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International CSI computer Conference, CSICC

作者： Parsa Azimi Sadegh Sulaimany Department of Computer Engineering Social & Biological Network Laboraroty (SBNA) University of Kurdistan Sanandaj Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

The application of artificial intelligence (AI) in the medical field has expanded significantly in recent years. Among its various subsets, machine learning (ML) demonstrates immense potential for enhancing the diagnosis and treatment of diseases. In this study, we aim to enhance the classification outcomes of recent lung cancer research by effective preprocessing techniques and emphasizing the impact of features derived from the ‘Survey Lung Cancer’ dataset. To improve classifier performance, different tasks such as SMOTE (Synthetic Minority Over-sampling Technique) for handling class imbalance and MinMaxScaler for feature scaling, were applied. These preprocessing steps significantly enhanced model accuracy. Specifically, the SVM classifier achieved a PR AUC of 99%, while the Random Forest (RF) classifier demonstrated the best performance across other evaluation metrics, achieving an accuracy of 97.91 %. Additionally, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) were employed to identify the most influential features contributing to each model's predictions. These techniques helped identify the most influential features contributing to each model's predictions, enhancing interpretability and understanding of their outputs.

关键词： Measurement Surveys Support vector machines Visualization Accuracy Explainable AI Lung cancer Predictive models Feature extraction Classification tree analysis

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A Critical Review on Flexibility Quantification and Evaluation Methods in Medium and Low Voltage networks

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IEEE Access 2025年 13卷 92622-92640页

作者： Georgia Eirini Lazaridou Efstratia Sarafoglou Nikolaos Fesakis Vasileios Boglou Athanasios Karlis Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Hellenic Electricity Distribution Network Operator (HEDNO) Athens Greece

With the increasing integration of Distributed Energy Resources (DERs), effective methods for flexibility quantification and evaluation become necessary to ensure grid stability and reliability. This review systematically explores two complementary approaches to flexibility quantification in medium voltage (MV) and low voltage (LV) networks: metric-based (MB) and region-based (RB) methodologies. MB methods provide concise evaluations of system-wide flexibility, facilitating benchmarking and operational insights. However, they often fail to capture the spatiotemporal complexities and interdependencies inherent in modern grids. RB methods, on the other hand, leverage Feasible Operating Regions (FOR) to map flexibility spatially and temporally but are hindered by low observability characterizing distributions systems and limited uncertainty modelling. Compared to previous studies that analyze MB and RB approaches separately, this review uniquely synthesizes both methodologies and proposes their combination as a more comprehensive tool for dynamic flexibility management in modern distribution networks. By enhancing FOR with flexibility metrics, the approach bridges gaps in uncertainty and time modelling, addressing key challenges posed by the increased penetration of DERs. This integration offers a practical and scalable solution for system operators to achieve a more comprehensive and dynamic assessment of flexibility, paving the way for enhanced grid stability and operational efficiency.

关键词： Measurement Reviews Power system stability Distributed power generation Uncertainty Power system reliability Renewable energy sources Indexes Reactive power Power measurement

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ImConv_RNN: Improved Convolutional Recurrent Model Based Channel Estimation for 6G networks 4

ImConv_RNN: Improved Convolutional Recurrent Model Based Cha...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kulkarni, Raghavendra Suresh Kumar Kondeti, Venkata Satya Pillai, Binu Sudhakaran Surendran, R. T-Mobile Usa Inc Principal Engineer Department of Network Technology BellevueWA United States Senior Systems Engineer Department of Professional Services Axyom. Core North AndoverMA United States Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

This research introduces a novel deep learning for channel estimation for the 6G networks. The channel estimation for the 6G networks is employed using the proposed Improved Convolutional Recurrent (ImConv-RNN) model. In this, the loss function optimization is employed using the proposed ChSO algorithm. The chaotic mapping based on Chebyshev is included in the skill optimization assist the algorithm to enhance the convergence rate. The proposed ImConv-RNN model is evaluated based on RMSE, R2, MAE and MAPE acquired the values of 0.0136, 0.9584, 0.0114, and 5.8969 respectively. © 2025 IEEE.

