Search Results for "predictor"
Sort By:
Showing 50 open source projects for "predictor"
View related business solutions-
8 Monitoring Tools in One APM. Install in 5 Minutes.AppSignal works out of the box for Ruby, Elixir, Node.js, Python, and more. 30-day free trial, no credit card required.
-
Shoplogix Smart Factory PlatformReal-time Visibility into Your Shop Floor's Performance. The Shoplogix smart factory platform enables manufacturers to increase overall equipment effectiveness, reduce operational costs, sustain growth and improve profitability by allowing them to visualize, integrate and act on production and machine performance in real-time. Manufacturers that trust us to drive efficiency in their factories. Real-time visual data and analytics provide valuable insights to make better informed decisions. Uncover hidden shop floor potential and drive rapid time to value. Develop a continuously improving culture through training, education and data-driven decisions. Compete in the i4.0 world by making the Shoplogix Smart Factory Platform the cornerstone of your digital transformation. Connect to any equipment or device to automate data collection and exchange it with other manufacturing technologies. Automatically monitor, report and analyze machine states to track real-time production.
-
1
Aviator Predictor
Aviator hack: seed-inspection of aviator crash predictor & aviator app
Our downloadable SHA256 analysis tool powers the Aviator predictor, Aviator predictor app, and aviator crash predictor. Available for desktop, it’s designed for research, fairness verification, and safe demo testing Demo-focused aviator predictor tools — seed-inspection helpers (SHA-512 / SHA-256), AI-assisted summaries, and demo bot templates for aviator crash predictor, Start in demo mode to test safely. -
2
Stake-predictor
The Stake Crash Predictor is a tool designed to predict the next multi
The Stake Crash Predictor is a tool designed to predict the next multiplier number in crash game on stake. It utilizes advanced machine learning techniques to provide predictions for crash game depending on the api data. Features Data Analysis: The predictor leverages historical market data to identify patterns and trends that may indicate an upcoming crash. -
3
Stake Crash Predictor
Stake Crash Predictor is a toolkit for stake mines predictor & Plinko.
The Stake Crash Predictor is a focused toolkit that combines statistical analysis, optional server fairness seed hash decrypt helpers, and AI-assisted summaries to help you study rounds on Stake.us. This project centers on the stake mines predictor and stake predictor workflows Demo-focused stake crash predictor app — seed-inspection helpers (SHA-512 / SHA-256), AI-assisted summaries, and demo bot templates for stake mines predictor too, Start in demo mode to test safely. ... -
4
Aviator-Predictor-v3
Aviator Predictor V3.0 - Tynix
The Aviator Predictor is a powerful application that utilizes advanced algorithms to analyze historical game data. It offers users real-time predictions, making it easier to decide when to place bets. Features Cross-Platform Compatibility: The app is available for Android, iOS, and Windows, ensuring that you can use it on your preferred device. -
Create and manage the email signature you needWith WiseStamp it’s easy to unify your brand and turn your emails into a powerful marketing tool. Get the most out of your emails with a professionally designed custom email signature.
-
5
vcf2maf
Convert a VCF into a MAF
vcf2maf is a Perl-based tool developed by the Memorial Sloan Kettering Cancer Center to convert Variant Call Format (VCF) files into Mutation Annotation Format (MAF) files. This conversion standardizes variant annotations, facilitating downstream analyses in cancer genomics. -
6
-
7
LogD_Predictor
Models to LogD Predictor Software
The repository contains a packaged set of machine-learning models and neural networks for predicting logD from NMR spectra and molecular fingerprints. For more details, check the Github repository: https://github.com/Prospero1988/logD_predictor -
8
GLM.jl
Generalized linear models in Julia
...The package supports modeling through both formula-based (e.g. @formula) and matrix-based interfaces, allowing both high-level convenience and low-level control. Under the hood, GLM.jl separates the linear predictor and response objects, allowing flexible combinations of link functions, variance structures, and fitting methods. -
9
plexe
Build a machine learning model from a prompt
plexe lets you build machine-learning systems from natural-language prompts, turning plain English goals into working pipelines. You describe what you want—a predictor, a classifier, a forecaster—and the tool plans data ingestion, feature preparation, model training, and evaluation automatically. Under the hood an agent executes the plan step by step, surfacing intermediate results and artifacts so you can inspect or override choices. It aims to be production-minded: models can be exported, versioned, and deployed, with reports to explain performance and limitations. ... -
Stigg | SaaS Monetization and Entitlements APIA monetization platform is a standalone middleware that sits between your application and your business applications, as part of the modern enterprise billing stack. Stigg unifies all the APIs and abstractions billing and platform engineers had to build and maintain in-house otherwise. Acting as your centralized source of truth, with a highly scalable and flexible entitlements management, rolling out any pricing and packaging change is now a self-service, risk-free, exercise.
-
10
DocTR
Library for OCR-related tasks powered by Deep Learning
...Seemlessly process documents for Natural Language Understanding tasks: we provide OCR predictors to parse textual information (localize and identify each word) from your documents. Robust 2-stage (detection + recognition) OCR predictors with pretrained parameters. User-friendly, 3 lines of code to load a document and extract text with a predictor. State-of-the-art performances on public document datasets, comparable with GoogleVision/AWS Textract. Easy integration (available templates for browser demo & API deployment). End-to-End OCR is achieved in docTR using a two-stage approach: text detection (localizing words), then text recognition (identify all characters in the word). ... -
11
AIMOON AI Predictor
AI-powered predictor for crash games, slots, and casino games.
