFID’s coil and resent to the receive RFID tags are also used for attendance which is updated directly to the database and displays the other student information.
Text Summarizing Using NLP
Authors: Vijay Kumar G. , Yadav Arvind , Vishnupriya B. , Naga Lahari M. , Smriti J. , Samved Reddy D.
Published: December 2021

In this era everything is digitalized we can find a large amount of digital data for different purposes on the internet and relatively it’s very hard to summarize this data manually. Automatic Text Summarization (ATS) is the subsequent big one that could simply summarize the source data and give us a short version that could preserve the content and the overall meaning. While the concept of ATS is started long back in 1950’s, this field is still struggling to give the best and efficient summaries. ATS proceeds towards 2 methods, Extractive and Abstractive Summarization. The Extractive and Abstractive methods had a process to improve text summarization technique. Text Summarization is implemented with NLP due to packages and methods in Python. Different approaches are present for summarizing the text and having few algorithms with which we can implement it. Text Rank is what to extractive text summarization and it is an unsupervised learning. Text Rank algorithm also uses undirected graphs, weighted graphs. keyword extraction, sentence extraction. So, in this paper, a model is made to get better result in text summarization with Genism library in NLP. This method improves the overall meaning of the phrase and the person reading it can understand in a better way.
A Compact Disc Shaped Microstrip Patch Antenna Using Inset Fed at 5GHz for Satellite Communications
Authors: Pon Bharathi A. , Wilson Allan J , Arun S. , Ramanathan V.
Published: December 2021

This examination work is focused around planning and simulating another kind of inset feed Disc Shaped Microstrip Patch Antenna (DSMPA) with Inset feed and Defected ground plane (DGP). By presenting a round space at the focal point of the ground plane, improved attributes of Microstrip patch antenna can be accomplished. The proposed Disc Shaped Microstrip patch antenna is reverberating at 5 GHz. Simulation has been finished by utilizing reenactment programming HFSS version15. From recreation results, it discovers that our examined Disc Shaped Microstrip patch antenna yields better return loss of - 25.1 dB & VSWR estimation of 0.96 dB. The examined DSMPA is yielding a higher radiation efficiency of 77.20 %. The minimized size and higher radiation efficiency contrasted with rectangular Microstrip patch antenna makes it all the more generally helpful for satellite communications.
MISP: Model for IaaS Security and Privacy
Authors: Sahu Indra Kumar , Nene Manisha J
Published: December 2021

Paradigm shift towards cloud computing offers plethora of advantages both for cloud users and Cloud Service Provider (CSP). For cloud users, it offers saving of cost, scaling of resources, pay per use, elastic and on-demand services. On the other hand, it offers centralized resource management and provisioning of operations, safety and security for CSP. By holding multiple virtual IT resources (CPUs, storage servers, network components and software) over the internet, Infrastructure-as-a-Service (IaaS) serves as fundamental layer for all other delivery models. Along with benefits of IaaS, there exists several security and privacy issues and threats to confidentiality, integrity, authentication, access control and availability. In this paper, detailed study of IaaS components, associated security and privacy issues are explored and counter measures for the same are determined. Furthermore, as a result of the study, Model for IaaS Security and Privacy (MISP) is proposed. The model presents a cubical structure and adds more features than the existing models to enhance the security and privacy of data and operations and guide security assessment for safer adoption by enterprises.
Data Security and Privacy-Preserving Framework Using Machine Learning and Blockchain in Big-Data to Data Middle Platform in the Era of IR 4.0
Authors: Chuqiao Chen , Goyal S.B.
Published: December 2021

The modem data is collected by using IoT, stored in distributed cloud storage, and issued for data mining or training artificial intelligence. These new digital technologies integrate into the data middle platform have facilitated the progress of industry, promoted the fourth industrial revolution. And it also has caused challenges in security and privacy-preventing. The privacy data breach can happen in any phase of the Big-Data life cycle, and the Data Middle Platform also faces similar situations. How to make the privacy avoid leakage is exigency. The traditional privacy-preventing model is not enough, we need the help of Machine-Learning and the Blockchain. In this research, the researcher reviews the security and privacy-preventing in Big-Data, Machine Learning, Blockchain, and other related works at first. And then finding some gaps between the theory and the actual work. Based on these gaps, trying to create a suitable framework to guide the industry to protect their privacy when the organization contribute and operate their data middle platform. No only academicians, but also industry practitioners especially SMEs will get the benefit from this research.
Performance Analysis of ML Algorithms to Detect Gender Based on Voice
Authors: Mohammad Sahar Raz , Srivinasa Rao T. , Anuradha S. , Srinivasa Rao B.
Published: December 2021

