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Evaluating Security of Big Data Through Fuzzy Based Decision-Making Technique

computer systems science & Engineering 2023年第1期44卷 859-872页

作者： Fawaz Alassery Ahmed Alzahrani Asif Irshad Khan Kanika Sharma Masood Ahmad Raees Ahmad Khan Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Applications Mangalmay Institute of Engineering&TechnologyGreater Noida201310Uttar PradeshIndia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia

In recent years,it has been observed that the disclosure of information increases the risk of *** restricting the accessibility of information,providing security is ***,there is a demand for time tofill the gap between security and accessibility of *** fact,security tools should be usable for improving the security as well as the accessibility of *** security and accessibility are not directly influenced,some of their factors are indirectly influenced by each *** play an important role in bridging the gap between security and *** this paper,we identify the key attributes of accessibility and security that impact directly and indirectly on each other,such as confidentiality,integrity,availability,and *** significance of every attribute on the basis of obtained weight is important for its effect on security during the big data security life cycle *** calculate the proposed work,researchers utilised the Fuzzy Analytic Hierarchy Process(Fuzzy AHP).Thefindings show that the Fuzzy AHP is a very accurate mechanism for determining the best security solution in a real-time healthcare *** study also looks at the rapidly evolving security technologies in healthcare that could help improve healthcare services and the future prospects in this area.

关键词： information security big data big data security life cycle fuzzy AHP

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Custom Object Detection for Fresh and Rotten Fruit Classification 5

Custom Object Detection for Fresh and Rotten Fruit Classific...

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5th International Conference on Advanced information Technologies, ICAIT 2024

作者： Thant, Khine Zin Khant Paing, Minn Thant, Win Win Hmway Tar, Hmway Oo, Aung Nway University of Information Technology Yangon Myanmar University of Information Technology Faculty of Computer Science Yangon Myanmar

ISBN: (纸本)9798331510046

Automated grading and processing of fruits is a complex task which requires the creation of effective fruit vision detection systems. This paper presents an introduction of Fruit detection and recognition system for grading apples, bananas and oranges though incorporation of deep learning methodologies. For this study, two separate databases of fruits including the Kaggle and Google databases were carefully prepared. The images were annotated with precision and augmented using the Roboflow web-based platform, categorizing each image into one of six classes: fresh apple, rotten apple, fresh orange, rotten orange, fresh banana, rotten banana. After data collection, the next step involved was data partitioning into training and validation datasets through a data splitting technique. Regarding the model training process, the YOLOv8 and YOLOv5 model was used. The evaluation that was conducted on the trained model was impressive, as the system returned a high value for accuracy, precision, recall, F1-score, and mAP. The proposed system not only can enhance fruit grading efficiency but also has a wide range of applications in other agricultural fields. Moreover, this study helps toward some significant concerns in agricultural industry, particularly in the area of fruit recognition. It aims to improve the automation the automation of post-harvest fruit quality selection, ensuring more efficient and sorting and grading processes. This extensive review is quite beneficial as it provides insights into the future direction on how to better improve automatic fruit picking technology for better efficiency and optimization. © 2024 IEEE.

关键词： Citrus fruits

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BERT-CNN: A Deep Learning Model for Detecting Emotions from Text

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computers, Materials & Continua 2022年第5期71卷 2943-2961页

作者： Ahmed R.Abas Ibrahim Elhenawy Mahinda Zidan Mahmoud Othman Department of Computer Science Faculty of Computer and InformaticsZagazig UniversityZagazig44519Egypt Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt11835Egypt

Due to the widespread usage of social media in our recent daily lifestyles,sentiment analysis becomes an important field in pattern recognition and Natural Language Processing(NLP).In this field,users’feedback data on a specific issue are evaluated and *** emotions within the text is therefore considered one of the important challenges of the current NLP *** have been widely studied in psychology and behavioral science as they are an integral part of the human *** describe a state of mind of distinct behaviors,feelings,thoughts and *** main objective of this paper is to propose a new model named BERT-CNN to detect emotions from *** model is formed by a combination of the Bidirectional Encoder Representations from Transformer(BERT)and the Convolutional Neural networks(CNN)for textual *** model embraces the BERT to train the word semantic representation language *** to the word context,the semantic vector is dynamically generated and then placed into the CNN to predict the *** of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR *** BERTCNN model achieves an accuracy of 94.7%and an F1-score of 94%for semeval2019 task3 dataset and an accuracy of 75.8%and an F1-score of 76%for ISEAR dataset.

