Sentiment analysis, also refers as opinion mining is a sub machine learning task where the general sentiment of a given document is determined. Using Machine Learning and Natural language processing (NLP) subjective information of a document is extracted and classified according to its polarity (Positive or Negative). It is a useful analysis as it provides an overall opinion of the viewer about a movie. Sentiment analysis is far from to be solved as the language is very complex. This complexity makes it interesting.

In this project, I choose to try to classify the reviews from IMDB Dataset into ‘positive’ or ‘negative’ sentiment by building a model based on neural networks. IMDB is an online database of information related to films, television etc., Here viewers share their opinion about any movie they watched. It is perfect source of data to determine the current overall opinion about any movie.

1. Downloading the data
2. Preparing and Processing the Data
3. Uploading the Data to S3
4. Building and Training the PyTorch Model
5. Testing the Model
6. Deploying the model for testing
7. Use the model for testing
8. Deploy the model for the web app
9. Use the model for the web app

An anime info app for teaching how to use Jetpack Compose (State, Navigation, Animation etc..) with Clean Architecture and shows how to write Unit & Ui test

• Minimum SDK level 21
• 100% Kotlin based + Coroutines and Flow
• Android Architecture Components – Collection of libraries that help you design robust, testable, and maintainable apps.
  • A single-activity architecture, using the Compose Navigation to manage composable transactions.
  • Lifecycle – perform an action when lifecycle state changes
  • ViewModel – Stores UI-related data that isn’t destroyed on UI changes.
  • UseCases – Located domain layer that sits between the UI layer and the data layer.
  • Repository – Located in data layer that contains application data and business logic.
• Jetpack Compose – is Android’s recommended modern toolkit for building native UI. It simplifies and accelerates UI development on Android. Quickly bring your app to life with less code, powerful tools, and intuitive Kotlin APIs
• Android Hilt – Dependency Injection Library
• Retrofit A type-safe HTTP client for Android and Java
• OkHttp An HTTP client that efficiently make network requests
• Coil Compose An image loading library for Android backed by Kotlin Coroutines
• Testing
  • Mockito A mocking framework that tastes really good. It lets you write beautiful tests with a clean & simple API
  • MockWebServer A scriptable web server for testing HTTP clients
  • Truth A library for performing assertions in tests
  • Turbine A small testing library for kotlinx.coroutines Flow
• Material Design 3 is the latest version of Google’s open-source design system.

This app uses MVVM (Model View View-Model) architecture

follow me for my next creations! 🤩

Designed and developed by 2022 halilozcan (Halil ÖZCAN)

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

An anime info app for teaching how to use Jetpack Compose (State, Navigation, Animation etc..) with Clean Architecture and shows how to write Unit & Ui test

• Minimum SDK level 21
• 100% Kotlin based + Coroutines and Flow
• Android Architecture Components – Collection of libraries that help you design robust, testable, and maintainable apps.
  • A single-activity architecture, using the Compose Navigation to manage composable transactions.
  • Lifecycle – perform an action when lifecycle state changes
  • ViewModel – Stores UI-related data that isn’t destroyed on UI changes.
  • UseCases – Located domain layer that sits between the UI layer and the data layer.
  • Repository – Located in data layer that contains application data and business logic.
• Jetpack Compose – is Android’s recommended modern toolkit for building native UI. It simplifies and accelerates UI development on Android. Quickly bring your app to life with less code, powerful tools, and intuitive Kotlin APIs
• Android Hilt – Dependency Injection Library
• Retrofit A type-safe HTTP client for Android and Java
• OkHttp An HTTP client that efficiently make network requests
• Coil Compose An image loading library for Android backed by Kotlin Coroutines
• Testing
  • Mockito A mocking framework that tastes really good. It lets you write beautiful tests with a clean & simple API
  • MockWebServer A scriptable web server for testing HTTP clients
  • Truth A library for performing assertions in tests
  • Turbine A small testing library for kotlinx.coroutines Flow
• Material Design 3 is the latest version of Google’s open-source design system.

This app uses MVVM (Model View View-Model) architecture

follow me for my next creations! 🤩

Designed and developed by 2022 halilozcan (Halil ÖZCAN)

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

In this research, we perfom analysis on wildfire dataset, and propose a model to classify the wildfires based on environmental and geographical data.

Full documentation is available inside “/docs” directory.

• Abstract
• Dataset
• Results
• Conclusion

Environmental, social, and economic causatums from wildfires have been continuously increasing around the world over the past decade. These fires not only devastated forest and grassland but also detrimentally impacted wildfire habitat, water quality & supply, tourism, and property values. In the past few years, a number of research studies have been conducted to monitor, predict and prevent wildfires using several Artificial Intelligence techniques such as Machine Learning, Deep Learning, Big data, and Remote Sensing. In this paper, we proposed the wildfire classification and prediction system to classify the wildfires into elven different types based on the data on temperature anomalies from satellites and geographical data using the CatBoost classifier. Quality metric – multi-class ROC-AUC has been considered to evaluate the performance of the system. The proposed system achieved high performance on the test set.