Gender classification is amongst the significant problems in the area of signal processing; previously, the problem was handled using different image classification methods, which mainly involve data extraction from a collection of images. Nevertheless, researchers over the globe have recently shown interest in gender classification using voiced features. The classification of gender goes beyond just the frequency and pitch of a human voice, according to a critical study of some of the human vocal attributes. Feature selection, which is from a technical point of view termed dimensionality reduction, is amongst the difficult problems encountered in machine learning. A similar obstacle is encountered when choosing gender particular features—which presents an analytical purpose in analyzing a human’s gender. This work will examine the effectiveness and importance of classification algorithms to the classification of gender via voice problems. Audial data, for example, pitch, frequency, etc., help in determining gender. Machine learning offers encouraging outcomes for classification problems in all domains. An area’s algorithms can be evaluated using performance metrics. This paper evaluates five different classification Algorithms of machine learning based on the classification of gender from audial data. The plan is to recognize gender using five different algorithms: Gradient Boosting, Decision Trees, Random Forest, Neural network, and Support Vector Machine. The major parameter in assessing any algorithm must be performance. Misclassifying rate ratio should not be more in classifying problems. In business markets, the location and gender of people are essentially related to AdSense. This research aims at comparing various machine learning algorithms in order to find the most suitable fitting for gender identification in audial data.
DSAE – Deep Stack Auto Encoder and RCBO – Rider Chaotic Biogeography Optimization Algorithm for Big Data Classification
Authors: Brahmane Anilkumar V. , Chaitanya Krishna B
Published: December 2021

In today’s era Big data classification is a very crucial and equally widely arise issue is many applications. Not only engineering applications but also in social, agricultural, banking, educational and many more applications are there in science and engineering where accurate big data classification is required. We proposed a very novel and efficient methodology for big data classification using Deep stack encoder and Rider chaotic biogeography algorithms. Our proposed algorithms are the combinations of two algorithms. First one is Rider Optimization algorithm and second one is chaotic biogeography-based optimization algorithm. So, we named it as RCBO which is integration is ROA and CBBO. Our proposed system also uses the Deep stack auto encoder for the purpose of training the system which actually produced the accurate classification. The Apache spark platform is used initial distribution of the data from master node to slave nodes. Our proposed system is tested and executed on the UCI Machine learning data set which gives the excellent results while comparing with other algorithms such as KNN classification, Extreme Learning Machine Random Forest algorithms.
Predicting COD and BOD Parameters of Greywater Using Multivariate Linear Regression
Authors: Sadik Shaikh Samir , Shahapurkar Rekha
Published: December 2021

Greywater reuse furthermore, reusing can be an incredible method to get non-consumable water. Since it contains broke down pollutions, greywater can’t be utilized straightforwardly. As an outcome, it is critical to decide the nature of water prior to utilizing it. Body estimations require five days to finish, while COD estimations require only a couple of hours. Not exclusively improve models for evaluating water quality are required; however, a more coordinated methodology is additionally getting more normal. Most of these models require a wide scope of information that isn’t in every case promptly available, making it a costly and tedious activity. Because of different issues in the enlistment with estimation included in water quality boundaries like BOD as well as COD, the principal objective of this investigation is to track down the best multivariate direct relapse models for foreseeing complex water quality outcomes. The code was written in Python for multi-variable information sources, and a Linear Regression Model was created. The projected COD versus estimated COD chart shows that the noticed and expected qualities are practically the same. The R-squared worth was 0.9973. A plot of extended BOD as an element of COD is likewise remembered for the outcome.
VirtualEye: Android Application for the Visually Impaired
Authors: Bagrecha Jay , Shah Tanay , Shah Karan , Gandhi Tanvi , Palwe Sushila
Published: December 2021

