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4th International Conference of science and information technology in Smart Administration, ICSINTESA 2024

作者： Amin, Muhammad Efendi, Syahril Candra, Ade Universitas Sumatera Utara Master of Information Technology Medan Indonesia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798350376111

The apriori algorithm is an algorithm that generates candidate itemsets incrementally and recursively to calculate and combine itemsets until no candidate itemset. Apriori algorithm has limitations, namely requiring a lot of time to scan the database to calculate support and confidence. In this study, the performance improvement is the execution time parameter for data scanning, and then researchers propose a method to speed up the performance of the apriori algorithm by combining the transaction reduction and hash-based methods, where the transaction reduction method is expected to be able to reduce the data to be scanned in the database by eliminating data whose value is below the minimum support. Then the hashbased method is able to reduce the data to be scanned in the database because the data scanned is only the hash code, while the large itemsets data will be represented by the hash code without making the performance of the validity of the rules formed does not decrease. Because the most useful information from finding patterns using the apriori algorithm is the rules formed. So the time required to execute a large database can be reduced a lot with association rules that run according to the procedure. From the results of tests conducted by researchers, at 2-itemsets in the second iteration the processing time is reduced about 20 times faster, then in the 3-itemsets in the third iteration the processing time was reduced by around 790 times faster compared to the apriori algorithm without transaction reduction and hash based methods. © 2024 IEEE.

关键词： Database systems

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Tensor-Based Factorial Hidden Markov Model for Cyber-Physical-Social Services

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IEEE Transactions on Services Computing 2025年第03期18卷 1825-1837页

作者： Lu, Zhixing Yang, Laurence T. Azman, Azreen Zhang, Shunli Zhou, Fang Hainan University School of Computer Science and Technology Hainan China Universiti Putra Malaysia Faculty of Computer Science and Information Technology Serdang Malaysia Zhengzhou University School of Computer and Artificial Intelligence School of Software Engineering Zhengzhou China Huazhong University of Science and Technology School of Computer Science and Technology Huazhong China St. Francis Xavier University Department of Computer Science Canada

With the rapid development and widespread application of information, computer, and communication technologies, Cyber-Physical-Social Systems (CPSS) have gained increasing importance and attention. To enable intelligent applications and provide better services for CPSS users, efficient data analytical models are crucial. This paper presents a novel data analytic framework for CPSS services. First, a Tensor-Based Factorial Hidden Markov Model (T-FHMM) is introduced to comprehensively analyze multi-user activity features, enhancing CPSS activity analytics. A tensor-based Forward-Backward algorithm is then designed for T-FHMM to efficiently perform evaluation tasks using multiple probabilistic computing micro-services. Additionally, a tensor-based Baum-Welch algorithm is developed to accurately learn model parameters via parameter optimization micro-services. Furthermore, a tensor-based Viterbi algorithm is implemented with specific micro-services to improve prediction tasks. Finally, the comprehensive performance of the proposed model and algorithms is validated on three open datasets through self-comparison and other-comparison. Experimental results demonstrate that the proposed method outperforms compared methods in terms of accuracy, precision, recall, and F1-score. © 2008-2012 IEEE.

关键词： Viterbi algorithm

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Handling Imbalanced Data for Improved Classification Performance: Methods and Challenges 3

Handling Imbalanced Data for Improved Classification Perform...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Abokadr, Somiya Azman, Azreen Hamdan, Hazlina Amelina, Nurul University Putra Malaysia Faculty of Computer Science & Information Technology Department of Intelligent Systems Seri Kembangan43400 Malaysia Al Zintan University Faculty of Science Libya University Putra Malaysia Faculty of Computer Science & Information Technology Seri Kembangan43400 Malaysia University Putra Malaysia Faculty of Computer Science and Information Technology Department of Computer Science Malaysia University Putra Malaysia Faculty of Computer Science and Information Technology Department of Multimedia Selangor Darul Ehsan Serdang Malaysia

ISBN: (纸本)9798350305333

Imbalanced data significantly impacts the efficacy of machine learning models. In cases where one class greatly outweighs the other in terms of sample count, models might develop a bias towards the majority class, thereby undermining the performance of the minority class. Imbalanced data act to increase the risk of overfitting, as the model may memorize the majority of class samples instead of learning underlying patterns. This paper addresses these challenges in the classification field by exploring various solutions, including under-sampling, oversampling, SMOTE, cost-sensitive learning, and ensemble deep learning methods. The evaluate the performance of these methods on different datasets and provide insights into their strengths and limitations. The paper presents a taxonomy of strategies for imbalanced binary and multi-class classification problems, including resampling, algorithmic, and hybrid methods. Ultimately, the paper furnishes guidelines to facilitate the selection of the most pertinent method for mitigating imbalanced data challenges within a specific classification context. © 2023 IEEE.

