bluuebunny/crossref_metadata_2025 huggingface dataset:
Reconnaissance of Professional Resources and Personal Potential of a Teacher in the Era of Social Turbulence and Transformation of Education
Authors: Lukyanova Margarita Ivanovna , Danilov Sergey Vyacheslavovich , Shustova Liubov' Porfir'evna
Published: November 2022

Introduction. The article defines the teacher's mission in the era of social turbulence and high uncertainty characteristic of the transition of Russian society to the post-industrial stage of development. The authors focus on training a teacher for the future, determined by social conditions and directions of development of the Russian education system. Relevance. The available scientific ideas about the professional resources and personal potential of the teacher, ways to support his development in modern conditions cannot be recognized as sufficient. This determines the purpose of the study: the definition of professional resources in the context of the development of the personal potential of the teacher, the most demanded in the era of social turbulence and transformation of education. Material and research methods: the work uses theoretical methods (analysis of philosophical, psychological and pedagogical literature), modeling, generalization of the experience of establishing additional pedagogical education; empirical methods (psychological diagnosis of professional resources and personal potential of teachers, readiness of teachers for professional activities in conditions of social turbulence and transformation of education). Study results and discussion. The authors propose a model of professional resources and personal potential of the teacher, indicate diagnostic methods for their research. A description of the possibility of additional pedagogical education is presented as a condition for the development of professional resources and the personal potential of the teacher. It was concluded that it is necessary to improve the advanced training and retraining of teachers in the context of modern requirements for the teacher's activities. The experience of the Ulyanovsk State Pedagogical University named after I. N. Ulyanov in this direction is described. Conclusions. The results of the study can be used in organizing comprehensive support for the development of professional resources and personal potential of teachers in the conditions of further education, managing a teacher's career, developing advanced training and professional retraining programs.
A Case Report of an Extensively Ossified Left Atrial Myxoma
Authors: Chosokabe Motohiro , Chosokabe Motohiro , Ariizumi Yasushi , Kobayashi Toshiya
Published: November 2022

A cardiac myxoma, a rare but relatively common cardiac tumor, was surgically excised. Histopathological examination of the lesion revealed an extensive internal ossification. Ossification of the interior of myxomas is rarely reported, and most are large tumors; however, this is a rare case of a small tumor. The cause of ossification has not yet been clearly reported.
Restrições semânticas de animacidade do objeto nulo do Português brasileiro
Authors: Guimarães Peixoto da Silva Gabriel
Published: November 2022

Com este trabalho, objetivamos fazer algumas reflexões sobre o objeto nulo do português brasileiro e suas restrições semânticas de animacidade. Muitos trabalhos sincrônicos e diacrônicos, formalistas e variacionistas (GALVES, 1984; DUARTE, 1989; CYRINO, 1994, SOLEDADE, 2011; PEIXOTO, 2021) atestam que o português brasileiro (PB) é uma língua que permite objeto nulo praticamente sem restrições. Isso significa que ele pode ocorrer em diversas estruturas sintáticas – coordenação e subordinação (relativas, adverbiais) – e com diversos tipos de antecedente/referente: [+/− animado, +/− específico], sendo preferencialmente [− animado]. Entretanto, um tipo de configuração enfraquece a possibilidade de objeto nulo no PB, apesar de ainda ser possível: quando seu antecedente é [+ animado, + específico]. Vários autores gerativistas propuseram interpretações para o fato, relacionando-o a diferentes naturezas da categoria vazia. Neste artigo, baseados em um corpus de cartas pessoais privadas escritas por brasileiros nascidos durante o século XX, a partir dos resultados de Peixoto (2021), trazemos algumas dessas discussões e resultados de análises quantitativas de dados acerca do objeto nulo no PB e de suas relações com a animacidade do seu referente, evidenciando que a restrição do objeto nulo não é de natureza estritamente sintática, mas semântica, o que Galves (2001) chama de restrição de logoforicidade.
FORMATION OF ANTI-CRISIS INVESTMENT POLICY WITHIN MANAGING CHANGE IN THE FIELD OF WELLNESS AND RECREATION IN THE COVID-19 PANDEMIC PERIOD
Authors: SHEVCHENKO H.M.
Published: November 2022

Topicality. Combined with the need to improve the health and efficiency of people who were infected with coronavirus, wellness recreation is one of the important strategic directions of change management in the context of normalization of the economy and all sustainable development processes in the pandemic and post-pandemic period. And accordingly, the sphere of wellness and recreation, especially given its state in Ukraine, needs to establish a systemic anti-crisis investment policy.Aim and tasks. The purpose of the article is to substantiate the prerequisites and factors for the formation of anti-crisis investment policy in the management of changes in the field of wellness and recreation in the conditions of COVID-19 pandemic in Ukraine.Research results. In the article the prerequisites for managing changes in the field of wellness and recreation during the pandemic period are analyzed and summarized, namely: during 2020 in the world and in Ukraine the volume of investments in the creation of new jobs in the fields of hospitality, tourism and recreation is reduced; during 2020-2021 there are no significant positive changes in the increase or redistribution of investment in recreation or health care in Ukraine, especially in those regions that have a greater number of coronavirus infected; in the field of international tourism, which has a direct impact on the formation of the domestic market of recreational services, further decline is observed in the number of Ukrainians traveling abroad and a gradual stabilization of domestic travel in Ukraine.Conclusion. In the article conceptual principles of managing change in the field of wellness and recreation in the context of the COVID-19 pandemic are formulated, within the anti-crisis investment policy, which provides for the development of appropriate programs, the implementation of which requires a set of organizational and economic measures aimed at creating a favorable investment climate, structural transformation and improving economic, social and environmental indicators of wellness and recreation effectiveness in the conditions of coronavirus pandemic.
Assessing the Impact of Electronic Word of Mouth on Brands Switching
Authors: Bhutto Sanam , Marvi Hina , Kumar Raj
Published: November 2022

