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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Kubo, Tomoya Yokogawa, Tomoyuki Arimoto, Kazutami Hokari, Masaki Kayano, Isao Okayama Prefectural University Faculty of Computer Science and Systems Engineering Soja Okayama Japan Kawasaki University of Medical Welfare Faculty of Health Science and Technology Kurashiki Okayama Japan

ISBN: (纸本)9798350383829

We developed an AI for Facial Expression Estimation to predict driver drowsiness levels, mimicking human evaluation. Traditional Image-based AIs struggle with limited data generalization in drowsiness prediction. Our proposed AI framework overcomes this by extracting Facial Action Units (FAU) from facial images, using these dynamic expression elements as primary features, making evaluation more humanlike. In our study involving 19 participants, we validated the generalization performance using a Gated Recurrent Unit (GRU) with FAU values and compared it with a Convolutional Neural Network (CNN) model based on VGGFACE (ResNet50). The proposed method demonstrated enhanced performance, showcasing potential improvements in drowsiness estimation with limited data, expecting to significantly reduce the data required for accurate drowsiness level estimation. Furthermore, the results obtained through the process of improving generalization performance imply that there exists a trade-off relationship between performance specialized to specific individuals and generalized performance in learning models utilizing features such as FAUs. © 2023 IEEE.

关键词： Advanced driver assistance systems

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection systems

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computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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Historical Arabic Images Classification and Retrieval Using Siamese Deep Learning Model

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computers, Materials & Continua 2022年第7期72卷 2109-2125页

作者： Manal M.Khayyat Lamiaa A.Elrefaei Mashael M.Khayyat Computer Science Department Umm Al-Qura UniversityMakkahSaudi Arabia Computer Science Department King Abdulaziz UniversityJeddahSaudi Arabia Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairoEgypt Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia

Classifying the visual features in images to retrieve a specific image is a significant problem within the computer vision field especially when dealing with historical faded colored ***,there were lots of efforts trying to automate the classification operation and retrieve similar images *** reach this goal,we developed a VGG19 deep convolutional neural network to extract the visual features from the images ***,the distances among the extracted features vectors are measured and a similarity score is generated using a Siamese deep neural *** Siamese model built and trained at first from scratch but,it didn’t generated high evaluation ***,we re-built it from VGG19 pre-trained deep learning model to generate higher evaluation ***,three different distance metrics combined with the Sigmoid activation function are experimented looking for the most accurate method formeasuring the similarities among the retrieved *** that the highest evaluation parameters generated using the Cosine distance ***,the Graphics Processing Unit(GPU)utilized to run the code instead of running it on the Central Processing Unit(CPU).This step optimized the execution further since it expedited both the training and the retrieval time *** extensive experimentation,we reached satisfactory solution recording 0.98 and 0.99 F-score for the classification and for the retrieval,respectively.

关键词： Visual features vectors deep learning models distance methods similar image retrieval

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Blockchain and IIoT Enabled Solution for Social Distancing and Isolation Management to Prevent Pandemics

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computers, Materials & Continua 2023年第7期76卷 687-709页

作者： Muhammad Saad Maaz Bin Ahmad Muhammad Asif Muhammad Khalid Khan Toqeer Mahmood Elsayed Tag Eldin Hala Abdel Hameed College of Computing and Information Sciences Karachi Institute of Economics&TechnologyKarachiPakistan Department of Computer Science Lahore Garrison UniversityLahorePakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Faculty of Engineering and Technology Future University in Egypt New Cairo11835Egypt Faculty of Computer and Information Systems Fayoum UniversityFaiyumEgypt Khaybar Applied College Taibah UniversityMedinaSaudi Arabia

Pandemics have always been a nightmare for humanity,especially in developing *** lockdowns are considered one of the effective ways to deal with spreading such ***,developing countries cannot afford such solutions because these may severely damage the country’s ***,this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations *** novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things(IIoT)and blockchain-enabled *** to existing studies,the immutable and tamper-proof contact tracing and quarantine management solution is *** use of advanced technologies and information security is a critical area for practitioners in the internet of things(IoT)and corresponding ***,this study also emphasizes information security,end-to-end solution,and experimental ***,a wearable wristband is proposed,incorporating 4G-enabled ultra-wideband(UWB)technology for smart contact tracing mechanisms in industries to comply with standard operating procedures outlined by the world health organization(WHO).Secondly,distributed ledger technology(DLT)omits the centralized dependency for transmitting contact tracing ***,a privacy-preserving tracing mechanism is discussed using a public/private key cryptography-based authentication ***,based on geofencing techniques,blockchain-enabled machine-to-machine(M2M)technology is proposed for quarantine *** step-by-step methodology and test results are proposed to ensure contact tracing and quarantine *** existing research studies,the security aspect is also considered in the realm of *** practical implementation of the proposed solution also obtains the *** results indicate the successful implementation of blockchain-enabled contac

关键词： Blockchain contact tracing distributed ledger technology geo-fencing internet of things industrial internet of things isolation management social distancing ultra-wideband

