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AI-Enabled Grouping Bridgehead to Secure Penetration Topics of Metaverse

computers, Materials & Continua 2022年第12期73卷 5609-5624页

作者： Woo Hyun Park Isma Farah Siddiqui Nawab Muhammad Faseeh Qureshi Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwon16419Korea Department of Computer Education Sungkyunkwan UniversitySeoul03063Korea Department of Software Engineering Mehran University of Engineering&TechnologyJamshoroPakistan

With the advent of the big data era,security issues in the context of artificial intelligence(AI)and data analysis are attracting research *** the metaverse,which will become a virtual asset in the future,users’communication,movement with characters,text elements,etc.,are required to integrate the real and ***,they can be exposed to ***,various hacker threats *** example,users’assets are exposed through notices and mail alerts regularly sent to users by *** the future,hacker threats will increase mainly due to naturally anonymous ***,it is necessary to use the natural language processing technology of artificial intelligence,especially term frequency-inverse document frequency,word2vec,gated recurrent unit,recurrent neural network,and long-short term ***,several application versions are ***,research on tasks and performance for algorithm application is *** propose a grouping algorithm that focuses on securing various bridgehead strategies to secure topics for security and safety within the *** algorithm comprises three modules:extracting topics from attacks,managing dimensions,and performing ***,we create 24 topic-based *** normal and spam mail attacks to verify our algorithm,the accuracy of the previous application version was increased by∼0.4%-1.5%.

关键词： Metaverse security computational linguistics grouping bridgehead AI

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TrafficNet: A Hybrid CNN-FNN Model for Analysis of Traffic Accidents in Seoul

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Journal of Computing Science and engineering 2023年第4期17卷 182-194页

作者： Mustafa, Ghulam Cho, Seong-je Hwang, Youngsup Department of Computer Engineering Sun Moon University Asan Korea Republic of Department of Software Science Dankook University Yongin Korea Republic of

The escalating global trend of traffic accidents with subsequent loss of lives is a matter of grave concern that requires immediate attention. Extensive efforts have been made to mitigate accidents and develop effective prevention strategies. This research paper focuses on a comprehensive analysis of traffic accidents in Seoul, aiming to identify factors and accident types that contribute to increased severity. To achieve this, we introduced a new approach called "TrafficNet: A Hybrid CNN-FNN Model" to evaluate effects of various parameters on the severity of traffic accidents in Seoul. Our main objective was to classify accidents into four distinct levels of severity: minor injuries, slander, fatalities, and injury reports. To assess the effectiveness of our proposed model, we conducted comprehensive experiments using publicly available traffic accident data provided by Seoul Metropolitan Government. These experiments involved six different models, including five machine learning models (decision tree, random forest, k-nearest neighbor, gradient boosting, and support vector machine) and one deep learning model (multilayer perceptron). The proposed model demonstrated exceptional performance, surpassing all other models and previous research findings using the same dataset. On the test dataset, TrafficNet achieved an impressive accuracy of 93.98% with a precision of 94.31%, a recall of 93.98%, and an F1-score of 93.89%. Copyright 2023. The Korean Institute of Information Scientists and Engineers

关键词： Support vector machines

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A model to measure the spread power of rumors

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13787-13811页

作者： Jahanbakhsh-Nagadeh, Zoleikha Feizi-Derakhshi, Mohammad-Reza Ramezani, Majid Akan, Taymaz Asgari-Chenaghlu, Meysam Nikzad-Khasmakhi, Narjes Feizi-Derakhshi, Ali-Reza Ranjbar-Khadivi, Mehrdad Zafarani-Moattar, Elnaz Balafar, Mohammad-Ali Computerized Intelligence Systems Laboratory Department of Computer Engineering University of Tabriz Tabriz Iran Department of Computer Engineering Naghadeh Branch Islamic Azad University Naghadeh Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Software Engineering Istanbul Topkapi University Istanbul Turkey Clinical Informatics Louisiana State University Health Sciences Center Shreveport Shreveport United States Department of Computer Engineering Shabestar Branch Islamic Azad University Shabestar Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering University of Tabriz Tabriz Iran

