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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Ticku, Amrita Tripathy, Niva Mishra, Pankaj Kumar Sinha, Anurag Jadon, Shivangi Raj, Aditya Kulshrestha, Harsh Rai, Vipin Shamim, Rejuwan Bharti Vidyapeeth's College of Engineering India Driems Autonomous Engineering College Cuttack India Rungta College of Engg. & Technology R1 Department of Electronics and Telecommunication Engg. CG Bhilai India Ignou School of Computing and Information Science New Delhi India Galgotias University Dept. of Computer Science and Engineering Noida India Galgotias University Department of Computer Science and Engineering Noida India Maharishi University of Information Technology Dept. of Computer Science and Engineering with Data Science Noida India

ISBN: (纸本)9798350335095

Stock prediction has always been a difficult task, and numerous strategies for predicting stock market behavior have been offered. Researchers have recently begun to investigate the use of sentimental analysis and deep learning models such as Long Short-Term Memory (LSTM) to predict stock prices. We offer a stock prediction model that integrates sentimental analysis and LSTM in this research. The model considers the mood of stock market news items and use LSTM to forecast future stock prices. The results of our suggested model's evaluation on the S& P 500 dataset reveal that it outperforms traditional stock prediction approaches. © 2023 IEEE.

关键词： Forecasting

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Novel Three-Phase Buck-Boost Inverter With Reduced Input Current Ripple and No Short-Circuit and Open-Circuit Faults

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IEEE Transactions on Power Electronics 2024年第5期39卷 5695-5706页

作者： Khan, Shah Nawaz Khan, Ashraf Ali Jamil, Mohsin Khan, Usman Ali Ahmed, Hafiz Furqan Ahmed, Shehab Memorial University of Newfoundland Department of Electrical and Computer Engineering St. JohnNLA1C 5S7 Canada Yonsei University Department of Electrical and Electronics Engineering Seoul03722 Korea Republic of National Sun Yat-sen University Department of Electrical Engineering Kaohsiung894 Taiwan King Abdullah University of Science and Technology CEMSE Divisions Thuwal23955 Saudi Arabia

This article proposes a new single-stage three-phase buck-boost inverter and control scheme, which remarkably reduces both the low and high-frequency ripple components in the input current. The low-frequency ripple component is eliminated by phase-shifting the output three-phase voltages by 120°. The high-frequency ripple component is decreased by doubling the effective switching frequencies and lowering the voltages of the input inductors. The proposed inverter has buck-boost dc-ac conversion ability and contains only six active switches. It is short-circuiting free and can avoid the reverse recovery losses of the body diodes of the mosfets. The proposed inverter has better spectral performance and has a lower distortion in the output waveforms due to no delays (equivalent to dead-time in the traditional three-phase inverter) in the switching signals. To validate the performance and advantages of the proposed inverter, detailed analytical, simulation, and experimental results are reported. Experiments have been conducted with a 1.5-kW laboratory prototype at a 50 kHz switching frequency for the line-line output voltage of 110-220 Vrms/60. At the end, integration of inductors is also suggested to decrease the magnetic volume. © 1986-2012 IEEE.

关键词： Electric inverters

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Assessing adversarial replay and deep learning-driven attacks on specific emitter identification-based security approaches

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Discover Internet of Things 2024年第1期4卷 1-21页

作者： Tyler, Joshua H. Fadul, Mohamed K. M. Hilling, Matthew R. Reising, Donald R. Loveless, T. Daniel Electrical Engineering Department The University of Tennessee at Chattanooga 735 Vine Street ChattanoogaTN37403 United States Department of Intelligent Systems Engineering Indiana University 700 N. Woodlawn Avenue BloomingtonIN47408 United States Department of Computer Science Tennessee Technological University 1 Williams L. Jones Drive CookevilleTN38505 United States