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Smart-Fragile Authentication Scheme for Robust Detecting of Tampering Attacks on English Text

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computers, Materials & Continua 2022年第5期71卷 2497-2513页

作者： Mohammad Alamgeer Fahd N.Al-Wesabi Huda G.Iskandar Imran Khan Nadhem Nemri Mohammad Medani Mohammed Abdullah Al-Hagery Ali Mohammed Al-Sharafi Department of Information Systems King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA

Content authentication,integrity verification,and tampering detection of digital content exchanged via the internet have been used to address a major concern in information and communication *** this paper,a text zero-watermarking approach known as Smart-Fragile Approach based on Soft Computing and Digital Watermarking(SFASCDW)is proposed for content authentication and tampering detection of English text.A first-level order of alphanumeric mechanism,based on hidden Markov model,is integrated with digital zero-watermarking techniques to improve the watermark robustness of the proposed *** researcher uses the first-level order and alphanumeric mechanism of Markov model as a soft computing technique to analyze English ***,he extracts the features of the interrelationship among the contexts of the text,utilizes the extracted features as watermark information,and validates it later with the studied English text to detect any *** has been implemented using PHP with VS code *** robustness,effectiveness,and applicability of SFASCDW are proved with experiments involving four datasets of various lengths in random locations using the three common attacks,namely insertion,reorder,and *** SFASCDW was found to be effective and could be applicable in detecting any possible tampering.

关键词： Watermarking soft computing text analysis hidden Markov model content authentication

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Decisioning-Based Approach for Optimising Control engineering Tools Using Digital Twin Capabilities and Other Cyber-Physical Metaverse Manufacturing System Components

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3212-3221页

作者： Mourad, Nahia Alsattar, Hassan A. Qahtan, Sarah Zaidan, Aws Alaa Deveci, Muhammet Sangaiah, Arun Kumar Pedrycz, Witold British University in Dubai Faculty of Engineering and IT Dubai United Arab Emirates University of Mashreq College of Administrative Science Department of Business Administration Baghdad10067 Iraq Middle Technical University College of Health and Medical Technology-Baghdad Department of Computer Center Baghdad10047 Iraq SP Jain School of Global Management SydneyNSW2141 Australia National Defence University Turkish Naval Academy Department of Industrial Engineering Istanbul34040 Turkey Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon International Graduate Institute of AI National Yunlin University of Science and Technology Yunlin64002 Taiwan University of Alberta Faculty of Engineering Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul34010 Turkey

The optimisation of control engineering tools based on digital twin capabilities and other cyber-physical metaverse manufacturing system (CPMMS) components are crucial for the successful performance. This study proposes a model for optimising control engineering tools using digital twin capabilities and other CPMMS components to solve the open issues. The main contributions and novelty aspects of the methodological process are outlined as follows: Formulated and developed is a decision matrix based on a utility procedure for 10 control engineering tools with digital twin capabilities and other three CPMMS components (Programmable-Logic-Controller and Human-Machine-Interface, Internet of Things connectivity and cybersecurity features). This matrix accounts for the uncertainty associated with tool assessment and transformation evaluation issue;formulated and develop an integrating fuzzy weighted with zero-inconsistency-interval-valued spherical fuzzy rough sets (IvSFRS-FWZIC) and combined compromise solution (CoCoSo) methods. The IvSFRS-FWZIC method is utilised to assign importance degrees to the digital twin capabilities and other CPMMS components. The applicability and robustness of the proposed approach are validated and evaluated through conducting sensitivity, correlation, and comparative analyses. The proposed approach can assist managers in analysing and selecting the most suitable tool for developing CPMMS. © 1975-2011 IEEE.

关键词： Internet of things

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Intelligent Algorithms for Gaming AI: Research Into Decision-Making Methods and Agent Behavior 3

Intelligent Algorithms for Gaming AI: Research Into Decision...

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3rd IEEE International Conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Popova, Anastasia R. Bondarenko, Gleb Oh. Syryh, Alyona S. Pavluk, Vladislav S. Geichenko, Evgenii I. Burlaka, Viacheslav D. Saint Petersburg Electrotechinical University 'LETI' Faculty of Information Measuring and Biotechnical Systems Saint-Petersburg Russia Computer Engineering Laboratory Saint-Petersburg Russia Saint-Petersburg Russia

ISBN: (纸本)9798331516321

This article delves into the exploration of the vector method of orientation within the scope of artificial intelligence (AI) systems. The vector approach, which forms the foundation of numerous contemporary algorithms, offers significant advantages in terms of accuracy and efficiency when tackling problems related to direction and distance determination. The primary objective of the paper is to thoroughly examine the fundamental principles and algorithms that employ vector methods, while also investigating their practical applications in diverse fields, including navigation, robotics, and autonomous systems. By providing a comprehensive analysis, the article aims to uncover the potential of vector-based methods in enhancing the performance and capabilities of AI systems. Additionally, the discussion will be enriched by practical examples and scenarios illustrating how these methods can be effectively utilized in real-world situations, thereby highlighting their relevance and versatility across various domains. Special attention will be given to potential future advancements and challenges in this area. © 2024 IEEE.

