Modern microblogging and social networking ecosystems serve as massive, continuous streams of public expression, generating expansive volumes of multilingual data that reflect global consumer sentiments and emotional trends. Parsing actionable insights from this cross-lingual text presents a major analytical challenge due to structural variations across different languages. To address this, this paper introduces an optimized, computationally lightweight multilingual opinion mining framework. The core architecture relies on a statistically robust Logistic Regression classifier designed to categorize user-generated text into precise Positive, Negative, or Neutral polarities. By harvesting real-time data from Twitter/X, the system implements a sequence of pipeline operations, including text sanitization, tokenization, and vocabulary normalization. These refined linguistic tokens are transformed into numeric arrays using optimized vectorization models before being processed by the predictive algorithm. Empirical testing indicates that this machine learning configuration delivers high classification accuracy across mixed-language inputs while maintaining remarkably low computational overhead, offering corporations and academic researchers an efficient alternative for scalable public feedback tracking.
Modern surveillance systems often struggle to detect and respond to threats in real time. This paper introduces a system that uses context-aware threat intelligence to detect weapons, recognize violent actions, analyze time-based patterns, and assess potential threats based on th…
In the era of modern technologies, introduced the widespread use of cloud computing and other solutions that revolutionized the storage and management of information. Cloud-based network threat identification and risk management using applying Log Analysis and Machine learning is…
Rising levels of carbon emissions have emerged as a key factor to climate change requiring smart mechanisms of monitoring and mitigation. In this paper, CarbonIQ, a machine learning-based, generative AI-based, and IoT-based data collection integrated carbon footprint prediction a…
In the digital age, the need for quick and reliable access to healthcare information is increasingly essential. This project introduces an AI-powered Symptom Checker Application that leverages Natural Language Processing (NLP) and Machine Learning (ML) to interpret user-described…
Microplastic contamination in coastal ecosystems has emerged as a critical environmental issue with significant ecological, economic, and public health consequences. Conventional monitoring approaches rely heavily on field sampling and laboratory-based analysis, which are time-co…
This paper presents an AI-driven self-adaptive irrigation framework integrating IoT sensing, computer vision, adaptive learning, and automated irrigation control. The proposed system continuously monitors soil moisture, temperature, humidity, water flow, and visual plant health c…