Reviewerly/exHarmony huggingface dataset:
Data Quality Assessment and Recommendation of Feature Selection Algorithms: An Ontological Approach

Feature selection plays an important role in machine learning and data mining problems. Identifying the best feature selection algorithm that helps to remove irrelevant and redundant features is a complex task. This research tries to address it by recommending a feature selection algorithm based on dataset meta-features. The main contribution of the work is the use of Semantic Web principles to develop a recommendation model for the feature selection algorithm. As a result, dataset meta-features are modeled in a domain ontology, and a set of Semantic Web rule language (SWRL) predictive rules have been proposed to recommend a feature selection algorithm. The result of this research is a feature selection algorithm recommendation based on the data characteristics and quality (FSDCQ) ontology, which not only helps with recommendations but also finds the data points with data quality violations. An experiment is conducted on the classification datasets from the UCI repository to evaluate the proposed ontology. The usefulness and effectiveness of the proposed method is evaluated by comparing it with the widely used method in the literature for the recommendation. Results show that the ontology-based recommendations are equally good as the widely used recommendation model, which is k-NN, with added benefits.
Hoax Detection Using Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) on Social Media

There are negative effects of the ease of obtaining information in today's society, one of which is the rise of hoaxes on the internet. As much as 92.40% of social media platforms such as Twitter are used to spread hoaxes. Launched on July 13, 2006, Twitter is a microblogging service where users can spread information at no cost to themselves or others. In this study, the authors will conduct hoax news detection on Twitter social media using the Long Short - Term Memory (LSTM) method and Gate Recurent Unit (GRU) and gloVe feature expansion. with a dataset of 25,234 data which produces accuracy results in TF-IDF on each model, namely 97.33% in LSTM and 96.75% in GRU, and an increase in accuracy of 0.22% in the tweet corpus on LSTM and an increase in accuracy of 0.15 in the BeritaTweet corpus on GRU.
Machine Learning Algorithms for Breast Cancer Prediction

There are numerous subtypes of breast cancer, each with its own unique outlook.The evaluation of the expression of small gene sets is the primary focus of the current stratification methods.In the upcoming years, Next Generation Sequencing (NGS) is anticipated to generate a significant amount of genomic data.We investigate the application of deep learning, or machine learning, to the subtyping of breast cancer in this case study.We used pan-cancer and non-cancer data to create semi-supervised settings because there weren't any publicly accessible data.A wide range of supervised and semisupervised designs are investigated with the help of Integrative omics data like microRNA expression and copy number variations.On our gene expression data challenge, accuracy results indicate that simpler models perform better than deep semi-supervised approaches.Deep model performance improves only marginally (if at all) when integrated combining several omics data types emphasises the need for additional research on bigger datasets of multi-omics data as they become accessible.In terms of biology, our linear model typically confirms.earlier classifications of gene subtypes.The development of a more varied and unexplored set of representative omics traits that may be helpful for subtyping breast cancer has resulted from deep methods, which imitate non-linear interactions.
CHSR: Cross-view Learning from Heterogeneous Graph for Session-Based Recommendation

Session-based recommendation (SBR) aims to predict the next item based on short behavior sequences for anonymous users. Most of the current SBR methods consider the scenario that a session just consists of a series of items. However, the multiple item attributes can also reflect user behaviors and provide information for recommendation. In other words, a session in the real world should consist of items and multiple item attributes, which means that the session is heterogeneous. In this paper, we propose a novel method for the anonymous recommendation with heterogeneous item attributes, named CHSR. Firstly, we construct homogeneous session graph and heterogeneous global graph for heterogeneous sessions to map the relationships among different item attributes. Secondly, homo-view and hetero-view of these two kinds of graph encoders are proposed to capture both intra and inter patterns of heterogeneous sessions. Thirdly, a cross-view fusion strategy with consistency loss is introduced to integrate the heterogeneous attribute information by fusing the representations from the two-type views. Finally, the interest preference of anonymous users is represented from the above steps. Extensive experiments conducted on three large-scale real-world datasets demonstrate the superior performance of CHSR over the state-of-the-art methods.
An Analysis Approach of The Influence of Customer Reviews and Ratings, Influencer Reviews, and References Group on The Purchase Decision of Frozen Food Products

