18 skills found
dotnetcore / Sharding Corehigh performance lightweight solution for efcore sharding table and sharding database support read-write-separation .一款ef-core下高性能、轻量级针对分表分库读写分离的解决方案，具有零依赖、零学习成本、零业务代码入侵
eloqdata / EloqdocFully elastic, MongoDB API compatible distributed JSON document database with compute-storage separation and robust ACID transactions.
fgnt / Sms WsjSMS-WSJ: Spatialized Multi-Speaker Wall Street Journal database for multi-channel source separation and recognition
chengdedeng / Perseus:zap:database read and write separation of java
mss-boot-io / Mss Boot AdminA front-end and back-end separation authority management system based on Gin, React, Ant Design v5, Umi v4, and mss-boot. Initialized with an environment variable, it supports multiple configuration sources, simplifies database setup, and eases service startup. Features include multi-tenancy, roles, users, menus, internationalization, system config
dioKaratzas / Fluent Dto MacroA Swift macro that simplifies handling Vapor Fluent models in API responses by auto-generating type-safe content structures, reducing boilerplate while keeping a clean separation between database models and API layers. 🚀
VakinduPhilliam / NodeJS Clean ArchitectureNodeJS Clean Architecture. Clean Architecture is an opinionated boilerplate for Node web APIs focused on separation of concerns and scalability with Uncle Bob Clean Architecture implementation. Features: > Union - Layered folder structure: code organization focused on codebase scalability. > Instant feedback and reload: use Nodemon to automatically reload the server after a file change when on development mode, makes the development faster and easier. > Scalable and easy to use web server. > Use Express for requests routing and middlewares. There are some essential middlewares for web APIs already setup, like body-parser, compression, and method-override. > Database integration: Mongoose, a MongoDB Database Connection, is already integrated; you just have to set the authentication configurations. > Prepared for testing: The test suite uses Mocha, Chai and is prepared to run unit, integration and functional tests right from the beginning. A FactoryGirl adapter for Mongo is setup to make your tests DRY as well, and the tests generate code coverage measurement with. You should read about the Chai plugins that are setup by default too. > Dependency injection: With Awilix, a practical dependency injection library, the code will not be coupled and it'll still be easy to resolve automatically the dependencies on the runtime and mock them during the tests. It's even possible inject dependencies on your controllers with the Awilix Express adapter. >Logging: The Log4js logger is highly pluggable, being able to append the messages to a file during the development and send them to a logging service when on production. Even the requests (through Morgan) and queries will be logged. > Linter: It's also setup with ESLint to make it easy to ensure a code styling and find code smells. How to use: Notice that the boilerplate comes with a small application for user management already; you can delete it with a npm script after you understand how the boilerplate works but please do the quick start first! 1. Clone the repository. 2. Setup the database on `config/database.js` (there's an example file there to be used with MongoDB). 3. Install the dependencies with `yarn`. 4. Create the development and test databases you have setup on `config/database.js` 5. Run the application in development mode with `npm run dev` 6. Access `http://localhost:3000/api/users` and you're ready to go! Compiled and presented by Vakindu Philliam.
spences10 / MCP Sqlite ToolsA Model Context Protocol (MCP) server that provides comprehensive SQLite database operations for LLMs. This server enables AI assistants to interact with local SQLite databases safely and efficiently, with built-in security features, advanced transaction support, and clear separation between read-only and destructive operations.
sfneal / Mysql ToolkitSyntax free MySQL toolkit... Build sophisticated queries programmatically, no need for tedious string manipulation. Handle's a remote MySQL database connection with a context manager, call almost any SQL query through a single import. Execute large SQL script (100mb+) on remote database, automatic command recognition, separation, and sequential execution.
