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11th International Symposium on Telecommunication, IST 2024

作者： Fallahreyhani, Maedeh Azmi, Paeiz Mokari, Nader Tarbiat Modares University Department of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350356250

In the evolving landscape of telecommunication systems, the emergence of semantic communication networks presents both opportunities and challenges, particularly in the face of physical adversarial attacks. This article explores innovative strategies to enhance the resilience of semantic communication systems against such attacks, leveraging advanced machine learning techniques and robust data encoding methods. Inspired by the behavior of natural noises, we implement a model and integrate it into our semantic network, and to improve classification accuracy, we transfer our analysis to the text. By analyzing existing vulnerabilities and proposing novel countermeasures, we aim to provide a comprehensive framework that not only improves the security of semantic communications but also enhances their efficiency and reliability. Our findings reveal that integrating adversarial training and semantic-aware encoding can significantly mitigate the impact of physical attacks, thereby paving the way for more secure communication infrastructures. © 2024 IEEE.

关键词： Cyber attacks

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Securing Electronic Health Records from Insider Threats in Smart City Healthcare Cloud Using Machine Learning Approach 5

Securing Electronic Health Records from Insider Threats in S...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Ramasami, S. Uma Maheswari, P. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam Erode India Anna University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350385649

In recent years, healthcare in smart cities is considered as significant to create a more resilient and well-informed healthcare ecosystem. The integration of cloud computing in healthcare industry has facilitated the storage and sharing of electronic health records (EHRs), enabling seamless access to medical information. However, this transition to cloud-based systems has introduced new security challenges, particularly the risk of insider threats. Insiders with authorized access to cloud-based EHR systems, such as healthcare professionals and cloud service providers, may exploit vulnerabilities or misuse sensitive patient data, leading to privacy breaches and unauthorized disclosure. This research work presents an innovative approach to securing EHRs from insider threats in the cloud using Gaussian Mixture Model and Classification algorithms. These techniques analyze user behaviour patterns and detect anomalous activities that may indicate insider threats. Here UK hospital electronic patient health record data set is used to conduct the experiments. The proposed approach comprises two key stages. In the initial stage, the unsupervised Gaussian Mixture Model is utilized to find the abnormal patterns in the dataset and label each record in the dataset as normal or anomaly. The second stage involves different supervised classification algorithms namely SVM, KNN, DT, NB and RF are used to classify the new instance. The results demonstrate that the Random Forest (RF) achieves high accuracy in detecting insider threats with an accuracy of 99.97%. The findings of this research contribute to the field of healthcare data security by offering an intelligent and proactive solution for mitigating insider threats to cloud-based EHRs in Smart city environments. © 2024 IEEE.

关键词： Support vector machines

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Deep Reinforcement Learning for Trajectory and Phase Shift Optimization of Aerial RIS in CoMP-NOMA Networks

Deep Reinforcement Learning for Trajectory and Phase Shift O...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Umer, Muhammad Mohsin, Muhammad Ahmed Mahmood, Aamir Dev, Kapal Jung, Haejoon Gidlund, Mikael Hassan, Syed Ali Pakistan Mid Sweden University Department of Computer and Electrical Engineering Sweden Munster Technological University Department of Computer Science Ireland Kyung Hee University Department of Electronics and Information Convergence Engineering Korea Republic of

ISBN: (纸本)9798350351255

This paper explores the potential of aerial reconfigurable intelligent surfaces (ARIS) to enhance coordinated multipoint non-orthogonal multiple access (CoMP-NOMA) networks. We consider a system model where a UAV-mounted RIS assists in serving multiple users through NOMA while coordinating with multiple base stations. The optimization of UAV trajectory, RIS phase shifts, and NOMA power control constitutes a complex problem due to the hybrid nature of the parameters, involving both continuous and discrete values. To tackle this challenge, we propose a novel framework utilizing the multi-output proximal policy optimization (MO-PPO) algorithm. MO-PPO effectively handles the diverse nature of these optimization parameters, and through extensive simulations, we demonstrate its effectiveness in achieving near-optimal performance and adapting to dynamic environments. Our findings highlight the benefits of integrating ARIS in CoMP-NOMA networks for improved spectral efficiency and coverage in future wireless networks. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Pole-Placement and Different PID Controller Structures Comparative Analysis for a DC Motor Optimal Performance 21

Pole-Placement and Different PID Controller Structures Compa...

