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arXiv 2024年

作者： Umair, Muhammad Sultana, Tangina Lee, Young-Koo Dept. of Computer Science and Engineering Kyung Hee University Global Campus. Yongin-si Korea Republic of Dept. of Electronics and Communication Engineering Hajee Mohammad Danesh Science and Technology University Bangladesh

Keyphrase Prediction (KP) is essential for identifying keyphrases in a document that can summarize its content. However, recent Natural Language Processing (NLP) advances have developed more efficient KP models using deep learning techniques. The limitation of a comprehensive exploration jointly both keyphrase extraction and generation using pre-trained language models spotlights a critical gap in the literature, compelling our survey paper to bridge this deficiency and offer a unified and in-dept. analysis to address limitations in previous surveys. This paper extensively examines the topic of pre-trained language models for keyphrase prediction (PLM-KP), which are trained on large text corpora via different learning (supervisor, unsupervised, semi-supervised, and self-supervised) techniques, to provide respective insights into these two types of tasks in NLP, precisely, Keyphrase Extraction (KPE) and Keyphrase Generation (KPG). We introduce appropriate taxonomies for PLM-KPE and KPG to highlight these two main tasks of NLP. Moreover, we point out some promising future directions for predicting keyphrases. © 2024, CC BY.

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Deep Learning-Based Spectrum Sharing for Dynamic Resource Allocation in 6G Cognitive Radio Networks

Deep Learning-Based Spectrum Sharing for Dynamic Resource Al...

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Power, Control and Computing Technologies (ICPC2T), Conference on

作者： Deepak Upadhyay Abhay Upadhyay Nookala Venu Kamlesh Kukreti Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

We conduct an extensive study on deep learningbased spectrum sharing to resolve dynamic resource allocation in 6G cognitive radio networks in this paper. The approach uses modern machine learning models to optimize spectrum usage, increase prediction accuracy of spectrum availability, and reduce interference among the primary and secondary users. We need to evaluate the performance of deep learning algorithms against the traditional spectrum allocation methods in simulation. In the paper, regular quality of service metrics such as throughput distribution and fairness among users are demonstrated to be much improved with these results, where both latency and energy consumption have been cut down dramatically. This work demonstrates the paradigm-shifting role of deep learning in spectrum management for $6 \mathbf{G}$ networks, pointing towards intelligent and effective resource allocation mechanisms in tomorrow’s communications.

关键词： Deep learning 6G mobile communication Accuracy Quality of service Interference Dynamic scheduling Throughput Resource management Cognitive radio Reliability

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Real-time Detection of Diverse Bangladeshi Traffic Signs Using YOLOv8

Real-time Detection of Diverse Bangladeshi Traffic Signs Usi...

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Future Machine Learning and Data Science (FMLDS), IEEE International Conference on

作者： Md. Habib Ehsanu Hoque Mrittika Mahbub Mohd Razali Bin Md Tomari Rezaul Bashar Md. Golam Rashed Dipankar Das Dept. of Information and Communication Engineering University of Rajshahi Bangladesh Dept. of Computer Science and Engineering Pundra University of Science and Technology Bangladesh Dept. of Electronic Engineering University Tun Hussein Onn Malaysia (UTHM) Malaysia Science Technology and Management Crescent Sydney Australia

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391220

The need for intelligent traffic management solutions is crucial in the rapidly evolving fields of autonomous traffic control and navigation systems, particularly for minimizing traffic accidents and ensuring the efficient operation of traffic signals. Improving traffic safety is of paramount importance, as road accidents claim over a million lives annually worldwide, with a significant proportion of these fatalities occurring in developing countries like Bangladesh. This research aims to develop a smart assistant capable of identifying and categorizing traffic signs to enhance road safety, especially in areas where drivers may not be fully aware of traffic regulations. To achieve this, we generated a comprehensive dataset containing 5,540 distinct images of traffic signs collected from various regions across Bangladesh, including highways, rural and urban roads, and challenging locations such as steep curves. The dataset comprises 41 categories of traffic signs. The dataset includes challenges such as color variations, blurriness, occlusion, small sign sizes, and low-light conditions. To further enhance the dataset's diversity, data augmentation techniques such as random rotations, shearing, and zooming were applied. We trained the YOLOv8 deep learning model, renowned for its real-time object detection capabilities, on this dataset. The model achieved impressive results, with a mean Average Precision (mAP) of 0.80, a recall of 0.87, a precision of 0.92, and an F1 score of 0.89, demonstrating its effectiveness in real-world traffic sign detection and classification.

关键词： Training Road accidents Virtual assistants Traffic control Streaming media Real-time systems Robustness Road safety Shearing Vehicles

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Robust Sensor Networks: Applying AI for Fault Identification in Challenging Operational Contexts

Robust Sensor Networks: Applying AI for Fault Identification...

