Reviewerly/exHarmony huggingface dataset:
RoBERTa as Semantic Approach for Big Five Personality Prediction using Artificial Neural Network on Twitter

Personality is a psychological construction that leads to variations in individual thought patterns, distinguishing human characteristics based on traits, feelings, and behavior. Hence, human beings have personalities and tend to be social creatures. Social media was created to facilitate people for remote communication. Twitter is one of the largest social media that is often used by the world community. Because the activities of social media users are quite numerous and varied, we are interested to predict the behavior of Twitter users based on their personality. These personalities are classified into five categories called Big Five Personality traits. By using Artificial Neural Network (ANN), it is one of the classification methods used as a reference in personality prediction, this method has good performance in classification. To overcome the overfitting while training the data, we used Synthetic Minority Oversampling Technique (SMOTE) technique to handle the imbalance data. Other features such as Linguistic Inquiry Word Count (LIWC) and Robustly Optimized BERT pre-training Approach (RoBERTa) with hyperparameter tuning can increase the performance of the system that we have built. The focus of this research is to present an analysis of Twitter user behavior that it can be used for the Big Five Personality Prediction from Twitter users using the ANN method. The use of this method has aspect to be considered, that is the accuracy of the classification of Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism (OCEAN) traits. The experimental results have shown that the ANN method gets 87.09% of accuracy.
Online feature Selection using Pearson Correlation Technique

The large volume of data that is generated nowadays in the era of real-time data processing takes a long time. Streaming feature selection is one way to get out of this real time processing challenge. In stream-wise feature selection, new features are assessed one by one for insertion into a forecasting model. When there is a large pool of acceptable characteristics, stream-wise feature selection performs well over traditional feature selection approaches, in which the features are known in advance. Over fitting can be avoided by adjusting the feature addition threshold dynamically. Unlike classic forward feature selection methods like stepwise regression, which assesses all available features at each step and choose the best one, stream-wise feature selection considers each feature only once when it is formed. In our work, we have used Pearson correlation technique for feature selection of streaming data blocks and compared with complete feature list of streaming data blocks for human activity recognition (HAR) dataset. The proposed algorithm gives promising results in terms of accuracy 94.85(%) over complete feature list.
Implementation of Blockchain and Peer-to-peer Network for Digital Document Management

Digital document storage usually implemented using conventional methods, namely by using a centralized database system that allows trust issues to arise due to the possibility of manipulation or interference from irresponsible parties. Therefore, we need a secure system that can prevent things like this from happening. For that, blockchain can be a solution to this problem. By using a blockchain-based system, the digital document management process can be carried out more transparently by only using the read and write method so that stored documents can still maintain their legal value. The end of this research results in the form of a decentralized application for digital document processing. Based on the results of white box testing, it is found that the web interface and smart contracts can run 100% however, the continuity of transactions is strongly influenced by several variables such as the computational ability of Rinkeby test network and the number of transactions that are executed at the same time. Based on the results of the black box test, the average file upload speed was 40.61 KBps with the component specifications as written in this journal. Furthermore, it was found that modifying 1 bit of data in a file would make the file identified as a unique file despite the content presented has not changed.
CUDA Parallelization of Teacher-Learner Optimization Algorithm

Evolutionary algorithms are effective methods for solving optimization problems that do not require any special assumptions to find optimal solutions. Optimization based on teaching and learning has been parallelized using CUDA parallelization technology. The results show that the proposed parallel version of the algorithm is fast and efficient in teaching and learning phases.
A Systematic Literature Review on Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) technology used by machines to understand, analyze and interpret human languages. In the past decade, NLP received more recognition due to innovation in information and communication technology which led to various research. Thus, it is essential to understand the development taken in the knowledge of literature. The present study aims to present a systematic literature review using bibliometric analysis in NLP research. The study identifies the publication trends, influential journals, cited articles, influential authors, institutions, countries, key research areas, and research clusters in the NLP field. 12541 NLP publications were extracted from the Web of Science (WoS) database and further analyzed using bibliometric analysis. The result indicated that the first NLP publication was in 1989, with the highest publication recorded in 2021. The IEEE access journal was the leading journal with the highest number of publications, and the highest number of citations received for NLP articles is 3174. The most productive author in the NLP field is Liu HF, whereas Harward university is the most influential institution. The US is the leading country in the total number of publications. Researchers extensively researched applied sciences area. The findings further revealed that most of the NLP research focused on five main clusters: modeling, neural networks, artificial intelligence, data mining using social media platforms, and data capturing and learning.
PCA-AdaBoost Method for a Low Bias and Low Dimension Toxic Comment Classification.

