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IEEE Open Journal of the Communications Society 2024年 5卷 1197-1203页

作者： Hung, Cheng-Feng Chen, You-Run Tseng, Chi-Heng Cheng, Shin-Ming National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106335 Taiwan

Open Radio Access Networks (O-RANs) represent a novel wireless access network architecture that decomposes traditional RAN functions and makes them openly accessible. O-RANs enable real-time coordination, RAN performance optimization, and management through RAN Intelligent Controllers (RICs) and their related xApps. Due to the openness of O-RAN, developers have the flexibility to download various pre-developed xApps from the Internet for deployment. They can even develop their xApps to enhance the flexibility and innovation of the RAN. The current O-RAN official WG11 has defined numerous specifications for secure implementations. However, not only is accurately detecting malicious xApps a significant challenge, but the existing H-Release also does not fully adhere to the specifications in its implementation. This could potentially introduce security risks and threats. In this paper, we implement an experimental O-RAN H-Release environment and discover significant threats from the absence of specified access control permissions for xApps. Malicious attackers can illegally access APIs to utilize other services or launch attacks on legitimate xApps and E2 nodes through the E2 interface, potentially causing a complete RAN disruption. We used the identified vulnerabilities to design three attacks, providing detailed explanations and a comprehensive analysis of their impact on the system. Finally, we also submit the discovered threats to CVEs (CVE-2023-42358 and CVE-2023-41628), providing a reference for O-RAN officials to improve security in the future. © 2020 IEEE.

关键词： Real time systems

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MAIDS: malicious agent identification-based data security model for cloud environments

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Cluster Computing 2024年第5期27卷 6167-6184页

作者： Gupta, Kishu Saxena, Deepika Gupta, Rishabh Singh, Ashutosh Kumar Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan School of Computer Science and Engineering The University of Aizu Aizuwakamatsu965-0006 Japan Department of Computer Science The University of Economics and Human Sciences Warsaw01-043 Poland Department of Computer Applications SRM Institute of Science & ampTechnology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad201204 India Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal462003 India

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent’s behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system’s sec

关键词： Forecasting

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The CWA Benchmark: A Seismic Dataset from Taiwan for Seismic Research

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Seismological Research Letters 2025年第3期96卷 2079-2091页

作者： Tang, Kuan-Wei Chen, Kuan-Yu Chen, Da-Yi Chin, Tai-Lin Hsu, Ting-Yu Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Seismological Center Central Weather Administration Taipei Taiwan Department of Civil and Construction Engineering National Taiwan University of Science and Technology Taipei Taiwan

In recent years, numerous studies have employed deep learning in seismology, with data-driven neural network models increasingly becoming the norm in emerging research paradigms. Alongside advancements in model architectures, several benchmark datasets have also been introduced. However, many of these datasets suffer from imbalanced distributions, particularly a scarcity of large-magnitude events, which can impair the performance of deep learning-based models. Taiwan, situated in a seismically active region experiences a high frequency of earthquakes annually. The Central Weather Administration (CWA) in Taiwan maintains comprehensive records of these seismic events. This study introduces a benchmark dataset, named CWA, specifically compiled from this extensive database. The CWA benchmark includes 331 events with magnitudes greater than five, collected from a high-density seismic network spanning from 2011 to 2021, making it well-suited for training deep learning models. In addition, the CWA benchmark features over 40 attributes and ∼500,000 seismograms, providing valuable data labels for various seismology-related tasks. © Seismological Society of America.

关键词： Seismic response

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Comparative Evaluation of Mango Fruit Diseases Classification Using Machine Learning

Comparative Evaluation of Mango Fruit Diseases Classificatio...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Kumar, Sandeep Gupta, Bhupesh Chhabra, Mohit Garg, Lucy Department of Computer Science & Engineering Mullana India Panipat Institute of Engineering Technology Department of Computer Science & Engineering Panipat India

ISBN: (纸本)9798350376470

A fruit valued for its great flavor, scent, and nutritional content;the mango (Mangifera indica L.) is one of the most significant tropical fruits in the world economically. However, several illnesses that compromise fruit quality and output significantly hinder the commercial cultivation and trading of mangoes. This widespread illness has significant financial consequences and represents a danger to mango farming worldwide. A key component of effectively managing the illness and protecting crops is the timely and accurate assessment of the disease's severity. The message highlights how critical it is to detect illnesses as soon as possible and with precision to enable effective crop management. The main illnesses that afflict mango fruits are covered in detail in this document, along with information on their genesis, symptoms, epidemiology, and management techniques. The primary disorders covered are anthracnose, mango malformation, powdery mildew, mango bacterial black spot, and mango abrupt decline (mango decline-associated virus). Every disease is thoroughly investigated, covering its causes, methods of spread, symptoms, and effects on fruit output and quality. Machine learning (ML) approaches have become more effective tools for agricultural system disease detection and categorization in recent years. To shed light on the efficacy and relevance of machine learning (ML) algorithms for categorizing mango fruit illnesses, this research study compares and contrasts them. © 2024 IEEE.