关键词： 6G networks channel estimation chebyshev mapping recurrent model skill optimization algorithm spatio-temporal feature extraction

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Multi-Representation Spatial-Temporal Graph Convolutional networks for network Traffic Prediction

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IEEE Internet of Things Journal 2025年

作者： Yang, Yang He, Yechen Zhao, Binnan Wu, Celimuge Gao, Zhipeng Rui, Lanlan Beijing University of Posts and Telecommunications School of Computer Science Beijing100876 China University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan

With the rapid proliferation of the Internet of Things, network traffic prediction has become crucial for intelligent network management, enabling more reliable and flexible services for a vast array of IoT devices and applications. The heterogeneous and dynamic nature of IoT networks introduces complex spatial relations and underlying periodic dependencies in spatial-temporal graphs that existing methods struggle to model effectively. In this paper, we propose Multi-Representation Spatial-Temporal Graph Convolutional networks (MRSTGCN), a novel unified framework specifically designed to address these challenges. MRSTGCN integrates a Multi-Representation Graph Convolutional network (MRGCN) module to model node heterogeneity and complex traffic propagation, and two complementary embedding modules - Historical Embedding and Temporal Embedding - to capture and fuse periodic dependencies across different fine-grained temporal cycles. Extensive experiments are conducted on two network traffic datasets, and the results demonstrate that MRSTGCN achieves state-of-the-art performance with obvious improvements in MAE, RMSE and MAPE on three prediction horizons. © 2025 IEEE.

关键词： Convolutional neural networks

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Navigating intent-based networking: from user descriptions to deployable configurations

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Neural Computing and Applications 2025年 1-36页

作者： AlSamarneh, Ala’ A. Al-Hammouri, Ahmad T. Al-Jarrah, Omar Y. Department of Network Engineering and Security Jordan University of Science and Technology P.O. Box 3030 Irbid22110 Jordan The Beacom College of Computer and Cyber Sciences Dakota State University 820 N Washington Ave. MadisonSD57042 United States

network automation development has accompanied network evolution due to its significant role in speeding up and simplifying network operations. Emerging networking and computing paradigms such as information-centric networks, next-generation networks, cloud, and edge computing and recent innovative technologies, such as the Internet of things (IoT), enabled novel network services (such as the Internet of Vehicles (IoV), context-aware applications, virtual reality, and augmented reality) that demand complex configurations and management. Intent-based networking (IBN) is a promising networking paradigm that provides abstract and autonomous network management. IBN promises to simplify configuring networking devices, allowing network engineers and service providers to focus on providing the expected services and continuously verifying that the network operates within the desired status. An IBN process starts by expressing the user requirement in a high-level descriptive format. Then, the IBN system translates these requirements to a low-level deployable format in a process called intent translation. In this work, we formally define the intent translation process and propose a generic intent translation system. Furthermore, we review the research on intent translation published between 2018 and 2022. We analyze and classify the proposed intent translation schemes and discuss the challenges and recent trends in intent translation. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Internet of things

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An Evaluation of Meteorological Variables Impact on Photovoltaic Power Generation Estimation Based on Deep Learning Model

An Evaluation of Meteorological Variables Impact on Photovol...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Husein Mauladdawilah E.J. Gago M.C. Pegalajar Mohammed Balfaqih School of Civil Engineering University of Granada Granadea Spain Department of Computer Science and Artificial Intelligence University of Granada Granadea Spain Department of Computer and Network Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Accurate forecasting of photovoltaic (PV) power generation is essential for efficient grid integration and operation of solar energy systems. This study assesses a PV station in Scotland, UK, by analyzing the correlation between meteorological variables and power output. Two methods are employed: Pearson Correlation Coefficient and deep learning forecasting using Long Short-Term Memory (LSTM) networks. The study uses four datasets, each incorporating a different meteorological variable alongside historical PV power data. Evaluation metrics include Mean Squared Error (MSE), Normalized Root Mean Squared Error (NRMSE), R-squared (R2), and Mean Absolute Percentage Error (MAPE). The analysis reveals that global total irradiance has the strongest positive correlation (0.92) with PV output, followed by temperature (0.66). Wind speed and relative humidity show negative correlations (−0.35 and -0.74, respectively). The LSTM model, trained with a 10-day input window, demonstrates the highest coefficient of determination with temperature range variable (99.48 % ) and the lowest NRMSE (1.77 % ). These findings highlight the significance of meteorological factors in optimizing PV power generation in Scotland.

