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Binary Classification of DR-Diabetic Retinopathy using CNN with Fundus Colour Images

Materials Today: Proceedings 2022年 58卷 212-216页

作者： Padmanayana B.K, Dr.Anoop Computer Science Department SUCET Mangaluru574146 India AI & ML Department SIT Mangaluru574143 India

Nowadays, with increasing cases of diabetes, one should control the blood sugar as well as perform regular examination of eyes to prevent oneself from blindness. Any person having diabetes is likely to develop Diabetic Retinopathy (DR). DR is triggered by high blood sugar due to diabetes. After some time, having excessive amount of sugar in blood, can damage retina. When sugar jams the tiny blood vessels the eyes are damaged and this will affect the blood vessels and result in leakage of fluid. Millions of working aged adults suffers from loss of sight due to diabetic retinopathy. DR cannot be treated completely but early detection of DR prevents the person from vision loss. We proposed a deep learning model for detection of Diabetic Retinopathy. Detection of DR is a slow process. Physical detection of DR involves a trained clinician to study and estimate the color fundus photo graphs of the retina. Normal process of identification takes a minimum of two days. In our paper Convolutional Neural Network architecture has been used to classify images into two classes which is no-diabetic retinopathy and with diabetic retinopathy. The performance of the network is compared with different optimizers like Adagrad, RMSPROP with momentum and Adam. APTOS-2019 Blindness Detection dataset has been used from Kaggle which contains high resolution Retinal images. Those images are used to train the model. Web based interface has been created for easy interaction with the model. © 2022

关键词： Blood

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Instagram Reach Analysis using Machine Learning Algorithms 1

Instagram Reach Analysis using Machine Learning Algorithms

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

作者： Bhat, Abhinav G. Tarun Rao, K. Devamane, Shridhar B. Udayaprasad, P.K. Ravindranath, R.C. Lakshmikantha, G.C. Global Academy of Technology Department of Computer Science Karnataka Bengaluru India Global Academy of Technology Department of Ai and Ml Karnataka Bengaluru India

ISBN: (纸本)9798350365405

One of the most widely used social media platforms right now is Instagram. Instagram is used professionally by those who want to promote their brands, develop their portfolios and blogs, and produce various types of content. Instagram is a well-known program used by millions of users in multiple niches, and it is constantly evolving to serve both users and content creators better. Nevertheless, as this fluctuates, it impacts the audience for our postings, which has a long-term impact on us. So, a content producer must examine the analytics of their Instagram reach if they hope to succeed on the platform over the long term. © 2024 IEEE.

关键词： Adversarial machine learning

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Hand Symbol Recognition Using Canny Edge Algorithm And Convolutional Neural Network 2

Hand Symbol Recognition Using Canny Edge Algorithm And Convo...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Mittal, Harshit Garg, Neeraj Maharaja Agrasen Institute of Technology Computer Science Department Delhi India Maharaja Agrasen Institute of Technology AI/ML Department Delhi India

ISBN: (纸本)9798350360523

Hand symbol recognition is a pivotal component in the domain of computer vision, with far-reaching applications spanning sign language interpretation, human-computer interaction, and accessibility. This research paper discusses the approach with the integration of the Canny Edge algorithm and Convolutional Neural Network. The significance of this study lies in its potential to enhance communication and accessibility for individuals with hearing impairments or those engaged in gesture-based interactions with technology. In the experiment mentioned, the data is manually collected by the authors from the webcam using Python codes, to increase the dataset augmentation is applied to original images which makes the model more compatible and advanced. Further, the dataset of about 6000 coloured images distributed equally in 5 classes (i.e., 1, 2, 3, 4, 5) are pre-processed first to gray images and then by the Canny Edge algorithm with threshold 1 and 2 as 150 each. After successful data building, this data is trained on the Convolutional Neural Network model giving Accuracy: 0.97834, Precision: 0.97841, Recall: 0.9783, and F1 score: 0.97832. For user purposes, a block of codes is built in Python to enable a window for hand symbol recognition. This research, at its core, seeks to advance the field of computer vision by providing a novel perspective on hand sign recognition. By leveraging the capabilities of the Canny Edge algorithm and Convolutional Neural Network, this study contributes to the ongoing efforts to create more accurate, efficient, and accessible solutions for individuals with diverse communication needs. © 2024 IEEE.

