Reviewerly/exHarmony huggingface dataset:
Sentimental analysis using machine learning in Twitter dataset

The analysis of sentiment is also known as opinion mining. Observation of sentiment is used for detecting different emotions of people through the feedback given by them. It is done to know whether the customer is satisfied by the organizations product, service and so on. Nowadays rating the product or service becomes very essential and important in everyones life. These are nothing without rating the sentiments of the customer. It is absolutely essential to collect the sentimental data since it helps in improving the product or its service, to satisfy the customer and to increase the sale. In this fast-changing world, Twitter is one of the most used and biggest sensation creating apps. Twitter is mostly used for delivering thoughts of people. This process is known as sentimental delivery. This research analysis is done with the help of views, likes, comments and shares of a particular tweet. The output of this analysis might be positive, negative or neutral. Machine learning is an algorithm or a method in which the task is conducted by an AI system. In this output value is predicted from the given input data.
Design a Car Sales Business Intelligence Using the K-Means Algorithm With Holt-winter's Smoothing Method

The use of Business Intelligence (BI) systems as decision support systems in the automotive environment, especially luxury cars, is still limited to support decision making regarding production and distribution policies. Forecasting is an important tool in planning for every organization and agency in need. The purpose of this study is to design BI that can be applied to luxury car show rooms as part of the automotive business organization by utilizing existing information systems. This research can also be used to see an overview of luxury car sales patterns using clustering techniques using K-Means algorithms. This study was designed by relating data on fuel increases from previous years. The results of this study are in the form of forecasting data using the smothing holtwinters method of luxury car sales in the next 5 years by adjusting to the conditions of fuel increase in the next 5 years for the process of making predictions of luxury car needs in Indonesia in the coming period.
Measuring the Effect of Income Inequality, Financial Inclusion, Investment, and Unemployment, on Economic Growth in Africa: A Moderating Role of Digital Financial Technology

The significance of understanding the effect of income inequality, financial inclusion, investment, unemployment on economic growth in Africa is imperative in academic literature, and contributes meaningfully to policy decision making. All the variables in the study play important roles in economic growth. This paper, therefore empirically analyses the contribution of income inequality, financial inclusion, investment, unemployment, on economic growth using secondary data spanning 2001 to 2022. Two-step system generalized method of moments (GMM) estimator is used to analyse the data extracted from the world development indicators (WDI). It was revealed that, income inequality and unemployment have a significant negative relationship with economic growth whilst financial inclusion and investment also have a significant positive relationship with economic growth. Also, financial technology, which is the moderator variable has significant relationship with economic growth. It was concluded that, policy makers should ensure equitable distribution of wealth, stabilize the economy, develop the skills of young entrepreneurs, and invest in financial technology to enhance economic growth in Africa.
Meta-Analysis Approaches in Machine Learning for Intelligent Chatbots

Meta-Analysis Approaches in Machine Learning for Intelligent Chatbots
FINANCIAL SUSTAINABILITY REPORTING: A FOCUS ON DEPOSIT MONEY BANKS IN NIGERIA

This study examined the prospects of adopting contemporary performance measures and how it affects sustainability reporting in Deposit Money Banks in Nigeria. This was achieved by using the traditional measures of performance that are normally found on the banks published annual reports and comparing it to other contemporary measures of performance. Data for the study was collected from the annual reports of seven (7) listed deposit money banks for a period of seven (7) years that is 2014-2020. Data obtained was analyzed using two selected contemporary measures of performance (Tobins Q and Loan deposit ratio (LDR)) and two selected traditional measures (Earnings per share (EPS) and Return on Assets (ROA)) in Performance management. Findings revealed that the new measures are quicker to identify loss of value and underperformance in standard. This shows that the new measures are more reliable when it comes to early prediction and rescue of sudden failure of listed deposit money banks in Nigeria. The study therefore recommends among others that deposit money banks should endeavor to use and include the performance results from the selected contemporary measures in their annual published reports for early detection of threats to their going concern.&#x0D; Keywords: Performance Management, Performance Measurement, Profitability, Value, Standard.
Designing An Information Security Framework For The Indonesia Water Industry Sector