The increasing amount of electronic word of mouth has significantly affected how consumers purchase. Customer behaviour known as "brand switching "gives them the opinion to select a product or service that best suits their needs. Individuals who are dynamic and relate to social media are free to comment on a good or service. With the rise of social commerce, has become an essential reference for users to make purchase decisions. A survey was conducted on a general audience, including both males and females, by using a questionnaire technique. A total of 199 respondents filled out the questionnaire. The result indicates that only one independent variable has been proved acceptable under the assumption: the other variables, brand image, brand attachment, and environmental effect comparative advertisement approved by the analysis. The study proves that electronic word of mouth plays a vital role in brand switching. Individuals who are dynamic and relate to the social network.
Comparative Study of Competitors Lead to Brands Switching in Pakistan: A Case Study of Hyderabad
Authors: Bhutto Sanam , Kumar Raj , Marvi Hina
Published: November 2022

This research aims to examine why consumers' brand switching behaviour happened, understand the critical factors behind brand switching in Hyderabad city, and illustrate the variables that significantly affect customer engagement in fashion clothing. Using a questionnaire, a survey was done with a public audience that included both men and females. A total of 199 people completed the survey. The outcome shows that just one independent variable has been validated following the hypothesis.The analysis did not approve the other four variables: brand image,attachment, word of mouth, and environmental effect. The current study proves that comparative advertisement plays a vital role in brand switching.
Car Parking Study of Hyderabad City, Sindh, Pakistan
Authors: Soomro Mehnaz , Marvi Hina , Khaskheli Rabia
Published: November 2022

We accept as true with any talk about stopping needs to start with a study of present stopping use.Regardless of whether your problem is parking in the city middle or on the other hand, how a great deal stopping expecting another development, you need to understand have a great deal stopping is as of now reachable and how is it used. Parking spots must normally be a piece of any study and any arrangements. Numerous straightforward tallies can be performed by the neighbourhood workforce or on the other hand, adjacent volunteers with insignificant preparation. The instruction under is meant exceptionally for the neighbourhood team of workers and neighbourhood volunteers in structuring and doing your survey, or for the neighbourhood team of supervising revising planned information assortment suggestions as a portion of the different permit methods.
Risk Managing Technique in Pakistan Industry: A Case from Pakistan
Authors: Khan Muhammad Mahmood Shah , Abid Sheikh Khurram , Tashfeen Rubeena
Published: November 2022

This study examines the risk management solutions used in the banking sector to meet the many risks. The report also evaluates how conventional and Islamic banks in Pakistan manage risk. This study used primary sources. First, senior managers, risk managers, and chief risk officers from Islamic and normal banks fill out a questionnaire. 51 financial institutions responded. Data analysis uses descriptive statistics, cross-tabulations, t-tests, an ANOVA, and the LSD test. Regular banks' operational risk management strategies and stress test results differ from Islamic banks statistically. The study found no statistically significant difference between Islamic and conventional banks in how well they used risk management tools and systems, how much
market risk VaR they used, how much credit risk exposure they had, how they reduced that risk, and how they analyzed their credit risk portfolios.
Lissencephaly with Congenital Hypothyroidism: A Case Report
Authors: Sahani Shambhu Kumar , Pathak Anil , Nepali Bishal , Rai Nilshan
Published: November 2022

Lissencephaly is a malformation of cortical development associated with deficient neuronal migration and abnormal formation of cerebral convolutions or gyri. The lissencephaly spectrum consists of agyria, pachygyria, and subcortical band heterotopia. At least 19 genes have been identified in the causation of lissencephaly and related syndrome. Lissencephaly is associated with many other congenital disorders but the association of lissencephaly with congenital hypothyroidism is rarely reported. We report a case of a 10-year-old girl having lissencephaly with congenital hypothyroidism. Early diagnosis of lissencephaly and genetic counselling can be made in suspected cases and further possible interventions can be taken. Also, regular follow-up, monitoring, and better conservative management lead to a better prognosis.
Mean Fluoride Concentration in Drinking Water Sources of a Municipality: A Descriptive Cross-sectional Study
Authors: Chaulagain Rajib , Chhatkuli Ayam , Shrestha Ashim Raj , Chhetri Bikram Karki , Pandey Sadhana
Published: November 2022

