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6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Krishna, J. Tejaswini, S. Viswa Sai Reddy, N. Susmitha, S. Sohail, S. Prasanna, G. Department of AI&ML AITS Rajampet India Department of Computer Science and Engineering AITS Rajampet India

ISBN: (纸本)9789819971367

Understanding digital media dishes is a fascinating issue, but it also involves a lot of challenge. The dish's complicated ingredient list presents a hurdle. Due to the growth of deep learning, a number of efficient tools can partially resolve the issue. The job of dish recognition in this work is thought about. Based on the EfficientNet architecture and transfer learning, a unique dish recognition algorithm is proposed. First, add a number of significant layers to the EfficientNet-B0. Next, employ transfer learning to retrain the model using the best parameters that were learned during the first pre-training on ImageNet on the UEH-VDR dataset, a fresh batch of dish pictures. The UEH-VDR dataset includes pictures of Vietnamese food gathered from a variety of sources. According to experimental findings, the suggested approach can identify a dish with an accuracy of 92.33%. Additionally, it performs better than models built on well-known Space Invariant Artificial Neural Networks (SIANN) like VGG and residual neural network. On the basis of the training data, a mobile application is also created to assist tourists who wish to learn about Vietnamese cuisine. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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A Novel Approach to Advancing Drowsiness Detection Using Head-Angle and Eye-Aspect Ratio 8

A Novel Approach to Advancing Drowsiness Detection Using Hea...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Shashidhar, V. Ahmed, Mueezuddin Razeena, M. Rajesh, K.S. Bharath, J. Bangalore India Rajarajeswari College of Engineering Department of Computer Science & Engineering Bangalore India Rajarajeswari College of Engineering Department of Ai&ml Bangalore India

ISBN: (纸本)9798331505462

Drowsiness is a significant risk factor for accidents, particularly in situations requiring sustained attention, such as driving or operating machinery. This research proposes an innovative methodology for detecting driver drowsiness using advanced computer vision algorithms and techniques. Our system employs a shapeshift predictor and face-tracking algorithms to monitor the driver's facial features and head movements. Specifically, it detects three distinct indicators of drowsiness: excessive head tilting to the right or left, significant drooping of the chin, and prolonged eye closure (defined as eyes remaining closed for more than 24 consecutive frames). When any of these conditions are met, an alarm is triggered, alerting the driver to their potentially hazardous state. The proposed system offers a robust and non-intrusive solution for real-time drowsiness monitoring, with potential applications in various fields where vigilance is critical for safety. © 2024 IEEE.

关键词： Eye movements

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AquaRescue: Intelligent Drowning Detection and Swift Rescue System Using Integrated Approach 4

AquaRescue: Intelligent Drowning Detection and Swift Rescue ...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Rakshitha Sowmya, P. Shettigar, Rashmitha Nishanth, N. Durgaprasad Kumar, Dhanush Department of Ai&ml Nitte574110 India Department of Computer Science and Engineering Nitte574110 India /CS East Mumbai400068 India

ISBN: (纸本)9798350387490

Drowning accidents, in pools continue to be a concern calling for creative solutions to improve safety standards. The research aims to establish a framework for identifying and saving drowning cases in pools. Cutting-edge technologies such as computer vision, Artificial Intelligence, and IoT are integrated into the system to quickly and precisely recognize incidents and trigger rescue operations. Utilizing cameras and advanced algorithms like YOLO v3 and CNN, the system continuously observes the pool environment. By assessing people's movements and locations in the water, it accurately detects drowning cases. Once an incident is identified, the system automatically starts rescue procedures by dispatching rescue tools and notifying lifeguards with alarms across the pool premises. Moreover, it enables emergency calls through GSM SIM900A module in case the lifeguard is not present nearby. This innovative detection and rescue system not only enhances swimmer safety but also reduces the workload on lifeguards, allowing them to react more effectively in emergencies. The ultimate goal of this system, driven by technological advancements, is to minimize drowning incidents in pools. The system's performance has been evaluated against other models like Faster R-CNN, YOLO v3, and SSD. YOLO v3 demonstrated superior accuracy of 90% with combined datasets (GF-1 & GF -2). © 2024 IEEE.