关键词： Classification (of information)

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DSWA: A Dilated Shift Window Attention Method for Chinese Named Entity Recognition 29

DSWA: A Dilated Shift Window Attention Method for Chinese Na...

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29th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2023

作者： Hou, Xinyu Zhu, Cui Zhu, Wenjun School of Computer Science Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)1891706578

In recent times, numerous models tried to enhance the performance of Transformer on Chinese NER tasks. The model can be enhanced in two ways: one is combining it with lexicon augmentation techniques, the other is optimizing the Transformer model itself. According to research, fully connected self-attention may scatter the attention distribution, which is the reason for worse performance of the original Transformer with self-attention. In this paper, we attempt to optimize the Transformer model especially attention layer. Therefore, a novel attention mechanism, Dilated Shift Window Attention, is proposed to address this problem. By using Window Attention, this method improves the model’s capacity to deal local information, meanwhile, the model can still manage long text and long-distance dependencies owing to the Window Dilatation mechanism. Experiments on various datasets also show that DSWA replacing fully connected self-attention improves the model’s performance on the Chinese NER task. Copyright © 2023 by KSI Research Inc. and Knowledge Systems Institute, USA.

关键词： Knowledge engineering

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Optimal Deep Belief Network Enabled Malware Detection and Classification Model

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Intelligent Automation & Soft Computing 2023年第3期35卷 3349-3364页

作者： P.Pandi Chandran N.Hema Rajini M.Jeyakarthic Department of Computer and Information Science Faculty of ScienceAnnamalai UniversityChidambaram608002India Department of CSE Alagappa Chettiar Government College of Engineering and TechnologyKaraikudi630003India Department of Computer and Information Science Annamalai UniversityChidambaram608002India

Cybercrime has increased considerably in recent times by creating new methods of stealing,changing,and destroying data in daily *** Docu-ment Format(PDF)has been traditionally utilized as a popular way of spreading *** recent advances of machine learning(ML)and deep learning(DL)models are utilized to detect and classify *** this motivation,this study focuses on the design of mayﬂy optimization with a deep belief network for PDF malware detection and classiﬁcation(MFODBN-MDC)*** major intention of the MFODBN-MDC technique is for identifying and classify-ing the presence of malware exist in the *** proposed MFODBN-MDC method derives a new MFO algorithm for the optimal selection of feature *** addition,Adamax optimizer with the DBN model is used for PDF malware detection and classiﬁ*** design of the MFO algorithm to select features and Adamax based hyperparameter tuning for PDF malware detection and classi-ﬁcation demonstrates the novelty of the *** demonstrating the improved outcomes of the MFODBN-MDC model,a wide range of simulations are exe-cuted,and the results are assessed in various *** comparison study high-lighted the enhanced outcomes of the MFODBN-MDC model over the existing techniques with maximum precision,recall,and F1 score of 97.42%,97.33%,and 97.33%,respectively.

关键词： PDF malware data classiﬁcation security deep learning feature selection metaheuristics

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Exploring Interoperability and Decentralized Applications for Personal Health Data using FHIR Standards 11

Exploring Interoperability and Decentralized Applications fo...

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11th International Conference on information technology: IoT and Smart City, ICIT 2023

作者： Sentausa, Dionysius Hareva, David Habsara School of Information Science and Technology Faculty of Computer Science Universitas Pelita Harapan Indonesia

ISBN: (纸本)9798400709043

This study explores the integration of Fast Healthcare Interoperability Resources (FHIR) standards in a system designed for secure and efficient management of Personal Health Records (PHRs). PHRs are crucial for patients to have control over their healthcare information and make informed decisions. Ensuring the security of this sensitive data is vital, and blockchain technology offers robust protection. However, the high cost of storing PHRs on the blockchain has led us to investigate the use of the Interplanetary File System (IPFS) alongside blockchain. Our proposed system comprises a PHR Decentralized Application (DAPPS), IPFS, Metamask, and the Ethereum Blockchain. Users input their PHR data through the DAPPS, which is stored in IPFS, and its location is registered on the Ethereum Blockchain via Metamask. Comprehensive testing validates the system's performance, with success across all test cases, confirming the feasibility of utilizing the Interplanetary File System and Ethereum Blockchain for secure PHR management while adhering to FHIR standards, fostering interoperability in healthcare data exchange. © 2023 ACM.

关键词： Blockchain

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Toward Addressing the Software Architecture Blind Spot of information System Success Factors in the Public Health Domain 44

Toward Addressing the Software Architecture Blind Spot of In...

