CSV Files Research Articles

Predictive Maintenance for Nasa’s Turbofan Engine

NASA's turbofan engine is a vital equipment used in its aircraft fleet. This engine is designed to provide the required thrust for various missions, from scientific research to astronaut training. However, this engine requires regular maintenance to ensure its optimal performance and safe operation. In this paper, we will find the remaining useful life of the turbofan engine by applying data science techniques and machine learning algorithms for predicting more accurate maintenance requirements. We will examine the performance metrics of different machine learning models and tune the parameters of the best model using random search. We will be deployed as an application using Streamlit. The final result of the web application is that it provides the results of the predictions done by the model as a csv file along with the model loss and accuracy.

Small Files Problem Resolution via Hierarchical Clustering Algorithm.

The Small Files Problem in Hadoop Distributed File System (HDFS) is an ongoing challenge that has not yet been solved. However, various approaches have been developed to tackle the obstacles this problem creates. Properly managing the size of blocks in a file system is essential as it saves memory and computing time and may reduce bottlenecks. In this article, a new approach using a Hierarchical Clustering Algorithm is suggested for dealing with small files. The proposed method identifies the files by their structure and via a special Dendrogram analysis, and then recommends which files can be merged. As a simulation, the proposed algorithm was applied via 100 CSV files with different structures, containing 2-4 columns with different data types (integer, decimal and text). Also, 20 files that were not CSV files were created to demonstrate that the algorithm only works on CSV files. All data were analyzed via a machine learning hierarchical clustering method, and a Dendrogram was created. According to the merge process that was performed, seven files from the Dendrogram analysis were chosen as appropriate files to be merged. This reduced the memory space in the HDFS. Furthermore, the results showed that using the suggested algorithm led to efficient file management.

Development and Evaluation of an Enhanced Virtual Reality Flight Simulation Tool for Airships

A real-time flight simulation tool is proposed using a virtual reality head-mounted display (VR-HMD) for remotely piloted airships operating in beyond-line-of-sight (BLOS) conditions. In particular, the VR-HMD was developed for stratospheric airships flying at low/high altitudes. The proposed flight simulation tool uses the corresponding aerodynamics characteristics of the airship, the buoyancy effect, mass balance, added mass, propulsion contributions and ground reactions in the FlightGear Flight Simulator (FGFS). The VR headset was connected to the FGFS along with the radio controller containing the real-time orientation/state of each button, which is also simulated to provide better situational awareness, and a head-up display (HUD) that was developed to provide the required flight data. In this work, a system was developed to connect the FGFS and the VR-capable graphics engine Unity to a PC and a wireless VR-HMD in real time with minimal lag between data transmission. A balance was found for FGFS to write to a CSV file at a period of 0.01 s. For Unity, the file was read every frame, which translates to around 0.0167 s (60 Hz). A test procedure was also conducted with a similar rating technique based on the NASA TLX questionnaire, which identifies the pilot’s available mental capacity when completing an assigned task to assure the comfortability of the proposed VR-HMD. Accordingly, a comparison was made for the aircraft control using the desktop simulator and the VR-HMD tool. The results showed that the current iteration of the system is ideal to train pilots on using similar systems in a safe and immersive environment. Furthermore, such an advanced portable system may even increase the situational awareness of pilots and allow them to complete a sizeable portion of actual flight tests with the same data transmission procedures in simulation. The VR-HMD flight simulator was also conceived to express the ground control station (GCS) concept and transmit flight information as well as the point of view (POV) visuals in real-time using the real environment broadcast using an onboard camera.

Aggregation of Italian Lichen Data in ITALIC 7.0.

The creation of a coordinated publishing and aggregation system of biodiversity data is a challenging task, which calls for the adoption of open data standards. ITALIC, the information system on Italian lichens, originated from the conversion of the first Italian checklist into a database. While the first version was "frozen", the present version is continuously updated and provides access to several other data sources and services, such as ecological indicator values, ecological notes and information, traits, images, digital identification keys, etc. The identification keys especially are an ongoing work that will lead to a complete national flora by 2026. Last year, new services were added, one for aligning lists of names with the national checklist, the other for aggregating occurrence data deriving from the digitization of 13 Italian herbaria, forming a total of ca. 88,000 records, which are distributed under a CC BY license and can be exported as CSV files in the Darwin Core format. An aggregator for lichen data will encourage the national community of lichenologists to produce and aggregate further data sets, and it will stimulate data reuse according to the paradigms of open science.

