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2024 International Research Conference on Smart Computing and Systems engineering, SCSE 2024

作者： Kavinda, Isuru Fernando, Harinda Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka

ISBN: (纸本)9798350375688

Illegible handwriting on medical prescriptions poses a significant challenge, often leading to the misinterpretation of drug names and dosages. This issue primarily stems from doctors' use of Latin abbreviations, driven by time constraints during patient consultations. The resultant discrepancies in prescription interpretation can potentially expose patients to incorrect medications and dosages, leading to adverse effects. In response to this concern, this study proposes a novel system integrating Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) models. Promising findings are reported in this study, achieving a loss of 0.4874 and an accuracy of 0.83 in the latest trial. The final model offers a potential solution to the persistent problem of illegible prescriptions, enhancing patient safety in the healthcare domain. © 2024 IEEE.

关键词： Long short-term memory

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Deep Reinforcement Learning for Delay-Optimized Task Offloading in Vehicular Fog Computing 29

Deep Reinforcement Learning for Delay-Optimized Task Offload...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Toopchinezhad, Mohammad Parsa Ahmadi, Mahmood Computer Engineering and Information Technology Department Razi University Kermanshah Iran

ISBN: (纸本)9798331523114

The imminent rise of Autonomous Vehicles (AVs) is revolutionizing the future of transport. The Vehicular Fog Computing (VFC) paradigm has emerged to alleviate the load of compute-intensive and delay-sensitive AV programs via task offloading to nearby vehicles. Effective VFC requires an intelligent and dynamic offloading algorithm. As a result, this paper adapts Deep Reinforcement Learning (DRL) for VFC offloading. First, a simulation environment utilizing realistic hardware and task specifications, in addition to a novel vehicular movement model based on grid-planned cities, is created. Afterward, a DRL-based algorithm is trained and tested on the environment with the goal of minimizing global task delay. The DRL model displays impressive results, outperforming other greedy and conventional methods. The findings further demonstrate the effectiveness of the DRL model in minimizing queue congestion, especially when compared to traditional cloud computing methods that struggle to handle the demands of a large fleet of vehicles. This is corroborated by queuing theory, highlighting the self-scalability of the VFC-based DRL approach. © 2025 IEEE.

关键词： Deep reinforcement learning

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An Improved Encoder-Decoder CNN with Region-Based Filtering for Vibrant Colorization

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computer Systems Science & engineering 2023年第7期46卷 1059-1077页

作者： Mrityunjoy Gain Md Arifur Rahman Rameswar Debnath MrimMAlnfiai Abdullah Sheikh Mehedi Masud Anupam Kumar Bairagi Computer Science and Engineering Discipline Khulna UniversityKhulna 9208Bangladesh Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif 21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif 21944Saudi Arabia

Colorization is the practice of adding appropriate chromatic values to monochrome photographs or videos.A real-valued luminance image can be mapped to a three-dimensional color ***,it is a severely ill-defined problem and not has a single *** this paper,an encoder-decoder Convolutional Neural Network(CNN)model is used for colorizing gray images where the encoder is a Densely Connected Convolutional Network(DenseNet)and the decoder is a conventional *** DenseNet extracts image features from gray images and the conventional CNN outputs a^(*)b^(*)color *** to a large number of desaturated color components compared to saturated color components in the training images,the saturated color components have a strong tendency towards desaturated color components in the predicted a^(*)b^(*)*** solve the problems,we rebalance the predicted a^(*)b^(*)color channel by smoothing every subregion individually using the average filter.2 stage k-means clustering technique is applied to divide the *** we apply Gamma transformation in the entire a^(*)b^(*)channel to saturate the *** compare our proposed method with several existing *** the experimental results,we see that our proposed method has made some notable improvements over the existing methods and color representation of gray-scale images by our proposed method is more plausible to ***,our suggested approach beats other approaches in terms of Peak Signal-to-Noise Ratio(PSNR),Structural Similarity Index Measure(SSIM)and Histogram.

关键词： Colorization DenseNet desaturation k-means

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Heuristic-based Multicasting Utilizing Network Coding over a Multi-Hop Wireless Mesh Network 18

Heuristic-based Multicasting Utilizing Network Coding over a...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Sharma, Neha Rathore, Prateek Shukla, Mahendra K. Pandey, Om Jee Indian Institute of Technology BHU Department of Electronics Engineering Varanasi India The LNM Institute of Information Technology Department of Communication and Computer Engineering Jaipur India ABV-Indian Institute of Information Technology Department of Information Technology Gwalior India

ISBN: (纸本)9798350391725

Multicasting over a multi-hop wireless mesh network is a challenging issue that recently received less attention. Various applications, such as distance learning, telemedicine, web radio, and online conferencing, require the same information to be sent to multiple recipients. In these applications, the destination nodes are often far from the source, necessitating the design of a multi-hop multicasting protocol. This paper proposes an efficient multi-hop multicasting approach for transmitting packets from the source to all intended destinations. By combining network coding with opportunistic routing, the proposed method significantly reduces the expected number of transmissions a packet must undergo in a lossy wireless network. Specifically, this work introduces a Heuristic-Based Multicasting (HBM) scheme that integrates network coding with opportunistic routing. First, it calculates the minimum route cost from the source node to all the destinations. Then, a novel packet transmission strategy is employed, where packets are combined using network coding and transmitted via a path derived from the Network Coding Assisted Multicasting (NCAM) route cost calculation. Extensive simulations demonstrate the effectiveness of the NCAM route cost calculation and the corresponding packet transmission strategy. The results show that the proposed HBM scheme significantly reduces the expected number of packet transmissions in the network, thereby increasing throughput compared to existing multicasting schemes such as MORE, Pacifier, and MLCAR. © 2024 IEEE.