winechit-dev / Tmdb AndroidA native Android application that allows users to browse and discover movies using The Movie Database (TMDB) API. The app follows clean architecture principles with a clear separation of concerns. The project is organized into multiple modules to enhance maintainability and scalability
shishirdas / Breast Cancer PredictionContent Past Usage: Attributes 2 through 10 have been used to represent instances. Each instance has one of 2 possible classes: benign or malignant. Wolberg,~W.~H., \& Mangasarian,~O.~L. (1990). Multisurface method of pattern separation for medical diagnosis applied to breast cytology. In {\it Proceedings of the National Academy of Sciences}, {\it 87}, 9193--9196. -- Size of data set: only 369 instances (at that point in time) -- Collected classification results: 1 trial only -- Two pairs of parallel hyperplanes were found to be consistent with 50% of the data -- Accuracy on remaining 50% of dataset: 93.5% -- Three pairs of parallel hyperplanes were found to be consistent with 67% of data -- Accuracy on remaining 33% of dataset: 95.9% Zhang,~J. (1992). Selecting typical instances in instance-based learning. In {\it Proceedings of the Ninth International Machine Learning Conference} (pp. 470--479). Aberdeen, Scotland: Morgan Kaufmann. -- Size of data set: only 369 instances (at that point in time) -- Applied 4 instance-based learning algorithms -- Collected classification results averaged over 10 trials -- Best accuracy result: -- 1-nearest neighbor: 93.7% -- trained on 200 instances, tested on the other 169 -- Also of interest: -- Using only typical instances: 92.2% (storing only 23.1 instances) -- trained on 200 instances, tested on the other 169 Relevant Information: Samples arrive periodically as Dr. Wolberg reports his clinical cases. The database therefore reflects this chronological grouping of the data. This grouping information appears immediately below, having been removed from the data itself: Group 1: 367 instances (January 1989) Group 2: 70 instances (October 1989) Group 3: 31 instances (February 1990) Group 4: 17 instances (April 1990) Group 5: 48 instances (August 1990) Group 6: 49 instances (Updated January 1991) Group 7: 31 instances (June 1991) Group 8: 86 instances (November 1991) Total: 699 points (as of the donated datbase on 15 July 1992) Note that the results summarized above in Past Usage refer to a dataset of size 369, while Group 1 has only 367 instances. This is because it originally contained 369 instances; 2 were removed. The following statements summarizes changes to the original Group 1's set of data: Group 1 : 367 points: 200B 167M (January 1989) Revised Jan 10, 1991: Replaced zero bare nuclei in 1080185 & 1187805 Revised Nov 22,1991: Removed 765878,4,5,9,7,10,10,10,3,8,1 no record : Removed 484201,2,7,8,8,4,3,10,3,4,1 zero epithelial : Changed 0 to 1 in field 6 of sample 1219406 : Changed 0 to 1 in field 8 of following sample: : 1182404,2,3,1,1,1,2,0,1,1,1 Number of Instances: 699 (as of 15 July 1992) Number of Attributes: 10 plus the class attribute Attribute Information: (class attribute has been moved to last column) Attribute Domain Sample code number id number Clump Thickness 1 - 10 Uniformity of Cell Size 1 - 10 Uniformity of Cell Shape 1 - 10 Marginal Adhesion 1 - 10 Single Epithelial Cell Size 1 - 10 Bare Nuclei 1 - 10 Bland Chromatin 1 - 10 Normal Nucleoli 1 - 10 Mitoses 1 - 10 Class: (2 for benign, 4 for malignant) Missing attribute values: 16 There are 16 instances in Groups 1 to 6 that contain a single missing (i.e., unavailable) attribute value, now denoted by "?". Class distribution: Benign: 458 (65.5%) Malignant: 241 (34.5%) Acknowledgements O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18. William H. Wolberg and O.L. Mangasarian: "Multisurface method of pattern separation for medical diagnosis applied to breast cytology", Proceedings of the National Academy of Sciences, U.S.A., Volume 87, December 1990, pp 9193-9196. O. L. Mangasarian, R. Setiono, and W.H. Wolberg: "Pattern recognition via linear programming: Theory and application to medical diagnosis", in: "Large-scale numerical optimization", Thomas F. Coleman and Yuying Li, editors, SIAM Publications, Philadelphia 1990, pp 22-30. K. P. Bennett & O. L. Mangasarian: "Robust linear programming discrimination of two linearly inseparable sets", Optimization Methods and Software 1, 1992, 23-34 (Gordon & Breach Science Publishers). Inspiration Rouse Tek Bio informatics Cytogenomics Project is an attempt to bring the human genome to the understanding of how cancers develop. All of our bodies are composed of cells. The human body has about 100 trillion cells within it. And usually those cells behave in a certain fashion. They observe certain rules, they divide when they’re told to divide, they’re quiescent when they’re told to remain dormant, they stay within a particular position within their tissue