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Enhanced Holistically Nested Edge Detection (eHED) Algorithm: A Reliable Edge Detection in Unconstrained Scenarios 5th

Enhanced Holistically Nested Edge Detection (eHED) Algorithm...

引用

5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Ramalakshmi, S. Vani, V. Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India

ISBN: (纸本)9789819767250

Understanding scenes, interpreting images, and identifying objects are all areas of computer vision that depend on edge detection. Many edge detection techniques, including traditional edge detection algorithms, have already been developed. The holistically nested edge detection (HED) system can accurately detect edges even under challenging circumstances, but it requires further improvements in its edge detection method. To address the constraints of the HED algorithm, this research introduces a novel enhanced HED (eHED) method. Integrating spatial attention (SA) into the HED method, eHED improves the ability to find edges, especially in complex scenarios. To improve the accuracy of edge detection, eHED focuses on informative parts of the image by successfully using spatial attention. This results in the exclusion of noise and features that are irrelevant to the current scenario. Extensive experiments are carried out to evaluate the effectiveness of the eHED algorithm on standard datasets including the ALPR dataset and PKU (License Plate Detection) dataset. The eHED algorithm, a substantial progress in edge detection technology, enhances performance and achieves an accuracy rate of 84% when deployed to the given dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer vision Edge detection Spatial attention The holistically nested edge detection

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Medicinal Plants Identification Using Deep Learning: A Survey 6

Medicinal Plants Identification Using Deep Learning: A Surve...

引用

6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Prasannan, Revathy Roy, Preethy Jithi, P.V. Amritha, R. Nair, Anjana V Jose, Ann Mariya Adi Shankara Institute of Engineering & Technology Computer Science and Engineering Kalady India

ISBN: (纸本)9798350381689

Medicinal plant identification has shown great benefit from deep learning, especially when CNNs are employed. CNNs are ideally suited for this task since they can extract complex features from photos. The promise of deep learning for medicinal plant identification is outlined in this research, which achieves up to 99% accuracy. This survey shows that deep learning can successfully detect medicinal plants in a variety of real-world contexts, such as open fields, rural places, and botanical gardens. Deep learning's ability to automate the identification process is a key benefit in this industry as it can assist alleviate the scarcity of knowledgeable and proficient botanists. Moreover, deep learning models can be implemented on mobile platforms, hence expanding their user base. This offers strong evidence that deep learning has potential applications in the recognition of medicinal plants. Deep learning models have the potential to aid in the building of an all-encompassing and dependable system for detecting medicinal plants in a variety of contexts with additional study and improvement. © 2024 IEEE.

关键词： Adversarial machine learning

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New Energy Power Data Prediction Research based on Cross Validation Method 5

New Energy Power Data Prediction Research based on Cross Val...

引用

5th International Conference on Advances in Electrical Engineering and computer Applications, AEECA 2024

作者： Sun, Lijuan Li, Lan Zhang, Xujuan Lanzhou Institute of Technology School of Computer Science & Artificial Intelligence Lanzhou China

ISBN: (纸本)9798350355253

In order to improve the prediction accuracy of photovoltaic power generation and reduce the impact of grid-connected operation of photovoltaic power station on the security, stability and economic operation of power system, a power prediction method combining data mining and machine learning algorithm was proposed. The statistical analysis technology in data mining is used to describe the real historical data of the output of a distributed photovoltaic power plant. Combined with correlation analysis, the main parameters affecting the generation power prediction are found. Three power prediction models are designed based on machine learning, including linear regression, gradient boosting tree and random forest. The experimental results show that the gradient boosting tree regression prediction model has the smallest prediction error and the highest generalization ability when the full sample data set is used as the training set to train the model. This method improves the prediction accuracy of photovoltaic power generation, and plays an important role in the optimal operation of photovoltaic power station, the scheduling of photovoltaic power system and the security and stability of power grid. © 2024 IEEE.

关键词： Prediction models

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Secure and Privacy-Preserving UAV-based Rescue Operations for Vehicles in Rugged Terrain 15

Secure and Privacy-Preserving UAV-based Rescue Operations fo...