A Bibliometric Analysis of the Psychology of Mobile Banking use Among Generation Z

This study tries to track how generation Z uses advances related to mobile banking. It is crucial to conduct research on generation Z's use of mobile banking because, according to the Central Bureau of Statistics, this group makes up the majority of the population in Indonesia. Researchers employed the quantitative descriptive with bibliometric analysis approach to obtain data for this study. 138 papers spanning the years 2018 to 2022 were searched using the Harzing's Publish or Perish application. The data from the PoP results were then descriptively examined based on the journal ranking, publication year, publisher name, and researcher productivity. PoP data is converted to an excel CSV file format (Comma Separated Values) and result as RIS (Research Information Systems) in order to obtain an accurate research development map. The software VOSviewer version 1.6.18 was used to analyze RIS data and create a pivot table from CSV data. The findings of this study demonstrate that, from 2018 to 2022, the number of publications on the subject of mobile banking research in generation Z increased yearly, reaching a peak of 72 articles in 2022. Although this research has not been extensively studied, it can stimulate research opportunities. Mapping the development of mobile banking research publications based on co-occurrence indicates that there is a psychological relationship to the use of mobile banking which is accompanied by 5 dominant clusters. Wordsbehavior, mobile applications, hedonic features, utilitarian benefits, utilitarian purposes, utilitarian features, sustainable consumption, mobile commerce, and financial behavior are all potential research topics.

Using Semantic Web to Create and Explore an Index of Toponyms Cited in Medieval Geographical Works

Western thought in European history was mainly affected by the image of the world created during the Middle Ages and Renaissance. The most popular reason to travel during the Middle Ages was taking a pilgrimage. Jerusalem, Rome, and Santiago de Compostela were the most popular destinations. It is not surprising that a lot of works written by travellers as guides for pilgrims exist. By the beginning of the Renaissance, a more precise image of the world was defined, thanks to the discovery of ancient geographical models, especially the work of Ptolemy. The Italian National Research Project (PRIN) IMAGO --- Index Medii Aevi Geographiae Operum --- (2020-2023) aims to provide a systematic overview of the medieval and renaissance Latin geographical literature using the Semantic Web technologies and the LOD paradigm. Indeed, until now, this literature has not been studied using digital methods. In particular, this article presents how we formally represented the knowledge about the toponyms, or place names, in the IMAGO ontology. To maximise the interoperability, we developed the IMAGO ontology as an extension of two reference vocabularies: the CIDOC CRM and its extension FRBRoo, including its in-progress reformulation, LRMoo. Furthermore, we used Wikidata as reference knowledge base. As case study, we chose to represent the knowledge related to the toponyms cited by the Italian poet Dante Alighieri in his Latin works. We carried out a first experiment for visualising the knowledge about these toponyms on a map and in the form of tables and CSV files.

Object Detection in Online Proctoring Through Two Camera Using Faster-RCNN

The COVID-19 pandemic has prompted changes in teaching methods from offline to online, including the implementation of exams. But many reports say that the potential for online exam cheating is very high which can compromise the credibility of the exam. The online exam monitoring system using one camera makes it difficult for officers to make decisions because of the lack of evidence and supporting data. In this study, we propose a monitoring approach using two cameras, namely a camera on a laptop to get a front view of the participant and a cellphone camera to get a side view of the examinee but because of the complexity of the problem, at this stage we only focus on the side camera. Implementation begins with the collection of video recording data, custom data sets for training and pretrained datasets from the zoo model. Training is carried out using a custom dataset to detect objects that are not recognized by the pretrained dataset. The evaluation of the training results using the COCO evaluator showed the average of the bbox-AP is 59,169. The fraud detection process is carried out using 6 exam videos with a total of 192,929 frames, producing two outputs, namely object detection videos and csv files. The csv file contains the frame number, time, object detected in each frame. The next process is to analyze the csv file and mark frames that have the potential to be fraudulent. The evaluation results show an accuracy of 0.884615385 and a recall of 0.821428571

LppDB - a Comprehensive Database for Lipopeptide based drugs: Exploring potential drug discovery aspects.

