Search Results for "lottery prediction algorithm"
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Showing 40 open source projects for "lottery prediction algorithm"
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Rezku Point of SaleRezku is an all-inclusive ordering platform and management solution for all types of restaurant and bar concepts. You can now get a fully custom branded downloadable smartphone ordering app for your restaurant exclusively from Rezku.
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MicroStation by Bentley Systems is the trusted computer-aided design (CAD) software built specifically for infrastructure design.MicroStation is the only computer-aided design software for infrastructure design, helping architects and engineers like you bring their vision to life, present their designs to their clients, and deliver their projects to the community.
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tsfresh
Automatic extraction of relevant features from time series
tsfresh is a python package. It automatically calculates a large number of time series characteristics, the so called features. tsfresh is used to to extract characteristics from time series. Without tsfresh, you would have to calculate all characteristics by hand. With tsfresh this process is automated and all your features can be calculated automatically. Further tsfresh is compatible with pythons pandas and scikit-learn APIs, two important packages for Data Science endeavours in python.... -
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alive-progress
A new kind of Progress Bar, with real-time throughput, ETA
alive-progress is an advanced Python progress bar library that introduces a highly animated and adaptive approach to tracking long-running tasks. Unlike traditional static progress indicators, it dynamically adjusts spinner speed and visual feedback based on actual throughput, giving users a more intuitive sense of activity. The library is designed with performance efficiency in mind, using multithreaded updates that minimize CPU overhead and terminal noise. It includes sophisticated ETA... -
3
Cryptocurrency prediction tool
Cryptocurrency prediction
I developed this program because I am not very familiar with the candlestick and other parameters, so I wanted the program to help analyze them. Therefore, I developed a cryptocurrency prediction tool, which mainly collects information about the coins that need to be queried through the software, such as candlestick, long short ratio of large investors, net inflow of funds, and other parameters. Finally, the XGBoost algorithm is used to vote for the rise or fall results. This software is for reference only and does not provide any investment guidance. ... -
4
NannyML
Detecting silent model failure. NannyML estimates performance
...By using NannyML, data scientists can finally maintain complete visibility and trust in their deployed machine learning models. When the actual outcome of your deployed prediction models is delayed, or even when post-deployment target labels are completely absent, you can use NannyML's CBPE-algorithm to estimate model performance. -
Iris Powered By Generali - Iris puts your customer in control of their identity.Iris Identity Protection API sends identity monitoring and alerts data into your existing digital environment – an ideal solution for businesses that are looking to offer their customers identity protection services without having to build a new product or app from scratch.
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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. Disclaimer: Provided for analytical and testing purposes only. No predictive... -
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miRDeep*
MiRDeep*
Please cite: An, J., Lai, J., Lehman, M.L. and Nelson, C.C. (2013) miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data. Nucleic Acids Res, 41, 727-737. We will create index for you if you tell us your interested species (j.an@qut.edu.au). download command line version "MDS_command_line_Vxx.zip" clicking "Browse All Files" please find miRPlant in sourceforge for plant miRNA prediction. -
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TextGen
textgen, Text Generation models
...This project implements the training and prediction of Seq2Seq, ConvSeq2Seq, and BART models based on PyTorch, which can be used for text generation tasks such as text translation. -
8
chimera
ChiMera: An easy-to-use pipeline for Genome-based Metabolic Network re
There is a scarcity of user-friendly tools that can be used in daily routine, providing insights about the metabolic network of a target organism for researcher’s groups. Here we present a novel tool, Chimera, which combines the most efficient tools in model reconstruction, prediction, and visualization and also implements new in-house algorithms for database integration and data manipulation. Our goals: Produce an organism-specific model based on the CarveMe algorithm Manage the model and perform growth predictions with COBRApy Create visualization for the metabolic network using PSAMM and Escher Add pathway information to metabolic maps using in house algorithm Perform single and double, gene and reaction, knockout in the organism -
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AmPEP and AxPEP
Sequence-based Antimicrobial Peptide Prediction by Random Forest
Antimicrobial peptides (AMPs) are promising candidates in the fight against multidrug-resistant pathogens due to its broad range of activities and low toxicity. However, identification of AMPs through wet-lab experiment is still expensive and time consuming. AmPEP is an accurate computational method for AMP prediction using the random forest algorithm. The prediction model is based on the distribution patterns of amino acid properties along the sequence. Our optimal model, AmPEP with 1:3 data ratio achieved a very high accuracy of 96%, MCC of 0.9, AUC-ROC of 0.99 and Kappa statistic of 0.9. AmPEP outperforms existing methods with respect to accuracy, MCC, and AUC-ROC when tested using the benchmark datasets. ... -
The AI workplace management platformBy combining AI workflows, predictive intelligence, and automated insights, OfficeSpace gives leaders a complete view of how their spaces are used and how people work. Facilities, IT, HR, and Real Estate teams use OfficeSpace to optimize space utilization, enhance employee experience, and reduce portfolio costs with precision.