关键词： BERT-CNN deep learning emotion detection semeval2019 text classification

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Security Threats to xApps Access Control and E2 Interface in O-RAN

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 1197-1203页

作者： Hung, Cheng-Feng Chen, You-Run Tseng, Chi-Heng Cheng, Shin-Ming National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106335 Taiwan

Open Radio Access Networks (O-RANs) represent a novel wireless access network architecture that decomposes traditional RAN functions and makes them openly accessible. O-RANs enable real-time coordination, RAN performance optimization, and management through RAN Intelligent Controllers (RICs) and their related xApps. Due to the openness of O-RAN, developers have the flexibility to download various pre-developed xApps from the Internet for deployment. They can even develop their xApps to enhance the flexibility and innovation of the RAN. The current O-RAN official WG11 has defined numerous specifications for secure implementations. However, not only is accurately detecting malicious xApps a significant challenge, but the existing H-Release also does not fully adhere to the specifications in its implementation. This could potentially introduce security risks and threats. In this paper, we implement an experimental O-RAN H-Release environment and discover significant threats from the absence of specified access control permissions for xApps. Malicious attackers can illegally access APIs to utilize other services or launch attacks on legitimate xApps and E2 nodes through the E2 interface, potentially causing a complete RAN disruption. We used the identified vulnerabilities to design three attacks, providing detailed explanations and a comprehensive analysis of their impact on the system. Finally, we also submit the discovered threats to CVEs (CVE-2023-42358 and CVE-2023-41628), providing a reference for O-RAN officials to improve security in the future. © 2020 IEEE.

关键词： Real time systems

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Deep learning-based method for detection of copy-move forgery in videos

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Neural Computing and Applications 2025年第14期37卷 8451-8464页

作者： Elbarougy, Reda Abdelfatah, Osama Behery, G.M. El-Badry, Noha M. Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Ha’il Ha’il81481 Saudi Arabia Department of Information Technology Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Computer Science Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Mathematics Faculty of Science Damietta University New Damietta Kafr Saad Egypt

Video forgery is one of the most serious problems affecting the credibility and reliability of video content. Therefore, detecting video forgery presents a major challenge for researchers due to the diversity of forgery types, the modernity of the programs used in forgery operations, and the abundance of information and content present in videos. The seriousness of this issue arises from the widespread use of videos in vital fields that require very high accuracy with no room for doubt or error, such as courtrooms, journalism, and others. Copy-move forgery is one of the most common and dangerous types of video forgery because of the difficulty in identifying it with the naked eye and the great diversity of forgery techniques involved in this particular type. One of the biggest challenges researchers have faced in the past is the complexity of the steps required to detect forgery in videos, leading to significant computational complexity and time consumption. The proposed method also aims to achieve better results than previous methods while reducing computational operations. Ultimately, forgery is detected with great efficiency. Compared to previous methods used for detecting copy-move forgery in the Rewind dataset, the proposed method achieves the highest F1, reaching 0.86, with a significant difference of 0.13 compared to the best result of the previous methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Deep learning

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IRS-enabled NOMA communication systems:A network architecture primer with future trends and challenges

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Digital Communications and Networks 2024年第5期10卷 1503-1528页

作者： Haleema Sadia Ahmad Kamal Hassan Ziaul Haq Abbas Ghulam Abbas Muhammad Waqas Zhu Han Faculty of Electrical Engineering and Telecommunications and Networking(TeleCoN)Research Center Ghulam Ishaq Khan Institute of Engineering and Technology(GIKI)Topi 23640Pakistan Faculty of Computer Science and Engineering GIKITopi 23640Pakistan Department of Computer Engineering College of Information TechnologyUniversity of Bahrain32028Bahrain School of Engineering Edith Cowan UniversityPerth WA6027Australia Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoul 446-701South Korea

Non-Orthogonal Multiple Access(NOMA)has already proven to be an effective multiple access scheme for5th Generation(5G)wireless *** provides improved performance in terms of system throughput,spectral efficiency,fairness,and energy efficiency(EE).However,in conventional NOMA networks,performance degradation still exists because of the stochastic behavior of wireless *** combat this challenge,the concept of Intelligent Reflecting Surface(IRS)has risen to prominence as a low-cost intelligent solution for Beyond 5G(B5G)*** this paper,a modeling primer based on the integration of these two cutting-edge technologies,i.e.,IRS and NOMA,for B5G wireless networks is *** in-depth comparative analysis of IRS-assisted Power Domain(PD)-NOMA networks is provided through 3-fold ***,a primer is presented on the system architecture of IRS-enabled multiple-configuration PD-NOMA systems,and parallels are drawn with conventional network configurations,i.e.,conventional NOMA,Orthogonal Multiple Access(OMA),and IRS-assisted OMA *** by this,a comparative analysis of these network configurations is showcased in terms of significant performance metrics,namely,individual users'achievable rate,sum rate,ergodic rate,EE,and outage ***,for multi-antenna IRS-enabled NOMA networks,we exploit the active Beamforming(BF)technique by employing a greedy algorithm using a state-of-the-art branch-reduceand-bound(BRB)*** optimality of the BRB algorithm is presented by comparing it with benchmark BF techniques,i.e.,minimum-mean-square-error,zero-forcing-BF,and ***,we present an outlook on future envisioned NOMA networks,aided by IRSs,i.e.,with a variety of potential applications for 6G wireless *** work presents a generic performance assessment toolkit for wireless networks,focusing on IRS-assisted NOMA *** comparative analysis provides a solid foundation for the dev

