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检索条件"机构=Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering"

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Avoidance of Ship Collision Using Deep Reinforcement learning with Bi-Directional Long Short-Term Memory in Continuous Action Spaces 2

Avoidance of Ship Collision Using Deep Reinforcement Learnin...

引用

2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Krishnamurthy, Revatthy Adnan, Myasar Mundher Sunil Kumar, V. Srinivas, T. Aditya Sai Aluvala, Srinivas Rajalakshmi Engineering College Artificial Intelligence & Machine Learning Department Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India School of Computer Science and Engineering Sr University Warangal India

ISBN: (纸本)9798350373110

Recent days, with the globalization of the world economy the ships count are increasing for marine transportation and the Waterways are growing increasingly overcrowded than previous. This situation may leads to problem of collision of ships which may cause losing of life and damage for property and to nature. Many automatically collision models of ships are implemented but many focused only on the ship-ship encounter situation only. By using a grid sensor which is virtual sensor, agents of Deep Reinforcement learning (DRL) classify approach of a multiple ships. This framework introduces an automatically collision detection algorithm for ships using DRL in continuous action spaces. DRL is used for avoidance of collision with a maximum distance of safe passing between ships. A unique method is developed named inside Obstacle Zone by Target (OZT) used to change learning capability that expands the OZT. Using Bi-directional Long Short-Term Memory (BI-LSTM) cell, network is redesigned and continuous action spaces training is carried out to train a model with longer safe distance of ships. In collision detection model that the bow cross range is effective for COLREGs compliant collision avoidance that is proposed in this model. The propose model also validates a scenario that included more ships and have passed that Imazu problem. The proposed BI-LSTM model achieved 80.43 % of accuracy, 95.67% of precision, 85.89% of recall and 93.54% of f1 score values. © 2024 IEEE.

关键词： Deep reinforcement learning

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Stock Market Value Prediction Using Attention Based Long Short-Term Memory Model 2

Stock Market Value Prediction Using Attention Based Long Sho...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Al-Farouni, Mohammed Ramachandra, A.C. Sasikumar, V.R. Sai Srinivas, T. Aditya Madhavi, R. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India Kristu Jayanti College Department of Computer Science Bangaluru India Jayaprakash Naryan College of Engineering Artificial Intelligence and Machine Learning Mahabubnagar India Keshav Memorial Engineering College Department of Cse Hyderabad India

ISBN: (纸本)9798350373110

Stock market value prediction is a trendy field these days with a lot of promise and challenges. An excess existing of methods for forecasting stock values have surfaced in recent times and the forecast accuracy of these systems is still far from optimal. This paper presents an Attention-Long Short- Term Memory model (Attention-LSTM) for stock market value prediction. The internet is the primary source of stock market data which offers historical data as well as technical indications such as stock posts and financial news. Next, combine historical and technical pointer's data to create a feature set for stock market value prediction, then utilize the Attention-LSTM network to make predictions which shows superior performance. The proposed model produces the better result of Mean Absolute Error (0.0479), Mean Squared Error (0.0050), Root Mean Squared Error (4.04), and Mean Absolute Percentage Error (1.0861) compared to previous models like Correlation Coefficient-Broad learning System (PCC-BLS), Deep learning-GuesSentiment Analysis (DL-GuesS), and LSTM. © 2024 IEEE.

关键词： Marketplaces

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Dual Channel Convolutional Neural Network for Dementia Disease Prediction 2

Dual Channel Convolutional Neural Network for Dementia Disea...

引用

2nd IEEE International Conference on Integrated intelligence and Communication Systems, ICIICS 2024

作者： Hussein, Layth Naveen, I.G. Aditya Sai Srinivas, T. Mukesh, S. Durgadevi, G. Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India Department of Biomedical Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

Dementia disease is a complex neurological condition that is affecting millions of population around the world. Dementia is known as a umbrella term because it does not represent a specific disease, it describes a range of cognitive impairments. Which are interfered significantly with the individual’s ability to function independently in their daily life. As a person’s age increases, the risk of dementia development also increases. This disease primarily affects the memory, thinking, and communication skills. The traditional methods failed to diagnose the disease accurately in early stage. This paper proposes a Dual Channel Convolutional Neural Network (DCCNN) to predict and classify the dementia disease. This algorithm is employed with the two different convolution layers that are internally consists of three convolutional layers and three fully connected layers and these layers are trained with the dataset first, and that are capable of processing two different type of data or features that are in that particular dataset simultaneously by utilizing two separate pathways or channels that are within the same model or architecture. Further, this method combines the features and provides the accurate prediction of the dementia. The proposed DCCNN model obtained 93% for R-fMRI dataset compared to the existing methods. © 2024 IEEE.