关键词： Wireless mesh networks (WMN)

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An Investigation on Open-RAN Specifications:Use Cases,Security Threats,Requirements,Discussions

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computer Modeling in engineering & Sciences 2024年第10期141卷 13-41页

作者： Heejae Park Tri-HaiNguyen Laihyuk Park Department of Computer Science and Engineering Seoul National University of Science and TechnologySeoul01811Republic of Korea Faculty of Information Technology School of TechnologyVan Lang UniversityHo Chi Minh City70000Vietnam

The emergence of various technologies such as terahertz communications,Reconfigurable Intelligent Surfaces(RIS),and AI-powered communication services will burden network operators with rising infrastructure ***,the Open Radio Access Network(O-RAN)has been introduced as a solution for growing financial and operational burdens in Beyond 5G(B5G)and 6G networks.O-RAN promotes openness and intelligence to overcome the limitations of traditional *** disaggregating conventional Base Band Units(BBUs)into O-RAN Distributed Units(O-DU)and O-RAN Centralized Units(O-CU),O-RAN offers greater flexibility for upgrades and network ***,this openness introduces new security challenges compared to traditional *** existing studies overlook these security requirements of the O-RAN *** gain deeper insights into the O-RAN system and security,this paper first provides an overview of the general O-RAN architecture and its diverse use cases relevant to B5G and 6G *** then delve into specifications of O-RAN security threats and requirements,aiming to mitigate security vulnerabilities *** providing a comprehensive understanding of O-RAN architecture,use cases,and security considerations,thisworkserves as a valuable resource for future research in O-RAN and its security.

关键词： O-RAN architecture use cases security issues security requirements security discussions

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Fusion of Multimodal Audio Data for Enhanced Speaker Identification Using Kolmogorov-Arnold Networks

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IEEE Access 2025年 13卷 87640-87653页

作者： Tamotia, Aryaman Karmokar, Dhruv Shantu Komal, Rethi Nawas, K. Khadar Shahina, A. Khan, A. Nayeemulla Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India Sri Sivasubramaniya Nadar College of Engineering Department of Information Technology Tamil Nadu Kalavakkam603110 India

Speaker identification using audio data is quite challenging because of inherent differences between people, ambient noise, and variable recording conditions. Although the classical deep learning methods are effective, they have rather high computational cost, which leads to usually cumbersome parameter tuning processes and hence reduce their applicability to real-world deployments. In this paper, we discuss an efficient version of Kolmogorov-Arnold Network for audio-based speaker identification using three different types of audio inputs: bone conduction, air conduction, and throat microphone recordings, alongside evaluation on the publicly available LibriSpeech dataset to enhance reproducibility. To optimize the performance of these modalities, we investigate data-level, feature-level, and decision-level fusion techniques. An exhaustive comparison is performed against some of the prominent deep learning architectures, namely VGG-19, ResNet-152, EfficientNetV2, and DenseNet-121, both as standalone architectures and within multimodal combinations for the purpose of evaluating the proposed framework. We demonstrate that our model achieves a significant increase in accuracy up to 99.375% through the use of MFCC and GFCC feature inputs with data-level fusion through MFCC features and achieves this level of accuracy using substantially fewer parameters than those of conventional deep learning models. This makes the KAN framework a highly efficient choice for audio-based speaker identification. One of the critical innovations within this design is a class of trainable activation functions that eliminate the need for traditional weight parameters, thereby providing scalable and computationally efficient solutions. This work contributes to the field of biometrics by providing a refined, resource-efficient approach to speaker identification based on multimodal audio data. © 2025 IEEE.

关键词： Deep learning

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Inductive Lottery Ticket Learning for Graph Neural Networks

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Journal of computer Science & technology 2024年第6期39卷 1223-1237页

作者： Yong-Duo Sui Xiang Wang Tianlong Chen Meng Wang Xiang-Nan He Tat-Seng Chua School of Data Science University of Science and Technology of ChinaHefei 230027China Department of Electrical and Computer Engineering The University of Texas at AustinAustin 78712U.S.A School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Computing National University of SingaporeSingapore

Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplored. The recent work Unified GNN Sparsification (UGS) studies lottery ticket learning for GNNs, aiming to find a subset of model parameters and graph structures that can best maintain the GNN performance. However, it is tailed for the transductive setting, failing to generalize to unseen graphs, which are common in inductive tasks like graph classification. In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity. To prune the input graphs, we design a predictive model to generate importance scores for each edge based on the input. To prune the model parameters, it views the weight’s magnitude as their importance scores. Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their importance scores. Although it might be strikingly simple, ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings. On 10 graph-classification and two node-classification benchmarks, ICPG achieves the same performance level with 14.26%–43.12% sparsity for graphs and 48.80%–91.41% sparsity for the GNN model.

