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Emergency vs Non-Emergency Vehicle Classification: Enhancing Intelligent Traffic Management Systems

Emergency vs Non-Emergency Vehicle Classification: Enhancing...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Madan, S. Shetty, Kishan Kodipalli, Ashwini Rao, Trupthi Kumaraswamy, S. Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept of Artificial Intelligence and Data Science Karnataka Bengaluru India Global Academy of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350300826

Due to its dense population, India frequently experiences traffic congestion, which puts lives in danger by trapping vehicles like ambulances, police cars, and fire trucks which run on road for emergency purpose. It becomes crucial to give these vehicles priority and allow for their easy passage. However, it becomes challenging or even impossible for traffic police to effectively handle such circumstances. Therefore, there is a requirement for an automated system that can locate emergency vehicles in high-traffic areas, alert the controller, or drive itself to direct other vehicles to make room. This study suggests an automated method for identifying emergency vehicles from CCTV footage that makes use of deep convolutional neural networks (CNN). The objective is to effectively identify and classify emergency vehicles in real-time, leveraging the power of advanced object detection techniques. With an accuracy of 91.73% and a loss of 0.2120, the suggested technique outperformed existing optimizers in accurately recognizing and classifying emergency vehicles. © 2023 IEEE.

关键词： Object detection

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Enhanced Vision Transformer with Lion Optimizer for Accurate Classification of Monkeypox Skin Images 4

Enhanced Vision Transformer with Lion Optimizer for Accurate...

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

作者： Bhatnagar, Roheet Hassan, Esraa Saber, Abeer Shams, Mahmoud Y. Elbedwehy, Samar Manipal University Jaipur Department of Computer Science and Engineering Rajasthan India Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning Kafrelsheikh33516 Egypt Damietta University Faculty of Computers and Artificial Intelligence Information Technology Dept. Damietta34517 Egypt Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Data Science Kafrelsheikh33511 Egypt

ISBN: (纸本)9798350387490

Monkeypox is a contagious viral infection caused by the monkeypox virus (Mpoxvirus). Characterized by skin lesions, fever, respiratory difficulties, swollen lymph nodes, and neurological complications, MPX can be fatal. Recent outbreaks have spread the disease across Europe, Australia, the United States, and Africa. Traditionally diagnosed through PCR testing of skin lesions, this process poses significant risks to medical personnel due to potential exposure to the highly contagious virus. However, the usage artificial intelligence (AI) is revolutionizing diagnostics, providing a safer and more efficient approach to MPX detection. In this paper, a new AI technique based on vision transformer and lion optimizer for classifying Monkeypox Skin Images. The presented model used for classifying the dataset and results reached accuracy of 99.6%. © 2024 IEEE.

关键词： Classification Deep Learning Diagnosis Monkeypox

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Leveraging XAI and Breakthrough Machine Learning Techniques for Trigeminal Neuralgia Severity Classification

Leveraging XAI and Breakthrough Machine Learning Techniques ...

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IEEE Region 10 Symposium

作者： Abhijna MB Akul P Ashwini Kodipalli Dept. of Information Science and Engineering Global Academy of Technology Dept. of Computer Science and Engineering Global Academy of Technology Dept. of Artificial Intelligence and Data Science Global Academy of Technology

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

Trigeminal Neuralgia (TN) is a debilitating chronic pain disorder that significantly diminishes overall well-being, making diagnosis and therapy more challenging. The quick and precise categorization of TN severity is critical to therapy effectiveness. To assess the severity of TN, this study makes use of advanced machine learning techniques and explainable AI (XAI) approaches. We use a range of bagging and boosting strategies, including AdaBoost, Random Forest, Decision Tree, K-Nearest Neighbours (KNN), Gradient Boosting, and Logistic Regression. Random Forest was 95.2% accurate, whereas AdaBoost and Gradient Boosting were 92% and 97.2% in terms of their accuracy. The Decision Tree classifier obtained an accuracy of 84.62%, while K-Nearest Neighbours and Logistic Regression also achieved 92.31% accuracy. F1- score, precision, and recall metrics were used to assess performance on a dataset that included patient demographics and medical histories. XAI techniques, such as LIME and SHAP values, were used to improve the models' readability and transparency, which helped to progress the creation of cutting-edge diagnostic tools and increase clinician and researcher confidence.

关键词： Logistic regression Accuracy Explainable AI Medical treatment Nearest neighbor methods Boosting Brain modeling Planning Random forests Regression tree analysis

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Grey Wolf Optimizer and Deep Neural Network based Feature Selection and Classification in Medical Image Analysis 18

Grey Wolf Optimizer and Deep Neural Network based Feature Se...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Chandrashekhar, Katha Leela, P. Madhavi, K. Reddy Avanija, J. Tangudu, Naresh Indur Institute of Engineering and Technology Computer Science and Engineering Siddipet India Mohan Babu University School of Computing Dept. of Data Science Tirupati India Mohan Babu University School of Computing Tirupati India Aditya Institute of Technology and Management Tekkali India

