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An Analysis on Cognitive Radio Adhoc Networks: Categories, Problems, and Solutions 1

14th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2022

作者： Vinya, Viyyapu Lokeshwari Sahithi, G. Gopal, S. Venu Rao, G. Venkateswara Shaikh, Mehwish Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Shamshabad India Department of Computer Science and Engineering CVR College of Engineering Telangana Ibrahimpatnam India Department of Artificial Intelligenceand Data Science Vardhaman College of Engineering Telangana Shamshabad India Department of Computer Science and Engineering GIT GITAM-Deemed to be University A.P. Visakhapatnam India Department of Software Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

The exploitation of finite spectrum resources is being addressed by the new technology known as Cognitive Radio (CR). It has emerged as a potential remedy for the spectrum shortage problem in the following generation of cellular networks by utilizing opportunities in the time, frequency, and spatial domains. Many group-oriented applications that are created for CRAHN deployment have grown significantly in relevance in recent years. Multicasting is a more effective method for enabling applications that rely on group communication. In contrast to traditional wired multicast routing protocols, multicast routing for CRNs must manage a range of challenges, because of the characteristics of CRNs, such as low bandwidth, mobility, and low power. In this paper, we discussed about various types, issues and solutions for CRAHN routing, Multicast Routing, Channel Assignment and Load balancing. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multicasting

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A Traffic Sign Recognition qnd Speech-BasedFeedback Generation System to Assist Drivers 5

A Traffic Sign Recognition qnd Speech-BasedFeedback Generati...

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5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Sarma, Himangshu Saharia, Navanath IIIT Department of Computer Science & Engineering Andhra PradeshSri City India IIIT Data Engineering Lab CSE Senapati Manipur India

ISBN: (纸本)9798350338287

According to the World Health Organization (WHO), 1.3 million people loss their life annually as a result of road accidents. By alerting or assisting the driver while they are driving, information technology such as computer vision can play a significant part in reducing the accident rate. Keeping that in mind, in this article we presents one traffic sign recognition tool using deep neural network (DNN) models with speech-based feedback for vehicle drivers. The proposed system can give voice-based feedback to driver to direct or inform them to take the appropriate action while driving, such as 'Drive slow, speed breaker ahead', 'Maximum Speed Limit is 80km/hr', 'School Ahead, drive slow', 'Don't take overtake' etc. © 2023 IEEE.

关键词： Deep neural networks

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An Auto Encoder-Decoder Approach to Classify the Bird Sounds Using Deep Learning Techniques

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SN computer science 2023年第3期4卷 1-13页

作者： Vamsi, Bandi Mahanty, Mohan Doppala, Bhanu Prakash Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology and Science Madanapalle 517326 India Department of Computer Science and Engineering Vignan’s Institute of Information Technology (A) Visakhapatnam 530046 India Data Analytics Generation Australia 88 Phillip St Sydney 2000 NSW Australia

Nowadays identification of bird species is the main problem for ornithologists and ecologists. The increase in volumes of data labelling, still there is a scope to implement an automated bird classification through deep learning techniques. Identifying the nature of a bird and its flock aids in analyzing different other species in the natural environment. This is considered as the main role in the problem of ecology. The species of birds act accordingly to the changes in ecology and this became a challenging task to identify the growth of the bird species with advanced technologies. In this work, our aim is to classify the different bird species based on voice recordings across various locations using autoencoders and decoders. This proposed work is a four-step process: data preprocessing of bird acoustic, feature extraction using a deep network, classification by random forest (RF) and support vector machine (SVM) and identifying the pitch intensities of a bird in low, medium and high ranges. In data pre-processing, the recorded audio is converted into spectrograms through frequency waves. The Mel Spectrogram filter is used to remove the noise from the recorded bird audio. For extracting the features in the spectrogram image, encoders and decoder convolutions are used to construct a fully connected vector. These features are given to RF and SVM for classifying the bird species to obtain standard prediction accuracy. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Bird classification CNN Decoder Encoder Mel spectrum

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An Attention-Based Model for Clinical Time Series Prediction: Enhancing ICU Readmission Prediction 14

