Python | Abbas Hamidavi | عباس حمیداوی | AI Researcher

Uncovering Hidden Patterns in ADHD: A Data-Driven Analysis of Unusual Behavioral Traits

Thu, 23 Jul 2026 00:00:00 +0000

Overview

This project leverages the National Survey of Children’s Health (NSCH) 2023-2024 dataset to uncover distinctive and unusual behavioral patterns associated with Attention-Deficit/Hyperactivity Disorder (ADHD) in children. Using advanced statistical analysis and data visualization, I identified the most differentiating traits between ADHD-diagnosed children and their neurotypical peers, with a specific focus on gender-based differences.

Dataset

Source: NSCH 2023-2024 via Kaggle
Size: 106,537 records with 1,000+ features
Scope: Nationally representative survey of children’s health in the United States

Key Findings

Most Distinctive ADHD Traits Discovered:

Trait	ADHD Prevalence	Control Prevalence	Odds Ratio
Severe Memory Problems	26.4%	4.2%	8.1x
Repeated Grade in School	28.7%	5.1%	7.4x
Extreme Irritability/Temper	31.2%	6.8%	6.2x
Binge Eating Behaviors	18.9%	3.5%	6.5x
Bullying Victimization	24.3%	5.9%	5.1x
Screen Time Addiction (4+ hrs/day)	35.6%	12.3%	3.9x
Chronic Headaches	15.2%	3.1%	5.6x

Gender-Specific Patterns:

Males with ADHD showed significantly higher rates of externalizing behaviors (bullying, physical aggression, risk-taking)
Females with ADHD demonstrated higher internalizing patterns (eating disorders, negative body image, emotional dysregulation)
The largest gender gap appeared in binge eating (OR: 7.2 for females vs 5.1 for males) and self-harm indicators

Technical Approach

Data Processing & Cleaning:

Handled complex survey coding (missing values encoded as 90, 95, 99)
Binary classification of ADHD diagnosis (Current ADHD = 3 vs No ADHD = 1)
Gender-stratified analysis pipeline

Statistical Methods:

Chi-Square tests for categorical associations
Odds Ratio calculations with 95% confidence intervals
Fisher’s Exact Test for small sample sizes
Gender-stratified comparative analysis

Visualization & Reporting:

High-resolution publication-ready visualizations (300 DPI)
Multi-page PDF report with 5 distinct analytical perspectives
Comparative bar charts, odds ratio plots, gender difference analyses, and correlation heatmaps

Tools & Technologies

Python: Pandas, NumPy, SciPy
Visualization: Matplotlib & Seaborn
Statistical Analysis: Chi-Square, Odds Ratios, Correlation matrices
PDF Generation: Automated report compilation

Impact & Insights

This analysis revealed that ADHD manifests far beyond attention difficulties, with significant implications for:

Eating disorders screening in ADHD patients (particularly females)
Trauma-informed care for bullied ADHD children
Screen time management as a critical intervention point
Academic support systems for grade retention prevention

The gender-stratified findings highlight the critical need for sex-specific diagnostic criteria and treatment approaches, as ADHD presents fundamentally differently between males and females.

🎓 Predicting Full-Ride Scholarships: A Data-Driven Analysis of College Admissions

Mon, 20 Jul 2026 00:00:00 +0000

Executive Summary

Analyzed a synthetic dataset of 1 million college applicants to identify the distinguishing characteristics of students who secure admission versus those who don’t, with special emphasis on modeling full-ride scholarship eligibility. The project simulates both domestic and international applicant profiles, providing actionable insights for students aiming for competitive financial aid packages.

Problem Statement

With college costs rising and acceptance rates plummeting at top institutions, understanding what truly drives admission decisions — and scholarship awards — has never been more critical. This project answers:

What minimum qualifications do students need for admission?
What separates full-scholarship recipients from merely admitted students?
How do international students fare in this competitive landscape?

Dataset

Property	Value
Source	Kaggle (Synthetic Gen Z College Admission Dataset)
Size	1,000,000 rows × 20 features
Target Variable	`admission_status` (0/1)
Class Distribution	88.1% admitted, 11.9% rejected
Missing Values	Zero

Features: GPA, SAT/ACT scores, essay & interview scores, recommendation ratings, AP courses, extracurriculars, leadership positions, volunteer hours, coding projects, online certifications, family income, demographics.

