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A Deep Reinforcement Learning-Based LSTM-Enhanced A3C Model for Predicting Bacterial Gill Disease in Aquaculture Water

A Deep Reinforcement Learning-Based LSTM-Enhanced A3C Model ...

引用

2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Kol, Bhawna Jairam Naik, K. National Institute of Technology Raipur Department of Computer Science & Engineering Raipur India

ISBN: (纸本)9798350369106

Aquaculture, a rapidly growing industry that plays a crucial role in global food security and contributes significantly to the economies of many countries, faces substantial challenges in managing aquatic diseases. Among such diseases is Bacterial Gill Disease (BGD), which may cause severe economic losses if not detected early and promptly treated. This paper introduces a novel deep reinforcement learning approach, the LSTM-Enhanced Asynchronous Advantage Actor-Critic (LE-A3C) model, which was proposed to predict BGD by analyzing multivariate water quality indicators. The approach integrates LSTM's sequential learning capabilities with the advantage-based optimization and the asynchronous parallel learning approach of the A3C algorithm. This could remarkably capture complex temporal dependencies as well as water quality patterns, which allows the proposed LE-A3C to identify early warning signs for outbreaking potential BGD outbreaks. The asynchronous learning in A3C would enable multiple water quality scenarios to be explored and optimized concurrently;this actually accelerates learning and increases its robustness in real-time predictive tasks. This boosts the speed and accuracy of detecting diseases and, therefore is a highly efficient tool for aquaculture systems. The proposed LE-A3C model was implemented on the 'Ponds data' dataset, and experimental results clearly demonstrate its superior performance compared to existing models, achieving a predictive accuracy of 97.50%. In comparison with the existing models M-DQN achieved an accuracy of 88.52%, the M-GRU model reached 78.92%, and the S-LSTM model recorded 74.31 % accuracy. © 2024 IEEE.

关键词： Deep reinforcement learning

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Research on Detecting Future Fake News Based on Multi-View and Knowledge Graph 4

Research on Detecting Future Fake News Based on Multi-View a...

引用

4th International Conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Zhu, Xuefeng Li, Liang Chongqing University of Technology College of Computer Science and Engineering Banan Chongqing China

ISBN: (纸本)9798350394375

In authentic scenarios, the fake news detector is designed with the purpose of training its model based on existing news data to identify instances of counterfeit information in future data. The current detection methods have been exclusively concerned with the model's capture of entity information problems that lack robust generalization, thus neglecting the intrinsic attributes of the news itself and its inability to effectively harness external knowledge for determining news credibility. To address these challenges, a novel framework for fake news detection is proposed, which integrates multiple perspectives within an entity-debiased paradigm, encompassing emotional and stylistic information, as well as knowledge graphs. By employing a multi-faceted approach to model news content, extract its unique features, and fuse knowledge graphs with entity information during the training phase, the impact of entity details on news authenticity is effectively modeled, thus enhancing the model's ability to generalize to future data. Experimental evaluations conducted on both Chinese and English datasets demonstrate that the proposed framework significantly elevates the performance of the fake news detector. © 2024 IEEE.

关键词： Knowledge graph

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CoMTAnet: An Enhanced Framework for Sleep Apnea Detection via Contrastive Multi-Temporal Respiratory Signal Analysis

CoMTAnet: An Enhanced Framework for Sleep Apnea Detection vi...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Huang, Zufang He, Kejing South China University of Technology School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350386226

Sleep Apnea Syndrome (SAS) is a prevalent sleep-related disorder that poses multiple health risks. Traditional detection methods, relying on polysomnography (PSG), are cumbersome and time-consuming, creating a pressing need for more convenient automated detection approaches. This paper presents CoMTAnet, a deep learning framework based on supervised contrastive learning, designed for automatic detection of SAS using multi-channel respiratory signals. The CoMTAnet framework employs an encoder-classifier architecture, where the encoder leverages a TimesBlock to extract deep temporal features from single-channel signals and Residual Blocks to derive high-level information across multi-channel features, trained using Supervised Contrastive Learning (SCL). The classifier categorizes the signal features into normal, insufficient breathing, and apnea classes. Tested on two extensive public datasets, MESA and SHHS, CoMTAnet demonstrates superior performance in terms of accuracy, precision, recall, and F1 score compared to other state-of-the-art models. This highlights the exceptional effectiveness of the supervised contrastive learning approach in the detection of SAS. © 2024 IEEE.

