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

作者： Suganya, N. Gouthami, P. Sathiya, R. Sivaranjani, S. Murugesan, M. Department of Computer Science and Business Systems Dr. N.G.P. Institute of Technology Coimbatore India Department of Information Technology Thangavelu Engineering College Chennai India Departement of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Departement of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (纸本)9798331527549

A computing system that is based on the internet and offers users all resources as on-demand services. Servers, storage, databases, software, and networking are among these on-demand services. Usually, the user must pay for the use of the resources;they are not their property. The number of people using this technology is increasing dramatically as a result of these features. Although it has benefits, cloud computing poses certain security and privacy risks. The authorized user’s data can be stolen easily. This current research thoroughly investigates modern strategies to these problems, highlighting identity management protocols, effective encryption and blockchain-based solutions. The article identifies research gaps, focuses attention to the inadequacies of current approaches, and recommends the possible ways to improve the efficiency, scalability and trustworthiness. © 2025 IEEE.

关键词： Cloud security

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Exploring the Integration of Educational Robotics and the Internet of Things in Learning Environments

Exploring the Integration of Educational Robotics and the In...

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International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Elena Kakoulli Salomi Evripidou Department of Computer Science Intelligent Systems Laboratory Neapolis University Pafos Pafos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

This study explores the integration of Educational Robotics (ER) and the Internet of Things (IoT) in learning environments, highlighting their collective impact on educational practices. It assesses ER and IoT’s application, challenges, and opportunities, offering insights into their role in enhancing pedagogy and learning outcomes. Specifically, our exploration uncovers the transformative potential of ER and IoT in fostering critical thinking, problem-solving skills, and digital literacy among students and reveals the pivotal role these technologies play in preparing learners for the demands of the digital age and Industry 4.0, while also highlighting the need for strategic implementation and teacher support. Our findings elucidate the potential of ER and IoT to innovate teaching strategies and curriculum design, serving as a guide for educational stakeholders, such as curriculum developers, educators, researchers, and policymakers, in leveraging these technologies to revolutionize instructional practices.

关键词： Training Information age Fourth Industrial Revolution Internet of Things Stakeholders Problem-solving Distributed computing

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Access-Redundancy Tradeoffs in Quantized Linear Computations

Access-Redundancy Tradeoffs in Quantized Linear Computations

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Ramkumar, Vinayak Raviv, Netanel Tamo, Itzhak Tel Aviv University Department of Electrical Engineering-Systems Tel Aviv Israel Washington University in St. Louis Department of Computer Science and Engineering St. LouisMO United States

ISBN: (纸本)9781665475549

Linear real-valued computations over distributed datasets are common in many applications, most notably as part of machine learning inference. In particular, linear computations which are quantized, i.e., where the coefficients are restricted to a predetermined set of values (such as ±1), gained increasing interest lately due to their role in efficient, robust, or private machine learning models. Given a dataset to store in a distributed system, we wish to encode it so that all such computations could be conducted by accessing a small number of servers, called the access parameter of the system. Doing so relieves the remaining servers to execute other tasks, and reduces the overall communication in the system. Minimizing the access parameter gives rise to an access-redundancy tradeoff, where smaller access parameter requires more redundancy in the system, and vice versa. In this paper we study this tradeoff, and provide several explicit code constructions based on covering codes in a novel way. While the connection to covering codes has been observed in the past, our results strictly outperform the state-of-the-art, and extend the framework to new families of computations. © 2023 IEEE.

关键词： Redundancy

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review 2

Trends, Opportunities, and Challenges in Detecting Depressiv...

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2nd International Conference on computer System, Information Technology, and electrical engineering, COSITE 2023

作者： Elwirehardja, Gregorius Natanael Isnan, Mahmud Perbangsa, Anzaludin Samsinga Muchtar, Kahlil Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mahmud Isnan Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350343069

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches. © 2023 IEEE.

关键词： Machine learning

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Assessing the Robustness of nnU-Net in the Detection of Prostate Lesions via Bi-Parametric MRI

Assessing the Robustness of nnU-Net in the Detection of Pros...