and they don’t move out of that. Occassionally however, a single cell, of those 100 trillion cells, behave in a different way. That cell keeps dividing when all its signals around it tell it to stop dividing. That cell ignores its counterparts around it and pushes them out of the way. That cell stops observing the rules of the tissue within which it is located and begins to move out of its normal position, invading into the tissues around it and sometimes entering the bloodstream and becoming a metastasis, depositing in another tissue of the body.. The reason the cell has gone rogue is because it has acquired within its genome, within its DNA, a number of abnormalities that cause it to behave as a cancer cell. All 100 trillion cells in the human body have got a copy of the human genome, they have 2 copies, 1 maternal, 1 paternal. Throughout Life all those copies of the genome in those 100 trillion cells, are acquiring abnormal changes or somatic mutations. These mutations are present in the cell and are not transmitted from parents to offspring. They are constrained to that individual cell. Those mutations occur in every cell of the body, normal and abnormal, for a number of different reasons. They occur because every time a cell divides possibly one letter of code out of 3 billion is replicated incorrectly. And that’s 1 source of somatic mutations. Another source is that our 100 trillion cells are being exposed to a number of different onslaughts like radiation, self generated chemicals from inhalation of things like tobacco smoke or even an unhealthy diet over time. Occasionally mechanisms in a particular cell make breakdown and the DNA of that cell begins to acquire somatic mutations rather more commonly than other cells. So in summary, every cell in the body acquires mutations throughout a lifetime, and as we get older we acquire more and more somatic mutations in which occasionally a particular type of gene is mutated where the protein that it makes is abnormal and drives the cell to behave in a rogue fashion that we call cancer.
JackMin1314 / Vue Admin SpiderThe project adopts the front-end and back-end separation design pattern. Back-end uses the Python Flask framework to provide data and permission verification, database backup, logging, timing tasks and email sending etc. Front-end both focus on UI and Service used Vue、Vue router、element-ui 、axios, etc.
pratishtha-agarwal / Automation Of Attendance Montoring SystemIt performs Facial recognition with high accuracy. This attendance project uses webcam to detect faces and records the attendance live in an excel sheet. In order to determine the distinctive aspects of the faces based on distance, convolutional neural networks are used. All you need to do is stand in front of the camera and your face is verified instantly in milliseconds, without recording the attendance more than once. Facial recognition systems are commonly used for verification and security purposes but the levels of accuracy are still being improved. Errors occurring in facial feature detection due to occlusions, pose and illumination changes can be compensated by the use of hog descriptors. The most reliable way to measure a face is by employing deep learning techniques. The final step is to train a classifier that can take in the measurements from a new test image and tells which known person is the closest match. A python based application is being developed to recognize faces in all conditions. We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.
alextanhongpin / Node RestA solid architecture for designing scalable RESTful apis, with auto-reload, validation, async/await support, dependency injection, clean separation of business logic from transport and testable code without mocking http requests and database call.
chenquan / Zero Sqlxzero-sqlx is a database orm framework based on go-zero implementation that supports read/write separation between leader and follower databases.
kardasch404 / MVCThis project aims to design a clean and modular MVC architecture in PHP, using PostgreSQL as the database. The goal is to have a strict separation of responsibilities, a Back Office for administration, and a Front Office for public display. The architecture should be extensible, secure, and well-structured, applying best development practices
yuexinok / QuerydbGolang for the MySQL query builder, support master-slave configuration, read-write separation, support library configuration. Referring to PHP Laravel framework database, it is simple to use, and database/sql is simply packaged.
tapan0810 / DotNet Api Practise 02This project is a RESTful Web API built using ASP.NET Core and Entity Framework Core, designed to practice real-world API development concepts. It demonstrates clean architecture with Controller–Service separation, DTO-based data transfer, and asynchronous CRUD operations backed by a relational database.
