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Journal articles on the topic 'Streamlit Interface'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Keerthi, MS P., G. Srikar Reddy, V. Sai Raghava, and K. Buchi Reddy. "Streamlit Interface for Multiple Disease Diagnosis." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (2023): 1159–64. http://dx.doi.org/10.22214/ijraset.2023.49166.

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Abstract: Nowadays most people are suffering with the many chronic diseases and getting effected by those without having the knowledge. In this paper we are trying to establish a single streamlet interface for three different machine learning disease prediction models. Our interface includes three diseases which are heart disease, diabetes, and pneumonia. Our heart disease model is developed using the logistic regression which explores whether the person have healthy heart or not by taking the needed inputs which are explored in further sections. Our diabetes model predicts whether the person

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Deeksha, Patel, Parasabaktula Jyostna, Mangal Yadav Shiv, Bhattacharyya Ritendu, and Kumar Depuru Bharani. "Facial Authentication using Deep-Learning: An Advanced Biosecure Login Model Employing an Integrated Deep-Learning Approach to Enhance the Robustness and Security of the Login Authentication Process." Facial Authentication using Deep-Learning: An Advanced Biosecure Login Model Employing an Integrated Deep-Learning Approach to Enhance the Robustness and Security of the Login Authentication Process 8, no. 12 (2024): 7. https://doi.org/10.5281/zenodo.10453298.

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Face recognition is a concept of the safest way of logging on; it entails that our facial images are acquired, detected, and subsequently authenticated by the particular interface. In this present digital generation, safe authentication of the interfaces is the primary cautionary aspect that should be maintained, and this model suggests a secure and strong authentication system. This paper recommends a face recognition login interface that involves deep learning models to provide a strong and secure authentication mechanism. It involves the extraction of facial images, proposes a solution to e

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Sujit Das, Dr, Ananya S Nair, and Pattela Aneesh. "AI Resume Analyzer: Smart Resume Evaluation and Enhancement." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44548.

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This paper presents an AI-driven resume analyzer designed to streamline and enhance the job candidate assessment process. The system utilizes Natural Language Processing (NLP) techniques and machine learning algorithms to process and evaluate resumes efficiently. By extracting key information, calculating an ATS compatibility score, identifying grammar errors, and providing improvement suggestions, the analyzer aims to assist both job seekers and recruiters optimize the resume screening process. The platform also evaluates resumes against job-specific requirements and offers curated resources,

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.Anusha M, Mrs. "Development of a Conversational AI Assistant for Real-Time Task Organization." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47247.

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Abstract This project unveils a smart assistant that transforms task management by weaving together speech-to-text, Retrieval-Augmented Generation (RAG), and OpenAI’s language models. Built with Streamlit and FastAPI, it offers an interactive platform that interprets voice commands, retrieves context, and crafts intelligent responses. The system tackles modern productivity hurdles by enabling seamless task creation, calendar syncing, and document querying, all through a natural, user- friendly interface. Testing reveals robust performance, setting the stage for scalable, innovative conversatio

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Sreelatha Bijili, Nandana Ande, Vinesh Doddi, Sai Aryan Meesala, Thilak Chinta, and Nithin Soma. "AI powered automatic test case creation using Chat GPT and Streamlit interface." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 470–79. https://doi.org/10.30574/wjaets.2025.15.2.0585.

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In today’s rapid software development landscape, ensuring software quality through comprehensive testing is both crucial and increasingly challenging. Traditional test case generation methods are time-consuming, error-prone, and often fail to adapt to evolving application requirements. This project introduces an AI-powered automated test case generation system that leverages OpenAI’s GPT language model integrated into a Streamlit-based interface to revolutionize the way test cases are created. By accepting inputs such as functional requirements, code snippets, or user stories, the system dynam

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Chavan, Dr Amrapali, and Soham Sakunde. "Home Safe: AI-Powered Anomaly Detection for Housing Inspection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44600.

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This paper presents HomeSafe, an AI-driven system for automated housing inspection using Machine Learning-based anomaly detection. The system detects structural cracks from images and estimates their severity, providing detailed reports for decision-making. A MobileNetV2-based CNN model is fine-tuned for crack detection, achieving high accuracy on a benchmark dataset. The system further estimates crack width and dimen sions using image processing techniques. A web-based interface (Streamlit) is developed for easy image uploads and report generation. The project aims to assist engineers, homeow

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Harshavardhan1, Polamarasetty. "Design and Implementation of a Fine-Tuned Llama-Based AI Chatbot with Voice and Text Interaction Using Streamlit and Ollama." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42961.