An Attention-Based Model for Clinical Time Series Prediction...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Madinei, Hananeh Sadat Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

ISBN: (纸本)9798331511272

— In recent years, time series prediction has become a highly interesting topic in various applied areas, including clinical time series analysis. Hospitals and other clinical healthcare systems collect Electronic Health Records. These records contain important information that needs to be mined. This information provides a solid basis for predicting hospital readmission, helping to improve healthcare, enhance patient outcomes, and optimize hospital resources. This study introduces a novel deep learning attention-based approach for predicting 30-day ICU readmissions using clinical time series information extracted from the MIMIC III dataset. This approach incorporates time-aware positional encoding into the attention mechanism to enhance predictive performance by capturing the temporal representation of data and considering the temporal order of clinical time series data. This model is compared with cutting-edge models. The significant enhancements in the model performance achieved by the proposed method highlight the effectiveness of incorporating time-aware positional encoding in capturing temporal dependencies and improving prediction outcomes. These results underscore the potential of advanced attention mechanisms in clinical time series analysis. ©2024 IEEE.

关键词： Electronic health record

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Diagnosis of Plant Infection for Optimised Authomatic Health Monitoring System Using Vision Transformer Models 5

Diagnosis of Plant Infection for Optimised Authomatic Health...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Thapliyal, Nitin Aeri, Manisha Satyarthi, Abhishek Kukreja, Vinay Sharma, Rishabh Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Artificial Intelligence & Data Science Iimt College of Engineering Uttar Pradesh Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350383522

Mosaic Virus is highly responsible for the crop loss, especially the pepper, which is considered part of food crop, an essential aspect of world's agriculture. This work aims at automating the classification of disease severity level-related the Pepper Leaf Mosaic Virus Disease threshold through vision transformer (ViT) model to ensure a critical job. The classification of the severity is more often accompanied by a fairly significant amount of workload and arbitrariness, resulting in equipment innovation in most of the locations. The work applies ViT-based approaches with sufficient precision to appropriately classify a dataset, which contains 10,800 meticulously selected photographs. The present model obtained a commendable average accuracy of 97.25%, which shows that the model was able to detect even smaller differences between the classes of severity. The operational criteria that include the tale of the precision, recall, and F1-score highlight the model's ability to minimize to Feggsor false Positives with its ability to discriminate cases of viruses at different levels of the spectrum. The more advanced comparison charts made hand methodologies stand at the pinnacle in plant pathology studies. This study is not only beneficial in terms of illuminating the understanding about the Pepper Leaf Mosaic Virus but also gives farmers and researchers a transformative tool that could change the way disease control methods are used. Technologies AI that power ViT model are helping the agriculture industries throughout the world to implement sustainability and resiliency of the systems by providing responses in the right time and place in addressing viral infections. © 2024 IEEE.

关键词： Agricultural Automation Agricultural Technology Artificial Intelligence Automated Diagnosis Crop Health Disease Management Global Food Security Image Classification Machine Learning Pepper Leaf Mosaic Virus Plant Pathology Precision Agriculture Severity Classification Vision Transformer ViT Model

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A Robust Framework for Epileptic Seizure Diagnosis: Utilizing GRU-CNN Architectures in EEG Signal Analysis 20

A Robust Framework for Epileptic Seizure Diagnosis: Utilizin...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Nikoupour, Maryam Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Alzahra University Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran Iranian Research Organization for Science and Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350383942

Seizures that take place repeatedly and without provocation are referred to as epilepsy. Epilepsy can be diagnosed with electroencephalography (EEG). One of the most influential challenges of the past few years has been the use of deep learning algorithms to replace manual inspection of medical signals by specialists, such as epilepsy signal classification. This paper presents a multi-label classification approach for epileptic seizures using deep learning. UCI machine learning repository's epileptic seizure dataset has been used to classify epileptic seizure patients. 178 features are present in each of the 11500 samples in the dataset. Based on a variety of criteria, the proposed method may have a positive impact on epilepsy diagnosis, in most cases by approximately 6% compared with existing methods utilizing long short-term memory (LSTM) and autoencoder. It is possible thus to develop and apply gated recurrent unit-based methods with good potential for categorizing EEG signals for epilepsy diagnosis based on gated recurrent unit (GRU)-CNN based methods. © 2024 IEEE.

