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Mendel 2023年第2期29卷 111-130页

作者： Chang, Wee Chea Sangodiah, Anbuselvan Faculty of Technology and Applied Science Open University Malaysia Malaysia School of Computing Faculty of Computing and Engineering Quest International University Malaysia

Semantic Web is the vision to make Internet data machine-readable to achieve information retrieval with higher granularity and personalisation. Semantic annotation is the process that binds machine-understandable descriptions into Web resources such as text and images. Hence, the success of Semantic Web depends on the wide availability of semantically annotated Web resources. However, there remains a huge amount of unannotated Web resources due to the limited annotation capability available. In order to address this, machine learning approaches have been used to improve the automation process. This Systematic Review aims to summarise the existing state-of-the-art literature to answer five Research Questions focusing on machine learning driven semantic annotation automation. The analysis of 40 selected primary studies reveals that the use of unitary and combination of machine learning algorithms are both the current directions. Support Vector Machine (SVM) is the most-used algorithm, and supervised learning is the predominant machine learning type. Both semi-automated and fully automated annotation are almost nearly achieved. Meanwhile, text is the most annotated Web resource;and the availability of third-party annotation tools is in-line with this. While Precision, Recall, F-Measure and Accuracy are the most deployed quality metrics, not all the studies measured the quality of the annotated results. In the future, standardising quality measures is the direction for research. © 2023, Brno University of Technology. All rights reserved.

关键词： Automation

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Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

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Computers, Materials & Continua 2023年第3期74卷 5607-5623页

作者： Abdallah Elhigazi Abdallah Mosab Hamdan Shukor Abd Razak Fuad A.Ghalib Muzaffar Hamzah Suleman Khan Siddiq Ahmed Babikir Ali Mutaz H.H.Khairi Sayeed Salih Faculty of Engineering Future UniversityKhartoum 10553Sudan Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Information Assurance and Security Research School of ComputingUniversiti Teknologi MalaysiaJohor81310Malaysia School of Psychology and Computer Science University of Central LancashirePreston PR12HEUK Deaptement of Management Information Systems College of Administrative ScienceApplied Science UniversityAl Eker623Bahrain Deaptement of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh11461Saudi Arabia

IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 *** to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate ***,the security design of the wireless structure is vulnerable to versatile *** example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false *** security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world *** paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks *** proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the *** scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in *** proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related *** experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.

关键词： 802.11 media access control(MAC) wireless local area network(WLAN) artificial neural network denial-of-service(DoS)

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CNN Architectures Exploration and Analysis for Multiclass Skin Diseases Identification 4

CNN Architectures Exploration and Analysis for Multiclass Sk...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Andryani, Nur Afny C. Chandra, Yonathan C. Mazava, Maulida Rosyidi, Mohammad Gondokaryono, Srie P. School of Computer Science Bina Nusantara University Jakarta Indonesia Research Center for Computing School of Applied Science Brin Telkom University Cibinong Indonesia Research Center for Computing Brin Cibinong Indonesia Universitas Padjadjaran Indonesian Pediatric Dermatology Study Group Faculty of Medicine Indonesian Society of Dermatology and Venereology Indonesia

ISBN: (纸本)9798331506490

Skin diseases have become one of the most common ailments, particularly in rural areas. Misdiagnosis, however, can lead to an even worse quality of life and inefficient treatments. Furthermore, countries with archipelagos, like Indonesia, are particularly impacted by an imbalance in the distribution of dermatological care. Here, general practitioners with less experience in the field typically provide most dermatological care in this area. Thus, misdiagnosis is virtually inevitable and leads to problems for the patient, even impacting the doctor's and dermatology organization's reputation. To solve this problem, it has recently been shown that general practitioners can identify skin disorders using artificial intelligence (AI). Unfortunately, not all AI implemented has considered dermatologists' knowledge in their systems, and specifically, most Indonesian dermatologists are not familiar with this AI thing. Thus, this work was purposed to help them predict skin disease classification through CNN-based Deep Learning Architecture methodologies for autonomous skin disease identification using mobile photos and dermoscopy. Here, the Fitzpatrick 17k and ISIC 2018 datasets with several deep learning methods, including ensemble techniques were used. The results showed that for mobile-based skin disease photos, the deep learning architecture offers a notable improvement over the handcrafted-based approach. The accuracy using an ensemble for the Fitzpatrick dataset was 55% and ISIC was higher, 80%. Although not all methods are improving slowly, further employment and refinement of ensemble techniques promise to automate further medical diagnostics which can enhance patient outcomes in dermatology. Finally, additional images will increase accuracy;specifically with the more intricate the photographs and sizes. Thus, this preliminary research showed the importance of the deep learning method in identifying skin disease classification. © 2024 IEEE.

