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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： He, Menglong Vithalani, Hrishikesh Catuneanu, Mircea Shetewy, Abdou Jamshidi, Kambiz Integrated Photonic Devices Group Faculty of Electrical and Computer Engineering TU Dresden Dresden Germany

We investigate the voltage-controlled index modulation in a silicon microring with a p-i-n junction. We demonstrate two notable regimes including reverse (linear) and forward bias (exponential-like), with a tuning ran... 详细信息

ISBN: (纸本)9798350379266

关键词： Silicon photonics

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Enhancing Stock Price Prediction: A Hybrid Approach Leveraging Large Language Models and Deep Learning

Enhancing Stock Price Prediction: A Hybrid Approach Leveragi...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Ahmed, Mohamed Erfan Ebrahim, Gamal A. Abdelaal, Marwa A. Ain Shams University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Sadat Academy for Management Sciences Faculty of Computers and Information Computer Science Department Cairo Egypt

ISBN: (纸本)9798350367560

Accurate stock price prediction is a challenging yet crucial goal in finance, with significant implications for investment decisions and risk management. This paper presents a comprehensive review of machine learning techniques for stock price prediction, examining traditional methods such as regression and ensemble models, as well as advanced approaches that integrate sentiment analysis and textual data sources. With the emergence of powerful Large Language Models (LLMs) such as ChatGPT, Llama and Gemini, we explore their potential for enhancing predictive accuracy using historical stock data. Key challenges are discussed, including data quality, model interpretability, and adapting to dynamic market conditions. Additionally, this paper proposes a trustworthy stock price prediction model based on LLMs enabling informed investment decision-making. Experimental results demonstrate that ChatGPT-4o model achieved a prediction accuracy of approximately 97%, which can be improved by tuning model parameters. Consequently, the paper highlights the potential of LLMs in improving stock price forecasting. © 2024 IEEE.

关键词： Prediction models

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Challenges in Securing UAV IoT Framework: Future Research Perspective

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2607-2629页

作者： Aljumah, Abdullah Ahanger, Tariq Ahamed Ullah, Imdad Prince Sattam Bin Abdulaziz University College of Computer Engineering and Sciences Saudi Arabia CoBA Prince Sattam Bin Abdulaziz University Department of Management Information Systems Saudi Arabia The University of Sydney School of Computer Science Faculty of Engineering SydneyNSW2006 Australia

Unmanned Aerial Vehicles (UAVs) offer the immense capability for allowing novel applications in a variety of domains including security, military, surveillance, medicine, and traffic monitoring. The prevalence of UAV systems is due to the collaboration and accomplishment of tasks efficiently and effectively. UAVs embedded with camcorders, GPS receivers, and wireless sensors propose enormous promise in realizing the Internet of Things (IoT) service delivery in vast domains. It results in establishing an airborne field of the IoT when empowered with communication protocols of LTE, 4G, and 5G/6G networks. However, numerous difficulties must be addressed before UAVs may be used effectively namely privacy, security, and administration. Conspicuously, in the current article, novel UAV-specific domains enabled by IoT and 5G/6G technology are explored. Moreover, the presented technique assesses sensor requirements and provides an overview of fleet management systems that address aerial networking, privacy, and security concerns. Furthermore, a framework based on the IoT-5G/6G aspect is proposed which can be deployed over UAVs. Finally, in a heterogeneous computational platform, the proposed framework provides a complete IoT architecture that enables secure UAVs. © 2004-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Dual-Critic Deep Deterministic Policy Gradient Approach for Task Offloading in Edge-Fog-Cloud Environment 5

A Dual-Critic Deep Deterministic Policy Gradient Approach fo...

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5th International Conference on computer and Applications, ICCA 2023

作者： Ebrahim, Moshira A. Ebrahim, Gamal A. Mohamed, Hoda K. Ain Shams University Faculty of Engineering Computer and Systems Engineering Department Cairo11517 Egypt

ISBN: (纸本)9798350303254

Computation intensive IoT applications have grown exponentially over the past few years, which has led to the rise in popularity of the edge, fog, and cloud computing paradigms among mobile devices with embedded smart chips. Mobile Edge Computing (MEC) and Fog Computing (FC) usually cooperate with cloud computing to fulfill the high-demand requirements and optimize the network resources of complex computational offloading environments. In this paper, the topic of computational offloading for the hierarchical Edge-Fog-Cloud (EFC) computing is examined where edge, fog, and cloud nodes function as relay nodes that are constructed for latency critical and computations intensive jobs. Hierarchical EFC makes use of both centralized and distributed computing architectures considering diverse channel quality, network selection, and cloud access. This study proposes an improved Deep Reinforcement Learning (DRL) algorithm to find the optimal decision for offloading a computational task in EFC networks. Simulation results validate the proposed DRL-based model by comparing it with other alternative algorithms. Experimental results demonstrate how the proposed approach can efficiently reduce the latency delay by 43% and energy cost by 32% of IoT end devices. Additionally, it can achieve a higher success rate compared with the other related algorithms with more than 16%. © 2023 IEEE.