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IoT Based Control Networks and Intelligent systems (ICICNIS), International Conference on

作者： Manjunatha A S Venkatramana Bhat P Ramakrishna M Matti Nidhi Prabhu Dept. of Computer and Communication Engineering NITTE (Deemed to be University) NMAM Institute of Technology Nitte Mangaluru Karnataka Dept. of Computer Science and Engineering NITTE (Deemed to be University) NMAM Institute of Technology Nitte Mangaluru Karnataka Dept. of Data Science and Computer Applications Manipal Academy of Higher Education Manipal Institute of Technology Manipal India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Wireless Sensor Networks (WSNs) are highly implemented in harsh and dynamic environments. In such configurations, faults and anomalies easily occur, causing unwanted disruptions in data accuracy and network performance. In the context of reliability and stability in WSN operations, effective fault detection techniques are vital. This research work discusses how several machine learning models may be applied, including Logistic Regression, Decision Tree, Random Forest, and K-Nearest Neighbors in order to detect fault in WSNs. The performance evaluation was performed based on major criteria, which are accuracy, precision, recall, and Fl score. This, therefore, makes these approaches to machine learning effective in identifying faults efficiently, hence offering a well-balanced solution that is scalable and robust for integrity and operational effectiveness of WSN s.

关键词： Fault diagnosis Wireless sensor networks Logistic regression Accuracy Fault detection Nearest neighbor methods Robustness Decision trees Random forests Testing

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Biomedical Image Security Based on 2D and 3D Chaotic systems 3

Biomedical Image Security Based on 2D and 3D Chaotic Systems

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3rd International Conference on Electronics and Sustainable communication systems, ICESC 2022

作者： Goje, Nitin S. Ravi, Renjith V Das, Debasis Patra, Sudhansu Shekhar Webster University Dept. of Business and Technology Tashkent Uzbekistan M.E.A Engineering College Electronics and Communication Engineering Malappuram India Kiit University School of Computer Applications Bhubaneswar India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Recently, identifying abnormalities via medical images became vital. As these images are transferred across the IoMT network, they require high safety. Unauthorised use of the data contained in these images might have disastrous consequences. There are numerous approaches to protecting images in IoMT. One of the most effective approaches for safeguarding medical images is encryption. Encryption methods rely heavily on the concepts of confusion and dispersion. This research developed effective encryption and decryption algorithms for secure image transmission based on the chaotic maps with two and three dimensions for the IoMT application. The RGB colour system is first converted to the YCbCr colour space via a colour transformation. Then each of these components will be divided into 16 blocks, and a block permutation will be used to alter the position of these blocks. To perform more confusion a pixel permutation technique is further used. As permutation is alone not sufficient to achieve a satisfactory encryption, the contents again undergone diffusion for getting the final ciphertext image. The provided encryption technique discussed in this paper has been verified and studied on certain colour images. The findings demonstrated that the statistical and differential assaults had a substantial impact on the security and validity of the results. Additionally, the ciphered image has a near-optimal information entropy and correlation coefficients close to zero. © 2022 IEEE.

关键词： Cryptography

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Deep Learning-Based Spectrum Sharing for Dynamic Resource Allocation in 6G Cognitive Radio Networks 4

Deep Learning-Based Spectrum Sharing for Dynamic Resource Al...

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4th International Conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Upadhyay, Deepak Upadhyay, Abhay Venu, Nookala Kukreti, Kamlesh Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9798331530402

We conduct an extensive study on deep learning-based spectrum sharing to resolve dynamic resource allocation in 6G cognitive radio networks in this paper. The approach uses modern machine learning models to optimize spectrum usage, increase prediction accuracy of spectrum availability, and reduce interference among the primary and secondary users. We need to evaluate the performance of deep learning algorithms against the traditional spectrum allocation methods in simulation. In the paper, regular quality of service metrics such as throughput distribution and fairness among users are demonstrated to be much improved with these results, where both latency and energy consumption have been cut down dramatically. This work demonstrates the paradigm-shifting role of deep learning in spectrum management for 6G networks, pointing towards intelligent and effective resource allocation mechanisms in tomorrow's communications. © 2025 IEEE.

关键词： Cognitive radio

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Leveraging Artificial Intelligence for Enhanced Data Generation in Addressing Imbalance in Binary Classification system

Leveraging Artificial Intelligence for Enhanced Data Generat...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Srijita Bandopadhyay Srimonti Dutta Imran Haider Bhavaraju Anuraag Jerry Zhu Saad Ahmed Bazaz Dept. of Electronics and Communication Engineering University of Engg. & Management Kolkata India School of Agro and Rural Technology Indian Institute of Technology Guwahati Guwahati India Dept. of Industrial and Systems Engineering Indian Institute of Technology Kharagpur Kharagpur India Dept. of Electronics and Communication Engineering Vellore Institute of Technology Vellore India Faculty of Mathematics University of Waterloo Ontario Canada Dept. of Computer Science National University of Computer & Emerging Sciences Islamabad Pakistan

This paper delves into the challenges of binary classification using imbalanced datasets, particularly when instances of interest are infrequent. It explores a comprehensive approach that integrates Synthetic Minority Over-sampling Technique (SMOTE), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs) to enhance classification outcomes. Traditional classification models tend to favor the majority class, while the impact of imbalanced misclassification costs is often overlooked. The integration of SMOTE, GANs, and VAEs in binary classification, or SMOTE-GAN-VAE, addresses these challenges by generating synthetic instances, refining data representations, and capturing latent features. To evaluate the effectiveness of various data generation methods, a credit card fraud dataset is used. The performance metrics considered include F0.5-score, F1-score, and F2-score, which account for both precision and recall. The results indicate that SMOTE-GAN-VAE outperforms individual methods, such as SMOTE, GANs, and VAEs, demonstrating its potential to enhance data representation and classification accuracy, and outperformed the β- VAE filtered approach employed in previous literature.