Toxic comment classification can be a solution to reduce the impact of one of these forms of online harassment, where the term frequency-inverse document frequency (TF-IDF) vectoring method can do feature extraction on comment text datasets. However, TF-IDF vectoring can result in a curse of dimensionality, which increases the computational cost exponentially. In addition, imbalanced data can also reduce classification performance. This study proposes the PCA-AdaBoost method for toxic comment classification with low dimension and low bias. We use the Jigsaw toxic comment classification dataset from Kaggle and apply AFINN’s lexicon for labeling toxic comments. DT and RF are two methods to benchmark the performance of our proposed classification method. The performance test applies several methods such as K-fold cross-validation, receiver operating characteristic (ROC) curve, the area under curve (AUC) value, confusion matrix, Accuracy, Precision, Recall, and F1-Score. The test results show that the application of PCA produces a Dimension Reduction that equals 7.3%, where 275 new features from dimension reduction have a Total Explained Variance of 96.6%. In addition, the results of the cross-validation test show that PCA can provide improved performance and a more robust classification model. Then the proposed PCAAdaBoost has the best Accuracy, Precision, Recall, and F1-Score compared to benchmark methods, namely, 0.86, 0.89, 0.82, and 0.85 respectively.
Implementation of Backward Key Chain and Hill Cipher for Securing Messages in the Wireless Sensor Networks

The development of the internet of things (IoT) has been growing, such as wireless sensor networks (WSN). The use of WSN in daily life has helped a lot in various fields. WSN has interconnected nodes with limited memory, resources, and computation. Because of these limitations, messages or data on these nodes are in danger of being tampered with or known by attackers. In this paper, a modification of the broadcast authentication scheme by adding confidentiality to the session key and message is proposed. The session key, produced by a backward key chain, is secured with a modified hill cipher encryption. The experiment results show that the average time required for the hill cipher method for encryption is higher than decryption with a difference of about 0.1579 sec. then the main result of the experiment is the comparison between the ECDH method and the Backward Keychain for both the encryption and decryption process, which results in the Backward Keychain method being better than the ECDH method.
Influence of Sentiment on Mandiri Bank Stocks (BMRI) Using Feature Expansion with FastText and Logistic Regression Classification

Problems that befell Bank Mandiri related to the issue of Bank Mandiri’s service error that occurred on July 20, 2019, it turned out that Bank Mandiri’s shares at the opening of trading on July 22, 2019, decreased by 0.95 per cent from the initial price. The decline continued to reach 1.27 per cent from the previous price, by looking for a correlation between the sentiments or opinions of Twitter or online news media such as CNN, Detik, Liputan 6, Stockbit, CNBC, Tempo, and Youtube with the stock price of Bank Mandiri (BMRI). Application of text mining to obtain data and optimise the model with the Logistic Regression and feature expansion with the FastText to reduce vocabulary mismatches in textual data to predict the sentiment of the data collected. Then perform a correlation test using the Pearson Correlation Coefficient on sentiment predictions with stock prices in May to September at the end of 2019. To know customer satisfaction with a Bank Mandiri service with the Bank Mandiri share price connectivity. To achieve accuracy in the model optimisation scenario by applying Logistic Regression and implementing feature expansion with the FastText scenario split of 90% of training and 10% of testing, 71.8% is obtained. After implementing the optimisation model on negative sentiment and positive sentiment, it can be seen that negative sentiment has the highest correlation to BMRI stock movements in the period May to the end of September 2019.
The Effect of Sentiment on Bank Mandiri Stocks (BMRI) Using the Feature Expansion Method with Word2vec and Backpropagation Neural Network

Investment in stocks has recently become a trend among the public. The problem that often occurs in stocks is that it is difficult to predict the increase or decrease in stock prices. In July 2019, Bank Mandiri experienced a system failure that caused customer balances to increase or decrease drastically. Incidental to the emergence of various opinions on social media and news media that spread the information. Based on this incident, a system is needed to see the relationship between sentiment and the company’s stock price. This research implements the Backpropagation Neural Network method to analyze sentiment and Word2vec feature expansion is used to find similarity words and reduce vocabulary mismatches in textual data. The result of this researchs, using feature expansion in the classification algorithm can improve performance up to 0.7% with an accuracy value of 72.58%. The sentiment analysis results on stock prices show that the sentiment value and stock prices have a very weak relationship.
Text Emotion Detection: Discover the Meaning Behind YouTube Comments Using Indo RoBERTa

