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Bayesian Belief Network Analysis for SPAD System in Railways

Bayesian Belief Network Analysis for SPAD System in Railways

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Multimedia Processing, Communication & Information technology (MPCIT), International Conference on

作者： Manjunath Sargur Krishnamurthy Bommisetty Rahul Chandrahas Reddy Bondala M Vishnu Vardhan Reddy Magha Raghul G Ambika B J JP Morgan & Chase Co. Houston Texas USA Dept. of AI&ML BMS Institute of Technology and Management Bengaluru Dept. of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Karnataka

ISBN: (数字)9798350375466

ISBN: (纸本)9798350375473

Even with a powerful network of signaling and warning systems in the country, there have been many examples of trains crossing the red signal due to various factors, even today. These occurrences, known as Signal Passed At Danger (SPAD) events, could potentially result in severe consequences such as train derailments, train collisions, infrastructure collisions, and other dangerous events. Traditionally, these events have been analyzed using the Fault Tree Analysis (FTA) approach Bayesian Network (BN) is considered to be a better model to represent this situation when it comes to handling complexity. Bayesian Network allows for integration and includes systematic knowledge about the system, which helps to get a very flexible concise, and simple graphed representation. This study demonstrates applications of Bayesian Belief Networks (BBN) analysing and enlightening the Signal Passed at Danger (SPAD) system in railway processes. Execution of BBN offers more dynamic and flexible models compared to outmoded Fault Tree Analysis (FTA), struggling with complexity. BBN efficiently catches the inter dependencies between factors that are contributing to SPAD events, such as signal conditions, human errors, and mechanical issues. Over simulations and probability calculations using OpenMarkov software, Study revealed key intuitions, such as identifying highly risk apparatuses within SPAD system. The outcomes include enhanced risk calculation accuracy and recommendations for improving signal systems and safety protocols. These conclusions contribute to a safer and reliable railway structure, highlighting the success of BBN for complex, safety critical systems in real world applications.

关键词： Analytical models Systematics Rail transportation Software Bayes methods Safety Complexity theory Software reliability Single-photon avalanche diodes Fault trees

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Malware Detection and Classification for Internet of Things Using Self-Attention Based Long Short-Term Memory 3

Malware Detection and Classification for Internet of Things ...

引用

3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Sudhakar, K. Husain, Saif O. Babu, D. Raja Kumar, M. Ashok Dineshkumar, R. Nitte Meenakshi Institute Of Technology Dept. Of Ai & Ds Bengaluru India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Skyline University Nigeria Department Of Computer Science Software Engineering And Cyber Security Kano State Nigeria Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350318609

The Internet of Things (IoT) connects numerous intelligent devices providing security features that interact with default settings accessed through applications. Additionally, Deep Learning (DL)-based mechanisms was involved for malware detection to identify and mitigate attacks. The Adaptive Synthetic Sampling (ADASYN) technique addresses class imbalances, especially when splitter class instances are distributed data in complex patterns. The proposed Self-Attention based Long Short-Term Memory (LSTM) classification method facilitates the handling of long-range dependencies in transferred data sequences offering high flexibility and adaptability to varying input lengths. For feature selection Particle Swarm Optimization (PSO) is utilized iteratively optimizes a solution in a high-dimensional space although it may have a low convergence rate due to its iterative process. The obtained results demonstrate the proposed Self-Attention based LSTM model achieves better accuracy reaching 98.05% on the NSL-KDD dataset and 99.01 % on the UNSW-NB15 dataset. These results ensure accurate classification compared to other existing methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). © 2024 IEEE.

关键词： Internet of things

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Design and Implementation Recommender System for Iraqi Terrorist Database Based on Deep Learning 7

Design and Implementation Recommender System for Iraqi Terro...

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7th International engineering Conference "Research and Innovation Amid Global Pandemic", IEC 2021

作者： Sattar, Ismael A. Alhamdani, Rafah S. Abdulla, Mohamed N. College of Sciences-Mustansiriyah University Dept. Computer Science Baghdad Iraq Al-Nahrain University College of Political Sciences Baghdad Iraq University of Technology Dept. Computer Engineering Baghdad Iraq

ISBN: (纸本)9780738132501

Recommendation system become important to deal with huge available information in the social media like Facebook, Twitter and other social networking to recommend and targeting the required users based on his/ her interest. Deep learning used as techniques to construct recommender system for achieving high accuracy with big available data. The proposed system used multi RBM neural network for building recommender system and measuring the interest of the users. The recommender system used to deal with security field through working on the Iraqi terrorism database obtained from global terrorist data. Many analysis process done over the data to visualize the importance of this area. The evaluation metric used Mean Squared Error (MSE) as well as Mean Absolute Error (MAE) as well as Precision and Recall and the proposed system reached good results. © 2021 IEEE.

关键词： Recommender systems

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Utilizing latent Features for Building Recommender system Based on RBM Neural Network 1

Utilizing latent Features for Building Recommender system Ba...

