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A Double-Branch Xception Architecture for Acute Hemorrhage Detection and Subtype Classification

computers, Materials & Continua 2023年第9期76卷 3727-3744页

作者： Muhammad Naeem Akram Muhammad Usman Yaseen Muhammad Waqar Muhammad Imran Aftab Hussain Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Software Engineering Capital University of Science&TechnologyIslamabad44000Pakistan

This study presents a deep learning model for efficient intracranial hemorrhage(ICH)detection and subtype classification on non-contrast head computed tomography(CT)*** refers to bleeding in the skull,leading to the most critical life-threatening health condition requiring rapid and accurate *** is classified as intra-axial hemorrhage(intraventricular,intraparenchymal)and extra-axial hemorrhage(subdural,epidural,subarachnoid)based on the bleeding location inside the *** computer-aided diagnoses(CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan ***,these approaches performonly binary classification and suffer from a large number of parameters,which increase storage ***,the accuracy of brain hemorrhage detection in existing models is significantly low for medically critical *** overcome these problems,a fast and efficient system for the automatic detection of ICH is *** designed a double-branch model based on xception architecture that extracts spatial and instant features,concatenates them,and creates the 3D spatial context(common feature vectors)fed to a decision tree classifier for final *** data employed for the experimentation was gathered during the 2019 Radiologist Society of North America(RSNA)brain hemorrhage detection *** model outperformed benchmark models and achieved better accuracy in intraventricular(99.49%),subarachnoid(99.49%),intraparenchymal(99.10%),and subdural(98.09%)categories,thereby justifying the performance of the proposed double-branch xception architecture for ICH detection and classification.

关键词： Computed tomography convolutional neural networks intracranial hemorrhage xception architecture

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OpenCV and its Applications in Artificial Intelligent Systems 5

OpenCV and its Applications in Artificial Intelligent System...

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5th International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2024

作者： Odeh, Ayman Odeh, Nada College Of Engineering Al Ain University Department of Software Engineering Al Ain United Arab Emirates College Of Information Technology UAEU Department of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798350354690

This paper explores the utilization of OpenCV (Open-Source computer Vision Library) in artificial intelligence (AI) systems, elucidating its pivotal role in advancing various applications across diverse domains. OpenCV, renowned for its comprehensive functionalities and real-time processing capabilities, has become instrumental in tasks such as object detection (OD), facial recognition (FR), and image processing (IP). However, despite its widespread adoption, there remains a need for a thorough examination of its integration within AI frameworks and the optimization of its functionalities. Through a comprehensive analysis, this study delineates the advantages, limitations, and potential areas for improvement of OpenCV in AI applications. Case studies exemplifying its efficacy in OD, FR, and autonomous systems further elucidate its practical implications. By delving into these aspects, this paper not only underscores the significance of OpenCV in AI but also provides valuable insights for researchers, developers, and practitioners aiming to leverage its capabilities in their endeavors. This study collectively underscores the significant role of OpenCV in enhancing the capabilities of AI systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Air Pollutant (CO) Forecasting: A Hybrid Approach Using Gooseneck Barnacle Optimization and Artificial Neural Networks 4

Air Pollutant (CO) Forecasting: A Hybrid Approach Using Goos...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Ahmed, Marzia Mohamad, Ahmad Johari Hassan, Md Maruf Bhuiyan, Touhid Department of Software Engineering Dhaka Bangladesh Faculty of Electrical & Electronics Engineering Technology Malaysia Southeast University Department of Computer Science and Engineering Dhaka Bangladesh School of IT VA United States

ISBN: (纸本)9798331518882

Air quality forecasting is critical for environmental monitoring and public health, and in this study, we propose a hybrid approach utilizing Gooseneck Barnacle Optimization (GBO) and Artificial Neural Networks (ANN) to enhance predictive accuracy. The air quality dataset, comprising pollutant concentrations (CO, NOx, benzene) and meteorological factors (temperature, humidity), is sourced from the UCI Machine Learning Repository. GBO is employed to fine-tune the ANN's hyperparameters (especially the weight and bias), as well as the number of layers, neurons, and activation functions, to improve regression-based forecasting. The GBOANN model achieves a Mean Squared Error (MSE) of 0.562 and a Root Mean Squared Percentage Error (RMSPE) of 5.63%, outperforming other metaheuristic methods like BMO, SSA, DA, MVO, and EMA. This study demonstrates the GBO-ANN model's potential for accurate real-time air quality forecasting, offering a robust, sustainable solution. © 2025 IEEE.