Specific Emitter Identification (SEI) has been put forward as an Internet of Things (IoT) Physical Layer Security (PLS) approach for its abilities to detect, characterize, and identify wireless emitters by exploiting distinct, inherent, and unintentional features in their transmitted signals. Since its introduction, a significant amount of work has been conducted;however, most assume the emitters are passive and their identifying signal features are immutable and challenging to mimic. This suggests the emitters are reluctant and incapable of developing and implementing effective SEI countermeasures;however, Deep Learning (DL) has been shown capable of learning emitter-specific features directly from their raw in-phase and quadrature signal samples, while Software-Defined Radios (SDRs) are capable of manipulating these signal samples. Based on these capabilities, it is fair to question the ease at which an emitter can effectively mimic the SEI features of another or manipulate its own to hinder or defeat SEI. This work considers SEI mimicry using three signal features mimicking countermeasures;"off-the-self" DL;two SDRs of different sizes, weights, power, and cost (SWaP-C);handcrafted and DL-based SEI processes, and assessment of an SEI mimicry countermeasure in a "coffee shop" deployment. Our results show "off-the-shelf" DL algorithms, and SDR enables SEI mimicry;however, adversary success is hindered by: (i) the use of decoy emitter preambles, (ii) the use of a denoising autoencoder and (iii) SDR SWaP-C constraints. © The Author(s) 2024.

关键词： Deep learning

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An ensemble model for predicting dispositions of emergency department patients

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BMC Medical Informatics and Decision Making 2024年第1期24卷 1-14页

作者： Kuo, Kuang-Ming Lin, Yih-Lon Chang, Chao Sheng Kuo, Tin Ju Department of Business Management National United University No.1 Miaoli Lienda 360301 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology No. 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Emergency Medicine E-Da Hospital Kaohsiung City Taiwan Department of Occupational Therapy I-Shou University Kaohsiung City Taiwan Department of Computer Science and Information Engineering National Taitung University 369 Sec. 2 University Rd Taitung Taiwan

Objective: The healthcare challenge driven by an aging population and rising demand is one of the most pressing issues leading to emergency department (ED) overcrowding. An emerging solution lies in machine learning’s potential to predict ED dispositions, thus leading to promising substantial benefits. This study’s objective is to create a predictive model for ED patient dispositions by employing ensemble learning. It harnesses diverse data types, including structured and unstructured information gathered during ED visits to address the evolving needs of localized healthcare systems. Methods: In this cross-sectional study, 80,073 ED patient records were amassed from a major southern Taiwan hospital in 2018–2019. An ensemble model incorporated structured (demographics, vital signs) and pre-processed unstructured data (chief complaints, preliminary diagnoses) using bag-of-words (BOW) and term frequency-inverse document frequency (TF-IDF). Two random forest base-learners for structured and unstructured data were employed and then complemented by a multi-layer perceptron meta-learner. Results: The ensemble model demonstrates strong predictive performance for ED dispositions, achieving an area under the receiver operating characteristic curve of 0.94. The models based on unstructured data encoded with BOW and TF-IDF yield similar performance results. Among the structured features, the top five most crucial factors are age, pulse rate, systolic blood pressure, temperature, and acuity level. In contrast, the top five most important unstructured features are pneumonia, fracture, failure, suspect, and sepsis. Conclusions: Findings indicate that utilizing ensemble learning with a blend of structured and unstructured data proves to be a predictive method for determining ED dispositions. © The Author(s) 2024.

关键词： Base-learner Emergency department disposition Ensemble learning Meta-learner Predictive disposition method

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Five Facets of 6G: Research Challenges and Opportunities

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ACM Computing Surveys 2023年第11期55卷 1-39页

作者： Shen, Li-Hsiang Feng, Kai-Ten Hanzo, Lajos Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