关键词： Convolution

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Dynamic Framerate SlowFast Network for Improving Autonomous Driving Performance

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IEIE Transactions on Smart Processing and Computing 2023年第3期12卷 261-268页

作者： Jeon, Byeong-Uk Chung, Kyungyong Department of Computer Science Kyonggi University Suwon Korea Republic of Division of AI Computer Science and Engineering Kyonggi University Suwon Korea Republic of

computer vision technology is used for autonomous driving and road traffic safety. Accordingly, studies on deep learning models that detect and analyze objects through images or videos are ongoing. On the other hand, deep learning algorithms that detect even the action of things require high computing performance. In addition, the computing performance of autonomous driving vehicles for processing such tasks is inferior. These classification processes are not used for recognizing and determining autonomous driving vehicles because it is impossible to process the classification of the action of autonomous driving vehicles in an autonomous driving vehicle on a real-time basis. This paper proposes a Dynamic Framerate SlowFast network for improving autonomous driving performance. Unlike pre-existing studies, the proposed model includes a cropping process through the YOLO model. In addition, it measures the similarity between unit frames through the SSIM and skips the input when the similarity exceeds a certain level. This process made it possible to reduce the number of frames entered into the model. Compared to the existing SlowFast Network, the performance evaluation compared the time required to analyze one image and the AUC of classification results when the number of input frames was reduced through similarity analysis techniques. The similarity analysis technique achieved the highest AUC when the SSIM was applied. The Dynamic Framerate SlowFast network proposed in this study achieved an AUC of 0.7126 and took an FPS of 0.7912 to analyze the entire verification video data. Compared to the pre-existing SlowFast network, which took an FPS of 0.5285 to achieve an AUC of 0.7531, the Dynamic Framerate SlowFast network achieved faster and more accurate results. Therefore, using the proposed technique, it is possible to achieve faster detection results while maintaining the object action detection AUC of the SlowFast network. © 2023 Institute of Electronics and Informatio

关键词： Autonomous vehicles

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Smart Interaction: Merging Augmented Reality and Internet of Things for Seamless Device Control

Smart Interaction: Merging Augmented Reality and Internet of...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： J. R. V. Jeny B. Sai Mahitha M. Shashank N. Vamsi Krishna Department of Computer Science and Engineering(AI & ML) Vignan Institute of Technology and Science

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

The combination of Augmented Reality (AR) and Internet of Things (IoT), often referred to as AR + IoT, offers exciting possibilities for future research and development. IoT serves as a platform for communication and remote control of connected devices, while AR provides an intuitive interface for visualizing and managing these devices through enhanced, information-rich displays. This research aims to develop an interactive and accessible home automation system that integrates IoT and AR technologies, enabling seamless remote control of electrical devices. Utilizing an ESP32/NodeMCU board interfaced with the Blynk IoT platform, the system supports device management through a mobile application and an AR interface designed in Unity. The AR interface facilitates intuitive interactions via URL-based signals, which are transmitted to the IoT hardware for execution. This dual-control mechanism enhances user flexibility and convenience, making smart home automation more accessible to users with varying technical expertise. The proposed system not only improves user interaction and control efficiency but also demonstrates the potential of AR in augmenting IoT applications, bridging the gap between physical and virtual control environments. Future enhancements could include advanced AR features, voice control, and cloud-based analytics to further optimize system performance and scalability.

关键词： Visualization System performance Scalability Smart homes Systems support Internet of Things Augmented reality Remote control Wireless fidelity Research and development

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Performance comparison of machine learning algorithms for coronary heart disease prediction 1

Performance comparison of machine learning algorithms for co...

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

作者： Bharath, R.C. Aneesh, R.T. Aniruddha, G.J. Devamane, Shridhar B. Ravindranath, R.C. Lakshmikantha, G.C. Global Academy of Technology Department of Computer Science Karnataka Bengaluru India Global Academy of Technology Department of Ai and Ml Karnataka Bengaluru India

ISBN: (纸本)9798350365405

The algorithms used in these machine learning models are quite straightforward and easy to understand, and they may produce predictions. These models have numerous business and scientific applications. Machine Learning model predictions are employed across many disciplines, including biology, behavioural sciences, environmental sciences, social sciences, and economics. We have discussed both the theoretical and practical elements in this paper. © 2024 IEEE.

关键词： Contrastive Learning

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Deep Learning in Medical Imaging: Image Processing - From Augmenting Accuracy to Enhancing Efficiency 24

Deep Learning in Medical Imaging: Image Processing - From Au...

