Hi Everyone! Econometrician, Mathematician, and Tech enthusiast from the Netherlands

Hello! I'm Mo, a Dutch native with an MSc in Econometrics from Erasmus School of Economics, specializing in Business Analytics and Quantitative Marketing. I'm also a member of the Institute of Electrical and Electronics Engineers (IEEE) and a part-time educator.

I'm passionate about delving into data to uncover insights that lead to impactful decisions, especially in the finance and tech sectors. My hands-on experience with projects in business intelligence and AI has solidified my appreciation for curiosity and persistence.

Currently doing research on Edge Computing and Network Optimization, hence my interest in Graphs came into fruition.

I'm excited about the possibilities with Neo4j and am eager to discover how I can contribute to and learn from the community. I’m looking forward to exploring collaborations that drive innovation within the tech domain.

Hi,
You Look a Person able to Set up Thermo és and solve complex issues.

My goal is to create True justice system to Remove bias and corruption In it. (My Effort gives away My históriája of being multiple Innocent victim).

I am Looking for Solution:

  1. being able to identify Source of information (able to mark original and/or all copies)
  2. able to mark If original Source of data is From human Or AI
  3. converting process flowcharts to graph system (to recognise where is Legal process standing/stuck)
  4. Automatic Creation of all Possible roures of a sceniario (One of it is true all other fictive) then narrow down with Evidence /s to the true path
  5. link /pair Evidence (doc) to Statements
  6. create Reference number to Evidence In chronological order submitted
  7. create a node Tree of Each law paragraph and add to each a precedent case - legal Basic database
  8. AI conparison - find Same, similar, parallel case to existing one and List all paragraphs Might be connected to case
  9. delete personal information From legal case After court decision
  10. judgement database - create database Why decision made upon What law paragraph and evidence
  11. difference In judgement decision In similar cases
  12. Benefit of doubt - not enough evidence….euther way
    Statistical Máthématical and próbability calculcukation - What path is more likely that happened.
  13. all Paths connected with boolean algebra (False Or true) upon evidence when narrowing down the multiple paths to 1.

You see the system i wish to create is merging diverse database (evidence, partira, external parties) flowchart/correct court procedure, logic combined with Basic node Tree - law, logic combined database combined with case (parallel case to law case), find nodes Do not link with evidence (does not Give Directions of path) and calculate highest probability of true path

Is it complex enough for you? Are you interested In Any way?
Anyone else From Community welcome.

I am Total Newbie to IT as i Worked on Electrical and Mechanical Field service (lack of equalised Degree) i had Couple of Years Java lectures at Uni when i studied 20 Years ago.

I am able to establish Concepts and theories and ways to solve complex issues. But i have 0 Knowledge how to Make My concepts into reality.

Please Help.

Let me explain this system using analogies from mechanical engineering and Java programming - areas you're familiar with.

Think of the justice system like a complex machine, similar to an automated production line. Just as a production line has different stations, quality checks, and decision points, our legal system needs similar controlled processes.

Justice System as Factory Production Line

Click to open diagram

  1. Document Management (Like Quality Control): Just as in manufacturing you need to verify raw materials before production, in our system we need to verify documents and evidence. Think of it like a Quality Control station:
  • Each piece of evidence gets a unique serial number (like parts in your factory)
  • We track where it came from (supplier tracking)
  • We verify its authenticity (quality inspection)
  1. Process Tracking (Like Assembly Line): If you've worked with Java, think of this like a State Machine or an Assembly Line:
public class LegalCase {
    private State currentState;
    private QualityCheck evidence;
    
    // Just like a production line moving from station to station
    public void moveToNextState() {
        if (qualityCheckPassed()) {
            currentState = currentState.next();
        }
    }
}
graph TD
    A[Event] --> B[Scenario 1]
    A --> C[Scenario 2]
    A --> D[Scenario 3]
    
    B --> E[Evidence 1]
    B --> F[Evidence 2]
    
    C --> G[Evidence 3]
    C --> H[Evidence 4]
    
    D --> I[Evidence 5]
    D --> J[Evidence 6]
    
    style A fill:#f96,stroke:#333
    style B,C,D fill:#69f,stroke:#333
    style E,F,G,H,I,J fill:#6f9,stroke:#333
  1. Scenario Analysis (Like Fault Tree Analysis): In mechanical engineering, when analyzing potential failure modes, you create fault trees. Our system does something similar:
  • Each possible scenario is like a different failure path
  • Evidence acts like sensor readings confirming or ruling out paths
  • Probability calculations work like reliability engineering

