Web Framework Research Articles

Web-based Application for Cancerous Object Segmentation in Ultrasound Images Using Active Contour Method

Segmentation, or the process of separating clinical objects from surrounding tissue in medical images, is an important step in the Computer-Aided Diagnosis (CAD) system. The CAD system is developed to assist radiologists in diagnosing cancer malignancy, which in this research is found in ultrasound (US) medical imaging. The manual segmentation process, which cannot be accessed remotely, is a limitation of the CAD system because cancer objects are screened frequently, continuously, and at all times. Therefore, this research aims to build a user-friendly web application called COSION (Cancerous Object Segmentation) that provides easy access for radiologists to segment cancer objects in US images by adopting an active contour method called HERBAC (Hybrid Edge & Region-Based Active Contour). The waterfall method was used to develop the web application with Django as the web framework. The successfully built web application is named Cosion. Cosion was tested on 114 radiology breast and thyroid US images. Functional, portability, efficiency, reliability, expert validation, and usability testing concluded that Cosion runs well and is suitable for use with a functionality value of 0.9375, an average GTmetrix score of 96.43±0.66%, 100% stress testing percentage, 77.5% expert validation, and 75.8% usability. These quantitative performances indicate that the COSION web application is suitable for implementation in the CAD system for US medical imaging.

YÜKSEK GERİLİM LABORATUVARLARI İÇİN IoT TABANLI HAVA İSTASYONU TASARIMI VE UYGULAMASI

This study presents the design and implementation of an internet of things (IoT) based weather station for high voltage laboratories using the Raspberry Pi 4 Model B and two BME680 sensors. The weather station calculates the relative air density and humidity correction coefficients using the temperature, pressure, and relative humidity data obtained from the sensors. The study investigates the effect of the constant and real-time calculation of these coefficients on the measurement of AC, DC and lightning breakdown voltage using spherical electrodes. Measurements were performed within a laboratory setting for a period of 12 hours, and the obtained results were subsequently compared. The findings reveal that the real time calculation of the correction coefficients leads to a reduction in measurement errors. The study also includes the development of a web-based user interface using HTML and CSS, which is hosted on the Raspberry Pi 4 using the Flask web framework. This interface allows users to access the weather station data from any device with a web browser and provides real-time monitoring of the current coefficients, as well as the capability to calculate actual parameters online.

Data-driven quantitative analysis of an integrated open digital ecosystems platform for user-centric energy retrofits: A case study in northern Sweden

This paper presents an open digital ecosystem based on a web-framework with a functional back-end server for user-centric energy retrofits. This data-driven web framework is proposed for building energy renovation benchmarking as part of an energy advisory service development for the Västerbotten region, Sweden. A 4-tier architecture is developed and programmed to achieve users’ interactive design and visualization via a web browser. Six data-driven methods are integrated into this framework as backend server functions. Based on these functions, users can be supported by this decision-making system when they want to know if a renovation is needed or not. Meanwhile, influential factors (input values) from the database that affect energy usage in buildings are to be analyzed via quantitative analysis, i.e., sensitivity analysis. The contributions to this open ecosystem platform in energy renovation are: 1) A systematic framework that can be applied to energy efficiency with data-driven approaches, 2) A user-friendly web-based platform that is easy and flexible to use, and 3) integrated quantitative analysis into the framework to obtain the importance among all the relevant factors. This computational framework is designed for stakeholders who would like to get preliminary information in energy advisory. The improved energy advisor service enabled by the developed platform can significantly reduce the cost of decision-making, enabling decision-makers to participate in such professional knowledge-required decisions in a deliberate and efficient manner. This work is funded by the AURORAL project, which integrates an open and interoperable digital platform, demonstrated through regional large-scale pilots in different countries of Europe by interdisciplinary applications.

Osteosarcoma Explorer: A Data Commons With Clinical, Genomic, Protein, and Tissue Imaging Data for Osteosarcoma Research.