The majority of Indonesia's water industry sectors have implemented smart water management systems as part of their business development, which has an indirect impact on enterprise information security. However, in general, water sector enterprises continue to place a low priority on information security, and the development of information system frameworks is based on generic norms employed by financial firms. There has been no research on information security frameworks especially built for water firms in Indonesia that use information security standards in the utilities sector. This article proposes a solution in the form of a new framework for Indonesian water firms that combines international information security requirements in the utilities sector with Indonesian government rules. This approach of development combines worldwide standards with national rules. The Cybersecurity Capability Maturity Model (C2M2) and ISO 27019 are two international standards commonly used by utility businesses globally. Government Regulation or Peraturan Pemerintah (PP) Number 71 of 2019 on the Implementation of Electronic Systems and Transactions is the relevant national regulation. The framework addresses information technology, telecommunications, and operational technology, with four approach categories: governance and ecosystem, protection, defense, and resilience. According to the research findings, the newly integrated framework can be applied and is worthy of recommendation. This framework also meets the standards for information security and can be used by Indonesian water corporations.
Multi-Label Classification with Label Clusters

Abstract Multi-Label Classification is the task of simultaneously predicting a set of labels for an instance. Typically, two approaches are used: global, which trains a single classifier to deal with all classes at once, and local, which divides the problem into many binary problems. In both approaches, learning label correlations is still an open issue. In this paper, we propose a method to cluster the label space and find partitions of disjoint correlated labels called hybrid partition, which can be considered in-between the local and global, combining the benefits of both. Our proposal entails: i) clustering the label space based on label correlations; ii) generating and validating the resulting hybrid partitions; iii) selecting the optimal partitions between them; and iv) evaluating their performances. Since we are also interested in analyzing how hybrid partitions are close to the best possible partitions, we compared our results with the ones obtained using partitions found by an oracle, an exhaustive, and a random search. The oracle and exhaustive strategies generate all possible label partitions for a dataset. While the oracle chooses the best one based on the test set alone, the exhaustive choices are based on validation. Experiments on eight well-known multi-label datasets revealed that both our proposal and the random partitions achieve superior or competitive results compared with the others. This indicates that the conventional global and local approaches may not accurately account for label correlations, requiring significant improvements. In this case, clustering the label space can be a solution.
Word Frequency and Predictability Dissociate in Naturalistic Reading

Many studies of human language processing have shown that readers slow down at less frequent or less predictable words, but there is debate about whether frequency and predictability effects reflect separable cognitive phenomena: are cognitive operations that retrieve words from the mental lexicon based on sensory cues distinct from those that predict upcoming words based on context? Previous evidence for a frequency-predictability dissociation is mostly based on small samples (both for estimating predictability and frequency and for testing their effects on human behavior), artificial materials (e.g., isolated constructed sentences), and implausible modeling assumptions (discrete-time dynamics, linearity, additivity, constant variance, and invariance over time), which raises the question: do frequency and predictability dissociate in ordinary language comprehension, such as story reading? This study leverages recent progress in open data and computational modeling to address this question at scale. A large collection of naturalistic reading data (six datasets, &amp;gt;2.2M datapoints) is analyzed using nonlinear continuous-time regression, and frequency and predictability are estimated using statistical language models trained on more data than is currently typical in psycholinguistics. Despite the use of naturalistic data, strong predictability estimates, and flexible regression models, results converge with earlier experimental studies in supporting dissociable and additive frequency and predictability effects.
EXPLORING THE POSSIBILITIES OF USING A DEVELOPED REMOTELY CONTROLLED MOBILE ROBOT

This article examines the construction of a remotely controlled mobile robot created 5 years ago at Alytus College, the possibilities of its use and the study of the possibilities of its use. During the study, the created mobile robot with other robots developed by foreign scientists is analyzed and compared and a corresponding way to use it is sought. Keywords: robot, construction, control, research
Description of Cognitive Function in The Elderly with Puzzle Therapy Intervention: Case Study

The elderly can experience a decrease in cognitive function because the body functions of the elderly also tend to decrease. Cognitive disorders in the elderly can interfere with and affect the behavior or activities of the elderly. The role of nurses is very important to provide interventions that can improve the cognitive function of the elderly. Non-pharmacological efforts are needed to overcome cognitive function problems in the elderly, one of which is through puzzle therapy. Puzzles in the form of pictures in the form of small pieces to hone intelligence, lead to patience, introduce the ability to share, and are a fun way to train the brain, especially for the elderly. This research method is a case study method with a nursing care approach to the elderly aged 80 years, the MMSE and SPSMQ questionnaires are used to assess cognitive and intellectual development in the elderly. The results showed that the application of puzzle therapy which had been carried out for 6 meetings with an intensity of 1x20 minutes per day showed an improvement in the results of the MMSE and SPSMQ scores, in other words there was an increase in cognitive and intellectual function in the elderly. For maximum results, puzzle preparation exercises should be carried out regularly and can be done together in nursing homes so that the elderly are more motivated and more enthusiastic.
A NOVEL APPROACH IN HILL CIPHER CRYPTOGRAPHY

