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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kulkarni, Raghavendra Suresh Kumar Kondeti, Venkata Satya Pillai, Binu Sudhakaran Surendran, R. T-Mobile Usa Inc Principal Engineer Department of Network Technology BellevueWA United States Senior Systems Engineer Department of Professional Services Axyom. Core North AndoverMA United States Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

This research introduces a novel deep learning for channel estimation for the 6G networks. The channel estimation for the 6G networks is employed using the proposed Improved Convolutional Recurrent (ImConv-RNN) model. In this, the loss function optimization is employed using the proposed ChSO algorithm. The chaotic mapping based on Chebyshev is included in the skill optimization assist the algorithm to enhance the convergence rate. The proposed ImConv-RNN model is evaluated based on RMSE, R2, MAE and MAPE acquired the values of 0.0136, 0.9584, 0.0114, and 5.8969 respectively. © 2025 IEEE.

关键词： 6G networks channel estimation chebyshev mapping recurrent model skill optimization algorithm spatio-temporal feature extraction

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EFNet: estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learning

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PeerJ computer Science 2025年 11卷 e2506-e2506页

作者： Ali, Waqas Alsabban, Wesam Shahbaz, Muhammad Al-Laith, Ali Almogadwy, Bassam Computer Science Department University of Engineering and Technology Lahore Pakistan Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Computer Science Department University of Copenhagen Copenhagen Denmark Department of Artificial Intelligence and Data Science Taibah University Medina Saudi Arabia

The ejection fraction (EF) is a vital metric for assessing cardiovascular function through cardiac ultrasound. Manual evaluation is time-consuming and exhibits high variability among observers. Deep-learning techniques offer precise and autonomous EF predictions, yet these methods often lack explainability. Accurate heart failure prediction using cardiac ultrasound is challenging due to operator dependency and inconsistent video quality, resulting in significant interobserver variability. To address this, we developed a method integrating convolutional neural networks (CNN) and transformer models for direct EF estimation from ultrasound video scans. This article introduces a Residual Transformer Module (RTM) that extends a 3D ResNet-based network to analyze (2D + t) spatiotemporal cardiac ultrasound video scans. The proposed method, EFNet, utilizes cardiac ultrasound video images for end-to-end EF value prediction. Performance evaluation on the EchoNet-Dynamic dataset yielded a mean absolute error (MAE) of 3.7 and an R2 score of 0.82. Experimental results demonstrate that EFNet outperforms state-of-the-art techniques, providing accurate EF predictions. Copyright 2025 Ali et al.

关键词： Convolutional neural networks

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Adaptive Cooperative Quality Weight Spectrum Sensing for Mitigating Byzantine Attacks in Cognitive Radio

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IEEE Access 2025年 13卷 80930-80940页

作者： Srisomboon, Kanabadee Pipanmekaporn, Luepol Prayote, Akara Lee, Wilaiporn Faculty of Engineering King Mongkut’s University of Technology Communication and Computer Network Research Group (C2NRG) Department of Electrical and Computer Engineering North Bangkok Thailand Faculty of Applied Science King Mongkut’s University of Technology Department of Computer and Information Science North Bangkok Thailand

Spectrum sensing data falsification (SSDF) attack, i.e., Byzantine attack, is one of the critical threats of the cooperative spectrum sensing where the Byzantine attackers (BAs) forward incorrect local sensing results to mislead the fusion center on channel availability decisions. By using traditional voting rule, the cooperative spectrum sensing performance deteriorates significantly due to incorrect local sensing results. Then, reliability weight strategy becomes the popular solution to avoid incorrect sensing results from BAs and unreliable cognitive radio users (CRUs). However, it is very difficult to detect the attackers since they also occasionally provide correct sensing results to the fusion center for concealing the attack objective. Based on existing techniques, the BAs and CRUs may be assigned with low reliability weights or distinguished from the data fusion account. However, it is very difficult to detect the attackers since they also occasionally provide correct sensing results to the fusion center for concealing the attack objective. Then, existing techniques still suffer from BAs and negative impact of unreliable CRUs. In this paper, we propose the adaptive cooperative quality weight algorithm for mitigating the Byzantine attack issue by distinguishing the BAs and CRUs from the data fusion account while selecting only useful CRUs since the number of members in the account is also the important factor for cooperative spectrum sensing. In our proposed algorithm, we adopt a stable preference ordering towards ideal solution (SPOTIS) for determining the reliability of SUs which shows low computational complexity as compared to other reliability weight-based techniques. To achieve high sensing performance, our global decision threshold is adapted according to the reliability of reliable users. From the simulation results, our proposed algorithm significantly improves global detection probability and total error probability compared to the traditional votin