关键词： Guards (shields)

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CAml: Cryptographic-Based Cloud Security for Healthcare Data with Machine Learning Technique

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Recent Patents on engineering 2025年第2期19卷 26-40页

作者： M.H, Chaithra Vagdevi, S. Department of Computer Science and Engineering REVA University & Research Scholar Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of AI & ML City Engineering College Karnataka Bangalore India

Background: The Cloud model is one of the most realistic frameworks with a vast range of social networking interactions. In medical data, security is a major constraint as it incorporates information about the patients. The cloud environment subjected to mobility and openness is exposed to security issues and limits authorization levels for data transmission. Objective: This patent paper aims to propose a security model for attack prevention within the healthcare environment. Methods: The proposed Cryptographic Attribute-based Machine Learning (CAml) scheme incorporates three stages. Initially, the homomorphic encryption escrow is performed for secure data transmission in the cloud. Secondly, the information of the users is evaluated based on the consideration of users' authorization. The authorization process for the users is carried out with the attribute-based ECC technique. Finally, the ml model with the classifier is applied for the detection and classification of attacks in the medical network. Results: The detected attack is computed and processed with the CNN model. Simulation analysis is performed for the proposed CAml with conventional ANN, CNN, and RNN models. The simulation analysis of proposed CAml achieves a higher accuracy of 0.96 while conventional SVM, RF, and DT achieve an accuracy of 0.82, 0.89 and 0.93, respectively. Conclusion: With the analysis, it is concluded that the proposed CAml model achieves higher classification accuracy for attack detection and prevention in the cloud computing environment. © 2025 Bentham science Publishers.

关键词： Cloud security

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Analysis on Detecting Cardiac Arrhythmia Using Advanced Technologies 8th

Analysis on Detecting Cardiac Arrhythmia Using Advanced Tech...

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Radhika, N. Sujatha, D. School of Computer Science & Engineering MRUH Telangana Hyderabad India CSE-AI&ML Department MRCET Telangana Hyderabad India Department of Computational Intelligence MRCET Telangana Hyderabad India

ISBN: (纸本)9789819786657

Essential features of the various cardiac diseases that affect the human heart are provided by the electrocardiogram (ECG). A significant portion of the diagnosis of heart illness comes from the categorization of arrhythmias. An arrhythmia is any alteration to the regular electrical impulse sequence. Conventional techniques for analyzing digital signals, machine learning, and the offshoots, including deep learning approaches are widely used for analyzing and classification of ECG signals and more importantly, for the timely diagnosis of cardiac disorders and arrhythmias. An extensive review of the literature regarding ECG analysis is presented in this document. Also address the computational barriers of machine learning techniques to wearable devices. The study started with a search for pertinent papers and then the gathered data was examined. Examining the assessed ECG features and the machine learning technique for identifying arrhythmias was the second step. The analysis shows that even though the MIT-BIH database requires a large amount of preprocessing work, it chose a sizable proportion of studies. We examine the substantial corpus of literature on pre-recorded ECG data, signal collection, ECG signal processing, and noise reduction, identifying ECG spectrographic states through function technology, classification of ECG signals, and relative perspectives among the examined modules. We reviewed the ultra-modern machine learning strategies for automated arrhythmia detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Enhancing Healthcare Data Security through Blockchain-Enhanced IDPS and Multi-Layered Firewall System

Enhancing Healthcare Data Security through Blockchain-Enhanc...

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Advancement in Technology (ICONAT), International Conference for

作者： Vishnu Kurnala Department of Computer Science and Engineering(AI&ML) Vardhaman College of Engineering

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

This paper introduces a novel security framework to fortify healthcare data against cyber threats, incorporating blockchain technology and ledger tokenization within an integrated Intrusion Detection and Protection System (IDPS). Faced with escalating cybersecurity challenges, our approach merges traditional protection mechanisms with cutting-edge technologies to offer unparalleled data integrity and security. By leveraging blockchain for immutable data trails and enhancing IDPS with advanced machine learning techniques, our solution provides a robust defense against a range of cyber threats, including ransomware and data breaches. Extensive testing in simulated environments confirms the effectiveness of our system, suggesting it as a viable blueprint for future developments in healthcare cybersecurity. This study not only demonstrates the application of blockchain in enhancing data security but also sets the stage for further innovative practices in protecting sensitive healthcare information.

关键词： Firewalls (computing) Data integrity Intrusion detection Medical services Machine learning Tokenization Blockchains Ransomware Protection Testing

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Meta-Learning-Based Multimodal Fake News Detection 5th

Meta-Learning-Based Multimodal Fake News Detection

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Varun, M.S. Bhattacharjee, Ishaan Venkatesha, Pratheek Karike Sathvik, A. Pramath, S. Das, Bhaskarjyoti Department of Computer Science and Engineering PES University Karnataka Bengaluru India Department of Computer Science and Engineering in AI and ML PES University Karnataka Bengaluru India

ISBN: (纸本)9789819745395

In the contemporary digital era, spreading false information via social media and Internet platforms poses a significant challenge. Not only the data has become increasingly multimodal, lack of sufficient labeled data due to prohibitive labeling costs and data privacy issues poses an additional challenge. An approach such as meta-learning can help address this issue. This work showcases meta-learning with scarcely labeled data for the detection of multimodal fake news and provides a performance comparison of the meta-learning approach with the fully supervised learning approach. We leverage the power of Model-Agnostic Meta-Learning (MAml) and Fast Context Adaption via Meta-Learning (CAml) in this work. The results are encouraging as these meta-learning models achieve heightened accuracy and speed even with insufficient labeled data for multimodal fake news detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Agriculture Wastage Management System Using Android Application

Agriculture Wastage Management System Using Android Applicat...

