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Automatic Modulation Classification in Wireless Communications Using Multi-Aspect Graph Attention Network with Avg-TopK Pooling and Osprey Optimization 4

Automatic Modulation Classification in Wireless Communicatio...

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

作者： Mohandas, R. Elangovan, Bharath Sivasubramanian, Soorya Sridhar, S. Dhanadhya, Trupti Chennai Institute of Technology Department of Electronics & Communication Engineering Tamil Nadu Chennai India C K College of Engineering & Technology Department of Artificial Intelligence & Data Science Tamil Nadu Cuddalore India V.R.S College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Arasur India Rajalakshmi Institute of Technology [Autonomous Department of Research Tamil Nadu Chennai India Dr. D Y Patil Institute of Technology Department of Electrical Engineering Maharashtra Pune India

ISBN: (纸本)9798331529635

AMC stands for automatic modulation classification, a significant tool in wireless communication for identification of the signal modulations of immense relevance to dynamic spectrum management, interference identification and cognitive radio networks. Many of the previous AMC approaches include feature extraction and manual classification which are not particularly portable and accurate in other contexts of communications. Recently, with the development of deep leaning, attention based methods have brought obviously enhanced performance in perceiving comprehensive signal characteristics;but there are still problems in focusing to multi-aspect features and training at a large scale signal data with high computational complexity. In order to address this problem, this research introduces a Avg-TopK pooling layer along with a Multi-Aspect Graph Attention Network (MAGAN) for the purpose of the AMC while using the Osprey Optimization Algorithm for the optimization of the proposed model. The Avg-TopK pooling only keeps K most important feature representations and decreases noise levels for better signal modulation classification. MAGAN uses multi-aspect attention mechanism allowing the emphasize various properties of the signal while perceiving mutual connections between the features of the signal. Additionally, the proposed Osprey Optimization Algorithm improves the training time of the model through optimization of hyperparameters. The presented results obtained from experimental analysis of standard AMC performance metrics show that the proposed model provides better AMC classification capability and insensitivity to the noise and existence of multiple types of modulation modes in contrast to other AMC models. In light of this, this work aims at solving some of the major challenges facing AMC through presenting a highly adaptive, efficient and accurate solution for the ever evolving wireless communication systems. The introduced approach attains higher accuracy as 99%

关键词： Radio interference

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Enhancing the Performance of E-Mode AlGaN/GaN HEMTs With Recessed Gates Through Low-Damage Neutral Beam Etching and Post-Metallization Annealing

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 150-155页

作者： Chen, Yi-Ho Ohori, Daisuke Aslam, Muhammad Lee, Yao-Jen Li, Yiming Samukawa, Seiji National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Graduate Degree Program of College of Electrical and Computer Engineering Hsinchu300093 Taiwan Tohoku University Institute of Fluid Science Sendai980-8577 Japan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Pioneer Semiconductor Innovation Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Hsinchu300093 Taiwan

This study investigated the electrical properties of AlGaN/GaN high-electron-mobility transistors (HEMTs) with varied recess depths under the gate electrode. We demonstrated a recess depth of approximately 6 nm, which was achieved through neutral beam etching (NBE) technique with a low etch rate of 1.8 nm/min, resulting in device enhancement-mode (E-mode) behavior with threshold voltage (Vth) of 0.49 V. The effects of post-metallization annealing (PMA) on the device performance were also examined. The results revealed that PMA treatment improves the DC characteristics of the devices, including maximum drain current (IDMAX), transconductance (gm), subthreshold swing (SS), on-off ratio, and off-state leakage current, with maximum enhancement percentage of 18.3% for IDMAX, 3758% for on-off ratio, and 54.3% for SS. Moreover, this study compared the recess depths of metal-insulator-semiconductor high-electron-mobility transistors (MIS-HEMTs) with the SiN dielectric layer. The results showed that MIS-HEMTs exhibit more negative Vth values, which can be attributed to the controlled surface states achieved through passivation. © 2020 IEEE.