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Natural language processing (NLP) drives artificial intelligence (AI)driven chatbots that have gained great popularity in many industries in recent years [15], therefore improving humancomputer interactions. Optimized for quick conversational reactions, this paper describes an AIdriven chatbot driven by a finely tuned Llama 3.2 model. Using Streamlit for an interactive user interface, Ollama for model deployment, and SpeechRecognition and pyttsx3 for smooth voice input and texttospeech (TTS) output [5][10[11]], the chatbot combines voice and textbased communication [2][4]. Using Sloth, a dedic

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Akhil, Buddhi. "Multi Disease Prediction Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49614.

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Abstract: Multiple Disease Prediction using Machine Learning, Deep Learning, and Streamlit is a comprehensive project aimed at predicting diseases such as diabetes, heart disease, and Parkinson’s disease. The system leverages a combination of machine learning and deep learning algorithms, including TensorFlow with Keras, Support Vector Machine (SVM), and Logistic Regression, to build accurate and reliable prediction models. These models are trained on publicly available datasets and are deployed using Streamlit Cloud, utilizing the Streamlit library to provide an intuitive and user-friendly in

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B, Sushma, and Benaka Raj. "Deep Learning-Based Web Application for Crop Disease Detection Using CNN and Streamlet." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem.spejss004.

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Crop diseases can cause significant yield losses and threaten food security if not diagnosed and treated in time. In this paper, we present a real-time web application for crop disease detection using deep learning techniques, primarily Convolutional Neural Networks (CNNs). The application allows farmers to upload leaf images through a Streamlet interface, which are then analyzed by a trained CNN model to detect diseases. The system provides fast, accurate predictions along with suggestions for treatments, promoting smart and sustainable farming. For scalability and maintainability, it makes u

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Wadhwani, Lakshay, Kushal Gupta, Akshat Kumar, Biraag S. Prabhakar, and Dr Archana Kumar. "Cognitive Query System Using Generative AI." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 963–67. http://dx.doi.org/10.22214/ijraset.2024.65247.

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Abstract: The Cognitive Query System using Generative AI is a cutting-edge tool designed to transform the way we retrieve and analyse data from a wide range of sources, including text, images, and documents. Powered by the Google Gemini API and deployed through Streamlit, this system allows users to easily ask questions, process images, and explore document contents via an intuitive, interactive interface. The system comprises three core modules: the Question-Answering Module, which uses advanced natural language processing (NLP) techniques to provide contextually accurate, conversational resp

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Yaganteeswarudu, Akkem, Saroj Kumar Biswas, and Varanasi Aruna. "Streamlit Application for Advanced Ensemble Learning Methods in Crop Recommendation Systems – A Review and Implementation." Indian Journal of Science and Technology 16, no. 48 (2023): 4688–702. https://doi.org/10.17485/IJST/v16i48.2850.

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Abstract <strong>Objectives:</strong> This article explores the integration of advanced ensemble machine learning methods within precision agriculture, aiming to enhance the reliability and practical utility of crop recommendation systems. The incorporation of the Streamlit framework in the development process underpins our objective to deliver a user-friendly tool that provides farmers and agricultural analysts with actionable insights. <strong>Methods:</strong> A thorough literature review of artificial intelligence applications in agriculture serves as the foundation of our s

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Harris, Preethi. "AI-DRIVEN REAL-TIME TRAFFIC AND EMERGENCY MANAGEMENT USING YOLO." international journal of advanced research in computer science 16, no. 3 (2025): 60–63. https://doi.org/10.26483/ijarcs.v16i3.7247.

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With rapid urbanization and increasing vehicle ownership, traditional traffic management systems that rely on fixed schedules and basic sensors are no longer sufficient to handle growing congestion. These outdated systems often result in longer travel times, frequent bottlenecks, and delayed emergency responses. To address these issues, AI-driven solutions powered by deep learning (DL) provide an intelligent alternative by dynamically adjusting traffic signals based on real-time conditions. This project presents an AI- powered traffic monitoring and management system that utilizes YOLO for rea

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Reche, Mr Nikhil. "Fake Social Media Profile Detection and Reporting." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45476.