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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： P. M. Naidu M. Anjali K. Sri Vaishnavi S. Chandralekha K. Tejaswini Reddy Department of Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

While numerous Agri-tech start-ups primarily focus on supply chain optimization and farm yield management, there is a growing need to address the unexplored challenge of managing agricultural stubble left after grain segregation. This project seeks to develop a solution for automating the stubble collection process, its transportation to designated sites, sending notifications about pickup and delivery, and facilitating the tracking of stubble disposal in an eco-friendly manner. The proposed Android application is versatile and can be utilized by various stakeholders, including farmers, transportation services, and small-scale industries. The application consists of modules to the specific needs of each user group, facilitating seamless interaction and problem resolution. The Agriculture Waste Management System Android application represents an innovative solution to efficiently and sustainably manage agricultural wastage. This system addresses the unique requirements of farmers, transporters, and industries, creating an interconnected ecosystem for responsible handling of surplus agricultural produce. Users can register, manage agricultural wastage, vehicles, and receive messages, enabling efficient information and resource flow within the agricultural supply chain. By digitizing and streamlining wastage management processes, this project aims to minimize waste, improve collaboration, and contribute to a more sustainable and interconnected agriculture. The Agriculture Waste Management System is not just a technological innovation but also a catalyst for positive change, promoting a greener, more efficient, and sustainable future for agriculture.

关键词： Waste management Technological innovation Supply chains Transportation Agriculture Registers Stakeholders Security Machinery Optimization

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Meta-learning Assisted Graph Representation Learning for Downstream Learning Tasks 5th

Meta-learning Assisted Graph Representation Learning for Do...

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Bhat, Nischal R. Kulkarni, Prerana Sanjay Menon, Ananya Meda, Smruthika S. Das, Bhaskarjyoti Department of Computer Science and Engineering PES University Karnataka Bengaluru India Department of Computer Science and Engineering in AI & ML PES University Karnataka Bengaluru India

ISBN: (纸本)9789819747108

The Graph Transformer Network (GTN) is a state-of-the-art solution for graph representation learning, which utilizes the transformer architecture to capture attention and long-range dependencies in a non-euclidean dataset such as a graph. However, even with GTN in a downstream learning task, there are usually a few data points that are harder to classify than others and are at risk of being wrongly classified. Metric-based meta-learning, which encompasses many meta-learning algorithms, proves promising in such scenarios. Through this work, we propose a novel 2-stage pipeline combining a Graph Transformer Network with metric-based meta-learning methodologies to make downstream classification tasks easier. The work described in this article investigates and evaluates this pipeline in two specific learning setups using two Metric Learning frameworks, i.e., a Siamese Network for binary classification of graphs and a Prototypical Network for multi-class classification of nodes, both appended to a representation learning block of Graph Transformer Network. The experiments described in this article conclusively prove the efficacy of such a pipeline both in full-shot and few-shot learning scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Deep Learning based air Quality Forecasting using Satellite Data 6

Deep Learning based Air Quality Forecasting using Satellite ...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sree, K. Navya Reddy, Mancharla Rakesh Saraswathi, S. Department of Computer Science and Engineering Specialization with AI & ML Sathyabama Institute of Science and Technology Chennai India Department Computer science and engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

air pollution is a significant threat to human health and the environment. Accurate air quality forecasting is essential for effective mitigation strategies, including public health advisories, emission control measures, and policy interventions. This research focuses on the development of an advanced air quality forecasting system using machine learning techniques. The system uses historical data on pollutants such as PM2.5, NO2, and O3, along with meteorological parameters, to predict future air quality levels. The model is enhanced with the inclusion of sophisticated machine learning algorithms to better capture the intricate spatiotemporal patterns and enhance the accuracy of forecasting. The system may potentially provide real-time air quality forecasts that enable proactive measures for protecting public health and inform decisions for environmental policy. This research contributes to sustainable and resilient urban environments by providing valuable insights into air quality dynamics and supporting effective air pollution mitigation strategies. © 2025 IEEE.

关键词： air pollution air quality forecasts cardiovascular diseases data-driven strategies environmental policy global climate international collaboration machine learning NO2 O3 PM2.5 policy-making pollution mitigation proactive alerts public health real-time monitoring respiratory diseases spatial variability temporal variability transboundary pollution

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