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1st Babylon International Conference on Information technology and Science, BICITS 2021

作者： Sattar, Ismael Abdul Alhamdani, Rafah Shihab Abdulah, Mohhammed Najm Dept. Computer Science College of Sciences Mustansiriyah University Baghdad Iraq College of Political Sciences Al-Nahrain University Baghdad Iraq Dept. Computer Engineering University of Technology Baghdad Iraq

ISBN: (纸本)9781665415736

Deep learning one of the hottest technologies that applied in various field for enhancing the available results which solving specific problems. The huge growth of the data over internet make recommender systems in need as effective techniques to overcome information overloaded, while employing deep learning methods lead to flourishing recommender system in several digital media. All in all, recommender system play best role based on user preferences. In this paper implement and analysis of building recommender system based on deep learning techniques for prediction movie rating as smart way to predict user preferences. Such technique would be useful in advertising management and social networking as well. © 2021 IEEE

关键词： Recommender systems

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Vehicle License Plate Detection and Recognition using YOLOv9 and Easy OCR

Vehicle License Plate Detection and Recognition using YOLOv9...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Rahul Arora Manan Agarwal Vinoth N A S Uma Maheswari K M Brindha R Computer Science and Engineering SRM Institute of Science and Technology Chennai Dept. of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The objective of the paper is to propose an approach for a real-time accurate Automatic Number Plate Recognition(ANPR) system, which recognizes vehicle license plates. The system is based on a common vision concept that combines Optical character recognition (OCR) and YOLOv9 deep learning algorithm. Intended to overcome challenges including poor light, partial obstruction, and different angles for high-speed vehicle plate recognition over multiple regions. This means that not only is the architecture future-friendly for license plate designs, but it will also work with existing infrastructure so this can be deployed in traffic management, law enforcement, and security use cases.

关键词： Deep learning Accuracy Law enforcement Training data Real-time systems Robustness Data models Security License plate recognition Intelligent transportation systems

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Bi-Directional LSTM Classification of Emotions in EEG Signals

Bi-Directional LSTM Classification of Emotions in EEG Signal...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Ashley Nand Immanuel Johnraja Jebadurai Ocheni Jeremiah Getzi Jeba Leelipushpam Paulraj Jebaveerasingh Jebadurai Dept. of Computer Science and Engineering Karunya Institue of Technology and Science Coimbatore India Dept. of Computer Science and Engineering Government Polytechnique College Tanjore India

ISBN: (纸本)9781665489638

In the field of neuroscience research, the analysis of EEG signals is crucial for comprehending both human behavior and the workings of the brain. The majority of research is being done to find out how EEG signals are usually used to distinguish between various psychological states and emotions. The proposed study has applied the Bi-LSTM approach for emotion categorization, which significantly raises the accuracy of emotion categorization based on the brain waves obtained from EEG. The results show that the system is reliable as the data has been trained by using a variety of classifiers but with the DEAP dataset, the proposed method obtains a higher and quicker accuracy rate with a training period of about 5ms and an average accuracy rate of 94.12%, and is working to classify the emotion such as valence, arousal, and dominance with single electrode channel with the highest accuracy.

关键词： Training Electrodes Neuroscience Databases Psychology Bidirectional control Electroencephalography

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Remote Motor Rehabilitation: EMG-IMU based Deep Learning Model Improves the Estimate of Wrist Kinematics 46

Remote Motor Rehabilitation: EMG-IMU based Deep Learning Mod...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Siviero, Ilaria Helmhold, Florian Ray, Andreas M. Bibián, Carlos Menegaz, Gloria Murguialday, Ander Ramos Storti, Silvia F. Dept. of Computer Science University of Verona Verona Italy Inst. of Medical Psychology and Behavioral Neurobiology University of Tübingen Tübingen Germany Gtc of Neuroscience Imprs for Cognitive and Systems Neuroscience Tübingen Germany Dept. of Engineering for Innovation Medicine University of Verona Verona Italy Tecnalia Basque Research and Technology Alliance San Sebastian Spain Dept. of Neurology and Stroke University of Tübingen Tübingen Germany Athenea Neuroclinics San Sebastian Spain

ISBN: (纸本)9798350371499

technology for motor rehabilitation faces challenges in uncontrolled settings, such as at home. In these real-world scenarios, robust signals like electromyographic (EMG) and inertial measurement unit (IMU) data are crucial for decoding continuous human actions. Classical modeling methods, such as linear, adaptive, or static filters, lack the capacity to capture complex relationships between surface EMG and kinematics, as well as generalizability across subjects. We propose a deep learning (DL) model for the continuous decoding of hand motion. Custom-made wearable devices acquired EMG-IMU data from 27 healthy subjects performing a wrist flexion/extension task. Two regression models were compared with a hybrid convolutional neural network and a gated recurrent unit. To address inter-subject variability, a leave-one-subject-out cross-validation approach was implemented. The DL model showed a mean R2 increase of 0.18 compared to the polynomial regressor. Our approach enhances wrist kinematics decoding providing a generalized model based on data captured with wearable devices. The findings hold potential for innovative home-based telemedicine solutions in motor rehabilitation. © 2024 IEEE.