ISBN: (纸本)9789380544519

Accurate and efficient medical image analysis is required for modern healthcare applications, facilitating timely disease diagnosis and treatment. Considering the importance of optimization algorithms and deep learning methodologies, this work presents a novel approach, Grey Wolf Optimizer-Assisted Deep Neural Networks (GWO-DNN), for feature selection and classification in medical image analysis. The proposed framework utilizes the power of the Grey Wolf Optimizer to intelligently select the most relevant features from complex medical images. The selected features are then integrated into Deep Neural Networks, to create accurate models for medical image classification tasks. This integrated approach is designed to improve diagnostic accuracy and enhance patient care. The experimentation and evaluation, reveal that the proposed GWO-DNN based model for image analysis is more efficient than the existing methods. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep Neural Networks (DNN) Disease Classification Feature Selection Grey Wolf Optimization (GWO) Medical Image Analysis

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Classification of Awareness in Disorders of Consciousness Using Common Spatial Pattern 13

Classification of Awareness in Disorders of Consciousness Us...

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13th International Winter Conference on Brain-Computer Interface, BCI 2025

作者： Lee, Chaewon Lee, Sunho Annen, Jitka Jeong, Ji-Hoon Massimini, Marcello Casarotto, Silvia Boly, Melanie Bodart, Olivier Thibaut, Aurore Rosanova, Mario Laureys, Steven Gosseries, Olivia Lee, Minji Dept. Data Science The Catholic University of Korea Bucheon Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of Dept. GIGA-Research & Neurology University and University Hospital of Liege Liege Belgium Dept. Data Analysis University of Ghent GhentB9000 Belgium Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical and Clinical Sciences "L. Sacco" University of Milan Milan Italy Wisconsin Institute for Sleep and Consciousness Dept. Psychiatry University of Wisconsin Madison United States Coma Science Group GIGA-Consciousness GIGA Research Center University of Liège Liège Belgium Centre du Cerveau Neurology department University Hospital of Liège Liège Belgium

ISBN: (纸本)9798331521929

Advances in intensive care have improved the survival rate of patients with severe acute brain injury, but diagnostic errors for patients with disorders of consciousness are still high. Accurate diagnosis of these patients is very important because effective treatment can vary depending on the diagnosis. In this study, we propose a framework for classifying unresponsive wakefulness syndrome and minimally conscious state, focusing on awareness. In particular, power spectral density and common spatial patterns were used together, considering that spatial information is a key feature in consciousness. The 16 patients with unresponsive wakefulness syndrome and 14 with minimally conscious state underwent resting-state electroencephalography measurements. In addition, we compared the performance by utilizing each frequency (delta, theta, alpha, beta, gamma bands) related to consciousness. As a result, the highest accuracy of 95.06% was achieved by the EEGNet classifier, especially in the beta frequency band. We demonstrated that spatial information is very important in consciousness, as we observed that classification performance improved when common spatial patterns were used. These results provide insight into various frameworks for diagnosing patients with disorders of consciousness and may help patients survive by increasing the diagnosis rate in the future. © 2025 IEEE.

关键词： Electroencephalography

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Virtual Sales Assistant: A Chrome Extension for E-commerce Websites

Virtual Sales Assistant: A Chrome Extension for E-commerce W...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Naman Chheda Pradnya Saval Dept. of Computer Science and Engineering(Data Science) Dwarkadas J Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The research presents an innovative browser extension that serves as a virtual sales assistant for shoppers across e-commerce platforms. Designed to foster trust and enhance the online shopping experience, this extension enables detailed product inquiries and provides immediate access to comprehensive specifications, empowering customers to make informed purchasing decisions. By seamlessly integrating with browsing sessions and leveraging natural language processing techniques, the virtual assistant generates accurate and informative responses to product-related queries, positioning e-commerce platforms as authoritative and responsive sources. Beyond functional capabilities, the extension aims to transform user engagement by acting as a personalized guide, akin to an in-store representative, thereby heightening satisfaction through tailored service and fostering deeper customer relationships. The research is motivated by factors such as increasing customer engagement, providing round-the-clock support, facilitating product differentiation and comparison, harnessing machine learning for enhanced experiences, building trust through expert guidance, ensuring unique integration with e-commerce platforms, iterating through feedback loops for continuous improvement, and supporting small businesses in enhancing customer interactions.

关键词： Feedback loop Virtual assistants Transforms Chatbots Mobile handsets Real-time systems Browsers Electronic commerce Continuous improvement Business

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Enhanced Predictive Modeling Techniques for Early Detection of COPD Utilizing 1D Convolutional Neural Networks

Enhanced Predictive Modeling Techniques for Early Detection ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Sneha Jha Prakash Sahu Santosh Kumar Dept. of Data Science CSVTU Chhattisgarh Computer Science & Engineering IIIT-Naya Raipur

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Artificial intelligence (AI) breakthroughs have created new opportunities in the field of medical diagnostics, especially for the early identification of respiratory conditions like Chronic Obstructive Pulmonary Disease (COPD), the most prevalent respiratory condition, is characterized by breathing difficulties, mucus (sputum) coughing, and wheezing. Early verification is critical for efficient administration. A one-dimensional convolutional neural network (1D CNN) model for COPD detection, optimized using Adam and RMSprop algorithms is proposed for this purpose. Utilizing the ICBHI 2017 dataset, the performance of each optimizer based on training and validation accuracy is evaluated. The Adam optimizer achieved a training accuracy of ${9 4 \%}$ and a validation accuracy of ${9 0 \%}$. In comparison, the RMSprop optimizer yielded a training accuracy of ${9 2 \%}$ and a validation accuracy of ${8 8 \%}$. These results demonstrated that the Adam optimizer surpassed RMSprop in terms of accuracy. It showcased the potential of the proposed 1D CNN model as a reliable diagnostic tool, contributing to the advancement of respiratory health analytics.