关键词： Decision making

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On Understanding Sports-HAR: Hierarchical, Mobile, Multi-Sensor based Classification of Table-Tennis Strokes 1

On Understanding Sports-HAR: Hierarchical, Mobile, Multi-Sen...

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1st International Conference of Intelligent Methods, systems and Applications, IMSA 2023

作者： Ezzeldin, Mustafa Ghoneim, Amr S. Abdelhamid, Laila Atia, Ayman Helwan University Faculty of Computers AI Software Engineering Cairo Egypt Helwan University Faculty of Computers and AI Computer Science Cairo Egypt Helwan University Faculty of Computers and AI Information Systems Cairo Egypt Helwan Univeristy HCI-LAB Faculty of Computers and AI Computer Science Cairo Egypt Faculty of Computer Science Giza Egypt

ISBN: (纸本)9798350335569

Table Tennis is a renowned competitive and recreational sport. An Olympic sport since 1988, table tennis in-cludes several movements, shots (i.e., strokes), and positions. Consequently, many factors can affect the strokes' accuracy and strength. This paper aims twofold: to classify the type of shot and to enhance the performance of the classification by improving the recognition accuracy and reducing the computational time. To track the movement of the body parts and extract table tennis stroke information, sensors (both Accelerometers and Gyroscopes) were utilized. The extracted data are processed and passed to a P-Dollar classifier to recognize six different table tennis strokes. This work utilizes two classification approaches to evaluate the performance, both linear and hierarchical. The presented results demonstrate that the hierarchical classification yields a better accuracy of 88%, achieved in 4 seconds, while traditional linear classification achieved 83% within 6 seconds. © 2023 IEEE.

关键词： Gyroscopes

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Enhancing School and College Attendance Using Advanced Technology 3

Enhancing School and College Attendance Using Advanced Techn...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Budiarto, Mukti Audiah, Sipah Astuti, Eka Dian Sanjaya, Yulia Putri Ayu Firli, M. Zaki University of Raharja Faculty of Informatics Engineering Tangerang Indonesia Cai Sejahtera Indonesia Faculty of Digital Business Tangerang Indonesia Asosiasi Dosen Indonesia Faculty of Digital Business Tangerang Indonesia Alfabet Inkubator Indonesia Faculty of Retail Management Tangerang Indonesia Rey Faculty of Computer Systems Tangerang Indonesia

ISBN: (纸本)9798350367492

In today digital era traditional manual attendance systems in educational institutions are both inefficient and outdated. This study assesses the impact of implementing technology-driven attendance systems developed using the Waterfall Model, which promises to revolutionize educational settings by enhancing operational efficiency and reducing administrative burdens. The research involved deploying automated attendance software across various institutions and comparing its efficacy with traditional methods in terms of time efficiency, accuracy, and cost. The results indicate that technology-based systems significantly reduce the time and effort required for attendance recording, minimize manual errors, and offer real-time data that helps quickly identify attendance patterns and issues. These improvements lead to enhanced student engagement and accountability. Moreover the transition to automated systems results in considerable administrative cost savings by decreasing reliance on extensive paperwork, thereby allowing for a better allocation of resources towards improving the educational experience. Ultimately, the study confirms that adopting modern attendance monitoring technologies not only streamlines processes but also enhances the educational framework, making institutions more efficient and responsive. This shift benefits administrative and educational outcomes and sets a robust foundation for future educational practices, highlighting the critical role of technology in advancing educational environments. © 2024 IEEE.

关键词： Educational technology

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Deep Learning for Diabetic Retinopathy (DR) Classifier 11th

Deep Learning for Diabetic Retinopathy (DR) Classifier

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11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021

作者： Kho, Sin Hui Mashohor, Syamsiah Hanafi, Marsyita Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9789811681288

High prevalence in diabetes contributed to the rising of the complications such as diabetic retinopathy (DR) which cause the patient to suffer from vision loss and this vision loss may become permanent if it is not well manage and treat. Including other medication non-adherence factor, this rising trend has been made more challenging for physician in keep apace with demand using manual method of retina screening to diagnose DR. Therefore, this project aimed to develop a classifier of deep learning for DR in 5 different level of severity which are normal (no DR), mild, moderate, severe and proliferative DR, to speed up the clinical assessment for early DR detection or DR monitoring progression of disease. The classification was applied using Scottish grading with consider three categories for fundus abnormalities which are hemorrhage, lipid exudates and microaneurysms for a training from scratch model and a pre-trained model using Inception V3. Verified and trustable dataset of retina screening or fundus image available online such as *** used to train and test of the system. Besides, the execution of this project was completed via on cloud training using Google Cloud Platform (GCP). The obtained results are measuring the classifier performance tested in term of accuracy, sensitivity, and specificity. Transfer learning using Inception V3 has been shown a better performance compare to own training model with accuracy 81.2% using transfer learning Inception V3 and 74.7% for own training model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Grading

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