Cryptography plays a vital role in securing sensitive information in various domains. Hill cipher, a classic encryption technique, has been widely used for its simplicity and effectiveness. However, the original Hill cipher is susceptible to certain attacks due to its limited key space and vulnerable characteristics. In this study, we propose an enhanced version of the Hill cipher called the “Block Hill Cipher” that overcomes the limitations of the original algorithm while preserving its fundamental principles. To increase the safety and effectiveness of the encryption process, the Block Hill Cipher offers a number of unique ideas. The Block Hill Cipher encrypts blocks of characters rather than single4 characters, making it possible to encrypt longer sequences at once. By expanding the key space and making the cipher more resistant to statistical attacks, this method greatly enhances the cipher’s overall security. In light drawbacks the present paper deals with a fresh strategy in which our algorithm by incorporating while upholding the Hill cipher’s fundamental ideas. Hence on the findings of the study may be useful cryptographic tool ranging from communication systems to data storage and transmission in modern digital environments.
IMPLEMENTASI SISTEM INFORMASI AKADEMIK PADA MADRASAH IBTIDAIYAH

This study was conducted to examine how technological advancement, particularly in the area of information technology, has affected academic institutions. This is significant because information technology now permeates all facets of modern civilization and human life. This study used a qualitative methodology and descriptive analysis. The Madrasah Ibtidaiyah is the locus. The information used in the analysis was gathered from the academic information system findings implemented in this madrasah. The outcomes of descriptive analysis have been able to support and facilitate the development of educational programs that bring together all academics, including educators, instructors, support staff, students, parents, and other stakeholders.Keywords: Academic Information System; Madrasah; Ibtidaiyah
The Use of Construction Management Information Systems: Efforts to Improve the Service Quality for Building Permit in Polewali Mandar Regency

Public service innovation offers the use of technology as an effort to improve service quality. The One-Stop Integrated Investment and Licensing Service of Polewali Mandar Regency has currently used the Building Management Information System (SIMBG) application to provide convenience in building permit services, but the implementation of the application has not been implemented optimally in providing services to the community. Through qualitative methods, this study aims to analyze the improvement in the quality of service for building permits/building applications in Polewali Mandar Regency, through the use of the SIMBG Building Management Information System, which has been in use since 2018. The data in this study were predominantly sourced from interviews. , which is also strengthened by the results of observation and documentation. Each data obtained before it reaches the analysis stage, is carried out by a reduction process, and triangulated to test the validity of the data. After the data is considered valid, the data is analyzed and presented in the form of narratives and pictures and conclusions are made. The results of data analysis show that the use of SIMBG has not been optimal in increasing the quality of building permit services. This is influenced by poor network quality which then also has an impact on employee performance in providing services. Through SIMBG, SIMBG employees and operators canimprove their skills and abilities in using technology to provide services.
Creating a Software Methodology to Analyze and Preserve Archaeological Legacy Data – ADDENDUM

An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.
Comparison of Multiple Linear Regression and Holt-Winter Exponential Smoothing in the Gold Jewelry Pricing Prediction

One important factor in someone's decision to purchase gold jewelry is the price factor. However, the price of gold jewelry varies from store to store. Therefore, a gold jewelry price forecasting system is needed to assist customers in determining the right time to buy gold jewelry. This study aims to develop a gold jewelry prediction system by comparing two methods: multiple linear regression and Holt-Winter exponential smoothing. These two methods were chosen because the researcher wants to compare which method is more accurate in predicting the price of gold jewelry. The second reason is that both multiple linear regression and Holt-Winter exponential smoothing methods share similarities in using three variables. The three variables in multiple linear regression include period (X1), profit (X2), and price (Y), while the variables in the Holt-Winter exponential smoothing method include level, trend, and season.
Implementation of The Flipped Classroom Learning Model With The Assistance of Interactive E-Books To Improve Science Literacy Competence Vector Material

The purpose of this study was to determine the increase in scientific literacy competence in vector material by implementing the flipped classroom learning model with the help of interactive e-books. This research is in the form of best practice in implementing learning with research samples of class X IPA-1 students at MAN 2 Mojokerto. The results of the research analysis showed that the classical average value had a significant increase from the pre-test value of 49.67 to 84.52 in the post-test and the completeness of student learning outcomes also increased from 37% in the pre-test to 93% in the post-test. The implementation of the flipped classroom learning model assisted by interactive e-books can improve vector material science literacy competence and become the best experience (best practice) for teachers and have a positive impact on learning activities carried out by teachers.
Face Mask Detection and Social Distance Monitoring with Deep Learning