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Abstract: The rise of fake profiles on social media has triggered serious concerns around misinformation, scams, and digital harassment. Manual detection methods are inefficient at large scale, hence automated detection powered by machine learning is the need of the hour. This paper presents a real-time, machine learning- based detection system using the XGBoost classifier. The model analyzes behavioral and content-based features such as follower-following ratio, post frequency, profile bio characteristics, hashtag usage, and engagement patterns. The system is implemented through a user-friend

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Urvashi, Urvashi, Syed Wajahat Abbas Rizvi, Dwivedi P.K., Ashish Kumar Pandey, and Sandhya Sandhya. "DEEP LEARNING-BASED MENTAL HEALTH DETECTION USING FINE-TUNED BERT: A MULTICLASS TEXT CLASSIFICATION APPROACH." Journal of Dynamics and Control 9, no. 5 (2025): 208–15. https://doi.org/10.71058/jodac.v9i5018.

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Mental health disorders, including anxiety, bipolar disorder, and suicidal tendencies, significantly affect individual well-being and necessitate timely detection for effective intervention. Traditional assessment methods, such as clinical evaluations and self-reported surveys, are often time-consuming and subjective. This paper introduces a deep learning-based approach utilizing a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) model for multi-class mental health classification through textual analysis. The system classifies text into four categories—depression, anxi

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M, Mrs Rajeshwari, Shodhan B C, and Gahan V S. "SOUND-BASED WILDLIFE PROTECTION WITH MACHINE LEARNING." International Journal of Engineering Applied Sciences and Technology 09, no. 01 (2024): 202–6. http://dx.doi.org/10.33564/ijeast.2024.v09i01.033.

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This research presents an automated system using acoustic and ML technologies to detect wildlife poaching. Utilizing Streamlit for user interface, users can upload and analyze audio files from restricted areas. The system alerts conservation officials via Telegram upon detecting suspicious sounds like gunfire or footsteps, ensuring swift responses. It comprises a user friendly interface for uploading and processing recordings, a ML algorithm trained to identify poaching-related noises using spectrograms, and real-time notification via Telegram for immediate action. This innovative approach enh

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Nagaraju, Dr V. Siva, and N. Ramya Sri. "IMAGE ANALYSIS FRAMEWORK FOR CARBON FOOTPRINT ESTIMATION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem39945.

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This study has been undertaken to develop an image analysis framework for estimating the carbon footprint of consumer products using advanced computational techniques. The framework leverages image recognition models alongside a dataset of product-specific carbon emission metrics to provide accurate assessments. Streamlit serves as the primary interface for user interaction, while Python, HTML, and CSS facilitate backend computation and frontend design. The analytical framework integrates data preprocessing, feature extraction, and environmental impact modelling to ensure robust analysis. Publ

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Kumar, R. Prapulla. "Cryptocurrency Price Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1838–48. https://doi.org/10.22214/ijraset.2025.68470.

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Abstract: Cryptocurrency markets exhibit high volatility, making accurate price prediction a challenging task. This project aims to develop a cryptocurrency price prediction model using linear regression. The model is trained on historical price data, considering key features such as closing prices, trading volume, and market trends. The implementation is built with Streamlit, allowing for an interactive and user-friendly interface where users can input parameters and visualize predictions dynamically.

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Varma, S. Sri Harsha. "Deepfake Video Detection with Explainable AI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47775.

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The increasing complexity of deepfake technology poses a serious threat to digital media integrity and strong detection and explanation mechanisms are needed. This project presents a complete system that detects not only deepfake videos but also gives detailed, interpretable explanations to support user understanding and trust. The detection pipeline integrates an InceptionV3-based Convolutional Neural Network (CNN) for spatial feature extraction and a Gated Recurrent Unit (GRU) for modeling temporal sequences, enabling accurate classification of video content as real or fake. Facial feature s

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Sufian, Md Abu, Wahiba Hamzi, Sadia Zaman, et al. "Enhancing Clinical Validation for Early Cardiovascular Disease Prediction through Simulation, AI, and Web Technology." Diagnostics 14, no. 12 (2024): 1308. http://dx.doi.org/10.3390/diagnostics14121308.

