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Multi-Language Medical Symptoms Analyzer and Hospital Locator Chatbot 4

Multi-Language Medical Symptoms Analyzer and Hospital Locato...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Ramani, Kasarapu Deepthi, Malepati Sai Naveen, N. Raju, Chintala Jeevan Krishna, Kandati Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Dept. of Data Science Tirupati India Sree Vidyanikethan Engineering College Department of IT Tirupati India

ISBN: (纸本)9798350385793

Not every hospital may have a help desk to respond to patient queries regarding the disease prediction based on their symptoms and suggesting appropriate department or medicine, providing precautions for that disease in multiple languages. An automated system, such as a chatbot, can effectively address this issue. The medical chatbot is trained using Hybrid Deep Neural Network-Recurrent Neural Networks (DNN-RNN). The user's query accepted through user interface is pre-processed using Natural Language Processing (NLP) and based on the symptoms disease is predicted using wikipedia summary dynamically. The chatbot is user-friendly, and allows the users to interact in three different languages-English, Hindi and Telugu. It also enables users to enter the location in text, and it using Geopy library, Google Places API, and Folium library hospitals' location information is displayed in the user-specified area. The performance of the proposed method is evaluated using accuracy and loss function. This proposed method is a highly effective learning process, making the chatbot a reliable tool for medical queries and disease prediction based on symptoms. © 2024 IEEE.

关键词： Recurrent neural networks

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Artificial Intelligence (AI) Enabled Music Player System for User Facial Recognition 4

Artificial Intelligence (AI) Enabled Music Player System for...

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4th International Conference for Emerging Technology, INCET 2023

作者： Rehash Rushmi Pavitra, A. Anushree, K. Akshayalakshmi, A.V.R. Vijayalakshmi, K. Sri Sairam Engineering College Dept. of Artificial Intelligence and Data Science TamilNadu Chennai India Sri Sairam Engineering College Department of Artificial Intelligence & Data Science TamilNadu Chennai India

ISBN: (纸本)9798350335750

Artificial Intelligence (AI) refers to the use of advanced computational algorithms and technologies to replicate or simulate human intelligence in machines or computer systems. Human expression is incredibly important in determining a person's current condition and mood. AI is utilized to analyze human facial expressions and extract emotions based on features such as cheeks, forehead, eyes, and smiles. In addition, 'songs' refers to an expressive medium that has always been the best option for analyzing and comprehending human emotions. The proposed 'smart music player' is a programmed system that functions under the assumption that we can tell someone's mood by the expression on his/her face. This model that recognizes facial micro-expressions with multicultural facial expression details, recommends music in accordance with corresponding mood. It is developed using a combination of song's features and micro-expression recognition technology of convolutional neural network. To do this, group facial expressions into seven distinct emotional groups, including happy, sad, angry, neutral, surprise and disgust. The primary objective of this study paper is to present an overview of a useful music player and social companion that automatically creates a playlist that will brighten your day based on your emotional condition, along with recommendations for future studies in the field of recommendation systems. © 2023 IEEE.

关键词： Computer vision

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Precise Diagnosis of Heart Disease with Stacking Classifier 5

Precise Diagnosis of Heart Disease with Stacking Classifier

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Jyothsna, V. Poojitha, B. Divya, A. Sai Ganesh, B. Srinija, D. Department of Data Science Mohan Babu University Tirupati India Dept. of information technology Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798350361155

Heart disease stands as a significant global health challenge, marked by a worrying rise in prevalence and substantial mortality rates. Despite the wealth of healthcare data, early detection of heart conditions remains elusive. This endeavor aims to address this issue by crafting a predictive model that integrates individual medical histories and lifestyle factors. The proposed methodology centers on the use of a Stacking classifier for heart disease prediction. To gauge the model's efficacy, the Framingham Heart Disease dataset is employed, sourced from the widely recognized Kaggle repository, endorsed by numerous researchers. Evaluation encompasses ensemble model assessments, incorporating critical metrics such as sensitivity, specificity, accuracy, and f-measure. Furthermore, the investigation delves into the application of ensemble methods for forecasting heart disease incidence over a decade, aiming to contribute to more effective strategies for early detection and management. Through the fusion of advanced modeling techniques and comprehensive datasets. © 2024 IEEE.