Lipopeptides are amphiphilic compounds consisting of a cyclic peptide attached to a fatty acid side chain. These are found with various medicinal properties ranging from broad-spectrum antimicrobial to anticancer properties. Elaborated studies on the therapeutic properties of peptides are hindered due to a lack of databases specifically dedicated to such classes of compounds where all the medicinal information of compounds is collectively studied in one place, which can accelerate the future scope of research on peptidolipidic compounds. The chemical information was retrieved from the PubChem compound database, and their metabolism information was generated through Swiss ADME. The application information about their particular pathways was obtained from different kinds of literature. A python script was developed to extract the information about lipopeptides from a CSV file, and the information was stored in MySQL Database. The database contains information for 70 lipopeptides and their biochemical information, structure, chemical and physical properties, information on industrial and medicinal applications, functions and references for lipopeptides. The development of web-based database applications allows the data stored in the database to be accessed and viewed publicly.

Ambient particulate matter monitoring system using SDS011 sensor utilizing machine learning approach and ambit of blockchain technology

In recent days, air contaminated with Particulate Matter Pollutants (PM2.5and PM10) plays a major role in affecting the quality of air and it has a strong effect on the environment. PM2.5and PM10 released in the air due to the huge increase of the manufacturing yards have led to significantly increased and severe health issues for all living beings around the globe. These Particulate Matters emitted from the industrial units can stay suspended in the air for a very prolonged time. Thus, it is essential to measure and monitor the ambient Particulate Matter emission rate near the industries. However, the existing fixed air quality monitoring stations are challenging, nevertheless, due to thehigh energy utilization, small-scale monitoring, and privacy concerns. To resolve this concern, a low-cost portable sensor NOVA SDS011 can be mounted near the industries to study the impact of the concentration level of the Particulate Matter Pollutants (PM2.5and PM10) released from those industries. The entire detected values are transmitted to a scalable IoT platform in the cloud, which allows the processing, analyzing, and exportation of data as.xls/.csv files. These exported data are pushed through various Machine Learning (ML) Classifier algorithms to classify the data into multiple classes based on its AQI (Air Quality Index) values. In this study, various ML classifier algorithms were used and analyzed in which Random Forest Classifier Algorithm had performed with utmost accuracy to classify the six different classes of AQI. This study also explains the limitation of privacy concerns in existing systems and the scope of using blockchain technology to overcome security concerns.

Pollock: A Data Loading Benchmark

Any system at play in a data-driven project has a fundamental requirement: the ability to load data. The de-facto standard format to distribute and consume raw data is csv. Yet, the plain text and flexible nature of this format make such files often difficult to parse and correctly load their content, requiring cumbersome data preparation steps. We propose a benchmark to assess the robustness of systems in loading data from non-standard csv formats and with structural inconsistencies. First, we formalize a model to describe the issues that affect real-world files and use it to derive a systematic "pollution" process to generate dialects for any given grammar. Our benchmark leverages the pollution framework for the csv format. To guide pollution, we have surveyed thousands of real-world, publicly available csv files, recording the problems we encountered. We demonstrate the applicability of our benchmark by testing and scoring 16 different systems: popular csv parsing frameworks, relational database tools, spreadsheet systems, and a data visualization tool.

Comparative Analysis of Machine Learning Performance in House Price Prediction for Ames Iowa

The price of housing is closely related to the quality of people's lives, which is reflected in various aspects (e.g., finding a place to live and managing their finances). On this basis, being able to predict house prices would be a great convenience, and this study examines the accuracy of multiple mechanistic learning regression models in predicting future house price changes. To be specific, this paper downloaded the dataset from Kaggle which includes the training and testing csv files. and ran many different regression techniques including lasso and more models on google Colab. Furthermore, a comparison between the results of different methods is carried out by estimating the mean square error. Then, this research combined some of the best yielded results of the model to combine into a final model and got a result with a high score. According to the analysis and evaluations, the study concluded that the hybrid model resulted in a more accurate prediction than the single model. These results shed light on guiding further exploration of real estate prediction.

Realtime And Centralized Solar Panel Online Monitoring System Design Using Thingspeak

Indonesia is a country with a tropical climate and has a high intensity of solar irradiation. Seeing this situation, Indonesia is the right region to implement the installation of new and renewable energy such as PLTS. In the use of solar panels, the magnitude of the output power is determined by several environmental conditions, such as the intensity of sunlight, temperature, and the direction in which sunlight comes. To prevent damage and deterioration in the performance of solar panels, research is needed for a more accurate and efficient use of renewable energy. The study was a solar panel system with a Wemos D1 microcontroller that monitored online using Thing speak with the help of INA 219 and LDR sensors. Online monitoring system by recording data on current, voltage, and light intensity in real time and centralized. The system will store and record measurement data every 15 seconds in the form of JSON, XML, CSV file extensions. Error on the voltage sensor of 1.7% for and 3.2% for the current sensor. average panel voltage value of 12.77 V, average panel current value of 0.43 A and 2410 lux for light intensity at interval testing from 07:00 a.m.to 16:00p.m.