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Cardiovascular diseases (CVDs) remain a major global health challenge and a leading cause of mortality, highlighting the need for improved predictive models. We introduce an innovative agent-based dynamic simulation technique that enhances our AI models’ capacity to predict CVD progression. This method simulates individual patient responses to various cardiovascular risk factors, improving prediction accuracy and detail. Also, by incorporating an ensemble learning model and interface of web application in the context of CVD prediction, we developed an AI dashboard-based model to enhance the ac

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Sabin Begum, R., A. Mohamed Anwar, and S. Anu Priya. "A Health-Based Deep Learning System for Rapid and Precise Detection of Acute Lymphoblastic Leukemia." Journal of Neonatal Surgery 14, no. 4S (2025): 87–94. https://doi.org/10.52783/jns.v14.1746.

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Acute Lymphoblastic Leukemia (ALL) is a prevalent and life-threatening form of cancer that requires accurate and timely diagnosis for effective treatment. Traditional diagnostic methods for ALL often involve time-consuming and subjective manual examination of blood smears, leading to potential errors and delays in diagnosis. To address these challenges, this project proposes a diagnostic system based on deep learning Convolutional Neural Networks (CNNs) and Streamlit, aimed at achieving fast and accurate classification of Acute Lymphoblastic Leukemia (ALL). The project leverages the power of d

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Ahmed,, Khan Abdullah Riyaz. "Real-Time American Sign Language Recognition Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50990.

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This paper presents the design and implementation of a real-time sign language recognition system focused on detecting static hand gestures representing the English alphabets (A–Z). The project leverages computer vision and machine learning to provide an accessible, low-cost tool for communication between hearing-impaired individuals and non-signers. The system captures webcam input, extracts hand landmarks using Google’s Mediapipe framework, and classifies gestures through a trained Random Forest model. A Streamlit-based user interface displays the detected letters and enables real-time sente

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Raju, Dr H. M. Naga. "Enhanced Age and Gender Estimation Using Opencv." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43052.

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The aim of this project is to improve age and gender estimation precision using advanced computer vision techniques along with user-friendly interfaces to a web application. It makes use of OpenCV for image processing and Streamlit for making an interactive web application. The system will predict age and gender precisely from images and live webcam feeds. It integrates pre-trained deep learning models specifically developed for age and gender classification with robust predictions made using Caffe-based networks. A Streamlit user interface is developed that effortlessly allows a user to uploa

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Srivastava, Shweta. "STOCKDIARY: Post-Development Analysis and Performance Evaluation of an Advanced Stock Market Prediction Platform." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35206.

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In the rapidly changing financial landscape, accurate stock market prediction is essential for investors to optimize their portfolios and manage risks effectively. STOCKDIARY is an innovative platform that leverages state-of-the-art technologies like Python, Streamlit, and Jupyter to provide real-time insights and predictions to its users. This paper conducts a thorough post-deployment evaluation of STOCKDIARY focusing processes, user engagement, predictive accuracy, and overall performance. Utilizing sophisticated predictive models trained on extensive historical data, including Linear Regres

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U, Anjali. "Enhancing Healthcare through Multi Disease Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 7 (2025): 12–16. https://doi.org/10.22214/ijraset.2025.72888.

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This study presents a unified machine learning-based system for predicting multiple diseases diabetes, Parkinson’s disease, and heart disease through a single interface. Support Vector Machine (SVM) is used for diabetes and Parkinson’s prediction, while Logistic Regression handles heart disease classification. The models are trained on publicly available datasets and integrated into an interactive web app using Streamlit. This approach enhances diagnostic efficiency, supports early detection, and provides a scalable solution to assist clinical decision-making.

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Gupta, Ritik. "MySQL based Chit Chat Application." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 761–67. http://dx.doi.org/10.22214/ijraset.2024.63199.

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Abstract: This project provides a modern chat application designed to work well with MySQL databases. The app uses Streamlit on the front end, providing a user interface through which users can query and manage data in natural language. The combination of OpenAI's GPT framework and LangChain provides powerful natural language processing and conversational capabilities, making data interaction more intuitive and efficient. This project demonstrates the integration of modern AI capabilities with traditional information management systems. The software bridges the gap between simple searches and

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Monks, Thomas, and Alison Harper. "Improving the usability of open health service delivery simulation models using Python and web apps." NIHR Open Research 3 (December 15, 2023): 48. http://dx.doi.org/10.3310/nihropenres.13467.2.