Objectives: This work proposes a real-time classification model that can accurately detect whether an individual is wearing a face mask and maintaining social distance with the goal of developing a lightweight and easily deployable system for surveillance purposes.Methods: The proposed method easily identifies the human by bounding boxes and wearing of face mask by realtime Face Detection Recognition System.This is a robust model that involves detection, tracking and validation as its features.Pre trained deep learning models like Inceptionv3, DenseNet are used and compared with the proposed Improved MobileNetv2.A tested deep learning model is developed to check social distancing, which uses the YOLO object detector and computes the Euclidean distance between people to confirm the safety of the system.Finally, the proposed method is evaluated in terms of precision, recall, F1-score, support, accuracy, sensitivity and specificity.Findings: The analysis of the results reveals that the improved MobileNetv2 model achieves precision of 98%, recall of 98% and accuracy of 98%.Hence, this deep learning system contributes to the management of COVID-19 outbreak in an efficient way and can be installed for operation in public places like shopping malls, stadiums etc. Novelty: The proposed system can be effortlessly integrated into embedded devices that have limited computational capabilities to the detection of face masks in photographs and real-time videos.
Strategi Meningkatkan Kinerja Pegawai Bagian Infomasi dan Pengolahan Data Sekolah Calon Perwira Angkatan Darat (Infolahta SECAPA AD)

The purpose of this study was to find out and analyze how the strategy for improving the performance of the Staff of the Information and Data Processing Section of the Army Officer Candidate School was. This research method uses qualitative methods. The data analysis technique used is SWOT analysis. This research data collection using interviews, observation and documentation review. The results showed that the strategy that should be used to improve the performance of the Staff of the Information and Data Processing Section for Army Officer Candidates is Supporting Aggressive Strategies. The strategy that is feasible to be implemented for the Staff of the Information and Data Processing Section of the Army Officer Candidate School is the Institutional strategy by improving employee performance which focuses on efforts to develop human resources, namely improving the quality of adequate human resources through training activities, increasing collaboration with outside organizations and consultants. , Support the provision of adequate facilities and infrastructure, Increasing the use of structured education and training programs, Providing awards for loyal and professional employees, Increasing collaboration between employees, Involvement of Infolahta staff in determining organizational policies, Increasing budget for technology development and the latest innovations.
An Enriched Model of Neutrosophical Fuzzy and Grasshopper Convolutional Neural Network Based Moving Object Detection and Classification to Improve Video Surveillance

Objectives:To identify a suitable object recognition method for video surveillance systems, especially in traffic monitoring, to track and sense multiple objects and classify them by employing conventional algorithms in order to boost accuracy.Methods: Neutrosophical Background Subtraction (NBS) and Grasshopper Optimized Convolutional Neural Network (GO-CNN) are employed to detect and track the objects in real time.The Neutrosophical Fuzzy Background Subtraction Method (NFBSM) is utilized to segment moving objects from the background in terms of truthful degree, false degree, and in-deterministic degree.CNN-based object tracking techniques, along with the grasshopper optimization algorithm (GO), are used for reliable object detection and hyper-parameter fine tuning.The NFBSM+GO-CNN model extracts the features from the dataset (collected from YOLO-V-3 from Kaggle) and selects a suitable function to perform object detection and classification of moving vehicles with a high accuracy rate.Four main features are used such as colour, texture, motion, and shape.PYTHON is used for implementation and comparative analysis, and the findings are compared with baseline models D-CNN and CNN-MOD.Findings: The proposed NFBSM+GO-CNN method outperforms with 98.2% accuracy, 96.3% improved precision, and 96.8% F-measure, which is comparatively higher than the baseline models in terms of real-time object detection and classification.Novelty: The outcome of NFBSM+GO-CNN clearly shows that the proposed model has the ability to detect the object in real-time and classify the vehicle in road traffic monitoring with a high accuracy rate.The proven results outperform existing models such as D-CNN and CNN-MOD.
A Study on Vocational High School Students’ Perception of School Changes and Departmental Restructure

A Study on the Development of Entry Level Jobs for the Smart Factory Field Manpower Training in Vocational High Schools - Smart Factory;High School Graduation Level;entry level job;Comparative Confirmation Act;Field Suitability Review
Object Detection by Network Based on CapsNet Architecture and Attention Layers

Abstract Object detection is responsible for categorizing and locating object in an image or video which has been widely used during recent years. This study represents a model based on Feature Pyramid Network (FPN) and new layers of capsule attention, the represented model is based on anchor and has had 79.5 % MAP in the experiments. On one hand, this imprecision shows improvement up to 7 % than the best existing methods like Yolo V3, Detectron2 etc, on the other hand it doesn’t have acceptable results considering runtime and the number of frames and it is not competitive.