In medical emergencies, rapid and accurate drug recommendation can be life-saving. The proposed system utilizes machine learning to suggest appropriate drugs based on patient symptoms, medical history, allergy profiles, and vital signs. By training models on a dataset of historical medical cases and drug efficacy records, the system predicts the most suitable medications for emergency conditions such as seizures, asthma attacks, heart failure, and allergic reactions. The system aims to assist paramedics, emergency room doctors, and caregivers by providing intelligent drug suggestions, reducing human error and saving crucial time.
The Text-to-Speech (TTS) Converter System is a software application that converts input text into human-like speech. This system is designed to help visually impaired individuals, people with reading disabilities, and users looking to convert written content into audio form. It takes typed or uploaded text as input and produces an audible speech output using a speech synthesis engine. The system aims to be simple, efficient, and accessible across different platforms. With advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP), modern TTS systems now produce highly realistic and context-aware voices, mimicking human intonation, pauses, and pronunciation. This project leverages these technologies to create an efficient and user-friendly TTS converter system
Automated bird species identification plays a crucial role in ecological monitoring, biodiversity assessment, and conservation efforts. This study presents a system that leverages audio signal processing and neural networks to accurately classify bird species based on their vocalizations. The proposed approach involves preprocessing raw bird song recordings using techniques such as noise reduction, Mel-frequency cepstral coefficients (MFCC) extraction, and spectrogram analysis to capture essential acoustic features. These features are then used to train a convolutional neural network (CNN) designed to learn and distinguish between the unique audio patterns of various bird species. The model demonstrates high classification accuracy across a diverse dataset of bird calls, highlighting its potential for real-time, scalable deployment in natural environments. This work contributes to the advancement of bioacoustic monitoring by offering an efficient, non-invasive tool for automated species recognition.
Natural disasters such as earthquakes, floods, wildfires, and hurricanes often cause catastrophic damage to life and infrastructure. Rapid and effective response is crucial to minimize casualties and ensure resource allocation. Traditional disaster management systems are often reactive, slow, and dependent on manual decision-making, which can delay rescue operations. This project proposes an AI-powered Disaster Response System that leverages machine learning, computer vision, natural language processing (NLP), and real-time data analytics to enhance disaster detection, prediction, and response. The system integrates multiple data sources, including satellite imagery, IoT sensors, social media feeds, and weather data, to predict disasters, assess damage. By automating early warnings, situational awareness, and resource allocation, the AI system aims to reduce human error, speed up rescue operations, and improve decision-making in emergency scenarios.
Visually impaired individuals face significant barriers in accessing technology, reading text, navigating spaces, and interacting with devices. This project proposes an AI Voice Assistant specifically designed to empower the visually impaired by allowing voice-based interaction with a computer or smartphone using speech recognition, text-to-speech, computer vision, and AI-based task automation. The system listens to user commands, processes them intelligently using Natural Language Processing (NLP), and responds with voice output. It can assist with daily tasks such as reading text aloud (OCR), recognizing objects or faces, accessing news, answering questions, sending emails, or opening applications, thus offering increased independence and accessibility.
This paper consists of fraud detection and measures to automate it fully. For every bank, it has become essential for Fraud detection. Fraud is rising significantly, which ends in many damages for the banks. Transactions create unique challenges for fraud exposure due to the lack of short-term processing. The foremost task is a feasibility study of chosen fraud detection methods. With the help of models, these transactions are to be tested individually and further proceeded. We first define a detection task: attributes of the dataset, the metric choice, and any techniques to control such unbalanced datasets. This leads to the fact that the underlying pattern generating the dataset results: For example, cardholders may improve their purchasing habits over periods, and fraudsters may change their tactics. Later, we highlighted several methods used to obtain the sequential features of credit card transactions.
Fake news has become a major threat to public discourse, elections, health campaigns, and even national security. With the rapid spread of false information on social media and websites, it is crucial to build systems that can automatically detect and classify news as real or fake. This project proposes a Fake News Detection System using Natural Language Processing (NLP) and Machine Learning techniques to analyze and predict the credibility of a news article based on its content. By training on a labeled dataset, the system learns linguistic patterns commonly found in fake news and accurately classifies new articles.
In this era of recent times, crime has become an evident way of making people and Society under trouble. An increasing crime factor leads to an imbalance in the Constituency of a country. In order to analyze and have a response ahead this type of Criminal activities, it is necessary to understand the crime patterns. This study imposes one such crime pattern analysis by using crime data obtained from Kaggle open source which in turn used for the prediction of most recently occurring crimes. The major aspect of this project is to estimate which type of crime contributes the most along with time period and location where it has happened. Some machine learning algorithms such as Naïve Bayes is implied in this work in order to classify among various crime patterns and the accuracy achieved was comparatively high when compared to pre composed works.
Build a lightweight web-application security scanner that automatically discovers input points (URLs, query parameters, forms), performs non-destructive tests to detect potential SQL Injection (SQLi) and Cross-Site Scripting (XSS) vulnerabilities, analyzes server responses for tell-tale signs (reflection, error messages, missing input sanitization), stores results, and shows a concise report. The tool is intended for educational and defensive use (pen-testing with permission), and focuses on detection and reporting rather than exploitation.
In today’s fast-paced lifestyle, managing finances is often neglected, leading to overspending and financial instability. This project aims to develop a Python-based Personal Expense Tracker and Budgeting System that enables users to record, categorize, analyze, and visualize their income and expenses. The system allows users to set budgets, track spending habits, receive alerts when nearing limits, and view reports in real time. With an intuitive interface, this tool promotes financial awareness and encourages better spending decisions.
Academic performance prediction is essential for early intervention and student support. Traditional evaluation methods only assess outcomes after exams, which limits proactive academic guidance. This project proposes a Student Performance Prediction System using Random Forest, a supervised machine learning algorithm, to predict a student’s performance based on multiple factors such as demographics, study time, health, attendance, previous grades, parental education, and internet access. This model helps educators identify at-risk students and improve academic outcomes through data-driven insights.
Recruiters often receive hundreds of resumes for a single job posting, making manual screening time-consuming and error-prone. This project automates the resume screening process using NLP techniques by matching the content of resumes with job descriptions. The system calculates a similarity score between each resume and the job post to identify the most suitable candidates, helping recruiters focus only on the most relevant applications.
The rapid urbanization of cities has led to major challenges in effective waste management. Traditional garbage collection systems are inefficient, often resulting in overflowing bins, increased pollution, and poor hygiene. This project proposes a Smart Garbage Management System using IoT and cloud technologies to automate the monitoring, scheduling, and optimization of waste collection in urban areas. The system involves smart bins equipped with sensors to monitor garbage levels in real-time, transmit data to a centralized dashboard, and dynamically notify collection units based on priority. This improves route planning, saves time, and reduces environmental hazards.
With the rapid growth of e-commerce platforms, recommending relevant products to users has become a vital part of improving user satisfaction and boosting sales. This project develops an intelligent recommendation system that suggests products to users based on their browsing behavior, purchase history, and user preferences using collaborative and content-based filtering techniques. Built using Python, the system employs machine learning models and integrates seamlessly into e-commerce platforms. Optional modules include sentiment analysis from reviews and a hybrid recommendation engine for enhanced accuracy.
In today’s data-driven world, organizations generate vast amounts of structured and unstructured data. Data Sphere is a unified data and AI platform that allows scalable and efficient management, analysis, and visualization of big data. This project leverages Datasphere to perform data ingestion, transformation, and advanced analytics, enabling stakeholders to extract actionable insights using built-in data science tools, AI, and machine learning capabilities.