关键词： Adaptive Thresholding Byzantine Attack Cooperative Spectrum Sensing CR SPOTIS

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A Systematic Review of the Future of Education in Perspective of Block Chain

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Journal of Mobile Multimedia 2025年第5期19卷 1221-1254页

作者： Shams Tabrez Siddiqui Haneef Khan Md Imran Alam Kamal Upreti Sarika Panwar Sheela Hundekari Department of Computer Science Jazan University Saudi Arabia Department of Computer and Network Engineering Jazan University Saudi Arabia CHIRST (Deemed to be University) Ghaziabad India Department of Electronics and Telecommunication Engineering AISSMS's Institute of Information Technology Pune India MIT ADT University Pune India

Blockchain is one of the most revolutionary technologies in the past decade due to its decentralisation, data integrity, reliability, and security. Blockchain technology is the next popular topic, and it has the potential to significantly alter the educational environment in many ways. Blockchain technology must be used in the education sector despite its challenges. Education is one of the sectors where blockchain-based solutions are still in use. Many academics possess extensive knowledge of the societal benefits blockchain technology might bring. The vast potential of blockchain can only be realised if education expands its knowledge of the technology. The primary goal of this research is to identify current problems related to educational institutions and identify blockchain features that could assist in addressing them. This research article will provide an overview of existing activities and address several perspectives on how blockchain can revolutionize the education sector. In prolongation, this article investigates the categories of blockchain technology applications, especially in the field of education. An in-depth discussion on the benefits and impact that blockchain brings to education is explored. Further, deliberate the abundant challenges of adopting blockchain in education. This study will direct the organizations/institutions to decide which blockchain application will benefit the most based on their requisites. The analysis will also provide information about other educational fields that may benefit from blockchain technology.

关键词： Privacy Electronic learning Federated learning Scalability Education Blockchains Security Reliability Secure storage Systematic literature review

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Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation

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IEEE Signal Processing Letters 2025年 32卷 1885-1889页

作者： Zhang, Dengke Tang, Quan Liu, Fagui Mei, Haiqing Chen, C. L. Philip South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Pengcheng Laboratory Department of New Network Shenzhen518000 China China Tobacco Guangdong Industrial Corporation Guangzhou510385 China Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100080 China

Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks, and directly applying them to Vision Transformers poses certain limitations due to conceptual disparities. To this end, we propose TokenSwap, a data augmentation technique designed explicitly for semi-supervised semantic segmentation with Vision Transformers. TokenSwap aligns well with the global attention mechanism by mixing images at the token level, enhancing the learning capability for contextual information among image patches and the utilization of unlabeled data. We further incorporate image augmentation and feature augmentation to promote the diversity of augmentation. Moreover, to enhance consistency regularization, we propose a dual-branch framework where each branch applies image and feature augmentation to the input image. We conduct extensive experiments across multiple benchmark datasets, including Pascal VOC 2012, Cityscapes, and COCO. Results suggest that the proposed method outperforms state-of-the-art algorithms with notably observed accuracy improvement, especially under limited fine annotations. © 1994-2012 IEEE.