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2023 IEEE EMBS Special Topic Conference on Data science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos Kalantzopoulos, Charalampos Pezoulas, Vasileios C. Koutsouris, Dimitrios D. Matsopoulos, George K. Marias, Kostas Tsiknakis, Manolis Fotiadis, Dimitrios I. National Technical University of Athens School of Electrical and Computer Engineering Biomedical Engineering Laboratory AthensGR15773 Greece Forth Biomedical Research Institute Ioannina Greece University of Ioannina Ioannina Greece Forth Institute of Computer Science Heraklion Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering Crete Heraklion Greece FORTH-IMBB Ioannina Greece University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department IoanninaGR 45110 Greece

ISBN: (纸本)9798350383386

Within the scope of prostate cancer diagnostic imaging, distinguishing lesions is challenging due to their subtle appearance and the prostate gland's complexity. This study employed the nnU-Net, a state-of-the-art medical image segmentation model, on a dataset of 301 patients from 2 openly available datasets to identify whether lesion sizes are affecting the model's performance. By applying t-SNE dimensionality reduction algorithm among dice score and respective lesion sizes, we found that nnU-Net behaves differently for lesions smaller than 9mm compared to lesions larger than 15mm. These insights can inform specialized training approaches for future deep learning models in prostate lesion *** Relevance: The main contributions of this study is, to investigate whether lesion sizes affects nnU-Net's deficiency leading to the development of more specialized models in respect to these factors. © 2023 IEEE.

关键词： Urology

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An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer with XAI Techniques

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IEEE Access 2025年 13卷 96317-96336页

作者： Haque, Fariha Asif Hasan, Mohammad Siddique, Md. Abu Ismail Roy, Tonmoy Kanti Shaha, Tonmoy Islam, Yamina Paul, Avijit Chowdhury, Muhammad E. H. Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi Bangladesh Utah State University Department of Data Analytics & Information Systems Old Main Hill Logan 84322 (435) 797-1000 UT United States Chittagong University of Engineering Technology Electrical Electronic Engineering Bangladesh International Islamic University Chittagong Department of Computer Science & Engineering Chittagong 4318 Bangladesh Qatar University Department of Electrical Engineering Doha 2713 Qatar

In the field of medical imaging, deep learning (DL) techniques have made significant contributions to the detection and classification of various cancers. Identifying the precise regions in medical images containing cancerous cells plays a crucial role in the diagnostic process. Early and accurate cancer detection is essential for effective treatment and improved patient outcomes. However, manual diagnosis is labor-intensive, requiring the specialized expertise of radiologists, and the increasing number of cancer cases presents challenges in processing large volumes of image data efficiently. To address these challenges, an end-to-end concatenated Convolutional Neural Network (CNN) attention model has been proposed for automatic lung cancer classification. This approach integrates two distinct CNNs, followed by a multi-layer perceptron (MLP) and a multi-head attention (MHA) mechanism, to enhance performance. The model leverages explainable AI techniques, such as gradient-weighted class activation mapping (grad-CAM) and Shapley additive explanations (SHAP), to highlight critical regions within the images that influence the decision-making process. This model achieves impressive performance, with an accuracy of 99.54%, precision of 99.31%, recall of 99.95%, F1-score of 99.66%, and an AUC of 99.97%. These results demonstrate that this approach not only surpasses existing methods but also provides a highly accurate and interpretable solution. By reducing the need for extensive manual intervention, this model enables faster and more reliable lung cancer diagnosis, paving the way for timely and effective treatments. © 2013 IEEE.

关键词： CLAHE convolutional neural network explainable AI gradient-weighted class activation mapping Gradio Lung cancer Shapley additive explanation

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Wind Power Prediction Based on Multi-class Autoregressive Moving Average Model with Logistic Function

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Journal of Modern Power systems and Clean Energy 2022年第5期10卷 1184-1193页

作者： Yunxuan Dong Shaodan Ma Hongcai Zhang Guanghua Yang IEEE State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering University of MacaoMacao S.A.R.999078China Institute of Physical Internet and the School of Intelligent Systems Scienceand Engineering Jinan UniversityZhuhai 519070China

The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e.,a wind power prediction model based on multi-class autoregressive moving average(ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method;the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy,but also the parameter estimation efficiency.

关键词： Wind power prediction wind generation time series analysis logistic function based classification

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A Game-Theoretic Framework for Distributed Load Balancing: Static and Dynamic Game Models

arXiv

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arXiv 2025年

作者： Fardno, Fatemeh Rasoul Etesami, S. The Department of Electrical and Computer Engineering and Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States The Department of Industrial and Systems Engineering Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States