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One aim of Open Science is to increase the accessibility of research. Within health services research that uses discrete-event simulation, Free and Open Source Software (FOSS), such as Python, offers a way for research teams to share their models with other researchers and NHS decision makers. Although the code for healthcare discrete-event simulation models can be shared alongside publications, it may require specialist skills to use and run. This is a disincentive to researchers adopting Free and Open Source Software and open science practices. Building on work from other health data science

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Monks, Thomas, and Alison Harper. "Improving the usability of open health service delivery simulation models using Python and web apps." NIHR Open Research 3 (October 5, 2023): 48. http://dx.doi.org/10.3310/nihropenres.13467.1.

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One aim of Open Science is to increase the accessibility of research. Within health services research that uses discrete-event simulation, Free and Open Source Software (FOSS), such as Python, offers a way for research teams to share their models with other researchers and NHS decision makers. Although the code for healthcare discrete-event simulation models can be shared alongside publications, it may require specialist skills to use and run. This is a disincentive to researchers adopting Free and Open Source Software and open science practices. Building on work from other health data science

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Venkateswarlu, Dr S. China. "Voice and Text-Based AI Healthcare Chatbot Using Local Language Models." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49087.

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Abstract -- In recent years, the integration of Artificial Intelligence (AI) into healthcare systems has transformed the way users access medical knowledge, offering enhanced accessibility, personalization, and efficiency. This project presents the design and development of an AI-based healthcare chatbot capable of functioning both offline and online, supporting voice and text input/output, and built entirely using free, open-source tools. Unlike most existing solutions that depend on expensive or cloud-based APIs, this chatbot utilizes locally hosted machine learning models from Hugging Face

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Patil, Vaibhav, Dr Sanjay Sutar, Sanskruti Ghadge, and Shubham Palkar. "Gesture Recognition for Media Interaction: A Streamlit Implementation with OpenCV and MediaPipe." International Journal for Research in Applied Science and Engineering Technology 11, no. 9 (2023): 1039–46. http://dx.doi.org/10.22214/ijraset.2023.55775.

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Abstract: This project aims to create a media player application that responds to hand gestures, using Python and the OpenCV library. The system taps into computer vision methods, like those used in depth-sensing cameras such as Kinect or Intel RealSense, to track and understand hand movements. It processes the depth data to extract hand features and employs machine learning (like CNNs or decision trees) to classify these into gestures. This lets the application accurately interpret user gestures and apply them to media commands—play, pause, volume, and more. All this works through a user-frie

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Thaniya, MD Shabhana. "Smart Detection of Parkinson’s Disease Using Random Forest and Streamlit." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49456.

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ABSTRACT Parkinson’s Disease (PD) is a chronic and progressive neurodegenerative disorder that primarily affects movement and coordination, often leading to significant impairments in daily life. Early diagnosis of Parkinson’s dis- ease is crucial for effective medical interven- tion and improved patient outcomes. In re- cent years, machine learning techniques have emerged as powerful tools in the early detec- tion of complex diseases by analyzing large sets of biomedical data. This study presents the development of a web-based application that utilizes a Random Forest Classifier for the accur

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Gajalakshmi N, Andalpriya C, Raja Lakshmi K, and Nuttrenai V. "Fit AI-Personalized Diet and Fitness Planner." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 03 (2025): 508–13. https://doi.org/10.47392/irjaem.2025.0080.

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Maintaining a healthy lifestyle is becoming increasingly challenging due to hectic schedules, unhealthy eating habits, and the lack of personalized diet and fitness guidance. Generic health plans often fail to address individual requirements, leading to ineffective results and poor adherence. To overcome these challenges, FitAI: Personalized Diet and Fitness Planner is developed as an AI-powered web application that provides customized diet and fitness recommendations based on user-specific data. The system collects key user inputs, including age, height, weight, gender, exercise frequency, di

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Deepak Rana, Yash Kapasiya. "Data Analysis and Extraction ChatBot: Leveraging Retrieval-Augmented Generation for Enhanced Document Processing." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47875.

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Abstract In the era of information overload, extracting meaningful insights from unstructured documents remains a significant challenge. This paper introduces a customized chatbot designed to extract and analyze textual data from various documents, utilizing Retrieval-Augmented Generation (RAG) techniques. By integrating frameworks like LlamaIndex and LangChain, and deploying a user-friendly interface via Streamlit, the system demonstrates enhanced capabilities in document comparison and data extraction. The chatbot's architecture, implementation, and potential applications are discussed in de

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Ferreira, Luís Otávio de Paula, and Hygor Santiago Lara. "Desenvolvimento e aplicação do TURDUS: uma ferramenta interativa para análise de sinais de aceleração com python." OBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA 22, no. 9 (2024): e6779. http://dx.doi.org/10.55905/oelv22n9-136.

