FuzzuTech Projects

━━━━━━━━━━━━━━━━━━━━ 📖 Introduction Can Artificial Intelligence predict what you are going to do next? In this project, we explore an AI Behavior Engine designed to analyze user actions, detect behavioral patterns, perform real-time analysis, calculate prediction confidence, and predict the most likely next action. The system observes actions such as clicking, scrolling, typing, and mouse movement. These actions are processed as behavioral data and analyzed to discover patterns that can help the AI estimate what action may happen next. Instead of simply recording what a user has already done, the AI Behavior Engine attempts to understand behavioral sequences and generate intelligent predictions from them. This project demonstrates concepts including real-time behavior analysis, action recording, prediction scoring, behavior mapping, prediction ranking, AI simulation, behavioral analytics, and intelligent software design. ━━━━━━━━━━━━━━━━━━━━ ✨...

  Demo : Click Video 👇👇👇 Features: Hand gestures mapped to PC actions (Launch apps, Show desktop, Lock screen) Modern GUI with Dark Mode Works with ML model or Random Simulation Code : import customtkinter as ctk import imageio import threading import numpy as np import pyautogui from PIL import Image, ImageTk import tensorflow as tf import os import random # ===== GUI Settings ===== ctk.set_appearance_mode("dark") ctk.set_default_color_theme("blue") class GestureApp(ctk.CTk):     def __init__(self):         super().__init__()         self.title("Fuzzu ML Hand Gesture Control (No CV2/No Mediapipe)")         self.geometry("500x500")         self.resizable(False, False)         # Camera         self.camera = None         self.gesture_enabled = False         # Title         self.la...

  Demo : Click Video 👇👇👇 Features: ✅ Embedded YouTube Video ✅ Keywords for SEO ✅ Tags from above ✅ Attractive thumbnail Code : # iot_federated_rl_gui.py # Single-file: Python – IoT & Autonomous Protection with Federated Learning + RL (CustomTkinter GUI) # Author: FuzzuTech # Notes: # - No external ML frameworks used; lightweight FedAvg + Logistic Regression (from scratch) using NumPy. # - RL via tabular Q-learning with epsilon-greedy policy. # - Live simulator of IoT traffic + attack scenarios (Normal, PortScan, BruteForce, DDoS). # - Actions: Block, Throttle, Isolate, Honeypot. Reward balances damage vs. mitigation cost. # - GUI: CustomTkinter with tabs (Dashboard, Simulator, Federated Learning, Logs, Settings). import sys import time import json import threading import random from collections import deque, defaultdict import numpy as np import tkinter as tk import customtkinter as ctk from tkinter import messagebox, filedialog from matplotlib.figure import Figure ...

  Demo : Click Video 👇👇👇 📌 Features Embedded YouTube Short 🎥 Project explanation (code + GUI screenshots) SEO keywords for AI in Cybersecurity, Python GNN, Threat Detection App Code : # gnn_threat_gui.py # Machine Learning – Graph Neural Networks (GNNs) for Threat Detection # "Network traffic ko graph ke form mein analyze karke attacks pakadna + CTkinter GUI" # Author: FuzzuTech Plan (Modern + Stylish + Attractive) import os import sys import time import math import threading import queue import random from dataclasses import dataclass, field from typing import Dict, List, Tuple # ---- Third-party ---- import numpy as np import torch import torch.nn as nn import torch.optim as optim import networkx as nx import matplotlib matplotlib.use("Agg")  # for off-screen draw; embedding handles canvas import matplotlib.pyplot as plt from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg import customtkinter as ctk import tkinter as tk from tkinter impor...

  Demo : Click Video 👇👇👇 Features: ✔ SEO keywords included (IDS, ML, Tkinter, Python GUI) ✔ Embedded YouTube Short ✔ Full code snippet (main.py) ✔ Download links (GitHub/GDrive optional) ✔ Call-to-action: Subscribe + Follow socials Code : """ main.py Single-file IDS demo: - Generates synthetic network-like dataset (if missing) - Trains IsolationForest model and saves model_iforest.pkl - Runs a modern-looking Tkinter GUI to simulate live packets,   score them with the model and highlight anomalies. Usage: 1) Create and activate a venv (recommended) 2) pip install -r requirements.txt 3) python main.py """ import os import threading import time import random import sys import queue from pathlib import Path import tkinter as tk from tkinter import ttk, messagebox # --- third-party libs --- try:     import numpy as np     import pandas as pd     from sklearn.ensemble import IsolationForest     import joblib except Exception as e:...