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A Hybrid Deep learning Approach for Early Detection of Diabetic Retinopathy 3

A Hybrid Deep Learning Approach for Early Detection of Diabe...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mary Vespa, M. Shajeena, J. Shiny, R.M. Gayathri Devi, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirapalli Campus India St Joseph's College of Engineering Department of Computer Science and Engineering Chennai India Rmd Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Diabetes is a malnutrition that results from elevated glucose levels. Diabetes eventually results in diabetic retinopathy (DR), a retinal condition that significantly impairs vision. It is an insulin-dependent difficulty that impacts ocular health. The receptive to cells at the back of the individual retina's veins gets injured, which is the cause of it. A favorable prognosis for this condition depends on its early identification. In this paper, the application of Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) on color fundus images is used for diabetic retinopathy is applied for understanding the task. The goal of the proposed DR identification method is to use deep learning to automatically identify the problem. An hybrid model that was trained and tested to distinguish between healthy and DR-affected fundi was able to achieve an accuracy of roughly 99.07% using fundus images from diabetic patients that were available in MESSIDOR. © 2025 IEEE.

关键词： Convolutional neural networks

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A Smart Vehicle Monitoring System Using Real-Time Data Processing and Advanced Sensor Integration 5

A Smart Vehicle Monitoring System Using Real-Time Data Proce...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Sathish, M. Prabha Devi, D. Kanchana, R. Pranesh, R.K. Kumaraguru College of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering Tamil Nadu Sathyamangalam India Sphoorthy Engineering College Department of Artificial Intelligence and Machine Learning Telangana Hyderabad India Kumaraguru College of Technology Department of Information Science and Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

The 'Smart Vehicle Monitoring System' presents a comprehensive solution for enhancing road safety and user authentication in the realm of modern transportation. This system integrates advanced technologies to monitor crucial aspects of vehicle usage, ensuring both the rider's safety and the vehicle's safety. The core functionalities encompass monitoring the usage of safety gear such as helmets and detecting alcohol consumption. User authentication is achieved through a combination of fingerprint recognition and Radio-frequency identification (RFID). This multi-layered approach ensures that only authorized individuals can operate the vehicle. The proposed system further extends its capabilities to monitor essential vehicle parameters, including tire pressure and fuel levels. In the event of an accident, a vibration sensor is employed to swiftly detect impact, triggering an alert system through GSM communication. This timely notification ensures prompt assistance and emergency response. Wireless communication plays a vital role in the system, with the helmet connected to an Arduino Mega via the ESP protocol. This acilitates real-time data exchange, allowing for seamless integration of various components. To enhance user interface and information dissemination, the system utilizes an LCD to present real-time status updates. Additionally, the integration of a GPS module enables the sharing of live location data, contributing to improved navigation and overall safety. In conclusion, the Smart Vehicle Monitoring System not only prioritizes the safety of the rider but also addresses authentication concerns and provides a holistic approach to vehicle monitoring. Through the amalgamation of cutting-edge technologies, this system aims to redefine the standards of safety and security in contemporary transportation. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Optimized Constrained Application Protocol for IoT Smart Home Data Security: A Novel Lightweight Approach

Optimized Constrained Application Protocol for IoT Smart Hom...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Subhashini, R. Jyothi, D.G. Bangalore Institute of Technology Affiliated to Visvesvaraya Technological University Department of Computer Science and Engineering Karnataka India Bangalore Institute of Technology Affiliated to Visvesvaraya Technological University Department of Artificial Intelligence and Machine Learning Karnataka India

ISBN: (纸本)9798350388602

By increasing simplicity and efficiency, a smart environment aims to enhance human existence. The IoT paradigm has evolved into a crucial technology for creating smart buildings, and the majority of computer networks now have IoT technologies. Nevertheless, during this shift, IoT devices are susceptible to cyberattacks. Thus, in practical smart home applications, security and privacy are key issues. In order to improve data security in smart home settings while lowering computing costs, this publication proposes the Broken-Stick Regressive Lightweight Speck Ephemeral Cryptography-based Constrained Application Protocol (BRLSC-CoAP). Real-time data is gathered by IoT devices and sent to authorized users. BRLSC-CoAP includes processes for authentication, encryption, decryption, and registration to safeguard this process. Forbes indexive Broken-stick regression is used to verify user authentication, and a lightweight Speck ephemeral cryptography technique is utilized for encryption. The experimental findings demonstrate that BRLSC-CoAP performs better than traditional techniques by increasing confidentiality, accuracy, and precision in authentication while lowering computing overhead, delay, and cost.. © 2024 IEEE.