Business in the food and beverage industry is currently growing rapidly.Changes in people's lifestyles, including food consumption styles, lead to instant food, one of which is frozen food.The frozen food product business industry is considered profitable, making competition in this field quite high.This is also supported by the style of people who currently like to shop online in e-commerce.With this, many people see the business of selling on e-commerce.This research aims to determine the effect of consumer reviews and stars, influencer reviews, and group references on consumer purchasing decisions.This research will find out what factors can encourage purchasing decisions for frozen food products based on the relationship shown in the results of the research.Analysis of research data will use the method of Structural Equation Modeling with Partial Least Square (PLS) model.The data will be processed assisted by the Microsoft Excel application and the SmartPLS 3.0 application.In this research, the questionnaire was filled out by 190 respondents and then removed according to the respondent's profile that matched the research so that as many as 169 respondents could be processed.The independent variables in this research are consumer reviews and stars (X1), influencer reviews (X2), and reference groups (X3) while the dependent variable in research on purchasing decisions (Y).The results showed that each independent variable had a positive and significant effect on purchasing decisions.
Biased logistic models applied to cervical cancer risk

In the present work, the CRISP-DM methodology was proposed to develop a set of machine learning models applied to evaluate cervical cancer risk suffer.For this research, a sample of 858 patients was taken, who were asked a series of questions regarding this pathology.The database has an unbalanced dependent variable, since this is a health study, the balancing technique will not be used to identify the variables that will enter the model, the Boruta library was used for variable selection.For the development, five algorithms will be used: Support Vector Machine (SVM), decision trees using the CHAID and CART algorithms, logistic regression and "asymmetric link" models.The models proposed in this work were refined by means of the Auc, Gini, Log loss and KS (Kolmogorov-Smirnov) indicators, as a result using the proposed models, AUC values of 98% were obtained.
Anime Recommendation System

Anime is popular nowadays, particularly among the younger generation. The project is an anime recommendation system. Recently everyone has been at home due to the corona virus but if you’re like me and really enjoy anime, finding a new one to watch is a job on its own. Finding a new anime in and of itself is not hard, but just randomly picking and watching an anime which can range from 7–1000 episodes can lead to some bad results. So we built a recommendation system using the data mining algorithms to try and find anime that will be enjoyable based on the user ratings of different anime as well as the sentiment, genre and vector scores of their reviews. Each user may rate anime and add it to their finished list. Thereafter, machine learning techniques have been applied to obtain top 10 matching recommendations and predicted ratings.
The Method of Photovoltaic Power Forecast based on Seasonal Classification and Limit Learning Machine

Abstract The seasonal distribution characteristics of photovoltaic power plant output fluctuation are analyzed, and a short-term power forecasting method based on seasonal classification is proposed. Firstly, the seasonal distribution characteristics of photovoltaic output and its fluctuation are analyzed. Secondly, the forecasting model of photovoltaic output in different seasons is established by the Limit Learning Machine neural network. Finally, an empirical analysis is carried out by using photovoltaic output data. The results show that the seasonal classification method of short-term PV power forecast is better than the unclassified model.
Analisis Sentimen Wisatawan terhadap Kualitas Layanan Hotel dan Resort di Lombok Menggunakan SERVQUAL dan CRISP-DM

The era of digital transformation has sparked innovations in product and service marketing strategies in various sectors, one of which is the tourism sector. In the hospitality industry context, product marketing using website-based digital media allows consumers as hotel guests to review the products and services received. The Tripadvisor website is a digital marketing platform that provides review features for app users, especially consumers, to give ratings and reviews. This study aims to analyze the quality of hotel services using the Service Quality (SERVQUAL) framework based on the results of the classification of hotel guest sentiment data using the Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithm by the stages of the Cross-Industry Standard Process for Data Mining (CRISP-DM). The CRISP-DM framework consists of six stages, namely: business understanding stage, data understanding stage, data preparation stage, modeling stage, evaluation stage, and deployment stage. The SERVQUAL framework consists of several dimensions: reliability dimension; responsiveness; assurance; empathy; tangibles. The review data that will be processed is the consumer review data of The Oberoi Beach Resort Lombok; Sheraton Senggigi Beach Resort; Sudamala Resort Sengiggi; Holiday Resort Lombok; Aston Sunset Beach Resort. The results of this study show that the SVM algorithm performs better than NBC, where the accuracy value is 98.57%, the precision value is 100%, the recall value is 97.14%, and the f-measure value is 98.54%. The AUC value is 100%, and the t-Test value is 98.6%. Unlike the case with the results of SVM's algorithm performance evaluation without using the SMOTE Upgrading Operator, where the accuracy value is 95.71%, the precision value is 95.71%, the recall value is 100%, and the f-measure value is 97.81%. In addition, the AUC value is 91.1%, and the t-Test value is 95.7%.
Aspect-based Sentiment Analysis on Social Media Using Convolutional Neural Network (CNN) Method