Introduction: Fluoride is essential for the growth and development of teeth and bone. Excess or less fluoride consumption can have harmful effects on our bodies. Fluoride level of 0.5-1.5 mg/l is said to be optimized according to the World Health Organization. The level of fluoride varies among the different geographical regions and water sources. It is essential to find out the fluoride content of various water sources used for drinking purposes. The aim of this study was to find the mean concentration of fluoride in drinking water sources of a municipality.
Methods: A descriptive cross-sectional study was conducted in a Municipality. The study was conducted from 1 December 2021 to 30 December 2021 after receiving ethical approval from the Ethical Review Board (Reference number: 1134). Water samples were collected and the fluoride content was estimated using 2-parasulfophenylazo-1,8-dihydroxy-3,6-napthalene-disulfonate colorimetric method. The data collected from the laboratory were calculated and presented in the form of a table. Point estimate and 95% Confidence Interval were calculated.
Results: The mean value of fluoride content in 160 collected water samples was 0.369±0.275 mg/l (0.33-0.41, 95% Confidence Interval). Among the different wards, the fluoride content was 0.708±0.27 mg/l in ward number 12 followed by a fluoride content of 0.57±0.19 mg/l in ward number 5.
Conclusions: In this study, the mean fluoride levels were lower when compared with similar studies conducted in similar settings. The levels were lower than that recommended by the World Health Organization. The various controlled methods of fluoridation have to be quickly initiated. Other means of fluoride consumption, like the use of fluoridated toothpaste, has to be recommended.
Penulisan Surat Dinas dan Pengelolaan Arsip Sekolah bagi Tenaga Kependidikan Se-Cabdin Wilayah VII Provinsi Sumatera Barat
Authors: Rahmah Elva , Marlini Marlini , Emidar Emidar , Zulfikarni Zulfikarni , Arief Ermawati
Published: November 2022

The purpose of community service activities is to improve the ability of educational staff in making official letters and managing school archives. The target of this activity is the education staff of SMA/SMK/SLB di Dinas Pendidikan Cabang Wilayah VII Provinsi Sumatera Barat. In this activity, problems will be completely solved related to the making of official letters and the management of school archives to realize school accountability. The approach used in implementing community service programs is Participatory Rural Appraisal (PRA) which emphasizes partner participation in all activities, from planning, implementation, and evaluation. The output target in the form of a process is an increase in the ability of the target audience in understanding the concept and process of making official letters and managing school archives. The results of the training and mentoring activities carried out are expected to create school archives that are systematically organized so that the school and the education office can access the archives easily and quickly.
Implementasi Seriang Game Edukids Sebagai Media Pembelajaran Menyenangkan di SDN Wonocolo 2 Sidoarjo
Authors: Muflihah Yusrida , Arifiana Isrida Yul , Siswoyo Siswoyo , Rizky Muhammad
Published: November 2022

Matematika menjadi salah satu mata pelajaran yang dianggap sukar oleh siswa. Berbagai macam keluhan juga muncul dari Guru, diantaranya capaian nilai akademik yang rendah, proses belajar matematika yang akhirnya berdampak pada perilaku bermasalah di kelas. Kondisi ini juga dihadapi oleh Guru kelas V di SDN Wonocolo 2 Sidoarjo. Berdasarkan identifikasi permasalahan yang dilakukan oleh pengusul, kondisi tersebut terjadi didasari oleh beberapa hal: 1)Penjelasan materi dan rumus kurang menarik dan cukup membosankan ; 2) Latihan soal yang kurang menarik dan membosankan untuk dikerjakan ; 3)Tidak ada keseruan dalam mengerjakan soal. Oleh sebab itu, prioritas dalam kegiatan pengabdian masyarakat ini adalah menyelesaikan permasalahan mitra terkait dengan penggunaan media pembelajaran matematika kelas V SDN Wonocolo 2 Sidoarjo. Solusi yang ditawarkan adalah mengimplementasikan Seriang Games Edukids serta pemberian penyuluhan terkait penggunaannya. Adapun Seriang Games Edukids yang akan diimplementasikan dilengkapi dengan fitur menampilkan materi dan rumus serta latihan soal yang dikemas dalam bentuk aktivitas permainan. Berdasarkan hasil implementasi, dapat disimpulkan bahwa Seriang Games Edukatif efektif untuk meningkatkan pemahaman akan konsep belajar matematika serta dapat dijadikan sebagai salah satu media pembelajaran yang menarik.
Sosialisasi Pencegahan Stunting di Desa Bandur Picak, Kecamatan Koto Kampar Hulu
Authors: Charlina Charlina , Putri Silvya Sischa Yulia , Fadilah Muhammad Arief , Ijjati Salsabila Filjania , Afsya Ghina Ans , Savira Dina , Putri Anggie Andika , Putri Almira Azalia Natasya , Ramadhan Nofriandi , Nasution Muhammad Naufal Ali Akbar , Ziansyah Mohammad Zascka
Published: November 2022

Stunting is a health problem that is often found in developing countries, including Indonesia. The prevalence of stunting in Indonesia is the fifth largest in the world. Stunting is a linear growth disorder caused by chronic malnutrition. People who live in the outlying areas receive less attention from the local health office so the stunting sufferers have not received a proper treatment. Currently, Indonesia has entered a new normal era where community activities run as usual, whether it’s the center of government, educational institutions, health agencies and others. This is the background for us to hold a stunting socialization program, especially in Bandur PicakVillage, Koto Kampar Hulu District. This article aims to provide information related to stunting socialization and stunting prevention interventions for pregnant women about nutrition during pregnancy in an effort to prevent stunting. This socialization is quite effective in providing information on how to prevent and reduce the incidence of stunting in children through pregnant women. With the socialization of stunting in Bandur Picak Village, this socialization can increase public awareness regarding child growth and development , so hoped that it can prevent stunting and reduce the percentage of stunting in Indonesia, especially in Bandur Picak Village, Koto Kampar Hulu District, Kampar Regency, Riau Province.

