Travel Insurance Prediction

The IFC File Analyzer software generates a spreadsheet or a set of CSV (comma-separated value) files from an IFC file. IFC (Industry Foundation Classes) is the data exchange standard used to facilitate interoperability in the building and construction industry. IFC is developed by buildingSMART and is an ISO standard—ISO 16739. Typical IFC viewers show avisualization of the building represented by the IFC file. The user can drill down to the individual attributes for a single building object. However, there is no way to view all of the entities and their attributes at once. The IFC File Analyzer provides this capability by creating a spreadsheet or CSV files from the IFC file. In the spreadsheet, a worksheet is created for each type of IFC entity in the file. Every row in the worksheet contains the attributes for an instance of an IFC entity. Multiple IFC files can be analyzed at once to compare entity usage. There are options to select which types of IFC entities are processed and to report some of the IFC entity Inverse relationships.

Objectives: To retrieve tweets from Twitter through Twitter API. The domain chosen for analysis is Make-In-India Dataset. Methods/Statistical Analysis: This paper consists of two phases of work: 1. Data Streaming from Twitter, 2. Knowledge Mining through R-Studio. Methods used for the two key operations are: 1. Twitter API and 2. Sentiment Analysis through R. Twitter application is created to request for connection with the Twitter database. Once connection establishes, ­authentication keys are generated. Providing the search key “Make-In-India” and number of keys required, a file with .df (data frame) is generated with the tweets and is converted into .CSV (Comma Separated Values) file which is suitable Analysis. Sentiment Analysis1 is also called Opinion mining talks about retrieving facts from the tweets such as how many people supporting Make-In-India (or) how many are negative with the scheme (or) how many are neutral with it. For this process, a negative words file and a positive words file is taken for comparison with the tweet data to calculate positive score and negative score of the tweet. The difference of these scores gives us with the final score of the tweet. Findings: The number of tweets identified as positive (or) negative (or) neutral so that the status of Make-In-India can be ­visualised in a graph. Firstly, the extraction of Tweets is from Twitter through R-Studio Environment About “Make-In-India”. Secondly, we parse the extracted raw tweets using R according to the types and store in .CSV format in R database. Scores are calculated for allthe tweets and stored in a file. In the third, we perform visual analysis from the stored data using R statistical software to conclude the impact of the program. Application/Improvements: Application of the ­methodology is to get findings2 from the public opinions which are available from Twitter tweets on a particular government issue, political parties and medical status around the country. Also it is useful to assess the popularity of the political leader and the program. Decision making is possible through sentiment analysis of user tweets. Keywords: Big Data, Data Analytics, Make-In-India Data Set, Streaming, R-Studio, Tweets, Twitter API

Web Services have become an important means of integration in an organisation and in business to business practices. Of particular interest is the large volumes of structured information that can result from a backup of data in a system, as part of a search requested on the stored data or from some other data exchange requirement. To be a viable in these areas the simple object acceptable proctorial (SOAP) message used must be able to move the data in a secure and fault tolerant manner, but be able to do it in a fast and efficient manner. One of the largest problems in efficiency of using SOAP messages is in the large amount of information describing the structure and content of the message. This paper presents an examination of the size of SOAP document’s used in data exchanges when compressed, which also compares the text based document, a comma separated value (CSV) file, containing the same information. Our results suggest significant size cost savings can be made when the SOAP message is compressed. Therefore, the SOAP message contains sufficient data entries this size cost saving makes to total size of the message comparable to the compressed CSV file.

A large percentage of the data published on the web is tabular data, commonly in the comma-separated values (CSV) format. URI fragment identifiers for CSV provide possibilities for data publishers (web servers) to refer to specific parts (i.e., fragments) of a CSV file using URIs as well as for data consumers (web clients) to retrieve URI-identified fragments over HTTP for further processing. However, few studies have focused on CSV fragment retrieval, and to our knowledge, so far there are no implementations of CSV fragment identifiers compliant web clients/servers. This study addresses the issue of implementation strategies for CSV fragment retrieval over the HTTP protocol. Two strategies (i.e., client-side fragment evaluation and server-side fragment evaluation) including the fragment evaluation algorithms were proposed and then implemented in order to demonstrate the implementability of CSV fragment retrieval over HTTP and to experimentally observe and compare the run-time efficiency of the two strategies. The results indicate that server-side fragment evaluation is a more efficient strategy in terms of running time and bandwidth cost, but requires extensions for existing web servers, whereas client-side fragment evaluation is the opposite. Our methods and findings provide valuable reference for potential implementers.