关键词： Self-supervised learning

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Critical Behavior Sequence Monitoring for Early Malware Detection 17

Critical Behavior Sequence Monitoring for Early Malware Dete...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Bisht, Tarun Babu, Sarath Singh, Virendra Indian Institute of Technology Bombay Computer Science and Engineering Department Mumbai India

ISBN: (纸本)9798331509736

The widespread use and the immense user base make Windows systems a prime target for attackers seeking to exploit vulnerabilities and maximize impact. Modern obfuscation techniques enable malware to evade detection tools, allowing it to intrude on systems and carry out malicious activities. Thus, to prevent potential harm to the victim's system, it is crucial to detect malware at the early execution stage and initiate adequate action. However, there is often a trade-off between accuracy and earliness in malware detection, as detecting threats at earlier stages may sometimes come at the cost of reduced detection accuracy. We introduce an early malware detection framework that balances this trade-off. Our proposed framework iteratively constructs API call prefix subsequence and applies security-sensitive embedding using API call parameters. The causal sequence encoder transforms these sequences into contextual vectors, which are then classified by a multi-layer perceptron. The proposed framework not only outperforms the state-of-the-art early malware detection method EarlyMalDetect but also demonstrates comparable performance to post-sequence malware detection methods like CTIMD and BD-MDLC, achieving accurate maliciousness prediction on or before analyzing just 3 % of the API sequence. © 2024 IEEE.

关键词： Causal encoder Deep learning Early detection Malware Sequence analysis

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Design Scheme of Copyright Management System based on Cryptography and Digital Watermarking 7

Design Scheme of Copyright Management System based on Crypto...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Shirley, C.P. Sujith, P. Karunya Institute of Technology And Sciences Department of Computer Science And Engineering Coimbatore India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359299

In recent years, piracy has become a common practice in the film and gaming industry. Previously, films were pirated, copied and distributed using compact disks (CDs), but now pirated content is shared online. In the gaming industry, competitors and fans often copy textures, characters, maps, and other content from each other. The proposed method aims to address the issue of piracy that occurs within the industry by proposing a security framework that uses cryptography for encryption and digital watermarking techniques for identification. Content creators usually have to share their work with other developers or creators in a work environment, which makes the content vulnerable to piracy. The aim of the proposed work is to put the created content into this security framework, encrypt it, and embed an invisible watermark specific to that user when sharing it with others. If any other creator needs that content, they can request it through the license server, and it will be decrypted using the keys from the server. The proposed method ensures that leaked content will have the watermark, which can help identify the person who leaked it, be it a city map in a game or a scene from a movie. This article will explain the methodologies used in this approach in detail. © 2024 IEEE.

关键词： Cryptography

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Detection and Segregation of Plastic Waste using a Robotic Arm 15

Detection and Segregation of Plastic Waste using a Robotic A...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kangabam, Bikash Huidrom, Bhupendra Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim India

ISBN: (纸本)9798331300579

Plastic waste pollution has become one of the most significant environmental concerns in recent years. The accumulation of plastic waste has been observed in various locations worldwide, leading to environmental damage and health hazards. In this paper, a system is proposed for the detection and segregation of plastic waste using a Robotic arm manipulator based on YOLOv5 and Raspberry Pi. YOLOv5 is an advanced object detection algorithm that can identify plastic waste in images. The Raspberry Pi is a low-cost, small-sized computer that can be used as the central processing unit for the proposed system. The system includes a camera module that captures images of the waste material in real time, and an ultrasonic sensor to measure distance of the object. It detects and identifies the plastic waste in the image. Then, using the 3-D coordinate values calculated from the data gathered from the camera and ultrasonic sensor, plastic waste is separated using a robotic arm by applying inverse kinematics, controlled by the Raspberry Pi. The proposed system's performance was evaluated using various custom datasets, and the results show that it can effectively detect and segregate plastic waste from other types of waste materials. © Grenze Scientific Society, 2024.

关键词： Inverse kinematics

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Lifelong Dynamic Timed A∗ (LTA∗) for Fastest Path Retrieval in Congested Road Networks Using Predicted Speeds 10

Lifelong Dynamic Timed A∗ (LTA∗) for Fastest Path Retrieva...