On Detailed Network Systems Configuration Management Automation using Python

In nowadays, communications are expanding in very high rates. New technologies are being born and some of them are taking so much importance in people’s lives. In a situation where people’s needs are getting more complicated and everybody’s lifestyle is advancing to another level, bigger and better infrastructure is needed. Managing all this process can’t be anymore a step by step process. It is strictly needed to evolve in automated process. Network configuration and reconfiguration may be a repetitive process, time consuming, and error prone process. To address this problem this paper is going to shed light on the benefits of an automated configuration and topology verification process. To this end, a proof of concept system, Netmiko, has been used in a case study. Netmiko scripts are able to read the current network state, can apply predefined configurations loaded from text-files or csv files, and automatically verify the network state. The goals of this paper are to demonstrate the development of Netmiko scripts, to illustrate the simplicity in the implementation, to compare the automated network system reconfiguration to a fully manual one, and, finally, to discuss potential pros and cons in switching to an automated network configuration process in everyday practice. A simple Packet Tracer simulation in joint with a GNS3 simulation are involved to evaluate this proof of concept. In Packet tracer a manual network configuration is performed while in GNS3 an automated network configuration is developed.

Sentiment Analysis and Text Classification for Depression Detection

Depression is an illness that can harm someone's life. However, many people still do not know that they are having depression and tend to express their feelings through text or social media. Thus, text-based depression detection could help in identifying the early detection of the illness. Therefore, the research aims to build a depression detection that can identify possible depression cues based on Bahasa Malaysia text. The data, in the form of text, has been collected from depressed and healthy people via a google form. There are three questions asked which are “Apakah kenangan manis yang anda ingat?”, “Apakah rutin harian anda?” and “Apakah keadaan yang membuatkan anda stress?” which obtained 172, 169 and 170 responses for each question respectively. All the datasets are stored in a CSV file. Using Python, TF-IDF was extracted as the feature and pipeline into several classifier models such as Random Forest, Multinomial Naïve Bayes, and Logistic Regression. The results were presented using the classification metrics of confusion matrix, accuracy, and F1-score. Also, another method has been conducted using the text sentiment techniques Vader and Text Blob onto the datasets to identify whether depressive text falls under negative sentiment or vice versa. The percentage differences were determined between the actual sentiment compared to Vader and Text Blob sentiment. From the experiment, the highest score is achieved by AdaBoost Classifier with a 0.66-F1 score. The best model is chosen to be utilized in the Graphical User Interface (GUI).

Sistem Monitoring Online Dan Analisis Perfomansi Plts Panel Surya Monocrystalline 100 Wp Berbasis Web

In this modern life, people's dependence on energy sources, especially electrical energy, has led to innovation in the use of cheap alternative energy sources that are environmentally friendly such as solar energy, one of which is PLTS. To support PLTS performance, a web-based monitoring system is needed for remote monitoring in monitoring PLTS performance, the data displayed is the result of measurements by voltage and current sensors installed in each solar panel on the PLTS performance detector. In this case, it is necessary to develop a Web-based monitoring system to help improve the efficiency of performance maintenance on 100 wp monocrystalline panels. By using the INA260 sensor in the form of a digital DC sensor that can measure a maximum current of 15 A, a maximum voltage of 36 v, and DC power and the DS18B20 sensor is a digital sensor that can measure the surface temperature of the panel. Sensor readings can be displayed offline via the HMI LCD while online readings can be displayed in the form of a Web which has a graphical display feature for each parameter and downloads data in the form of a .csv file as a feature that supports data analysis. The results of the sensor validation test or calibration using a measuring instrument have an average error sensor reading of 0.34 %. The average current sensor error is 0.63%, and the power sensor error average is 0.56%, while the average panel surface temperature sensor error is 0.34%

Machine Learning Based Heart Disease Prediction System

Abstract: Heart attack disease is one of the leading causes of the death worldwide. In today’s common modern life, deaths due to the heart disease had become one of major issues, that roughly one person lost his or her life per minute due to heart illness. Predicting the occurrence of disease at early stages is a major challenge nowadays. Machine learning when implemented in health care is capable of early and accurate detection of disease. In this work, the arising situations of Heart Disease illness are calculated. Datasets used have attributes of medical parameters. The datasets are been processed in python using ML Algorithm i.e., Random Forest Algorithm. This technique uses the past old patient records for getting prediction of new one at early stages preventing the loss of lives. In this work, reliable heart disease prediction system is implemented using strong Machine Learning algorithm which is the Random Forest algorithm, which read patient record data set in the form of CSV file. After accessing dataset the operation is performed and effective heart attack level is produced. Advantages of proposed system are High performance and accuracy rate and it is very flexible and high rates of success are achieved

On the design of linked datasets mapping networks of collaboration in the genomic sequencing of Saccharomyces cerevisiae, Homo sapiens, and Sus scrofa.