With technologies that have democratized the production and reproduction of information, a significant portion of daily interacted posts in social media has been infected by rumors. Despite the extensive research on rumor detection and verification, so far, the problem of calculating the spread power of rumors has not been considered. To address this research gap, the present study seeks a model to calculate the Spread Power of Rumor (SPR) as the function of content-based features in two categories: False Rumor (FR) and True Rumor (TR). For this purpose, the theory of Allport and Postman will be adopted, which it claims that importance and ambiguity are the key variables in rumor-mongering and the power of rumor. Totally 42 content features in two categories "importance" (28 features) and "ambiguity" (14 features) are introduced to compute SPR. The proposed model is evaluated on two datasets, Twitter and Telegram. The results showed that (i) the spread power of False Rumor documents is rarely more than True Rumors. (ii) there is a significant difference between the SPR means of two groups False Rumor and True Rumor. (iii) SPR as a criterion can have a positive impact on distinguishing False Rumors and True Rumors. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cell proliferation

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NLP-Based Subject with Emotions Joint Analytics for Epidemic Articles

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computers, Materials & Continua 2022年第11期73卷 2985-3001页

作者： Woo Hyun Park Isma Farah Siddiqui Dong Ryeol Shin Nawab Muhammad Faseeh Qureshi Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwon16419Korea Department of Software Engineering Mehran University of Engineering&TechnologyJamshoroPakistan Department of Computer Education Sungkyunkwan UniversitySeoul03063Korea

have been focused on addressing the Covid-19 pandemic;for example,governments have implemented countermeasures,such as quarantining,pushing vaccine shots to minimize local spread,investigating and analyzing the virus’characteristics,and conducting epidemiological investigations through patient management and ***,researchers worldwide require funding to achieve these ***,there is a need for documentation to investigate and trace disease ***,it is time consuming and resource intensive to work with documents comprising many types of unstructured ***,in this study,natural language processing technology is used to automatically classify these *** used statistical methods include data cleansing,query modification,sentiment analysis,and ***,owing to limitations with respect to the data,it is necessary to understand how to perform data analysis suitable for medical *** solve this problem,this study proposes a robust in-depth mixed with subject and emotion model comprising three *** first is a subject and non-linear emotional module,which extracts topics from the data and supplements them with emotional *** second is a subject with singular value decomposition in the emotion model,which is a dimensional decomposition module that uses subject analysis and an emotion *** third involves embedding with singular value decomposition using an emotion module,which is a dimensional decomposition method that uses emotion *** accuracy and other model measurements,such as the F1,area under the curve,and recall are evaluated based on an article on Middle East respiratory syndrome.A high F1 score of approximately 91%is *** proposed joint analysis method is expected to provide a better synergistic effect in the dataset.

关键词： Computational linguistic AI epidemic healthcare classification

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Gastrointestinal Diseases Classification Using Deep Transfer Learning and Features Optimization

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computers, Materials & Continua 2023年第4期75卷 2227-2245页

作者： Mousa Alhajlah Muhammad Nouman Noor Muhammad Nazir Awais Mahmood Imran Ashraf Tehmina Karamat College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Engineering HITEC UniversityTaxila47080Pakistan Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan

Gastrointestinal diseases like ulcers, polyps’, and bleeding areincreasing rapidly in the world over the last decade. On average 0.7 millioncases are reported worldwide every year. The main cause of gastrointestinaldiseases is a Helicobacter Pylori (H. Pylori) bacterium that presents in morethan 50% of people around the globe. Many researchers have proposeddifferent methods for gastrointestinal disease using computer vision *** of them focused on the detection process and the rest of themperformed classification. The major challenges that they faced are the similarityof infected and healthy regions that misleads the correct classificationaccuracy. In this work, we proposed a technique based on Mask Recurrent-Convolutional Neural Network (R-CNN) and fine-tuned pre-trainedResNet-50 and ResNet-152 networks for feature extraction. Initially, the region ofinterest is detected using Mask R-CNN which is later utilized for the trainingof fine-tuned models through transfer learning. Features are extracted fromfine-tuned models that are later fused using a serial approach. Moreover, anImproved Ant Colony Optimization (ACO) algorithm has also opted for thebest feature selection from the fused feature vector. The best-selected featuresare finally classified using machine learning techniques. The experimentalprocess was conducted on the publicly available dataset and obtained animproved accuracy of 96.43%. In comparison with state-of-the-art techniques,it is observed that the proposed accuracy is improved.