关键词： Etching

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An ideal deeply contextual prediction model for an effective prediction and mitigation of cyberbullying in social networks

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1561-1579页

作者： Prathaban, Banu Priya Vaishnavi, T. Ramesh, Addanki Purna Sujaritha, M. Rama, R. Senthil Gracewell, J. Jeffin Department of Networking and Communications School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai 603203 India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Tamil Nadu Chennai 600062 India Department of Electronics and Communication Engineering DNR College of Engineering and Technology West Godavari Andhra Pradesh Bhimavaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Kuniyamuthur 641008 India Department of Electrical and Electronics Engineering DMI College of Engineering Tamil Nadu 600123 Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai 602105 India

Social media have evoked a host of problems, especially Twitter and Instagram, considering misconceived notions of free speech. Among the concerns that have been seen as crucial is the impact of cyberbullying;it is a global issue affecting an individual and society in general. Though various methods have been suggested for deterring cyberbullying, most of these depend on victim interaction and are hence ineffective in the task of independently identifying and preventing cyberbullying. Within this context, the research introduces a new, innovative framework that is ideal and deeply contextual in terms of prediction, thus creating an approach to detecting cyberbullying on social networks without directly engaging with any victim. Consequently, the key contribution of this work is developing the Embedded Residual Recurrent Convolution Network, a hybrid deep learning architecture for the detection of cyberbullying data with more precision and accuracy. Further, this is optimized by a Tenacious Projectile Optimizer for minimizing computational time and improving classifier performance. In this regard, the novelty of the proposed I-DCP framework is established in its superior performances on different social media datasets, with an average accuracy of over 99% and an execution time as low as 13 s, hence highly efficient for real-time detection of cyberbullying. These represent important advances compared to previous methodologies and constitute a strong and scalable response in the fight against cyberbullying. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： And classification Artificial intelligence (AI) Cyberbullying detection Deep learning Instagram Optimization Social network Twitter

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Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

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computer Modeling in engineering & sciences 2024年第6期139卷 3353-3385页

作者： Murugan Palanikumar Nasreen Kausar Dragan Pamucar Seifedine Kadry Chomyong Kim Yunyoung Nam Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Mathematics Faculty of Arts and ScienceYildiz Technical UniversityEsenlerIstanbul34220Turkey Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of BelgradeBelgrade11000Serbia College of Engineering Yuan Ze UniversityTaoyuan City320315TaiwanChina Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos1102-2801Lebanon ICT Convergence Research Center Soonchunhyang UniversityAsan31538South Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538South Korea

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this *** is provided of the scoring function,accuracy function and operational laws of the log q-rung *** algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming ***,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify *** chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile *** numbers q are used to express the results of the *** demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

关键词： Vague set aggregating operators euclidean distance hamming distance decision making

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Robustness of Explainable AI Algorithms for Disease Biomarker Discovery from Functional Connectivity datasets

Robustness of Explainable AI Algorithms for Disease Biomarke...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Deepank Girish Yi Hao Chan Sukrit Gupta Jing Xia Jagath C. Rajapakse College of Computing and Data Science Nanyang Technological University Singapore Department of Computer Science and Engineering Indian Institute of Technology Ropar India

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Machine learning (ML) models have been used in functional neuroimaging for wide-ranging tasks, ranging from disease diagnosis to disease prognosis. There have been successive functional connectivity-based ML studies focused on improving model performances for disease detection. An increasing number of such studies use their trained models to detect and evaluate salient features that could be potential biomarkers of these neuro-logical conditions. The evaluation of these salient features is often qualitative and limited to cross-referencing existing literature for similar findings. In this study, we present objective quantitative metrics to evaluate the robustness of these salient features. Building upon existing generic evaluation metrics, we propose metrics that capture topological properties known to be characteristic of brain functional connectomes. Using existing and newly proposed measures on a set of baselines and state-of-the-art graph neural networks (GNN) models, we found that when GNNExplainer is used with models that incorporate attention, the scores produced are relatively more robust than other combinations. On datasets of patients with Autism Spectrum Disorder (ASD) or Attention-deficit Hyperactivity Disorder (ADHD), our proposed metrics highlighted that salient features identified in both disorders are highly involved in functional specialization, while salient ASD features expressed stronger functional integration than ADHD. We package these existing and novel metrics together in the RE-CONFIRM framework that holds promise to set the foundations for the quantitative evaluation of salient features detected by future studies.