引用

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

作者： Lama, Radhika Rathod, Bhukya Nithin Raman, Rakesh Karmakar, Sandip National Institute of Technology Computer Science and Engineering West Bengal Durgapur India

ISBN: (纸本)9798350370249

Traditional rescue efforts encounter difficulties in challenging terrain, hindered by obstacles and natural calamities. Unmanned Aerial Vehicles (UAVs) integrated with Vehicular Ad Hoc Networks (VANETs) present a viable solution, particularly in mountainous areas. UAVs function as airborne communication hubs, swiftly establishing temporary networks and overcoming geographical constraints. Their agility enables access to remote locations, offering vital assistance during emergencies. However, concerns regarding security and privacy emerge due to the openness of communication channels. To address this, we present a secure system for UAV-based rescue operations for vehicles. Our approach includes the application formal verification using 'Scyther,' an informal analysis of other possible attacks towards the proposed scheme, and a comparative evaluation with existing solutions, ensuring security robustness in emergency scenarios. © 2024 IEEE.

关键词： Privacy-preserving Rugged Terrain Security Unmanned Aerial Vehicle

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Advancements and Challenges in Federated Learning : A Survey

Advancements and Challenges in Federated Learning : A Survey

引用

2024 International Conference on Advances in computing Research on science Engineering and technology, ACROSET 2024

作者： Prajapat, Vivek Singh Rathore, Narendra Pal Sethi, Kamal Kumar Rajput, Shiv Shankar Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Machine Learning (ML) has significantly impacted daily life by automating tasks and enhancing decision-making across diverse sectors such as healthcare, finance, and transportation. However, concerns regarding data privacy, particularly in sensitive domains like healthcare and finance, have impeded its widespread adoption. Federated Learning (FL), pioneered by Google in 2016, presents a promising solution by enabling devices to collaborate on model training without sharing raw data. In FL, each device retains its data locally, and only model updates are exchanged with a central server for aggregation. This decentralized approach preserves data privacy while still improving model performance through collaborative learning. FL has garnered interest across industries, with approximately 32% of companies intending to integrate it into their systems soon. Furthermore, investment in FL is projected to rise substantially from 107 million in 2020 to 538 million by 2025, as indicated by forecasts from KPMG. This paper provides an overview of FL, its applications across various sectors, current adoption trends, and future growth prospects, highlighting its significance in addressing data privacy concerns while advancing machine learning capabilities. © 2024 IEEE.

关键词： Federated learning

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Deep Learning-based Classification of Cataract Infected Eyes and Normal Eyes using Convolutional Neural Network 4

Deep Learning-based Classification of Cataract Infected Eyes...

引用

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

作者： Ashitha Sandrin, A. Denisha, M. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385793

Cataract, a common eye disease characterized by clouding of the natural lens of the eye, is a serious threat to visual health. If left untreated, they can lead to blurred vision and even blindness, underscoring the importance of timely intervention. To address the urgent need for accurate cataract diagnosis, this study provides an improved approach using convolutional neural networks (CNN) to distinguish cataract eyes from eyes with normal vision. Because cataracts are the most common cause of visual impairment worldwide, recognition and accurate classification are critical for prompt intervention and mitigation of potential harms. Taking advantage of recent advances in deep learning, especially CNNs, this method introduces a new feature extraction technique that uses hyperparameter optimization. The systematic exploration of the hyperparameter space determines the optimal configuration that maximizes the efficiency of the classification algorithm to distinguish cataract from normal eyes. Experimental validation on a comprehensive fundus image dataset demonstrates the effectiveness of this approach, achieving robust performance and high classification accuracy. A deep learning approach with optimization techniques will improve the efficiency and accuracy of cataract diagnosis, offering promising solutions to alleviate the global burden of vision loss and blindness. © 2024 IEEE.

关键词： Convolutional neural networks

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Cloud-Driven Machine Learning with AWS: A Comprehensive Review of Services 2

Cloud-Driven Machine Learning with AWS: A Comprehensive Revi...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Ravindranathan, Manya K. Sendil Vadivu, D. Rajagopalan, Narendran National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798350306415

With the rise of cloud computing, people now have 24/7 online access to IT resources, which is a transformational force. Amazon Web Services (AWS) is a market leader in the field of cloud service providers, providing a broad range of platform, software, and infrastructure services. In this review article, we explore the domain of cloud-driven Machine Learning (ML) with an emphasis on the extensive resources and support provided by AWS. The ability of the AWS ecosystem to provide scalable and effective ML applications is highlighted in this study. It looks at the range of AWS services created to make building, training, and deploying cloud-based ML applications easier. Our analysis includes an AWS Machine Learning as a Service (MLaaS) product categorization, illuminating the range of resources and options available to ML practitioners. We also take into account findings from comparable studies that have used AWS services for a range of purposes. In conclusion, this article offers a thorough introduction for individuals wishing to explore the world of cloud- driven ML using AWS. It provides insightful information on AWS services, their uses, and how they are influencing ML in the cloud. © 2024 IEEE.