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21st International Learning and Technology Conference, L and T 2024

作者： Salem, Nema Ali, Mirna Effat University Electrical and Computer Engineering Department Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

The pole-placement method is a popular technique used in control system design to assign desired closed loop system poles. By strategically placing these poles, the system's dynamic response can be tailored to meet specific performance requirements. This study focuses on the design and simulation of the pole-placement method and various structures of Proportional-Integral-Derivative PID controllers to determine the best-performing controller for a modeled DC motor. The study explores different PID controller structures, including parallel, series, (PI - D), and (I - PD). In addition, it employs a comprehensive analysis by utilizing a range of performance metrics such as settling time, overshoot, and rise time. The pole placement utilizes the state space technique to assign the desired closed loop poles while the controllers are tuned using Ziegler-Nichols, to achieve optimal performance. The results shows that the (I - PD) controller is the optimal controller for this application, with a rise time of 0.507 seconds, a settling time of 2.3 seconds, and an 0.51% overshoot. © 2024 IEEE.

关键词： DC motors

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Fostering Human-AI interaction: development of a Clinical Decision Support System enhanced by eXplainable AI and Natural Language Processing 2

Fostering Human-AI interaction: development of a Clinical De...

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Joint of the 2nd World Conference on eXplainable Artificial Intelligence Late-Breaking Work, Demos and Doctoral Consortium, xAI-2024:LB/D/DC

作者： Bergomi, Laura Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

Artificial Intelligence (AI) is increasingly integrated into Decision Support Systems (DSS). The explainability of AI-based systems becomes crucial in sensitive and critical domains, such as healthcare, where ethical considerations and reliability are paramount concerns. In the clinical setting, it is important to evaluate how humans and AI can collaborate on cognitive tasks. Collaboration protocols (HAI-CP) allow for the investigation of the usefulness of AI models and their impact on users (both positive and negative). Although research on the application of these methods is blooming, there is little understanding of the impact on clinical decision-making, especially for eXplainable AI (XAI) systems, due to the lack of user studies. Therefore, the goal of this proposal is to develop a clinical DSS enhanced by XAI and Natural Language Processing (NLP): their synergy can add value to the interaction between users and AI, fostering a more linguistically natural, comprehensible, trustworthy, and supporting interfacing, that blends into the existing workflows. This proposal explores potential solutions to tailor natural language explanations and data visualizations to the end-user, improving the comprehensibility of the reasons behind a decision, and increasing the user’s confidence in the decision;investigates and tests possible strategies to "get the patient-in-the-loop";explores uncertainty quantification and counterfactual approaches, and finally assesses the impact on naturalistic (i.e., real-world) decision-making and long-term effects and biases. © 2024 Copyright for this paper by its authors.

关键词： Decision support systems

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Forecasting Smart Grid Stability Using Machine Learning Models

Forecasting Smart Grid Stability Using Machine Learning Mode...

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2024 IEEE International Conferences of AI, science, engineering, and Technology, AIxSET 2024

作者： Kandukuri, Raja Banad, Yaser M. Southern University Department of Computer Science Baton RougeLA United States School of Electrical and Computer Engineering University of Oklahoma NormanOK United States

ISBN: (纸本)9798350390995

This paper presents a comprehensive study on designing and evaluating machine learning models for forecasting smart power grid stability. The stability of power grids is crucial for balancing electricity supply and demand, but it is challenged by the increasing use of renewable resources and dynamic pricing. Concepts like Decentralized Smart Grid Control (DSGC) aim to address this issue. We developed and compared machine learning models, including Support Vector Machines (SVM), LightGBM, and deep learning architectures, to identify the most accurate approach for forecasting grid stability. Our results showed that the SVM model achieved the highest accuracy of 99.9%, outperforming other models. These outcomes provide valuable insights and practical recommendations for deploying effective forecasting models in real-world smart grid scenarios, ensuring stability, efficient energy management, and seamless integration of renewable resources. © 2024 IEEE.

关键词： Power grids

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Robust CNN for facial emotion recognition and real-time GUI

AIMS Electronics and Electrical Engineering

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AIMS Electronics and electrical engineering 2024年第2期8卷 217-236页

作者： Ali, Imad Ghaffar, Faisal Department of Computer Science University of Swat KP Swat Pakistan System Design Engineering Department University of Waterloo Waterloo Canada

computer vision is witnessing a surge of interest in machines accurately recognizing and interpreting human emotions through facial expression analysis. However, variations in image properties such as brightness, contrast, and resolution make it harder for models to predict the underlying emotion accurately. Utilizing a robust architecture of a convolutional neural network (CNN), we designed an efficacious framework for facial emotion recognition that predicts emotions and assigns corresponding probabilities to each fundamental human emotion. Each image is processed with various pre-processing steps before inputting it to the CNN to enhance the visibility and clarity of facial features, enabling the CNN to learn more effectively from the data. As CNNs entail a large amount of data for training, we used a data augmentation technique that helps to enhance the model's generalization capabilities, enabling it to effectively handle previously unseen data. To train the model, we joined the datasets, namely JAFFE and KDEF. We allocated 90% of the data for training, reserving the remaining 10% for testing purposes. The results of the CCN framework demonstrated a peak accuracy of 78.1%, which was achieved with the joint dataset. This accuracy indicated the model's capability to recognize facial emotions with a promising level of performance. Additionally, we developed an application with a graphical user interface for real-time facial emotion classification. This application allows users to classify emotions from still images and live video feeds, making it practical and user-friendly. The real-time application further demonstrates the system's practicality and potential for various real-world applications involving facial emotion analysis. © 2024 the Author(s).