关键词： Convolutional neural networks

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Protecting Medical Image Transmission: Color Secret Sharing Protocol for Enhanced Security

Protecting Medical Image Transmission: Color Secret Sharing ...

引用

2024 International Conference on artificial intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Maram, Balajee Sai, Manikonda Srinivasa Sesha Vanniappan, Malathy Srinivas, T. Aditya Sai Nagaraju, Pamarthi Prasan, U.D. School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India KKR & KSR Institute of Technology and Sciences Department of Information Technology Guntur India SR University Department of Electronics and Communication Engineering Telengana Warangal India Jayaprakash Naryan College of Engineering Artificial Intelligence and Machine Learning Mahabubnagar India Department of CSE Bhimavaram India Aditya Institute of Technology and Management Andhra Pradesh Tekkali India

ISBN: (纸本)9798331517953

The safety in the transmission of medical images cannot be compromised in the digital age, as the information contained is very sensitive. This paper presents a new approach to providing security for medical image transmission using the Color Secret Sharing protocol. This is ensured by the proposed approach of dividing the medical image into many shares, each of which is encoded with independent color information, to ensure that the restoration of the original image can be done only if all the shares are combined. Thus, the risk related to unauthorized access and data breaching is much reduced during the transmission process. Security analysis and experimental validation are provided that demonstrate that the compared methods tend to efficiently resist attacks. This study embeds CSS into medical imaging systems to solve the secure and trustworthy solution on the confidentiality, integrity, and availability of patient data in healthcare communications. © 2024 IEEE.

关键词： Color Secret Sharing Cryptography Healthcare Data Protection Image Encryption Medical Image Security Secure Transmission

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Avoidance of Ship Collision Using Deep Reinforcement learning with Bi-Directional Long Short-Term Memory in Continuous Action Spaces

Avoidance of Ship Collision Using Deep Reinforcement Learnin...

引用

Data Science and Network Security (ICDSNS), International Conference on

作者： Revatthy Krishnamurthy Myasar Mundher Adnan Sunil Kumar V T. Aditya Sai Srinivas Srinivas Aluvala Artificial Intelligence & Machine Learning Department Rajalakshmi Engineering College Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic university Najaf Iraq Department of Artificial intelligence and Machine learning Nitte Meenakshi Institute of Technology Bengaluru India Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India School of Computer Science and Engineering SR University Warangal India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

关键词： Accuracy Biological system modeling Bidirectional control Power system stability Deep reinforcement learning Stability analysis Vectors Marine vehicles Collision avoidance Long short term memory

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An Automatic Extraction of Features from Water Bodies by using Contrast Vision Transformer 2

An Automatic Extraction of Features from Water Bodies by usi...

引用

2nd IEEE International Conference on Integrated intelligence and Communication Systems, ICIICS 2024

作者： Madhumitha, A. Al-Fatlawi, Muhammed Sai Srinivas, T. Aditya Ramachandra, A.C. Venkatasubramanian, R. Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam Erode India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

In recent days, for strengthening the climate change adaption water features are utilizing as the most crucial environmental element. The integration of Remote Sensing (RS) with artificial intelligence (AI) is becoming the major sought-after approach for extracting the features of water information. In existing, the Faster Regional Convolution Neural Network (FR_CNN) is utilized for the water bodies’ extraction. However, the existing FR_CNN model struggled with computational complexity. To overcome this issue, Contrast Vision Transformer (CoVT) is proposed to extract features from water bodies. The transformer with self-attention mechanism reduces the computational complexity by eliminating regional proposal networks. Initially, the satellite images are taken from sentinel-2 dataset and further processed into preprocessing. In preprocessing Gaussian Median Filter (GMF) and Atmospheric Correction (AC) are utilized to reduce the noise and atmospheric effects from images. After that, proposed CoVT extract features from preprocessed images and fed into projection head to generate projected features. Finally, the projected features are further processed with prediction head to predict the satellite image features of water bodies. From the results, the proposed CoVT provided better results when compared to existing FR_CNN by providing results such as accuracy (99.4%), precision (99.1%), recall (98.5%) and F1-score (98.7%) respectively. © 2024 IEEE.

关键词： Median filters

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Stock Market Value Prediction Using Attention Based Long Short-Term Memory Model

Stock Market Value Prediction Using Attention Based Long Sho...