Keywords : Stunting, Educational, Health Agencies
HOW IS THE LEADERSHIP OF THE LATIN-AMERICAN YOUTH? LEADERSHIP, SEX AND COUNTRY
Authors: Rivera-Mata Juan
Published: November 2022

Many papers studied leadership but not much about leaders in Latin America. We evaluated 427 students from 16 countries in Latin America and Spain participating in the Global Competitiveness Leadership Program of Georgetown University during 2007-2019 period, trying to find if their leadership profile was different from those in Europe and USA. We found their profiles are very similar, without significant differences by country or sex and with high homogeneity in the group, but they could be more characterized as “paternalistic leaders”(Hiller et. col, 2019) maybe due to country and organizational cultures. The results suggest there is a significant similarity among leaders, regardless of their sex or country of origin. Different countries, different sex, similar leadership.
CORRUPTION, COMPETITIVENESS AND ECONOMIC GROWTH: EVIDENCE FROM LATIN AMERICAN AND CARIBBEAN COUNTRIES 2004-2017
Authors: Useche Alejandro J. , Reyes Giovanni E.
Published: November 2022

The generally accepted idea is that corruption is an unethical behavior that breaks legitimate and socially accepted norms, harming social well-being. The aim of this paper is to analyze the evolution and determine the relationships between competitiveness, economic growth and corruption perception in a sample of 20 Latin American and Caribbean countries from 2004 to 2017, using correlation and performance analysis. Findings show that there has been a short advance against corruption during this period. Although most of the countries increased their competitive score, GDP growth decreased considerably. Mixed evidence appeared on the correlation between corruption, competitiveness and economic growth.
DESEMPEÑO DE LOS MERCADOS ACCIONARIOS DESARROLLADOS Y EMERGENTES UN AÑO ANTES Y UN AÑO DESPUÉS DEL PRIMER BROTE DE COVID-19 ORIGINADO EN WUHAN, CHINA
Authors: Sandoval Álamos Eduardo , Lastra Piña Carlos , LLantén Figueroa Sergio
Published: November 2022

Este artículo investiga el desempeño de los mercados accionarios desarrollados y emergentes, un año antes y después del primer brote de Covid-19. Para lo anterior, se utiliza el “Alfa de Jensen”, a partir del modelo de 5 factores de Fama & French, estimado por mínimos cuadrados generalizados. Los resultados indican que durante la crisis, los mercados accionarios de Alemania, Austria, España, Holanda, Portugal, Brasil, Chile, Malasia, Rusia, Sudáfrica y Tailandia destruyen valor mientras que China y Corea del Sur presentan un buen desempeño asociado a políticas monetarias y fiscales exitosas, representando así una opción para el refugio de inversionistas globales.
Deep Learning-Based Solar Activity Prediction Using Sunspot Number and Solar Radio Flux
Authors: Waykar Sanjay B. , Kale Anil W. , Karlekar Nandkishore P.
Published: November 2022

Nowadays, solar spectral irradiances are modelled by solar activity indices, which are used to identify the solar energy absorbed in the environment. This paper devises the Deep LSTM model for predicting solar activity using Sunspot Number (SSN) and Solar Radio Flux (SRF). The processing steps involved in the solar activity prediction are technical indicator extraction and solar activity prediction. In this paper, the solar indices are considered as an input of solar activity prediction, which is acquired from the solar cycle progression dataset. The technical indicators, like Simple Moving Average (SMA), Average True Range (ATR), Relative Strength Index (RSI), William’s %R, Stochastic %D, and Commodity Channel Index (CCI)are extracted for attaining a better prediction performance. In addition, the solar activity prediction is carried out using Deep LSTM based on SSN and SRF. The Deep LSTM is an effective deep learning technique that is widely utilized for prediction purposes since it has a better prediction ability. Moreover, the experimental result demonstrates that the devised Deep LSTM attained the minimum Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) of 1.186, 2.869 and 1.693, correspondingly.
Analysis on Characteristics and Management of Retinoblastoma Using Machine Learning
Authors: Ashwini S  , Minu R I , Kumar Jeevan
Published: November 2022

Retinoblastoma is an embryonic intraocular tumor arising in the retina of the eye. It is a dangerous tumor that can damage the eye and its surrounding components. Chromosome 13q14.1-14.2 is the cytogenetic location of the RB1 gene. As a result, early identification of Retinoblastoma in children is essential. Over the last few decades, Retinoblastoma treatment has improved with the goal of not only saving life and the eye but also optimizing residual vision. In oncology, machine learning approaches used to predict cancer patient treatment outcomes include data collection and preprocessing, text mining of clinical literature, and constructing prediction models. This paper discusses recent advances in the management of Retinoblastoma, as well as data preparation and model construction for identifying patterns between Retinoblastoma clinical factors and predicting therapy success using machine learning.
Multimodal Machine Learning Framework to Detect the Bipolar Disorder
Authors: Sivagnanam Lingeswari , Karthikeyani Visalakshi N.
Published: November 2022