Scenario Analysis as Fault Tree

Click to open diagram

  1. Evidence Linking (Like Object-Oriented Programming): Just as in Java you create objects with relationships, our evidence system creates a web of connected information:
```java
// Similar to how you might structure classes in Java
class Evidence {
    private String id;
    private List<Statement> linkedStatements;
    private DateTime timestamp;
}

class Statement {
    private String content;
    private List<Evidence> supportingEvidence;
}

5. Decision Making (Like Control Systems): Think of this like a feedback control system in mechanical engineering:

* Input: Evidence and statements
* Process: Legal analysis
* Feedback: Previous similar cases
* Output: Decision based on strongest supported path

The whole system works like a well-oiled machine where:

* Quality control (evidence verification) ensures good input
* Assembly line (legal process) ensures proper procedure
* Sensors (evidence) confirm or deny possible scenarios
* Control systems (decision making) produce consistent output


> So to answer your question more specifically:
> 
> 
> 1. Source Identification Just like parts tracking in manufacturing, each piece of information gets a unique serial number (hash) that follows it throughout the system. Think of it like quality control stamps that can't be forged.
> 2. Human vs AI Source Marking Similar to how mechanical sensors detect different materials, we use pattern recognition to identify content origins. It's like having a sophisticated gauge that can tell the difference between machine-made and hand-crafted parts.
> 3. Process Flowcharts to Graph System Think of this like converting a mechanical assembly diagram into a digital monitoring system. Each state in the legal process becomes a node (like a station on an assembly line), and the connections become edges (like conveyor belts between stations).
> 4. Automatic Scenario Generation This works like failure mode analysis in engineering. Just as you map all possible ways a machine could fail, we map all possible scenarios that could explain the evidence. Then, like using sensor data to identify the actual failure, we use evidence to identify the true scenario.
> 5. Evidence-Statement Linking Similar to Java object relationships, we create connections between evidence (objects) and statements (other objects). It's like having machine parts (evidence) connected to their specification documents (statements).
> 6. Chronological Reference Numbers Like a production line timestamp system, each piece of evidence gets a sequential identifier based on when it enters the system. Think of it as a parts tracking system in manufacturing.
> 7. Law Paragraph Node Tree Similar to how Java inheritance works, we create a hierarchical structure of law paragraphs. Each paragraph is like a class that can inherit from or reference other paragraphs, with precedent cases serving as implementation examples.
> 8. Case Comparison This works like comparing engineering blueprints. The system looks for similar patterns in previous cases, much like how you might search a database of previous machine designs for similar solutions.
> 9. Personal Information Removal Like sanitizing machine logs of operator-specific data, the system automatically removes personal identifiers while maintaining the essential technical details of the case.
> 10. Judgment Database Creation Similar to maintaining a machine maintenance log, this creates a structured record of decisions, linking each judgment to specific evidence and law paragraphs that supported it.
> 11. Judgment Difference Analysis Like comparing quality control reports across different production runs, this system analyzes variations in judgments across similar cases to identify inconsistencies.
> 12. Mathematical Probability Calculation This uses reliability engineering principles to calculate the most likely scenario. Just as you might calculate the probability of machine failure based on component reliability data, we calculate scenario probabilities based on evidence strength.
> 13. Boolean Path Analysis Similar to digital logic in control systems, we use true/false conditions based on evidence to narrow down possible scenarios. It's like a sophisticated diagnostic system that uses sensor readings to identify machine states.
> 
> All these components work together like a modern automated factory:
> 
> * Input validation (evidence verification)
> * Process control (legal procedure tracking)
> * Quality assurance (scenario verification)
> * Output consistency (judgment standardization)

HI @modaoud,

Are you currently open for new collaborations? We are looking for a future CTO.

My name is Manuel and I'm the founder of Spiintel, a system powered by Graph-AI technology, designed to deliver tailored solutions for SMEs to address specific challenges in their operations. Like an Iron Man suit, it equips businesses with adaptive, mission-focused tools to streamline processes, overcome challenges, and achieve success with precision and efficiency.

if you are open to have a chat you can reach me via phone +31643848695 or email manuel@spiintel.com