The rapid growth of YouTube videos is directly proportional to their comments. This comment attracts researchers to extract the meaning of public response and feedback about specific content. Utilizing insights from advertising YouTube comments can be beneficial in evaluating advertising. To see if the company can increase the effectiveness of the ad with emotion detection. This study build deep learning text-based emotion detection uses CNN, Multinomial Naive Bayes, and Indo RoBERTa to compare and evaluate which one is the most accurate and uncover the emotions behind comments. The models are extract the emotion of Indonesian comments as any one of the five basic emotions (anger, fear, joy, sadness, and surprise). Relating to the reaction of the public to the advertisement for one of the shampoo products on the YouTube Channel, the result shows that the text’s emotional expressions are primarily those of joy and surprise. This study successfully detects five text emotions and identifies Indo RoBERTa as the best model for domain and language-specific text-emotion detection.
Two-Layer SVM, Towards Deep Statistical Learning

Support Vector Machine (SVM) is originally a binary large-margin classifier emerged from the concept of structural risk minimization. Multiple solutions such as one-versus-one and one-versus-all have been proposed for creating multi-class SVM using elementary binary SVMs. Also multiple solutions have been proposed for SVM model selection, adjusting margin-parameter C and the Gaussian kernel variance. Here, an improved classifier named SVM-SVM is proposed for multi-class problems which increases accuracy and decreases dependency to margin-parameter selection. SVM-SVM adopts two K-class one-vs-one SVMs in a cascaded two-layer structure. In the first layer, input features are fed to one-vs-one SVM with non-linear kernels. We introduce this layer as a large-margin non-linear feature transform that maps input feature space to a discriminative K*(K-1)/2 dimensional space. To assess our hierarchical classifier, some datasets from the UCI repository are evaluated. Standard one-vs-one SVM and one-vs-one fuzzy SVM are used as reference classifiers in experiments. Results show significant improvements of our proposed method in terms of test accuracy and robustness to the model (margin and kernel) parameters in comparison with the reference classifiers. Our observations suggest that a multi-layer (deep) SVM structures can gain the same benefits as is seen in the deep neural nets (DNNs).
XGBoost based solutions for detecting fraudulent credit card transactions

Due to the emigration of businesses to the internet, credit cards have become a widely used tool for customers to pay for purchases both online and offline. However, fraudsters try to attack these payment tools using various techniques, and credit card fraud transactions have become dangerous. Credit card payment security is becoming an important research topic, and different ways are proposed to overcome this problem. In this article, we will propose a solution that combines the strengths of three methods to secure credit card fraud transactions. The Differential Evolution (DE) algorithm is used to select the best hyperparameters of the XGBoost algorithm (eXtreme Gradient Boosting). Synthetic Minority Oversampling (SMOTE) combined with Edited Nearest Neighbour (ENN) to handle the imbalanced data issues. Finally, we will classify fraud transactions using the optimized XGBoost algorithm. The proposed solution is evaluated using the European data set in terms of Accuracy, Recall, F1score, and AUC. As a result, the proposed solution generally achieved the best score in terms of all the measurements used in this study. In terms of accuracy, we achieved 99.94%, similarly, we reached the best score in terms of the Precision score, which is 80.68%, in addition, we got 86.02% of recall score, 83.27 of F-measure, and 99.21 of AUC score. Thus, we emphasize the superiority of our proposed method over the competitive machine learning model utilized in this work.
Multilingual Cyberbullying Detector (CD) Application for Nigerian Pidgin and Igbo Language Corpus

In recent years, difficulties related to cyberbullying have emerged as a result of the expansion of social media platforms and community interaction. Naive Bayes classifiers and other well-known models have been used successfully by several academics to create sentiment analysis systems for various use cases. Recent advances in the detection and management of multilingual cyberbullying actions on forums and social networking sites have built on the success of these sentiment analysis efforts. In order to reduce cybercrime in Nigeria, the study's goal is to create an improved Cyberbullying Detector (CD) that is interactive, affordable, and helps identify, monitor, and regulate cyberbullying. The application is the first of its kind in Nigeria to monitor and regulate cyberbullying on Twitter in Pidgin English and Igbo Language. A custom pidgin library was developed with comprehensive translations. The TextBlob library is appropriate for the study, which focuses on cyberbullying, in terms of sentiment prediction. From the sentiment analysis of Twitter data collected using SNScrape, the results show language-specific models that worked perfectly in flagging cyberbullying at manageable runs.
Local Walsh-Hadamard spectra in video sequence image classifiers