关键词： Diagnosis

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Investigating Principle Component Analysis Impact on the Performance of Machine Learning Classifiers: A Health Informatics Application 25

Investigating Principle Component Analysis Impact on the Per...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Banimustafa, Ahmed Almatarneh, Sattam Bulkrock, Olla Alazaidah, Raed Almahasneh, Hossam Samara, Ghassan Higher Colleges of Technology Computer Information Science Dept. Abu Dhabi United Arab Emirates Zarqa University Data Science & Artificial Intelligence Dept. Zarqa Jordan University of Business and Technology College of Engineering Jeddah Saudi Arabia Princess Sumaya University for Technology Software Engineering Dept. Amman Jordan Zarqa University Computer Science Dept. Zarqa Jordan

ISBN: (纸本)9798331540012

Principal Components Analysis (PCA) is a popular dimensionality reduction and feature selection technique. It works by swiping non-informative features while retaining those that are still potentially useful. However, regardless of its wide applications in statistical analysis, the influence of PCA on the performance of classification algorithms has been rarely studied, particularly in the domain of medical image classification. In this paper, we present a thorough analysis of the influence of applying PCA on the classification of medical images. The dataset used in this study consists of thousands of images that represent four types of blood cells. These images are used to train three machine-learning classifier models after transforming images into numerical vectors using a CNN pre-trained embedding algorithm, where each image is encoded using one thousand numerical vectors. The study involves building three classification models using (1) K-nearest Neighbor (KNN), (2) Logistic Regression (LR), and (3) Random Forests (RF). Each model was built twice, once after the application of PCA to reduce the dimensionality of the embedded features and once with the original embedded features. The performance of each model is evaluated using Classification accuracy, F1, AUC, Recall, precision, and specificity metrics. The recorded performance is then compared to drawing a decisive conclusion. © 2024 IEEE.

关键词： Dimensionality reduction

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Real-Time Stress Level Monitoring Using IOT

Real-Time Stress Level Monitoring Using IOT

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Asha Patil, Nandini Preeti, B.M. Laxmi Sharnbasva University Dept. of Artificial Intelligence and Data Science Karnataka Kalaburagi India Sharnbasva University Dept. of Computer Science and Engineering Karnataka Kalaburagi India Sharnbasva University Dept. of Electrical and Electronics Engineering Karnataka Kalaburagi India

ISBN: (纸本)9798350315455

Stress is a prevalent and debilitating issue in contemporary society, contributing to a range of physical and mental health problems. As our lives become increasingly fastpaced and interconnected, addressing stress effectively has become imperative. This introduces a pioneering approach to tackle challenge through real-time stress level monitoring utilizing the Internet of Things (IoT). We propose a holistic system that merges IoT sensors, advanced data analytics, and machine learning techniques to provide continuous stress assessment and management solutions. The data is collected through the designed model then preprocessed, and essential features are extracted to construct a predictive model. The heart of the system is a machine learning model, which constantly updates itself based on user-specific stress patterns, yielding highly accurate real-time stress level estimations. Users are provided with actionable feedback and personalized recommendations, ranging from deep breathing exercises to lifestyle adjustments. Extensive experimentation demonstrates the system's robustness, with stress level predictions boasting a mean absolute error of under 5%. © 2023 IEEE.

关键词： data preprocessing feature extraction health monitoring Internet of Things (IoT) machine learning personalized recommendations physiological sensors real-time stress monitoring well-being

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Automatic Feature Extraction from High-Resolution Satellite Imagery using Deep Learning techniques

Automatic Feature Extraction from High-Resolution Satellite ...