In India, almost 18 million visually impaired people have difficulties in managing their day-to-day activities. Hence, there is a need to develop an application that can assist them every time and give vocal instructions in both English and Hindi. In this paper, we introduced a robust lightweight Android application that facilitates visually impaired individuals by providing a variety of essential features such as object and distance detection, Indian currency note detection, and optical character recognition that can enhance their quality of life. This application aims to have a user-friendly GUI well suited to the needs of the blind user and modules like Object Recognition with Image Captioning so that the visually challenged user can gain a better understanding of their surroundings.
A Novel Technique for Handling Small File Problem of HDFS: Hash Based Archive File (HBAF)
Authors: Shankar Sharma Vijay , Barwar N.C
Published: December 2021

Now a day’s, Data is exponentially increasing with the advancement in the data science. Each and every digital footprint is generating enormous amount of data, which is further used for processing various tasks to generate important information for different end user applications. To handle such enormous amount of data, there are number of technologies available, Hadoop/HDFS is one of the big data handling technology. HDFS can easily handle the large files but when there is the case to deal with massive number of small files, the performance of the HDFS degrades. In this paper we have proposed a novel technique Hash Based Archive File (HBAF) that can solve the small file problem of the HDFS. The proposed technique is capable to read the final index files partly, that will reduce the memory load on the Name Node and offer the file appending capability after creation of the archiv.
Preprocessing and Skull Stripping of Brain Tumor Extraction from Magnetic Resonance Imaging Images Using Image Processing
Authors: Suryawanshi Shweta , Patil Sanjay B.
Published: December 2021

Many neuroimaging processing functions believe the preprocessing and skull strip (SS) to be an important step in brain tumor diagnosis. For complex physical reasons intensity changes in brain structure and magnetic resonance imaging of the brain, a proper preprocessing and SS is an important part. The method of removing the skull is relayed to the taking away of the skull area in the brain for medical investigation. It is more correct and necessary techniques for distinguishing between brain regions and cranial regions and this is believed a demanding task. This paper gives detailed review on the preprocessing and traditional transition to machine learning and deep learning-based automatic SS techniques of magnetic resonance imaging.
Machine Learning: A Quantum Perspective
Authors: Jhanwar Aishwarya , Nene Manisha J.
Published: December 2021

Recently, increased availability of the data has led to advances in the field of machine learning. Despite of the growth in the domain of machine learning, the proximity to the physical limits of chip fabrication in classical computing is motivating researchers to explore the properties of quantum computing. Since quantum computers leverages the properties of quantum mechanics, it carries the ability to surpass classical computers in machine learning tasks. The study in this paper contributes in enabling researchers to understand how quantum computers can bring a paradigm shift in the field of machine learning. This paper addresses the concepts of quantum computing which influences machine learning in a quantum world. It also states the speedup observed in different machine learning algorithms when executed on quantum computers. The paper towards the end advocates the use of quantum application software and throw light on the existing challenges faced by quantum computers in the current scenario.
A Wi-Fi Based Smart Irrigation Monitoring for an Agricultural Environment
Authors: Patankar Nikhil , Charmal Manoli , Bhaskar Nikhil , Janrao Swati , Kamble Aniket
Published: December 2021

A producer who controls irrigation using a smartphone. A firebase that uses weather data to predict when to water crops. Sensors that read how much water is in the soil. From water availability and unpredictable weather patterns to regulations from outside entities, it can be a challenge to irrigate crops. Nowadays IoT has different solutions to overcome with this kind of problem. The particular research targeted successfully by development effected system using NodeMCU, Sensors, firebase, and assertive application. These tools which could conserve a good amount of water, it will become vital to success as the population groves and water availability dries up. The tool works by helping farmers with limited irrigation capacity determine the best time to water their crops. The main ai m of automating the system is to provide a certain amount of water required by crops by monitoring the moisture of soil and surrounding temperature. This obtains with the help of sensors and NodeMCU for interfacing their values. The values are displayed on a mobile application in real-time using Google’s firebase. Irrigation using IoT is a key component of precision agriculture. By changing manual irrigation with automatic valves and systems reduces the human error. Farmer can monitor his crop yield from anywhere at any time.
Exploring Cardiac Disease Based on ML in IOT: A Comprehensive Study
Authors: Safa M. , Pandian A.
Published: December 2021

