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Intelligent Accident Detection and Severity Analysis System for Enhanced Emergency Response

Intelligent Accident Detection and Severity Analysis System ...

引用

Computing and Data Science (ICCDS), International Conference on

作者： Harini P Chidambarathanu K Lalitha Ramachandran Dept. Computer Science and Business Systems R.M.K Engineering College Kavarapettai Dept. Science and Humanities R.M.K Engineering College Kavarapettai

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

Road accidents cause serious injuries and deaths worldwide. The previous year's Ministry of Road Transport and Highways Annual Report on Road Accidents in India showed that many road accidents were fatal. A large percentage of Indian deaths occur without rapid medical intervention or first assistance. Delays in reporting incidents to emergency services increased fatalities. A sophisticated method using deep learning and analytics to locate CCTV accidents, classify their severity, and speed up emergency response. Accident detection and analysis are efficient with cutting-edge technologies. The accident detection phase uses YOLOv7, a deep-learning object detection model, to evaluate CCTV footage in real time. A stratified K-fold deep neural network classifies accidents as major or minor based on collision severity. Real-time GPS tracking alerts ambulance drivers within three km of the accident using the mobile app. This expedites emergency response, lowering mortality by reducing the time between the accident and medical assistance.

关键词： Deep learning YOLO Road accidents Artificial neural networks Transforms Emergency services Real-time systems

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A Sequential Pattern Mining Approach for Situation-Aware Human Activity Projection

A Sequential Pattern Mining Approach for Situation-Aware Hum...

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IEEE International Conference on systems, Man and Cybernetics

作者： Giuseppe D'Aniello Roberto Falcone Matteo Gaeta Zia Ur Rehman Giancarlo Fortino Dept. Information and Electrical Engineering and Applied Mathematics University of Salerno Fisciano SA Italy Dept. Computer Engineering Modeling Electronics and Systems University of Calabria Rende Cs Italy

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Human activity prediction has become increasingly prevalent in a plethora of time-critical applications. To realize accurate identification and prediction of human behaviour, we propose a situation-aware wearable computing system. A wearable computing system has the capability to perceive, comprehend and project situations by analyzing the human behavioral patterns in different environments. In particular, this work proposes a situation-aware human activity prediction (SA-HAP) approach based on sequential pattern mining that aims to anticipate future activities and tailor its responses according to situations by analyzing frequent sequential patterns and their correlations to understand how these situations are interrelated. The approach not only improves prediction accuracy but also provide the foundation for a more informed decision-making process, as the projected situations can be explained using the identified behavioral patterns. The approach is compared with other traditional techniques for activity prediction (LSTM and HMM), achieving better performance on the Extrasensory dataset.

关键词： Accuracy Correlation Decision making Hidden Markov models Medical services Predictive models Time factors Wearable devices Long short term memory Cybernetics

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ATSC Core Network: Encryption Analysis and Evaluation

ATSC Core Network: Encryption Analysis and Evaluation

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IEEE International Symposium on Broadband Multimedia systems and Broadcasting (BMSB)

作者： M. Amezola E. Iradier R. Cabrera O. Landrove J. Montalban P. Angueira Dept. Computer Languages and Systems UPV/EHU Bilbao Spain Dept. of Communications Engineering UPV/EHU Bilbao Spain Dept. Electronics Technology UPV/EHU Bilbao Spain

ISBN: (数字)9798350364262

ISBN: (纸本)9798350364279

The ever-increasing consumer demand for new applications and use cases has revolutionized standards for delivering multimedia content. Regarding broadcast standards, ATSC is leading the way in technological development and has positioned itself at the forefront of the industry. In particular, ATSC 3.0 is the first IP-based broadcast standard, which enables the integration of a Broadcast Core Network (BCN) into the traditional architecture to coordinate the transmission facilities. It also facilitates implementing futuristic use cases that were inconceivable a few years ago. However, the development of the BCN also poses new technological challenges regarding data and information management and security. This paper aims to analyze the possible alternatives of the BCN for providing cybersecurity through data encryption. It presents an analysis of current alternatives and a prototype based on a client-server architecture that implements encryption techniques in the BCN. The results show that depending on the cybersecurity level to be guaranteed, different costs in terms of processing time have to be assumed. In addition, a set of guidelines for guaranteeing cybersecurity in the BCN is defined.

