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arXiv 2023年

作者： Bannai, Eiichi Miezaki, Tsuyoshi Nakasora, Hiroyuki Fukuoka819-0395 Japan Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama719-1197 Japan

Let C be a two and three-weight ternary code. Furthermore, we assume that C are t-designs for all by the Assmus-Mattson theorem. We show that t ≤ 5. As a corollary, we provide a new characterization of the (extended)... 详细信息

关键词： C (programming language)

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Masked Face Recognition by Applying SSD and ResNet Model for Attendance System

Masked Face Recognition by Applying SSD and ResNet Model for...

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2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2021

作者： Efanntyo Mitra, Aditya R. Universitas Pelita Harapan Electrical Engineering Faculty of Science and Technology Tangerang Indonesia Universitas Pelita Harapan Information Systems Faculty of Computer Science Tangerang Indonesia

ISBN: (纸本)9781665499699

The rapid development of technology in artificial intelligence (AI) includes face recognition. Some of these developments in face recognition systems can be found easily in everyday life, such as entry access, payment, or attendance recording systems. Notably, the demand for image-based face recognition methods to record attendance increases due to its effectiveness and efficiency. Compared to conventional methods like fingerprint or RFID, facial recognition offers better results. On the other hand, with the arising needs in dealing with the novel coronavirus (COVID-19) pandemic, official health protocols require mask-wearing and maintaining a minimum distance of 1 meter between individuals to prevent the spread of the virus. Along with such a situation, a face recognition system to record attendance can reduce the occurrence of direct contact and allow each individual to maintain a safe distance, including from the attendance device. This paper presents the performance of a masked-faced recognition system that implemented SSD (Single Shot Detection) and ResNet feature extraction. The face recognition system application developed using Python and related libraries show a stable level of masked face recognition accuracy. This evaluation was made at predetermined distances between the face and the camera and measured at room lighting of 200 lux with an average accuracy of 67%. The application also has a feature to send notification emails to every employee who is unable to attend work on their scheduled workdays. © 2021 IEEE.

关键词： Face recognition

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Architecting Digital Twins for Intelligent Transportation systems

arXiv

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arXiv 2025年

作者： Bhatt, Hiya Sahil Vaidhyanathan, Karthik Biju, Rahul Gangadharan, Deepak Trestian, Ramona Shah, Purav Software Engineering Research Centre IIIT Hyderabad India Computer Systems Group IIIT Hyderabad India Faculty of Science and Technology Middlesex University London United Kingdom

Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency. © 2025, CC BY.

关键词： Adaptive control systems

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Citrus Limon L. Ripeness Identification Using Feature Extraction of Mean RGB, HSV, LBP and K-Nearest Neighbor

Citrus Limon L. Ripeness Identification Using Feature Extrac...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Priyo Utomo, Ade Cahyo Sari, Christy Atika Rachmawanto, Eko Hari Hadi, Heru Pramono Mostafa, Salama A. Al-Dayyeni, Wisam Subhi Talib University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Ada University School of Information Technologies and Engineering Baku Azerbaijan

ISBN: (纸本)9798350339215

Citrus Limon L. (Lemon) is a type of fruit that is currently widely consumed, because it contains abundant vitamin C, fiber, and antioxidants. This fruit have high potential in the agribusiness sector and is widely cultivated by traditional farmers. However, during harvest time, traditional farmers generally still use manual methods using the human eye in distinguishing the maturity level of lemons which is less efficient because it has a low accuracy. Digital image processing is one solution to this problem. In the research on the classification of lemon maturity levels using digital image processing with the feature extraction method of the Mean RGB, HSV, and LBP methods and the K-Nearest Neighbor classification algorithm in this study, a total of 120 lemon images were used which were divided into 80 training image data and 40 image data. testing. The results of model performance measurements in the form of the highest accuracy level in the Mean RGB method of 100%, the highest accuracy in the HSV method of 98%, and the highest accuracy in the LBP method of 82.5%. © 2023 IEEE.

关键词： Citrus fruits

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Cooperative Autonomous Driving Control among Vehicles of Different Sizes Using Deep Reinforcement Learning

Cooperative Autonomous Driving Control among Vehicles of Dif...