Every human being in today’s age has high importance in a stable human life. With daily new technology is launched in a world loaded with innovative business sectors to tackle more effectively the prevalent challenges of the world. We ought to figure out if as many people as possible can be spared. The death rate of multiple non-communicable heart disease forms is rising steadily each year. Many non-communicable disorders are long-term, gradual and too serious that a patient’s situation is too critical to control. This leads to a sudden heart attack for most people or learning about their condition before it is too late. In this document we suggest a cardiovascular state prediction approach for IoT and Machine Learning, which will use IoT system (sensors) to capture the data needed from the human body and move it to the cloud where data is saved with user verification. The obtained information from the human body is then normalized to quantify and forecast the total condition before AI calculations are applied to them.
A Wide Survey on Data Mining Approach for Crop Diseases Detection and Prevention
Authors: Nirgude Vikas N , Malik Sandeep
Published: December 2021

India is agriculture land and major revenue manufacturing sector. However, because of amendment in temporal parameters and uncertainty in climate directly have an effect on quality and amount of the assembly and maintenance of crops. Also, quality even a lot of degrade once the crops area unit infected by any malady. The main focus of this analysis in agriculture is to increment the crop quality and potency at lower price and gain profit as result of in India the majority of the population depends on agriculture. Big selection of fruits is growing up in India such as apple, banana, guava, grape, mango, pomegranate, orange is the main one. Fruit production gives around 20% of the country’s development. However, because of absence of maintenance, inappropriate development of fruits and manual investigation there has been scale back in generate the standard of fruits.So, Data Mining Approach used in the agriculture domain to resolve several agricultural issues of classification or prediction. During this paper complete survey of several data mining approach for crop disease management has been done. Detection of disease in early state will improve in quality of crop still as decrease the production cost. Also, we can improve the production of the particular crop. Several major parameters are used for the crop disease classification or prediction.
A Review on BCI Emotions Classification for EEG Signals Using Deep Learning
Authors: Chavan Puja A. , Desai Sharmishta
Published: December 2021

Emotion awareness is one of the most important subjects in the field of affective computing. Using nonverbal behavioral methods such as recognition of facial expression, verbal behavioral method, recognition of speech emotion, or physiological signals-based methods such as recognition of emotions based on electroencephalogram (EEG) can predict human emotion. However, it is notable that data obtained from either nonverbal or verbal behaviors are indirect emotional signals suggesting brain activity. Unlike the nonverbal or verbal actions, EEG signals are reported directly from the human brain cortex and thus may be more effective in representing the inner emotional states of the brain. Consequently, when used to measure human emotion, the use of EEG data can be more accurate than data on behavior. For this reason, the identification of human emotion from EEG signals has become a very important research subject in current emotional brain-computer interfaces (BCIs) aimed at inferring human emotional states based on the EEG signals recorded. In this paper, a hybrid deep learning approach has proposed using CNN and a long short-term memory (LSTM) algorithm is investigated for the purpose of automatic classification of epileptic disease from EEG signals. The signals have been processed by CNN for feature extraction from runtime environment while LSTM has used for classification of entire data. Finally, system demonstrates each EEG data file as normal or epileptic disease. In this research to describes a state of art for effective epileptic disease detection prediction and classification using hybrid deep learning algorithms. This research demonstrates a collaboration of CNN and LSTM for entire classification of EEG signals in numerous existing systems.
Prediction of Heart Disease Severity Measurment Using Deep Learning Techniques
Authors: Patil Rina S. , Gangwar Mohit
Published: December 2021