关键词： Reinforcement learning

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The Effect of Balanced and Imbalanced Dataset on Voice Pathology Detection Using Online Sequential Extreme Learning Machine (OSELM)

The Effect of Balanced and Imbalanced Dataset on Voice Patho...

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2024 IEEE International Conference on Advanced Telecommunication and Networking Technologies, ATNT 2024

作者： Sazihan, N. F. S. Md Latiff, N. M. Abdul Malik, N. N. N. Abd Mashohor, S. Alaghbari, K. Al-Dhief, F.T. Universiti Teknolgi Malaysia Utm Faculty of Electrical Engineering Johor Bahru Malaysia Universiti Putra Malaysia Faculty of Engineering Dept. of Computer and Communication Systems Engineering Serdang Malaysia

ISBN: (纸本)9798350353501

Voice pathology detection is crucial for early diagnosis and treatment of vocal disorders. This research studies the effect of balanced and imbalanced datasets on the accuracy of voice pathology detection using Online Sequential Extreme Learning Machine (OSELM) on the Saarbruecken Voice Database (SVD). The dataset's balance has been identified as having a significant influence on the performance of the machine learning models. In this work, the voice signals from vowel /a/ of pathology and non-pathology classes are taken from SVD. The dataset is divided into two category which are balanced and imbalanced dataset. Then, the features of the voice signals are then extracted using Mel-Frequency Cepstral Coefficient (MFCC) and fed into classifier called OSELM. The result of balanced and imbalanced dataset is assessed for its accuracy. The results showed that the model achieved an accuracy of 66.25% with the balanced dataset compared to 48% for the imbalanced dataset. These findings highlight the importance of addressing dataset balance to enhance the reliability and effectiveness of voice pathology detection systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Robust Stability Analysis of Smith Predictor Based Interval Fractional-Order Control systems:A Case Study in Level Control Process

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 762-780页

作者： Majid Ghorbani Mahsan Tavakoli-Kakhki Aleksei Tepljakov Eduard Petlenkov Department of Computer Systems Tallinn University of TechnologyTallinn 12618Estonia Faculty of Electrical Engineering K.N.Toosi University of TechnologyTehran ***Iran IEEE

The robust stability study of the classic Smith predictor-based control system for uncertain fractional-order plants with interval time delays and interval coefficients is the emphasis of this *** uncertainties are a type of parametric uncertainties that cannot be avoided when modeling real-world ***,in the considered Smith predictor control structure it is supposed that the controller is a fractional-order proportional integral derivative(FOPID)*** the best of the authors'knowledge,no method has been developed until now to analyze the robust stability of a Smith predictor based fractional-order control system in the presence of the simultaneous uncertainties in gain,time-constants,and time *** three primary contributions of this study are as follows:ⅰ)a set of necessary and sufficient conditions is constructed using a graphical method to examine the robust stability of a Smith predictor-based fractionalorder control system—the proposed method explicitly determines whether or not the FOPID controller can robustly stabilize the Smith predictor-based fractional-order control system;ⅱ)an auxiliary function as a robust stability testing function is presented to reduce the computational complexity of the robust stability analysis;andⅲ)two auxiliary functions are proposed to achieve the control requirements on the disturbance rejection and the noise ***,four numerical examples and an experimental verification are presented in this study to demonstrate the efficacy and significance of the suggested technique.

关键词： Interval uncertainty FOPID controller fractional-order systems robust stability analysis smith predictor

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High-Sensitivity Plasmonic Sensors Probe for Uric Acid Detection using Surface Funtionalized Gold-Graphene Quantum Dotes stacked Nanocomposites

High-Sensitivity Plasmonic Sensors Probe for Uric Acid Detec...

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2024 JSAP-Optica Joint Symposia, JSAP 2024

作者： Noor, Ahmad Shukri Muhammad Onifade, Olabisi Abdullahi Abu Bakar, Muhammad Hafiz Mahdi, Mohd Adzir Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Malaysia Wireless and Photonics Network Research Centre of Excellence Faculty of Engineering Universiti Putra Malaysia Malaysia

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A Novel Drone-Station Matching model in Smart Cities based on Strict Preferences

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无人系统（英文） 2023年第3期11卷 261-271页

作者： Debolina Nath Anjan Bandyopadhyay Ankit Rana Tarek Gaber Aboul Ella Hassanien Department of Computer Science and Engineering Amity University Kolkata KolkataWest BengalIndia Department of Computer Science and Engineering Gitam University BangaloreKarnatakaIndia School of Science Engineeringand Environment University of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailiaEgypt Scientific Research Group in Egypt(SRGE) Egypt Faculty of Computer and Artificial Intelligence Cairo UniversityCairoEgypt Scientific Research Group in Egypt(SRGE) Egypt