关键词： Biological system modeling Biomarkers Feature extraction Brain modeling Functional neuroimaging Graph neural networks Robustness Medical diagnosis Prognostics and health management Diseases

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Iot Based Condition Monitoring of electrical Equipment 2

Iot Based Condition Monitoring of Electrical Equipment

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Raja, J. Kalyani, N. Singh, Mukul Kumar Garg, Akriti Sarojwal, Atul Devi, S. Rukmani Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering School of Computing Tamil Nadu Chennai600062 India School of Computing Sastra Deemed University Tamil Nadu Thirumalaisamudram613401 India Mahatma Jyotiba Phule Rohilkhand University Department of Electrical Engineering Uttar Pradesh Bareilly243006 India Simats Deemed to be University Department of Computer Science Saveetha College of Liberal Arts and Sciences Tamil Nadu Chennai600124 India

ISBN: (纸本)9798350364699

In recent years, the surveillance of the health of electrical appliances has been transformed by the Internet of Things (IoT). This investigation offers a comprehensive account of a system that employs the Internet of Things (IoT) to conduct predictive maintenance and analysis of electrical equipment. The network of interconnected sensors in the proposed system enables the real-time monitoring of a variety of operating characteristics, including temperature, current, voltage, and vibration. Upon the identification of equipment health indicators, the data collected by these sensors is transmitted to a centralised cloud platform. Sophisticated analytics and machine learning algorithms analyse the data on this platform to detect any patterns. The system's predictive warnings and real-time insights facilitate the implementation of preventive maintenance, reduce downtime, and enhance the reliability and efficacy of electrical equipment. The article explores the benefits of utilising Internet of Things (IoT) condition monitoring in industrial and utility settings. It also addresses pertinent topics, including system architecture, data processing methodologies, and other pertinent topics. © 2024 IEEE.

关键词： Predictive maintenance

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Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model

arXiv

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arXiv 2024年

作者： Sartipi, Shadi Cetin, Mujdat Department of Electrical and Computer Engineering University of Rochester Rochester United States Department of Electrical and Computer Engineering Goergen Institute for Data Science University of Rochester Rochester United States

Automated emotion recognition using electroencephalogram (EEG) signals has gained substantial attention. Although deep learning approaches exhibit strong performance, they often suffer from vulnerabilities to various perturbations, like environmental noise and adversarial attacks. In this paper, we propose an Inception feature generator and two-sided perturbation (INC-TSP) approach to enhance emotion recognition in brain-computer interfaces. INC-TSP integrates the Inception module for EEG data analysis and employs two-sided perturbation (TSP) as a defensive mechanism against input perturbations. TSP introduces worst-case perturbations to the model's weights and inputs, reinforcing the model's elasticity against adversarial attacks. The proposed approach addresses the challenge of maintaining accurate emotion recognition in the presence of input uncertainties. We validate INC-TSP in a subject-independent three-class emotion recognition scenario, demonstrating robust performance. © 2024, CC BY-NC-SA.

关键词： Emotion Recognition

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Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks

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IEEE Internet of Things Journal 2025年

作者： Yu, Hanzhi Liu, Yuchen Yang, Zhaohui Sun, Haijian Chen, Mingzhe University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Georgia School of Electrical and Computer Engineering AthensGA30602 United States

