Reviewerly/exHarmony huggingface dataset:
Strange Approach of Movie Rating Prediction Using Logistic Regression Comparing to Gaussian Naive Bayes Algorithm

The aim is to find movie ratings using logistic regression and comparing the result with naive bayes based on Accuracy. A total of 6040 samples were collected from movie datasets available in kaggle. Two algorithms are used; one is Logistic Regression and another is naive bayes algorithm. The computation processes were executed and verified for exactness. Sample size N=5 is taken for both algorithms. SPSS was used for predicting significance value of the dataset considering G-Power value as 80%. Logistic Regression achieved mean accuracy of 80.83% when compared to Naive Bayes Algorithm with 82.53%. Results were obtained with a level of significance with 0.003 (p&lt;0.05). Applied strange recommendation model confirms to have higher accuracy than Naive Bayes algorithm.
Classification of Fertiliser Type Based on Soil Minerals Using Voting Classification Over Decision Tree

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&lt;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.
Analyzing Ola Data for Precise Price Prediction Using XGBoost Technique Comparing with LASSO Regression

XGBoost algorithm and Lasso regression and compare r-square, Mean Square Error (MSE), Root MSE, and RMSLE values. 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 of the groups considered. It was iterated 20 times for efficient and accurate prediction of cab price prediction with G power in 80% and threshold 0.05%, CI 95% mean and standard deviation. The sample size calculation was done with clincle. The pretest analysis was kept at 80%. The sample size calculation was done using clincalc. The statistical analysis shows that the significance value for calculating r-squared and MSE was 0.63 and 0.581(p&gt;0.05), respectively. The XGBoost algorithm gives a slightly better accuracy rate with a mean r-squared percentage of 72.62%, and the Lasso regression algorithm has a mean r-square of 70.47%. Through this, the prediction is made for the online booking of cabs or taxis, and the Xgboost algorithm gives a slightly better r-squared value and MSE values than the Lasso regression algorithm.
Classifications of E-MAIL SPAM Using Deep Learning Approaches

Spamming is an art used to deceive people and, in most cases, send unwanted messages that can be used by cybercriminals to trick victims and get confidential credentials from the victim. Spamming occurs via email, SMS, social networking websites calling the victim’s phone number, etc. Spamming can exist for various reasons, but it can be used for malicious purposes mainly like trying to forge the victim for gathering the personal information, bank details card details, passwords, and other confidential data about the intended user. To overcome the security breach, the spam messages are classified for the understanding of spam has been done using different methods like Machine Learning and Natural Language Processing. We propose a method to identify the email spam messages and also to classify the message using deep learning approaches such as Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), and Bilateral Long Short-Term Memory (BLSTM). We have used an email spam dataset with over 5000 spam text samples are used to train our deep learning models. The proposed method separated the sentences from the email into words followed by their root words, and each word is assigned an index number before training. We have used regularization and dropout layers in our models to reduce the chances of overfitting. The proposed method is evaluated and compared with other existing models based on the history of the loss curve, Precision-Recall, and accuracy. Based on the results, it is observed that our Bi-LSTM model produced higher accuracy than other existing systems.
Designing Deep Classifier for Effective Detection of Spoof News in Twitter

A Spoof news is a fraud content meant to misguide the reader about the event with ill motive. In this article a reactive technique using deep learning is proposed to deal with it effectively. Spoof news are innumerable in number over microblog twitter and have wide range of bad effects overall. This is causing chaos and hoax among the readers about the issue. They are getting mislead about the issue a lot. As of now automatic locators of fake news are ineffective and few in number. This emphasized us to come up with smart locator with deep learning mechanism. One way of dealing with this issue is to make “blacklist” of origins and composers of counterfeit news. Here we need to examine all irksome instances of origins and creators in gradual manner. To cater this need we came up with a classifier based on deep learning mechanism that studies linguistic, network account aspects of twitter news and then distinguishes them into spoof and legitimate ones. We set up a deep learning model that takes both legitimate and spoof news elements as input and learns by analyzing their constructs. Then do the binary classification of news effectively thus avoiding the user not to misled by fake.
Predicting Customer Behaviour Using Self-Oganizing Map and Comparing with K Nearest Neighbour