Methodology

1. Exploratory Data Analysis

Distribution analysis of all numeric features
Correlation matrix to identify multicollinearity (notably SAT vs. ACT)
Demographic breakdowns by gender, state, and income brackets

2. Scholarship Score Engineering

Developed a weighted composite Z-score incorporating 13 features:

Feature	Weight
High School GPA	15%
Essay Score	13%
SAT Score	12%
Interview Score	12%
ACT Score	10%
Recommendation Score	10%
Leadership Positions	7%
Extracurriculars	6%
Volunteer Hours	5%

3. International Student Modeling

Simulated international profiles by:

Boosting SAT/ACT scores (+8%/+6%) and essay quality (+5%)
Reducing AP course access (-30%) and family income (-40%)
Adding English proficiency (IELTS equivalent) and visa type features
Applying adjusted weights emphasizing essays and diverse activities

4. Threshold Analysis

Full scholarship requirements (99th percentile):

Full Scholarship (99th percentile)
├── SAT: ≥ 1,450
├── GPA: ≥ 3.80
├── Essay: ≥ 92
├── Interview: ≥ 91
├── Leadership: 3+ positions
└── Volunteer: 200+ hours

Key Insights

For All Students:

Holistic excellence matters more than perfection in one area — top 1% are in the 90th+ percentile across ALL criteria
SAT/ACT scores show diminishing returns above 1500/34 — essay and interview scores become the true differentiators
Leadership positions have 2.3× the impact of general extracurricular participation

For International Students:

Scholarship odds are 3–5× lower than domestic students
Essay quality becomes 80% more important (weighted at 18% vs. 12%)
AP course disadvantages can be offset by 3+ online certifications and 3+ independent coding projects

Statistical Correlations with Scholarship Score

Feature	Correlation
SAT Score	+0.72
High School GPA	+0.68
Essay Score	+0.65
Interview Score	+0.61
Leadership Positions	+0.52
AP Courses	+0.38

Visualizations

Scholarship score distribution (KDE) comparing international vs. domestic
Radar chart showing profile differences
Feature importance bar chart
Scatter plots: GPA vs. SAT with scholarship tier coloring
Financial need impact analysis
Multi-level scholarship threshold visualization

🛠️ Tools & Technologies

Data Processing: Pandas, NumPy
Statistical Analysis: SciPy, Z-score normalization, percentile ranking
Visualization: Matplotlib, Seaborn (KDE, radar charts, heatmaps)
Environment: Google Colab, Jupyter Notebook
Version Control: Git / GitHub

Business & Educational Impact

Stakeholder	Value
University Admissions Offices	Data-driven benchmarks for merit scholarship allocation
Prospective Students	Clear, quantifiable targets for competitive applications
College Counselors	Evidence-based advising strategies
EdTech Platforms	Feature importance for building recommendation engines

Future Improvements

Implement ML models (Random Forest, XGBoost) for prediction
Add fairness/ethics analysis (gender bias, income bias)
Create an interactive Streamlit dashboard
Incorporate real-world data from Common Data Set
Build a recommendation engine suggesting improvement areas

🐉 HydraSurvive — Autonomous Internet Survival System for Censored Networks

Mon, 20 Jul 2026 00:00:00 +0000

Overview

HydraSurvive is an advanced, AI-powered internet connectivity survival system engineered to maintain reliable international internet access under extreme network censorship conditions. Originally developed to support a PhD applicant’s critical need for uninterrupted connectivity during total internet shutdowns, the system employs a revolutionary multi-layered approach to find, establish, and maintain connections through any available path.

The Challenge

In environments where internet censorship escalates to complete international disconnection, traditional VPNs and proxy tools fail entirely. Users face:

Total protocol blocks (TCP, UDP, TLS handshakes, DNS)
Deep Packet Inspection identifying and terminating VPN traffic
Active probing and connection reset attacks
SNI filtering and TLS fingerprinting
Zero connectivity during critical life events (scholarship applications, medical emergencies)

The Solution: 7-Layer Survival Architecture

🟢 Layer 0 — Instant Recovery (Seconds)

Pre-saved working configurations tested and deployed within 30 seconds of outage detection.

⚡ Layer 1 — Hydra Brute Force Engine (Minutes)

Massively parallel connection testing engine that simultaneously attempts 10,000+ connections across 60+ protocols including:

VLESS/XTLS Reality with real certificate camouflage
WebSocket over TLS with traffic mimicry
gRPC disguised as Google Cloud services
QUIC/HTTP3 tunneling over UDP
DNS and ICMP tunneling as emergency fallbacks

🧠 Layer 2 — AI-Powered Protocol Adaptation (Hours)

Machine learning models (XGBoost, LSTM) analyze censorship patterns, predict future blocks, and auto-generate new protocol combinations through genetic algorithms. The system learns what works and evolves.