关键词： Photovoltaic systems Deep learning Temperature distribution Correlation Meteorological factors Wind speed Solar energy Predictive models Forecasting Long short term memory

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Satellite-UAV networks for 6G Control: A Sensing-Communication-Computing-Control Closed Loop Perspective

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IEEE network 2025年

作者： Lei, Chengleyang Fang, Xinran Feng, Wei Chen, Yunfei Xiao, Ming Ge, Ning Mao, Shiwen The Department of Electronic Engineering State Key Laboratory of Space Network and Communications Tsinghua University Beijing100084 China The Department of Engineering University of Durham DurhamDH1 3LE United Kingdom The Department of Information Science and Engineering School of Electrical Engineering and Computer Science Royal Institute of Technology Sweden The Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States

The future sixth-generation (6G) networks are envisioned to support machines and robots besides human beings. In order to efficiently support machines and robots working in remote and post-disaster areas, satellites and unmanned aerial vehicles (UAVs) can be utilized. In this article, we investigate how satellite-UAV networks can serve robots for their control tasks. We introduce the typical use cases of the satellite-UAV networks for control and outline the relevant challenges. The control process usually involves sensing, communication, computing, and control components, which forms a sensing-communication-computing-control (SC3) closed loop. In analogy with the reflex arc structure of humans, we reveal the indivisibility of the SC3 closed loops and propose a closed-loop-oriented system design framework. Within this framework, we take the closed-loop control performance as the design target, and highlight the holistic consideration of sensing, communication, computing, and control components in the SC3 closed loop. Furthermore, we discuss the extended SC3 closed loop with multiple sensors and robots to decompose complex control systems. A case study is provided to show the performance gain of the proposed closed-loop-oriented system design. Finally, we outline open issues in designing more efficient satellite-UAV networks for control. ©1986-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Systematic Review of the Future of Education in Perspective of Block Chain

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Journal of Mobile Multimedia 2025年第5期19卷 1221-1254页

作者： Shams Tabrez Siddiqui Haneef Khan Md Imran Alam Kamal Upreti Sarika Panwar Sheela Hundekari Department of Computer Science Jazan University Saudi Arabia Department of Computer and Network Engineering Jazan University Saudi Arabia CHIRST (Deemed to be University) Ghaziabad India Department of Electronics and Telecommunication Engineering AISSMS's Institute of Information Technology Pune India MIT ADT University Pune India

Blockchain is one of the most revolutionary technologies in the past decade due to its decentralisation, data integrity, reliability, and security. Blockchain technology is the next popular topic, and it has the potential to significantly alter the educational environment in many ways. Blockchain technology must be used in the education sector despite its challenges. Education is one of the sectors where blockchain-based solutions are still in use. Many academics possess extensive knowledge of the societal benefits blockchain technology might bring. The vast potential of blockchain can only be realised if education expands its knowledge of the technology. The primary goal of this research is to identify current problems related to educational institutions and identify blockchain features that could assist in addressing them. This research article will provide an overview of existing activities and address several perspectives on how blockchain can revolutionize the education sector. In prolongation, this article investigates the categories of blockchain technology applications, especially in the field of education. An in-depth discussion on the benefits and impact that blockchain brings to education is explored. Further, deliberate the abundant challenges of adopting blockchain in education. This study will direct the organizations/institutions to decide which blockchain application will benefit the most based on their requisites. The analysis will also provide information about other educational fields that may benefit from blockchain technology.