关键词： Scalability Prototypes Encryption Information management Reliability Noise measurement Multimedia communication

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Classifying Driver Distraction with Textile Electrocardiograms

Classifying Driver Distraction with Textile Electrocardiogra...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Kaveti Pavan Vishal Singh Roha Tomohiko Igasaki P. A. Karthick Digvijay S. Pawar Nagarajan Ganapathy Dept. of Biomedical Engineering IIT Hyderabad Telangana India Dept. of Electrical & Computer Systems Monash University Melbourne Australia Dept. of Biomedical Engineering Kumamoto University Kumamoto Japan Dept. of Instrumentation Engineering NIT Tiruchirappalli Tamil Nadu India Dept. of Civil Engineering IIT Hyderabad Telangana India

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Textile sensor-based vital sign assessment plays an important role in continuous monitoring due to its unobtrusive and non-invasiveness. Textile electrocardiography (ECG) sensors allow mental wellbeing assessments in drivers during driving. In this study, we assess the effectiveness of a single-lead ECG obtained from a non-medical-grade ECG shirt for detecting driver distraction due to induced stress. Using ECG shirts, a single-lead ECG (256Hz, 12 bits) is acquired from N=10 healthy volunteers having driving licenses in three distinct driving situations (Baseline, Texting, Calling) in a controlled environment. ECG data is manually checked, and segmented into short durations (10, 30, 60 seconds). These segments are applied to a customized convolution neural network (ccNN). The proposed approach is able to classify the driver's distraction with ccNN yielding a weighted F-Score of 0.65 and an average accuracy of 67.12% on the validation set. Leave-One-Subject-Out Cross-Validation results showed weighted F-Scores ranging from 0.53 to 0.75. Thus, a single-lead, wearable textile ECG provides informative insights into a driver's mental wellbeing.

关键词： Accuracy Roads Pipelines Neural networks Electrocardiography Licenses Sensors Monitoring Textiles Vehicles

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Dual-Modality Deep Feature-based Anomaly Detection for Video Surveillance

Dual-Modality Deep Feature-based Anomaly Detection for Video...

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Parth Lalitkumar Bhatt Dhruva Shah Christopher Silver Wandong Zhang Thangarajah Akilan Dept. of Computer Science Lakehead University Thunder Bay Canada Dept. of Electrical and Computer Engineering Lakehead University Thunder Bay Canada Dept. of Electrical and Computer Enggineering Western University London Canada Dept. of Software Engineering Lakehead University Thunder Bay Canada

Detecting anomalies in videos is not only crucial but also an intriguing task in surveillance systems. It is a sequential modeling problem in nature that requires careful selection of spatial and temporal dependent patterns from a sequence of frames. There are several research works from traditional approaches to modern deep learning-based techniques introduced to address this problem. However, there is a huge demand for research and development to ameliorate the performance of the existing solutions. In response to that, this study proposes an improved video anomaly detection model using deep features extracted from a dual-modality input representation. The proposed model demonstrates effectiveness in the benchmark–UCF crime dataset by achieving the best AUC of 87.52%, which is ≈ 12.3% improvement compared to a baseline. The application aspect of this work includes strengthening the security measures in common places, viz. airports, banks, public transits, schools, and shopping complexes by detecting aberrational or suspicious activities in surveillance videos.

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Classifying Emotions in Roman Urdu Posts using Machine Learning 1

Classifying Emotions in Roman Urdu Posts using Machine Learn...