Machine learning enables AI and is used in data analytics to overcome many challenges. Machine learning was the growing method of predicting outcomes based on existing data. The computer learns characteristics from the test implementation, then applies characteristics to an unknown dataset to predict the result. Classification is an essential technique of machine learning which is widely used for forecasting. Some classification techniques predict with adequate accuracy, while others show a small precision. This research investigates a process called machine learning classification, which combines different classifiers to enhance the precision of weak architectures. Experimentation using this tool was conducted using a database on heart disease. The collecting and measuring data method were designed to decide how to use the ensemble methodology to improve predictive accuracy in cardiovascular disease. This paper aims not only to enhance the precision of poor different classifiers but also to apply the algorithm with a neural network to demonstrate its usefulness in predicting disease in its earliest stages. The study results show that various classification algorithmic strategies, such as support vector machines, successfully improve the forecasting ability of poor classifiers and show satisfactory success in recognizing heart attack risk. Using ML classification, a cumulative improvement in the accuracy was obtained for poor classification models. That process efficiency was further improved with the introduction of feature extraction and selection, and the findings show substantial improvements in predictive power.
Analysis of Brain Tumor Disease Detection Using Convolutional Neural Network
Authors: Deshmukh Yogesh , Dahe Samiksha , Belote Tanmayeeta , Gawali Aishwarya , Choudhary Sunnykumar
Published: December 2021

Brain Tumor detection using Convolutional Neural Network (CNN) is used to discover and classify the types of Tumor. Over a amount of years, many researchers are researched and planned ways throughout this area. We’ve proposed a technique that’s capable of detecting and classifying different types of tumor. For detecting and classifying tumor we have used MRI because MRI images gives the complete structure of the human brain, without any operation it scans the human brain and this helps in processing of image for the detection of the Tumor. The prediction of tumor by human from the MRI images leads to misclassification. This motivates us to construct the algorithm for detection of the brain tumor. Machine learning helps and plays a vital role in detecting tumor. In this paper, we tend to use one among the machine learning algorithm i.e. Convolutional neural network (CNN), as CNNs are powerful in image processing and with the help of CNN and MRI images we designed a framework for detection of the brain tumor and classifying its Different types.
Natural Language Querying and Visualization System
Authors: Banerjee Trishali , Bhattacharjee Upasana , Jansi K. R.
Published: December 2021

Data is the new gold; everything is data driven. But it is impossible for everyone to possess technical skills to be able to write queries and know different python tools used for data visualizations. The process of extracting information from a database is a mammoth task for non-technical users as it requires one to have extensive knowledge of DBMS language. But these data and visualizations are required for various everyday presentations and interactions in the professional world. This application would enable the users to overcome these obstacles. Our project aims at integrating two systems, an NLP interface to fetch data from simple English queries, and a second system where the fetched data with the help of natural language processing is used to form visualizations as demanded by the users will be created. This system would essentially help the people who are not techno-savvy or are not in the field of tech to interact with data using simple English.
LORA (Long Range) Based Low Power Emergency Tracking System in Disaster Areas
Authors: Sweetline Shamini S. , Anuja A.P. , Archana B. , Premalakshmi P.
Published: December 2021

The communication network is one of the major assistances for disaster management purposes. At each disaster phase, the information flow between the disaster management departments and the population is critical, as it destroys the whole communication infrastructure where the base station to Mobile Switching Centre (MSC) connectivity is broken, power generators are exhausted, and the network may suffer from congestion. Even when radio equipment works, police officers, rescue teams, medical teams cannot communicate with each other because of incompatible radios. To analyze the characteristics of the geological environment in the disaster region and to alert the Disaster Management Department, this solution based on LoRa has been proposed. The penultimate goal of this paper is to fasten the rescue operations.
Design and Analysis of 64 GHz Millimetre Wave Microstrip Patch Antenna
Authors: Sarkar Swagata , Nagappan Sivakami , Kadhir Badhusha Shafin
Published: December 2021

Millimetre Wave frequencies (30–300 GHz) can be used for different major applications of modern world like telecommunications, security screening, imaging, automotive radars, military applications, remote sensing, radio astronomy and many more. The internationally reserved frequency spectrum is used for Radio Frequency Energy. In this work 64 GHz antennas are compared with different design and a comparative study is taken. In this work Microstrip patch antenna with carpet architecture, and fractal island are designed and compared. The general comparative parameters for antenna are directivity, gain, return loss, bandwidth, specific absorption rate etc. After the comparison, it is found that return loss gave better result for carpet design at 64 GHz compare to fractal island design.