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arXiv 2025年

作者： Yarham, Sara Behjati, Mehran Alobaidy, Haider A.H. Abdul Majeed, Anwar P.P. Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University 47500 Malaysia Department of Information and Communications Engineering College of Information Engineering Al-Nahrain University Baghdad64046 Iraq Taicang Suzhou215400 China

Monitoring air quality and environmental conditions is crucial for public health and effective urban planning. Current environmental monitoring approaches often rely on centralized data collection and processing, which pose significant privacy, security, and scalability challenges. Federated Learning (FL) offers a promising solution to these limitations by enabling collaborative model training across multiple devices without sharing raw data. This decentralized approach addresses privacy concerns while still leveraging distributed data sources. This paper provides a comprehensive review of FL applications in air quality and environmental monitoring, emphasizing its effectiveness in predicting pollutants and managing environmental data. However, the paper also identifies key limitations of FL when applied in this domain, including challenges such as communication overhead, infrastructure demands, generalizability issues, computational complexity, and security vulnerabilities. For instance, communication overhead, caused by the frequent exchange of model updates between local devices and central servers, is a notable challenge. To address this, future research should focus on optimizing communication protocols and reducing the frequency of updates to lessen the burden on network resources. Additionally, the paper suggests further research directions to refine FL frameworks and enhance their applicability in real-world environmental monitoring scenarios. By synthesizing findings from existing studies, this paper highlights the potential of FL to improve air quality management while maintaining data privacy and security, and it provides valuable insights for future developments in the field. Copyright © 2025, The Authors. All rights reserved.

关键词： Data quality

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Data-Driven Analysis of Skin Cancer Classification with Convolutional Neural Networks for E-Health Applications

Data-Driven Analysis of Skin Cancer Classification with Conv...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ahmed, Imran Ahmad, Misbah Chehri, Abdellah Jeon, Gwanggil Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom Hartpury University Animal and Agriculture Department Gloucester United Kingdom University of the West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

ISBN: (纸本)9798350351255

This study explores the effectiveness of Convolutional Neural Networks (CNNs) in automatically classifying skin cancer for e-health applications. The trained model showcases impressive performance by leveraging the HAM10000 dataset, which includes a wide range of skin lesion images from seven different classes. The parameters and architecture of the CNN model are presented in a systematic manner, providing valuable insights into the reasoning behind its design. The model is optimized using the Adam optimizer and annealing techniques to ensure efficient convergence. The model's performance is assessed on validation and test datasets, showcasing an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. This study highlights the significant potential of CNN as a powerful tool for automating the diagnosis of skin cancer, which is in line with the growing trend of using deep learning for medical image analysis. © 2024 IEEE.

关键词： Electronic health record

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Entrepreneurship, Business Strategies and Innovative Concepts towards Wastes Management, Resource Recovery and Circular Economy 5

Entrepreneurship, Business Strategies and Innovative Concept...

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5th IEEE International Conference on Electro-computing Technologies for Humanity, NIGERCON 2024

作者： Oyebode, Oluwadare Joshua Adeniyi, Adekunle Tirimisiyu Ojo, Temitope Mary Ajibade, Samuel-Soma M. Apeh, Sunday Afolalu, Sunday Adeniran Odokuma, Ogagaoghene Emmanuel Afe Babalola University Civil and Environmental Engineering Department Ekiti State Ado-Ekiti Nigeria Afe Babalola University Chemical and Petroleum Engineering Department Ekiti State Ado-Ekiti Nigeria Sunway University School of Engineering and Technology Department of Computing and Information Systems Malaysia Ekiti State University Department of Civil Engineering Ekiti Ado-Ekiti Nigeria Afe Babalola University Mechanical and Mechatronics Engineering Department Ekiti State Ado-Ekiti Nigeria Covenant University College of Engineering Department of Civil Engineering Ogun State Ota Nigeria