Motivated by applications in job scheduling, queuing networks, and load balancing in cyber-physical systems, we develop and analyze a game-theoretic framework to balance the load among servers in both static and dynamic settings. In these applications, jobs/tasks are often held by selfish entities that do not want to coordinate with each other, yet the goal is to balance the load among servers in a distributed manner. First, we provide a static game formulation in which each player holds a job with a certain processing requirement and wants to schedule it fractionally among a set of heterogeneous servers to minimize its average processing time. We show that this static game is a potential game and admits a pure Nash equilibrium (NE). In particular, the best-response dynamics converge to such an NE after n iterations, where n is the number of players. We then extend our results to a dynamic game setting, where jobs arrive and get processed in the system, and players observe the load (state) on the servers to decide how to schedule their jobs among the servers in order to minimize their averaged cumulative processing time. In this setting, we show that if the players update their strategies using dynamic best-response strategies, the system eventually becomes fully load-balanced and the players' strategies converge to the pure NE of the static game. In particular, we show that the convergence time scales only polynomially with respect to the game parameters. Finally, we provide numerical results to evaluate the performance of our proposed algorithms under both static and dynamic settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Queueing networks

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Boosting efficiency and stability of 2D alternating cation perovskite solar cells via rational surface-modification: Marked passivation efficacy of anion

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Journal of Energy Chemistry 2023年第9期84卷 354-362页

作者： Hualin Zheng Xuefeng Peng Tingxi Chen Ting Zhang Shihao Yuan Lei Wang Feng Qian Jiang Huang Xiaodong Liu Zhi David Chen Yanning Zhang Shibin Li State Key Laboratory of Electronic Thin Films and Integrated Devices and School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China(UESTC)Chengdu 610054SichuanChina Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China(UESTC)Chengdu 610054SichuanChina Department of Electrical&Computer Engineering and Center for Nanoscale Science&EngineeringUniversity of KentuckyLexingtonKY 40506USA Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan 523808GuangdongChina

Two-dimensional(2D) alternating cation(ACI) perovskite surface defects,especially dominant iodine vacancies(V_Ⅰ) and undercoordinated Pb^(2+),limit the performance of perovskite solar cells(PVSCs).To address the issue,1-butyl-3-methylimidazolium trifluoro-methane-sulfonate(BMIMOTF) and its iodide counterpart(BMIMI) are utilized to modify the perovskite surface *** find that BMIMI can change the perovskite surface,whereas BMIMOTF shows a nondestructive and more effective defect passivation,giving significantly reduced defect density and suppressed charge-carrier nonradiative *** mainly attributes to the marked passivation efficacy of OTF-anion on V_Ⅰ and undercoordinated Pb^(2+),rather than BMIMI^(+) *** from the rational surface-modification of BMMIMOTF,the films exhibit an optimized energy level alignment,enhanced hydrophobicity and suppressed ion ***,the BMIMOTF-modified devices achieve an impressive efficiency of 21.38% with a record open-circuit voltage of 1.195 V,which is among the best efficiencies reported for 2D PVSCs,and display greatly enhanced humidity and thermal stability.

关键词： 2D ACI perovskite solar cells Charge-carrier nonradiative recombination Surface defects passivation Energy level alignment Ionic migration Stability

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Policy for Optimal Avoidance of Multiple Engagement Zones

Policy for Optimal Avoidance of Multiple Engagement Zones

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Milutinović, Dejan Von Moll, Alexander Weintraub, Isaac E. Casbeer, David Department of Electrical and Computer Engineering University of California Santa Cruz Santa CruzCA95064 United States Control Science Center Air Force Research Laboratory WPAFB OH45433 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This work formulates the feedback control strategies for vehicles to reach a goal point amongst a field of dynamic risk regions. Whereas previous work has considered deterministic versions of this problem, we consider the scenario in the context of uncertainty. This uncertainty could be due to unknown wind or other external disturbances. The risk regions’ dynamics are tightly coupled to the state of the vehicle and as a result the task of navigating through a field dynamically changes as the vehicle traverses through it. Rather than using a nonlinear program solver, the approach taken here is one of formulating the problem as a stochastic optimal control problem wherein a feedback control law is derived. This feedback control law allows a single or multiple vehicles to reach the desired goal location efficiently and with minimal risk incursion. When inside a risk region, there is a probability rate of the vehicle being neutralized. The stochastic dynamical system is modeled as a hybrid system, i.e., regime switching diffusion. The hybrid system is comprised of continuous spatial dynamics and discrete operational states corresponding to "normal" and "neutralized". The optimal control problem is discretized into a field of vehicle states and control. Using Value Iteration, the control signal at each discrete location is optimized, providing a feedback control strategy over the field. Following a discussion of the methods and approaches, this problem is formulated, simulations are performed, and conclusions and remarks are made © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Stochastic systems

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