关键词： Electroencephalography

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RAUIE: A Relation-Augmented Document-Level Event Extraction Model Based on UIE 5

RAUIE: A Relation-Augmented Document-Level Event Extraction ...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Liang, Yiyuan Jia, Hong Wang, Licai Huang, Yangchen Yang, Zhe Big Data Research and Development Center North China Institute of Computing Technology Beijing China Tsinghua University Department of Computer Science and Technology Beijing China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350385557

In document-level event extraction tasks, multiple event types often coexist within a document (the multi-event problem), and arguments may play different roles across different events (the argument intertwining problem). Existing methods often suffer from significantly reduced event extraction accuracy, mainly when focusing on scenarios with scarce annotated data and imbalanced event-type distributions. This paper emphasizes enhancing the correlation between event triggers and arguments to address these challenges. Therefore, we propose the Relation-Augmented UIE Model (RADIE), which introduces entity relationships into prompts using prompt-based learning and designs a relation-augmentation algorithm to improve the accuracy and completeness of event extraction. Experimental results demonstrate that this approach significantly outperforms baseline methods, mainly showcasing excellent performance on datasets with long-tail distributions. © 2024 IEEE.

关键词： document-level event extraction relation augment Universal Information Extraction

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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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Adaptive Operator Selection for Meta-Heuristics: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Pei, Jiyuan Mei, Yi Liu, Jialin Zhang, Mengjie Yao, Xin Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington New Zealand Lingnan University School of Data Science Hong Kong Hong Kong

Appropriate selection of search operators plays a critical role in meta-heuristic algorithm design. Adaptive selection of suitable operators to the characteristics of different optimization stages is an important task that owns promising potential to improve the performance of a meta-heuristic algorithm. A variety of adaptive operator selection methods have been proposed in last decades, from the machine learning and optimization communities. However, the existing studies have not been systematically reviewed so far. To fill the gap, this paper provides a comprehensive survey of adaptive operator selection for meta-heuristics. According to the information required for selection, adaptive operator selection methods are classified into two categories: (i) stateless methods and (ii) state-based methods. Each category is further summarized into several key components. The strategies of each component belonging to the two categories are reviewed respectively. The motivation, strengths and weaknesses of the proposed strategies are also discussed. Furthermore, studied meta-heuristics and optimization problems in the literature are summarized. The effects from the difference of meta-heuristics and problems to the specific design of methods are discussed, together with the guidance of selecting the suitable method in different application scenarios. At the end, emerging challenges that could guide further research are discussed. © 2020 IEEE.

关键词： Adaptive algorithms

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Hummingbird-Optimizer and Otsu/ Kapur-Assisted Segmentation of Foot Ulcer 1st

Hummingbird-Optimizer and Otsu/ Kapur-Assisted Segmentation ...

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1st International Conference on data engineering and Machine Intelligence, ICDEMI 2023

作者： Mohan, Ramya Kadry, Seifedine Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates

ISBN: (纸本)9789819776153

In addition to traditional approaches, several computerized techniques have been developed to enhance the results. The automation in the medical domain considerably reduced the burden and improved disease diagnosis, treatment planning, and implementation. With the advent of automation, the medical domain has greatly reduced the burden of diagnosing diseases, developing treatment plans, and implementing them. This research aims to develop image-processing assisted detection of Diabetic Foot Ulcer (DF) from digital photographs. Digital photographs are used to assist in diagnosing Diabetic Foot Ulcer (DF). As part of the developed tool, (i) images are collected and resized, (ii) Otsu/Kapur and Hummingbird-optimizer thresholding is supported, (iii) level-set segmentation is used to extract the DF region, and (iv) the developed scheme is compared with the binary mask and its performance is verified. A total of 300 images are examined in this work, and MATLAB software is used to conduct the experiments. Comparing Otsu + LS with Kapur + LS, the overall result achieved with Kapur’s enhanced image is better than that of Otsu + LS. Authentic clinical DF images can be used to verify the proposed scheme in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： MATLAB

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