关键词： Dermatology

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Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

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Computers, Materials & Continua 2024年第12期81卷 4787-4832页

作者： Israa Ibraheem Al Barazanchi Wahidah Hashim Reema Thabit Mashary Nawwaf Alrasheedy Abeer Aljohan Jongwoon Park Byoungchol Chang College of Computing and Informatics Universiti Tenaga Nasional(UNITEN)Kajang43000Malaysia College of Engineering University ofWarith Al-AnbiyaaKarbala56001Iraq Faculty of Information Science&Technology Universiti Kebangsaan MalaysiaBangi43600Malaysia Department of Computer Science Applied CollegeUniversity of HailHail55424Saudi Arabia Department of Computer Science and Informatics Taibah UniversityMedina42353Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Republic of Korea

This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning ***,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state *** models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal *** diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed *** goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic *** propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies ***,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN *** further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and *** validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’***,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model *** findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.

关键词： Computer science clinical decision support system(CDSS) medical queries healthcare deep learning recurrent neural network(RNN) long short-term memory(LSTM)

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Tube-based Model Reference Adaptive Control for Vibration Suppression of Active Suspension Systems

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 728-731页

作者： Yashar Mousavi Alireza Alfi Ibrahim Beklan Kucukdemiral Afef Fekih IEEE the Department of Applied Science School of ComputingEngineering and Built EnvironmentGlasgow Caledonian University the Faculty of Electrical Engineering Shahrood University of Technology the Electrical and Computer Engineering Department University of Louisiana at Lafayette

Dear editor,With the developments of industrial automation in recent years,vehicle suspension systems have received a great deal of attention in industry and academia due to their critical role in the chassis performance of vehicles[1].The suspension system is expected to guarantee the vehicle’s maneuverability and provide satisfactory ride comfort by absorbing the vibrations arising from the road surface excitations and ensuring road-holding capability and suspension *** by the desirable performance of the model reference adaptive control(MRAC)approach,various literature studies have investigated its performance in diverse linear and nonlinear practical systems[2].

关键词： editor Active guarantee

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Towards new realities: Implications of personalized online layers in our daily lives

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i-com 2024年第2期23卷 221-229页

作者： Herder, Eelco Stojko, Laura Strecker, Jannis Neumayr, Thomas Yigitbas, Enes Augstein, Mirjam Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Department of Computer Science University of the Bundeswehr Munich Neubiberg Germany Institute of Computer Science University of St. Gallen St. Gallen Switzerland Research Group Personalized Environments and Collaborative Systems University of Applied Sciences Upper Austria Hagenberg Austria Faculty of Electrical Engineering Computer Science and Mathematics Paderborn University Paderborn Germany

We are currently in a period of upheaval, as many new technologies are emerging that open up new possibilities to shape our everyday lives. Particularly, within the field of Personalized Human-Computer Interaction we observe high potential, but also challenges. In this article, we explore how an increasing amount of online services and tools not only further facilitates our lives, but also shapes our lives and how we perceive our environments. For this purpose, we adopt the metaphor of personalized 'online layers' and show how these layers are and will be interwoven with the lives that we live in the 'human layer' of the real world. © 2024 the author(s), published by De Gruyter, Berlin/Boston.

关键词： Human computer interaction

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Deploying Agrivoltaics in Sub-Saharan Africa: A Sustainable Pathway Towards Energy-Food Security-Challenges and Opportunities: A Review

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IEEE Access 2025年 13卷 87810-87833页

作者： Abubakar, Suleiman Ibrahim See, Chan Hwang Sukki, Firdaus Muhammad Mahendiran, Ramasamy Sundaram, Senthilarasu Abidin, Mohamad Izani Zainal Kamarudin, Mohd Khairul Amri Edinburgh Napier University Merchiston Campus School of Computing Engineering and the Built Environment EdinburghEH10 5DT United Kingdom University Sultan Zainal Abidin Faculty of Applied Social Science Gong Badak Campus Terengganu Kuala Nerus21300 Malaysia Tamil Nadu Agricultural University Department of Renewable Energy Engineering AEC&RI Coimbatore India Teesside University Tees Valley School of Computing Engineering and Digital Technologies MiddlesbroughTS1 3BX United Kingdom Higher Colleges of Technology Faculty of Applied Media Abu Dhabi United Arab Emirates