Osteosarcoma research advancement requires enhanced data integration across different modalities and sources. Current osteosarcoma research, encompassing clinical, genomic, protein, and tissue imaging data, is hindered by the siloed landscape of data generation and storage. Clinical, molecular profiling, and tissue imaging data for 573 patients with pediatric osteosarcoma were collected from four public and institutional sources. A common data model incorporating standardized terminology was created to facilitate the transformation, integration, and load of source data into a relational database. On the basis of this database, a data commons accompanied by a user-friendly web portal was developed, enabling various data exploration and analytics functions. The Osteosarcoma Explorer (OSE) was released to the public in 2021. Leveraging a comprehensive and harmonized data set on the backend, the OSE offers a wide range of functions, including Cohort Discovery, Patient Dashboard, Image Visualization, and Online Analysis. Since its initial release, the OSE has experienced an increasing utilization by the osteosarcoma research community and provided solid, continuous user support. To our knowledge, the OSE is the largest (N = 573) and most comprehensive research data commons for pediatric osteosarcoma, a rare disease. This project demonstrates an effective framework for data integration and data commons development that can be readily applied to other projects sharing similar goals. The OSE offers an online exploration and analysis platform for integrated clinical, molecular profiling, and tissue imaging data of osteosarcoma. Its underlying data model, database, and web framework support continuous expansion onto new data modalities and sources.

A Blockchain-Based Semantic Exchange Framework for Web 3.0 Toward Participatory Economy

Web 3.0 is the next-generation Internet that enables participants to read, write, and own contents in a decentralized manner. It is mainly driven by blockchain, semantic communication, edge computing, and artificial intelligence, which can construct value networks to achieve participatory economics based on participatory decision making. Web 3.0 can capture the characteristics of blockchain, semantic extraction, and communication to achieve decentralized semantic sharing and transfer information precisely. However, current Web 3.0 solutions focus on the blockchain while overlooking other new technologies' roles in Web 3.0. To further unleash the advantages of semantic extraction and communication in Web 3.0, in this paper, we propose a blockchain-based semantic exchange framework to realize fair and efficient interactions. In this framework, we first attempt to tokenize semantic information into Non-Fungible Token (NFT) for semantic exchange. Then we utilize a Stackelberg game to maximize buying and pricing strategies for semantic trading. We also leverage Zero-Knowledge Proof to share authentic semantic information without publishing it before receiving payments, which can achieve a fair and privacy-preserving trading compared with current NFT marketplaces. A case study about urban planning is given to show clearly the proposed mechanisms. Finally, several challenges and opportunities are identified.

Agile Machine Learning Model Development Using Data Canyons in Medicine: A Step towards Explainable Artificial Intelligence and Flexible Expert-Based Model Improvement

Over the past few decades, machine learning has emerged as a valuable tool in the field of medicine, driven by the accumulation of vast amounts of medical data and the imperative to harness this data for the betterment of humanity. However, many of the prevailing machine learning algorithms in use today are characterized as black-box models, lacking transparency in their decision-making processes and are often devoid of clear visualization capabilities. The transparency of these machine learning models impedes medical experts from effectively leveraging them due to the high-stakes nature of their decisions. Consequently, the need for explainable artificial intelligence (XAI) that aims to address the demand for transparency in the decision-making mechanisms of black-box algorithms has arisen. Alternatively, employing white-box algorithms can empower medical experts by allowing them to contribute their knowledge to the decision-making process and obtain a clear and transparent output. This approach offers an opportunity to personalize machine learning models through an agile process. A novel white-box machine learning algorithm known as Data canyons was employed as a transparent and robust foundation for the proposed solution. By providing medical experts with a web framework where their expertise is transferred to a machine learning model and enabling the utilization of this process in an agile manner, a symbiotic relationship is fostered between the domains of medical expertise and machine learning. The flexibility to manipulate the output machine learning model and visually validate it, even without expertise in machine learning, establishes a crucial link between these two expert domains.

DESAIN APLIKASI ABSENSI SANTRI PONDOK ABDUL DHOHIR MENGGUNAKAN PENGENALAN WAJAH BERBASIS WEB

The web-based face recognition application will provide convenience for students to LDII organizations or Islamic boarding school, it is possible for the students themselves to give their best in carrying out their activities. Attendance is crucial to any organization. Where participation is one of the key factors that may encourage and support any action that will take place. Operating the attendance system manually at the moment, namely utilizing manual paper. This might lead to a number of errors.and unhygienic times during the pandemic, example is the inefficiency of utilizing paper, forgetting to fill in the absences, missing paper used and using pens alternately causing the spread of the COVID-19 virus. By using Face Recognition, students only show the face to be scanned using a webcam provided by the boarding school teacher. The strategy utilized in this study is rapid application development using modeling using the Unified Modeling Language (UML). The Django web framework's tools were then used to create this application, Postgresql and Python programming. This application was built with the aim of making it easier for boarding school teacher and students.

TBX and ‘Lemon’: What perspectives in terminology?