ISBN: (纸本)9798331542559

Huge benefits, entrepreneurial skills, business strategies, and innovative concepts can be deployed towards environmentally friendly activities, resource efficiency, and circular economy. This paper examined entrepreneurship, business strategies and creative concepts towards waste management, resource recovery and circular economy. The methodology used includes literature reviews and checks on engineering facilities and systems for handling wastes for health and wealth. Various business ideas and innovative strategies were checked for possible implementation. The finding revealed that many resources have been wasted due to a lack of appropriate techniques for handling waste in most developed and developing nations of the world. There are a lot of opportunities and funding for research and capacity building in waste recycling. All aspects of entrepreneurship, business strategies, and innovative concepts related to resource recovery, waste management, and the circular economy must be welcomed. Although waste is a major issue worldwide, efforts are being made to develop strategies for using trash as a valuable resource. Among these is the conversion of garbage into biogas, power, and other useful resources. It was concluded that entrepreneurship, business strategies and innovative concepts can be adopted in most nations for adequate waste management, resource recovery and circular economy. Management skills, design of engineering facilities, public awareness, and public-private partnership with proper collaboration of various stakeholders are very essential in this regard. Adequate research, industrial input, manpower development, skill acquisitions, funding and notable academic input are needed to achieve potential business, resource recovery, circular economy and effective waste management. © 2024 IEEE.

关键词： Business Strategies Effective Waste Management Entrepreneurship Innovative Concepts Resource Efficiency

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FDD based Cell-free massive MIMO with angular reciprocity over rician fading channels

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Telecommunication systems 2025年第3期88卷 1-18页

作者： Hassan, Beenish Baig, Sobia Aslam, Saad Tahzeeb, Shahab Hasan, Syed Faraz Department of Electrical Engineering The Islamia University of Bahawalpur Bahawalpur Pakistan Electrical Engineering Department College of Engineering Qassim University Unaizah Saudi Arabia School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer and Information Systems Engineering NED University of Engineering and Technology Karachi Pakistan Directorate of Research Services University of New England Armidale Australia

We present a combined angle reciprocity and Rician K factor based feedback for up/downlink scenarios using MMSE estimator for frequency division duplex (FDD) based massive Multiple Input Multiple Output (MIMO) system. It considers phase variation for spatially correlated Rician fading channel and also takes into account the phase shift in the line-of-sight component of Rician channel that is incurred due to user mobility and/or phase noise. The cell-free massive MIMO system has largely been implemented in the context of time division duplex (TDD) systems. However, FDD is still prevalent in communication standards and therefore, it is essential to assess its performance for FDD based channel estimation schemes in a typical cell-free massive MIMO environment. To begin with, we present an analysis of estimation schemes including the minimum mean square error (MMSE), linear MMSE (LMMSE), and least-square (LS) in terms of their spectral efficiency, using prior channel knowledge at the access points. We also present an angle-based beamforming technique that combines the quantized channel feedback based on Rician K factor with angle reciprocity-based channel estimates. The multipath channel estimation for MMSE and LMMSE schemes is also presented and simulation results are provided that show the superior performance of proposed angle and feedback MMSE over the LMMSE and theoretical MMSE schemes. Numerical simulations of the overall system confirm that the combined angular reciprocity and feedback-based MMSE estimates perform better with a value of $$12.6\%$$ for spectral efficiency and $$27\%$$ for energy efficiency, thus, producing an overall improved system performance compared to the conventional FDD based estimation schemes.

关键词： Cell-free Massive MIMO Feedback Rician fading FDD MMSE LMMSE

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Case-based Explanation of Classification Models for the Detection of SQL Injection Attacks 31

Case-based Explanation of Classification Models for the Dete...