引用

1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021

作者： Nabeel, Zahra Mehmood, Maseeha Baqir, Anees Amjad, Anam Dept. of Software Engineering Sialkot Pakistan Ca'Foscari University of Venice Dept. of Environmental Sciences Informatics and Statistics Venice Italy National University of Sciences and Technology Dept. of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665424134

Emotion, a state of human mind has a significant impact on human behavior, social interactions and decision making. Few emotions from Carroll E. Izard model such as long-term fear, anger and sadness cause mental disorders which can lead to deterioration of mental and physical health. For the well-being of mental health, different psychological parameters such as voice, facial expressions and body movements were used to extract these emotions in earlier times. This paper focuses on verbal language for emotion classification. With the huge popularity of Web 3.0, people feel more comfortable in sharing their emotions over social media due to the anonymity of the web. Recently, researchers around the globe are interested to classify the emotions from social media posts (i.e., tweets, comments or micro-blogs etc.) using resource-rich languages e.g., English. However, less attention is paid towards the resource-poor language such as Urdu in Roman script. According to the Gallup survey in Pakistan, Urdu in Roman script is widely used to transmit text. To detect emotions in Roman Urdu text is a big challenge due to its scripting and morphology. In this paper, the following contributions are made;1) A corpus of Roman Urdu is developed for emotion classification. 2) We aim to identify user's mental states using textual posts on social media plat-forms;Instagram, Facebook, Twitter and YouTube. Particularly, Natural Language Processing methods incorporated with feature extraction methods (TF-IDF) and four supervised classifiers such as Support Vector Machine (SVM),Naïve Bayes, Random Forest and K-nearest Neighbor are used. By utilizing 80% training and 20% testing data, Naive Bayes secures 70.57% accuracy. 3) Moreover, suitability analysis is performed to identify the most suitable classifier in terms of accuracy, training and testing time. Results show that Naive Bayes is the most suitable classifier for emotion classification. © 2021 IEEE.

关键词： Deterioration

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GenAir: Generative AI for Resilient Urban Air Mobility with VTOLs in Disaster Evacuation

GenAir: Generative AI for Resilient Urban Air Mobility with ...

引用

Simulation Winter Conference

作者： Abdullah Alsaheal William Hoy Priscila Haro Asad Rehman Nurcin Celik Dept. of Industrial and Systems Engineering University of Miami Coral Gables FL USA Dept. of Mathematics and Computer Science Saint Louis University Madrid SPAIN

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

Coastal regions face growing threats, making timely and safe evacuation paramount. Current plans rely heavily on congested ground transportation, leading to delays and heightened stress levels. The emerging field of Urban Air Mobility (UAM), utilizing Vertical Take-Off and Landing Vehicles (VTOLs), promises to alleviate these issues by providing solutions for quick evacuation strategies. Here, we aim to leverage Generative Adversarial Networks (GANs) to expand limited datasets with synthetic data specific to disaster scenarios, evacuation routes, airspace considerations, and the impact of real-time weather events, enabling robust simulation of UAM deployment in disaster evacuations. We identified two applicable scenarios: i) UAM for Extreme Weather Emergency Evacuation and ii) Hospital Evacuations using VTOLs as use cases and illustrated their impact. This research seeks to pave the way for optimized, data-driven evacuation planning with UAM and VTOLs, ultimately enhancing the safety and efficiency of evacuations in the face of extreme events.

关键词： Hospitals Disasters Land transportation Sea measurements Real-time systems Planning Faces Stress Meteorology Synthetic data

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Rare and Common Types of Retinal Disease Recognition Using Ensemble Deep Learning

Rare and Common Types of Retinal Disease Recognition Using E...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Jesika Debnath Al Shahriar Uddin Khondakar Pranta Arafath Bin Mohiuddin Amira Hossain Anamul Sakib Md. Moniruzzaman Shanto Shamim Forhad Abdullah Al-Sakib Rezaul Haque Shafiur Rahman Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Computer Science Wright State University Dayton USA Department of Collage of Engineering and Technology Westcliff University CA USA Department of Business Administration International American University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Information Technology Westcliff University CA USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Detecting retinal diseases is crucial for preventing vision loss, but it comes with challenges, such as data imbalance and accurately classifying rare conditions. This study introduces a new ensemble transfer learning model that combines EfficientNetB7, SE-ResNet, DenseNet-169, and NASNet to improve the classification of common and rare retinal diseases. The ensemble uses a probability averaging approach to maximise each architecture’s strengths. Essential preprocessing techniques such as Global Contrast Normalization (GCN) and Total Variation Denoising (TVD) were used to enhance image quality. The model was trained and evaluated on the Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0, achieving 98.65% accuracy and an F1-score of 0.96% for common diseases and 99.10% accuracy with an F1-score of 0.97 for rare diseases, outperforming individual models significantly. The ensemble has been integrated into a web-based application that allows clinicians to upload retinal images for instant diagnostic feedback, enabling real-time analysis and visual insights. Despite its strengths, challenges such as high computational demands and reliance on high-resolution photos remain. Future work will improve computational efficiency through model compression techniques and extend the model to other imaging modalities, such as Optical Coherence Tomography (OCT). In conclusion, this study presents a robust and scalable solution for retinal disease classification, potentially improving early detection and providing real-time support in clinical settings, particularly in resource-limited environments.