关键词： Smart homes

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The Impact of AI-Powered Personalization on Academic Performance in Students 5

The Impact of AI-Powered Personalization on Academic Perform...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Shete, Shweta G. Koshti, Priyanka Pujari, Vinayak I. Sanjay Ghodawat University Department of Artificial Intelligence and Machine Learning Kolhapur India Sanjay Ghodawat University Department of Computer Science Kolhapur India D.Y. Patil School of Engineering and Management Department of Computer Application Kolhapur India

ISBN: (纸本)9798350351378

This study investigates how learners' performance is affected by AI-driven personalization. This goal compares students' academic performance in AI-driven personalized learning environments to that of students in conventional learning environments. This study shows the rapid development and application of artificial intelligence (AI) in education has led to the emergence of personalized learning platforms that utilize AI to provide tailored learning experiences for individual students. The study shows a beneficial relationship between enhanced academic accomplishment, engagement, and satisfaction with individualized AI-based adaptive learning through quantitative and qualitative analysis. The results demonstrate how AI-driven customization can improve student achievement and change the way that education is delivered. © 2024 IEEE.

关键词： Students

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An Effective machine learning Algorithm for Forecasting the Market Value of a House 3

An Effective Machine Learning Algorithm for Forecasting the ...

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3rd International Conference on Applied artificial intelligence and Computing, ICAAIC 2024

作者： Ancy, S.G. Praveenchandar, J. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore641114 India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore641114 India

ISBN: (纸本)9798350375190

machine learning plays a virtual role in everyday speech commands, product recommendation, and even medical fields. But instead of providing better customer service, it provides safer autonomous vehicle systems. House price prediction using machine learning is a hot topic in data science and AI. With the increasing availability of data and the advancement of machine learning algorithms, accurately predicting house prices has never been easier. This study will not only assist housing developers and researchers to determine the most significant factors affecting housing prices but will also help determine the best machine learning model to use in this research. This model will allow people to allocate resources to a request without relying on a broker. According to the results of this study, the random forest regression is the most accurate. Real estate market analysis is one of the most important tools for predicting changes in house prices and rental income. Several techniques have been developed in recent years to solve this problem, including statistical models and data mining methodologies and machine learning algorithms. However, a few issues, including data quality and availability, needed to be fixed. Missing numbers, outliers, and inconsistent formats are common in housing databases. These issues could impair the performance of the prediction models. To solve these issues, thorough data preprocessing is required. Collaboration with data providers and governmental agencies is also required to ensure data coverage and accuracy. © 2024 IEEE.

关键词： Forecasting

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Titanic Survivors Prediction Analysis Using machine learning Algorithms 1

Titanic Survivors Prediction Analysis Using Machine Learning...

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1st International Conference on Computational intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Devamane, Shridhar B. Abhijith, B.H. Purushotham, Bhushan Ravindranath, R.C. Lakshmikantha, G.C. Udayaprasad, P.K. Global Academy of Technology Department of Computer Science and Engineering Bangalore India Global Academy of Technology Department of Artificial Intelligence and Machine Learning Bangalore India Global Academy of Technology Department of Ai and Ml Bangalore India

ISBN: (纸本)9798350365405

machine learning algorithms are computational models that allow computers to acquire knowledge and improve performance on a task by automatically learning patterns and rules from input data provided by the user. In this work we are predicting the Titanic disaster survivors based on a particular dataset. Based on characteristics such as age, sex, ticket class, etc., we applied the Support Vector machine, K-Nearest Neighbors, Decision Tree, and Random Forest algorithm to identify which passenger would survive. © 2024 IEEE.

关键词： Adversarial machine learning

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Touchless Mouse Control Via Hand Gestures Using Resnet and Mediapipe

Touchless Mouse Control Via Hand Gestures Using Resnet and M...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Jagadeeswara Rao Palisetti Digumarthi Srilikhitha Karna Bhargava Basa Bhavana Devi Mettu Bharath Kumar Computer Science and Engineering(Artificial Intelligence and Machine Learning) Lakireddy Bali Reddy College of Engineering Mylavaram India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Touchless control of systems using hand gestures is a trending technology in the field of Human-computer Interaction (HCI). One of the most common ways to interact with systems is through the mouse. In this paper, a touchless mouse is focus on, which can perform the same operations as a physical mouse using finger and hand gestures. All of these operations are carry out by the system without requiring any external devices. In the physical mouse, we hold and operate it using the index and middle fingers. Similarly, we use the same hand pose to control the touchless mouse system. Nowadays, every system has an inbuilt webcam. This system uses a low-resolution webcam as input to capture hand gestures with 21 landmarks using OpenCV and MediaPipe. Based on the movements of the landmarks in the hand, the cursor can navigate, and various actions such as right-click, left-click, double-click, zoom in and zoom out, drag and drop files, multiselect files, increase, and decrease volume, adjusting the brightness, are easily performed, ResNet18 is used to stop the cursor movement. Using different finger and hand gestures. The system has the potential to replace traditional HCI tools in the future and help optimize hu-man-computer communication.

