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Combined Effect of Concept Drift and Class Imbalance on Model Performance During Stream Classification

computers, Materials & Continua 2023年第4期75卷 1827-1845页

作者： Abdul Sattar Palli Jafreezal Jaafar Manzoor Ahmed Hashmani Heitor Murilo Gomes Aeshah Alsughayyir Abdul Rehman Gilal Department of Computer and Information Sciences Universiti Teknologi PETRONAS(UTP)Seri Iskandar32610Malaysia Centre for Research in Data Science UTPPerak32610Malaysia High Performance Cloud Computing Centre(HPC3) UTPPerak32610Malaysia School of Engineering and Computer Science Victoria University of WellingtonWellington6012New Zealand AI Institute University of Waikato WellingtonHamilton3240New Zealand Anti-Narcotics Force Ministry of Narcotics ControlIslamabad46000Pakistan College of Computer Science and Engineering Taibah UniversityMadinah42353Saudi Arabia

Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more *** existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s *** analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream ***, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority *** study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.

关键词： Classification data streams class imbalance concept drift class imbalance ratio

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FedHZone: Federated Learning Based Hazardous Zone Detection Using Internet of Drone Things

FedHZone: Federated Learning Based Hazardous Zone Detection ...

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2024 IEEE International Conference on Communications, computing, Cybersecurity and Informatics, CCCI 2024

作者： Biswas, Payel De, Debashis Obaidat, Mohammad S. Hazra, Arnab Sadoun, Balqies Jogesh Chandra Chaudhuri College Department of Computer Science Kolkata India Maulana Abul Kalam Azad University of Technology Centre of Mobile Cloud Computing Department of Computer Science and Engineering West Bengal WB Nadia India School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China School of Computing SRM University Department of Computational Intelligence SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India Dr. Sudhir Chandra sur Institute of Technology & Sports Complex Department of Computer Science and Engineering Kolkata India College of Engineering Al Balqa ' Applied University Al-Salt19117 Jordan

ISBN: (纸本)9798350349832

Air pollution can affect human health, so it is necessary to predict the air quality index (AQI) in advance. In this work, air quality data collected by the Internet of Drone Things (IoDT) is predicted and analyzed to detect highly polluted zones using a distributed privacy-preserving federated learning (FL) approach. Air quality data for a particular zone is collected and then fed into a Long short-Term memory (LSTM) based model to predict the pollution level in local training. Global aggregation is done by an IoDT-mounted central server to aggregate the received models from the local IoDT level. Privacy-preserving FedHZone predicts and analyses the AQI values from the past time series dataset in a multi-drone environment. This work detects the most hazardous zone in a larger region of Jaipur, Rajasthan using real-Time data. The result shows FedHZone with stacked LSTM having ''2%-7% better accuracy than the vanilla LSTM-based model. © 2024 IEEE.

关键词： Federated learning

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Multimodal Learning in Image Processing

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computers, Materials & Continua 2025年第2期82卷 3615-3618页

作者： Zhixin Chen Gautam Srivastava Shuai Liu School of Educational Science Hunan Normal UniversityChangsha410081China Institute of Interdisciplinary Studies Hunan Normal UniversityChangsha410081China Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Research Centre for Interneural Computing China Medical UniversityTaichung406040Taiwan Institute of Engineering and Technology Chitkara UniversityChandigarh140401India

1 Introduction onMultimodal Learning in Image Processing IP(Image processing),as a classical research domain in computer application technology,has been researched for *** is one of the most important research directions in computer vision,which is the basis for many current hotspots such as intelligent transportation/education/industry,*** image processing is the strongest link for AI(artificial intelligence)applying to real world application,it has been a challenging research field with the development of AI,from DNN(deep convolutional network),Attention/LSTM(long-short term memory),to Transformer/Diffusion/Mamba based GAI(generated AI)models,e.g.,GPT and Sora[1].Today,the description ability of single-model feature limits the performance of image *** comprehensive description of the image is required to match the computational performance of current large scale models.

