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A draft drawing structure tool for improve 3D modeling process in student

A draft drawing structure tool for improve 3D modeling proce...

International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Jirawat Sookkaew Rachen Sukmuang Pheeraphon Changjaikla Department of Computer graphic and Multimedia School of Information and Communication Technology University of Phayao Phayao Thailand Department of Visual Communication Design School of of Architecture Kasem Bundit University Bangkok Thailand

In this research, the concept of applying the student’s original learning based on the course or skills related to the use of hand drawing and sketching was applied by creating a 3D structure drawing and dividing it into 2 groups between Non-Draft Groups and Draft Groups Both groups created 3D models and assessed the structures. Upon examination, it was found that the structural drawing group produced 3D models with higher 3D structural evaluation scores than the other group. To show that the use of the sketch helps the 3D structure to be more complete.

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An Efficient Intrusion Detection System using Advanced Machine Learning Techniques in SDN for Healthcare System

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Asif, Muhammad Waseem Aqdus, Aqsa Amin, Rashid Chaudhry, Shehzad Ashraf Alsubaei, Faisal S. Iqbal, Sajid Department of Computer Science and IT The University of Lahore Lahore54000 Pakistan Department of Computer Science University of Chakwal Chakwal48800 Pakistan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul Turkey The Department of Cybersecurity College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

The quick advancement of healthcare systems necessitates robust and efficient network security keys to defend sensitive patient records and guarantee uninterrupted service delivery. The current IDS has many challenges, such as a high false positive rate, poor accuracy of detection, slow response to threats, and inability to scale well. This paper proposes an efficient and real-time intrusion detection system (IDS) using advanced machine learning techniques within a software-defined networking (SDN) framework specifically tailored for healthcare systems. The proposed architecture implements a Machine Learning (ML) model that combines the SVM and KNN to better identify malicious activities. Full sets of detection and mitigation capabilities are implemented to address different types of traffic in the network with the least interference. Through the different evaluation measures, the efficiency of the proposed model is assured. Network performance is determined by success rate queries, packet losses in each domain path, and the CPU being used by the system. Responsiveness is measured through delay metrics grounded on end-to-end delay, hop-to-hop packet delay, latency rate, and propagation delay. Moreover, model accuracy fidelity is reviewed via precision assessment, alpha (α) affecting the accuracy of the model, and confusion matrix with different techniques with the proposed hybrid SVM-KNN model. Last of all, a comparison of the security of the models in question strengthens the argument in favor of the proposed model. More specifically, flow and network topology diagrams are included to show how integration may be accomplished in linkage or merger with existing healthcare networks. The results also present a 30% overall advancement in detection and mitigation by presenting the hybrid SVM-KNN model to overcome other traditional models. This proposed model shows significant improvements not less than 20-30% improvement in CPU use, 30-50% reduction in end-to-end delay,

关键词： Decision trees

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Optimization of antique assembly building frame construction based on BIM technology and Matlab genetic algorithm

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Zhang, Han Ping, Hongming School of Civil Engineering and Architecture Henan University of Science and Technology Henan Luoyang471000 China Department of Electrical and Computer Engineering University of Washington SeattleWA98101 United States

With the rapid development of science and technology, the traditional construction industry is facing the urgent need for transformation and upgrading, and how to achieve the performance optimization of the assembly building frame has become an urgent problem to be solved. The research constructs the antique assembly building frame using BIM technology and uses a Matlab genetic algorithm to optimize the model of the building frame. The efficacy of the optimized assembly building is analyzed, and the safety performance of the BIM-Matlab frame building proposed in this paper is compared with that of the traditional frame building and the anti-buckling frame building to explore the effectiveness of the performance optimization of the BIM-Matlab frame building. The values of the structural non-lateral stiffness ratio of the BIM-Matlab assembly building are all greater than or equal to 1, and the seismic shear weight ratio meets the requirements. The displacement ratio and inter-story displacement ratio meet the code requirements under all working conditions. The building foundation's zero stress zone is 0.00%, and the minimum stiffness-to-weight ratio value of 38.69 is enough to pass the overturning resistance and stability test. The BIM-Matlab framed building can endure earthquakes with greater intensity and safety reserves than conventional framed buildings and anti-flexural framed buildings, which are more secure in earthquakes. © 2024 Han Zhang et al., published by Sciendo.

