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Deployment of a Mobile Application Using a Novel Deep Neural Network and Advanced Pre-Trained Models for the Identification of Brain Tumours

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IETE JOURNAL OF RESEARCH 2023年第10期69卷 6902-6914页

作者： Precious, J. Glory Kirubha, S. P. Angeline Evangeline, I. Keren SRM Inst Sci & Technol Dept Elect & Commun Engn Chennai Tamil Nadu India SRM Inst Sci & Technol Dept Biomed Engn Chennai Tamil Nadu India

Medical image processing is significant in clinical diagnosis and treatment. However, the traditional approach for examining these images has hit its performance limit, and requires a considerable amount of time and effort. In addition, the misdiagnosis of brain tumour types from these images can hinder patients from receiving proper medical treatment and can diminish their chances of survival. The emerging deep learning technique has shown good results in classification problems. In the current study, existing transfer learning models and a novel model were applied to classify augmented magnetic resonance images (n = 12,256 images). These methods were used to identify three different kinds of brain tumours from the images: meningioma, glioma, and pituitary tumours. Accuracies of 83.30%, 79.54%, 81.83%, 82.49%, 85.21%, and 91.73% were obtained using ADAM optimizers for VGG-16, VGG-19, Inception V3, Xception, Mobile net, and the proposed Lightweight Sequential net, respectively. Furthermore, we achieved accuracies of 50.45%, 60.24%, 57.85%, 61.98%, 75.14%, and 84.82% while using the SGDM optimizer for the aforementioned deep neural networks, respectively. We also developed an Android and IOS mobile application using the novel deep neural network and a flutter framework. The mobile application was tested using brain tumour images collected from SRM hospital and an accuracy of 82.5% was obtained. The untrained data of 155 images collected from hospital were used to validate this application. In terms of execution speed, the proposed architecture surpassed the existing pre-trained models. Thus, when compared to approximately similar approaches, our newly developed network outperformed them.

关键词： Brain tumour deep neural network flutter framework mobile application

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flutter-Based Cross-Platform Data Visualization of Real-Time Road Incident Analysis & Prediction 5

Flutter-Based Cross-Platform Data Visualization of Real-Time...

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5th International Conference on Artificial Intelligence, Robotics and Control (AIRC)

作者： Walee, Nafeeul Alam Shalan, Atef Georgia Southern Univ Dept Informat Technol Statesboro GA 30458 USA

ISBN: (纸本)9798350385083;9798350385076

Due to the proliferation of digital technologies and interconnected systems, the production of data is growing at an astounding rate. As a result, the sheer volume and complexity of data make it incredibly challenging to read and interpret for a human. In addition, the continuous flow of data presents even more significant challenges in terms of processing, analyzing, and interpreting the information. To resolve this issue, visualization of data can be the key to making sense of a complex set of data. Furthermore, data visualization plays a vital role in representing a real-time stream of data. In this paper, the flutter framework is used to interpret real-time data and visualize it across platforms. Furthermore, 3 machine learning algorithms are used to predict the contributing factors of these incidents, also to evaluate which model is performing better. The results of the predictions as well as visualization are shown in different charts. The charts offer a snapshot of the Realtime data in an organized manner which gives the user an immediate insight.

关键词： real-time data visualization machine learning flutter framework cross-platform

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FDM: NutriGuide - A Cross-Platform flutter Application for Managing and Educating on Nutritional Deficiencies 2

FDM: NutriGuide - A Cross-Platform Flutter Application for M...

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2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Vidhya, K. Thiyagu, T. M. Taurshia, Antony Nagarajan, B. Jenefa, A. Jebas, Joshua Flower, I Karunya Inst Tech & Sci Sch Cst Coimbatore Tamil Nadu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept CSE Morai India KPR Inst Engn & Technol Dept Artificial Intelligence & Data Sci Coimbatore Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

In today's fast-paced world, many individuals struggle to maintain a balanced diet, leading to various nutritional deficiencies. Effective management and education on these deficiencies are crucial for improving public health. Traditional methods of addressing nutritional deficiencies often lack personalization and user engagement. The NutriGuide application, developed using the flutter framework, aims to revolutionize nutritional management by providing a user-centric, cross-platform solution. Utilizing a comprehensive database of nutrients and symptoms, the application offers personalized dietary recommendations based on user inputs. The dataset includes detailed information on common deficiencies, symptoms, and nutrient-rich foods. Experimental results indicate high user satisfaction, with an average satisfaction score of 4.5 out of 5, and significant improvements in nutritional intake, such as a 28.6% increase in Vitamin A intake and a 50% increase in Vitamin D intake. In conclusion, NutriGuide presents an innovative approach to nutritional health, combining modern technology with evidence-based recommendations to empower users in managing their dietary needs.