This data note describes a unique two-step methodology to construct six linked datasets covering the sequencing of Saccharomyces cerevisiae, Homo sapiens, and Sus scrofa genomes. The datasets were used as evidence in a project that investigated the history of genomic science. To design the datasets, we first retrieved all sequence submission data from the European Nucleotide Archive (ENA), including accession numbers associated with each of our three species. Second, we used these accession numbers to construct queries to retrieve peer-reviewed scientific publications that first described these sequence submissions in the scientific literature. For each species, this resulted in two associated datasets: 1) A .csv file documenting the PMID of each article describing new sequences, all paper authors, all institutional affiliations of each author, countries of institution, year of first submission to the ENA (when available), and the year of article publication, and 2) A .csv file documenting all institutions submitting to the ENA, number of nucleotides sequenced and years of submission to the database. We utilised these datasets to understand how institutional collaboration shaped sequencing efforts, and to systematically identify important institutions and changes in the structure of research communities throughout the history of genomics and across our three target species. This data note, therefore, should aid researchers who would like to use these data for future analyses by making the methodology that underpins it transparent. Further, by detailing our methodology, researchers may be able to utilise our approach to construct similar datasets in the future.

Elementary Statistics Projects Using Covid Data

This article describes five elementary statistics projects involving the Covid-19 data made available to the public in csv files by the Centers for Disease Control and Prevention. The first project examined data available at the beginning of the covid surge in New York City in spring, 2020, and used the correlation coefficient to estimate the total number of deaths that could be expected as the spike ran its course. The second project is an easy one on the concept of excess deaths and on the mechanics of extracting parts of a data file that answer relevant questions. The data is from a spike in deaths in the particularly bad flu surge in the winter of 2017–2018. The third and fourth projects ask the student to fit a logistic growth curve to observed cumulative numbers of deaths in a spike, like the Covid spikes in New York City and Wisconsin and the nationwide 2017–2018 flu spike. The method is a simple linear regression with transformed variables. The fifth project involves hypothesis testing and judging when a Poisson model might be useful. The paper also documents difficulties and adaptations of the sort familiar to all teachers who have taught during the Covid-19 pandemic.

On automating network systems configuration management

In nowadays, communications are expanding in very high rates. New technologies are being born and some of them are taking so much importance in people’s lives. In a situation where people’s needs are getting more complicated and everybody’s lifestyle is advancing to another level, bigger and better infrastructure is needed. Managing all this process can’t be anymore a step by step process. It is strictly needed to evolve in automated process. Network configuration and reconfiguration may be a repetitive process, time consuming, and error prone process. To address this problem this paper is going to shed light on the benefits of an automated configuration and topology verification process. To this end, a proof of concept system, Netmiko, has been used in a case study. Netmiko scripts are able to read the current network state, can apply predefined configurations loaded from text-files or csv files, and automatically verify the network state. The goals of this paper are to demonstrate the development of Netmiko scripts, to illustrate thesimplicity in the implementation, to compare the automated network system reconfiguration to a fully manual one, and, finally, to discuss potential pros and cons in switching to an automated network configuration process in everyday practice. A simple Packet Tracer simulation in joint with a GNS3 simulation are involved to evaluate this proof of concept. In Packet tracer a manual network configuration is performed while in GNS3 an automated network configuration is developed.

Mobile Screen Addiction Using Addictive Attribute by Combination of Algorithm-In Addiction Disorder

Mobile Screen spent by teenage who use lot of time on screen experience psychiatric problems such as frustration, fatigue, depression, anxiety, and so on. Matplotlib pyplot with histogram figure is used to evaluate the psychiatric condition data (CSV file). For the purpose of rating the condition data, the standard deviation and mean are determined. For data analysis, there is a correlation matrix that compares every row and column. Gradient Booster, Logistic Regression, Knn algorithm is used to compare the precision of the machine learning process, which merged the characteristics of addictive and non-addictive data to find rating disorders.