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International Joint Conference on Neural Networks (IJCNN)

作者： Akito Takenaka Tomohiro Harada Yukiya Miura Kiyohiko Hattori Johei Matuoka Graduate School of Systems Design Tokyo Metropolitan University Tokyo Japan Graduate School of Science and Engineering Saitama University Saitama Japan Faculty of Systems Design Tokyo Metropolitan University Tokyo Japan Faculty of Computer Science Tokyo University of Technology Tokyo Japan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

This study explores acquiring cooperative control in an environment where only multiple autonomous vehicles with different sizes navigate using deep reinforcement learning (DRL). Previous works proposed a cooperative learning approach utilizing information obtained through vehicle-to-vehicle communication. However, the previous research assumed all vehicles to be the same size. Indeed, in the real environment, vehicles of various sizes, such as compact cars and large trucks, coexist on the roads and must cooperatively drive. This study aims to achieve autonomous driving control among vehicles of different sizes using DRL and analyze the impact of vehicle size on cooperative behavior. We conducted simulation experiments using vehicles of sizes corresponding to regular cars and medium-sized trucks. Experimental results revealed that cooperative autonomous driving control was successfully acquired even in environments where vehicles of different sizes coexisted. Furthermore, the model trained exclusively with medium-sized trucks demonstrated the ability to mitigate collisions when operating even in environments with a mixture of vehicle sizes. In contrast, the model trained with a mixture of vehicle sizes demonstrated more collisions in the medium truck-only environment. This result indicates that a model trained in a challenging environment (where only large-sized vehicles exist) performs better in simpler environments (environments with only small-sized vehicles or a mix of small and large-sized vehicles) in terms of collision avoidance.

关键词： Training Navigation Roads Neural networks Vehicular ad hoc networks Deep reinforcement learning Automobiles

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Early Detection of Childhood Malnutrition Using Survey Data and Machine Learning Approaches

Early Detection of Childhood Malnutrition Using Survey Data ...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Thoufiq Zumma Anikur Rahaman Nuzhat Noor Islam Prova Tasriful Haque Julias Caeser Joy Bose Ravina Akter Youki Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science Washington University Science and Technology New York USA Department of Seidenberg School of CSIS Pace University New York USA Master of Information Technology and Systems Victorian Institute of Technology Faculty of Artificial Intelligence and Science University of Wollongong Faculty of Engineering and IT University of Technology Sydney

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Children's physical, mental and emotional development depends heavily on sleep, with age-specific sleep needs fluctuating. Malnutrition may result from eating too little, absorbing nutrients poorly, being unwell, living in poverty or not knowing enough about nutrition. Food allergies and iron insufficiency are two examples of illnesses whose onsets might differ depending on environmental exposures and genetics. By providing proper diet, getting medical attention when necessary and encouraging healthy behaviors, parents play a crucial role in supporting their children's health. For children to grow and develop properly, they need meals high in fruits, vegetables, milk, protein, calcium, iron and fiber. Key advantages of a balanced diet include aiding bone formation, improving heart health and encouraging regular bowel motions and digestion. In general, children's general health and wellness depend on caregivers being aware of their dietary requirements and giving them the proper attention. The findings of the research imply that machine learning algorithms may be useful in understanding and predicting the prevalence of malnutrition in children. Furthermore, these algorithms could provide perceptive data that might help politicians create effective policies. In this research, we make use of five different machine learning algorithms. With a decision tree, the accuracy is 0.92%, followed by svm which provides 0.91%, KNN, which provides 0.84%, GBM classifier, which provides 0.79% and random forest, which provides 0.65%.

关键词： Support vector machines Deep learning Surveys Pediatrics Machine learning algorithms Predictive models Prediction algorithms Iron Decision trees Medical diagnostic imaging

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Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

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computer systems science & engineering 2023年第6期45卷 3037-3052页

作者： Fatma S.Alrayes Najm Alotaibi Jaber S.Alzahrani Sana Alazwari Areej Alhogail Ali M.Al-Sharafi Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud Al Faisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to *** the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so ***,malware attacks and software piracy pose serious risks in compromising the security of *** can steal confidential data which results infinancial and reputational *** advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud *** article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud *** presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud *** proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification *** EAG-TODL-CTD model pre-processes the input data to transform to a compatible *** threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class ***,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our *** performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and *** experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

关键词： Cybersecurity computer networks threat detection internet of things cloud computing deep learning