关键词： Graphical user interfaces

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Boosting Wind Harvest: FOPID Pitch Angle Controller for Turbines 21

Boosting Wind Harvest: FOPID Pitch Angle Controller for Turb...

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21st International Learning and Technology Conference, L and T 2024

作者： Salem, Nema Jamjoom, Jude Qashqari, Maha Alzahrani, Mariah Effat University Electrical and Computer Engineering Department Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Wind turbine blades are subjected to a variety of loads, including aerodynamic and gravitational loads. These loads produce aerodynamic strain and vibration in the blades, resulting in rotor blade damage and a reduction in the wind turbine's system efficiency. This could be prevented by implementing a proper pitch angle controller that plays a crucial role in boosting the energy capture and overall performance of wind power systems. The conventional Proportional-Integral-Derivative, P ID, controller has been widely utilized for pitch control, but it often faces challenges in meeting the requirements of complex and dynamic wind conditions. To address these limitations, this study explores the implementation of the Fractional Order Proportional-Integral-Derivative, F OP ID, controller for wind turbine pitch control. This paper presents a comparative analysis between the P ID and F OP ID controllers for wind turbine pitch control. The performance of both controllers is evaluated through Simulink. The results demonstrate that the F OP ID controller exhibits superior performance in terms of faster response time, and improved steady-state error compared to the P ID controller. © 2024 IEEE.

关键词： Controllers

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Cloud-Enabled Bio-Inspired Optimization: A Scalable Solution for Complex Real-World Problems 3

Cloud-Enabled Bio-Inspired Optimization: A Scalable Solution...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shreya Sakthi, U.R. Sruthi, S. Shruti, S.G. Rajganesh, N. Department of Computer Science and Engineering Sri Venkateswara College of Engineering Sriperumbudur India

ISBN: (纸本)9798331527549

Bio-inspired optimization algorithms, encompassing swarm intelligence, neural networks, fuzzy logic, and evolutionary algorithms, are powerful tools for addressing complex real-world optimization problems. However, their effectiveness is often constrained by the computational demands of training and execution, which surpass the capabilities of traditional computing platforms—particularly when dealing with massive datasets or intricate optimization challenges. These limitations restrict the practical applicability of such algorithms. This work introduces a framework that leverages cloud computing as a scalable and efficient platform to overcome these computational barriers. By integrating bio-inspired optimization techniques with the elastic and high-performance resources of cloud environments, this research demonstrates significant improvements in both the scope and efficiency of these algorithms. Experimental results validate the proposed approach, showcasing enhanced performance, reduced execution time, and the ability to solve larger and more complex real-world problems, thereby unlocking the full potential of bio-inspired optimization in practical applications. © 2025 IEEE.

关键词： Cloud platforms

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Decoding the Script: A Bi-LSTM Based Journey into Doctor's Handwriting for Precise Prescription Understanding

Decoding the Script: A Bi-LSTM Based Journey into Doctor's H...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Khan, Mohd Ayan Arif, Hamza Singh, Vishal Sharma, Dolly Amity University Department of Computer Science and Engineering Noida India 3rd Amity University Department of Computer Science and Engineering Noida India

ISBN: (纸本)9798350394412

Deciphering a doctor's handwritten prescription has been a complex task for human beings. Research has been done to automate the understanding of handwritten text, but transcribing doctor's handwritten prescriptions still requires the advancement to build a method that can be applied in real life. This paper presents a new hybrid model built on a fusion of CNN and Bi-LSTM architectures trained with CTC loss for the difficult task of decoding non-printed handwritten physicians' prescriptions. The unification of signals such as spatial feature extraction (CNN) as well as sequential dependency decrease (Bi-LSTM) with the aim of misinterpretation mitigation retrieves a very high recognition accuracy over a single, segmented test collection. With iterative training methodology, which focuses on computational limitations, the approach results in continuous learning, thus eliminating the problem of overfitting. The given model is very good at understanding the heterogeneity of handwriting and contextual relationships in prescription texts. This scientific addition leads to improvements in patient care, streamlined workflows, and healthcare information management improvements as the ultimate performance of the model is the astonishing 98.40% accuracy level it offers. © 2024 IEEE.

关键词： Deep learning

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