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Design of efficient single-etch grating couplers for silicon nitride photonics at 1550 nm

Design of efficient single-etch grating couplers for silicon...

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2024 Photonics North, PN 2024

作者： Fraser, William Benedikovic, Daniel Korcek, Radovan Milanizadeh, Maziyar Bucio, Thalía Domínguez Vitali, Valerio Gardes, Frederic Y. Schmid, Jens H. Cheben, Pavel Ye, Winnie N. Dept. Electronics Carleton University Ottawa Canada Dept. Multimedia and Information-Communication Technologies University of Zilina Zilina Slovakia University Science Park University of Zilina Zilina Slovakia National Research Council Ottawa Canada Optoelectronics Research Centre University of Southampton Highfield Campus Southampton United Kingdom Electrical Computer and Biomedical Engineering Department University of Pavia Pavia27100 Italy

ISBN: (纸本)9798350366365

We present the design of single-etch grating couplers implemented on native and a hybrid silicon nitride (SiN) photonic platform with amorphous silicon (α-Si) overlays, operating in the telecom waveband. The SiN-only grating coupler has a coupling loss of -3.6 dB and a 1-dB bandwidth of 37 nm. A hybrid α-Si-on-SiN platform yields improved coupling loss of -1.4 dB and -1 dB for uniform and subwavelength grating apodized designs, respectively. © 2024 IEEE.

关键词： Silicon nitride

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Real-Time Person Re-Identification to Improve Human-Robot Interaction

Real-Time Person Re-Identification to Improve Human-Robot In...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Carlsen, Per Antoine Taylor, Angelique M. Chan, Darren M. Uddin, Md Zia Riek, Laurel Torresen, Jim Logistics Oda Oslo Norway Cornell University College of Computing and Information Science Cornell Tech New York United States HRL Laboratories LLC MalibuCA United States Sustainable Communication Technologies SINTEF Digital Oslo Norway University of California Dept. of Computer Science and Engineering San Diego United States University of Oslo Dept. of Informatics and RITMO Oslo Norway

ISBN: (纸本)9798350372601

Mobile robots deployed in the homes of disabled people and older adults can play a vital role as companions and aid in activities of daily living, enhancing people's quality of life. For these robots to effectively assist, it can be helpful if they possess social and interactive skills to facilitate natural and personalized interactions. Personalization, including using proper names and remembering preferences, is crucial for building strong social connections and fostering trust in human-robot interaction. However, achieving personalized interactions necessitates the robot's ability to autonomously recognize and re-identify individuals without relying on specific constraints or prior knowledge. This work introduces two lightweight Dual convolutional neural networks, LuNet Light and LuNet Lightest, designed for person re-identification in a robotic context without the limitations of existing systems. We introduce a lightweight architecture, LuNet Lightest which achieves near state-of-the-art performance on the MARS dataset. It is well-suited for real-time applications on low-cost, hardware-constrained robots. This person-identification system enables assistive mobile robots to accurately identify users, fostering trust and facilitating natural, personalized interactions. © 2024 IEEE.

关键词： Convolutional neural networks

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Employment of task scheduling based on water wave optimization in multi robot system 2

Employment of task scheduling based on water wave optimizati...

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： Amer, Dina A. Attiya, Gamal Zeidan, Ibrahim Nasr, Aida A. Zagazig University Faculty of Engineering Computer and System Engineering Dept. Egypt Menoufia University Faculty of Electronic Engineering Computer Science and Engineering Dept. Egypt Tanta University Faculty of Computers and Information Egypt

ISBN: (纸本)9781665418423

The task allocation problem in a multi-robot system (MRS) may be characterized as the problem of assigning a set of tasks to a group of robots in order to improve some performance parameters. However, when many requests are received simultaneously by a robot, the current scheduling approaches may make unreasonable decisions, delaying the execution of some requests and so affecting the system performance. This paper addresses this issue and proposes a new scheduling mechanism for task allocation in MRS. The presented approach implements a universal allocation depend on Water Wave Optimization (WWO) algorithm with the Genetic Algorithm (WWO_GA). The result of this approach is compared with the conventional Water Wave Optimization (WWO) and the Particle Swarm Optimization (PSO) algorithms. Simulation results prove that the introduced approach enhances the performance of the multi-robot system in terms of total traveled distance and computation time. © 2021 IEEE.

关键词： Multipurpose robots

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