While the fifth-generation systems are being rolled out across the globe, researchers have turned their attention to the exploration of radical next-generation solutions. At this early evolutionary stage, we survey five main research facets of this field, namely Facet 1: next-generation architectures, spectrum, and services;Facet 2: next-generation networking;Facet 3: Internet of Things;Facet 4: wireless positioning and sensing;and Facet 5: applications of deep learning in 6G networks. In this article, we provide a critical appraisal of the literature of promising techniques ranging from the associated architectures, networking, and applications, as well as designs. We portray a plethora of heterogeneous architectures relying on cooperative hybrid networks supported by diverse access and transmission mechanisms. The vulnerabilities of these techniques are also addressed and carefully considered for highlighting the most of promising future research directions. Additionally, we list a rich suite of learning-driven optimization techniques. We conclude by observing the evolutionary paradigm shift that has taken place from pure single-component bandwidth efficiency, power efficiency, or delay optimization toward multi-component designs, as exemplified by the twin-component ultra-reliable low-latency mode of the fifth-generation system. We advocate a further evolutionary step toward multi-component Pareto optimization, which requires the exploration of the entire Pareto front of all optimal solutions, where none of the components of the objective function may be improved without degrading at least one of the other components. © 2023 Association for Computing Machinery.

关键词： Internet of things

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Future Security Issues in Internet of Energy 4

Future Security Issues in Internet of Energy

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4th International Conference on Advanced science and engineering, ICOASE 2022

作者： Benrebbouh, Chahrazed Cherbal, Sarra Mansouri, Houssem Pathan, Al-Sakib Khan Univ Ferhat Abbas Setif 1 Lrsd Laboratory Faculty of Sciences Department of Computer Science Setif Algeria United International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665472227

One of the most popular new technologies today is the Internet of Energy (IoE), which utilizes the Internet for collecting, organizing, optimizing and managing network energy information from various edge devices. In this way, a distributed smart energy infrastructure is developed. As IoE is essentially linked with the Internet, the cyber security concerns for this environment are also significant. Like any other technology connected with the Internet, IoE also is vulnerable to various kinds of attacks and threats. In this paper, we investigate the security issues in Internet of Energy. We study various security techniques proposed or developed for this environment during the recent years and discuss what to expect in the future. © 2022 IEEE.

关键词： Internet of Energy

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Information undergraduate and non-information undergraduate on an artificial intelligence learning platform: an artificial intelligence assessment model using PLS-SEM analysis

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Education and Information Technologies 2023年第4期29卷 4371-4400页

作者： Zhong, Hua-Xu Chang, Jui-Hung Lai, Chin-Feng Chen, Pei-Wen Ku, Shang-Hsuan Chen, Shih-Yeh Department of Engineering Science National Cheng Kung University 1 Tainan Republic of China Computer and Network Center and Department of Computer Science and Information Engineering National Cheng Kung University Tainan Republic of China Department of Computer Science and Information Engineering National Cheng Kung University 1 Tainan Republic of China Department of Computer Science and Engineering National Taiwan Ocean University Keelung Taiwan

Artificial intelligence (AI) education is becoming an advanced learning trend in programming education. However, AI subjects can be difficult to understand because they require high programming skills and complex knowledge. This makes it challenging to determine how different departments of students are affected by them. This study draws on research in programming education and STEM education to explore the different factors that affect students in AI learning. Therefore, the purpose of this study is to investigate the impact of AI learning platforms on information undergraduate and non-information undergraduate by using a research model. The course was implemented for 65 students in the information undergraduate group and 39 students in the non-information undergraduate group. The findings showed that the two groups had different learning effects under different variables. Students with different cognitive styles may use different skills to positively influence self-regulated learning. This study provides important evidence to understand the learning impact of artificial intelligence among university students from different disciplines.

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Comparative Analysis of Differential Privacy Implementations on Synthetic Data 15

Comparative Analysis of Differential Privacy Implementations...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Said, Huwida E. Mahmoud, Qusay H. Goyal, Mandeep Hashim, Faiza College of Technological Innovation Zayed University Department of Computing and Applied Technolgy Dubai United Arab Emirates Ontario Tech University Department of Electrical Computer and Software Engineering OshawaONL1G 0C6 Canada College of Information Technology Uae University Department of Computer Science and Software Engineering AI-Ain United Arab Emirates

ISBN: (纸本)9798331507695

Differential privacy offers a promising solution to balance data utility and user privacy. This paper compares two prominent differential privacy tools-PyDP and IBM's diffprivlib-that are applied to a synthetic dataset with medical attributes. We evaluate these tools based on their effectiveness in maintaining data privacy while preserving the statistical integrity of the data. Our results reveal that PyDP provides synthetic data that closely matches real-world data, making it appropriate for tasks requiring accuracy while striking a better balance between data utility and privacy. However, IBM's diffprivlib is more suitable for privacy-critical applications due to its stronger privacy guarantees, but at the cost of increased noise and decreased data utility. This paper contributes to the practical understanding of implementing differential privacy in machine learning and software applications and enhances the tools available for developers in sensitive data environments. © 2025 IEEE.