关键词： Deep learning Visualization Accuracy Computational modeling Optical coherence tomography Transfer learning Imaging Retina Real-time systems Diseases

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Coordinate Attention-enabled Ship Object Detection with Electro-optical Image 11

Coordinate Attention-enabled Ship Object Detection with Elec...

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11th International Conference on Computing and Pattern Recognition, ICCPR 2022

作者： Xu, Hongbin Jiang, Xiantao Yin, Tao Cen, Qi Zhang, Zhijian Song, Tian Yu, F. Richard Dept.Information Engineering Shanghai Maritime University Shanghai China Ningbo Special Equipment Inpection and Research Institute Ningbo China Dept. Electrical and Electronics Engineering Tokushima University Tokushima Japan Dept. Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (纸本)9781450397056

Shipping safety is one of the factors restricting the development of navigation. In particular, the route near the shore is prone to unknown risks due to the existence of multiple types of ships, the density of ships, the shielding between ships, and other reasons. This paper presents a method for detecting medium-range ships, which can improve security for ships. This method is based on the You Only Look Once Version 5 network (YOLOv5). To improve the accuracy, the coordinate attention model is integrated into the detection network. The main research content and experimental work of this paper are as follows. Firstly, the YOLOv5 network and spatial attention mechanism are analyzed. Then, detection experiments were carried out based on YOLOv5 and Singapore Maritime Data Set (SMD). Then, the coordinate attention model was used to improve the network. Finally, by adjusting training parameters and improving attention, the mAP of test results of the object detection network reaches 73%, and the feasibility of object detection of the YOLOv5 algorithm with coordinate attention is confirmed. © 2022 ACM.

关键词： Object detection

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Weapon detection system for surveillance and security

Weapon detection system for surveillance and security

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2023 International Conference on IT Innovation and Knowledge Discovery, ITIKD 2023

作者： Khalid, Shehzad Waqar, Abdullah Ain Tahir, Hoor Ul Edo, Onome Christopher Tenebe, Imokhai Theophilus Bahria University Dept. of Computer Engineering Islamabad Pakistan Auburn University at Montgomery Department of Information Systems Alabama United States Mineta Transportation Institute San Jose State University CA United States

ISBN: (纸本)9781665463720

Weapon detection is a critical and serious topic in terms of public security and safety, however, it's a difficult and time-consuming operation. Due to the increase in demand for security, safety, and personal property protection, the requirement for deploying video surveillance systems capable of recognizing and interpreting scenes and anomalous occurrences plays an important role in intelligence monitoring. In certain regions of the globe, mass shootings and gun violence are on the increase. Timely detection of the presence of a gun is critical to prevent loss of life and property. Several object detection models are available, which struggle to recognize firearms due to their unique size and form, as well as the varied colors of the background. In this study, we proposed a state-of-the-art system based on a deep learning model YOLO V5 for weapon detection that will be sufficiently resilient in terms of affine, rotation, occlusion, and size. We evaluated the performance of our system on a publicly available dataset and achieved the F1-score of 95.43%. For the detection and segmentation of firearms, we implemented Instance segmentation or Pixel level segmentation which employed Mask-RCNN. The system achieved the detection accuracy (DC) of 90.66% and 88.74% Mean intersection over union (mIoU). The purposed methodology combined different data augmentation and preprocessing methods to improve the accuracy of the proposed weapon detection system. © 2023 IEEE.

关键词： Object detection

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