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A análise de sinais de aceleração é crucial para a manutenção preditiva e o monitoramento de condições em máquinas rotativas, como motores, turbinas e compressores. Esses equipamentos são fundamentais em diversas indústrias, e a detecção precoce de falhas pode prevenir paradas inesperadas e reduzir custos de manutenção. A crescente complexidade dos sistemas mecânicos e a necessidade de maior eficiência operacional demandam ferramentas avançadas que possam oferecer análises precisas e rápidas. Este artigo apresenta o desenvolvimento e aplicação do Turdus, um software interativo para análise de

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Smith, Duddu Guna Sai. "Deep Learning-Based Background Removal using MODNet with Green Spill Correction." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49662.

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Abstract - Background removal (portrait matting) is an important pre-processing step for photography, augmented reality, and videography. Traditional chroma-key tools require manual trimaps or controlled lighting, limiting flexibility. We propose an automated deep learning solution that uses the MODNet portrait matting model in a Streamlit-based web application. Users upload a subject image (e.g. human on green/any background) and a new background image; the system runs MODNet to compute an alpha matte, applies green-spill correction, and overlays the foreground onto the new background. The UI

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Li, Yichen, Meina Zhang, Jiayu Wang, Yuxuan Liu, Yutong Xing, and Xinyuan Wang. "Multi-Platform Electric Vehicle Detection System in Elevators." Frontiers in Computing and Intelligent Systems 10, no. 1 (2024): 22–25. http://dx.doi.org/10.54097/2d8v6c75.

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This project addresses elevator safety by proposing a solution based on an improved lightweight YOLOv3 model. The system is trained on a custom-built dataset of electric vehicles inside elevators, achieving efficient and accurate object detection suitable for edge computing environments. It demonstrates excellent performance through transfer learning and comparative experiments. The user interface, developed with PyQt5 and Streamlit, supports image, video, and real-time detection, along with result-saving capabilities. Tests on elevator videos show outstanding accuracy and practicality, making

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., Srisailanath. "Language Translator with Machine Learning (English-French)." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 5506–9. https://doi.org/10.22214/ijraset.2025.69523.

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In today’s globalized world, the need for efficient language translation tools has become critical. This project aims to develop a “Language Translation System” by integrating GNMT (Google Neural Machine Translation) Translation API and a user-friendly Streamlit interface. The system leverages advanced deep learning models trained on extensive datasets to provide accurate and context-aware translations. By focusing on accessibility and precision, the project seeks to bridge linguistic barriers and facilitate seamless communication. Users can input English text, select the desired target langua

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Shankar, Prof Vidya, and Ripunjay Raj. "MULTIPLE DISEASE PREDICTION USING MACHINE LEARNING." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem.spejss007.

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In this research paper, a prediction system is designed based on machine learning for multiple diseases. The full system uses pre-trained machine learning models to assess the chances of diabetes, heart disease, Parkinson's disease and breast cancer based on the medical input data given by the user. This paper explores how these model(s) can be transformed into a common interface using user friendly software for real-time disease detection for enhanced early diagnosis & medical decision making. Key Words: Machine Learning, Disease Prediction, Streamlit, Healthcare AI, Chronic Diseases

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Aditya, Bhosale. "Integration of IoT and Machine Learning for Real-Time Plant Health Monitoring and Disease Detection System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46349.

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Abstract – Agricultural yield is highly dependent on timely disease management and optimal growing conditions. In mango cultivation, especially for the Alphonso variety, diseases such as anthracnose and rust cause significant damage. This paper introduces an IoT-based system that combines environmental monitoring with machine learning-driven leaf disease detection. Temperature, humidity, and soil moisture are tracked using DHT11 and soil moisture sensors interfaced with a NodeMCU ESP8266. This data is visualized on ThingSpeak. For disease diagnosis, a trained Convolutional Neural Network (CNN)

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Syafarina, Gita Ayu, and Zaenuddin Zaenuddin. "Implementasi Framework Streamlit Sebagai Prediksi Harga Jual Rumah Dengan Linear Regresi." METIK JURNAL 7, no. 2 (2023): 121–25. http://dx.doi.org/10.47002/metik.v7i2.608.