关键词： Hands Human computer interaction Performance evaluation Webcams Tracking Navigation Hospitals Brightness Gesture recognition Indexes

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Crop Monitoring System with Water Moisture Level using Arduino 5

Crop Monitoring System with Water Moisture Level using Ardui...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Sowmia, K.R. Prithi, S. Vijay, K. Berna, I. Eugene Bhuvaneswaran, B. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computational Intelligence School of Computing Srm Ist Kattankulathur Campus India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9781665474672

Many factors-including sunlight, water, soil, and pest control-need to be considered when cultivating crops. The farmer has other responsibilities besides keeping an eye on the seedlings. In this way, the productivity of farming is enhanced by a machine that tends to all the plants with minimal human intervention. The amount of water a plant receives should be proportional to the amount of sunlight it gets. This research work employs the idea of IoT to connect the monitored plant to the network and transmit data. The microcontroller is an Arduino UNO, and components include a photoresistor for light detection and a moisture sensor for measuring the amount of moisture in the soil. The information is transmitted over a network, and the result is viewed using a mobile application. The data that has been collected is displayed in the app. It warns the user when the plant is in danger of drying out due to a lack of water or sunlight. If the crop is dry, a motordriven water pump is used to saturate the soil, and if it's too wet, a motor-operated shutter opens to let the water out. This project intends to make farming as mechanized and labor-free as possible. © 2023 IEEE.

关键词： Crops

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AI -Driven Detection of Gastrointestinal Disease based on Advanced Deep learning Techniques 4

AI -Driven Detection of Gastrointestinal Disease based on Ad...

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Izankar, Sakshi V. Puri, Chetan Burle, Rushikesh Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India

ISBN: (纸本)9798331523923

Esophageal, gastric, and colorectal cancers (CRC) are some of the gastrointestinal (GI) cancers threatening global health with CRC being particularly concerning due to its high fatality rate. While early diagnosis is crucial in improving treatment efficiency, barriers such as image noise and visual subjective assessments during diagnostics such as colonoscopies often render it inconceivable. For better diagnostic accuracy in detecting gastrointestinal disease via endoscopic images, the research explores whether highly advanced artificial intelligence (AI) models such as Vision Transformers (ViTs) and pre-trained DenseNet201 can be effective. We compare these models to popular deep learning techniques such as Support Vector machines (SVM), ResNet50, VGG19, and GoogleNet. At a training accuracy of 99.9% and a validation accuracy of 98.8%, experiments on the KVASIR dataset indicate that Vision Transformer is superior to other models since it can learn long-range dependencies. The robustness of deep learning models for identifying GI disease is exemplified by the superior performance of DenseNet201, ResNet50, and InceptionV3, all of which possess high accuracy, precision, and recall values. The paper illustrates the potential of Vision Transformers, when combined with transfer learning and pre-trained CNN models, to revolutionize endoscopic diagnoses and provide a valuable resource to help physicians manage gastrointestinal conditions more effectively. © 2025 IEEE.

关键词： Endoscopy

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Deep learning for Enhanced Spectrum Sensing in Cognitive Radio Networks with Blockchain-based Security 3

Deep Learning for Enhanced Spectrum Sensing in Cognitive Rad...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Solanki, Surendra Brahma, Banalaxmi Manipal University Jaipur Department of Artificial Intelligence & Machine Learning Rajasthan Jaipur India Dr. B. R Ambedkar National Institute of Technology Department of Computer Science and Engineering Punjab Jalandhar India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354348

The growing need for wireless communication requires the optimum use of spectrum. Cognitive Radio (CR) technology enables the utilization of unused frequency channels through the identification and exploitation of these bands. Nevertheless, conventional spectrum sensing approaches are plagued by drawbacks such as instances of missed detections and false alarms. This study presents a new framework that combines deep learning and blockchain technology to improve the accuracy of spectrum sensing and ensure safe spectrum management in cognitive radio networks. Deep learning models are highly proficient at acquiring intricate signal characteristics, which allows them to attain exceptional performance in spectrum sensing. Blockchain technology, characterized by its decentralized recordkeeping system, inability to be altered, and methods for reaching agreement, promotes confidence and thwarts tampering with spectrum sensing data and user access privileges. Our platform utilizes deep learning models on CR devices to achieve precise spectrum sensing. Additionally, it leverages the secure data storage and access control features of blockchain. This integration improves the accuracy of spectrum sensing, guarantees secure and transparent management of the spectrum, and encourages efficient utilization of the spectrum in cognitive radio networks. The evaluation technique and anticipated results are well defined, setting the stage for future investigations in this field. © 2024 IEEE.

关键词： Radio systems

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