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Global-local term fusion for optimised community Q&A topic modelling

International Journal of Innovative Computing and Applications 2024年第1期15卷 50-69页

作者： Prabha, Sneh Sardana, Neetu Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Community question and answer (Q&A) websites have become invaluable information and knowledge-sharing sources. Effective topic modelling on these platforms is crucial for organising and navigating the vast amount of user-generated content. To address these challenges, we propose a novel global-local term fusion with optimised community (GLOCOM) Q&A topic modelling approach that leverages both local and global term importance to enhance topic modelling on community Q&A websites. GLOCOM combines term frequency-inverse document frequency for local importance and entropy for global importance. Further, we employ fuzzy clustering to enhance the representation of multifaceted topics. Furthermore, clustering results are optimised using a genetic algorithm (GA) to refine cluster assignments and centroids. We compared the proposed model with baseline models LDA and FLSA. GLOCOM has performed consistently well for all topic numbers. It has shown an improvement of 8.86% in silhouette score as compared to LDA and excelled for datasets with size > 3 MB. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Inverse problems

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Predicting Liver Cirrhosis Stages Using Extra Trees, Random Forest, and SVM with Data Mining Techniques

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第21期48卷 15-26页

作者： Ali, Duaa S. Aljabery, Maalim A. Faculty of Computer Science & Information Technology Computer Science Dept University of Basrah Basrah Iraq

Liver cirrhosis often occurs as a result of the lengthy and persistent progression of chronic liver disorders. It is a key crucial cause of death on a global scale. Early diagnosis and identification of cirrhosis are essential for preventing the disease's progression and the complete devastation of liver tissue. This paper aims to build an intelligent automated system that can predict the stages of cirrhosis employing Machine Learning (ML) algorithms, including Random Forest (RF), Extra Trees (ET), and Support Vector Machine (SVM). The dataset used in this research is sourced from the Zenodo website and linked to the GitHub website. This was our initial use of the data, which is publicly accessible and consists of 70 features and 10,000 records. In addition, data mining techniques were used to analyze the data before predicting the outcome. This involved data balancing due to the significant imbalance in the dataset's classes. To address this, we employed the Synthetic Minority Oversampling Technique (SMOTE) to mitigate a bias problem in a machine learning model. Then, feature selection techniques were applied, such as Chi-Square, Mutual information (MI), and Recursive Feature Elimination and Cross-Validation (RFECV) based on classifiers RF and SVM (RF-RFECV, SVM-RFECV) to select relevant features. Lastly, the experimental findings showed that the Extra-Trees model with the Chi-square feature selection method (ET-Chi-Square) achieved the maximum level of accuracy of 93.87%. Additionally, it obtained recall, F1-score, and precision values of 94% each and an Area Under Curve (AUC) of 99%. Our method exhibited exceptional performance as compared to previous relevant research. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Support vector machines

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Detection and Diagnosis of Small Target Breast Masses Based on Convolutional Neural Networks

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Tsinghua science and technology 2024年第5期29卷 1524-1539页

作者： Ling Tan Ying Liang Jingming Xia Hui Wu Jining Zhu School of Computer Science Nanjing University of Information Science&TechnologyNanjing 210044China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing 210044China

Breast mass identification is of great significance for early screening of breast cancer,while the existing detection methods have high missed and misdiagnosis rate for small *** propose a small target breast mass detection network named Residual asymmetric dilated convolution-Cross layer attention-Mean standard deviation adaptive selection-You Only Look Once(RCM-YOLO),which improves the identifiability of small masses by increasing the resolution of feature maps,adopts residual asymmetric dilated convolution to expand the receptive field and optimize the amount of parameters,and proposes the cross-layer attention that transfers the deep semantic information to the shallow layer as auxiliary information to obtain key feature *** the training process,we propose an adaptive positive sample selection algorithm to automatically select positive samples,which considers the statistical features of the intersection over union sets to ensure the validity of the training set and the detection accuracy of the *** verify the performance of our model,we used public datasets to carry out the *** results showed that the mean Average Precision(mAP)of RCM-YOLO reached 90.34%,compared with YOLOv5,the missed detection rate for small masses of RCM-YOLO was reduced to 11%,and the single detection time was reduced to 28 *** detection accuracy and speed can be effectively improved by strengthening the feature expression of small masses and the relationship between *** method can help doctors in batch screening of breast images,and significantly promote the detection rate of small masses and reduce misdiagnosis.

