Volume 13 | Issue 4 |

 Abstract

The AI system called "Road Traffic Analysis Using YOLO-V4 & Deep Sort" uses advanced technology to automate vehicle tracking in real time. Increasing numbers of urban residents and cars place heavy burdens on current traffic management systems that operate slowly and have limits. This project solves video monitoring difficulties by using YOLO-V4 for object detection and Deep Sort for multi-object tracking to process both streaming and recorded videos. The system recognizes motor vehicles, gives those ID numbers, and follows them across several images to reveal road patterns and vehicle movement. The system uses improved processing tools such as non-max suppression and Kalman filter methods to detect objects reliably. The system uses these technologies together with Python and TensorFlow to work in all types of roads and smart city settings at top speed. This project lets transportation authority's control road activity better to ease congestion and better protect their users. Our project will keep improving through the addition of traffic flow prediction systems based on IoT technology plus cloud architecture for wider implementation. Most modern traffic systems would benefit from the addition of smart tolling functions plus automatic detection of crimes and self-driving car assistance as part of intelligent transportation research.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

YOLO-V4 , Deep Sort, Kalman Filter, Python , Tensorflow, Object Detection.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Intelligent SMS Spam Classifier is a Flask web tool that uses a Multinomial Naive Bayes model to spot and block spam messages. Text preprocessing through CountVectorizer lets the system find spam accurately while identifying regular messages. More sophisticated rules are required to handle the growing number of spam messages which use phishing and fraud tactics. The project improves how well machine learning can detect spam by making it more flexible across different environments. The easy-to-use Flask framework lets people use their phones or PCs to classify messages live. The system can detect spam patterns from different kinds of text through its training on a wide variety of SMS data. Our system reliability increases through security features which include input protection checks and HTTP data encryption plus constant incoming message speed management. The application provides flexibility to use either local resources or cloud services such as Heroku and AWS as its deployment platforms. The system will gain more capabilities through support for different languages along with deep learning technology and API-based filtering systems with a mobile app addition.

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 Keywords

CountVectorizer, Intelligent SMS Spam Classifier, Naive Bayes Model.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The evolving e-commerce environment requires consumers to overcome product deal identification difficulties because prices consistently change among various platforms. Through web-based price comparison tool PriceMatch users can access real-time pricing data because of its use of advanced web scraping techniques data analytics alongside automation which extracts data from major online retailers Amazon, Flipkart and Croma. Users access knowledgeable purchasing decisions because the system tracks prices in real-time while providing historical data analysis and automatic price alert notifications. The application constructs its infrastructure from Flask and Selenium combined with BeautifulSoup thus delivering fast data retrieval and user-friendly operation. The system provides future-enhancing capabilities through its modular design structure while giving users options for making their selection between AI-assisted price predictions and mobile-based platform upgrades and broader marketplace reach expansion. The price comparison automation feature in PriceMatch provides shoppers with affordable shopping recommendations that eliminate repetitive work while improving their entire online shopping experience.

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 Keywords

E-Commerce Environment, PriceMatch, Amazon, Flipkart, Croma, BeautifulSoup.

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 Abstract

The machine learning system called CardioMyx analyzes multiple medical parameters through its designed algorithm to detect heart disease risk in individuals. This system applies the Random Forest Classifier ensemble learning technique as a robust method to evaluate patient information which generates high-risk or low-risk categories. The model analyzes age, sex, cholesterol levels, blood pressure, and blood sugar together with lifestyle factors to predict precise risk ratings. Medical personnel gain essential diagnostic insights from advanced analytics built into CardioMyx which leads to fast medical interventions. The technical platform supports preventive cardiology decisions by letting doctors provide individualized medical treatments or lifestyle recommendations based on calculated risk outcomes. As extra educational data enters the model it becomes better at producing accurate risk results and maintains such precision across multiple patient populations. CardioMyx shows promise to change heart disease prognosis by cutting down human mistakes while supporting physicians to detect diseases early. Through its functions the system assists healthcare providers while allowing patients to understand their cardiac state which promotes active cardiovascular risk management.

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 Keywords

CardioMyx, Pressure, Blood Sugar, Cholesterol Levels, Heart Disease Prognosis.

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 Abstract

The digital healthcare platform EPharma uses artificial intelligence together with machine learning capabilities to connect patients' symptoms detection to the generation of correct prescription recommendations. The Random Forest-based learning model in the system evaluates user symptoms to recommend medications effectively thus helping users avoid the dangerous implications of self-diagnosis and wrong self-treatment. EPharma uses strong authentication systems to guarantee safe user access between different user classes including patients and healthcare workers and pharmacists. The Flask framework creates the backend system and enables real-time data retrieval and maintains a MySQL database that operates with structured security parameters. The credibility of medical information in EPharma improves when users interact with external APIs such as Wikipedia and PubChem because this provides verified medication information along with their side effects and proper usage guidelines. Through its intuitive layout EPharma makes medical prescription tasks easy which lets healthcare reach more people with better operational efficiency. The system has built-in AI prediction technology which prevents medical mistakes and a security system using authentication protocols and encryption techniques protects confidential medical data.Through its medication management system EPharma assists healthcare providers with prescription automation which simultaneously decreases their workload while delivering better care outcomes to patients. Users of the platform can learn about their medication through educational resources which empower them with vital knowledge. The document explains how the system functions as well as describes installation methods and testing procedures while demonstrating its capabilities in modernizing healthcare operations. EPharma showcases how it changes digital healthcare by implementing intelligent automation with secure data handling in addition to improved user experiences while fixing current prescription system issues.