We explore product reviews of Amazon and Flipkart in three large product categories: Electronics, Books, and Food, to systematically analyze the relative strengths and weaknesses of both platforms with the help of machine learning. The reviews were web scraped, preprocessed, and preserved in Comma-Separated Values (CSV) files for training and testing. Sentiment labeling was done using both the Bag of Words model and Valence Aware Dictionary and sEntiment Reasoner (VADER) to allow both rule-based and statistical views of sentiment identification. Trained models were run on the server side, and their accuracy was determined through accuracy scores, precision, recall, F1-scores, and confusion matrices. To facilitate interpretability, interactive visualizations were built with Streamlit so that users can analyze sentiment distributions, model comparisons, and temporal changes in customer opinions. Results show clear differences in customer sentiment trends between platforms and product categories, which are actionable inputs for enhancing customer experience and platform stability. This work reinforces the usefulness of integrating web scraping, natural language processing, and machine learning to uncover insights on consumer behavior in e-commerce. Potential future expansions can involve expanding to other domains, implementing deep learning-based sentiment models, and using advanced feature extraction methods for higher predictive quality.

Contracts differ in the extent to which software providers risk financial loss in the case of cost overruns. In this paper, we hypothesise that an increase in the provider's risk of financial loss is related to an increased rate of problematic software projects. The hypothesis is tested by comparing software projects using target-price contracts with and without an upper limit on risk sharing. These two contract variants differ mainly in the provider's risk of financial loss. If the provider's risk of a financial loss makes a difference, we would expect that projects using target-price contracts with an upper limit on risk sharing would perform worse than projects using target-price contracts without an upper limit on risk sharing. Data were collected from 24 software professionals who had project experience with both contract variants. The software professionals were asked to assess the success of their last projects when using each of the two contract variants and their general experience with using them. We found that projects using target-price contracts with an upper limit on risk sharing were, on average, less successful. In addition, the respondents' general experience with the two target-price contract variants suggested that including an upper limit on risk sharing is related to less provider focus on delivering client benefits and with more client effort spent on project administration and monitoring. We conclude that placing more of the financial risk for cost overruns on the provider side tend to increase the likelihood of project problems.

In the Broadband Service Provider (BSP) industry, billing data can originate from various sources like customer subscriptions, payment platforms and network telemetry. The data is often imported using Comma Separated Values (CSV) file uploads from billing systems. To get the best outcome of the Software as a Service (SaaS) platform the data must be cleaned up and standardized prior to feeding to the product. The smallest error rate in billing data can lead to revenue leakage for a business. Poor data hygiene is a leading cause of billing system projects failing to meet their targets. BSPs cannot afford to have data management as a reactive measure anymore. Ensuring data quality before feeding into critical products is imperative for a successful business, however, the scope of such efforts is not always simple. Using automation for cleaning and restructuring of data with Extract, Transform and Load (ETL) tools ensures an effective and reliable methodology of data accuracy. Product adoption is as much reliant on operations and training, as it is on high accuracy in billing and network data. In modern broadband services, having healthy data is key to a strong foothold in the BSP market.

Background and aimOver the past decades, we have witnessed an immense expansion in the arsenal and performance of machine learning (ML) algorithms. One of the most important fields that could benefit from these advancements is biomedical science. To streamline the training and evaluation of binary classifiers, we constructed a universal and flexible ML framework that uses tabular biomedical data as input.Methods and resultsOur framework requires the input data to be provided as a comma-separated values file, in which rows correspond to subjects and columns represent different features. After reading the content of this file, the framework enables the users to perform outlier detection, handle missing values, rescale features, and tackle class imbalance. Then, hyperparameter tuning, feature selection, and internal validation are performed using nested cross-validation. If an additional dataset is available, the framework also provides the option for external validation. Users may also compute SHapley Additive exPlanations values to interpret the individual predictions of the model and identify the most important features. Our ML framework was implemented in Python (version 3.9), and its source code is freely available via GitHub. In the second part of this paper, we also demonstrate the usage of the framework through a case study from the field of cardiovascular imaging.ConclusionsThe proposed ML framework enables the efficient training and evaluation of binary classifiers on tabular biomedical data. We hope our framework will serve as a useful resource for both learning and research purposes and will promote further innovation.