We collect the environmental data from National Centers for Environmental Prediction (NCEP)

Source: NCEP data

Russian Cities data

In this project, we analysed the wildfire data and proposed a model to classify the wildfires based on ensemble learning.

In this research, we perfom analysis on wildfire dataset, and propose a model to classify the wildfires based on environmental and geographical data.

Full documentation is available inside “/docs” directory.

• Abstract
• Dataset
• Results
• Conclusion

Environmental, social, and economic causatums from wildfires have been continuously increasing around the world over the past decade. These fires not only devastated forest and grassland but also detrimentally impacted wildfire habitat, water quality & supply, tourism, and property values. In the past few years, a number of research studies have been conducted to monitor, predict and prevent wildfires using several Artificial Intelligence techniques such as Machine Learning, Deep Learning, Big data, and Remote Sensing. In this paper, we proposed the wildfire classification and prediction system to classify the wildfires into elven different types based on the data on temperature anomalies from satellites and geographical data using the CatBoost classifier. Quality metric – multi-class ROC-AUC has been considered to evaluate the performance of the system. The proposed system achieved high performance on the test set.

We collect the environmental data from National Centers for Environmental Prediction (NCEP)

Source: NCEP data

Russian Cities data

In this project, we analysed the wildfire data and proposed a model to classify the wildfires based on ensemble learning.

✨ Application for instant text translation and enhancement via Gemini API with hotkey support

• One-click translation of selected text
• Translation enhancement using Gemini AI
• Flexible configuration via config file

1. Select the text you want to translate.
2. Press ctrl+shift+t (or your configured hotkey).
3. The translated and enhanced text will be automatically pasted in place of the original.

Clone the repository:

git clone https://github.com/anacesh/Hotkey-translator-gemini
cd text-translator

Install dependencies:

pip install -r requirements.txt

1. Go to Google AI Studio.
2. Click “Get API key” in the menu.
3. Create a new API key.

Copy the key to config.json:

"gemini_api_key": "YOUR_GEMINI_KEY"

❗ Important: The Gemini API is not available in all countries. Check the list of supported regions.

The config.json file contains the main settings:

{
  "hotkey": "ctrl+shift+t",
  "enable_alt_copy": true,
  "default_target_language": "en",
  "use_gemini": true,
  "send_original_to_gemini": true,
  "system_prompt": "...",
  "gemini_extra_prompt": "...",
  "languages": {
    "ru": { "target": "en" },
    "en": { "target": "ru" }
  }
}

• hotkey: Hotkey for activation (e.g., ctrl+shift+t).
• use_gemini: Enable translation enhancement via Gemini API (true or false).
• system_prompt: System prompt for the Gemini model.
• languages: Translation language settings (e.g., {"ru": {"target": "en"}}).

python main.py

text-translator/
├── clipboard_handler.py  # Clipboard handling
├── config.py             # Configuration manager
├── main.py               # Entry point
├── translator.py         # Translation and Gemini API logic
├── tray_icon.py          # System tray icon
├── config.json           # Configuration file
└── requirements.txt      # Dependencies

-   Administrative privileges are required for keyboard event handling.
-   The Gemini API may have request limitations.
-   X11/Wayland is required for Linux operation.

📄 License MIT License © 2025 [anacesh]

Gopher HTTP requests (POST/GET)

URL: gopher: // :/_ followed by TCP data stream

Tip for determing POST request length:

└─$ echo “username=admin&password=admin” | wc -c

30

Subtract 1 from this result. = 29 -> Then add this to content length header.

Example output:

└─$ python3 gopher-requests.py

1. Gopher GET Request
2. Gopher POST Request

[*] Please select GET (1) or POST (2) request: 1

[x] Gopher GET Request:
gopher://localhost:80/_GET%20/%20HTTP/1.1%0D%0AHost%3A%20127.0.0.1%0D%0AContent-Type%3A%20application/x-www-form-urlencoded%0D%0A

1. Gopher GET Request
2. Gopher POST Request

[*] Please select GET (1) or POST (2) request: 2

[?] Informational: The four HTTP headers above are required for POST requests, namely POST, Host, Content-Type and Content-Length. If it is missing, an error will be reported, but GET does not use it.

[x] Gopher POST Request:
gopher://127.0.0.1:80/_POST%20/admin%20HTTP/1.1%0D%0AHost%3A%20127.0.0.1%0D%0AContent-Type%3A%20application/x-www-form-urlencoded%0D%0AContent-Length%3A%2029%0D%0A%0D%0Ausername%3Dadmin%26password%3Dadmin%0D%0A

Update: This script was helpful during a recent offensive-security course I took, I thought it would be nice to put it out in the universe. It will help you form GET and POST requests that will be able to be used in SSRF.

A simple Leaflet plugin that provides a collapsible search box.