关键词： Privacy Electronic learning Federated learning Scalability Education Blockchains Security Reliability Secure storage Systematic literature review

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Toward Drought Modeling in South Asia: Machine Learning Approaches, Challenges, and Opportunities

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IEEE Access 2025年 13卷 87654-87671页

作者： Mana Saleh Al Reshan Muhammad Owais Raza Naeem Ahmed Mahoto Adel Rajab Asadullah Shaikh Mohamed A. Elmagzoub Khairan D. Rajab Department of Information Systems College of Computer Science and Information Systems Najran University Najran Saudi Arabia Emerging Technologies Research Laboratory (ETRL) College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Computer Engineering Istanbul Sabahattin Zaim University İstanbul Türkiye Department of Software Engineering Mehran University of Engineering and Technology Jamshoro Sindh Pakistan Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Network and Communication Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia

Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and ecosystem sustainability. Machine all are severely affected by drought. Due to the increasing frequency and severity of droughts caused by climate change. Effective drought modeling is crucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) techniques have been developed as potential drought modeling tools, which offer accurate and reliable drought detection. This review paper summarizes the drought modeling(Drought Prediction, Drought Detection and Drought Forecasting) approaches. This paper focuses on three main aspect. 1) The selection of the region for this study, for this study South Asia(SA) is selected as region of interest (ROI) that offer accurate drought modeling, providing policymakers and decision-makers with insightful information. The geographical scope of this study is the region of South Asia. This region is selected because of its heavy reliance on agriculture. 2) This paper focuses on the current and future trends, challenges, and advances of and vulnerability to droughts. The review offers a thorough grasp of how drought conditions are evaluated by gathering and analyzing the most important drought indicators and metrics specific to South Asia. The paper explores the current state-of-the-art in ML and DL for drought modeling. 3) This review encapsulates the indicator and metrics (Complex Machine learning and deep learning models) for drought modeling which are most relevant to the SA region. This study sum up as most common challenges in drought modeling are, highlighting current challenges such as incomplete and inconsistent datasets, lack of explainable and interpretable models, and unavailability of data for model uncertainty analysis. This study proposes that these problems can be solved with modern machine learning techniques such as explainable machine learning and federa

关键词： Droughts Machine learning Asia Deep learning Indexes Water resources Remote sensing Predictive models computer science Climate change Environmental monitoring Economics

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Optimal Multi-Bitrate Video Caching and Processing in Edge Computing: A Stackelberg Game Approach

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IEEE Internet of Things Journal 2025年

作者： Guo, Min Zhang, Di Xing, Weiwei Shao, Xun Liu, Zhi Zhang, Yaoxue Beijing Jiaotong University School of Software Engineering Beijing100044 China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Aichi441-8580 Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan Tsinghua University Department of Computer Science and Technology Beijing100084 China

By caching and transcoding video files on edge servers, video edge caching (VEC) can alleviate network congestion and improve user experience. To achieve this, VEC needs to address resource allocation and pricing problems and develop caching and transcoding strategies. However, existing works have not effectively coordinated and resolved these issues. Towards this end, we propose a game-theoretic multi-bitrate video caching and processing method for VEC. Specifically, to address these interdependent issues concurrently, we first incorporate multidimensional strategies into the system model and formalize the profit maximization problems of the network service provider and multiple video providers (VPs) as a constrained multi-objective optimization problem. Second, we transform the problem into a Stackelberg game and identify a Nash equilibrium as the solution. To solve the game problem, we decompose the game process into two stages focusing on resource competition and price determination, respectively. Then, we address the problems of transcoding and caching strategies development, and resource allocation in parallel across varying resource price conditions. The transcoding strategy is determined based on the constraints imposed by delay, value, and the source video. For the caching strategy development and resource allocation, we transform them into two grouped knapsack problems and apply the "dominant strategy» from game theory to identify the "feasible strategy» of VPs. Finally, we determine the resource prices in a traversal manner. Theoretical analysis shows that our proposed method achieves the optimal solution. Experimental results show that our algorithm achieve a 6.04% increase in overall profit compared to the greedy strategy algorithm. © 2025 IEEE.

关键词： Costs

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