关键词： Intelligent reflecting surface Non-orthogonal multiple access 6G Beamforming Sum rate Energy efficiency

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Analysis and Classification of Fake News Using Sequential Pattern Mining

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Big Data Mining and Analytics 2024年第3期7卷 942-963页

作者： M.Zohaib Nawaz M.Saqib Nawaz Philippe Fournier-Viger Yulin He College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060 Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Department of Computer Science Faculty of Computing and Information TechnologyUnivesity of SargodhaSargodha 40100Pakistan

Disinformation,often known as fake news,is a major issue that has received a lot of attention *** researchers have proposed effective means of detecting and addressing *** machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual *** introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and *** this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper ***,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic *** obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of ***,the discovered frequent patterns are used as features for fake news *** framework is evaluated with eight classifiers,and their performance is assessed with various *** experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.

关键词： disinformation fake news sequential pattern mining(SPM) frequent patterns classification

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Per-user network access control kernel module with secure multifactor authentication

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Journal of Supercomputing 2024年第1期80卷 970-1008页

作者： Cheng, Sheng-Tzong Horng, Gwo-Jiun Hsu, Chih-Wei Su, Z.-Yu Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering Southern Taiwan University of Science and Technology Tainan Taiwan

Network attacks, such as botnets stealing sensitive data, constitute a critical concern for administrators in the Internet area. Such attacks can be prevented using a network access control (NAC) scheme. However, existing NAC mechanisms all rely on the user account mechanism provided by the operating system. Herein, we propose a protocol involving a Linux security module for user-based NAC. The module requires neither user accounts nor secure user space;it loads signed rules and keys for a user from a USB security key, securely authenticates the user, and controls network permissions directly from the Linux kernel. Moreover, we present extensions for the protocol, developed using additional authentication methods and devices (such as a smartphone), to prevent man-in-the-middle, replay, and phishing attacks. The protocol can securely authenticate users from kernel space by using USB security keys and a user smartphone despite an insecure user space, is resistant to various attacks, and can guarantee that authorized users obtain their corresponding network privileges. At the same time, the analysis of Linux operating system security enhancement technology is proposed to achieve the goal of trusting machines. Through the use of empirical algorithm analysis, enhanced virtualization technology, and other methods, the risk of hacker intrusion on information platforms is reduced in multiple aspects, and management mechanisms are used to set and use resource permissions. On the other hand, after mastering the key technologies for network blocking development in the core layer of the system, there is also an opportunity to independently build modules for the security core of operating systems in special fields and future 5G/6G SDNs. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Network security

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EduGym Post-Mortem: Open Educational Resource for Educational Game

EduGym Post-Mortem: Open Educational Resource for Educationa...

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2025 International Conference on Advancement in Data science, E-learning and information system, ICADEIS 2025

作者： Tarigan, Jos Timanta Zamzami, Elviawaty Muisa Zendrato, Niskarto Nababan, Anandhini Medianty Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798331513320

This paper aims to delve into the development of EduGym, an educational microlearning game specifically designed for mobile platforms. The discussion highlights the implementation and integration of Open Educational Resources (OER) and examines their significant impact on the development process. The project strategically used OER as the primary content source to ensure seamless future content expansion without requiring changes to the core application. This approach also empowers other developers to repurpose and adapt the content for various educational applications. Furthermore, the paper addresses key challenges encountered during the development process from a software engineering standpoint, including maintaining flexibility, ensuring scalability, and managing resource allocation. The study outlines the overall architecture of EduGym, detailing the development workflow and the ways in which OER integration influenced both development and resource management. The aim is to offer valuable insights and practical guidance for developers and educators who are exploring similar methodologies, informed by the lessons learned and experiences gained through the creation of EduGym. © 2025 IEEE.

关键词： Game design

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