The main aim of this paper is to analyse the performance of customer behaviour in online shopping by using precision value.A novel Self-organizing map with sample size 10 and k nearest neighbour with sample size 10 was iterated at different times for predicting accuracy percentage of customer behaviour. The minimum power of analysis is fixed as 0.8 and the maximum accepted error is fixed as 0.5. The result proved that the Self-organizing map got significant results with 96% accuracy compared to K Nearest Neighbour with 93% accuracy. The self-organizing map appears to perform significantly better than K-Nearest Neighbour with the value of p=1.000. 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.
Abnormality Detection in Human Action Using Thermal Videos

Anomaly detection is a challenging task in the surveillance system due to the factors like extracting appropriate features, inappropriate differentiation among the normal vs abnormal behaviours, the sparse occurrence of abnormal activities and environmental variations. In the dark environment, detection of human actions is still difficult as more features for recognizing the key point are not visible. Hence the proposed work is focused on overcoming the environmental variations task that too in a less bright environment by using thermal videos. Variations in the actions can be easily identified as it works on the property of infrared radiations. For recognizing actions, the skeleton-based approach is used as it helps with the joint-wise segregation of human parts, resulting in more accuracy. The motion pattern of humans in the thermal video is tracked to classify the level of abnormality.
A Novel Approach for Movie Suggestion System with Empirical Risk Minimization and Decision Tree Algorithms

The major objective of this study is to find accuracy in movie suggestion systems using machine learning algorithms namely Empirical Risk Minimisation Algorithm and Decision Tree Algorithm. Empirical Risk Minimisation algorithm and Decision Tree algorithm with sample size N = 10 was iterated 20 times for finding accuracy in movie suggestion systems.The Empirical riskminimisation algorithm produces a better accuracy of [92.02%] than the Decision tree algorithm [80.58%]. The significance of Empirical risk minimisation[p&lt;0.05] for independent sample T-test is high.The comparison of results showed that the Empirical risk minimisation algorithm is significantly better for finding accuracy in movie suggestion systems when compared to the Decision tree algorithm
Reconnaissance of Professional Resources and Personal Potential of a Teacher in the Era of Social Turbulence and Transformation of Education

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.
Guiding Vector Fields for Following Occluded Paths

Accurately following a geometric desired path in a two-dimensional space is a fundamental task for many engineering systems, in particular mobile robots. When the desired path is occluded by obstacles, it is necessary and crucial to temporarily deviate from the path for obstacle/collision avoidance. In this paper, we develop a composite guiding vector field via the use of smooth bump functions, and provide theoretical guarantees that the integral curves of the vector field can follow an arbitrary sufficiently smooth desired path and avoid collision with obstacles of arbitrary shapes. These two behaviors are reactive since path (re)-planning and global map construction are not involved. To deal with the common deadlock problem, we introduce a switching vector field, and the Zeno behavior is excluded. Simulations are conducted to support the theoretical results.
Vulnerability Assessment Website E-Government dengan NIST SP 800-115 dan OWASP Menggunakan Web Vulnerability Scanner

E-Government is one of the local government's means of implementing service-oriented values and public information disclosure by providing the local government's official website. Vulnerability Assessment (VA) is a vulnerability assessment method by testing security vulnerabilities to find out all potential critical weaknesses of e-government websites, the aims is to support convenience and service improvement when using e-government websites. The method is using descriptive quantitative to analyze and evaluate security level of both e-government websites based on the NIST SP 800-115 method regarding technical guidelines for testing and assessing information security parameters and OWASP using two web vulnerability scanners. This study is to analyze and evaluate the results of vulnerability scanning on two different types of e-government web, namely the e-government web belonging to the local government and village government, the sample assessment using the semarangkab.go.id website belonging to the local government of Semarang Regency, Central Java Province. and gunungtumpeng.id which is the official e-government website owned by the Gunung Tumpeng Village Government, Suruh District, Semarang Regency, Central Java Province, Indonesia. Hasil penelitian menunjukkan ditemukan persamaan penilaian dalam hal kategori level ancaman dan jumlah kerentanan menggunakan kedua alat web vulnerability scanner, dan hasil yang berbeda terdapat pada durasi, kecepatan pemindaian, dan jenis temuan kerentanan. Kedua parameter OWASP dapat memberikan petunjuk dan penjelasan kompleks untuk membantu pengembang atau pihak pemerintah daerah melakukan pengambilan keputusan mengenai keamanan informasi pada web e-government yang dikelola.
PREDICTION OF QU AND Ru CAPACITIES OF PILE IN CLAYEY SOIL LAYER USING GEOSPATIAL ANALYSIS