AIMOON AI Predictor is a specialized artificial intelligence system designed to forecast outcomes in online casino games, including crash games (Aviator, Spaceman, Lucky Jet), slots, roulette, blackjack, and even sports/esports betting. The AI analyzes game patterns in real time to predict the optimal cash-out point or round result. The system integrates with major game providers such as PG Soft, TaDa Gaming, Pragmatic Play, and Hacksaw Gaming. -
12
Dice Bot
predict outcomes on the well-known gambling site Dice-Bot.
Overview Dice-Bot Predictor is a software tool designed to predict outcomes on the well-known gambling site Dice-Bot. This tool is intended to help users make informed decisions when participating in gambling activities on Dice-Bot. Features Trial Version: You can use Dice-Bot Predictor for a trial period of 2 hours to try out its features. Windows Support: Dice-Bot Predictor is compatible with Windows operating systems. -
13
PLEK
predictor of long non-coding RNAs and mRNAs based on k-mer scheme
INSTALLATION Pre-requisite: 1. Linux 2. C/C++ compiler (i.e. gcc, g++) 3. Python 2.5.0 or later versions (http://www.python.org/) Steps: 1. Download PLEK.1.2.tar.gz from https://sourceforge.net/projects/plek/files/ and decompress it. $ tar zvxf PLEK.1.2.tar.gz 2. Compile PLEK. $ cd PLEK.1.2 $ python PLEK_setup.py USAGE python PLEK.py -fasta fasta_file -out output_file -thread number_of_threads -minlength min_length_of_sequence -isoutmsg 0_or_1... -
14
MFACE
A high-resolution model of field-aligned currents
MFACE is a high-resolution field-aligned currents model derived from ten years of CHAMP data with a novel technique based on EOF decomposition. Predictor variables include MLat, MLT, DoY, IMF conditions, Vsw and AE index. Compared to existing models, MFACE yields significantly better spatial resolution, reproduces typically observed FAC thickness and intensity, improves on the magnetic local time distribution, and gives the seasonal dependence of FAC latitudes and the NBZ current signature. ... -
15
vits_chinese
Best practice TTS based on BERT and VITS
vits_chinese is an implementation of the VITS end-to-end text-to-speech (TTS) architecture tailored for Chinese (and possibly multilingual) speech synthesis. VITS is a model combining variational autoencoders (VAEs), normalizing flows, adversarial learning, and a stochastic duration predictor — a design that enables generation of natural, expressive speech, capturing variations in rhythm and prosody. By customizing or porting VITS for Chinese, this project aims to produce high-quality TTS outputs in a language that can be challenging due to tones, pronunciation variability, and prosody. The repository offers full training and inference pipelines: preprocessing, mel-spectrogram generation, training scripts, and audio synthesis. ... -
16
Emb-GAM
An interpretable and efficient predictor using pre-trained models
Deep learning models have achieved impressive prediction performance but often sacrifice interpretability, a critical consideration in high-stakes domains such as healthcare or policymaking. In contrast, generalized additive models (GAMs) can maintain interpretability but often suffer from poor prediction performance due to their inability to effectively capture feature interactions. In this work, we aim to bridge this gap by using pre-trained neural language models to extract embeddings for... -
17
Auto-PyTorch
Automatic architecture search and hyperparameter optimization
While early AutoML frameworks focused on optimizing traditional ML pipelines and their hyperparameters, another trend in AutoML is to focus on neural architecture search. To bring the best of these two worlds together, we developed Auto-PyTorch, which jointly and robustly optimizes the network architecture and the training hyperparameters to enable fully automated deep learning (AutoDL). Auto-PyTorch is mainly developed to support tabular data (classification, regression) and time series... -
18
DisPC – a large scale disorder prediction and analysis tool which is a meta-predictor in one hand and also performs a proteome wide analysis of function, binding region prediction and evolutionary conservation analysis of the intrinsically disordered proteins (IDPs). DisPC is tested in several bacterial and fungal genomes for detailed genome wide analysis of IDPs. This pipeline can be used for large scale identification and characterization of IDPs in different domains of life.
-
19
snpEff is a fast variant effect predictor (SNP, MNP and InDels) for genomic data. It is integrated with Galaxy so it can be used either as a command line or as a web application.
-
20
Generic predictor that not depends data itself, this assumes original data is in L2(R) and there exists sampling theorem.
-
21
-
22
MAP
Microsatellite instability Absolute single sample Predictor
-
23
esPOS
A predictor to identify essential protein from PPI network
Predicting essential proteins from protein-protein interactions using order statistics -
24
Spectrator
Utility for manipulate predictor and L1 policy MSR
Simple utility for control CPU IA32_SPEC_CTRL /IA32_PRED_CMD/IA32_FLUSH_CMD When active Windows Spectre Protection write in MSR IA32_SPEC_CTRL incorect! -
25
NanoGLADIATOR
Real-time detection of copy number alterations from nanopore data.
Nano-GLADIATOR is a novel software tool that allows to analyze WGS data generated during a MinION or GridION X5 run for the detection of copy number alterations (CNAs). Nano-GLADIATOR (Nanopore Genomic aLterations AnD allelIc frAcTiOn pRedictor) is capable to perform CNVs/CNAs detection and allelic fraction prediction during the sequencing run ("On-line" mode for real time molecular karyotyping) and once the experiment is completed ("Off-line" mode, for parallel analysis of multiple samples). In "On-line" mode the tool must be launched simultaneously to a nanopore experiment so that, by exploiting the data flow generated during the sequencing process, it can perform real-time CNVs/CNAs detection. ...