The current approaches for diagnosing mental disorders rely heavily on self-reported and clinical interview ratings. The development of an automatic recognition system assists in the early detection and discovery of biological markers for diagnostic purposes. In this paper, develops a multimodal machine learning model, where it processes multiple modalities like visual, acoustic and textual features using a cross-modality correlation. The study uses a Denoising Autoencoder that finds the multimodal representations and then it adopts Fisher Vector encoding to form session-level descriptors. Paragraph Vector (PV) are used for textual modality that embeds the interview sessions transcripts into document representations that tends to capture the mental disorder cues. Finally, the textual and audio-visual features are fused before training the 3-layered denoising autoencoder (DAE) with Residual Neural Network classifier. The proposed model is validated using two bipolar disorders that includes depression and bipolar disorder. The study uses two different datasets including Extended Distress Analysis Interview Corpus (E-DAIC) and Bipolar Disorder Corpus (BDC) to analyse the depression and bipolar disorders. The experimental evaluation is conducted to show the performance improvement using the proposed multimodal model than other state-of-the art methods in detecting the depression and bipolar disorders. The results of simulation show that the proposed method obtains an improved detection rate than existing models.
Automatic Bug Tracking System Using Text Analysis and Machine Learning Predictions
Authors: Meena Kumari P
Published: November 2022

Every day, many bugs are raised, which are not fully resolved, and a large number of developers are using open sources or third-party resources, which leads to security issues. Bug-triage is the upcoming automated bug report system to assign respective security teams for an ample rate of bug reports submitted from different IDEs within the organization (on-premises). Furthermore, by predicting the appropriate team (who can resolve the bug) in an organization, the bugs can be assigned once it is tracked. With this, cost and time can be saved in tracking and assigning the bugs. In this paper, we are implementing an Automatic bug tracking system (ABTS) to assign the team for the reported bug using the Text analysis for bug labeling and classification machine learning algorithm for predicting developer.
Predictive Analysis for Identifying Survival of Covid-19 Patients Using Support Vector Machine over Logistic Regression
Authors: Ganesh K. , Nelson Kennedy Babu C
Published: November 2022

The use of Logistic Regression (LR) and Support Vector Machines (SVM) to identify the survival of Covid 19 patients in a novel way is the prime work. To forecast the survival of Covid-19 patients, The LR algorithm and the SVM algorithm are iterated 20 times with a sample size of 10.In comparison to the 86.4% accuracy of the logistic regression technique, the Support Vector Machine approach offers a 91.2% accuracy. In independent sample T-test, LR has a high level of significance. The range of this study, reveals SVM algorithm aids in data analysis and classifier performance.
Predicting Customer Behaviour Using a Self-Organizing Map and Comparing with Random Forest
Authors: Venkatesh I. , Poovizhi T.
Published: November 2022

The main of this paper is to analyse the performance of customer behaviour in online shopping by using precision value.A novel Self-organizing map with Random Forest was iterated at different times for predicting accuracy percentage of customer behaviour. The result proved that the Novel Self-organizing map got significant results with 96% accuracy compared to Random Forest with 93% accuracy. Self-organizing mapis a simple and most effective algorithm to build fast machine learning models. The self-organizing map helps in predicting with more accuracy the percentage of customer behaviour.
A Novel Scheme for Efficiency of Customer Loyalties Using Pattern Growth Method with Upper Bound Algorithm
Authors: Kavya Sree Kurapati , Ashok Kumar S.
Published: November 2022

The aim is to improve and develop a novel scheme to detect loyalties of customers using pattern growth method.Novel Pattern growth method compared with upper bound taxonomy sequence algorithm are used to detect online sales customer loyalties. Sample size is determined using the G Power calculator and found to be 10 per group. Totally 20 samples are used. Pretest power is 80% with CI of 95%. Based on the analysis Novel Pattern growth method has an accuracy of 80.8% and upper bound taxonomy sequence algorithm has 67.25%. Significance value is 0.0001 (p<0.05, two-tailed). Proposed algorithm Novel Pattern-Growth method has higher accuracy than Upper Bound Taxonomy for selected datasets for more reviews.
Detection of Tumor for Breast Cancer Patient Using Regression Tree Compared with Linear SVM Algorithm
Authors: Saimonika R. , Sriramya P.
Published: November 2022