关键词： Air Quality GBO Metaheuristic Neural Network Time series forecasting

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IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1360-1372页

作者： Wang, Shuyue Liu, Zhunga Zhang, Zuowei Bennamoun, Mohammed Northwestern Polytechnical University School of Automation Shaanxi Xi’an710072 China The University of Western Australia Department of Computer Science and Software Engineering PerthWA6009 Australia

Multimodal domain adaptation (MMDA) aims to transfer knowledge across different domains that contain multimodal data. Current methods typically assume that both the source and target domains have paired multimodal data with the same modalities, allowing for direct knowledge transfer between corresponding types of data. However, in certain applications, the source domain benefits from advanced sensors and equipment, capturing more modalities than those available in the target domain. As a result, the information from the source modalities may not strongly align with that of the target modalities. This weak correlation hinders the effective utilization of all source data for the target domain. To address this challenge, we propose a weakly correlated multimodal domain adaptation (WCMMDA) method for pattern classification. WCMMDA is designed to acquire the modality-independent and category-related knowledge from the source domain, enabling the full utilization of available source modalities for effective knowledge transfer. Specifically, modality-invariant features are first extracted from the multimodal data to bridge the heterogeneity gap within each domain. Subsequently, domain-invariant features are further learned from these modality-invariant features to align the feature distributions across the source and target domains. A source-specific classifier is employed here, which predicts pseudo-labels for the target data and enables the feature extractor to explore category-related information in source features. Finally, a target-specific classifier is trained using the pseudolabeled target data, where highly reliable pseudolabels are selected based on confidence to improve classification performance. Extensive experiments are performed on the real-world multimodal datasets to demonstrate the superiority of WCMMDA. © 2020 IEEE.

关键词： Labeled data

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An aspect-based sentiment analysis model for Arabic game reviews based on hybrid transformers models

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Neural Computing and Applications 2025年第16期37卷 10309-10331页

作者： Hammad, Mahmoud AbuEnnab, Noor Al-Refai, Mohammed IT Department Ajman University Ajman United Arab Emirates Software Engineering Department Jordan University of Science and Technology Irbid Jordan Computer Science Department Jordan University of Science and Technology Irbid Jordan

Aspect-based sentiment analysis (ABSA) is a natural language processing (NLP) technique to determine the various sentiments of a customer in a single comment regarding different aspects. The increasing online data content generated by interested customers and reviewers motivated researchers and data scientists to conduct ABSA. ABSA has become increasingly popular in recent years due to its versatility in e-commerce, social media, and customer feedback analysis. However, ABSA faces several significant challenges, including determining the aspects and their sentiment polarities (positive, negative, or neutral) in a given text. Moreover, ABSA faces particular challenges in non-English languages such as Arabic due to the lack of resources and mature models. Typically, ABSA tackles one or more of the ABSA research tasks: (T1) aspect term extraction, (T2) aspect term polarity, (T3) aspect category identification, and (T4) aspect category polarity. To identify the aspects and their corresponding sentiment polarities in a given text, accurate and efficient NLP techniques are required. Despite growing interest in Arabic ABSA, the lack of annotated datasets and pre-trained models has hindered its development. In this research, we have collected a dataset of Arabic game reviews and annotated them using three annotators, and then we trained an ABSA deep learning model based on the BERT pre-trained model combined with zero-shot learning (ZSL) to tackle all the four aforementioned tasks. Our best performing model achieved a high accuracy on all four tasks with an accuracy of 91.61% on T1, 90.99% on T2, 79.08% on T3, and 88.17% on T4. Finally, we compared our model’s accuracy with the state-of-the-art Arabic-based ABSA models on different datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Sales

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Adaptive Resource Planning for AI Workloads with Variable Real-Time Tasks

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computers, Materials & Continua 2023年第3期74卷 6823-6833页

作者： Sunhwa Annie Nam Kyungwoon Cho Hyokyung Bahn Department of Computer Science and Engineering Ewha UniversitySeoul03760Korea Embedded Software Research Center Ewha UniversitySeoul03760Korea

AI(Artificial Intelligence)workloads are proliferating in modernreal-time *** the tasks of AI workloads fluctuate over time,resourceplanning policies used for traditional fixed real-time tasks should be *** particular,it is difficult to immediately handle changes inreal-time tasks without violating the deadline *** cope with thissituation,this paper analyzes the task situations of AI workloads and findsthe following two ***,resource planning for AI workloadsis a complicated search problem that requires much time for ***,although the task set of an AI workload may change over time,thepossible combinations of the task sets are known in *** on theseobservations,this paper proposes a new resource planning scheme for AIworkloads that supports the re-planning of *** of generatingresource plans on the fly,the proposed scheme pre-determines resourceplans for various combinations of ***,in any case,the workload isimmediately executed according to the resource plan ***,the proposed scheme maintains an optimized CPU(Central Processing Unit)and memory resource plan using genetic algorithms and applies it as soonas the workload *** proposed scheme is implemented in the opensourcesimulator SimRTS for the validation of its *** show that the proposed scheme reduces the energy consumptionof CPU and memory by 45.5%on average without deadline misses.