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This research aims to develop an Artificial Intelligence (AI)-based application using the Streamlit framework to predict house sale prices in Banjarmasin City using Linear Regression methodology. The increase in demand and supply of properties in Banjarmasin City poses a complex challenge in determining house sale prices. The Linear Regression method was chosen as the primary analytical tool to identify factors influencing house sale prices. This application utilizes historical data of house sale prices and variables such as land area, building area, number of rooms, proximity to public facili

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Shivendra, Malviya, Kale Bhavna, Nimbalkar Khushika, and Malviya Deepika. "Implementation of Job Alert System." Recent Trends in Cyber Criminology Research 1, no. 1 (2025): 32–36. https://doi.org/10.5281/zenodo.15189227.

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<em>The Job Alert System is a comprehensive platform designed to streamline job searching, saving, and notification processes for users. Built using Python and Streamlit, the system allows users to search for job opportunities across various platforms, save their preferred jobs, and receive timely alerts through email, SMS, and WhatsApp. The platform integrates advanced features such as dynamic job searching, personalized notification settings, and automated alert delivery, ensuring a seamless user experience. The system leverages Python libraries and APIs to fetch job listings, proc

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Anderková, Viera, František Babič, Zuzana Paraličová, and Daniela Javorská. "Intelligent System Using Data to Support Decision-Making." Applied Sciences 15, no. 14 (2025): 7724. https://doi.org/10.3390/app15147724.

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Interest in explainable machine learning has grown, particularly in healthcare, where transparency and trust are essential. We developed a semi-automated evaluation framework within a clinical decision support system (CDSS-EQCM) that integrates LIME and SHAP explanations with multi-criteria decision-making (TOPSIS and Borda count) to rank model interpretability. After two-phase preprocessing of 2934 COVID-19 patient records spanning four epidemic waves, we applied five classifiers (Random Forest, Decision Tree, Logistic Regression, k-NN, SVM). Five infectious disease physicians used a Streamli

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Ganjigatti, Abhishek. "Plant Identification and Query System Using Deep Learning and RAG-Based Search." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 6056–60. https://doi.org/10.22214/ijraset.2025.71619.

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In this project, we present a dual-module system that utilizes a Convolutional Neural Network (CNN) for leaf-based plant identification and integrates a Retrieval-Augmented Generation (RAG) system for answering plant-related queries. The CNN was trained and fine-tuned using the LeafSnap dataset comprising 184 plant classes, achieving a final accuracy of 88%. Additionally, the RAG module leverages LangChain tools incorporating Wikipedia, Arxiv, and a custom PDF retriever for context-aware plant information search. This end-to-end solution is implemented using Flask and Streamlit, offering users

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Reddy, RajanaS aikiran, Madhusudhan Reddy, and Dr V. Vijayakumar Dr.V.Vijayakumar. "Machine learning technology for classification of diseases in Oranges." International Journal of Engineering and Science Invention 14, no. 3 (2025): 06–12. https://doi.org/10.35629/6734-14030612.

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—A Convolutional Neural Network (CNN) is used in this study, "Machine Learning Technology for Classification of Diseases in Oranges," to automatically identifyorangedisorders.Fourclassesofimages— Blackspot, Canker, Fresh, and Greening—are used to train the model. Thesystemcorrectlyclassifiesphotosintovariouscategories byutilizingdeeplearningandimageprocessing, whichhelps with early detection and minimizes manual effort. Orange photographs can be uploaded through an easy-to-use Streamlit interface, and they are instantly processed and classed withhighconfidencescores.Thegoalofthissolution is to

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Ashwini, M. Rayannavar, Chouhan Rakshit, Ali Gazi Aman, and Rajesh Patel Maitree. "SpeakVision: A Comprehensive Survey on End-to-End Sentence Level Lipreading." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 11 (2024): 2209–12. https://doi.org/10.5281/zenodo.14330071.

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SpeakVision is a speech reading framework capable of extracting speech from audio-video inputs using an AI-based model. A new integrated approach, using both sight and sound, is needed for situations when a voice signal is obscured, or when seeing the apparatus is much easier than hearing it. SpeakVision leverages AI technologies, such as, 3D convolutional layers for extracting spatial features, Bidirectional LSTMs for temporal information and CTC decoding for generating text. Video preprocessing techniques were applied to optimize model performance, and the results were developed into an easy

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Sathwika, Vaddepelli. "Generative Adversarial Network Powered Image Transformation Enhancing, Completing and Converting Visuals." International Journal for Research in Applied Science and Engineering Technology 13, no. 7 (2025): 252–57. https://doi.org/10.22214/ijraset.2025.72977.