Social media are a platform for people to express their opinions on various topics, one of which is Twitter. Movie reviews are a frequently found topic on Twitter that contains a person's opinion of a movie that has been watched. But since opinions are subjective, it is difficult to determine an accurate assessment of a movie. In addition, the diverse aspects of a movie make it difficult to judge whether a review is positive or negative. Referring to that problem, a method is needed to perform sentiment analysis of the problem. In this study, sentiment analysis of movie reviews was carried out based on aspects of plot, acting, and director. This research also performs classification using a CNN model and combines several techniques, namely TF-IDF feature extraction, FastText feature expansion, and SMOTE to calculate the accuracy value and F1-Score. The final results obtained in this study are in the aspect of the plot getting an accuracy of 73.81% (+12,22%) and F1-score 73.72% (+15,93%), the acting aspect obtaining an accuracy value of 89.30% (+0,54%) and F1-score 89.26% (+50,80%), and in the aspect of the director having an accuracy of 87.37% (+0,28%) and F1-score 87.35% (+84,39%). Based on these results, each application of techniques such as TF-IDF, FastText, and SMOTE can increase the accuracy value and F1-Score of the model built.
Aspect-Level Sentiment Analysis on Social Media Using Gated Recurrent Unit (GRU)

Twitter is one of the popular social media for sharing opinions, one of which is about movie reviews. There are many opinions related to movie reviews on Twitter social media so the assessment of a movie can vary. Therefore, aspect-level sentiment analysis is needed to classify movie reviews to provide optimal results. This research was conducted by building a system using the Gated Recurrent Unit (GRU) method to perform sentiment analysis at the aspect level on movie reviews taken from Twitter. The aspects used in this research are plot, acting, and director. This research also conducted experiments by combining three techniques, which are feature extraction using TF-IDF, feature expansion with GloVe, and the application of SMOTE to improve model accuracy. The results show that each test scenario can improve the accuracy and F1-Score values of each aspect. The final value of each aspect is the accuracy value for the plot aspect is 70.40%(+7.62%) and F1-Score is 70.35%(+9.70%), the accuracy value is 93.75%(+6.28%) and F1-Score is 93.70%(+65.19%) for the acting aspect, and the accuracy value is 90.44%(+4.60%) and F1-Score is 90.17%(+122.80%) for the director aspect.
Foreword to AniNex workshop 2022

The use of social media has become so popular that people share photos every day on them. Automatic face recognition and tagging of people’s photos have caused privacy preservation issues and some methods have been proposed for hiding the identity of presented people in these images. Blurring and blacking the face area, adding physical adversarial patches to the face, and adding adversarial masks are some proposed methods for this purpose. However, these methods particularly suffer from dissimilarity of the input and output images and inadequate performance in identity concealment from automatic face recognition (AFR) systems. In this paper, we propose the Generative Mask-guided Face Image Manipulation (GMFIM) model based on Generative Adversarial Networks (GANs) to apply imperceptible edits to the input face image to preserve the identity of the person in the image. Our model consists of a face mask module, a GAN-based optimization module, and a merge module. Different criteria are considered in the objective function of the optimization step to produce high-quality images that are as similar as possible to the input image while they cannot be recognized by AFR systems. The results of the experiments on different datasets show that our model provides promising results in terms of the quality of the generated images and the identity concealment performance.
FEATURE SUBSET GENERATION FOR ENSEMBLE LEARNING USING FEATURE CLUSTERING AND MUTUAL INFORMATION

Ensemble learning is a powerful technique for constructing accurate predictive models. Feature subset generation is an important step for ensemble learning. This paper proposes feature clustering and mutual information as a new feature subset generation method for ensemble learning. The proposed feature subset generation technique clusters the features using a hierarchical clustering algorithm. Mutual information is used to compute the similarity between the features within each cluster. Feature subset generation is then performed by selecting the most informative features from each cluster. Experiments are conducted on a real-world dataset to compare the proposed feature subset generation technique to other existing feature subset generation techniques. The experimental results show that the proposed technique outperforms existing feature subset generation techniques.&#x0D;
LARAVEL PHP FRAMEWORK AND PROJECT CREATED USING LARAVEL

This research paper consist about working and Fundamentals of leading technology Laravel -PHP framework PHP is Object Oriented Methodology.Object Oriented Methodology is a methodology employed in the development of computer software that aims to guide the development process by analyzing the system requirements in a structured manner.It is designed to facilitate the creation of robust and efficient software systems using an approach that is centered around the use of objects, which are the fundamental building blocks of the software.Its working and Implementations we have included in this research Paper.Laravel is a PHP framework where PHP is a scripting language that is versatile in its application likewise Laravel has introduced many more features in the field of development it is used to develop web panels , e-Commerce websites , CRM, and Api's for Android and IOS applications.
Online Streaming Platform