关键词： Resource planning artificial intelligence real-time system task scheduling optimization problem genetic algorithm

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Characterization of Memory Access in Deep Learning and Its Implications in Memory Management

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computers, Materials & Continua 2023年第7期76卷 607-629页

作者： Jeongha Lee Hyokyung Bahn Institute of Artificial Intelligence and Software Ewha UniversitySeoul03760Korea Department of Computer Science and Engineering Ewha UniversitySeoul03760Korea

Due to the recent trend of software intelligence in the Fourth Industrial Revolution,deep learning has become a mainstream workload for modern computer *** the data size of deep learning increasingly grows,managing the limited memory capacity efficiently for deep learning workloads becomes *** this paper,we analyze memory accesses in deep learning workloads and find out some unique characteristics differentiated from traditional ***,when comparing instruction and data accesses,data access accounts for 96%–99%of total memory accesses in deep learning workloads,which is quite different from traditional ***,when comparing read and write accesses,write access dominates,accounting for 64%–80%of total memory ***,although write access makes up the majority of memory accesses,it shows a low access bias of 0.3 in the Zipf ***,in predicting re-access,recency is important in read access,but frequency provides more accurate information in write *** on these observations,we introduce a Non-Volatile Random Access Memory(NVRAM)-accelerated memory architecture for deep learning workloads,and present a new memory management policy for this *** considering the memory access characteristics of deep learning workloads,the proposed policy improves memory performance by 64.3%on average compared to the CLOCK policy.

关键词： Memory access deep learning machine learning memory access memory management CLOCK

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Correntropy-Based Data Selective Adaptive Filtering

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 754-766页

作者： Chien, Ying-Ren Wu, Sheng-Teng Tsao, Hen-Wai Diniz, Paulo S.R. Adaptive and Autonomous Communication Laboratory Department of Electrical Engineering National Ilan University Yilan City260007 Taiwan College of Electrical Engineering and Computer Science Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Program of Electrical Engineering Department of Electronics and Computer Engineering COPPE/Poli Universidade Federal do Rio de Janeiro Rio de Janeiro68504 Brazil

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Adaptive filters

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Utilizing Evidential Reasoning (ER) Approach for software Components Selection

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SN computer Science 2025年第3期6卷 1-13页

作者： Ahmad, Nabot Al-Qerem, Ahmad Omar, Firas Jebreen, Issam Department of Software Engineering Al Zaytoonah University of Jordan Amman Jordan Computer Science Department Zarqa University Zarqa Jordan Business Intelligence Department Al Zaytoonah University of Jordan Amman Jordan Software Engineering Department Middle East University Amman Lebanon

software component selection is one of the most challenging problems for software developers to meet customer needs. Despite various methods researchers propose to aid in component selection, a gap exists in addressing the complexities of decision-making under uncertainty. This study utilizes the Evidential Reasoning (ER) approach, which effectively handles multi-criteria decision-making (MCDM) scenarios to evaluate software components for an online bookstore. The methodology involves assessing quality attributes using the ER approach incorporating evaluation grades, weights, and belief degrees to manage information uncertainty. The results demonstrate that the ER approach provides a robust framework for software component selection leading to informed decisions. This approach offers significant advantages in scenarios where traditional MCDM methods are insufficient, thereby enhancing the reliability of component selection in software engineering. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making Evidential reasoning Multi-criteria software component

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3-Multi ranked encryption with enhanced security in cloud computing

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Digital Communications and Networks 2023年第2期9卷 313-326页

作者： YeEun Kim Junggab Son Reza M.Parizi Gautam Srivastava Heekuck Oh Major in Bio Artificial Intelligence Dept.of Computer Science and EngineeringHanyang UniversityAnsanRepublic of Korea Department of Computer Science Kennesaw State UniversityMariettaUSA Department of Software Engineering Kennesaw State UniversityMariettaUSA Department of Mathematics and Computer Science Brandon UniversityBrandonCanada Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Dept.of Computer Science and Engineering Hanyang UniversityAnsanRepublic of Korea

Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users,and even return the top-k most relevant search results when *** refer to a model that satisfies all of the conditions a 3-multi ranked search ***,SE schemes that have been proposed to date use fully trusted trapdoor generation centers,and several methods assume a secure connection between the data users and a trapdoor generation *** is,they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords,but it will never attempt to use it in any other manner than that requested,which is impractical in real *** this study,to enhance the security,we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security *** proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center,thereby preventing attackers from determining whether two different queries include the same ***,we develop a method for managing multiple encrypted keywords from every data owner,each encrypted with a different *** evaluation demonstrates that,despite the trade-off overhead that results from the weaker security assumption,the proposed scheme achieves reasonable performance compared to extant schemes,which implies that our scheme is practical and closest to real life.

关键词： Searchable encryption Multi-keyword search Multiple data owners/users Ranked search Enhanced security

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