The basic purpose of foundation design is to predict ultimate soil bearing (Qu) and to determine actual load-carrying capacities (Ru) at the final set installation.One specific case study was when the project owner was only allowed to use limited CPT data of the high-cost reason for complete soil investigation.Besides that, the location was not only relatively densely populated, but also the soil investigation work could cause some noise during the implementation of field-testing methods using SPT and Borlogs.CPT has been selected and easy in predicting the Qu value of a single pile.Two points at one of the configurations of single piles were evaluated randomly by the PDA test in determining the actual load capacity or Ru.In general conclusion, the Qu was less than Ru values for all existing points of the single pile at the study area.To find out other values of Qu and Ru in all piles were analyzed by a geospatial analysis.By using isotropic Moran's method, there was a positive autocorrelation in adjacent pile location since I = 0.1767 > I0 = -0.25.Graphically, the semivariogram also validated Gaussian interpolation models as the best correlation in searching pair; distance; and semivariance values among the single pile.Finally, the ordinary Kriging method was used to plot all points graphically in 3 D based on Moran's and semivariogram of Qu and Ru data.Thus, this method yields good results of Qu and Ru for other single piles with an error of around 13.2% at the final set installation between 8.9 to 9.5 m.
Effect of Pedagogical Competences of English Language Teachers on Their Students’ Academic Achievement: A Qualitative Study

Teachers play a vital role in education. The academic results are related to the professional abilities of the teachers. Therefore, teachers must possess pedagogical competence. In this context, the objectives of this research study are to investigate the pedagogical competencies of college teachers in Sukkur, Sindh, Pakistan. This study involves a qualitative case study. The findings show that the teachers' competence to teach affects how well the students do in their academics.
Contextualization to Enhance Students’ Writing Ability

The current study used a qualitative methodology to elicit data from ten EFL teachers of Irbid private schools in order to ascertain the responses to the research questions. The researcher gathered data from participants prior to the study using a convenience sample drawn from the public. Convenience sampling is used in research to acquire a decent approximation of the truth, and it gives valuable information Interviews and observations were used to acquire data for this investigation. Additionally, the present research made use of a technology for non-participant observation. When an observer visits a place and takes notes without being engaged in the activities of the participants, this is referred to as a non-participant observation. To ensure the study's authenticity and trustworthiness, a member check was conducted by presenting data from classroom observations and interviews. Participants had access to their audiotaped interviews as well as their written transcripts, classroom observation instructions, and field notes. As a result, this study's data was acquired through observations and interviews. Qualitative data analysis entails ascribing meaning to texts and visuals in order to address research concerns. The majority of the participating teachers were convinced that teaching students how to write in context and from the context of actual life was important. They supported the use of teaching in context in promoting students’ EFL writing creativity.
Technology application in the mathematical learning

The teaching of mathematics these days can turn multidisciplinary with a great and significant content when making use of technology and project-based learning. This work arises as an answer of the Comprehensive Reform in the Development of Competencies petition, the proposal in the educational improvement by the constructive lineup of John Biggs and Collis, as well as Marzano´s use of significant knowledge and mental habits. The implementation of this engineering project was with teams of students from the first semester of different engineering majors in the subjects of algebra, geometry, and trigonometry. The evaluation of collaborative work in the classroom and computer room about mathematic laws, calculations, and mathematical software, as well as the reliable investigation on internet about the context and dimension of projects resulted in the creation of the first project contest, where every student showed their motivation and excellent development, the interaction of technology use and the mathematical learning.
MIND MAPPING UNTUK MENINGKATKAN KEMAMPUAN MENULIS TEKS EKSPOSISI SISWA MAN 1 ACEH TIMUR