引用

Data Science and Network Security (ICDSNS), International Conference on

作者： Mohammed Al-Farouni Ramachandra A C Sasikumar V R T. Aditya Sai Srinivas R. Madhavi Department of Computers Techniques Engineering College of Technical Engineering The Islamic university Najaf Iraq Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science Kristu Jayanti College Bangaluru India Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India Department of CSE Keshav Memorial Engineering College Hyderabad India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

关键词： Training Analytical models Soft sensors Predictive models Network security Time factors Stock markets Forecasting Long short term memory Investment

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Protecting Medical Image Transmission: Color Secret Sharing Protocol for Enhanced Security

Protecting Medical Image Transmission: Color Secret Sharing ...

引用

artificial intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Balajee Maram Manikonda Srinivasa Sesha Sai Malathy Vanniappan T. Aditya Sai Srinivas Pamarthi Nagaraju U D Prasan School of Computer Science and Artificial Intelligence SR University Warangal Telangana Department of information technology KKR & KSR institute of technology and sciences Guntur Department of Electronics and Communication Engineering SR University Warangal Telengana India Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar Department of CSE Shri Vishnu engineering College for women (A) Bhimavaram India Aditya Institute of Technology and Management Tekkali Andhra Pradesh

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

关键词： Protocols Image color analysis Image communication Medical services Resists Image restoration Encryption Safety Colored noise Biomedical imaging

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Dual Channel Convolutional Neural Network for Dementia Disease Prediction

Dual Channel Convolutional Neural Network for Dementia Disea...

引用

Integrated intelligence and Communication Systems (ICIICS), International Conference on

作者： Layth Hussein I G Naveen T. Aditya Sai Srinivas S Mukesh G. Durgadevi Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India Department of Biomedical Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Dementia disease is a complex neurological condition that is affecting millions of population around the world. Dementia is known as a umbrella term because it does not represent a specific disease, it describes a range of cognitive impairments. Which are interfered significantly with the individual's ability to function independently in their daily life. As a person's age increases, the risk of dementia development also increases. This disease primarily affects the memory, thinking, and communication skills. The traditional methods failed to diagnose the disease accurately in early stage. This paper proposes a Dual Channel Convolutional Neural Network (DCCNN) to predict and classify the dementia disease. This algorithm is employed with the two different convolution layers that are internally consists of three convolutional layers and three fully connected layers and these layers are trained with the dataset first, and that are capable of processing two different type of data or features that are in that particular dataset simultaneously by utilizing two separate pathways or channels that are within the same model or architecture. Further, this method combines the features and provides the accurate prediction of the dementia. The proposed DCCNN model obtained 93% for R-fMRI dataset compared to the existing methods.

关键词： Accuracy Annotations Predictive models Prediction algorithms Feature extraction Classification algorithms Pattern recognition Convolutional neural networks Diseases Dementia

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An Automatic Extraction of Features from Water Bodies by Using Contrast Vision Transformer

An Automatic Extraction of Features from Water Bodies by Usi...

引用

Integrated intelligence and Communication Systems (ICIICS), International Conference on

作者： Madhumitha A Muhammed Al-Fatlawi T. Aditya Sai Srinivas Ramachandra A C R. Venkatasubramanian Department of Computer Science and Business Systems Bannari Amman Institute of Technology Erode India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

In recent days, for strengthening the climate change adaption water features are utilizing as the most crucial environmental element. The integration of Remote Sensing (RS) with artificial intelligence (AI) is becoming the major sought-after approach for extracting the features of water information. In existing, the Faster Regional Convolution Neural Network (FR_CNN) is utilized for the water bodies' extraction. However, the existing FR_CNN model struggled with computational complexity. To overcome this issue, Contrast Vision Transformer (CoVT) is proposed to extract features from water bodies. The transformer with self-attention mechanism reduces the computational complexity by eliminating regional proposal networks. Initially, the satellite images are taken from sentinel-2 dataset and further processed into preprocessing. In preprocessing Gaussian Median Filter (GMF) and Atmospheric Correction (AC) are utilized to reduce the noise and atmospheric effects from images. After that, proposed CoVt extract features from preprocessed images and fed into projection head to generate projected features. Finally, the projected features are further processed with prediction head to predict the satellite image features of water bodies. From the results, the proposed CoVt provided better results when compared to existing FR_CNN by providing results such as accuracy (99.4%), precision (99.1%), recall (98.5%) and F1-score (98.7%) respectively.

关键词： Computer vision Head Accuracy Atmospheric modeling Noise Feature extraction Transformers Satellite images Computational complexity artificial intelligence

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