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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Shah, Kashish D. Patel, Dhaval K. Raval, Mehul S. Zaveri, Mukesh Merchant, S.N. School of Engineering and Applied Science Ahmedabad University India Svnit Department of Computer Science and Engineering Gujarat Surat India LIT Bombay Department of Electrical Engineering Mumbai Powai India

ISBN: (纸本)9798350383119

As of recent, the majority of developing countries have faced traffic congestion issues. As a result, vehicular communication technologies such as DSRC and Cellular-V2X are standardized by the 3GPP to support the transportation community. The C-V2X standard proposed in 3GPP Re114 provides high data transmission facilities for V2V and V2I communication. In this paper, we attempt to solve the traffic congestion problem by considering the V2I communication facility as per the C-V2X standard. We propose an online learning strategy based on Deep Reinforcement Learning for smart signaling to avoid traffic congestion at the intersections. These are the key aspects to address the congestion issue. We conduct extensive near-real-time simulations in PTV VISSIM considering the Swastik Cross Road intersection at Ahmedabad, Gujarat, India, imported via OpenStreetMap. With the proposed smart signaling strategy, we found significant improvement in vehicles' average travel time and average space occupancy compared to existing traffic signaling methods. Uplifting this research and implementing such real-time strategies will significantly impact the current transportation system. © 2024 IEEE.

关键词： Cellular-V2X Deep Reinforcement Learning Intelligent Transportation Systems Smart Signaling Vehicular Communication

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A Hybrid Approach for Medical Image Fusion using SVD and PCA 4

A Hybrid Approach for Medical Image Fusion using SVD and PCA

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Kusuma, J. Meera, K.N. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India Amrita School of Engineering Department of Mathematics Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350307757

The process of combining multiple images into a single one is called image fusion. This facilitates extracting the information from one image instead of collating the information from multiple images. Image fusion reduces the randomness and redundancy and gives better clarity for human perception especially in the field of medical sciences. In order to improve the information contained in an image, we provide a novel technique in this study termed SVDPCA. We choose two distinct types of images for this experiment, namely Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Initially singular value decomposition (SVD) is used on the two input images. The final image is created by processing the SVD result with the Principal Component Analysis (PCA) technique. To perform the experiment, we used 30 image sets. The results are compared with other techniques like PCA, SVD and maximum method. The experiments show that the proposed method SVDPCA which combines both methods gives the best result. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Multimodal attention-driven visual question answering for Malayalam

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Neural computing and Applications 2024年第24期36卷 14691-14708页

作者： Kovath, Abhishek Gopinath Nayyar, Anand Sikha, O.K. Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam

Visual question answering is a challenging task that necessitates for sophisticated reasoning over the visual elements to provide an accurate answer to a question. Majority of the state-of-the-art VQA models are only applicable to English questions. However, applications such as visual assistance and tourism necessitate the incorporation of multilingual VQA systems. This paper presents an effective deep learning framework for Malayalam visual question answering (MVQA), which can answer a specific natural language question about an image in Malayalam. As there is no available dataset in English–Malayalam VQA, a MVQA dataset was created by translating English question–answer pairs from the visual genome dataset. The paper proposes an attention-driven MVQA model on the developed dataset. The proposed MVQA model uses a deep learning-based co-attention mechanism to jointly learn the attention for images and Malayalam questions. A second-order multimodal factorized high-order pooling is used for multi modal feature fusion. Different VQA models using combinations of classical CNNs and RNNs were experimented on the developed MVQA dataset, and the performance was compared against the proposed attention-driven model. Experimental results show that the proposed attention-driven MVQA model achieves state-of-the-art results as compared to other models for MVQA on the custom Malayalam VQA dataset. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep learning

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Real-Time Road Damage Detection and Infrastructure Evaluation Leveraging Unmanned Aerial Vehicles and Tiny Machine Learning

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IEEE Internet of Things Journal 2024年第12期11卷 21347-21358页

作者： Khan, Muhammad Waseem Obaidat, Mohammad S. Mahmood, Khalid Batool, Dania Badar, Hafiz Muhammad Sanaullah Aamir, Muhammad Gao, Wu Comsats University Islamabad Sahiwal Campus Department Of Computer Science Sahiwal57000 Pakistan Superior University Department Of Computer Science Lahore54000 Pakistan The University Of Jordan King Abdullah Ii School Of Information Technology Amman11942 Jordan University Of Science And Technology Beijing School Of Communication And Computing Engineering Beijing100083 China Amity University Amity School Of Engineering And Technology Uttar Pradesh Noida201301 India National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan University Of Palermo Department Of Engineering Palermo90133 Italy Emerson University Department Of Emerging Computing Technologies Multan60000 Pakistan Northwestern Polytechnical University Center For Bio-Inspired System-on-Chip School Of Computer Science Xi'an710060 China