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Workshops at the 31st International Conference on Case-Based Reasoning, ICCBR-WS 2023

作者： Recio-Garcia, Juan A. Orozco-Del-Castillo, Mauricio G. Soladrero, Jose A. Tecnológico Nacional de México IT de Mérida Department of Systems and Computing Merida Mexico Association for the Advancement of Artificial Intelligence Mexico Department of Software Engineering and Artificial Intelligence Instituto de Tecnologías del Conocimiento Universidad Complutense de Madrid Spain

This paper investigates the interpretability of various machine learning models in the context of detecting SQL injection attacks. The models under investigation include decision trees, multi-layer perceptron, random forests, support vector machines, ADA Boost, naive Bayes, and quadratic discriminant analysis. Our main objective is to gain insights into how these models make decisions when classifying SQL commands as either legitimate or potentially malicious. To achieve this, we employ a set of commonly used features from the literature to train and evaluate the models. By prioritizing explainability, our aim is to uncover the underlying factors and decision rules that drive the models’ predictions. Through this research, we seek to bridge the gap between model accuracy and human understanding, thereby facilitating the practical application of machine learning models in real-world security scenarios. The findings of this study contribute to the development of a case-based explanation system that provides valuable insights for cybersecurity practitioners and researchers. © 2023 CEUR-WS. All rights reserved.

关键词： Decision trees

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MFANet: Mixed Feature Attention Network for Port Lane Detection 42

MFANet: Mixed Feature Attention Network for Port Lane Detect...

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42nd Chinese Control Conference, CCC 2023

作者： Zhang, Jinwei Su, Jinya Yi, Dewei Yang, Jun Ministry of Education The School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom

ISBN: (纸本)9789887581543

With the advancement of industrial automation and artificial intelligence technology, unmanned port autonomy has gained increasing attention. Port automatic driving technology is a critical component of unmanned autonomous systems, enabling rubber tire gantry cranes (RTGs) to autonomously navigate by detecting port lanes and implementing control commands. In this study, a deep learning-based method is proposed for detecting port lanes in complex port scenes. The method employs the Inception module and attention mechanism to enhance lane feature extraction, and a structural loss is introduced to explicitly constrain the detection results and incorporate the prior characteristics of port lanes. A novel Mixed Feature Attention Network (MFANet) is proposed to implement the method for port lane detection. Experimental results demonstrate that MFANet effectively improves the accuracy of port lane detection while maintaining high computational efficiency. Furthermore, the performance of MFANet is evaluated in a real-world application scenario. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Computational efficiency

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Development of a Universal RNA Dual-Terminal Labeling Method for Sensing RNA-Ligand Interactions

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CCS Chemistry 2023年第1期5卷 221-233页

作者： Longhuai Cheng Dejun Ma Jie Zhang Xueying Kang Yang Wu Yi Zhao Long Yi Zhen Xi State Key Laboratory of Elemento-Organic Chemistry Department of Chemical BiologyCollege of ChemistryNational Pesticide Engineering Research CenterCollaborative Innovation Center of Chemical Science and EngineeringNankai UniversityTianjin 300071 State Key Laboratory of Organic-Inorganic Composites and Beijing Key Laboratory of Bioprocess Beijing University of Chemical Technology(BUCT)Beijing 100029 Key Laboratory of Intelligent Information Processing Research Center for Ubiquitous Computing SystemsInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190

Dual labeling of an RNA can provide Förster resonance energy transfer(FRET)sensors for studying RNA folding,miRNA maturation,and RNA-protein ***,we report the development of a highly efficient strategy for direct dual-terminal labeling of any RNA of *** explored new Michael cycloaddition for facile labeling of 5′-terminal RNA with improved *** chemical tetrazinylation of RNA at the 3′-terminus was achieved with the highly efficient and catalysis-free tetrazine-cycloalkyne *** single-terminal labeling methods were combined for dual-terminal labeling of an RNA including short hairpin RNA,pre-miRNA,riboswitch,and noncoding ***,these dual-labeled RNA-based FRET sensors were used to monitor RNA-ligand interactions in vitro and in live *** is anticipated that these universal RNA labeling strategies will be useful to study RNA structures and functions.