关键词： Nutritional Deficiencies Personalized Dietary Recommendations flutter framework Cross-Platform Application Nutritional Health Management User-Centric Design Data-Driven Algorithms Mobile Health Applications Nutritional Education

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Location-Based QR Attendance Application 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Shaikh, Nishat Shah, Prathi Vaghani, Deep Tejani, Jeel Lakhani, Harsh CHARUSAT Univ Smt Kundanben Dinsha Patel Dept Informat Technol CSPIT Anand Gujarat India

ISBN: (数字)9789819713295

ISBN: (纸本)9789819713288;9789819713295

The "Location-Based QR Attendance Application" has the potential to completely transform how educational institutions handle attendance. Using the flexible flutter framework, this application streamlines the process of keeping track of attendance. Teachers can record attendance in a secure and accurate manner by creating QR codes that are connected to their GPS locations. By using their mobile devices to scan the QR code, students can use the system to verify their position and ensure attendance only when they are inside the specified geographic area. This improves data integrity and accountability. The app offers instructors a feature-rich dashboard for managing attendance records. By allowing data export as Excel sheets for simple sharing, it simplifies administrative tasks and lessens the workload for administrators and educators. Viewing their attendance history and quickly learning whether their submission was approved or denied are helpful features for students. They now have more visibility into and control over their attendance records thanks to this. The "Location-Based QR Attendance Application" enhances education by merging technology, efficiency, and transparency. It improves attendance management, reduces administrative work, and empowers students for better engagement, elevating education quality.

关键词： GPS location and QR-based attendance flutter framework Firebase Excel sheet Attendance tracking Data integrity Transparency Faculty-Student Module

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EduBusGuru: Enhancing College Bus Tracking and Management with Real-Time Mobile App 15

EduBusGuru: Enhancing College Bus Tracking and Management wi...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Yeruva, Sagar Grandhi, Aishwarya Katta, Sai Deepthi Palutla, Sai Sharad Chandra Amruthaluri, Gagan Datta Hyderabad India

ISBN: (纸本)9798350370249

An efficient transportation system is essential for modern educational facilities to ensure smooth daily operations. Current systems often rely on fixed schedules and lack real-time monitoring, leading to inefficiencies and security issues. This paper proposes a new college bus tracking system that uses GPS technology on the driver's smartphone to improve traffic safety and efficiency. The system leverages the Firebase real-time database for data synchronization and the flutter framework for cross-platform mobile app development. Administrators manage buses, routes, stops, and student assignments, while students benefit from real-time bus tracking, route updates, and direct communication with drivers. Bus drivers can broadcast messages, monitor missions, and provide continuous GPS updates. Through extensive research into existing systems, user experiences validate the system's performance, demonstrating its cost-effectiveness, scalability, usability, and affordability compared to traditional GPS systems. This paper proposes a structured approach to improve campus traffic management through the innovative use of mobile and cloud technologies. © 2024 IEEE.

关键词： and real-time Alerts Firebase flutter framework GPS (Global Positioning System) Messaging Module Mobile Application Real-time tracking Route Management User Communication

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Development and Implementation of a Smart Parking System in Coimbatore, Tamil Nadu: Enhancing Urban Mobility and Efficiency 4

Development and Implementation of a Smart Parking System in ...

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4th International Conference on Artificial Intelligence and Signal Processing

作者： Aswin, S. Ruthra, L. Jashwanth, Sudheep G. Anand, R. Amrita Vishwa Vidyapeetham Dept Elect & Elect Engn Amrita Sch Engn Coimbatore Tamil Nadu India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661;9798350350654

This paper discusses the creation of a detailed smart parking app designed to improve the effectiveness and ease of use of city parking systems. The application includes many advanced features and technologies to streamline the parking process for users and optimize parking lot *** early stages of development focused on creating a user-friendly interface, including a sign-up page built using the flutter framework. The integration of webcam functionality enabled real-time detection of the number of cars within the camera's focus, providing users with accurate information about parking availability. Payment integration was seamlessly integrated into the application, allowing users to securely pay for parking services. Leveraging machine learning techniques, specifically random forest detection, further increased the accuracy of car detection within the camera feed. Additionally, analogic modeling was used to simulate traffic flow at intersections and junctions, helping to understand vehicle traffic patterns and optimize parking slot. The application fetches and stores data on user activity and parking availability using Firebase, ensuring efficient data management and real-time updates. The culmination of these efforts is a robust smart parking application capable of providing users with real-time parking information, seamless payment options and customized parking management solutions. The work demonstrates the potential of integrating different technologies to solve complex urban challenges and pave the way for smarter and more efficient parking solutions.

关键词： flutter framework Real-time detection Payment integration Car detection Traffic flow

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