关键词： mammography diagnosis mass detection deep learning cross-layer attention adaptive positive sample selection

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Cloud Computing Security: Assured Deletion

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第3期48卷 485-496页

作者： Ahmed, Sarmad Mahmood Mahmood, Baban Ahmed Department of Computer Science College of Computer Science and Information Technology Kirkuk University Kirkuk Iraq

With the advent of cloud computing, many organizations, institutions, and individuals have chosen to store their data in the cloud as a way to compensate for limited local storage capabilities and reduce expenses. However, the process of uploading data to the cloud results in relinquishing control over it, leaving the data owner unaware of the details of its storage and location. Hence, ensuring data confidentiality and integrity has emerged as a critical concern, especially with regard to cloud employees and other potential attacks. A significant amount of investigation has been conducted on the security aspects of cloud computing, with a particular focus on permanent removals. Hence This study investigates the deployment of deletion assurance in cloud computing on two separate cloud platforms, employing strategies such as cryptographic key partitioning, cryptographic-based random writing, and link deletion within confirmed deletion scenarios. The characteristics of this approach include security safeguards like asymmetric encryption (specifically Elliptic Curve Cryptography (ECC)) and multiple hashing algorithms like hash 256 are used to fortify the security and confidentiality of data stored in the public cloud and the secrecy of files kept in the cloud is unaffected by invasions from the outside or from within an encrypted file prevents a threat from reading it and the owner may easily encrypt each file with a fresh key by managing and storing the keys using the key management center also authorized users are able to request this key thanks to secure access enforcement and In the event of assure deletion, the file is overwritten with random encryption and uploaded to the cloud, updating all copies while deleting all links and keys from the key management. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Cloud platforms

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EAROE:Enabling Body-Channel Voice Interaction Interface on Earphones via Occlusion Effect

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IEEE Internet of Things Journal 2025年第12期12卷 18651-18664页

作者： Han, Feiyu Zuo, You Jiang, Weiwei Yan, Dawei Zhao, Yuxin Yang, Panlong Yan, Yubo School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science and Technology University of Science and Technology of China Hefei230021 China

Nowadays, voice input on earphones has become one of the most paramount human-computer interaction approaches. Traditional voice interaction is built on the air channel, which is highly noise-susceptible and suffers from being falsely triggered by nearby competing users. In our work, we design a noise-resistant voice interaction interface on earphones, named EAROE, which takes advantage of the narrow-bandwidth body channel to reconstruct high-fidelity audible speech. Although promising, directly taking the body channel as a voice interaction interface is non-trivial since the limited bandwidth of body-channel speech causes the original timbre information and linguistic content to be lost. To address these issues, we employ an electro-acoustic (EA) model for occlusion effect-based cross-channel correlation analysis. Based on that, we carefully design an attention-based encoder-decoder network to embrace cross-channel correlation for high-quality wide-bandwidth spectrum synthesis. To accommodate individual differences and improve model generalization, we implement a physics-based data augmentation strategy to expand the scale of the training dataset. Through extensive real-world experiments with 28 participants, EAROE can achieve an average Mel-cepstral distance of 8.08, an average modulation spectra distance of 0.82, and an average log-spectral distance of 11.16, outperforming existing solutions. © 2014 IEEE.

关键词： Bandwidth

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Hybrid IT? Algorithm for Large-Scale Colored Traveling Salesman Problem

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Chinese Journal of Electronics 2024年第6期33卷 1337-1345页

作者： Xueshi DONG College of Computer Science and Technology Qingdao University Beijing Key Laboratory of Urban Spatial Information Engineering School of Computer Science Wuhan University

In the fields of intelligent transportation and multi-task cooperation, many practical problems can be modeled by colored traveling salesman problem(CTSP). When solving large-scale CTSP with a scale of more than 1000dimensions, their convergence speed and the quality of their solutions are limited. This paper proposes a new hybrid IT?(HIT?) algorithm, which integrates two new strategies, crossover operator and mutation strategy, into the standard IT?. In the iteration process of HIT?, the feasible solution of CTSP is represented by the double chromosome coding, and the random drift and wave operators are used to explore and develop new unknown regions. In this process, the drift operator is executed by the improved crossover operator, and the wave operator is performed by the optimized mutation strategy. Experiments show that HIT? is superior to the known comparison algorithms in terms of the quality solution.