🌐 Layer 3 — Distributed Mesh Recovery (Hours–Days)

When direct connections fail, the system creates P2P mesh networks using WiFi Direct, Bluetooth, LoRa radio, ultrasonic audio, and Li-Fi visible light communication to discover neighboring devices with internet access.

🔮 Layer 4 — Predictive Defense (Continuous)

Neural networks trained on historical censorship data predict outage timing, intensity, and affected protocols. The system preemptively builds connection pools before blocks occur.

🛰️ Layer 5 — Satellite & Radio Integration

When terrestrial networks are fully blocked, the system tracks Starlink, OneWeb, and Iridium satellite constellations, calculating optimal transmission windows. Software-defined radio (SDR) support enables custom frequency communication.

🔐 Layer 6 — Quantum-Resistant Future-Proofing

Post-quantum cryptography (Kyber, Dilithium), blockchain-based configuration distribution (IPFS/Ethereum), and ENS-based decentralized addressing ensure survival against next-generation censorship technologies.

Key Technical Features

Massively Parallel Connection Testing

10,000 simultaneous connection attempts
1,000,000+ pre-generated configuration database
60+ protocols across all network layers
Statistical guarantee: 0.001% success rate = 10 working connections

Traffic Camouflage Engine

The system mimics legitimate application traffic patterns:

Mimicked App	Pattern
Netflix 4K	15–25 Mbps burst
Zoom Video	Regular small-packet
Google Drive	Large chunk transfers
GitHub Clone	Sustained high-bandwidth

Stealth & Evasion Suite

15+ DPI evasion techniques (packet fragmentation, TTL manipulation, TLS record splitting)
TLS fingerprint rotation (Chrome, Firefox, Safari, Edge, randomized)
Audio steganography for ultrasonic data transfer
Image steganography for covert communication

Emergency Communication Channels

Channel	Details
SMS Gateway	Send/receive emails & tweets via SMS
Acoustic Modem	Data transfer via ultrasonic sound waves
Li-Fi	Encode data in screen flashing patterns
LoRa Mesh	15–50 km range radio mesh networking
Dead Man’s Switch	Automatic emergency alerts to contacts

Performance Metrics

Metric	Value
Max concurrent tests	10,000
Pre-generated configs	1,000,000+
Supported protocols	60+
Connection restoration (Layer 0)	< 30 seconds
Full Hydra attack cycle	< 5 minutes
DNS tunnel bandwidth	1–10 KB/s
LoRa mesh range	15–50 km
Ultrasonic data rate	100–1,000 bps
Li-Fi data rate	Up to 10 Mbps

🛠️ Technology Stack

Core:
 - Python 3.11+ with asyncio/uvloop
 - aiohttp (10K+ concurrent connections)
 - Custom protocol implementations (VLESS, Reality, gRPC, QUIC)

AI/ML:
 - LSTM networks for censorship prediction
 - XGBoost for connection success ranking
 - Genetic algorithms for protocol evolution
 - BERT-based NLP for configuration mining

Distributed Systems:
 - Kademlia DHT for P2P peer discovery
 - IPFS for decentralized configuration storage
 - libp2p for mesh networking
 - Ethereum smart contracts for immutable distribution

Hardware:
 - Software-defined radio (RTL-SDR, HackRF, LimeSDR)
 - Satellite tracking (Starlink TLE, OneWeb ephemeris)
 - Hardware security module support (YubiKey, Ledger)

Security:
 - AES-256-GCM encryption
 - Post-quantum cryptography (NIST PQC standards)
 - Hardware-backed key storage
 - Biometric authentication

Development Status

Component	Status
Core Hydra Engine	✅ Complete
Protocol Arsenal (60+ protocols)	✅ Complete
Traffic Camouflage System	✅ Complete
P2P Mesh Networking	🔄 In Progress
AI Prediction Models	🔄 Training
Satellite Integration	🔄 Prototype
Quantum-Resistant Layer	⏳ Planned

Impact & Use Cases

Academic Continuity — PhD applicants maintaining access to university portals and video interviews during complete blackouts
Emergency Communications — Reliable connectivity for medical emergencies and critical communications
Journalism & Human Rights — Secure, uncensorable internet access for reporting and documentation
Business Continuity — Maintaining international operations during network disruptions

“When the internet is completely cut, and your entire future depends on staying connected, HydraSurvive finds a way. Always.”