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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Lakshmi Vara Prasad, G. Murali, A. Bhaskar Reddy, Y.V. Marturi, Haribabu Paul, Valeru Vision Centre for Data Science AI and ML QIS College of Engineering and Technology Andhra Pradesh Ongole India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798400709586

Deep learning has transformed medical imaging by significantly improving accuracy and efficiency in image processing tasks such as disease detection, segmentation, and classification. This paper explores the role of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other advanced architectures in enhancing image interpretation across various medical imaging modalities. By analyzing MRI, CT, and ultrasound data, this study demonstrates the efficacy of deep learning in automating critical diagnostic tasks, reducing human error, and improving patient outcomes. We propose a comprehensive framework for applying deep learning techniques in medical imaging, providing insights into future advancements in the field. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

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BERTing Emotions: Exploring Textual Emotion Recognition using NLP

BERTing Emotions: Exploring Textual Emotion Recognition usin...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： R. Praveen Kumar Pannala Ramya G.Sai Teja V.Rama Krishna Department of Computer Science and Engineering(AI & ML) Vignan Institute of Technology and Science

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Emotion recognition in text is a crucial area within Natural Language Processing (NLP), serving diverse applications such as sentiment analysis, mental health assessment, and human-computer interaction. This study explores the use of Bidirectional Encoder Representations from Transformers (BERT) for this purpose, capitalizing on its bidirectional transformer architecture to grasp intricate contextual relationships in text. By fine tuning a pretrained BERT model on a dataset annotated with emotional categories like happiness, sadness, anger, surprise, and fear, the model achieves precise emotion classification. BERT's pre-trained linguistic knowledge and ability to interpret complex semantic and syntactic patterns provide a strong foundation for this task. Experimental results demonstrate that BERT surpasses traditional machine learning approaches, including Support Vector Machines (SVM) and logistic regression, in terms of accuracy, owing to its advanced contextual embeddings. Additionally, the model exhibits excellent generalization capabilities across diverse emotional expressions, highlighting its effectiveness for emotion-aware NLP applications. This project also leverages Streamlit to develop interactive web applications that integrate NLP-based emotion detection, showcasing its practicality and accessibility for real-world use cases.

关键词： Support vector machines Emotion recognition Sentiment analysis Semantics Bidirectional control Mental health Syntactics Transformers Encoding Tuning

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Waste Product Identification to assist EPR using Object Detection 15

Waste Product Identification to assist EPR using Object Dete...

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

作者： Anil Prasad, R. John, Ayain Gandiboina, Harika Jayadev, Gopika Frugal Scientific Pvt Ltd Bengaluru India Dayananda Sagar University School of Engineering Department of Computer Science and Engineering Bengaluru India Dayananda Sagar University School of Engineering Department of Ai & Ml Bengaluru India

ISBN: (纸本)9798350370249

With the increase in population, there has been a large increase in waste production. This has affected the environment and in turn have led to land pollution. To increase the recycling of products and increase the lifespan of raw materials, several countries of the world have implemented EPR policy. EPR requires collection of data related to the amount of non biodegradable waste generated by each company. With the recent technological advancements in machine vision, it has become possible to develop an infrastructure to easily collect such data in realtime using various object detection models like Yolo, SSD, RCNN and so on. This paper provides the details of a complete prototype that can help assist in the identification of non biodegradable waste products into their respective producers which can in turn help to implement the EPR policy and also elaborates on the model selection process for this application. The models that were used and compared in this paper includes CNN, Faster RCNN and Yolo v5 © 2024 IEEE.

关键词： CNN Extended Producer Responsibility (EPR) Faster RCNN Object Detection Techniques Waste Management YOLO

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Next-Generation Cyber Defense: Deep Q-Learning-Based Adversarial Detection and Prevention in Autonomous Vehicle Networks 4

Next-Generation Cyber Defense: Deep Q-Learning-Based Adversa...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Srivastava, Shubhi Bhargav, H.K. Gurani, Vijayakumar Chaturvedi, Arpana Gandhi, Ankita Dharangutti, Yogesh New Horizon College of Engineering Department of Information Science and Engineering Bangalore India Shridevi Institute of Engineering and Technology Department of Computer Science and Engineering Tumakuru India Karnatak University Department of Computer Science Dharwad India New Delhi Institute of Management Department of IT/AI/ML New Delhi India Parul Institute of Engineering and Technology Department of Computer science and Engineering Gujarat India Faculty of Law Pune India

ISBN: (纸本)9798350352931

This paper presents a novel ai-powered super-resolution technique for real-time video processing, which significantly enhances the visual quality of low-resolution video frames. The proposed framework employs a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Generative Adversarial Networks (GANs) to reconstruct high-resolution frames with minimal computational overhead. Experimental results on benchmark video datasets show that the proposed method achieves a peak signal-to-noise ratio (PSNR) of 34.56 dB and a structural similarity index (SSIM) of 0.94, outperforming state-of-the-art methods by 12.3% in PSNR and 8.7% in SSIM. Additionally, the system demonstrates an average processing speed of 30 frames per second (fps) on a GTX 1080Ti GPU, making it suitable for real-time applications. The qualitative and quantitative results validate the effectiveness of the proposed method in preserving fine details, reducing artifacts, and enhancing visual clarity in various real-time video processing scenarios. © 2024 IEEE.

关键词： Generative adversarial networks

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