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44th International Conference on information Systems: Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies, ICIS 2023

作者： Dang, Anthony Beydoun, Ghassan School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9781713893622

Context: In the public health domain, there is no shortage of failed information Systems projects. In addition to overblown budgets and elapsed deadlines (ad nauseam), technical issues exist. These include poor usability, instability, system performance, and data inconsistency issues. The issues relate to Software Engineering, and specifically Software Architecture. The role of Software Architecture underpins these issues. However, the enquiries and analyses of these failed information Systems projects have focused on the perspective of stakeholders (e.g., government ministers and health practitioners) and project managers. While stakeholders provide input into project requirements and goals, the project failure or success does not emanate from these roles exclusively. For example, system instability is not specifically a stakeholder issue but rather a Software Architecture concern. Aim: To bring to bear and address public sector health information System failure from the Software Architecture perspective. Method: A literature survey was conducted to ascertain the perceived failure and success factors within the public health domain. Results: We observed that the available literature on health information Systems appears to lack success factors which have been noted in the Software Engineering and Software Architecture literature. Software Architecture appears to be an understudied area within the public health domain. Contribution: To bring awareness to the public health domain that the information System's success requires multi-faceted perspectives and actions. Specifically, perspectives and actions involving Software Architects and Software Engineers are required to successfully address the quality attributes of the proposed systems during development. Copyright © 2023 Anthony Dang and Ghassan Beydoun.

关键词： Software architecture

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Enhanced Object Detection and Classification via Multi-Method Fusion

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computers, Materials & Continua 2024年第5期79卷 3315-3331页

作者： Muhammad Waqas Ahmed Nouf Abdullah Almujally Abdulwahab Alazeb Asaad Algarni Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073South Korea

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented *** substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled *** address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection *** proposed approach involves the integration ofmultiple methods in a complementary *** process commences with the application of Gaussian filters tomitigate the impact of noise *** images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent *** Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented *** precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms *** Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved *** method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize *** minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall *** proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,***,detection accuracies of 87.2%and 86.6%have been *** ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex *** these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

关键词： BRIEF features saliency map fuzzy c-means object detection object recognition

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Multiple-Person Tracking with Criss-Cross Attention and Deep Matching 8

Multiple-Person Tracking with Criss-Cross Attention and Deep...

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8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Liu, Zhuoyang Zhang, Liming University of Macau Faculty of Science and Technology Department of Computer and Information Science Macau China

ISBN: (纸本)9798350397932

Multi-pedestrian tracking is one of the important application fields of computer vision, among which the real-time tracking of pedestrians and the ability to solve occlusion problems are two big challenges to improve the recognition accuracy. In this paper, we propose a new standardized tracking-by-detection framework that can effectively alleviate the above two problems. We first introduce the Criss-Cross Attention (CCA) mechanism in the detector. The CCA module can reduce the computational complexity of the detector and improve the detection accuracy. Then a pedestrian deep feature extractor fused with cosine metric learning for two-layer convolution is added to further improve the tracking accuracy and real-time performance. Finally, Kalman Filter prediction combined with pedestrian deep features effectively reduces identity switching under the occlusion. We conduct experiments on the MOT16 datasets and evaluate on HOTA. Experimental results demonstrate the competitiveness of our method against some selected state-of-the-art methods. © 2023 IEEE.

关键词： computer vision

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Text Classification Based (PWACNN and Context-BiLSTM Methods

Text Classification Based (PWACNN and Context-BiLSTM Methods

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2023 International Conference on Intelligent Management and Software Engineering, IMSE 2023

作者： Zhang, Xinyu Zhu, Wenjun Zhu, Cui Li, Hongyang School of Computer Science Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9798350394146

Text classification is an important task in natural language processing, where text is classified into different categories by labelling. However, due to the complex structure and deep semantics of natural language, it is still challenging to deeply analyze the intrinsic structure of text and extract text features. In this paper, we adopt a parallel combination of local and global features to extract potential semantic information from text data. Among them, global features are obtained by the Context-BiLSTM method. The method fuses the hidden states in both the forward and backward directions through a gate mechanism before entering the cell, instead of simply summing the forward and backward hidden states at the end. The local features are obtained through the Pooling Word Attention CNN(PWACNN) model. This method generates the pooling results with embedding participation, so the pooling results can focus on its relevant features in the original embedding. Finally, the PWACNN and Context-BiLSTM fusion model into the softmax layer for classification. Experimental results show that the proposed model performs well and can improve the accuracy of classification significantly. ©2023 IEEE.

关键词： Embeddings

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