The paper aims to detect the tumor in breast cancer images using a Regression Tree and compare it with a Support Vector Machine to evaluate accuracy values.The regression tree algorithm is applied to 10 sample images from a dataset of more than 250 images. For the same input images taken accuracy, values are evaluated. The dataset taken consists of breast cancer images in this research study a Novel Regression Tree and Linear Support Vector Machine is evaluated with a total of 20 sample sizes. Based on the statistical analysis the significance value for calculating accuracy was found to be p<0.05(significant two-tailedtests). Detection of breast cancer was performed by using a Regression Tree and Support Vector Machines which achieved the accuracy of 95% and 86% respectively. By the above two results, we conclude that the performance of the Novel Regression tree is significantly better than the Linear Support Vector Machine in terms of accuracy.
Bird Species Identification Using Convolutional Neural Network
Authors: Dharaniya R  , Preetha M  , Yashmi S
Published: November 2022

Identification of bird species is an important task in biodiversity preservation and ecosystem maintenance. Birds also help in various activities like agriculture, landscape formation, coral reef formation, etc. Identification and observation of the bird species is a vital task in ecology. Due to the development in the field of machine learning, the automatic classification of bird species has been made simple. The advancements in technology led to the development of various bird species classification systems. Mostly, a web application or mobile application will be developed to display the result of the classification model. This paper presents the implementation details of bird species identification using a Convolutional Neural Network.
Multi-Objective Optimization Technique for Channel Allocation in Wireless Networks
Authors: Reeginal Y. , Anu N. , Archana M. , Sathyavathi V. , Sai Ramesh L
Published: November 2022

The channel allocation is the primary concept for enhancing the throughput and channel quality. The target channel allocation will enhance the performance by minimizing the noise rate as the resource utilization leads to an important problem for primary user. The dynamic channel allocation technique is maintained in this paper through the multi-objective optimization technique. The poor resource allocation leads to desperate problem in the wireless network, the proposed technique shows the improved throughput and energy proficiency.
Smart Household Object Detection Using CNN
Authors: Vatchala S. , Sasidevi S. , Dhanalakshmi R  , SaiRamesh L
Published: November 2022

Object detection is one of the most basic and central tasks in computer vision. object detection is a subset of object recognition. Its task is to find all the interested objects in the image, and determine the category and location of the objects. Object detection is widely used and has strong practical value and research prospects. Applications include face detection, pedestrian detection and vehicle detection. In recent years, with the development of convolutional neural network, significant breakthroughs have been made in object detection. This work aim to detect objects in the video frames. It detects household objects and predicts the object where it may be present. Convolutional Neural Networks (CNN) is used to detect objects in the environment. Then Resnet50 is used to classify the images into objects. Then Support vector machine (SVM) is used to train objects and stored in object database. It will be retrieved whenever neural networks sent object for verification.
Classification of Fertiliser Type Based on Soil Minerals Using Voting Classification Over Decision Tree
Authors: Bandaiah K. , Rama Parvathy L.
Published: November 2022

To estimate the type of fertilizer based on the soil minerals using the voting classifier. For forecasting fertilizer type accuracy %, a Voting Classifier with a sample size of 10 and a Decision Tree with a sample size of 10 was iterated at various times. A supervised learning algorithm is a Decision Tree. It constructs a “forest” using an array of decision trees, typically trained to use the “bagging” method. A Novel Voting Classification is a predictive model that learns from several models and predicts an output (class) based on the result representing the greatest likelihood of being the chosen class. The Novel Voting Classifier produced substantial results with 96 percent accuracy, compared to 94% accuracy for the Decision Tree. The Novel voting classifier and the Decision Tree showed statistical evidence of p=0.001 (p<0.05). Voting Classifier is the most effective algorithm that classifies the type of fertilizer based on soil minerals with more accuracy than the Decision Tree.
Cost Optimization Analysis Using Hybrid Cloud Optimized Cost Scheduling Algorithm for Efficient Packet Delivery
Authors: Mahitha K. , Sridhar S.
Published: November 2022

To Minimizes the computing cost in the packet delivery of Hybrid Cloud using an Efficient Cost Scheduling Algorithm. The following Hybrid Cloud Optimized Novel Cost scheduling algorithm is analyzed in the group 1 (596 samples), and the Cuckoo Search Optimization is analyzed in the group 2 (596 samples). Cuckoo Search Optimization has significantly better cost reduction (57%) compared to Hybrid Cloud Optimized Novel Cost Scheduling algorithm (20%). There was a statistical significance between Hybrid Cloud Optimized Cost Scheduling Algorithm and Cuckoo Search Optimization Algorithm. Efficient packet delivery for cloud resources using the novel Cuckoo Search Optimization Algorithm helps in cost reduction with a significance value of 0.033 (p<0.05). The cost optimization was better in Hybrid Cloud Optimized Cost Scheduling algorithm than Cuckoo search algorithm
Ola Data Analysis for Dynamic Price Prediction Using Multiple Linear Regression and Random Forest Regression
Authors: Venkat Sai Tarun G. , Sriramya P.
Published: November 2022