A method and software have been developed for classifying video images. The Walsh-Hadamard transform was used to form descriptors of “weak” classifiers. The software allows you to create a database of class features, determine the two-dimensional Walsh-Hadamard spectrum of segments, train fully connected neural networks, and perform exploratory analysis to study the relevance of two-dimensional spectral coefficients. Application of the method and software was carried out on ultrasound images of the pancreas.
Mobile Banking Transaction Authentication using Deep Learning

The use of smartphones is growing in tandem with the rapid advancement of mobile technologies. The most critical key to future mobile banking and market activation is a safe financial transaction. This study offers a safe authentication solution to strengthen the security of mobile banking applications using Convolutional Neural Network model to provide an embedded facial recognition model in mobile bank application. The System works with two computer vision models working together, the Firebase Machine Learning vision model to perform the face detection and preprocessing of the image, and the MobileFaceNet model to process, classify and transform into a data structure ‘savable’ by a database (an array of numbers). Data were collected by taking four pictures of each user, recording key facial features such as the eyes, brows, nose, lips, and ears in real-time with a smartphone's front facing camera. The System provides a user-friendly interface that was developed using Java programming language and Dart programming language. After being tested in a variety of scenarios, the system achieved very encouraging authentication accuracy on real faces. It is a promising application that can reduce insecurity in banking sector.
A blind steganalysis-based predictive analytics of numeric image descriptors for digital forensics with Random Forest &amp; SqueezeNet

Image steganalysis have been a prominent study in digital forensics and the data science use case of artificial intelligence has been widely adopted in conceptual frameworks. In existing studies, deep learners gain prominence for intrusion detection systems while other dissimilar modules are used for feature extraction. Hence, this study rather employs deep learners as image embedding networks aimed at feature extraction for a predictive analytics of image steganalysis. The extracted numeric image descriptors trains three learner algorithms for pattern recognition using a 10 fold cross-validation system. Experimental result indicates the ensemble of Random forest algorithm and SqueezeNet image embedder as the best for steganalysis in digital forensics while the size of the training set turns out to be insignificant for the supervised machine learning study.
A Community Cloud-Based Store for Forensic Operations in Cybercrime Control

Digital forensics is intended to unearth evidence from concluded activities in the cyberspace and such workflow is data-intensive. A typical workflow would include gathering, analysis, preservation, authentication, and presentation. Generally, building a cyber-forensic system that supports digital evidence, forensic analysis, investigation, and collaborative engagements among anti-cybercrime agencies, and stakeholders is a complex task. In this paper, we present a practical model that could support the management of forensic workflows and collaborations among agencies such as the Economic and Financial Crimes Commission (EFCC), and the Police. We adopt a modified object-oriented methodology in the investigation, analysis, and design of components required to implement a cloud store for forensic operations. The model was validated against the forensic procedures of an anti-crime agency as well as a cloud database service provider using the MongoDB NoSQL platform, to ascertain the level of assurance on its implementability.
Fraud Detection System for Effective Healthcare Administration in Nigeria using Apache Hive and Big Data Analytics: Reflection on the National Health Insurance Scheme

Nigerian researchers have shown that the lack of adequate mechanisms for fraud detection has impaired both providers and beneficiaries of this scheme. This work develops a fraud detection program for Nigeria's National Health Insurance Scheme (NHIS). Nigeria's National Health Insurance Scheme (NHIS) and Health Maintenance Organizations (HMOs) are the subjects of this study. The study was conducted using available data from NHIS-registered healthcare facilities and HMOs. Unified Modeling Language (UML) tools were used to create the framework. The framework was built with Apache Derby DB, Hadoop Distributed File System (HDFS), and Apache MapReduce as the big data processing platform. Using Apache Hive and Big Data Analytics, a system for detecting healthcare fraud is developed. This system used data from the Nigerian National Health Insurance Scheme (NHIS), which was broken down into three categories: enrolment, referral, and claim data. The analysis of current healthcare investigative methods is conducted, and a new framework is proposed.
Framework for Hausa Speech Recognition

The idea of this paper is to present a framework for the development of the Hausa Speech recognition system. The framework comprises the creation of a dataset, and the development of acoustic, language, and speech models. The goal is to enhance speech recognition research for under-resourced languages such as the Hausa language. The paper presented work achieved so far by the researchers are creating and developing the Hausa dataset, acoustic model, language model and speech respectively. The dataset and models can be put together for the development of speech to text, text to speech, dictation application and speech to speech translator