Road damage detection (RDD) through computer vision and deep learning techniques can ensure the safety of vehicles and humans on the roads. Integrating unmanned aerial vehicles (UAVs) in RDD and infrastructure evaluation (IE) has also emerged as a key enabler, contributing significantly to data acquisition and real-time monitoring of road damages, such as potholes, cracks, and surface anomalies, facilitating proactive maintenance and improved road conditions. These UAVs are low-powered and resource-constrained devices that work autonomously to perform pattern detection and decision making leveraging tiny machine learning (Tiny ML) algorithms. These Tiny ML algorithms are designed to run on edge devices, IoT devices, UAVs, etc. In this study, the RDD2022 data set collected using UAVs and dashboard cameras of vehicles was utilized to train pure and mixed models that exhibit class instance imbalance in certain classes which is addressed by implementing data augmentation as a regularization technique. State-of-the-art two-stage detectors: Faster R-CNN ResNet101 and one-stage detectors: SSD MobileNet V1 FPN, YOLOv5, and Efficientdet D1 are employed. The results indicate that the two-stage detector achieved an impressive mean average precision (mAP) of 88.49% overall and 96.62% for focused classes. Notably, the state-of-the-art Efficientdet D1 approach achieved a competitive mAP of 86.47% overall and 95.12% for focused classes, with significantly lower computational cost. These findings highlight the potential of advanced object detection techniques, particularly Efficientdet D1, to enhance the accuracy and efficiency of RDD systems, thereby improving passenger safety and overall performance. © 2014 IEEE.

关键词： Object detection

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A Review of Deep Convolutional Neural Networks in Mobile Face Recognition

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第23期17卷 4-19页

作者： Chi, Jing On, Chin Kim Zhang, Haopeng Chai, Soo See Faculty of Computing and Informatics University Malaysia Sabah Sabah Malaysia School Information and Electrical Engineering Hebei University of Engineering Hebei China Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Sarawak Malaysia

With the emergence of deep learning, Convolutional Neural Network (CNN) models have been proposed to advance the progress of various applications, including face recognition, object detection, pattern recognition, and number plate recognition. The utilization of CNNs in these areas has considerably improved security and surveillance capabilities by providing automated recognition solutions, such as traffic surveillance, access control devices, biometric security systems, and attendance systems. However, there is still room for improvement in this field. This paper discusses several classic CNN models, such as LeNet-5, AlexNet, VGGNet, GoogLeNet, and ResNet, as well as lightweight models for mobile-based applications, such as MobileNet, ShuffleNet, and EfficientNet. Additionally, deep CNN-based face recognition models, such as DeepFace, DeepID, FaceNet, and SphereFace, are explored, along with their architectural characteristics, advantages, disadvantages, and recognition accuracy. The results indicate that many scholars are researching lightweight face recognition, but applying it to mobile devices is impractical due to high computational costs. Furthermore, noise label learning is not robust in actual scenarios, and unlabeled face learning is expensive in manual labeling. Finally, this paper concludes with a discussion of the current problems faced by face recognition technology and its potential future directions for development. © 2023 by the authors of this article. Published under CC-BY. All Rights Reserved.

关键词： Neural network models

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Evaluating Large Language Models for Enhanced Intrusion Detection in Internet of Things Networks

Evaluating Large Language Models for Enhanced Intrusion Dete...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Nwafor, Ebelechukwu Baskota, Ujjwal Parwez, Md Salik Blackstone, Jeremy Olufowobi, Habeeb Villanova University Department of Computing Sciences PA United States University of Texas at Arlington Department of Computer Science & Engineering TX United States Howard University Department of Electrical Engineering & Computer Science WashingtonDC United States

ISBN: (纸本)9798350351255

The Internet of Things (IoT) landscape has grown exponentially in recent years, making robust and efficient intrusion detection systems (IDS) even more critical. While Large Language Models (LLMs) have gained significant traction, their effectiveness in network intrusion detection remains largely unexplored. This paper proposes an LLM-based framework for enhanced threat detection and analysis in IoT networks. We explore using advanced LLMs like OpenAI's Generative Pre-trained Transformer (GPT) model, focusing on techniques such as fine-tuning and embedding similarity. Using real-world intrusion datasets, we evaluate the proposed LLM's performance in detecting common network attacks and compare it with ensemble-based IDS solutions. We assess the efficiency of the LLM in binary class and multiclass classification task using standard metrics, such as accuracy, recall, precision, and F1 scores. While the fine-tuning approach does not produce comparable results to the current baseline ensemble-based IDS models, the embedding approach, however, yields comparable results. This analysis represents a starting point for exploring the utilization of advanced large language models for intrusion detection within an IoT ecosystem. © 2024 IEEE.