This research aims to create the most efficient and accurate cab fare prediction system using two machine learning algorithms, the Multiple linear Regression algorithm and the random forest algorithm, and compare parameters r-square, Mean Square Error (MSE), Root MSE, and RMSLE values to evaluate the efficiency of two machine learning algorithm. Considering Multiple linear Regression as group 1 and random forest algorithms as implemented, the 2 group process was to predict prices and get the best accuracy to compare algorithms. The algorithm should be efficient enough to produce the exact fare amount of the trip before the trip starts. The sample size for implementing this work was N=10 for each group considered. The sample size calculation was done with clincle. The pretest analysis was kept at 80%. The sample size is estimated using G-power. Based on the statistical analysis significance value for calculating r-squared, MSE was 0.945 and 0.266(p>0.05), respectively. The Multiple linear algorithms give a slightly better accuracy rate with a mean r-squared percentage of 71.69%, and the Random forest algorithm has a mean r-square of 71.29%. Through this, prediction is made for online booking of cabs or taxis, and the Multiple linear algorithms give a slightly better r-squared value than the Random forest algorithm.
Higher Classification Accuracy of Income Class Using Decision Tree Algorithm over Naive Bayes Algorithm
Authors: Zaid Mohamed , RajendranT
Published: November 2022

Developing two machine learning classifiers with higher accuracy for classifying income classes for people earning less and a higher salary scale between 50,000. Decision Tree Algorithm (DTA) and Naive Bayes Algorithm (NBA) are the two classifier mechanisms employed. On a dataset of 32516 records, the methods were implemented and tested. Implemented each algorithm through programs and performed ten rounds on both methods to determine distinct scales of income class for who earns lesser and higher salary scale between 50,000. The G-power test is around 80% accurate. The findings of the programming experiment showed that the Decision Tree Algorithm had a mean accuracy of 84.3790 and the Naive Bayes Algorithm had a mean accuracy of 79.3170 for classifying income categories. The variation in accuracy between the two classifiers is statistically significant (p=0.53), which is insignificant when employing the unpaired samples t-test. The primary purpose of this work is to apply a novel technique to modern Machine Learning Classifiers to forecast income class classification. When the Decision Tree Algorithm is compared to the Naive Bayes Algorithm, the results show that the DTA outperforms the NBA.
Analyzing Ola Data for Predicting Price Based Trip Distance Using Random Forest and Linear Regression Analysis
Authors: Venkat Sai Tarun G. , Sriramya P.
Published: November 2022

The paper aims to create a most efficient and accurate cab fare prediction system using machine learning algorithms and comparing them. The machine learning algorithms are Random forest algorithm and Linear regression and comparing the r-square, mean square error (MSE), Root MSE and Root Mean Squared Logarithmic Error (RMSLE) values. We implement the Random forest and linear regression algorithms to predict the prices of the system and to get the best accuracy when comparing both the algorithms. The algorithms should be efficient to predict the prices of the trips before the starting of the trip. The sample size considered for this work is N=10 for each of the groups considered. Totally it was iterated 20 times for efficient and accurate analysis on prediction of price with G-power in 80% and threshold 0.05%, CI 95% mean and standard deviation. The sample size calculation was done with clincle. Based on the statistical analysis the significance value for calculating the r-square was found to be 0.034. The Random forest algorithm gives a slightly better accuracy rate with a mean r-square percentage of 71.67% and the linear regression has mean r-square value of 70.57%. By this process, the prediction is done for the price prediction of the online cab rental system and the Random forest algorithm gives a better r-square value compared to the Linear regression algorithm.
Real Estate Price Prediction and Analysis Using Voting Regression Compared with Linear Regression
Authors: Hanuma Reddy G. , Sriramya P.
Published: November 2022

The primary goal of this research work is to predict housing prices that are frequently overstated, using efficient machine learning algorithms to obtain better accuracy. This study compares the price prediction accuracy of Novel Voting Regression (Group 2) and Linear Regression (Group 1) algorithms. For each of the groups studied, the sample size was N=10. Clincle was used to figure out the sample size. The pretest analysis was maintained at 80%. Using G-power, the sample size is calculated. Statistical analysis yielded a value of 0.584 for significance. The accuracy of the Novel Voting Regression method for house price prediction is 80.92%, which is greater than the Linear Regression algorithm’s 69.81%. The Independent Sample T-test has a statistical significance of 0.584. So it can be concluded that the Novel Voting Regression technique can give near accurate values than the Linear Regression technique.
Human Emotions Recognition Using Softmax Classifier and Predict the Error Level Using OpenCV Library
Authors: PavanreddyA  , Senthil Kumar R
Published: November 2022

The goal of this research project is to compare the accuracy of the CNN method using the Softmax classifier and the OpenCV library by taking out new features and comparing them to the accuracy of the CNN method. To analyse the suggested system, CNN algorithms with softmax classifier and OpenCV library were used with a sample size of 20 to find facial expression recognition by comparing the accuracy and error rate between the techniques. In the proposed study, the dataset was used to collect data on the outcome and accuracy of the OpenCV error rate, and the OpenCV improvement was compared to the CNN softmax classifier technique. The average accuracy of OpenCV over techniques that use convolutional neural networks is 95.5%. The T-Test doesn’t show anything important (p 0.05). Face recognition systems were used to get information from a number of places for the study. Compared to the convolution neural network method, the OpenCV algorithm made the result more accurate and less likely to make mistakes.
Host Intrusion Detection System Using Novel Predefined Signature Patterns by Comparing Random Forest over Decision Tree Algorithm
Authors: Sai Chowdary P. , Vinod D.
Published: November 2022