关键词： Transportation Traveling salesman problems Multitasking Encoding Standards Biological cells Convergence

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Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment

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computers, Materials & Continua 2024年第9期80卷 4663-4686页

作者： Aljuaid Turkea Ayedh M Ainuddin Wahid Abdul Wahab Mohd Yamani Idna Idris Faculty of Computer Science and Information Technology Universiti MalayaKuala Lumpur50603Malaysia Faculty of Computer Science and Information Technology Shaqra UniversityShaqra11961Saudi Arabia Center for Mobile Cloud Computing Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala Lumpur50603Malaysia

Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce ***,this trend introduces security challenges,such as unauthorized *** access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and *** paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN *** technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access *** proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy ***,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern ***,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.

关键词： BYOD security access control access control decision-enforcement deep learning neural network techniques TabularDNN multilayer dynamic adaptable flexibility bottlenecks performance policy conflict

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Pushing the Limits of WiFi-Based Gait Recognition Towards Non-Gait Human Behaviors

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6137-6153页

作者： Yan, Dawei Yang, Panlong Shang, Fei Han, Feiyu Yan, Yubo Li, Xiang-Yang University of Science and Technology of China School of Computer Science and Technology Hefei230021 China Nanjing University of Information Science & Technology School of Computer Science Nanjing210044 China

WiFi-based gait recognition technologies have seen significant advancements in recent years. However, most existing approaches rely on a critical assumption: users must walk continuously and maintain a consistent body posture. This poses a substantial challenge when users engage in non-periodic or discontinuous behaviors (e.g., stopping, starting, or turning mid-walk), which can disrupt the extraction of gait-related features and degrade recognition performance. To address this issue, we propose freeGait, a novel approach designed to mitigate the impact of non-gait behaviors in WiFi-based gait recognition systems. Our solution models this problem as domain adaptation, where we learn domain-independent representations to isolate gait features from behavior-dependent noise. We treat human behaviors with labeled user data as source domains and behaviors without user labels as target domains. However, applying domain adaptation directly is challenging due to the ambiguous classification boundaries in the target domains for WiFi signals. To overcome this, we align the posterior distributions between the source and target domains and constrain the conditional distribution within the target domains to enhance gait classification accuracy. Additionally, we implement a data augmentation module to generate data resembling the labeled data, while supervised learning ensures distinctiveness between users. Our experiments, conducted with 20 participants across 3 different scenarios, demonstrate that freeGait can accurately predict data across 15 domains by labeling only a small subset from 6 source domains, achieving up to a 45% improvement in user classification accuracy compared to existing methods. © 2002-2012 IEEE.

关键词： Wi-Fi

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Efficient and lightweight CNN model for COVID-19 diagnosis from CT and X-ray images using customized pruning and quantization techniques

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Neural Computing and Applications 2025年 1-20页

作者： Sriwiboon, Nattavut Department of Computer Science and Information Technology Faculty of Science and Health Technology Kalasin University Kalasin Thailand

The coronavirus disease 2019 (COVID-19) has posed significant challenges globally, with image classification becoming a critical tool for detecting COVID-19 from chest X-ray and CT images. Convolutional neural network (CNN) architectures are widely employed for this purpose, yet many previous models have struggled to balance high accuracy with low storage requirements, limiting their applicability in resource-constrained environments. This paper introduces an efficient and lightweight CNN model optimized for COVID-19 diagnosis through pruning and quantization techniques, addressing these limitations. Our approach significantly reduces storage size and parameter count while maintaining high accuracy. We customized several CNNs, particularly EfficientNetV2 and MobileNetV2, to achieve these goals. We specifically selected EfficientNetV2 and MobileNetV2 due to their superior balance of accuracy and efficiency, making them well-suited for lightweight medical applications. EfficientNetV2’s storage size has been reduced from 568 to 142 KB through customized pruning, achieving 98.9% accuracy for 2-class and 98.28% for 3-class X-ray classification, as well as 98.3% and 98.05% for 2-class and 3-class CT classification, respectively. MobileNetV2, after hybrid pruning and quantization, achieved a remarkably low storage size of 70 KB, making it highly effective for deployment in environments with limited resources. Despite a slight accuracy decrease of 0.9 to 1.58%, both models have maintained robust performance suitable for COVID-19 diagnosis. The results demonstrate that our pruning and quantization techniques are effective in developing lightweight, high-performance CNN models, making them suitable for broader applications in medical imaging where both efficiency and accuracy are critical. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： COVID-19

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A Novel CAPTCHA Recognition System Based on Refined Visual Attention

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computers, Materials & Continua 2025年第4期83卷 115-136页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Monir Abdullah Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha67714Saudi Arabia Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.

关键词： Text-based CAPTCHA recognition refined visual attention web security computer vision

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