• Basic Demo
• Fuse.js Demo

• Include the Leaflet.AnimatedSearchBox.css file in the head section of your document:

<link href="src/AnimatedSearchBox.css" rel="stylesheet">

• Include the Leaflet.AnimatedSearchBox.js file in your document:

<script src="src/AnimatedSearchBox.js"></script>

• Add the search icon image file to img/search_icon.png

• Create a new L.Control.Searchbox and add it to the map:

var searchbox = L.control.searchbox({
    position: 'topright',
    expand: 'left'
}).addTo(map);

• position: Sets the position of the searchbox (Default: 'topright').
• expand: Sets the direction in which the search box expands. (Default: 'left').
• collapsed: Sets the initial state of the searchbox (Default: false).
• id: Sets the id of the container of the searchbox.
• class: Adds custom classes to the container of the searchbox.
• width: Sets the width of the input field of the searchbox. (Example: '450px')
• iconPath: Sets the path for the search icon (Default: 'img/search_icon.png').
• autocompleteFeatures: Activates the given features (Default: []).
  Possible features:
  • 'setValueOnClick': Set the value of the searchbox to the value of the clicked autocomplete list item.
  • 'arrowKeyNavigation': Coming soon
  • 'setValueOnHover': Coming soon
  • 'setValueOnEnter': Coming soon

// Expand the searchbox
searchbox.show()

// Collapse the searchbox
searchbox.hide()

// Toogle the searchbox
searchbox.toggle()

// Returns true if searchbox is collapsed
searchbox.isCollapsed()

// Returns current value of the text field of the searchbox
searchbox.getValue()

// Sets the value of the text field of the search box
searchbox.setValue(value)

// Adds an item to the autocomplete list
searchbox.addItem(item)

// Adds items to the autocomplete list
searchbox.addItems(items)

// Sets items of the autocomplete list
searchbox.setItems(items)

// Clears the autocomplete list
searchbox.clearItems()

// Clears the text field of the search box
searchbox.clearInput()

// Clears the text field and the autocomplete list of the search box
searchbox.clear()

// Adds a listener function (handler) to a particular DOM event (event)
// of the input field of the searchbox
searchbox.onInput(event, handler);

// Removes a previously added listener function (handler) of a particular DOM event (event)
// from the input field of the searchbox
searchbox.offInput(event, handler);

// Adds a listener function (handler) to a particular DOM event (event)
// of the button of the searchbox
searchbox.onButton(event, handler);

// Removes a previously added listener function (handler) of a particular DOM event (event)
// from the button of the searchbox
searchbox.offButton(event, handler);

// Adds a listener function (handler) to a particular DOM event (event)
// of the autocomplete list
searchbox.onAutocomplete(event, handler);

// Removes a previously added listener function (handler) of a particular DOM event (event)
// from the autocomplete list
searchbox.offAutocomplete(event, handler);

• Add option to use <datalist> for autocomplete.
• Support for npm etc.

This project is about a python script that tells basic information about YouTube videos and download them as a .mp4 file format. I’m planning on make this project light as possible, and possibly add downloader for other websites too, currently working on one for Instagram and Twitter.

First, we need to clone this repository. As there aren’t any functions on the script to make it auto-update itself, you can choose whenever you want to check for updates! Keep in mind you will need Python 3 or above to run this script, the tested and debugged version used to make this project was v3.9.8 as mentioned in the tags on top of this README file.

git clone https://github.com/KarboXXX/python-media.git

cd python-media

And now, we need to install our python package dependecies in the requirements.txt file, running:

pip3 install -r requirements.txt

If you’re on Linux and lazy like me, you can just:

git clone https://github.com/KarboXXX/python-media.git && cd python-media && pip3 install -r requirements.txt

Then, you ready to go! Make sure to run python3 main.py for help.

Please, if you run this project on your computer, and it doesn’t work, report it, and help me to improve my coding! Any tips for performance or making the code look better are welcome as well!

This project is about a python script that tells basic information about YouTube videos and download them as a .mp4 file format. I’m planning on make this project light as possible, and possibly add downloader for other websites too, currently working on one for Instagram and Twitter.

First, we need to clone this repository. As there aren’t any functions on the script to make it auto-update itself, you can choose whenever you want to check for updates! Keep in mind you will need Python 3 or above to run this script, the tested and debugged version used to make this project was v3.9.8 as mentioned in the tags on top of this README file.

git clone https://github.com/KarboXXX/python-media.git

cd python-media

And now, we need to install our python package dependecies in the requirements.txt file, running:

pip3 install -r requirements.txt

If you’re on Linux and lazy like me, you can just:

git clone https://github.com/KarboXXX/python-media.git && cd python-media && pip3 install -r requirements.txt

Then, you ready to go! Make sure to run python3 main.py for help.

Please, if you run this project on your computer, and it doesn’t work, report it, and help me to improve my coding! Any tips for performance or making the code look better are welcome as well!