In this paper, we investigate an accurate synchronization between a physical network and its digital network twin (DNT), which serves as a virtual representation of the physical network. The considered network includes a set of base stations (BSs) that must allocate its limited spectrum resources to serve a set of users while also transmitting its partially observed physical network information to a cloud server to generate the DNT. Since the DNT can predict the physical network status based on its historical status, the BSs may not need to send their physical network information at each time slot, allowing them to conserve spectrum resources to serve the users. However, if the DNT does not receive the physical network information of the BSs over a large time period, the DNT's accuracy in representing the physical network may degrade. To this end, each BS must decide when to send the physical network information to the cloud server to update the DNT, while also determining the spectrum resource allocation policy for both DNT synchronization and serving the users. We formulate this resource allocation task as an optimization problem, aiming to maximize the total data rate of all users while minimizing the asynchronization between the physical network and the DNT. The formulated problem is challenging to solve by traditional optimization methods, as each BS can only observe a partial physical network, making it difficult to find an optimal spectrum allocation strategy for the entire network. To address this problem, we propose a method based on the gated recurrent units (GRUs) and the value decomposition network (VDN). The GRU component allows the DNT to predict future status using the historical data, effectively updating itself when the BSs do not transmit the physical network information. The VDN algorithm enables each BS to learn the relationship between its local observation and the team reward of all BSs, allowing it to collaborate with others in determining whe

关键词： Resource allocation

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Evaluation of Adaptive Synthetic Resampling Technique for Imbalanced Breast Cancer Identification

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Procedia computer science 2024年 235卷 1000-1007页

作者： Tsehay Admassu Assegie Ayodeji Olalekan Salau Kanimozhi Sampath Rajkumar Govindarajan Sangeetha Murugan B. Lakshmi Department of Computer Science College of Engineering and Technology Injibara University Injibara Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Department of Computer Science & Engineering (Cyber Security) Madanapalle Institute of Technology and Science Madanapalle Andhra Pradesh India Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology and Science Madanapalle Andhra Pradesh India Department of Computer Science & Engineering Madanapalle Institute of Technology and Science Madanapalle Andhra Pradesh India Department of Computer Science & Technology Madanapalle Institute of Technology and Science Madanapalle Andhra Pradesh India

As one of the most common types of cancer among women, breast cancer is a serious health concern worldwide. Early detection is crucial for successful treatment and improved survival rates. However, detecting breast cancer is challenging due to imbalanced classification, where the minority class (cancerous) is ominously smaller than the majority class (non-cancerous). In this paper, we explore the use of logistic regression (LR) and the adaptive synthetic resampling (ADASYN) technique to address imbalanced classification in breast cancer detection. To that end, we collected the Wisconsin Breast Cancer dataset, which contains 569 instances. The dataset is imbalanced, with 212 malignant (cancerous) cases and 357 benign (non-cancerous) cases. Then, we trained support vector machine, LR, K-nearest neighbor, gradient, and adaptive boosting on the imbalanced dataset. Finally, we trained these algorithms on resampled data with the ADASYN oversampling and we evaluated their performance using cross-validation score with 5-folds. The results of the experiment showed that using ADASYN with LR significantly improved the performance the LR model. The LR model achieves 99.46% accuracy on breast cancer diagnosis. Moreover, the confusion matrix shows that among the 188 samples, the model misclassified one cancerous instance. Thus, we concluded that the proposed model is effective for breast cancer diagnosis.

关键词： Breast cancer detection imbalanced classification machine learning Adaptive synthetic oversampling

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FedCal: achieving local and global calibration in federated learning via aggregated parameterized scaler 24

FedCal: achieving local and global calibration in federated ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Hongyi Peng Han Yu Xiaoli Tang Xiaoxiao Li College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Vancouver BC Canada and Vector Institute Canada

Federated learning (FL) enables collaborative machine learning across distributed data owners. However, this approach poses a significant challenge for model calibration due to data heterogeneity. While prior work focused on improving accuracy for non-iid data, calibration remains under-explored. This study reveals existing FL aggregation approaches lead to sub-optimal calibration, and theoretical analysis shows despite constraining variance in clients' label distributions, global calibration error is still asymptotically lower bounded. To address this, we propose a novel Federated Calibration (FedCal) approach, emphasizing both local and global calibration. It leverages client-specific scalers for local calibration to effectively correct output misalignment without sacrificing prediction accuracy. These scalers are then aggregated via weight averaging to generate a global scaler, minimizing the global calibration error. Extensive experiments demonstrate that FedCal significantly outperforms the best-performing baseline, reducing global calibration error by 47.66% on average.

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