关键词： lottery ticket hypothesis graph neural networks neural network pruning

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Answer Code Validation Program with Test Data Generation for Code Writing Problem in Java Programming Learning Assistant System

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engineering Letters 2024年第5期32卷 981-994页

作者： Wai, Khaing Hsu Funabiki, Nobuo Aung, Soe Thandar Lu, Xiqin Jing, Yanhui Kyaw, Htoo Htoo Sandi Kao, Wen-Chung Department of Information and Communication Systems Okayama University Okayama Japan Department of Computer and Information Science Tokyo University of Agriculture and Technology Tokyo Japan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

In order to support the learning of novice students in Java programming, the web-based Java Programming Learning Assistant System (JPLAS) has been developed. JPLAS offers several types of exercise problems to foster code reading and writing skills at different levels. In JPLAS, the code writing problem (CWP) asks a student to write a source code that will pass the test code given in the assignment where the correctness is verified by running them on JUnit. In this paper, to reduce the teacher’s workload during the marking process, we present the answer code validation program that verifies all the source codes from a large number of students for each assignment and reports the number of passing tests for each source code in the CSV file. Besides, to test a source code with various input data, we implement the test data generation algorithm that identifies the data type, generates new data, and replaces it for each test data in the test code. Furthermore, to verify the correctness of the implemented procedures in the source code, we introduce the intermediate state testing in the test code. For evaluations, we applied the proposal to source codes and test codes in a Java programming course in Okayama university, Japan, and confirmed the validity and effectiveness. © 2024, International Association of Engineers. All rights reserved.

关键词： Students

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A multimodal sentiment analysis system for recognizing person aggressiveness in pain based on textual and visual information

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4489-4501页

作者： Ghosh, Anay Dhara, Bibhas Chandra Pero, Chiara Umer, Saiyed Department of Computer Science and Engineering University of Engineering and Management Newtown Kolkata India Department of Information Technology Jadavpur University Kolkata India Department of Computer Science University of Salerno Fisciano Italy Department of Computer Science and Engineering Aliah University Kolkata India

This article proposes a multimodal sentiment analysis system for recognizing a person’s aggressiveness in pain. The implementation has been divided into five components. The first three steps are related to a text-based sentiment analysis system to perform classification tasks such as predicting the classes into non-aggressive, covertly aggressive, and overtly aggressive classes. The remaining two components are related to an image-based sentiment analysis system. A deep learning-based approach has been employed to do feature learning and predict the three types of pain classes. An aggression dataset for the text-based system and the UNBC-McMaster database for an image-based system has been employed, respectively. Experimental results have been compared with the state-of-the-art methods, showing the superiority of the proposed approach. Finally, the scores due to text-based and image-based sentiment analysis systems are fused to obtain the performance for the proposed multimodal sentiment analysis system. © 2023, The Author(s).

关键词： Sentiment analysis

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Socially Aware V2X Localized QoS

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IEEE Internet of Things Journal 2024年第15期11卷 25925-25938页

作者： Kaliski, Rafael Han, Yue-Hua National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei115 Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan

Vehicle to Everything (V2X) is a core 5G technology. V2X and its enabler, Device-to-Device (D2D), are essential for the Internet of Things (IoT) and the Internet of Vehicles (IoV). V2X enables vehicles to communicate with other vehicles (V2V), networks (V2N), and infrastructure (V2I). While V2X enables ubiquitous vehicular connectivity, the impact of bursty data on the network's overall Quality of Service (QoS), such as when a vehicle accident occurs, is often ignored. In this work, we study both 4G and 5G V2X utilizing Evolved Universal Terrestrial Radio Access New Radio (E-UTRA-NR) and propose the use of socially aware 5G NR dual connectivity (en-DC) for traffic differentiation. We also propose localized QoS, wherein high-priority QoS flows traverse 5G road side units (RSUs) and normal-priority QoS flows traverse 4G base station (BS). We formulate a max-min fair QoS-aware nonorthogonal multiple access (NOMA) resource allocation scheme, QoS reclassify. QoS reclassify enables localized QoS and traffic steering to mitigate bursty network traffic's impact on the network's overall QoS. We then solve QoS reclassify via integer linear programming (ILP) and derive its approximation. We demonstrate that both optimal and approximation QoS reclassify resource allocation schemes in our socially aware QoS management methodology outperform socially unaware legacy 4G V2X algorithms (no localized QoS support, no traffic steering) and socially aware 5G V2X (no localized QoS support, yet utilizes traffic steering). Our proposed QoS reclassify scheme's QoS flow end-to-end latency requires only approx 15% of the time legacy 4G V2X requires. © 2014 IEEE.

关键词： Quality of service

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