关键词： Genetic algorithms

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Deep Reinforcement Agent for Failure-aware Job scheduling in High-Performance Computing 27

Deep Reinforcement Agent for Failure-aware Job scheduling in...

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27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Yang, Kang Cao, Rongyu Zhou, Yueyuan Zhang, Jiawei Shao, En Tan, Guangming Institute of Computing Technology CAS State Key Laboratory of Computer Architecture Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665408783

Job scheduling is crucial in high-performance computing (HPC), which is dedicated to deciding when and which jobs are allocated to the system and placing the jobs on which resources, by considering multiple scheduling goals. Along with the incremental of various resources and dazzling deep learning training (DLT) workloads, job failure becomes a quite common issue in HPC, which will affect user satisfaction and cluster utilization. To alleviate the influence of hardware and software errors as much as possible, in this paper, we aim to tackle the problem of failure-aware job scheduling in HPC clusters. Inspired by the success of previous studies of deep reinforcement learning-driven job scheduling, we propose a novel HPC scheduling agent named FARS (Failure-aware RL-based scheduler) by considering the effects of job failures. On the one hand, a neural network is applied to map the information of raw cluster and job states to job placement decisions. On the other hand, to consider the influence of job failure for user satisfaction and cluster utilization, FARS leverages make-span of the entire workload as the training objective. Additionally, effective exploration and experience replay techniques are applied to obtain effectively converged agent. To evaluate the capability of FARS, we design extensive trace-based simulation experiments with the popular DLT workloads. The experimental results show that, compared with the best baseline model, FARS obtains 5.69% improvement of average make-span under different device error rates. Together, our FARS is an ideal candidate for failure-aware job scheduler in HPC clusters. © 2021 IEEE.

关键词： Scheduling

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MetaCity: Data-driven sustainable development of complex cities

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The Innovation 2025年第2期6卷 88-103,87页

作者： Yunke Zhang Yuming Lin Guanjie Zheng Yu Liu Nicholas Sukiennik Fengli Xu Yongjun Xu Feng Lu Qi Wang Yuan Lai Li Tian Nan Li Dongping Fang Fei Wang Tao Zhou Yong Li Yu Zheng Zhiqiang Wu Huadong Guo Department of Electronic Engineering Beijing National Research Center for Information Science and Technology(BNRist)Tsinghua UniversityBeijing 100084China John Hopcroft Center for Computer Science Shanghai Jiao Tong UniversityShanghai 200240China University of Chinese Academy of Sciences Beijing 100049China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Civil and Environmental Engineering Northeastern UniversityBostonMA 02115USA School of Architecture Tsinghua UniversityBeijing 100084China School of Civil Engineering Tsinghua UniversityBeijing 100084China Big Data Research Center University of Electronic Science and Technology of ChinaChengdu 611731China JD iCity JD Technology&JD Intelligent Cities ResearchBeijing 100176China College of Architecture and Urban Planning Tongji UniversityShanghai 200292China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China These authors contributed equally

Cities are complex systems that develop under complicated interactions among their human and environmental *** generates substantial outcomes and opportunities while raising challenges including congestion,air pollution,inequality,etc.,calling for efficient and reasonable solutions to sustainable ***,booming technologies generate large-scale data of complex cities,providing a chance to propose data-driven solutions for sustainable urban *** paper provides a comprehensive overview of data-driven urban sustainability *** this review article,we conceptualize MetaCity,a general framework for optimizing resource usage and allocation problems in complex cities with data-driven *** this framework,we decompose specific urban sustainable goals,e.g.,efficiency and resilience,review practical urban problems under these goals,and explore the probability of using data-driven technologies as potential solutions to the challenge of *** the basis of extensive urban data,we integrate urban problem discovery,operation of urban systems simulation,and complex decision-making problem solving into an entire cohesive framework to achieve sustainable development goals by optimizing resource allocation problems in complex cities.