Smart fish farming faces critical challenges in achieving comprehensive automation, real-time decision-making, and adaptability to diverse environmental conditions and multi-species aquaculture. This study presents a novel Internet of Things (IoT)-driven intelligent decision-making system that dynamically monitors and optimizes water quality parameters to enhance fish survival rates across various regions and species setups. The system integrates advanced sensors connected to an ESP32 microcontroller, continuously monitoring key water parameters such as pH, temperature, and turbidity which are increasingly affected by climate-induced variability. A custom-built dataset comprising 43,459 records, covering ten distinct fish species across diverse pond environments, was meticulously curated. The data were stored as a comma-separated values (CSV) file on the IoT cloud platform ThingSpeak and synchronized with Firebase, enabling seamless remote access, control, and real-time updates. Advanced machine learning techniques, with feature transformation and balancing, were applied to preprocess the dataset, which includes water quality metrics and species-specific parameters. Multiple algorithms were trained and evaluated, with the Decision Tree classifier emerging as the optimal model, achieving remarkable performance metrics: 99.8% accuracy, precision, recall, and F1-score, a 99.6% Matthews Correlation Coefficient (MCC), and the highest Area Under the Curve (AUC) score for multi-class classification. Our framework's capability to manage complex, multi-species fishpond environments was validated across diverse setups, showcasing its potential to transform fish farming practices by ensuring sustainable climate-adaptive management through real-time water quality optimization. This study marks a significant step forward in climate-smart aquaculture, contributing to enhanced fish health, survival, and yield while mitigating the risks posed by climate change on aquatic ecosystems.

This dataset was generated from a preclinical study that used the relapsing-remitting experimental autoimmune encephalomyelitis (EAE) model in SJL/J mice to examine lipid signalling in neuroinflammation. The study examined how the reference compounds FTY720 (fingolimod, 0.5 mg/kg/day) affected the autotaxin/lysophosphatidic acid (ATX/LPA) axis and related lipid mediators. The mice were divided into three groups: control (no EAE), EAE, and EAE plus fingolimod. Tissue samples were collected from the plasma, cerebellum, hippocampus, and prefrontal cortex, resulting in 26 biological samples. Targeted lipidomics was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to quantify 62 lipid species, including lysophosphatidic acids, ceramides, sphingoid bases, and endocannabinoids. The dataset is provided in both raw and imputed formats, along with comprehensive sample-level metadata.The data are organized into three comma-separated values (CSV) files: (1) the original quantitative lipidomics data matrix with missing values, (2) a log₁₀-transformed imputed dataset with missing values addressed using random forest imputation, and (3) a metadata file detailing the characteristics of the samples, group assignments, and tissue type. Standardized variable naming and detailed metadata facilitate cross-referencing and integration with other datasets.This resource enables comparative analyses of lipid profiles across tissues and treatment groups. It supports statistical and machine learning applications and enables the evaluation of data augmentation strategies, including statistical and generative AI approaches. This dataset can be reused in studies of neuroinflammation, lipid signalling, and biomarker discovery, as well as in the development of methods for computational biology and omics data analysis.

Classification and prediction tasks are common in health research. With the increasing availability of vast health data repositories (e.g. electronic medical record databases) and advances in computing power, traditional statistical approaches are being augmented or replaced with machine learning (ML) approaches to classify and predict health outcomes. ML describes the automated process of identifying ("learning") patterns in data to perform tasks. Developing an ML model includes selecting between many ML models (e.g. decision trees, support vector machines, neural networks); model specifications such as hyperparameter tuning; and evaluation of model performance. This process is conducted repeatedly to find the model and corresponding specifications that optimize some measure of model performance. ML models can make more accurate classifications and predictions than their statistical counterparts and confer greater flexibility when modelling unstructured data or interactions between covariates; however, many ML models require larger sample sizes to achieve good classification or predictive performance and have been criticized as "black box" for their poor transparency and interpretability. ML holds potential in family medicine for risk profiling of patients' disease risk and clinical decision support to present additional information at times of uncertainty or high demand. In the future, ML approaches are positioned to become commonplace in family medicine. As such, it is important to understand the objectives that can be addressed using ML approaches and the associated techniques and limitations. This article provides a brief introduction into the use of ML approaches for classification and prediction tasks in family medicine.