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AUDACITY
AUtozygosity iDentification And ClassIfication Tool
AUDACITY is novel computational approach for the identification of Runs of Homozygosity by using VCF files from whole-exome and whole-genome sequencing data generated by second generation sequencing technologies. AUDACITY is a tool integrating novel RoH detection algorithm and autozygosity prediction score for prioritization of mutation-surrounding regions. ###################################################################### The AUDACITY tool has been published on Computational and Structural Biotechnology Journal (CSBJ). . You can find it at https://www.sciencedirect.com/science/article/pii/S2001037020303354 ###################################################################### -
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GWOVina 1.0
The Hybrid Grey Wolf Optimization for Protein-Ligand Docking
Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein. Based on the implementation of AutoDock Vina, GWOVina employs grey wolf optimization (GWO) algorithm to speed up the search for optimal ligand poses. Our rigid docking experiments show that GWOVina has enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, GWOVina is also competitive in pose ranking. ... -
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Machine Learning From Scratch
Bare bones NumPy implementations of machine learning models
ML-From-Scratch is an open-source machine learning project that demonstrates how to implement common machine learning algorithms using only basic Python and NumPy rather than relying on high-level frameworks. The goal of the project is to help learners understand how machine learning algorithms work internally by building them step by step from fundamental mathematical operations. The repository includes implementations of algorithms ranging from simple models such as linear regression and... -
13
CCZero (中国象棋Zero)
Implement AlphaZero/AlphaGo Zero methods on Chinese chess
ChineseChess-AlphaZero is a project that implements the AlphaZero algorithm for the game of Chinese Chess (Xiangqi). It adapts DeepMind’s AlphaZero method—combining neural networks and Monte Carlo Tree Search (MCTS)—to learn and play Chinese Chess without prior human data. The system includes self-play, training, and evaluation pipelines tailored to Xiangqi's unique game mechanics. -
14
DACO-algorithm
A novel transcription factor complex prediction algorithm.
Eukaryotic gene expression is controlled through molecular logic circuits that combine regulatory signals of many different factors. Complexation of transcription factors and other regulatory proteins is a prevailing and highly conserved mechanism of signal integration within critical regulatory pathways and enable to infer controlled genes as well as the exerted regulatory mechanism. We developed DACO (domain-aware cohesiveness optimization), a novel algorithm that combines... -
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PF_HP
Prediction of proteinfolding in 2D HP model
Even in the simplified two dimensional HP-model (hydrophob/polar) the prediction of proteinfolding is NP complete. We implement a brute force algorithm with serial and parallel execution to solve short inputs of HP sequences (0-1 bitstrings). Selbst im vereinfachten zweidimensionalen HP-Modell (hydrophob/polar) ist die Proteinfaltung bereits NP-vollständig. Hier implementieren wir einen brute-force Algorithmus zur Lösung kurzer Eingabesequenzen (0-1-Bitstrings) für die Proteinfaltung. ... -
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BioPPSy
A biochemical property prediction system
Computationally predicts the pharmacokinetic properties of drug candidates using Quantitative Structure Property Relationships (QSPR) modelling. Assembles a set of tools and databases for predicting the physical properties of small molecules. The program models a given property's dependence on a collection of molecular and structural descriptors using a training set of molecules. Neural networks and support vector regression are available, as well as linear models. The models generated by... -
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MUSE
A Multi-algorithm Collaborative Structure-prediction Environment
MUSE is short for Multi-algorithm-collaborative Universal Structure-prediction Environment, which was developed for easy use in structure prediction of materials under ambient or extreme conditions, such as high pressure. It was written in Python and organically combined the multi algorithms including the evolutionary algorithm, the simulated annealing algorithm and the basin hopping algorithm to collaboratively search the global energy minimum of materials with the fixed stoichiometry. ... -
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cfPred
Chou & Fasman algorithm implemented using C language.
Chou & Fasman algorithm implemented using C language. It is a CUI tool used for protein secondary structure prediction. -
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faif
C++ header only library with AI and bioinformatics algorithms
C++ header only library, small and fast; Naive Bayesian Classifier, Decision Tree Classifier (ID3), DNA/RNA nucleotide second structure predictor, timeseries management, timeseries prediction, generic Evolutionary Algorithm, generic Hill Climbing algorithm and others. -
20
node2vec
Learn continuous vector embeddings for nodes in a graph using biased R
The node2vec project provides an implementation of the node2vec algorithm, a scalable feature learning method for networks. The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction. ... -
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Random Bits Forest
RBF: a Strong Classifier/Regressor for Big Data
We present a classification and regression algorithm called Random Bits Forest (RBF). RBF integrates neural network (for depth), boosting (for wideness) and random forest (for accuracy). It first generates and selects ~10,000 small three-layer threshold random neural networks as basis by gradient boosting scheme. These binary basis are then feed into a modified random forest algorithm to obtain predictions. In conclusion, RBF is a novel framework that performs strongly especially on data... -
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Community profiling (CP) is a guilt-by-association approach for predicting functional associations between proteins: two proteins are predicted to be associated if they share similar presence and absence profiles (called community profiles) across microbial communities. Community profiling is conceptually similar to the phylogenetic profiling approach to functional prediction, however with fundamental differences. This package provides different profile construction methods and correlation metrics, which we have shown are important for optimizing the performance of this new approach. Please refer to our paper for details of the algorithm: Dazhi Jiao, Wontack Han and Yuzhen Ye. Functional association prediction by community profiling. ...
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ingap-cdg
codon-based de Bruijn graph algorithm for gene construction
Currently, most gene prediction methods detect coding sequences (CDSs) from transcriptome assembly when lacking of closely related reference genomes. However, these methods are of limited application due to highly fragmented transcripts and extensive assembly errors, which may lead to redundant or false CDS predictions. Here we present a novel algorithm, inGAP-CDG, for effective construction of full-length and non-redundant CDSs from unassembled transcriptomes. inGAP-CDG achieves this by combining a newly developed codon-based de bruijn graph to simplify the assembly process and a machine learning based approach to filter false positives. ... -
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PSICOV
The unofficial binary for PSICOV: Protein Sparse Inverse COVariance
PSICOV (Protein Sparse Inverse Covariance estimation program) is a coevoultion algorithm applied to very large (typically >=1000 sequences) multiple sequence alignments for precise protein structural contact prediction. This is the unofficial precompiled Windows binary for PSICOV compiled by Chengxin Zhang at Fudan University. The source code is copyrighted by David T. Jones, University College London.