关键词： resource usage sustainable development urbanization resilience data driven urban systems simulation complex cities complexity

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Critical assessment of opal mining techniques, tools, market system, environmental impact challenges, and prospects — A systematic review

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Arabian Journal of Geosciences 2025年第6期18卷 1-14页

作者： Adamu, Anteneh Yayeh Eneyew, Demessie Mulu Ejigu, Assamen Ayalew Dubiniewicz, Andrzej Kajetan Endalew, Sisay Awoke Nigussie, Asmare Belay Amognehegn, Asnake Enawgaw School of Civil and Water Resource Engineering & Architecture Kombolcha Institute of Technology Kombolcha Ethiopia Department of Electrical and Computer Engineering Kombolcha Institute of Technology Kombolcha Ethiopia Faculty of Earth Sciences and Spatial Management Nicolaus Copernicus University Torun Poland Department of Chemistry College of Natural Science Wollo University Dessie Ethiopia

The study presents the existing challenges in opal mining in the Delanta area (mining methods, tools, market system, and environmental impact) and provides possible recommendations to overcome these challenges. Literature review, data collection, field survey, and data analysis were used as methods for this study. Environmental impact assessment also applied in the study area. Based on the study, the opal mining system in the Delanta area is performed in traditional ways and this method is not benefiting the local miners as well as the country. In the area, due to roof collapse of mining sites, three to eight artesian small-scale miners are dying each year. Opal quality deterioration (wastes locally called Ajara) because of cracking is the other main challenge in the study area. Moreover, environmental problems such as deforestation, concealed underground channels, landslides, the loss of numerous species’ habitats, annihilation of flora, land degradation, soil erosion and pollution, water pollution, air pollution, downstream sedimentation, and the covering of topsoil on agricultural land, which affects the farming system are the main findings of this study. In the study area, only 3% of the opal is processed, while 97% is rough, and this results in loss of income for the country. Finally, the study indicated the possible solutions in opal exploration and mining methods, as well as market system for the safe and sustainable extraction of Delanta opal resources. The study recommended for future work to design a safe mining method, a ventilation shaft, develop a smartphone app for quality grading and branding for selling, developing a low-cost opal cutting and polishing machine for value addition, and establishing training centers. In addition, the sector in the area also needs solar systems planting for lighting of mining sites, design and development of waste soil removal technology/conveyors and hammers for safe mining, sustainable development and management.

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Cell Phone Image-Based Persian Rice Detection and Classification Using Deep Learning Techniques

arXiv

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arXiv 2024年

作者： Kelishami, Mahmood Saeedi Kelishami, Amin Saeidi Kelishami, Sajjad Saeedi Department of Mathematics Rasht Branch Islamic Azad University Iran Department of Computer Engineering Sharif University of Technology Iran Department of Architecture Rasht Branch Islamic Azad University Iran

This study introduces an innovative approach to classifying various types of Persian rice using image-based deep learning techniques, highlighting the practical application of everyday technology in food categorization. Recognizing the diversity of Persian rice and its culinary significance, we leveraged the capabilities of convolutional neural networks (CNNs), specifically by fine-tuning a ResNet model for accurate identification of different rice varieties and employing a U-Net architecture for precise segmentation of rice grains in bulk images. This dual-methodology framework allows for both individual grain classification and comprehensive analysis of bulk rice samples, addressing two crucial aspects of rice quality assessment. Utilizing images captured with consumer-grade cell phones reflects a realistic scenario in which individuals can leverage this technology for assistance with grocery shopping and meal preparation. The dataset, comprising various rice types photographed under natural conditions without professional lighting or equipment, presents a challenging yet practical classification problem. Our findings demonstrate the feasibility of using non-professional images for food classification and the potential of deep learning models, like ResNet and U-Net, to adapt to the nuances of everyday objects and textures. This study contributes to the field by providing insights into the applicability of image-based deep learning in daily life, specifically for enhancing consumer experiences and knowledge in food selection. Furthermore, it opens avenues for extending this approach to other food categories and practical applications, emphasizing the role of accessible technology in bridging the gap between sophisticated computational methods and everyday tasks. © 2024, CC BY.