关键词： AWS AWS Free Tier Services Cloud computing Distributed computing Machine Learning as a Service

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Anomaly Detection in Medical Imaging: Bridging the Gap between 2D Processing and Interactive 3D Visualization

Anomaly Detection in Medical Imaging: Bridging the Gap betwe...

引用

2024 IEEE International Conference on Recent Advances in science and Engineering technology, ICRASET 2024

作者： Abhisri, M. Sasidhar, Manikrishneshwar Subramanian, Aditya Sreeraghavan, Mirdhula Sivakumar, Surindernath Prabakaran, N. Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India

ISBN: (纸本)9798350388602

This paper mainly aims to present the use of transform a 2D image into a 3D interactive model which will help us visualize the anomaly, allowing the medical sector a better understanding of anomalies in medical images, which may even unearth new findings. Using stable diffusion algorithms, the generative model and computer vision converts 2D images into interactive 3D models. Existing structures usually struggle with complex 2D images when the anomaly is detected, hence focus on building a 3D model for deeper understanding is necessary. In this model, the challenges and roadblocks in normal 2D images for anomaly detection and why a 3D model will provide a better insight into the anomaly from medical images is discussed. Next, it explores the technological benefits of having an advanced 3D model and how the 3D model will help in interacting with the anomaly by employing 3D models to show the expected progression in the anomaly extraction process. Finally outlining the previously implemented models to generate 3D interactive objects such as, generative adversarial networks (GANs) and convolutional neural networks (CNNs), as they have exhibited promising results in extracting meaningful features and detecting anomalies in medical images. It emphasizes the importance of bridging the gap between 2D image processing and interactive 3D visualization in anomaly by incorporating advanced techniques, leveraging 3D information, and enabling interactive exploration, that enhance the precision and efficacy of anomaly detection, aiding in achieving quicker diagnosis and treatment as outcomes in healthcare. © 2024 IEEE.

关键词： Generative adversarial networks

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Biological Profile Estimation of the Human Skeleton Based Forensic Identification Using Deep Learning Image Classification 5

Biological Profile Estimation of the Human Skeleton Based Fo...

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

作者： Dutta, Sriparna Keerthana, R. Shekar, D Chandra Kumar, S. Satheesh Institute of Aeronautical Engineering Computer Science and Information Technology Hyderabad Dundigal India

ISBN: (纸本)9798331518097

This research study proposes an innovative methodology for the automated estimation of biological profiles through deep learning techniques, with a particular focus on the classification of age and gender derived from chest X-ray imaging. Utilising Convolutional Neural Networks (CNNs) within the TensorFlow/Keras framework, this investigation confronts the inherent limitations associated with traditional forensic anthropology, such as examiner bias, protracted processing durations, and the variability encountered in instances of poorly preserved skeletal remains. The proposed system capitalises on chest X-rays to extend the analytical reach of skeletal assessments beyond the typically examined osseous structures, attaining an impressive 96% accuracy in gender classification alongside negligible error rates in age estimation. These findings highlight the capacity of CNNs to effectively extract and scrutinise intricate anatomical features, thereby facilitating accurate forensic profiling. In contrast to conventional methodologies, this paradigm significantly reduces the necessity for human involvement, presenting a more objective, efficient, and scalable alternative for forensic identification. The incorporation of this system into a user-centric interface enhances its applicability in practical contexts, further illustrating its practical significance. Although this study reveals considerable advancements, subsequent endeavours will concentrate on augmenting the diversity of the dataset and investigating sophisticated architectures to improve generalisability and performance metrics. This research accentuates the transformative potential inherent in AI-driven methodologies within forensic science and medical imaging, establishing a solid foundation for future innovations in these domains. © 2024 IEEE.

关键词： Convolutional neural networks

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Detection of Cyber Attacks using AI 10

Detection of Cyber Attacks using AI

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Saravanan, Gurunishal Dhas, Julia Punitha Malar Devapriya, I. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353068

Several cyber attacks happen globally on several commercial and industrial technological devices. If these attacks are detected early, the damages can be reverted or even prevented. Models such as Gaussian Naive Bayes, Decision Tree, SVM, Random Forest, and Logistic Regression are used to detect these attacks. After these algorithms are trained, a custom model is designed using ensemble learning methods. The ensemble learning method used is known as Stacking, which integrates many algorithms to provide a better accuracy value. The model's performance is assessed and compared to consider the best model to detect cyber attacks. The accuracy score is used to evaluate the models. Based on these results, the most suitable model is chosen. © 2024 IEEE.

关键词： Decision trees

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