关键词： Network intrusion

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Data driven approach to identify a flow-based Botnet Host using Deep Learning

Data driven approach to identify a flow-based Botnet Host us...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Mishra, Aniket Bharathi, Indira School of Computing Amrita Vishwa Vidhyapeetham Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798350396485

The internet's technological advancements exposed the globe to its weaknesses as well. The risk of exploitation has also increased as a result of larger network cores cooperating to combat cyber threats, which continue to be a severe problem for the entire world. Recurrent Neural Networks (RNN)-based deep learning techniques have recently advanced to new levels in a variety of fields and applications. The risk of forged accounts is greater than ever thanks to increased network use and traffic. The challenge to identify a malicious host on the internet has always been a challenge from the development perspective. The job of binary classification to label a host as a botnet has not made any significant progress and thus still, the internet faces the issue of botnets taking over many active and important connections exploiting the network, controlling compromised hosts to spam other hosts on the network, launch DDoS attacks and more. This paper attempts to provide a novel approach for evolving the comprehensive framework for controlling botnet host prediction and uses them to handle real time cases. To attain greater recognition accuracy, we use Gated Recurrent Unit (GRU) as a hybrid Recurrent Neural Network (RNN) model. We take an evolving time series input from a network station for several days which depicts data flow i.e., count of connections from different devices recognized by their IPs, and these features are used from the IP flow to provide capability to recognize the host on a network as a potential threat. Threat detection of such botnets is important not only from the perspective of stopping them but also to find significant insights about the targeted attack to understand future trends and make the networks persistent against them. © 2023 IEEE.

关键词： Time series

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Carbon Prognosticator: A Triple Ensemble Regressor and SHAP Analysis for Enhanced CO2 Emission Modeling 4

Carbon Prognosticator: A Triple Ensemble Regressor and SHAP ...

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4th International Conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： George, Angelina Kumar, T. M. Mohan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Chemistry Bengaluru India

ISBN: (纸本)9798331529635

Tracking carbon emissions from both vehicles and humans is crucial for understanding and mitigating climate change. Accurate prediction models are essential to address the diverse nature of carbon emission datasets. An ensemble regression model combining Random Forest, CatBoost, and Deep Neural Networks has been proposed to effectively handle various types of datasets. Evaluations showed that the model achieved an R2 score of 99.717% on the CO2 Emission (Vehicles) dataset with a 70-30 train-test split and an R2 score of 98.425% on the Carbon Footprint (Humans) dataset with an 80-20 split. These results underscore the model's capability to provide robust and accurate predictions across different carbon emission contexts, enhancing environmental analytics. © 2024 IEEE.

关键词： Carbon capture and utilization

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Enhanced Deep Learning Model for Superior Multi-Class Classification Performance

Enhanced Deep Learning Model for Superior Multi-Class Classi...

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2024 International Conference on Microelectronics, ICM 2024

作者： Aydoghmishi, Faezeh Mohammadi Abdel-Raheem, Esam Rueda, Luis University of Windsor Department of Electrical and Computer Engineering WindsorON Canada School of Computer Science University of Windsor WindsorON Canada

ISBN: (纸本)9798350379396

Skin cancer is a highly prevalent form of cancer worldwide. The clinical assessment of skin lesions is crucial for evaluating the disease's characteristics. However, this assessment is often hindered by the variability in interpretations and lengthy timelines, resulting in delayed diagnoses. An advanced computer-aided diagnosis (CAD) system is needed to improve patient survival rates. This paper presents a Multi-class Skin Cancer Classification system using an enhanced VGG-16 model to improve the diagnosis of skin cancer. Our approach focuses on classifying multiple skin lesions types, specifically Melanocytic Nevus (NV), Basal Cell Carcinoma (BCC), Melanoma (MEL), and Vascular Lesions (VASC). The system was trained and evaluated on the Human Against Machine with 10,000 training images (HAM10000) dataset. We have conducted a comparative study between our method and several previously introduced techniques on the International Skin Image Collaboration (ISIC) dataset, and the results show that the proposed model outperforms previously proposed techniques. © 2024 IEEE.

关键词： Lung cancer

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Quantum Machine Learning Techniques based on Nurturing Agri-Ontology Framework in Agricultural science 3

Quantum Machine Learning Techniques based on Nurturing Agri-...

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3rd International Conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Bennet Prabhu, A. Surendran, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Drbccc Hindu College Department of Computer Applications Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Kone Elevator India Technology & Engineering Centre Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

The absence of a consistent ontological framework often impedes efficient knowledge organization and sharing within the agricultural community, despite the quantity of data created in agricultural research. The lack of defined vocabulary and conceptual frameworks hinders data integration, agricultural research, and innovation by preventing interoperability between databases and systems. To address these issues, this research work proposes the Nurturing Agri-Ontology Framework (NAOF), which offers a systematic approach to creating and updating agricultural ontologies. Ontologies that faithfully portray agricultural ideas and connections can be more easily created with the help of NAOF, which combines ideas from knowledge management, agricultural science, and ontology engineering. NAOF aims to ensure that the ontologies produced reflect the wide range of viewpoints and terminology used in agricultural research by encouraging cooperation among stakeholders, ontology engineers, and domain specialists. In this study, the improvement of knowledge organization, sharing and integration in the agricultural area is shown with the application of NAOF. Using this research study, scientists can build ontologies that improve agricultural decision-making, data interoperability, and the discovery of new information. Using quantum machine learning techniques, a strong foundation of knowledge can be laid for several agricultural applications, such as precision agriculture, crop management, and the formulation of agricultural policies. Ontology coverage, interoperability, scalability, and usability are established evaluation parameters that are used to assess the efficacy of NAOF in this research work. The analysis shows that NAOF effectively solves the problems and creates a better agricultural knowledge ecosystem by thoroughly evaluating it using these standards. © 2024 IEEE.

关键词： Ontology

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