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 Keywords

Epharma, Artificial Intelligence, Flask Framework, Wikipedia, Pubchem.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Agricultural disease management remains vital in the field because plant diseases directly affect crop production levels and they create threats for food safety together with economic stability. The conventional approach to plant disease diagnosis consists of agricultural experts conducting visual examinations which prove to be irregular yet lengthy and prevents adequate services to farmers who reside beyond normal accessibility. The Smart Agro-Cure project specifies the development of an AI-powered pesticide recommendation system based on Convolutional Neural Networks which function through image input. The system uses leaf images for automatic disease detection which then recommends appropriate pesticides to improve both accuracy and efficiency in accessible disease management operations. The deep learning model reaches high precision identification of diseases because it receives training on multiple plant disease varieties within its diverse dataset. A curated pesticide database within this system enables users to get green and optimal treatment suggestions. Image preprocessing methods including resizing, normalization and augmentation help the project reach higher accuracy because they enhance model generalization abilities. Through the Flask-based user-friendly graphical interface (GUI) system users can easily upload images to get immediate disease analysis. The software demonstrates excellent scalability together with deployment features that make it suitable for mobile applications and edge computing platforms to serve farmers worldwide.

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 Keywords

Agricultural Disease, Crop Production, Agro-Cure, Convolutional Neural Networks.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Machine learning serves stock market prediction as an essential application through which people alongside organizations obtain data-driven investment intelligence. The system establishes stock market prediction through the implementation of machine learning approaches and deep learning techniques toward stock price forecasting. The system runs on Python while using Flask for web-based deployment which enables users to obtain real-time predictions through an effortless interface. The model acquires historical stock information from Yahoo Finance through the use of Pandas and NumPy and yfinance libraries for processing. Deep learning acceptance requires MinMaxScaler to normalize the data before deep learning processing. The system implements a Sequential model which applies Long Short-Term Memory layers and Dropout layers and Dense layers using TensorFlow Keras for effective prediction of temporal patterns. The system performs five stages as part of its operational scope which include the collection of data followed by preprocessing procedures and model development and evaluation before deployment. Additional possible enhancements to this design will integrate multi-stock forecasting together with sentiment analysis functionality and mobile device compatibility. The main mission is to establish a powerful deep learning algorithm that does stock forecast with superior precision yet the system will also showcase an easy-to-use interface and instant prediction capabilities for user-defined stocks.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine Learning, Investment Intelligence, Stock Market Prediction.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Thyroid nodules are abnormal growths in the thyroid gland that can be benign or malignant. Early detection and classification are crucial for effective treatment. This project utilizes Deep Learning, specifically Convolutional Neural Networks (CNN) with an extended VGG algorithm, to improve accuracy in thyroid nodule detection. The model processes ultrasound images to differentiate between benign and malignant nodules, reducing dependency on manual diagnosis.

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 Keywords

Thyroid nodules, Deep Learning, Convolutional Neural Networks (CNN), VGG algorithm (extended VGG), Ultrasound images, Benign nodules, Malignant Nodules, Early detection, Accuracy improvement, Manual diagnosis, Medical imaging, and Artificial Intelligence (AI) in healthcare.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Feminism is a social and political movement that advocates for the rights and equality of women. Thus, feminism from the start has been a mix of a movement and an ideology that seeks to acquire all kinds of equal rights for women in society. The history of feminism includes early feminist movements, such as the women's suffrage movement, which fought for the right of women to vote in political elections. The women's suffrage movement was a significant part of the broader feminist movement, as it sought to secure basic civil and political rights for women. First-wave feminism, a key part of the women's suffrage movement, focused on gaining political and legal rights for women, such as the right to vote, own property, and access education and employment opportunities. After the suffrage movement ended feminist movement took seat. Second wave feminism marks key aspects in the politics of Marxist and liberal feminist movement. After that, third wave feminism include intersectionality, inclusive, diversity, focusing on individuality and social justice. Fourth wave feminism have digital, inclusive and focusing on online activism, body autonomy and consent. Feminist theory provides framework analysing women oppression, challenging, patriarchal structure, and promoting gender equality. It encompasses various branches, including liberal, radical, Marxist, postcolonial, and intersectional feminism, to understand and address gender-based injustices.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Feminism, rights, equality and social justice.

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Creative Commons Attribution 4.0 and The Open Definition