关键词： Adaptive filters

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Wavelet Frequency Transformation for Specific Weeds Recognition 1

Wavelet Frequency Transformation for Specific Weeds Recognit...

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1st International Conference on Artificial Intelligence and Data Analytics, CAIDA 2021

作者： Habib, Shaban Khan, Ishrat Islam, Muhammad Albattah, Waleed Mohammed Alyahya, Saleh Khan, Sheroz Kamrul Hassan, Mohammad Qassim University College of Computer Dept. of Information Technology Buraydah Saudi Arabia Prince Sultan University College of Computer and Inofrmation Science Dept. of Information Technology Computer Science Department Riyadh11586 Saudi Arabia College of Engineering and Information Technology Onaizah Colleges Dept. of Electrical Engineering Saudi Arabia Universiti Kebangsaan Malaysia Center for Cyber Security Bangi Malaysia

ISBN: (纸本)9780738131771

Saudi Arabia is experiencing depleting water level which ultimately leading to having reduced level of weed and crops farms. The ongoing practice of watering for all kind of weeds at farms is manual which is laborious and slow besides waste of unregulated use of water. Therefore, there arises the need of automated water control, making the automated watering system as the viable option for precision weed control system. This paper has presented the development of real-time automated water sprinkle system for the target weeding area. The technique of wavelet frequency is developed as a software interface using MATLAB program detecting need of water sprinkling from the pictures of the leaves obtained. Software-based results are applied to hardware for real-time grass detection and classification based on shape and density due to reason that the leaves may be wide open, shrunk and leaves those with curved in features. The real-time system is capable of thus deciding the proportionate amount of water needed to be sprinkled over the weeds using a purposely developed hardware system. The system can detect areas where more, more or less water is needed, through a high-accuracy connected camera. © 2021 IEEE.

关键词： Real time systems

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IOT Based Smart Agriculture Using LIFI

IOT Based Smart Agriculture Using LIFI

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International Conference on Multimedia, Signal Processing and Communication Technologies, (IMPACT)

作者： A Vijayakrishna Gopichand G Mohd Dilshad Ansari G Suryanarayana Department of Computer Science and Technology G Narayanamma Institute of Technology … Science India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Dept. of Computer Science … Engineering Guru Nanak University Hyderabad India Dept. of Computer Science … Engineering Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9781665476485

Modernization of the conventional farming procedure is one of the major concerns for a nation like India that needs to import agro items from various nations to cater to the needs of around 1.2 billion people. One of the main challenges of agriculture is that most of the conventional methods of agriculture are majorly manual which is still prevalent among the farmers. These results in unnecessary usage of pesticides and wastage of water in irrigating fields when not required. The proposed work is an IoT Based Smart Agriculture system using Light Fidelity (LiFi) that is fully automated and checks the farm field using IoT. Besides uncommon instruments, web related things or gadgets, things inserted with devices, sensors, programming forms Internet of Things (IoT) which is a key part of future web. The framework consists of sensors that are deployed in the field and they sense parameters like temperature, light and soil moisture and then data is analyzed by Micro Controller Unit (MCU). The data is then transferred to the receiver end through LiFi transmitter. The system has been developed by using PIC microcontroller as it is very convenient to use. LiFi offers more security over Wi-Fi or any wired connection and is faster hence the integrity of the agricultural data can be secured while transmitting for analysis. LiFi is eco- friendly too. Through IoT farmer can access the information from anywhere at any time. Automated monitoring of field parameters helps in producing high quality yield.

关键词： Smart agriculture Temperature sensors Wireless communication Temperature distribution Transmitters Light fidelity Internet of Things

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ML based Pre-Diagnosis Framework for early detection of Neurodevelopmental Disorders

ML based Pre-Diagnosis Framework for early detection of Neur...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Sumit Pandey Srishti Sharma Anant Agarwal Dept. of Computer Science School of Engineering and Technology The Northcap University Gurugram Haryana India Dept. of Electronics Engineering Zakir Husain College of Engg. and Tech Aligarh Muslim University Aligarh Uttar Pradesh India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Neurodevelopmental disorders cover a wide range of behavioral and cognitive symptoms mapped to various conditions. Early diagnosis and planned interventions are crucial for improving outcomes and the quality of life for people suffering from such disorders. This research work talks about a detailed framework with the objective of assisting in the early pre-diagnostic phase of neurodevelopmental disorders. The motivation for the development of a ML-based framework in pre-diagnosis of neurodevelopmental disorders is the effective and zero-cost getting diagnosed. This framework will integrate knowledge from diverse domains to systematize and make recognition of early signs and symptoms associated with neurodevelopmental disorders much more efficient. Additionally, the framework emphasizes two very critical parameters: accessibility and cost-effectiveness. The developed pre-diagnostic mechanism can be implemented in a diversified variety of scenarios, especially with limited resources, towards zero cost. The implementation of advanced machine learning techniques can assist in the analysis of the response to a customized questionnaire in identifying patterns that may suggest a risk of neurodevelopmental disorders. This proactive approach not only supports and facilitates early detection and intervention but also seeks to add towards the overall well-being of patients with these disorders.

关键词： Neurological diseases Costs Random forests

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