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Noisy ECG Signal Data Transformation to Augment Classification Accuracy

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computers, Materials & Continua 2022年第5期71卷 2191-2207页

作者： Iqra Afzal Fiaz Majeed Muhammad Usman Ali Shahzada Khurram Akber Abid Gardezi Shafiq Ahmad Saad Aladyan Almetwally M.Mostafa Muhammad Shafiq Department of Information Technology University of GujratGujrat50700Pakistan Department of Computer Science University of GujratGujrat50700Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh 11421Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

In this era of electronic health,healthcare data is very important because it contains information about human *** addition,the Internet of Things(IoT)revolution has redefined modern healthcare systems and management by providing continuous *** this case,the data related to the heart is more important and requires proper *** the analysis of heart data,Electrocardiogram(ECG)is *** this work,machine learning techniques,such as adaptive boosting(AdaBoost)is used for detecting normal sinus rhythm,atrial fibrillation(AF),and noise in ECG signals to improve the classification *** proposed model uses ECG signals as input and provides results in the form of the presence or absence of disease AF,and classifies other signals as normal,other,or *** article derives different features from the signal using Maximal Information Coefficient(MIC)and minimum Redundancy Maximum Relevance(mRMR)technique,and then classifies them based on their *** the ECG contains some kind of noise and irregular data streams so the purpose of this study is to remove artifacts from the ECG signal by deploying the method of Second-Order-Section(SOS)(filter)and correctly classify *** features were extracted to improve the detection of ECG *** with existing methods,this work gives promising results and can help improve the classification accuracy of the ECG signals.

关键词： ECG atrial fibrillation adaptive boosting heart rate variability

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Lag Synchronization and Lag H∞ Synchronization for Multiweighted Coupled Reaction-Diffusion Neural Networks Suffering Topology Attacks

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IEEE Transactions on Control of Network systems 2025年

作者： Wang, Jin-Liang Zhao, Xin-Yu Ren, Shun-Yan Huang, Tingwen Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tianjin300387 China Tiangong University School of Electronics & Information Engineering Tianjin300387 China University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu611731 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper mainly discusses two kinds of coupled reaction-diffusion neural networks (CRNN) under topology attacks, that is, the cases with multistate couplings and with multiple spatial-diffusion couplings. On one hand, by adopting state feedback control scheme, the problem of the lag synchronization for the multistate coupled CRNN is addressed. Moreover, the influences of external disturbances for the lag synchronization of the CRNN with multistate couplings are also taken into consideration. On the other hand, several lag and lag H∞ synchronization conditions for CRNN with multiple spatial-diffusion couplings are derived under the help of the Kronecker product and the state feedback control method. Finally, two numerical examples are provided to verify the correctness of the derived criteria. © 2014 IEEE.

关键词： State feedback

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Quantum Annealing for the Mechanics of Robot Inspectors on Electrical Transmission Lines' Conductors

Quantum Annealing for the Mechanics of Robot Inspectors on E...

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Control, Robotics and Informatics (ICCRI), International Conference on

作者： Hadi Salloum Mohammad Reza Bahrami Artem Matevosian Sofia Gamershmidt Ali Jnadi Manuel Mazzara Faculty of Computer Science and Engineering Innopolis University Innopolis Russia Cyber-physical systems lab Innopolis University Innopolis Russia Institute of Robotics and Computer Vision Innopolis University Innopolis Russia

ISBN: (数字)9798350380309

ISBN: (纸本)9798350380316

This paper explores the integration of quantum computing, specifically quantum annealing, into robotics for inspecting electrical transmission lines. By using quantum annealing's computational power, we address the dynamics of robot inspectors on transmission line conductors, enhancing the computational efficiency of managing the movements of robot inspectors on transmission line conductors. The use of these robots is crucial for performing efficient and safe inspections. We discuss the mathematical foundations of quantum annealing, transforming differential equations governing robot dynamics into Ising model Hamiltonians suitable for quantum annealing devices. Through experiments using D-Wave's ‘Advantage’ quantum annealer, we demonstrate the viability of quantum annealing in solving complex robotics problems, paving the way for advanced applications in the field.

关键词： Annealing Power transmission lines Computational modeling Quantum mechanics Differential equations Inspection Quantum annealing Conductors Mathematical models Robots

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