Agrivoltaics (APV) offer an innovative solution to the pressing energy and food security challenges in Sub-Saharan Africa (SSA). Over 600 million people in this region lack access to energy, and food insecurity remains pervasive. By combining photovoltaic (PV) systems with agricultural production, APV optimizes land-use efficiency, enabling concurrent renewable energy generation and enhancing agricultural productivity. This review critically evaluates the potential of APV systems to address these dual challenges, focusing on SSA’s unique socioeconomic and environmental contexts. Key findings highlight the APV’s ability to mitigate agricultural constraints, such as water scarcity and climate variability, while providing decentralized energy solutions to rural communities. The analysis emphasizes strategic APV design considerations, including panel height, spacing, and crop compatibility, which are essential for optimizing energy and crop yields. However, challenges such as high initial costs, limited technical capacity, and sociocultural acceptance pose significant barriers to its widespread adoption. This review discusses policy recommendations for addressing these barriers, including financial incentives, technology transfer frameworks, and stakeholder engagement strategies. The novelty of this work lies in its tailored approach to SSA, integrating evidence from global APV case studies and proposing localized implementation strategies that align with the region’s development priorities. This study advances the understanding of APV as a pathway to achieving Sustainable Development Goals (SDGs) in SSA by offering a dual-purpose model for energy access and food security. Overcoming adoption barriers through innovative governance, research, and community engagement will be pivotal to unlocking the transformative potential of APV in SSA’s energy-food landscape. © 2013 IEEE.

关键词： Food security

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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Computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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A Communication-efficient Conjunctive Query Scheme under Local Differential Privacy

A Communication-efficient Conjunctive Query Scheme under Loc...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Ellen Z. Guan, Yunguo Lu, Rongxing Zhang, Harry University of New Brunswick Faculty of Computer Science FrederictonE3B5A3 Canada Eastern Michigan University School of Information Security and Applied Computing YpsilantiMI48197 United States

ISBN: (纸本)9798350351255

Crowdsourcing has become a widely used method for data collection and analysis, yet its privacy remains a challenge. In this paper, we present a new efficient and privacy-preserving conjunctive query scheme for crowdsourcing scenarios. The scheme employs the Local Differential Privacy (LDP) technique to ensure both query privacy and high communication efficiency. Specifically, when an aggregator launches a conjunctive query to a set of crowdsourcing users, the query condition will not be leaked. To respond the query, each user just needs to return one bit back to the aggregator. By integrating prefix encoding technique, our proposed scheme can also efficiently support conjunctive queries with one range query condition. Detailed security analysis shows our proposed scheme can achieve desirable security requirements. In addition, performance evaluations also indicate its efficiency. Furthermore, extensive experiments demonstrate our proposed scheme can achieve high accuracy while ensuring ϵ-LDP. © 2024 IEEE.

关键词： Differential privacy

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EXISTENCE OF WEAK SOLUTIONS FOR OBSTACLE PROBLEMS WITH VARIABLE GROWTH IN ORLICZ-SOBOLEV SPACES

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Journal of Mathematical sciences (United States) 2024年第2期289卷 155-167页

作者： Allalou, Mouad Ait Temghart, Said Raji, Abderahmane Applied Mathematics and Scientific Computing Laboratory Faculty of Science and Technics Sultan Moulay Slimane University BP 523 Beni Mellal 23000 Morocco National Higher School for Artificial Intelligence and Data Sciences Ibn Zohr University Taroudant 83000 Morocco

This article delves into the exploration of weak solutions’ existence within the context of obstacle problems associated with the following inequality: ∫ΩV(z,w,Dw):D(v-w)dz≥g,v-w, where v lies in a convex set FO,B... 详细信息

This article delves into the exploration of weak solutions’ existence within the context of obstacle problems associated with the following inequality: ∫_ΩV(z,w,Dw):D(v-w)dz≥g,v-w, where v lies in a convex set F_O,B. The primary methodology employed in this investigation involves the utilization of You ng’s measure theory, complemented by a theorem originating from Kinderlehrer and Stampacchia, specifically tailored for reflexive Orlicz-Sobolev spaces. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： 28A33 35J88 46E30 46E35 Kinderlehrer and Stampacchia Obstacle problem Orlicz-Sobolev spaces Young’s measures

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