Abstract Different solutions are offered today for modelling multilingual terminological data. In this article, we focus on the description of two approaches: on the one hand, the model proposed in the context of ISO TC 37/SC 3, based on the adoption of the Terminological Markup Framework/TermBase eXchange standards; on the other hand, the ‘Lemon’ model and, more generally, the Ontology Web Language adopted in the framework of the Semantic Web. The aim of this study is to propose a contrastive multilevel analysis between these two paradigms, with the ultimate goal of highlighting their divergences and convergences. The terminological case study chosen to test the two approaches is represented by the pathology known as ‘body dysmorphic disorder’, which poses challenges in terms of conceptual and linguistic representation. Starting from the phenomena of reconceptualization and denominative variation of this disorder, we will show that the two models are based on diverging but complementary theoretical and methodological perspectives.

A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron.

Osteosarcoma is the most common type of bone cancer that tends to occur in teenagers and young adults. Due to crowded context, inter-class similarity, inter-class variation, and noise in H&E-stained (hematoxylin and eosin stain) histology tissue, pathologists frequently face difficulty in osteosarcoma tumor classification. In this paper, we introduced a hybrid framework for improving the efficiency of three types of osteosarcoma tumor (nontumor, necrosis, and viable tumor) classification by merging different types of CNN-based architectures with a multilayer perceptron (MLP) algorithm on the WSI (whole slide images) dataset. We performed various kinds of preprocessing on the WSI images. Then, five pre-trained CNN models were trained with multiple parameter settings to extract insightful features via transfer learning, where convolution combined with pooling was utilized as a feature extractor. For feature selection, a decision tree-based RFE was designed to recursively eliminate less significant features to improve the model generalization performance for accurate prediction. Here, a decision tree was used as an estimator to select the different features. Finally, a modified MLP classifier was employed to classify binary and multiclass types of osteosarcoma under the five-fold CV to assess the robustness of our proposed hybrid model. Moreover, the feature selection criteria were analyzed to select the optimal one based on their execution time and accuracy. The proposed model achieved an accuracy of 95.2% for multiclass classification and 99.4% for binary classification. Experimental findings indicate that our proposed model significantly outperforms existing methods; therefore, this model could be applicable to support doctors in osteosarcoma diagnosis in clinics. In addition, our proposed model is integrated into a web application using the FastAPI web framework to provide a real-time prediction.

MetaPro: a web-based metabolomics application for LC-MS data batch inspection and library curation

IntroductionMetabolomics analysis based on liquid chromatography-mass spectrometry (LC-MS) has been a prevalent method in the metabolic field. However, accurately quantifying all the metabolites in large metabolomics sample cohorts is challenging. The analysis efficiency is restricted by the abilities of software in many labs, and the lack of spectra for some metabolites also hinders metabolite identification.ObjectivesDevelop software that performs semi-targeted metabolomics analysis with an optimized workflow to improve quantification accuracy. The software also supports web-based technologies and increases laboratory analysis efficiency. A spectral curation function is provided to promote the prosperity of homemade MS/MS spectral libraries in the metabolomics community.MethodsMetaPro is developed based on an industrial-grade web framework and a computation-oriented MS data format to improve analysis efficiency. Algorithms from mainstream metabolomics software are integrated and optimized for more accurate quantification results. A semi-targeted analysis workflow is designed based on the concept of combining artificial judgment and algorithm inference.ResultsMetaPro supports semi-targeted analysis workflow and functions for fast QC inspection and self-made spectral library curation with easy-to-use interfaces. With curated authentic or high-quality spectra, it can improve identification accuracy using different peak identification strategies. It demonstrates practical value in analyzing large amounts of metabolomics samples.ConclusionWe offer MetaPro as a web-based application characterized by fast batch QC inspection and credible spectral curation towards high-throughput metabolomics data. It aims to resolve the analysis difficulty in semi-targeted metabolomics.

Winnowing Algorithm: A Powerful Tool for Identifying Plagiarism in Assignments

Plagiarism refers to using other ideas or works as their own without giving proper acknowledgment. The act of plagiarism is inappropriate and untrue for many reasons, especially in the academic world. Academicians are aware of this and try to avoid the act of plagiarism by any means necessary. In the present context, the digital way of teaching and learning is in practice which has more chance of plagiarized content. This research provides plagiarism detection features due to the lack of such features in digital-based teaching-learning activities. This proposed system handles the document in text format and uses Winnowing Algorithm for fingerprinting the assignment documents, and the hashing technique chosen for this algorithm is the Rolling Hash function. The similarity value is calculated using Jaccard coefficient. The test results show the combinations of parameters (n-gram, window length, and the base prime number) for the successful implementation of the system. The system successfully detects plagiarism on student assignments. The overall system is developed by using Python Web Framework Django and MySQL as a database.