关键词： Diseases deep learning endoscopy gastrointestinal tract transfer learning

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Framework for Agent-Based Multistage Application Partitioning Algorithm in Mobile Cloud Computing

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SN computer Science 2024年第4期5卷 330页

作者： Kanwal, Asia Amjad, Tehmina Ashraf, Humaira Department of Computer Science and Software Engineering International Islamic University H10 Islamabad 44000 Pakistan

The idea of computational offloading is quickly catching on in the world of mobile cloud computing (MCC). Today’s applications have heavy demands on power and computing resources, creating issues with energy consumption, storage capacity, and mobile device performance. Mobile devices may efficiently offload their calculations to cloud servers using the offloading paradigm and then get the processed results back onto the device. The investigation relates to identifying the specific application components that should be offloaded and run remotely and which parts are supposed to be treated locally. The applications need to be partitioned to differentiate between remote and local codes. In this paper, an agent-based multistage graph partitioning (ABMP) scheme is proposed. The framework of the scheme is based on three-tier architecture that includes mobile, cloudlet, and cloud for the execution of application tasks. The main goal is to provide an efficient partitioning and offloading scheme in the mobile cloud computing area. The results show that incorporating both agent-based multistage graph partitioning and offloading algorithms yields superior performance as compared to previous methods in terms of reducing execution costs and conserving battery life for mobile devices. © The Author(s) 2024.

关键词： Agent-based partitioning Application partitioning Edge computing Mobile cloud computing Multistage graph partitioning Ready time partitioning offloading

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Scene and Aerial Image Classification Based on Advanced Deep Learning Technique 8th

Scene and Aerial Image Classification Based on Advanced Deep...

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8th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2024

作者： Mehmood, Maryam Hussain, Farhan Ijaz, Asad Department of Computer and Software Engineering National University of Sciences and Technology Islamabad44000 Pakistan Department of Software Engineering National University of Modern Languages Islamabad Pakistan Department of Mechanical Engineering Mirpur University of Science and Technology MUST AJK Mirpur Pakistan

ISBN: (纸本)9789819786947

Classifying scenes and aerial imagery is a critical component in applications such as land-use analysis, land cover mapping, and remote sensing technologies. Numerous existing models leverage Convolutional Neural Networks for classification, but they often involve intricate designs with a vast number of parameters, necessitating extensive computational power and prolonged training periods. This paper introduces a refined strategy employing a pre-trained EfficientNet B0 framework. Modification of parameters and layers through careful fine-tuning has resulted in notable enhancements in the model's classification precision. The superiority of EfficientNet B0 over comparative CNN architectures was confirmed by testing with three disparate datasets, particularly within the sphere of remote sensing imagery. The EfficientNet B0's superior performance is credited to its optimal structure that strikes a balance between model complexity and efficiency. Effective fine-tuning coupled with judicious computational resource allocation yields substantial accuracy in categorization. Moreover, its adaptability to assimilate remote sensing data exemplifies its widespread applicability. This study accentuates the significance of refining prevailing deep learning models to cater to specified objectives. Tailoring pre-trained models such as EfficientNet B0 enables marked advancements in classification precision, circumventing the necessity for intricate systems or significant computational demands. This methodology is promising for elevating both the proficiency and impact of scene and aerial image classification across a range of fields, with particular implications for remote sensing endeavors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Resource allocation

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A Package Level Regression Test Selection Approach for Java software Systems

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IEEE Access 2025年 13卷 87848-87861页

作者： Al-Refai, Mohammed Hammad, Mahmoud M. Jordan University of Science and Technology Faculty of Computer and Information Technology Computer Science Department Irbid22110 Jordan Faculty of Engineering and Information Technology Ajman United Arab Emirates Jordan University of Science and Technology Faculty of Computer and Information Technology Software Engineering Department Irbid22110 Jordan