关键词： dual labeling Förster resonance energy transfer sensor Michael cycloaddition tetrazinecycloalkyne ligation RNA-ligand interaction

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Classifying Textual Sentiment Using Bidirectional Encoder Representations from Transformers

Classifying Textual Sentiment Using Bidirectional Encoder Re...

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International Conference on Computer and Information Technology (ICCIT)

作者： Shawly Ahsan Fairooz Tasnia Nafisa Tabassum Avishek Das Mohammed Moshiul Hoque Nazmul Siddique Department of Computer Science & Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh School of Computing Engineering and Intelligent Systems Ulster University UK

Textual sentiment analysis (TSA) has gained significant attention recently for its wide-ranging applications across various research domains and industries. However, most existing research and sentiment analysis tools are primarily tailored for English texts. The unique linguistic complexities of the Bengali language, coupled with a paucity of comprehensive resources and tools, pose distinctive challenges for TSA in Bengali. This paper introduces an intelligent approach, leveraging transformer-based learning techniques by harnessing the potent capabilities of self-attention mechanisms for dealing with Bengali sentences containing ungrammatical structures or local dialects. To tackle the downstream TSA task in Bengali, this work explores a range of machine learning (ML), deep learning (DL), and transformer-based baselines. Experimental results reveal that the Bangla BERT model outperforms the other baselines, achieving the highest weighted f 1 -score of 0.69.

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Mathematical Modelling of Quantum Kernel Method for Biomedical Data Analysis

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Computers, Materials & Continua 2022年第6期71卷 5441-5457页

作者： Mahmoud Ragab Ehab Bahauden Ashary Maha Farouk S.Sabir Adel A.Bahaddad Romany F.Mansour Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

This study presents a novelmethod to detect themedical application based on Quantum computing(QC)and a few Machine Learning(ML)*** has a primary advantage i.e.,it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of ***,this model is suggested for medical application only by recent researchers.A novel strategy i.e.,Quantum KernelMethod(QKM)is proposed in this paper for data *** this QKM process,Linear Tunicate Swarm Algorithm(LTSA),the optimization technique is used to calculate the loss function initially and is aimed at medical *** output of optimization is either 0 or 1 i.e.,odd or even in *** this output value,the data is identified according to the ***,the method also reduces time,saves cost and improves the efficiency by feature selection process i.e.,Filter *** the features are extracted,QKM is deployed as a classification model,while the loss function is minimized by *** motivation of the minimal objective is to remain ***,some computations can be performed more efficiently by the proposed *** testing,the test data was evaluated by minimal loss *** outcomes were assessed in terms of accuracy,computational time,and so *** this,databases like Lymphography,Dermatology,and Arrhythmia were used.

关键词： Medical data classification feature selection qkm classifier ltsa optimization

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Multilabel Aggressive Text Classification from Social Media using Transformer-based Approaches

Multilabel Aggressive Text Classification from Social Media ...

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International Conference on Computer and Information Technology (ICCIT)

作者： Jawad Hossain Avishek Das Mohammed Moshiul Hoque Nazmul Siddique Department of Computer Science & Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh School of Computing Engineering and Intelligent Systems Ulster University UK

The prevalence of multilabel aggressive text content on social media has a detrimental societal impact attracting the attention of government agencies and tech corporations to undertake measures against the spread of it. Hitherto research has focused on high-resource languages like English, leaving low-resource languages like Bengali out of the spotlight. This work presents a transformer-based technique to classify multilabel aggressive texts in Bengali into their targets to aid research in this area. A dataset (EM-BAD) containing 13728 texts is developed into five target classes: Religious Aggression (ReAG), Political Aggression (PoAG), Verbal Aggression (VeAG), Gender Aggression (GeAG), and Racial Aggression (RaAG) to perform the aggressive texts classification. Experimental results demonstrate that the Bangla-BERT with adjusted pooling layer and fine-tuning outdoes all ML, DL, and transformer-base baselines and existing techniques. The Bangla-BERT shows the highest weighted f1-score of 0.89 in the multilabel aggressive text classification task.

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