Writing an exposition text has not yet obtained the maximum score according to the minimum completeness criteria of the school. So we need a method to make it easier for students, namely mind mapping. Obtaining the results of increasing the ability to write expository texts through mind mapping is the goal of the research. MAN 1 Aceh Timur became the research site. A total of 18 students of class X MIA 1 in the academic year 2021/2022 became the subjects of the research. Classroom Action Research (CAR) is a research design through four research steps, namely planning (plan), action (action), observation (observation) and reflection through two cycles. Tests and observations become instruments in research. Data analysis was carried out through a qualitative approach by calculating the class average value using the classical completeness percentage. Students who get a KKM score of 75 are declared complete. The results showed that the ability to write expository texts through mind mapping at MAN 1 Aceh Timur in the first cycle was 55.55%% or 10 students in the poor category. Meanwhile, after being given the second cycle, the results showed 100% or 18 students. Based on the results of the study, it was concluded that students' ability to write expository texts through mind mapping could increase. ABSTRAKMenulis teks eksposisi belum memperoleh nilai yang maksimal sesuai dengan kriteria ketuntasan minimal sekolah. Sehingga diperlukan suatu metode untuk memudahkan siswa yaitu dengan mind mapping. Memperoleh hasil peningkatan kemampuan menulis teks eksposisi melalui mind mapping merupakan tujuan dari penelitian. MAN 1 Aceh Timur menjadi tempat penelitian. Sebanyak 18 siswa kelas X MIA 1 pada tahun ajaran 2021/2022 menjadi subjek dalam penelitian. Penelitian Tindakan Kelas (PTK) merupakan desain penelitian melalui empat langkah penelitian yaitu perencanaan (plan), Tindakan (action), pengamatan (observasi) dan refleksi melalui dua siklus. Tes dan observasi menjadi instrument dalam penelitian. Analisis data dilakukan melalui pendekatan kualitatif dengan menghitung nilai rata-rata kelas menggunakan persentase ketuntasan secara klasikal. Siswa yang mendapatkan nilai KKM ?75 dinyatakan tuntas. Hasil penelitian menunjukkan kemampuan menulis teks eksposisi melalului mind mapping di MAN 1 Aceh Timur pada siklus I sebesar 55,55%% atau 10 siswa dengan kategori kurang. Sedangkan setelah diberikan siklus II menunjukkan hasil sebesar 100% atau 18 siswa. Berdasarkan hasil penelitian disimpulkan kemampuan siswa dalam menulis teks eksposisi melalui mind mapping dapat meningkat.
Typos’ Effects on Web-Based Programming Code Output: A Computational Linguistics Study

Computational linguistics is concerned with understanding language from a computational perspective and constructing artifacts that are useful in processing and generating language. In the use of language, whether human language or programming language, there can be an error that makes the language not understood properly. One of the errors that often occurs is syntax error. In language, a syntax error is a mistake in using a language that involves organizing words and phrases that do not make sense. While in programming, a syntax error is an error in writing code in a program that makes the format or information unrecognizable by the computer system. Such errors are the simplest of errors but can affect many aspects of the final code output. This article aims to show how writing errors or typos in programming code can affect some or all of the results. The data obtained is data from web programming code that is used to make the website display the Geographic Information System Clustering the Distribution of Stunting Disease in Banggai Regency with K-Means, and also the R programming code used to calculate the stunting distribution using K-Means. The results of this article will show that a typo, even just a letter or any single punctuation, can affect the program’s final result.
Metaverse in Tourism and Hospitality: Empirical Evidence on Generation Z from Italy

The paper intends to analyze the change taking place in tourism marketing due to the metaverse. The tastes and needs of today's new generations of tourists, especially Generation Z, are becoming more and more in line with new technologies. Tourism marketing requires a change in the way of promoting and telling a product and a brand. The objective of the paper will be to analyze the digital tools of the Metaverse and of the “immersive” technologies. The results of a survey that investigates people's propensity to use digital tools and new technologies will also be presented. Further objectives will be to understand the role these technologies will have in the world of travel and to provide a “guide” for tourism-related businesses, trying to help them face and understand a highly dynamic sector.