Regression testing of software systems is an important and critical activity yet expensive and resource-intensive. An approach to enhance its efficiency is Regression Test Selection (RTS), which selectively re-executes a subset of relevant tests that are impacted by code modifications. Previous studies on static and dynamic RTS for Java software have shown that selecting tests at the class level is more effective than using finer granularities like methods or statements. Nevertheless, RTS at the package level, which is a coarser granularity than class level, has not been thoroughly investigated or evaluated for Java projects. To address this gap, we propose PKRTS, a static package-level RTS approach that utilizes the structural dependencies of the software system under test to construct a package-level dependency graph. PKRTS analyzes dependencies in the graph and identifies relevant tests that can reach modified packages, i.e., packages containing altered classes. In contrast to conventional static RTS techniques, PKRTS implicitly considers dynamic dependencies, such as Java reflection and virtual method calls, among classes belonging to the same package by treating all those classes as a single cohesive node in the dependency graph. We evaluated PKRTS on 885 revisions of 9 open-source Java projects, with its performance compared to Ekstazi, a state-of-the-art dynamic class-level approach, and STARTS, a state-of-the-art static class-level approach. We used Ekstazi as the baseline to measure the safety and precision violations of PKRTS and STARTS. The results indicated that PKRTS outperformed static class-level RTS in terms of safety violation, which measures the extent to which relevant test cases are missed. PKRTS showed an average safety violation of 2.29% compared to 5.94% safety violation of STARTS. Despite this, PKRTS demonstrated lower precision violation and lower reduction in test suite size than class-level RTS, as it selects higher number of irrelevant te

关键词： Java programming language

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A Multimodal Multiobjective Evolutionary Algorithm for Filter Feature Selection in Multilabel Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4428-4442页

作者： Hancer, Emrah Xue, Bing Zhang, Mengjie Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Mehmet Akif Ersoy University Department of Software Engineering Burdur15039 Turkey

Multilabel learning is an emergent topic that addresses the challenge of associating multiple labels with a single instance simultaneously. Multilabel datasets often exhibit high dimensionality with noisy, irrelevant, and redundant features. In recent years, multilabel feature selection (MLFS) has gained prominence as a crucial and emerging machine learning task due to its ability to handle such data effectively. However, existing approaches for MLFS often prioritize top-ranked features based on intrinsic data criteria, disregarding relationships within the feature subset. Additionally, compared with conventional feature selection, multiobjective evolutionary algorithms (MOEAs) have not been widely explored in the context of MLFS. This study aims to address these gaps by proposing a multimodal multiobjective evolutionary algorithm (MMOEA) called MMDE_SICD which incorporates a preelimination scheme, an improved initialization scheme, an exploration scheme inspired by genetic operations and a statistically inspired crowding distance scheme. The results show that the proposed MMDE_SICD algorithm can outperform a variety of MOEAs and MMOEAs as well as conventional MLFS algorithms. Notably, this study is the first of its kind to consider MLFS as a multimodal multiobjective problem. © 2020 IEEE.

关键词： Multiobjective optimization

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Efficient Defense Against Model Stealing Attacks on Convolutional Neural Networks 22

Efficient Defense Against Model Stealing Attacks on Convolut...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Khaled, Kacem Dhaouadi, Mouna De Magalhaes, Felipe Gohring Nicolescu, Gabriela Polytechnique Department of Computer Engineering and Software Engineering Montreal Canada University of Montreal Department of Computer Science and Operations Research Canada

ISBN: (纸本)9798350345346

Model stealing attacks have become a serious concern for deep learning models, where an attacker can steal a trained model by querying its black-box API. This can lead to intellectual property theft and other security and privacy risks. The current state-of-the-art defenses against model stealing attacks suggest adding perturbations to the prediction probabilities. However, they suffer from heavy computations and make impracticable assumptions about the adversary. They often require the training of auxiliary models. This can be time-consuming and resource-intensive which hinders the deployment of these defenses in real-world applications. In this paper, we propose a simple yet effective and efficient defense alternative. We introduce a heuristic approach to perturb the output probabilities. The proposed defense can be easily integrated into models without additional training. We show that our defense is effective in defending against three state-of-the-art stealing attacks. We evaluate our approach on large and quantized (i.e., compressed) Convolutional Neural Networks (CNNs) trained on several vision datasets. Our technique outperforms the state-of-the-art defenses with a ×37 faster inference latency without requiring any additional model and with a low impact on the model's performance. We validate that our defense is also effective for quantized CNNs targeting edge devices. © 2023 IEEE.

关键词： Deep learning Model stealing attacks Privacy Quantization Security

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