关键词： Semantic Segmentation

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Navigating intent-based networking: from user descriptions to deployable configurations

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Neural Computing and Applications 2025年 1-36页

作者： AlSamarneh, Ala’ A. Al-Hammouri, Ahmad T. Al-Jarrah, Omar Y. Department of Network Engineering and Security Jordan University of Science and Technology Irbid Jordan The Beacom College of Computer and Cyber Sciences Dakota State University Madison USA

network automation development has accompanied network evolution due to its significant role in speeding up and simplifying network operations. Emerging networking and computing paradigms such as information-centric networks, next-generation networks, cloud, and edge computing and recent innovative technologies, such as the Internet of things (IoT), enabled novel network services (such as the Internet of Vehicles (IoV), context-aware applications, virtual reality, and augmented reality) that demand complex configurations and management. Intent-based networking (IBN) is a promising networking paradigm that provides abstract and autonomous network management. IBN promises to simplify configuring networking devices, allowing network engineers and service providers to focus on providing the expected services and continuously verifying that the network operates within the desired status. An IBN process starts by expressing the user requirement in a high-level descriptive format. Then, the IBN system translates these requirements to a low-level deployable format in a process called intent translation. In this work, we formally define the intent translation process and propose a generic intent translation system. Furthermore, we review the research on intent translation published between 2018 and 2022. We analyze and classify the proposed intent translation schemes and discuss the challenges and recent trends in intent translation.

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Ultra-Wideband Mode-Compressed Dipole Antenna with Folded U-Shaped Reflector

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IEEE Transactions on Antennas and Propagation 2025年

作者： Wang, Heesu Park, Yong Bae Park, Ikmo Ajou University Department of Electrical and Computer Engineering 206 Worldcup-ro Youngtong-gu Suwon16499 Korea Republic of Ajou University Department of AI Convergence Network Department of Electrical and Computer Engineering 206 Worldcup-ro Youngtong-gu Suwon16499 Korea Republic of

This paper proposes an ultra-low-profile ultrawideband antenna comprising a mode-compressed dipole and U-shaped reflector. The radiating element of the antenna is a wide-width mode-compressed dipole structure loaded with short stubs and slots, and it is positioned on top of the dielectric substrate. The wide-width dipole compresses the third-order mode of the dipole, while the added short stubs generate a resonant mode at lower frequencies and compress the fifth-order mode of the dipole. The slots added to each wide-width dipole arm generate a resonant mode for the antenna and improve its radiation pattern characteristics. For the impedance matching of the antenna, indirect feeding was applied using a driving element located at the bottom of the dielectric substrate. The proposed antenna has a –10 dB impedance bandwidth range of 0.99–5.15 GHz (135.5%), and its gain varies from 3.5 to 6.4 dBi within the operating bandwidth. The electrical size of the proposed antenna is only 0.257 λL × 0.132 λL × 0.0528 λL © 1963-2012 IEEE.

关键词： Reflection

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An explainable multi-objective hybrid machine learning model for reducing heart failure mortality

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PeerJ computer Science 2025年 11卷 e2682-e2682页

作者： Shamrat, F.M. Javed Mehedi Khalid, Majdi Qadah, Thamir M. Farrash, Majed Alshanbari, Hanan Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia Department of Computer Science and Artificial Intelligence Umm Al-Qura University Makkah Saudi Arabia Department of Computer and Network Engineering Collge of Computing Umm Al-Qura University Makkah Saudi Arabia

As the world grapples with pandemics and increasing stress levels among individuals, heart failure (HF) has emerged as a prominent cause of mortality on a global scale. The most effective approach to improving the chances of individuals’ survival is to diagnose this condition at an early stage. Researchers widely utilize supervised feature selection techniques alongside conventional standalone machine learning (ML) algorithms to achieve the goal. However, these approaches may not consistently demonstrate robust performance when applied to data that they have not encountered before, and struggle to discern intricate patterns within the data. Hence, we present a Multi-objective Stacked Enable Hybrid Model (MO-SEHM), that aims to find out the best feature subsets out of numerous different sets, considering multiple objectives. The Stacked Enable Hybrid Model (SEHM) plays the role of classifier and integrates with a multi-objective feature selection method, the Non-dominated Sorting Genetic Algorithm II (NSGA-II). We employed an HF dataset from the Faisalabad Institute of Cardiology (FIOC) and evaluated six ML models, including SEHM with and without NSGA-II for experimental purposes. The Pareto front (PF) demonstrates that our introduced MO-SEHM surpasses the other models, obtaining 94.87% accuracy with the nine relevant features. Finally, we have applied Local Interpretable Model-agnostic Explanations (LIME) with MO-SEHM to explain the reasons for individual outcomes, which makes our model transparent to the patients and stakeholders. Copyright 2025 Shamrat et al. Distributed under Creative Commons CC-BY 4.0