Software testing is essential in the software development process. Modified Condition / Decision Coverage (MC/DC) is a test case derivation technique that enhances the stability and reliability of software by effectively verifying complex conditions and decision structures. We propose the MCC Test Cases (MTC) generator in this study. This generator generates Multiple Condition Coverage (MCC) test cases using binary numbers to confirm the maximum coverage value of MC/DC testing. The proposed MTC generator utilizes some conditions from the Traffic Alert and Collision Avoidance System (TCAS)-II specification. It converts them into a Comma-Separated Values (CSV) file and then validates the coverage results through the VectorCAST program. So, MC/DC testing was performed using the MCC test case to confirm the maximum coverage value when performing MC/DC tests for each condition of the TCAS-II specification. This research is helpful for the verification of MC/DC test cases by confirming the maximum coverage value when performing MC/DC tests. Moreover, having more test cases increases the likelihood of discovering defects. Therefore, it can improve the efficiency of software test coverage verification, as well as the quality and stability of software.

This study presents an automated workflow for delineating research plot boundaries and creating multiple polygon shape files with unique identifiers for overlaying drone multi-spectral MS imagery with centimetre-level accuracy for obtaining zonal statistics. The study used a high-accuracy real-time kinematic global navigation satellite System (RTK-GNSS) mounted on a planter, georeferenced unmanned aerial vehicle (UAV) imagery, and automated workflows that converted RTK-GNSS points into plot polygons, generated shapefiles, comma-separated values (CSV) files, and maps for downstream analysis. The method enabled efficient zonal statistics extraction with minimal manual adjustments and maintained consistent alignment across temporal datasets. This automated pipeline provided streamlined data processing, reduced labour, enhanced reproducibility, and a scalable open-source approach for high-throughput phenotyping and statistical analysis of breeding trials.

Open-pit mining is very difficult to automate because the bot must be efficient enough to track itself and then extract the minerals even with poor visibility in the mine. In this chapter, we propose methods to make an autonomous bot to work in an open-pit mine. This research is done on wall follower, line follower driving through lane detection, and AprilTag. It compares all the existing techniques and proposes a new approach, i.e., AprilTag. These work much better in comfort driving the bot based on the coordinates of the mining pit. The live coordinates from the camera above the pit are compared with the coordinates of the path on which it was previously driven. These coordinates are stored to and accessed from a comma-separated values (CSV) file. A python-based graphical user interface was also designed to make the process of training and testing the bot more efficient. A walk-through of the same is also provided in this chapter. The autonomous bot is also capable with some fallback conditions to prevent itself from falling inside the pit and getting damaged. The bot exchanges data between Arduino and Raspberry Pi using serial communication. Our proposed approach is suitable for industrial use and robust in real-time scenarios; therefore, it can be applied in modern industrialized systems.

Evolutionary studies require extensive examination of genomic information across all domains of life. Despite the availability of a large number of genomes through GenBank, the effective visualization or comparison of the information they contain is challenging due to many reasons, including their size. We introduce genome-based retrieval and analysis parser, a comprehensive software tool to analyze genome files, and an online database housing an extensive collection of carefully curated, high-quality genome statistics for all the organisms available in the RefSeq database of National Center for Biotechnology Information. Users can either directly search, or select from precategorized groups, the organisms of their choice and retrieve data, and the output is generated as tables containing more than 200 columns of useful genomic information (base counts, GC content, Shannon entropy, codon usage, etc.) separately calculated for different genomic elements (e.g. coding sequences, introns, transfer RNA, ribosomal RNA, noncoding RNA, etc.). The data are independently displayed (if applicable) for each chromosomal, mitochondrial, plastid, or plasmid sequence. All the data can be visualized on the database or downloaded as comma-separated value or Excel files. The genome-based retrieval and analysis parser database is free to access without any registration and is publicly available at http://tacclab.org/gbrap/