关键词： Image computer LSTM

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Rule-oriented Approach for Knowledge acquisition from Database 15

Rule-oriented Approach for Knowledge acquisition from Databa...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Aakash, B. Uma Sankari, S.S. Thangavel, Senthil Kumar Palanisamy, T. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Vikram Sarabhai Space Centre ISRO Scientist Thiruvananthapuram India Amrita Vishwa Vidyapeetham Amrita School of Physical Sciences Department of Mathematics Coimbatore India

ISBN: (纸本)9798350370249

In today's world, where importance is provided more towards knowledge rather than just information, the need for transformation of databases to knowledgebase in an efficient way such that data is not lost during the process of transformation. This paper explores the scalability of the rule-based approach, demonstrating its effectiveness across various domains and dataset sizes. Through illustrative examples and case studies, it elucidates how this methodology can adapt to evolving data structures and accommodate complex relationships within the information ecosystem. Moreover, the research investigates the interpretability and transparency of the transformed knowledge base. By harnessing the explicit logic encoded in PROLOG rules, users gain insights into the underlying reasoning process, fostering trust and facilitating collaboration between domain experts and data practitioners. The research highlights the practical implications of deploying a rule-oriented transformation strategy in real-world scenarios. From facilitating decision support systems to enabling semantic search functionalities, the transformed knowledge base serves as a valuable asset for organizations seeking to unlock the latent insights buried within their data repositories. This paper discusses one such possible way which is Rule-oriented Approach where the Database transform to Knowledgebase through PROLOG script which has more advantages like Improved Data Understanding, Enhanced Querying and Retrieval and steps of the transformation are well defined in this paper. © 2024 IEEE.

关键词： Latent semantic analysis

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A Detailed Investigation into the Brain Cancer Detection Capabilities of CNN

A Detailed Investigation into the Brain Cancer Detection Cap...

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International Health Informatics Conference, IHIC 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Choudhury, Tanupriya Kotecha, Ketan School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International University Lavale Campus Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819771899

Brain tumour detection is an accepted testing task in the beginning phases of life. Yet, presently it has progressed with different AI calculations. Along these lines, to identify the brain tumour of a patient we consider the data of patients like MRI pictures of a patient’s brain. Here our concern is to spot if the tumour is available in persisting’s brain. It’s crucial to identify the tumour at the beginning level for a sound lifetime of a patient. During this task, we gauge the seriousness utilizing Convolutional Neural Network calculationwhich gives us precise outcomes. Tumour division is a pivotal and burdensome assignment inside the territory of clinical picture preparation as a human-helped manual characterization may bring about erroneous expectations and findings. We suggested using convolutional neural organization to distinguish tumours from 2D appealing reverberation brain images (MRI). The exploratory analysis was performed on a dataset that included tumours of various sizes, regions, forms, and picture forces. We used convolutional neural networks, which are implemented using Keras and Tensorflow because they respect a much better display than standard ones. CNN achieved an accuracy of 83.54% in our research. The recently developed CNN design may be used as a productive decisionaid device for radiologists in clinical diagnostics because of its high speculation capacity and quickness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neuroimaging

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ADAPTIVE STOCHASTIC GRADIENT ALGORITHM FOR BLACK-BOX MULTI-OBJECTIVE LEARNING 12

ADAPTIVE STOCHASTIC GRADIENT ALGORITHM FOR BLACK-BOX MULTI-O...

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12th International Conference on Learning Representations, ICLR 2024

作者： Ye, Feiyang Lyu, Yueming Wang, Xuehao Zhang, Yu Tsang, Ivor W. Department of Computer Science and Engineering Southern University of Science and Technology China Australian Artificial Intelligence Institute University of Technology Sydney Australia Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Shanghai Artificial Intelligence Laboratory China School of Computer Science and Engineering Nanyang Technological University Singapore