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This study addresses conventional image processing shortcomings via one cohesive system integrating SRGAN, DeepFillv2, and CycleGAN (Generative Adversarial Networks). Image enhancement along with clever completion also versatile style transformations including colorization and decolorization are accessible through an intuitive Streamlit web interface. SRGAN is known to excel at improving image resolution and DeepFillv2 reconstructs damaged areas skillfully with full contextual awareness. CycleGAN eases style changes between domains without need of matched datasets. The system performs during r

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Dubey, Priyanka. "A Multilingual Customer Support Assistant Using Machine Learning and Streamlit for Real-Time Global Communication." International Journal of Innovative Research in Engineering and Management 12, no. 2 (2025): 130–38. https://doi.org/10.55524/ijirem.2025.12.2.21.

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In this paper, a customer support language assistant is developed, which can be used through a web-based interface to enhance real-time multilingual communication. The tool leverages automated language detection, language translation, and reply to conversion across several scripts and languages through Machine Learning (ML) and natural language processing (NLP). This application combines a Naive Bayes classifier with Google Translate API, and a Streamlit based frontend that provides a cohesive user experience for anyone, regardless of their language proficiency. This assist offers a quick opti

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Kumar, Ashmit. "Tourismo AI: Smart Travel & Hospitality Hub." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47916.

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Abstract- Tourismo AI: Smart Travel & Hospitality Hub is a specially designed travel platform powered by AI that allows one to change travel and traveling the way one wants through a smart intelligent interface. The system synergies machine learning, NLP, and cloud technology in the sense of trip planning people to be always aware of various options and get real-time results. The platform is most recognized for the following features: a set of custom itineraries equipped with points of interest information, the ability to make real-time adjustments according to the weather and road conditi

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Ashrith Sambaraju, Marikanti Sathvika, Mainam Krupa, Velagapudi Venkata Sai Mahendra Kumar, Mrs.K. Revathi, and Dr. M. Ramesh. "Automated Brain Tumor Classification Using Hybrid Deep Learning Models." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 05 (2025): 2171–77. https://doi.org/10.47392/irjaeh.2025.0318.

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Agricultural price forecasting plays a vital role in empowering farmers with market intelligence, enhancing crop planning, and supporting economic resilience. This project presents an efficient and user-friendly system for predicting the Minimum Support Price (MSP) of crops using machine learning techniques, with a real-time interface built using Streamlit. The system leverages an XGBoost regression model trained on historical crop price datasets, including commodity name, crop variety, type, and year. To increase accessibility and impact, the application incorporates Twilio SMS integration, e

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Nelavetla Sridhar Reddy, Vaduka Manideep, Gaddala Sumanth, Yasarapu Sai Charan Goud, Mohammed Ayaz Uddin, and Dr. M. Ramesh. "CROP MSP Forecasting and OTP-Verified SMS Notification System." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 05 (2025): 2163–70. https://doi.org/10.47392/irjaeh.2025.0317.

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Agricultural price forecasting plays a vital role in empowering farmers with market intelligence, enhancing crop planning, and supporting economic resilience. This project presents an efficient and user-friendly system for predicting the Minimum Support Price (MSP) of crops using machine learning techniques, with a real-time interface built using Streamlit. The system leverages an XGBoost regression model trained on historical crop price datasets, including commodity name, crop variety, type, and year. To increase accessibility and impact, the application incorporates Twilio SMS integration, e

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Deogire, Kartik. "Smart Neuro-Oncology Assistant: A Chatbot for Brain Tumour Detection and Primary Cancer Prediction." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 3291–303. https://doi.org/10.22214/ijraset.2025.68888.

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The Smart Neuro-Oncology Assistant introduces a novel AI-powered platform that combines deep learning-based brain tumor detection with primary cancer prediction capabilities, particularly for tumors associated with Glioma and Pituitary regions. This interactive system supports patients and healthcare professionals throughout the neuro-oncological assessment process. Utilizing the Xception convolutional neural network architecture, the system achieves 96% overall accuracy in classifying brain MRI scans into four categories: Glioma, Meningioma, Pituitary Tumor, and No Tumor. The platform extends

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