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2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Ramani, Kasarapu Suchith, G.S. Indrasena Reddy, C. Chinna, A. Reshma, G. Mohan Babu University Erst. Sree Vidyanikethan Engineering College Dept. of Data Science Tirupati India Mohan Babu University Erst. Sree Vidyanikethan Engineering College Dept. of CSSE Tirupati India

ISBN: (纸本)9798350354539

Automated feature extraction from high-resolution satellite data has great potential to locate lucrative resources on Earth. The main elements that are being extracted include Land, Buildings, Roads, Water bodies, and all other necessary elements seen in high-resolution satellite photography. The input dataset includes high-resolution satellite images of New York together with matching map pictures of buildings, roads, landscapes, and water bodies, all in common file formats ***. This paper introduces an innovative feature extraction approach utilizing Deep learning techniques such as Pix2Pix GAN (Generative Adversarial Network) for semantic segmentation and improving the object delineation, such as land, buildings, roads, and water features, in image-to-image translation tasks. U-Net enables precise semantic segmentation by retaining spatial information. Simple CNN (Convolutional Neural Netwok) processes image patches sequentially, extracting relevant features such as edges, textures, and patterns, enabling accurate classification and segmentation of objects in satellite imagery. Pix2Pix GAN is shown to perform better than other models based on Structural Similarity Index Measure (SSIM), a metric used to quantify the similarity between two pictures. It achieved the highest SSIM score of 0.904. © 2024 IEEE.

关键词： Semantic Segmentation

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Robust Sensor Networks: Applying AI for Fault Identification in Challenging Operational Contexts 5

Robust Sensor Networks: Applying AI for Fault Identification...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Manjunatha, A.S. Venkatramana Bhat, P. Ramakrishna, M. Prabhu, Matti Nidhi Dept. of Computer and Communication Engineering NMAM Institute of Technology Nitte Karnataka Mangaluru India Dept. of Computer Science and Engineering NMAM Institute of Technology Nitte Karnataka Mangaluru India Manipal Academy of Higher Education Dept. of Data Science and Computer Applications Manipal Institute of Technology Manipal576104 India

ISBN: (纸本)9798331518097

Wireless Sensor Networks (WSNs) are highly implemented in harsh and dynamic environments. In such configurations, faults and anomalies easily occur, causing unwanted disruptions in data accuracy and network performance. In the context of reliability and stability in WSN operations, effective fault detection techniques are vital. This research work discusses how several machine learning models may be applied, including Logistic Regression, Decision Tree, Random Forest, and K-Nearest Neighbors in order to detect fault in WSNs. The performance evaluation was performed based on major criteria, which are accuracy, precision, recall, and Fl score. This, therefore, makes these approaches to machine learning effective in identifying faults efficiently, hence offering a well-balanced solution that is scalable and robust for integrity and operational effectiveness of WSN s. © 2024 IEEE.

关键词： Random forests

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ABC-DE-WOA: A New Hybrid Algorithm for Optimization Problems 4

ABC-DE-WOA: A New Hybrid Algorithm for Optimization Problems

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4th International Conference on Circuits, Control, Communication and Computing, I4C 2022

作者： Pradeep Kumar, D. Sowmya, B.J. Kanavalli, Anita Vincent, Annvin Dept. of Computer Science and Engineering Bangalore India Dept. of Artificial Intelligence and Data Science Bangalore India