关键词： Fruits

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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike Information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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ConciseNet: A Robust Model for Enhancing Signal Reconstruction in Wireless Sensor Networks 4

ConciseNet: A Robust Model for Enhancing Signal Reconstructi...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kumari, P. Meena Sekar, K. Jagadeesh, P. Dheepa, T. Avn Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India National Institute of Technology Silchar Department of Computer Science and Engineering Assam Silchar India Barath University Department of Computer Science and Engineering Tamilnadu India Kongunadu College of Engineering and Technology Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350343670

This paper introduces a novel data collection model called ConciseNet that leverages a cutting-edge data reconstruction method to significantly enhance signal reconstruction accuracy in wireless sensor networks (WSNs). In traditional WSNs, data collection is prone to errors and losses, especially in long-range scenarios. The proposed model addresses this challenge by employing ConciseNet, an attentive sequential model that effectively compresses sensor signals without compromising information fidelity. The unique strength of ConciseNet lies in its ability to capture essential features from raw sensor data, allowing for efficient and precise reconstruction over extended periods. The result is a more robust and accurate representation of collected data, thereby improving the quality of information gathered by the WSN and maximizing the lifetime of sensors and WSNs. © 2024 IEEE.

关键词： Compressive sensing Data Reconstruction Time dependency Transformer Neural Network Wireless Sensor Network

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A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

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computer Modeling in engineering & sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barnawi Krishan Kumar Neeraj Kumar Bander Alzahrani Amal Almansour Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

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Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability

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Tungsten 2025年第1期7卷 137-151页

作者： Geethukrishnan Onkar Apte Kiran Kumar Tadi Energy Electrodics and Electrocatalysis (EEE) Lab Department of Chemistry School of Advanced Sciences Vellore Institute of Technology School of Computer Science and Engineering Vellore Institute of Technology

The precise detection and measurement of dopamine（DA）,a crucial neurotransmitter in the human body,plays a significant role in diagnosing,preventing,and treating neurological diseases associated with its levels.A highly sensitive DA electrochemical sensor was constructed by combining molybdenum disulfide quantum dots（MSQDs） with multiwalled carbon nanotubes（MWCNTs）.The MSQDs were synthesized using the shear exfoliation *** sensors consist of MSQDs with Mo-S edge catalytic centers for the DA redox reaction,and MWCNTs amplify the sensor *** linearity of the sensor for the detection of DA was tested in the presence of ascorbic acid(AA,50 μmol·L-1) and uric acid(UA,200 μmol·L-1),and exhibited linearity from 2 to 966 μmol·L-1of DA with 0.097 μA(mol·L-1)-1sensitivity and a low limit of detection of0.6 μmol·L-1（the ratio between signal and noise,S/N=3）.Moreover,the sensitivity and selectivity of the sensor were also studied using *** is no increase in amperometric current after adding the most potentially interfering *** sensor was successfully applied to recover DA in human blood sera ***,machine learning algorithms were operated to aid in the near-precise detection of DA in the heterogeneous mixture containing AA and *** algorithms facilitate the identification and quantification of DA amidst coexisting interferents,including AA,that are commonly present in biological matrices.

关键词： Dopamine MoS_2 Biosensor Multiwalled carbon nanotubes Machine learning Complex mixture

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District-Level Rainfall and Cloudburst Prediction Using XGBoost: A Machine Learning Approach for Early Warning Systems

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第2期49卷 375-396页

作者： Kumar, Guru Dayal Tyagi, Shekhar Pradhan, Kalandi Charan Shah, Akshat Migration and Development Research Group School of Humanities and Social Sciences Indian Institute of Technology Indore452020 India Department of Computer Science and Engineering Indian Institute of Technology Indore452020 India Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur440001 India

This research presents a novel methodology for cloudburst forecasting using the XGBoost (Extreme Gradient Boosting) machine learning algorithm. With the escalating impact of climate change, accurately predicting extreme weather events like cloudbursts is crucial due to their potential to trigger floods. Cloudburst events were identified from daily rainfall data. Our study leverages historical weather data, focusing on intricate rainfall patterns, to forecast future cloudburst occurrences. Comparative analysis against Random Forest and LSTM models confirmed XGBoost’s effectiveness, consistently outperforming alternatives across multiple performance metrics. The XGBoost model, known for its ability to handle complex datasets, demonstrated strong predictive performance, with an RMSE of 0.12 and an MAE of 0.09, indicating high accuracy. This research provides a reliable tool for advanced weather forecasting and early warning systems, offering valuable support to policymakers, disaster management teams, and agricultural planners in mitigating risks associated with extreme rainfall events. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Acid rain

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DD-SPP: Dynamic and Distributed Service Placement Policy for Optimal Scheduling in Fog-Edge Computing

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SN computer science 2024年第7期5卷 801页

作者： Kaur, Navjeet Bhardwaj, Vivek Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University School of Computer Science and Engineering Manipal University Jaipur

The existing cloud model unable to handle abundant amount of Internet of Things (IoT) services placed by the end users due to its far distant location from end user and centralized nature. The edge and fog computing are the latest technologies for developing smart cities that are becoming popular to efficiently support latency-sensitive IoT application services. But these advanced technologies suffer from limited size and computation capabilities;therefore, optimal placement of services among available resources is an open issue. Therefore, the proposed work target the stated issue and presents a Dynamic and Distributed Service Placement Policy (DD-SPP) considering the edge and fog devices. The model is divided into three main phases, that is, the Service Type Estimator (STE), the Service Dependency Estimator & Resolution (SDER), and Resource Demand Estimator & Scheduling (RDES). STE and RDES modules depend on each other to perform optimal service placement. The results of the implementation showed satisfactory improvement with respect to present state-of-the-art policy where delay is improved by around 41%, energy is improved by 27%, response time by 28% and overall cost by 28%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud computing DD-SPP Edge computing Fog computing IoT Scheduling Service placement policy

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