There has been a considerable increase in the use of drones,or unmanned aerial vehicles(UAVs),in recent times,for a wide variety of purposes such as security,surveillance,delivery,search and rescue operations,penetration of inaccessible or unsafe areas,*** increasing number of drones working in an area poses a challenge to finding a suitable charging or resting station for each drone after completing its task or when it goes low on its *** classical methodology followed by drones is to return to their pre-assigned charging station every time it requires a *** approach is found to be inefficient as it leads to an unnecessary waste of time as well as power,which could be easily saved if the drone is allotted a nearby charging station that is ***,we propose a drone-allocation model based on a preference matching algorithm where the drones will be allotted the nearest available station to land if the station is *** problem is modeled as three entities:Drones,system controllers and charging *** matching algorithm was then used to design a Drone-Station Matching *** simulation results of our proposed model showed that there would be considerably less power consumption and more time saving over the conventional *** would save its travel time and power and ensure more efficient use of the drone.

关键词： Unmanned Aerial Vehicle(UAV) stable matching resource allocation smart city matching algorithm

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CNN-Based Multi-Output and Multi-Task Regression for Supershape Reconstruction from 3D Point Clouds

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第5期49卷 37-48页

作者： Remmach, Hassnae Razak, Siti Fatimah Abdul Ullah, Arif Yogarayan, Sumendra Sayeed, Md Shohel Mrhari, Amine Computer Systems Engineering Laboratory Cadi Ayyad University Marrakesh40000 Morocco Faculty of Information Science and Technology Multimedia University Ayer Keroh75450 Malaysia Research in Computer Science Laboratory Faculty of Sciences Ibn Tofail University Kenitra Morocco

In a number of industries, including computer graphics, robotics, and medical imaging, three-dimensional reconstruction is essential. In this research, a CNN-based Multi-output and Multi-Task Regressor with deep learning capabilities is proposed for three-dimensional object reconstruction from 3D point cloud. Our approach is grounded in the original Point Net architecture, which addresses the difficulties associated with convolution when applied to point clouds. Firstly, this paper is modified using a Multi-Output Regressor to accurately recreate Super forms from 3D point clouds. Using this method, we first extract features from the 3D point cloud using Point Net. After that, a Multi-Output Regressor receives these data and uses them to anticipate the Super shape parameters needed to reconstruct the shape. Taking in the data, the Multi-Output Regressor retrieved characteristics from Point Net and simultaneously predicts several outcomes. Second, a Multi-Task Regressor is used to modify the Point Net. The network gains from the capacity to transfer knowledge from one task to another, improving the model's overall performance. The model would forecast the ten parameters needed to create the shape in the case of rebuilding Super shapes. The test findings were better than expected;they are intriguing in terms of prediction accuracy and cost, and they update the result by 80%, which is a good accomplishment for the study. © 2025 Slovene Society Informatika. All rights reserved.

关键词： 3D reconstruction

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Performance Comparison of the Invasive Weed Optimization K-Means Algorithm With Particle Swarm Optimization K-Means for Banking Data 6

Performance Comparison of the Invasive Weed Optimization K-M...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Suwirmayanti, Ni Luh Gede Pivin Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Setyaningsih, Emy Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Computer Systems Engineering Faculty of Applied Science Universitas AKPRIND Indonesia Yogyakarta Indonesia

ISBN: (纸本)9798331539603

Banking produces extensive and diverse data, so a clustering process is needed to understand customer behavior patterns and transactions more effectively. This clustering has been widely utilized with the K-Means algorithm due to its simplicity and efficiency. However, this algorithm is limited in determining the optimal cluster center, affecting the accuracy of the clustering results. This study proposes applying two metaheuristic algorithms, Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO), as additional solutions combined with K-Means. These two algorithms were chosen because of their ability to find global solutions and convergence speed to handle large and complex banking data. The results show the superiority of IWOKM compared to PSOKM in terms of SSE and DBI. From the SSE value, IWOKM produces a lower value (3029.77), which indicates that this method is better at minimizing clustering errors and producing more accurate clustering than PSOKM (80007.09). From the DBI value, IWOKM also produces a better value (1.6684) than PSOKM (1.6782), which shows that the clusters produced by IWOKM are more compact and more separated. However, in terms of computation time, PSOKM is more efficient, with a faster average computation time. Although IWOKM produces better clustering quality, PSOKM offers advantages in terms of processing speed. This finding confirms that the selection of the IWO algorithm is more appropriate for use on datasets with characteristics such as the German Credit Dataset. © 2024 IEEE.

关键词： Invasive weed optimization

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