ISBN: (纸本)9798350397475

One of the most effective swarm intelligence-based algorithms for many global optimization issues is the Artificial Bee Colony (ABC) strategy. Despite the fact that there are many Artificial Bee Colony (ABC) variants, the algorithm normally has a low convergence rate. Therefore, it is still essential to moderate an algorithm's intensity and diversity. In this instance, the standard Artificial Bee Colony (ABC) algorithm has been combined with the Whale Optimization Algorithm (WOA) and Differential Evolution Algorithm (DE) to generate a novel Hybrid Artificial Bee Colony algorithm (ABC), Artificial Bee Colony-Differential Evolution-Whale Optimization Algorithm (ABC-DE-WOA). For simple benchmark problems with up to 100 dimensions, 50 dimensions, 30 dimensions, and 10 dimensions, the proposed hybrid technique is compared with Artificial Bee Colony (ABC) variants like Artificial Bee Colony-Whale Optimization Algorithm (ABC-WOA), Artificial Bee Colony-Differential Evolution Algorithm (ABC-DE), and original Artificial Bee Colony Algorithm (ABC). The results show that the proposed technique performs better than its competitors in terms of convergence speed. © 2022 IEEE.

关键词： Global optimization

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A Vision-Based Autonomous AI Tic-Tac-Toe Manipulation System 12

A Vision-Based Autonomous AI Tic-Tac-Toe Manipulation System

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12th International Electrical engineering Congress, iEECON 2024

作者： Rakprayoon, Panjawee Ounsiri, Akechai Chongyoa, Ratchanon Bangkok University Faculty Of Engineering Dept. Ai Engineering And Data Science Pathum Thani Thailand Dna Robotics And Automation System Company Limited Pathum Thani Thailand Bangkok University Faculty Of Engineering Dept. Computer And Robotics Engineering Pathum Thani Thailand

ISBN: (纸本)9798350383591

For an AI tic-tac-toe manipulator application, a real-time vision-based approach is proposed. The technique employs the RealSense camera to capture color and dept. images. Combining object detection and image processing techniques to identify X's and O's positions on the game board. However, we recognize that errors can occur during object movement when using only 2D images. To solve this problem, a calibration method between RealSense and the manipulator based on the point-to-plane ICP algorithm is presented. This method provides the 3D object position in the robot coordinate, resulting in increasing the accuracy from 86.25% to 97.25%. Our experiment shows that this technique is highly effective and can accurately identify position objects. © 2024 IEEE.

关键词： Object detection

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Silent Skies: Machine Learning for Aircraft Noise Reduction Strategies

Silent Skies: Machine Learning for Aircraft Noise Reduction ...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Singh, Nageshwar Pawar, Kunal Afnan, Syed Nashath Hurthivk Hegde, Ramakrishna N Kannadaguli, Prashanth Srinivas Institute of Technology Dept. Aeronautical Engineering Mangalore India Dhaarini Academy of Technical Education Senior Data Science Trainer Bengaluru India

ISBN: (纸本)9798331517892

Aircraft noise pollution poses a significant challenge to communities near airports, impacting their health, well-being, and overall quality of life. Traditional noise mitigation approaches have predominantly focused on physical modifications to aircraft design and flight procedures. However, the emergence of machine learning (ML) techniques offers promising avenues for innovative noise reduction strategies. This paper introduces 'Silent Skies,' a groundbreaking framework utilizing machine learning algorithms to address aircraft noise. Silent Skies integrates diverse data sources, including aircraft telemetry, weather conditions, airport infrastructure, and community feedback, to train predictive models for accurately estimating noise levels. These models inform decision-making processes, optimizing flight paths, engine settings, and airport operations to minimize noise emissions. Key components of the Silent Skies framework include data preprocessing techniques for handling heterogeneous and noisy data, feature engineering methods for extracting relevant information from raw data sources, and ensemble learning approaches to enhance prediction accuracy. Additionally, the framework incorporates real-time monitoring and feedback mechanisms to continually refine noise reduction strategies in response to evolving conditions. To demonstrate the effectiveness of Silent Skies, we present case studies conducted at major airports, showcasing significant reductions in noise pollution while maintaining operational efficiency. These case studies underscore the framework's adaptability across diverse airport configurations, airspace constraints, and environmental contexts. In summary, Silent Skies represents a significant advancement in aircraft noise mitigation, offering scalable and adaptive solutions that prioritize community well-being without compromising aviation safety or efficiency. Through the integration of machine learning techniques, airports can transition towards q

关键词： Noise abatement

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