Development of a Web-Based Kasem Language Learning System

The concept of language involves using words in an organized and traditional way, be it through speaking, writing, or gesturing, as a means of human communication. The inability to communicate efficiently in a specific language is known as language barrier. Such limitation can result in difficulties performing everyday tasks and create challenges on a global levels, such as when businesses move to other nations or on a local level, like when a family or student moves to a region where English is not the primary language. The effects of linguistic barriers can negatively impact the quality of life for everyone involved, regardless of the context. In this paper, the researchers have designed and implemented a web-based Kasem language learning system (KLLS). The technology provides audio output, with corresponding text in the Kasem language to the end user, allowing them to study and become fluent in it. High-level Python web framework, Django, was used to develop and implement the system. Finally, a series of tests were conducted by different levels of users. Each end user reported a high level of satisfaction with the system after navigating through and receiving a clear audio output.

Student Feedback System using Flask

The student feedback system is used to get student feedback about the staff through the web-based application. This Student Feedback System is used to determine whether students understand the teaching concepts or not, whether their performance is going well or poorly, and how they should direct their subsequent efforts. In this way, feedback is essential to scholars' learning and growth. Flask and MySQL technologies are used to develop this student feedback system. Flask is a web Framework used to develop the web application and MySQL is the Relational Database that is used to store the data and manipulate data. This student feedback system is very useful for schools and colleges to get staff feedback. Four characters are involved in this application. They are admin, hod, staff, and student, and then this application consists of the four important modules: the admin module, the hod module, the staff module, and the student module. The admin can add the HOD to each department, and the admin can view all the students' feedback. This process was managed by the Admin Module. The HOD can add the staff to their department, and the HOD can view only their department’s student feedback. This process was managed by the HOD module. The Staff can add the students and only can view their feedback. They cannot see other staff's feedback. This process was managed by the Staff Module. Finally, Students can only provide feedback to their staff. This process was handled by the student module. These are all the four important modules and characteristics of this application

A web framework for information aggregation and management of multilingual hate speech

Social media platforms have led to the creation of a vast amount of information produced by users and published publicly, facilitating participation in the public sphere, but also giving the opportunity for certain users to publish hateful content. This content mainly involves offensive/discriminative speech towards social groups or individuals (based on racial, religious, gender or other characteristics) and could possibly lead into subsequent hate actions/crimes due to persistent escalation. Content management and moderation in big data volumes can no longer be supported manually. In the current research, a web framework is presented and evaluated for the collection, analysis, and aggregation of multilingual textual content from various online sources. The framework is designed to address the needs of human users, journalists, academics, and the public to collect and analyze content from social media and the web in Spanish, Italian, Greek, and English, without prior training or a background in Computer Science. The backend functionality provides content collection and monitoring, semantic analysis including hate speech detection and sentiment analysis using machine learning models and rule-based algorithms, storing, querying, and retrieving such content along with the relevant metadata in a database. This functionality is assessed through a graphic user interface that is accessed using a web browser. An evaluation procedure was held through online questionnaires, including journalists and students, proving the feasibility of the use of the proposed framework by non-experts for the defined use-case scenarios.

Secure QR-Code Based Message Sharing System Using Cryptography and Steganography

Abstract: Numerous cryptographic methods are accessible for filling the need of data security over the web, servers, and neighbourhood frameworks. Be that as it may, there is consistently request of greater security which may not be meet by such cryptographic calculations alone due to realized security assaults and numerical intricacy. Consequently, envisioning the essential mix of cryptography and steganography strategies can give a more significant level of safety. Speedy Reaction (QR) codes are utilized broadly because of their advantageous attributes.

Visualising and Forecasting Stock Prices with Flask

Abstract: Stock price forecasting and visualisation are critical tasks for investors and traders in financial markets. In this project, we have developed a web application that can visualise stock prices of the company in form of charts and predicts future stock prices using machine learning algorithms. The web application is built using Flask(python), a popular web framework, and integrates several APIs and libraries, including News API, Alpha Vantage API, Beautiful Soup (bs4), Pandas, Numpy, Plotly, and Scikit-learn. The application provides users with a basic overview of the company, current prices of the stock, news related to the company, and visualisations of historical stock prices in the form of charts. The historical stock prices are fetched using Alpha Vantage API, and the charts are generated using Plotly library. The web application also includes a predictive model built using Support Vector Regression (SVR) algorithm which forecasts the stock prices. This paper demonstrates the potential of machine learning algorithms and web technologies in the field of stock price forecasting and visualisation. The web application developed, provides users with valuable insights and information related to stock prices, and it can be used as a powerful tool in financial markets.