关键词： Data integrity

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Wind disturbance compensated path-following control for fixed-wing UAVs in arbitrarily strong winds

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Chinese Journal of Aeronautics 2024年第2期37卷 431-445页

作者： Hao LU Lan GAO Yunda YAN Mingzhe HOU Chenliang WANG Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Science and Technology Hangzhou Normal UniversityHangzhou 311121China Department of Computer Science University College LondonLondon WC1E 6BTUnited Kingdom Center for Control Theory and Guidance Technology Harbin Institute of TechnologyHarbin 150001China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

Wind is the primary challenge for low-speed fixed-wing unmanned aerial vehicles to follow a predefined flight *** cope with various wind conditions,this paper proposes a wind disturbance compensated path following control strategy where the wind disturbance estimate is incorporated with the nominal guiding vector field to provide the desired airspeed direction for the *** the control input vector for the outer-loop kinematic subsystem needs to satisfy a magnitude constraint,a scaling mechanism is introduced to tune the proportions of the compensation and nominal ***,an optimization problem is formulated to pursue a maximum wind compensation in strong winds,which can be solved analytically to yield two scaling factors.A cascaded inner-loop tracking controller is also designed to fulfill the outer-loop wind disturbance compensated guiding vector ***-fidelity simulation results under sensor noises and realistic winds demonstrate that the proposed path following algorithm is less sensitive to sensor noises,achieves promising accuracy in normal winds,and mitigates the deviation from a desired path in wild winds.

关键词： Path following Guiding vector field Wind disturbance Disturbance rejection Fixed wings

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Joint Phase-Shift Design and Power Control for Near-and Far-Field Communications in Extremely Large RIS-aided UAV Networks

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IEEE Internet of Things Journal 2025年第13期12卷 22658-22668页

作者： Bui, Tinh T. Huynh, Dang Van Nguyen, Long D. Jung, Haejoon Duong, Trung Q. Memorial University Faculty of Engineering and Applied Science St. John’sNLA1B 3X5 Canada Duy Tan University Da Nang550000 Viet Nam Kyung Hee University Department of Electronics Engineering Yongin-si17104 Korea Republic of Queen’s University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

This paper investigates the integration of drone (aka UAV)-assisted networks with a reconfigurable intelligent surface (RIS) to enhance energy efficiency in near-and far-field communication scenarios. The coexistence of near-field and far-field communications introduces unique challenges in ensuring efficient resource allocation, managing interference, and meeting quality of service requirements for users. Primary users in the near-field areas have stronger signal links, while secondary users and primary far-field users face increased path loss and interference, necessitating sophisticated optimisation strategies to balance their performance. To address these challenges, we propose a joint optimisation framework for transmission power allocation and RIS phase-shift design. The framework aims to maximise energy efficiency while maintaining reliable communication for all user groups, leveraging the complementary characteristics of UAV and RIS technologies. The low-complexity optimisation approach is developed, leveraging advanced successive convex approximation techniques and iterative algorithms. The framework consists of the Dinkelbach algorithm for the outer loop and a combination of linear and convex optimisation algorithms for the inner loop. Linear programming is employed to handle the large number of variables, such as phase-reflecting coefficients, while convex programming is used to optimise power allocation in UAVs, with convergence guaranteed. Simulation results reveal significant energy efficiency gains compared to baseline methods, demonstrating the effectiveness of the proposed framework in managing the coexistence of near-and far-field communications. The findings underscore the importance of energy-efficient design in enabling scalable and sustainable UAV-assisted networks, offering valuable insights for the development of high-performance next-generation communication systems. © 2014 IEEE.

关键词： Convex optimization

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