关键词： Heart

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Optimization of Photovoltaic Power Forecasting: A Comparative Study of Deep Learning Architectures, Optimization Techniques, and Evaluation Metrics

Optimization of Photovoltaic Power Forecasting: A Comparativ...

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Learning and Technology Conference (L&T)

作者： Husein Mauladdawilah Mohammed Balfaqih Zain Balfagih E.J. Gago M.C. Pegalajar School of Civil Engineering University of Granada Granada Spain Department of Computer and Network Engineering University of Jeddah Jeddah Saudi Arabia Computer Science Department Effat University Jeddah Saudi Arabia Department of Computer Science and Artificial Intelligence University of Granada Granada Spain

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

This study presents a systematic evaluation of deep learning architectures for photovoltaic (PV) power forecasting, comparing nine model configurations across three architectures (MLP, LSTM, CNN) and three optimizers (Adam, RMSprop, Adagrad). Using six years of hourly data from a 350kWp grid-connected PV system in Scotland, we demonstrate that architectural choice and optimizer selection significantly impact forecasting accuracy. The LSTM-RMSprop configuration achieved superior performance with RMSE of 2.651 kWh and MAE of 1.197 kWh, showing a 90.29% coefficient of determination (R 2 ). This outperforms both CNN (RMSE: 2.767-2.902 kWh) and MLP architectures (RMSE: 3.104-3.115 kWh) across all optimizers. Our main contributions include: (1) comprehensive optimization of model architectures through hyperparameter evaluation, revealing optimal configurations for each model type; (2) systematic evaluation of optimizer impact, demonstrating RMSprop's superiority for LSTM with improved accuracy across architectures; (3) detailed error analysis showing model stability across different conditions, with NRMSE ranging from 31.15% to 39.07%; and (4) practical insights into computational requirements, where CNN architectures achieve fastest training times (2.3-3.8 minutes/epoch) compared to LSTM (3.9-5.9 minutes/epoch) and MLP (36.54-46.72 minutes/epoch). Results demonstrate that LSTM architectures with appropriate optimization can outperform simpler models in PV power forecasting, providing valuable guidance for practical implementations.

关键词： Photovoltaic systems Training Analytical models Accuracy Computational modeling computer architecture Predictive models Forecasting Optimization Long short term memory

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HIAWSC: An Immune Algorithm Based Heuristic Web Service Composition Framework

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Chinese Journal of Electronics 2025年第3期23卷 579-585页

作者： Jiuyun Xu Reiff-Marganiec Stephan School of Computer and Communication Engineering China University of Petroleum Qingdao China Network and Switch Technology Key Laboratory Beijing University of Posts and Telecommunication Beijing China Department of Computer Science University of Leicester Leicester UK

The introduction of of web services has led to web service composition being a focus of many researchers. Composing web services using workflows is seen as the most realistic method from an industrial view-point. Amongst other method, the use of natural computing methods has been proposed previously to automate web service composition. The need for a fast response when computing the most suitable sequence of services is addressed in this paper. In particular, we propose a novel heuristic immune algorithm with an efficient encoding and mutation method. The algorithm involves two steps: an immune selection operation, which is maintaining antibody population diversity and the clonal selection. The use of a vaccine during the evolution provides heuristic information that accelerates the convergence. Our experimental results illustrate that the proposed heuristic immune algorithm is very effective in improving the convergence speed. We also provide a schema analysis for this method.

关键词： Web services Heuristic algorithms Antibodies Encoding Vaccines Convergence

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