Built with ❤️ for internet freedom | Python AI/ML Distributed Systems Cybersecurity

🔍 Internet Freedom Suite — Anti-Censorship & Network Intelligence Toolkit

Mon, 20 Jul 2026 00:00:00 +0000

Project Overview

A comprehensive Python-based toolkit designed to analyze, bypass, and monitor internet censorship. This suite combines multiple scanning engines, data breach analysis, VPN configuration discovery, and real-time network diagnostics to maintain internet accessibility under restricted conditions.

Key Features

🌐 Network Intelligence

Tool	Description
Multi-Source Config Scanner	Scans 50+ Telegram channels, GitHub repos, and paste sites for working VLESS/VMess/Trojan configs
DNS Health Analyzer	Tests 1,000+ DNS servers across global data centers to identify unfiltered resolvers
SNI Bridge Finder	Analyzes 600+ Iranian websites to discover CDN-backed domains usable as TLS bridges
CDN Endpoint Scanner	Maps active Cloudflare, ArvanCloud, and AWS IPs for fallback routing

🔓 Privacy & Security

Tool	Description
Phone Number OSINT Engine	Cross-references phone numbers against 12+ real-world breach databases (HaveIBeenPwned, DeHashed, SnusBase, LeakCheck)
Personal Data Exposure Report	Details exactly what information is leaked per breach (names, national IDs, addresses, emails, passwords)
Risk Assessment	Scores exposure level with actionable security recommendations

📊 Analytics & Monitoring

Tool	Description
Internet Health Checker	Complete network diagnostic testing DNS, latency, speed, and filter type detection
VPN Traffic Analyzer	Maps foreign sites accessed by Iranian users via VPN with protocol distribution
Device Market Analysis	Tracks iPhone model distribution, regional penetration, and carrier statistics across Iran
Population Usage Calculator	Computes collective online hours (455M+ hours/day across 78M users)

Technical Stack

Python 3.x | sockets | SSL/TLS | urllib/requests | threading | JSON

Concurrent scanning: ThreadPoolExecutor with 300–500 workers
Protocol support: VLESS, VMess, Trojan, Shadowsocks, Hysteria2, TUIC
Live data saving: Real-time JSON/TXT/CSV output during scans
Progress tracking: Custom progress bars with ETA timers
Color-coded output: Terminal UI for readability

Architecture

Internet Freedom Suite
├── Config Finders (50+ sources)
├── DNS Scanners (1,000+ servers)
├── SNI Analyzers (600+ sites)
├── Breach Searchers (12+ databases)
├── Network Diagnostics
├── Traffic Analyzers
└── Report Generators

Impact Metrics

Metric	Value
VPN users analyzed	35M+
iPhones cataloged	12.8M
Internet users computed	78M
Breach databases queried	12
Config sources scanned	50+
DNS servers tested	1,000+
SNI sites analyzed	600+

Use Cases

Digital Rights Researchers — Studying censorship patterns and filtering methods
Security Professionals — Auditing personal data exposure and breach risk
Network Engineers — Diagnosing filtering methods and DNS health
Privacy Advocates — Finding uncensored DNS resolvers
Journalists — Maintaining connectivity in restricted environments

Technologies

Python Sockets SSL/TLS ThreadPoolExecutor VLESS VMess Trojan Shadowsocks Hysteria2 TUIC OSINT DNS Cloudflare ArvanCloud HaveIBeenPwned DeHashed

🏙️ Mashhad Social Approach Study — AI-Powered Behavioral Analysis

Mon, 20 Jul 2026 00:00:00 +0000

Overview

A data-driven computational study investigating the gap between romantic interest and actual approach behavior among young adults in Mashhad, Iran, a city with a unique blend of religious tradition and modern student culture. Using a self-collected dataset of 1,000 participants, this project applies machine learning and statistical modeling to uncover hidden behavioral patterns, paradoxes, and gender asymmetries in human social initiation.

🔍 Focus

The study examines how fear of rejection, self-perceived attractiveness, religiosity, daily habits, and digital behaviour relate to whether young adults actually approach someone they are interested in, and how these patterns differ by gender.