Troid: A Real-Time Multiplayer FPS Game

Abstract: Troid is a real-time multiplayer first-person shooter (FPS) game designed to be played on a web browser without the need for a high-end PC or dedicated graphics card. The game allows two players to join a room and engage in a fast-paced, action-packed gameplay experience. The purpose of Troid is to provide a high-quality FPS game that can be enjoyed on any device with a modern web browser and an internet connection, regardless of hardware specifications. Unlike most FPS games that require high-end hardware to run smoothly, Troid is optimized for minimal requirements. Troid's front-end is built using a reactive web framework that facilitates the creation of fast and interactive user interfaces, while the game's graphics and animation are created using a 3D graphics library. Real-time communication between players is facilitated using a reliable real-time communication library, and an in-memory data store is utilized to store player coordinates and other in-game data. Overall, Troid exemplifies the potential of creating highquality multiplayer games that can be played on any device with a modern web browser and an internet connection, without compromising on the gameplay experience.

Web Interface of NER and RE with BERT for Biomedical Text Mining

The BioBERT Named Entity Recognition (NER) model is a high-performance model designed to identify both known and unknown entities. It surpasses previous NER models utilized by text-mining tools, such as tmTool and ezTag, in effectively discovering novel entities. In previous studies, the Biomedical Entity Recognition and Multi-Type Normalization Tool (BERN) employed this model to identify words that represent specific names, discern the type of the word, and implement it on a web page to offer NER service. However, we aimed to offer a web service that includes Relation Extraction (RE), a task determining the relation between entity pairs within a sentence. First, just like BERN, we fine-tuned the BioBERT NER model within the biomedical domain to recognize new entities. We identified two categories: diseases and genes/proteins. Additionally, we fine-tuned the BioBERT RE model to determine the presence or absence of a relation between the identified gene–disease entity pairs. The NER and RE results are displayed on a web page using the Django web framework. NER results are presented in distinct colors, and RE results are visualized as graphs in NetworkX and Cytoscape, allowing users to interact with the graphs.

Haplogrep 3 -aninteractive haplogroup classification and analysis platform.

Over the last decade, Haplogrep has become a standard tool for haplogroup classification in the field of human mitochondrial DNA and is widely used by medical, forensic, and evolutionary researchers. Haplogrep scales well for thousands of samples, supports many file formats and provides an intuitive graphical web interface. Nevertheless, the currently available version has limitations when applying it to large biobank-scale data. In this paper, we present a major upgrade to the software by adding (a) haplogroup summary statistics and variant annotations from various publicly available genome databases, (b) an interface to connect new phylogenetic trees, (c) a new state-of-the-art web framework managing large scale data, (d) algorithmic adaptions to improve FASTA classification using BWA-specific alignment rules and (e) a pre-classification quality control step for VCF samples. These improvements will give researchers the opportunity to classify thousands of samples as usual but providing additional ways to investigate the dataset directly in the browser. The web service and its documentation can be accessed freely without any registration at https://haplogrep.i-med.ac.at.

PERFORMANCE ANALYSIS BETWEEN INTERPRETED LANGUAGE-BASED (LARAVEL) AND COMPILED LANGUAGE-BASED (GIN) WEB FRAMEWORKS

Web frameworks are essential for web developers, allowing them to build web applications more efficiently and effectively. This research paper compared two web frameworks with different programming language implementations: interpreted programming language and compiled programming language. PHP/Laravel was chosen as the representative interpreted programming language, and Go/Gin was chosen as the representative compiled programming language, because they were the most popular based on their star counts on GitHub. Based on a research paper by the University of Minho, Go was nearly 10 times faster than PHP in terms of execution time. Despite this, PHP is still the most commonly used web technology among the top 1 million websites. The results of a series of tests comparing the two showed that Gin was over 86 times faster than Laravel in terms of requests per second and time per request. The choice of which web framework to use will depend on the specific needs and goals of the project. However, if performance is the only criterion, then Gin is a better choice than Laravel.