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10th International Conference on Vehicle technology and Intelligent Transport Systems, VEHITS 2024

作者： Sondhi, Kartikey Dalapati, Poulami Kumar, Saurabh Department of Computer Science and Engineering The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9789897587030

Efficient transportation systems are crucial for the ever-growing smart cities. With the increasing urbanization and growth in vehicular traffic, congestion has become a significant challenge. This research paper addresses the critical issue of identifying the fastest, least congested path in road transport networks, aiming to enhance overall travel efficiency and reduce the negative impact of traffic congestion. The study employs an improved version of the Lifelong Planning A∗ (LPA∗) that helps find the fastest route between two points in dynamic changing environments. The proposed methodology is called the Lifelong Dynamic Timed A∗(LTA∗) algorithm with an optimal bound weight factor integrated with it to make the search more guided and efficiently predict optimized traffic paths to provide real-time recommendations. To validate the effectiveness of the developed algorithm, extensive simulations and case studies are conducted on a small area inWashington as well as on GridWorlds. The experimental results show that LTA∗, within accurate weight bounds, always managed to find the fastest path, and in some cases, the time taken was close to half of that produced by A∗. Copyright © 2024 by SCITEPRESS - science and technology Publications, Lda.

关键词： Traffic congestion

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DemoCraft: Using In-Context Learning to Improve Code Generation in Large Language Models 3

DemoCraft: Using In-Context Learning to Improve Code Generat...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Joshua, Kapu Nirmal Sreejith, Mihit Indian Institute of Technology Department of Electrical Engineering Kanpur India Indian Institute of Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331515911

Producing executable code from natural-language directives via Large Language Models (LLMs) involves obstacles like semantic uncertainty and the requirement for task-focused context interpretation. To resolve these difficulties, we introduce DemoCraft, a framework that refines code generation through in-context learning and demonstration selection, in tandem with latent concept learning. Latent concept learning appends extra concept tokens, which act as trainable embeddings encapsulating task-oriented insights. We evaluate our approach on two promi- nent datasets: MBPP and Humaneval. Our experimental findings reveal that this method attains roughly a 2x boost in the pass@k metric compared to baseline models. Moreover, we propose two new evaluation measurements: correctness@k and similarity@k. Empirical analyses indicate that our system achieves nearly a 3x advancement in these measures as well. © 2025 IEEE.

关键词： Semantics

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VGGAE-Net: A Two Stream Ensemble CNN for Diabetes Retinopathy Classification 4

VGGAE-Net: A Two Stream Ensemble CNN for Diabetes Retinopath...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Girepunje, Shashank Singh, Pradeep National Institute of Technology Dept. of Computer Science and Engineering Raipur India

ISBN: (纸本)9798350349900

Diabetic retinopathy is a retinal disease that leads to vision impairment in people suffering from diabetes mellitus. Early detection of disease requires expert supervision and computer diagnosis systems to avoid vision loss. Diabetic Retinopathy grading is suffering from small lesions and data imbalance problems. In this work, we proposed a VGG based attentive ensemble Network (VGGAE-Net) for the classification of diabetes Retinopathy. We have also utilized the two attention blocks channel attention module and class attention module with extracted feature maps from the CNN. Then their prediction outputs are combined to provide the final output for the classification task. The experiment shows that the ensemble approach of two attention blocks with VGG model gives 96.66% accuracy as compared to the existing recent models. © 2024 IEEE.

关键词： class attention module diabetes retinopathy global attention module

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Machine Learning-Powered Traffic Sign Tracking: A Step-by-Step Framework for Recognition 2

Machine Learning-Powered Traffic Sign Tracking: A Step-by-St...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Rkhi Krishna, C.R. Pallavi, N.R. Computer Science Engineering Department BGS Institute of Technology Adichunchanagiri University India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

One of the biggest challenges in machine vision is finding and identifying items in natural scenes. When real-time processing capabilities are needed, this difficulty is exacerbated. Intelligent transport systems rely heavily on identifying and detecting traffic signs through video technology. While a great deal of research has shown that several state-of-the-art approaches' "most notably, deep learning methods' "perform admirably on public datasets, these methods have significant processing needs. In contrast to previous studies, we provide a computational framework based on machine learning for the detection, tracking, and recognition of traffic signs using video captured from a moving vehicle for Indian Traffic Sign boards. The main advantages of our suggested system are: 1. Improving traffic sign detection performance in challenging lighting scenarios. 2. Using content analysis and important information recognition, a region of interest (ROI) extraction approach was created to improve the accuracy of visual inspection using YOLO. 3. The DNN method is used to enhance tracking performance, which includes the traffic lights, and produce a more stable classification result. The model underwent testing using various epoch values, revealing an accuracy increase to 96.8% after 150 epochs. Additionally, a comparison was made with input based on images, yielding an accuracy of 95.4% also after 150 epochs. © 2024 IEEE.

关键词： Traffic signs

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