Multi-objective optimization (MOO) has become an influential framework for various machine learning problems, including reinforcement learning and multitask learning. In this paper, we study the black-box multi-objective optimization problem, where we aim to optimize multiple potentially conflicting objectives with function queries only. To address this challenging problem and find a Pareto optimal solution or the Pareto stationary solution, we propose a novel adaptive stochastic gradient algorithm for black-box MOO, called ASMG. Specifically, we use the stochastic gradient approximation method to obtain the gradient for the distribution parameters of the Gaussian smoothed MOO with function queries only. Subsequently, an adaptive weight is employed to aggregate all stochastic gradients to optimize all objective functions effectively. Theoretically, we explicitly provide the connection between the original MOO problem and the corresponding Gaussian smoothed MOO problem and prove the convergence rate for the proposed ASMG algorithm in both convex and non-convex scenarios. Empirically, the proposed ASMG method achieves competitive performance on multiple numerical benchmark problems. Additionally, the state-of-the-art performance on the black-box multi-task learning problem demonstrates the effectiveness of the proposed ASMG method. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Multiobjective optimization

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Towards AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom University of West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Hartpury University Department of Animal and Agriculture Gloucester United Kingdom Aston University College of Engineering and Physical Sciences Birmingham United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating Artificial Intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model's effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model's capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision-making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step towards recognising the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2025 IEEE.

关键词： Artificial intelligence (AI) autonomous vehicles deep leaning edge computing Internet of Vehicles (IoV)

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Predicting Estimated Time of Arrival Using Boosting Models 6

Predicting Estimated Time of Arrival Using Boosting Models

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Kam, Say-Hong Chong, Yung-Wey Ibrahim, Noor Farizah Keoh, Sye-Loong Phon-Amnuaisuk, Somnuk Abdul Rahim, Sharul Kamal School of Computer Sciences Universiti Sains Malaysia Penang Malaysia National Advanced IPv6 Centre Universiti Sains Malaysia Penang Malaysia School of Computing Science University of Glasgow Glasgow United Kingdom School of Computing and Informatics Universiti Teknologi Brunei Brunei Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Malaysia

ISBN: (纸本)9798350344349

Estimating the time of arrival (ETA) in public transportation can be challenging due to incomplete data and the complex nature of the urban environment. This study aims to address persistent criticism of the poor punctuality problem in Malaysian buses through the modeling of bus arrival time predictions. The study uses geographical and time data to predict bus arrival times through several boosting models. The data cleaning method enhanced data quality by eliminating invariable entries, segmenting the bus route for a more granular analysis, and encoding the data for improved structure and reliability. Through the implementation of Boruta for feature selection, relevant variables crucial for prediction were identified, contributing to the model's precision. The results highlighted LightGBM's superiority over AdaBoost and XGBoost, exhibiting the highest accuracy and a balanced level of complexity. This integrated methodology not only presents a robust prediction model but also showcases a potential practical implementation. © 2024 IEEE.

关键词： Forecasting

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DeepCNN:Spectro-temporal feature representation for speech emotion recognition

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin Database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

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Train for Stain: Adapting for Diverse H&E Staining Profiles Across Centers in Classification of Mitotic Figures (Glioma-MDC 2025)

Train for Stain: Adapting for Diverse H&E Staining Profiles ...

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IEEE International Symposium on Biomedical Imaging

作者： Andrew Brown Victoria Porter Alexander Baine Richard Gault Centre for Intelligent Sustainable Computing School of Electronics Electrical Engineering and Computer Science Queen's University Belfast

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Mitotic figure detection is an important aspect of grading for many forms of cancer however, there can be very high interrater variability. With the emergence of computational digital pathology, there is great potential for this classification task to be carried out objectively and consistently by a data-driven approach. However, this process is somewhat hampered by the diversity of cellular characteristics across cancer sites and the size of the domain shift between datasets originating from different centers. Building upon a previously developed classifier for H&E stained samples (HPVNet), this study aims to see if the established practices to minimize the impact of domain shift by stain variation can be minimized through a range of augmentation approaches and fine-tuning. The developed model obtained a very high ROC AUC measure (0.99 in train and validation) as well as good accuracy (0.94 in validation) and F1 score (0.94 in validation). The findings show potential but more work is needed to establish a truly generalizable approach.

关键词： Pathology Adaptation models Accuracy Buildings Biomedical measurement Cancer Biomedical imaging

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