The objective of the work is to identify anomaly intrusion detection in a user network environment using information accessing and retrieval from homogeneous network databases. Here machine learning algorithms namely Random Forest and Decision Tree are been used to categorize passive and active attack. To accomplish focused accuracy, sample size of n=10 in Random Forest and n=10 in Decision Tree was repeated for 20 intervals for well-organized and precise investigation on categorized images with G power in 80% and threshold 0.05%, CI 95% mean and standard deviation. The existing works proves the sequential implemented in focus to intrusion detection, while comparing Random Forest and Decision Tree has classified and predicted the values from the network intrusion to generate accuracy with Random Forest has higher accuracy (76.37%) compared to Decision Tree accuracy (71.57%) with a significance of P<0.001 (2-tailed). Prediction in identifying anomaly intrusion detection systems shows that Random Forest has higher accuracy over Decision Tree.
A Systematic Literature Review of User Experience Evaluation Scales for Human-Robot Collaboration
Authors: Prati Elisa , Borsci Simone , Peruzzini Margherita , Pellicciari Marcello
Published: November 2022

In the last decade, the field of Human-Robot Collaboration (HRC) has received much attention from both research institutions and industries. Robot technologies are in fact deployed in many different areas (e.g., industrial processes, people assistance) to support an effective collaboration between humans and robots. In this transdisciplinary context, User eXperience (UX) has inevitably to be considered to achieve an effective HRC, namely to allow the robots to better respond to the users’ needs and thus improve the interaction quality. The present paper reviews the evaluation scales used in HRC scenarios, focusing on the application context and evaluated aspects. In particular, a systematic review was conducted based on the following questions: (RQ1) which evaluation scales are adopted within the HRI scenario with collaborative tasks?, and (RQ2) how the UX and user satisfaction are assessed?. The records analysis highlighted that the UX aspects are not sufficiently examined in the current HRC design practice, particularly in the industrial field. This is most likely due to a lack of standardized scales. To respond to this recognized need, a set of dimensions to be considered in a new UX evaluation scale were proposed.
Object-Oriented Ontology Enterprise Architecture Framework Supporting Enterprise Knowledge Integration
Authors: Chao M.H. , Trappey A.J.C. , Chen Neil K.T.
Published: November 2022

In the era of Industry 4.0 and digital transformation (DT), the way the manufacturing industry provides value has shifted from selling products to directly creating value for customers. The synergy between business and information technology is the most important criterium for DT. This study proposed an object-oriented ontology-based enterprise architecture (OOOEA) framework, based on the unified modeling language (UML), to connect and integrate all databases and modules of the enterprise’s existing information system (or ERP). The enterprise domain knowledge must be depicted to achieve business-IT alignment. This research starts from the physical and philosophical views of object-oriented ontology (OOO), pays attention to the essence of all things observed or understood in business operations, and constructs the core ideas of the OOOEA framework. The contribution comes from three aspects. One is to provide guidance for the first step of DT, the other is to propose a concise and applicable methodology as the basis for communication between employees at all levels, and the third is to verify the feasibility and effectiveness of the framework proposed in this research through a practical case study.
Confidence in Models and Simulations: A Multi-Stakeholder Analysis
Authors: Chaudhari Ashish M. , Rebentisch Eric , Rhodes Donna H.
Published: November 2022

System stakeholders from multiple disciplines increasingly interact with computer models and simulations to make critical decisions. Advanced digital models have transformed how engineers interact, analyze requirements, develop and verify system elements, and test them to validate that they meet stakeholder needs. However, these models are often developed by a specific discipline for its own purposes. Convincing other stakeholders to accept the results of these tools can be a challenge, and indeed, the adoption of models and simulations at the level of system development still lags the pace of the underlying computational and application advances. The acceptance of models and simulations remains largely a function of the subjective preferences of engineers and other stakeholders. In this paper, we investigate the social and technical factors that contribute to the acceptance and effectiveness of models and simulations, what we refer to as model confidence. We combine a literature review with practitioner interviews to identify constructs and attributes influencing model confidence. Model confidence results from the interplay of model-related, modeler-related and stakeholder-related constructs. The constructs identified in this study populate a model confidence framework currently being developed. They highlight important considerations for future research and practice to enable improved and increased use of models and simulations in multidisciplinary settings.
Requirements Handling in Multidisciplinary Product Development – A Company Study
Authors: Stolt Roland , André Samuel
Published: November 2022

When developing new products there are a multitude of requirements from different domains to consider making it a multidisciplinary activity. It involves stakeholders from the company and from the broader society. Requirements come from for example the customers, the production facility, suppliers, and governmental agencies placing safety and environmental regulations on the product. To manage all these heterogeneous requirements, a need of formalization and support emerges. The requirements can be managed using IT support. This functionality is often provided in contemporary PLM-systems. In this paper, it is investigated how a large industrial company with in-house design and manufacturing of consumer products formalize the requirements and handles them through the product development stages. The study involves corporate documents and interviewing staff from several departments of the company. Results show that while some aspects such as compliance with health and safety regulations are well supported, others are being addressed differently in each project with little formalization and IT support. This increases the risk of extra iterations in the development process. The paper discusses how to mitigate those risks and highlights some areas of possible improvement of the practices and IT support in product – as well as in production development.