Video traffic demand over mobile networks have been difficult task, the quality of service get reduced when there is a gap between the traffic demand and link capacity. The quality of video streaming gets poor when there occurs the long buffering and intermittent disruptions. In the cloud computing technology, we propose a new mobile video streaming framework consist of AMES cloud, which is dubbed with: adaptive mobile video streaming and efficient social video sharing these construct a private agent to provide video streaming efficiently for each mobile user. AMO uses the scalable video coding technique to adjust the streaming. SOV monitors the social network interactions among mobile users and as to pre fetch video in advance. The website will have a user-friendly interface, allowing users to easily navigate and access the content of the website. The website will also provide features such as creating playlists, sharing content with friends, and rating videos. Additionally, the website will provide a way for users to upload and publish their own videos. The website will also include a search engine to help users find content quickly and easily.
Request-Granting-Resistance Sequence in Chinese Public Service Calls

This study draws on a database of 200 citizens' telephone calls to a Chinese radio program phone-in helpline and uses conversation analysis as the methodology to examine citizens' requests for assistance, officials' granting responses to citizens' requests, citizens' or the host's resistance to officials' granting responses.It is found that citizens make complaints about their previous failure to solve their problems in a way that is not merely to legitimize their current requests for assistance but also to ask for an account of their previous failure to have matters satisfactorily resolved, since in many cases even when officials grant citizens' requests, the granting is followed by those citizens' pursuit of reasons for or remedy to their previous failed resolution attempts.The study also analyzed how citizens' resistance to officials' responses is handled and how the final agreement is reached.The findings of this study contribute to the study of turn design of requests and preference organization of responses to requests and have implications for responses to requests in service encounters.
Analysis of Different Text Features Using NLP

Abstract: A single letter can create a word, then that single word can create a paragraph and many paragraphs together make a perfect bound of a text collection, make a perfect article. These texts, these words can be said a way through which one expresses themselves. Be it for personal reasons, or professional, Texts do play an important role to convey the feelings. So, with these texts, many operations can be performed on it. Be it making a very large paragraph shorter - as is done in para summarizer, be it changing the language of a given text - as in language translator, be it automatically guessing of the next word - as in automatic word generator, or be it the spelling a grammar corrector. Here, NLP takes its role. Natural Language Processing i.e., NLP includes studies of how computers and humans communicates in a particular language, more precisely natural language. It is a field that blends computer science, linguistics, and machine learning. NLP aspires to enable computers to understand and generate human language. There are many more tasks one can perform with the text given, to get a more fruitful output of it. In this paper, work done on some of these operations or features of the texts are discussed and summarized.
Which of the book recommendation sections is the most similar to the user selections in LibraryThing?

Purpose This study aims to determine the most similar set of recommendation books to the user selections in LibraryThing. Design/methodology/approach For this purpose, 30,000 tags related to History on the LibraryThing have been selected. Their tags and the tags of the related recommended books were extracted from three different recommendations sections on LibraryThing. Then, four similarity criteria of Jaccard coefficient, Cosine similarity, Dice coefficient and Pearson correlation coefficient were used to calculate the similarity between the tags. To determine the most similar recommended section, the best similarity criterion had to be determined first. So, a researcher-made questionnaire was provided to History experts. Findings The results showed that the Jaccard coefficient, with a frequency of 32.81, is the best similarity criterion from the point of view of History experts. Besides, the degree of similarity in LibraryThing recommendations section according to this criterion is equal to 0.256, in the section of books with similar library subjects and classifications is 0.163 and in the Member recommendations section is 0.152. Based on the findings of this study, the LibraryThing recommendations section has succeeded in introducing the most similar books to the selected book compared to the other two sections. Originality/value To the best of the authors’ knowledge, itis for the first time, three sections of LibraryThing recommendations are compared by four different similarity criteria to show which sections would be more beneficial for the user browsing. The results showed that machine recommendations work better than humans.
Smart IoT Based Healthcare Sector

Abstract: The Internet of Things (IoT) circumscribes the real-world commodity to communicate and interact with each other by the help of Internet. The enormous growth in the field of IoT devices has drawn wide attention towards wireless-based area networks (WBANs) to eliminate implications like lack of central entity, rigid security demands, mass data processing, low-latency service provision and resource limitation. Moreover, this paper represents a broad discussion over IoT healthcare protocols. This paper also brings us an extensive insight over the IoT surrounding healthcare over its privacy, challenges privacy, challenges, and security issues. This paper also proposes a solution to reduce potential causes of human error and to provide with the bestin-class efficiency in the healthcare sector. This paper highlights IoT based approach in the healthcare industry for higher reliability and efficiency.