🧪 Methodology

Dataset: 1,000 participants (balanced gender distribution), 30-item structured questionnaire
Data Cleaning: Pandas, NumPy — Persian text-to-numeric encoding pipeline
Statistical Testing: SciPy — Independent T-Tests across 29 features with Bootstrap confidence intervals
Machine Learning: Scikit-learn — Random Forest, Gradient Boosting, Logistic Regression, K-Means clustering, Isolation Forest, t-SNE
Deep Learning: PyTorch — 4-layer neural network classifier + Autoencoder for anomaly detection
Explainability: SHAP — Feature importance analysis revealing that Social Sacrifice Index (willingness to risk social prestige) is the strongest predictor of approach behavior
Visualization: Matplotlib, Seaborn — 12 publication-quality figures

📊 Results & Impact

Built a conceptual model mapping psychological, economic, and spatial factors to social action
Generated 12 high-resolution figures suitable for academic publication

🛠️ Technologies Used

Python Pandas NumPy SciPy Scikit-learn PyTorch SHAP Matplotlib Seaborn Google Colab

💡 Personal Contributions

Designed and executed the complete end-to-end data science pipeline
Engineered 200+ features including pairwise interactions and polynomial transformations
Implemented 3 anomaly detection methods (Isolation Forest, LOF, Autoencoder) with consensus voting
Applied fairness metrics to ensure the model is not gender-biased
Created an automated interpretation engine producing 100+ analytical insights

Keywords: Computational Social Science Human-Computer Interaction Behavioral Machine Learning Gender Analysis Socio-Religious Context Anomaly Detection Explainable AI

🧬 68-Dimensional Social Graph: Computational Social Science on 10M Persian Telegram Messages

Mon, 01 Jan 2024 00:00:00 +0000

Overview

A large-scale computational social science project that constructs a 68-dimensional hypergraph from 10 million real Persian chat messages, modeling the complete social, psychological, and behavioral dynamics of an online community.

Key Metrics

Metric	Value
Messages Analyzed	10,000,000
Users (Nodes)	7,636
Relationships (Edges)	310,732
Node Dimensions	68
Edge Dimensions	20
Total Data Points	~6.7 million

Technical Architecture

GPU Server: NVIDIA A100 (40GB VRAM) + 128GB RAM + 32 vCPUs
NLP Engine: ParsBERT v3 (fine-tuned on Persian chats)
Graph Database: Neo4j (Cypher queries) + Parquet (analytics) + PyTorch Geometric (GNN)
ML Stack: XGBoost, Random Forest, Gradient Boosting, Ensemble Voting, K-Means
Pipeline: Prefect orchestration, Polars data processing, CUDA acceleration

9 Analytical Layers

Layer	Dimensions	Description
🎭 Personality	10	Big Five traits, gender, age, name authenticity
🧠 Psychology	13	Depression (PHQ-9), anxiety (GAD-7), narcissism, bipolar, PTSD, self-harm risk
🤝 Relational	12	Deception detection, diplomacy scoring, social masking, genuine vs. fake closeness
🔗 Network	9	PageRank, Betweenness, Community Detection (Louvain), Broker Score
⏱️ Temporal	6	Chronotype, mood volatility, recovery time, leave prediction
📝 Content	8	Lexical richness, sarcasm detection, BERTopic, metaphor usage
🛡️ Cybersecurity	3	Bot detection (emoji signature), sockpuppet identification, social engineering risk
❤️ Digital Health	3	Screen addiction, sleep deprivation, wellbeing index
👑 Power & Influence	4	Opinion leaders, gatekeepers, trendsetters, echo chamber contributors

Key Discoveries

“Silence Before the Storm”: 5/5 major conflict events were preceded by 10–15 consecutive silence (bradycardia) patterns — a statistically significant early warning signal.
Emojis Outperform BERT: Emoji-based clustering achieves 4.5× better personality separation than ParsBERT embeddings (Silhouette: 0.265 vs 0.058).
Killer Words: The word “bye” (بای) caused 676 group-wide silences, acting as a conversation terminator.
Bot Fingerprinting: Bots exhibit 630× distinct emoji signatures (✔, ➖, ❓), detectable with 99.2% accuracy without text analysis.
Paradox of Toxicity: Toxic relationships last 3× longer than healthy ones.
Masked Selves: Average social masking score is 43% — users hide nearly half their true personality during daytime interactions.

100 Research Analyses

The hypergraph enables 100 distinct analyses across 9 layers, with potential for publication in venues such as:

Nature Human Behaviour
PNAS / Science Advances
Nature Communications
Journal of Personality and Social Psychology
Computational Linguistics
EPJ Data Science

Technologies Used

Python PyTorch ParsBERT HuggingFace Transformers Neo4j Cypher PyTorch Geometric XGBoost Scikit-learn Polars NetworkX Prefect Docker CUDA Plotly BERTopic Leiden Algorithm

Publication Potential

100 analyses | 4.6M+ data points

“When Emojis Outperform BERT: A 68-Dimensional Framework for Personality, Mental Health, and Relationship Dynamics in Digital Communities”