关键词： Training Analytical models Accuracy Computational modeling Predictive models Chronic obstructive pulmonary disease Convolutional neural networks Reliability Medical diagnosis Artificial intelligence

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Optimized 1D CNNs for Enhanced Early Detection and Accurate Prediction of COPD and Other Pulmonary Diseases

Optimized 1D CNNs for Enhanced Early Detection and Accurate ...

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IEEE Region 10 Symposium

作者： Prakash Sahu Sneha Jha Santosh Kumar Computer Science & Engineering IIIT-Naya Raipur Dept. of Data Science CSVTU Chhattisgarh

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

Artificial intelligence (AI) advancements have opened new avenues in medical diagnostics, particularly for the early detection of respiratory conditions such as Chronic Obstructive Pulmonary Disease (COPD). COPD, the most common respiratory disease, is marked by breathing difficulties, mucus (sputum) production, coughing, and wheezing. Early diagnosis is crucial for effective management and treatment. A one-dimensional convolutional neural network (1D CNN) model optimized with Adam and RMSprop algorithms is proposed for COPD detection. Using the ICBHI 2017 dataset, the model's performance with each optimizer was evaluated based on training and validation accuracy. The Adam optimizer achieved a training accuracy of 94% and a validation accuracy of 90%. In comparison, the RMSprop optimizer attained a training accuracy of 92% and a validation accuracy of 88%. These findings indicate that the Adam optimizer outperformed RMSprop in terms of accuracy. It showcased the potential of the proposed 1D CNN model as a reliable diagnostic tool, contributing to the advancement of respiratory health analytics.

关键词： Training Analytical models Accuracy Production Chronic obstructive pulmonary disease Convolutional neural networks Reliability Medical diagnosis Artificial intelligence IEEE Regions

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Vaffel - a tool for forecasting transmission line failures based on Bayesian updating, renalysis data and medium range weather forecasts

Vaffel - a tool for forecasting transmission line failures b...

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International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Øystein Rognes Solheim Ingeborg Ligaarden Boye Annfelt Høverstad Thomas Trøtscher Markus Andersen Magnus Korpås Dept. of Data Science Statnett SF Oslo Norway Dept. of Electric Power Engineering NTNU Oslo Norway Dept. of Data Science Statnett SF Trondheim Norway Dept. of Computer Science NTNU Trondheim Norway Dept. of Electric Power Engineering NTNU Trondheim Norway

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

This paper presents a method for forecasting the probability of weather-related transmission line failures. The method combines failure statistics, a Bayesian updating scheme and reanalysis weather data to produce fragility curves that model the relationship between weather exposure for a component and the probability of failure. The fragility curves are combined with medium range weather forecasts extracted at each span of the individual transmission lines to obtain a forecast of the hourly probability of failure for each line. A tool that implements the method has been developed and is currently being tested operationally within the Norwegian TSO Statnett. We show the data requirements and demonstrate the use of a dashboard that expose the forecasts to an end-user.

关键词： Power transmission lines Weather forecasting Probabilistic logic data models Power system reliability Bayes methods Reliability

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Exploring the Impact of IoT data Diversity on Machine Learning Model 5

Exploring the Impact of IoT Data Diversity on Machine Learni...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Bisen, Dhananjay Venu, Nookala Dubey, Aditya Garg, Priyanka Salendra, Suresh Devi, T. Lakshmi Madhav Institute of Technology & Science Center for Internet of Things M.P Gwalior474 005 India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing M.P Gwalior474 015 India Balaji Institute of Technology & Science Dept. of Compter Science and Engineering Telangana Warangal506 331 India Siddhartha Institute of Engineering and Technology Dept.of CSE-Data Science Hyderabad501506 India

ISBN: (纸本)9798331530013

The past few years have witnessed a remarkable change by the rapid expansion of the Internet of Things (IoT). The increasing spread of networked devices in various areas of the Internet of Things has resulted in a huge amount of machine data. This data contains rich information that can be harnessed across domains such as healthcare, agriculture, smart cities, and manufacturing. To effectively analyze and capitalize this information, the integration of machine learning (ML) methods is essential. Thus, well-known machine learning techniques including Logistic Regression, Decision Tree, KNN, SVM, Random Forest, and Naive Bayes are used in paper. The performance of the algorithms is analyzed using three diverse IoT datasets and compared to one another using pre-defined measures such as accuracy, precision, recall, F1-score and execution time. Furthermore, test findings shown that the random forest algorithm consistently outperformed other algorithms for a variety of datasets with up to 98% accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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