关键词： Convolutional neural networks

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Ml-experiment: A Python framework for reproducible data science 4

Ml-experiment: A Python framework for reproducible data scie...

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4th International Conference on Applied Physics, Simulation and Computing, APSAC 2020

作者： Molner Domenech, Antonio Guillén, Alberto Computer Architecture and Technology Department University of Granada Spain

Nowadays, data science projects are usually developed in an unstructured way, which makes it difficult to reproduce. It is also hard to move from an experimental environment to production. Operational workflows such as containerization, continuous deployment, and cloud orchestration allow data science researchers to move a pipeline from a local environment to the cloud. Being aware of the difficulties of setting those workflows up, this paper presents a framework to ease experiment tracking and operationalizing machine learning by combining existent and well-supported technologies. These technologies include Docker, Mlflow, Ray, among others. The framework provides an opinionated workflow to design and execute experiments either on a local environment or the cloud. ml-experiment includes: an automatic tracking system for the most famous machine learning libraries: Tensorflow, Keras, Fastai, Xgboost and Lightgdm, first-class support for distributed training and hyperparameter optimization, and a Command Line Interface (CLI) for packaging and running projects inside containers. © Published under licence by IOP Publishing Ltd.

关键词： Machine learning

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Practice research of the training mode of students' innovative and entrepreneurial talents based on computer courses 16

Practice research of the training mode of students' innovati...

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16th IEEE International Conference on computer Science and Education, ICCSE 2021

作者： Zhou, Yi Yu, Zhe Yu, Kang-Bo Wang, He Chen, Ling-Ling Changshu Institute of Technology College of Computer Science and Engineering SuZhou China Harbin Branch of United Network Telecommunications Corporation Harbin China Central China University Technology School of Architecture and Art ChangSha China Changshu Institute of Technology College of Computer Science and Technology SuZhou China

ISBN: (纸本)9781665414685

At present, the innovation and entrepreneurship education system of college students in colleges and universities in our country is faced with the problems of single resources, disconnect from majors and emphasis on theory and practice, which seriously restrict the effect and efficiency of college students' innovation and entrepreneurship education. This paper puts forward the use of service learning in the teaching of computer courses to carry out teaching activities, through the revision of talent training programs and curriculum, optimize innovative entrepreneurship education resources, build innovative entrepreneurship education information platform and other measures to strengthen the management and implementation of innovative entrepreneurship education for college students, improve the effectiveness and efficiency of innovative entrepreneurship education in colleges and universities. © 2021 IEEE.

关键词： Students

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Optimal Qubit Reuse for Near-Term Quantum computers

Optimal Qubit Reuse for Near-Term Quantum Computers

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Sebastian Brandhofer Ilia Polian Kevin Krsulich Institute of Computer Architecture and Computer Engineering and Center for Integrated Quantum Science and Technology University of Stuttgart Stuttgart Germany IBM Quantum IBM TJ Watson Research Center Yorktown Heights NY USA

Near-term quantum computations are limited by high error rates, the scarcity of qubits and low qubit connectivity. Increasing support for mid-circuit measurements and qubit reset in near-term quantum computers enables qubit reuse that may yield quantum computations with fewer qubits and lower errors. In this work, we introduce a formal model for qubit reuse optimization that delivers provably optimal solutions with respect to quantum circuit depth, number of qubits, or number of swap gates for the first time. This is in contrast to related work where qubit reuse is used heuristically or optimally but without consideration of the mapping effort. We further investigate reset errors on near-term quantum computers by performing reset error characterization experiments. Using the hereby obtained reset error characterization and calibration data of a near-term quantum computer, we then determine a qubit assignment that is optimal with respect to a given cost function. We define this cost function to include gate errors and decoherence as well as the individual reset error of each qubit. We found the reset fidelity to be state-dependent and to range, depending on the reset qubit, from 67.5% to 100% in a near-term quantum computer. We demonstrate the applicability of the developed method to a number of quantum circuits and show improvements in the number of qubits and swap gate insertions, estimated success probability, and Hellinger fidelity of the investigated quantum circuits.

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